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		<title>Former NASA Physicist: The UAP Maneuver That Should Have Exploded &#124; Kevin Knuth</title>
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					<description><![CDATA[Former NASA Physicist: The UAP Maneuver That Should Have Exploded &#124; Kevin Knuth https://www.youtube.com/watch?v=vumiiCdAv70 Transcripts: Brian KeatingWell, this is a two-part episode. we&#8217;re discussing today, fascinating lecture that summarizes a lot of the current cutting edge research and investigations that you&#8217;ve done as a physicist. And the reason that I&#8217;m having you on for this in a podcast as well is because I think you&#8217;re doing really important and heroic work in terms of bringing scientific rigor to this problem. And one of the problems that I see most and afflicting the UAP phenomenon is. Is that there are very few actual real scientists, professors. Yes, I&#8217;m sorry to be a gatekeeper, but but sometimes we need gatekeeping. You and I met through a a chat thread with about 300 other people, some accusing others of different nefarious things, and everyone saying that they have their own truth. Well, no, there&#8217;s there&#8217;s only one scientific truth. It&#8217;s our job to investigate it, interrogate nature. And you do that through the lens of a pr practicing professional scientist. And you&#8217;re also a professor. And these these things matter. Yes, it&#8217;s important to have other people and voices as well, but we need people from agencies, from industry, from the military, but we certainly do need from the professoriate. And that&#8217;s why I think it&#8217;s really important what you&#8217;re doing. So today you&#8217;re gonna describe the the research that you&#8217;ve gone into in terms of a presentation you made at a very important conference you gave over the summer, and you&#8217;re gracious enough to provide it here, and then we&#8217;re gonna have a lot nice long conversation. Depending on which order you&#8217;re watching this in, you may have already seen the conversation with Kevin on the Into the Impossible podcast. This shows you I&#8217;m dedicating two hours to this phenomenon because I think it&#8217;s so important, so pr and it&#8217;s so prescient that Kevin&#8217;s been involved in this, bringing scientific rigor, bringing academic rigor. We hear a lot about peer review is broken, science is broken, Fauci&#8217;s this, lockup string theorist. and you know, I&#8217;m guilty of some of it too. But but the reality is Kevin&#8217;s a working professor. you can check out his website. I&#8217;ll have all of his links below. And just really grateful for your presence today, Kevin. So if you wouldn&#8217;t mind, you could take us through this presentation. I might interrupt here and there, but just to ask clarifying questions that the audience might be interested and unable to ask at this point. Prof. Kevin KnuthAbsolutely and th thank you for the very very kind introduction. Yeah, I gave I gave this talk at the scientific coalition for UAP Studies meeting, the SCU meeting this last summer and in Toronto. And I think one of the there there&#8217;s a few things that co that characterize academic scientists and make them different than others and perhaps in this field more useful is we study things and we&#8217;re trained to study things and we&#8217;re trained to be skeptical and we&#8217;re I I I don&#8217;t know how many times I&#8217;m told by somebody, I&#8217;m skeptical. well I am too. I mean I&#8217;m I&#8217;m skeptical of my own ideas, and most people aren&#8217;t. And that that&#8217;s skeptics. Yeah, skepticism works both ways. You have to be able to catch mistakes. And the fastest way to catch mistakes is for you to catch them. and then and and then on in addition to that, physicists think about things differently than other people. And so I&#8217;m I can&#8217;t say that that&#8217;s the only good way to think about it. I know that it&#8217;s not, but Brian KeatingThat&#8217;s a sign of a good scientist. Yep, that&#8217;s a sign of a good scientist. Yep. Prof. Kevin KnuthPhysicists often bring a unique perspective that&#8217;s that you can&#8217;t get anywhere else. And so this talk is basically that. I&#8217;m asking the question how anomalous is anomalous, and when it comes to UAP, and I&#8217;m going to focus on energy and power. And I think the reasons for focusing on energy and power are that in some cases we can estimate the energies and powers involved in a sighting or an interaction with these objects. And very often th those energies or powers are as much, much greater than humans are capable of dealing with. And I think this is the real evidence that we have that we&#8217;re dealing with non-human tech in many of these cases. And it&#8217;s pretty and when I go through some of these cases, it&#8217;ll be pretty obvious that this cannot possibly be human technology. I know Lockheed Martin can do some pretty cool things, but Brian KeatingYeah. Prof. Kevin Knuththere&#8217;s a lot of things that they can&#8217;t do. Prof. Kevin KnuthSo I think it&#8217;s also difficult to for people to remember that UAP are a class of phenomena, not a single thing. There are very certainly different types of UAP. Not all of them are alien spacecraft. In fact, most of them probably aren&#8217;t. And in this slide, I actually show several pictures of UAPs. one of them is actually identified. and it&#8217;s the one here in the lower the lower left. Can you see my cursor moving? Okay. You can see this one in the lower left. Excuse me. when I first saw this image years ago, I got very excited because I had recognized several other images that looked similar. And I thought, wow, maybe it&#8217;s the same type of craft. And I Brian KeatingFine. Brian KeatingYeah, that&#8217;s perfect. Yeah. Brian KeatingMm. Prof. Kevin KnuthPulled all those images together and looked at them at the same time and realized, no, it&#8217;s it&#8217;s a bird. It&#8217;s a seagull. Which is precisely why the photographer didn&#8217;t notice it while he took the pictures, because he was taking a picture of the boats in the background that I have covered up with the text. that&#8217;s actually a seagull. I&#8217;m gonna try]]></description>
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					<h2 class="elementor-heading-title elementor-size-default">Former NASA Physicist: The UAP Maneuver That Should Have Exploded | Kevin Knuth</h2>				</div>
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									<h2><strong>Transcripts:</strong></h2><p>Brian Keating<br />Well, this is a two-part episode. we&#8217;re discussing today, fascinating lecture that summarizes a lot of the current cutting edge research and investigations that you&#8217;ve done as a physicist. And the reason that I&#8217;m having you on for this in a podcast as well is because I think you&#8217;re doing really important and heroic work in terms of bringing scientific rigor to this problem. And one of the problems that I see most and afflicting the UAP phenomenon is.</p><p>Is that there are very few actual real scientists, professors. Yes, I&#8217;m sorry to be a gatekeeper, but but sometimes we need gatekeeping. You and I met through a a chat thread with about 300 other people, some accusing others of different nefarious things, and everyone saying that they have their own truth. Well, no, there&#8217;s there&#8217;s only one scientific truth. It&#8217;s our job to investigate it, interrogate nature. And you do that through the lens of a pr practicing professional scientist.</p><p>And you&#8217;re also a professor. And these these things matter. Yes, it&#8217;s important to have other people and voices as well, but we need people from agencies, from industry, from the military, but we certainly do need from the professoriate. And that&#8217;s why I think it&#8217;s really important what you&#8217;re doing. So today you&#8217;re gonna describe the the research that you&#8217;ve gone into in terms of a presentation you made at a very important conference you gave over the summer, and you&#8217;re gracious enough to provide it here, and then we&#8217;re gonna have a lot nice long conversation.</p><p>Depending on which order you&#8217;re watching this in, you may have already seen the conversation with Kevin on the Into the Impossible podcast. This shows you I&#8217;m dedicating two hours to this phenomenon because I think it&#8217;s so important, so pr and it&#8217;s so prescient that Kevin&#8217;s been involved in this, bringing scientific rigor, bringing academic rigor. We hear a lot about peer review is broken, science is broken, Fauci&#8217;s this, lockup string theorist. and you know, I&#8217;m guilty of some of it too. But but the reality is Kevin&#8217;s a working professor.</p><p>you can check out his website. I&#8217;ll have all of his links below. And just really grateful for your presence today, Kevin. So if you wouldn&#8217;t mind, you could take us through this presentation. I might interrupt here and there, but just to ask clarifying questions that the audience might be interested and unable to ask at this point.</p><p>Prof. Kevin Knuth<br />Absolutely and th thank you for the very very kind introduction. Yeah, I gave I gave this talk at the scientific coalition for UAP Studies meeting, the SCU meeting this last summer and in Toronto.</p><p>And I think one of the there there&#8217;s a few things that co that characterize academic scientists and make them different than others and perhaps in this field more useful is we study things and we&#8217;re trained to study things and we&#8217;re trained to be skeptical and we&#8217;re I I I don&#8217;t know how many times I&#8217;m told by somebody, I&#8217;m skeptical. well I am too. I mean I&#8217;m</p><p>I&#8217;m skeptical of my own ideas, and most people aren&#8217;t. And that that&#8217;s skeptics. Yeah, skepticism works both ways. You have to be able to catch mistakes. And the fastest way to catch mistakes is for you to catch them. and then and and then on in addition to that, physicists think about things differently than other people. And so I&#8217;m I can&#8217;t say that that&#8217;s the only good way to think about it. I know that it&#8217;s not, but</p><p>Brian Keating<br />That&#8217;s a sign of a good scientist. Yep, that&#8217;s a sign of a good scientist. Yep.</p><p>Prof. Kevin Knuth<br />Physicists often bring a unique perspective that&#8217;s that you can&#8217;t get anywhere else. And so this talk is basically that. I&#8217;m asking the question how anomalous is anomalous, and when it comes to UAP, and I&#8217;m going to focus on energy and power. And I think the reasons for focusing on energy and power are that in some cases we can estimate the energies and powers involved in</p><p>a sighting or an interaction with these objects. And very often th those energies or powers are as much, much greater than humans are capable of dealing with. And I think this is the real evidence that we have that we&#8217;re dealing with non-human tech in many of these cases. And it&#8217;s pretty and when I go through some of these cases, it&#8217;ll be pretty obvious that this cannot possibly be human technology. I know Lockheed Martin can do some pretty cool things, but</p><p>Brian Keating<br />Yeah.</p><p>Prof. Kevin Knuth<br />there&#8217;s a lot of things that they can&#8217;t do.</p><p>Prof. Kevin Knuth<br />So I think it&#8217;s also difficult to for people to remember that UAP are a class of phenomena, not a single thing. There are very certainly different types of UAP. Not all of them are alien spacecraft. In fact, most of them probably aren&#8217;t. And in this slide, I actually show several pictures of UAPs.</p><p>one of them is actually identified. and it&#8217;s the one here in the lower the lower left. Can you see my cursor moving? Okay. You can see this one in the lower left. Excuse me. when I first saw this image years ago, I got very excited because I had recognized several other images that looked similar. And I thought, wow, maybe it&#8217;s the same type of craft. And I</p><p>Brian Keating<br />Fine.</p><p>Brian Keating<br />Yeah, that&#8217;s perfect. Yeah.</p><p>Brian Keating<br />Mm.</p><p>Prof. Kevin Knuth<br />Pulled all those images together and looked at them at the same time and realized, no, it&#8217;s it&#8217;s a bird. It&#8217;s a seagull. Which is precisely why the photographer didn&#8217;t notice it while he took the pictures, because he was taking a picture of the boats in the background that I have covered up with the text. that&#8217;s actually a seagull. I&#8217;m gonna try to jump over to another set of slides and I can show you that. Oops, one second.</p><p>Prof. Kevin Knuth<br />Right, can you see this new set? Alright. Alright, here we go. I&#8217;ll go to full screen mode here too. Alright, so this is from an earlier talk. This is the same picture and the person was taking a picture of the the ships here. I&#8217;ve cropped it a bit. we can zoom in and maybe still not obvious what it is. It&#8217;s not obvious until I do that. now it&#8217;s pretty obvious what this is. it&#8217;s a seagull and I don&#8217;t wanna</p><p>Brian Keating<br />Yeah, I see it. Yep.</p><p>Brian Keating<br />Yeah.</p><p>Prof. Kevin Knuth<br />I feel bad about raining on this guy&#8217;s parade of his cool UAP photo. But but it&#8217;s it&#8217;s that&#8217;s my job as a scientist. We&#8217;ve been ruining all the fun for what four hundred years now. So it&#8217;s so I I like I joke with people, I tell them I&#8217;m here to ruin the fun. And</p><p>And that&#8217;s really what we&#8217;ve been doing. It&#8217;s very funny. We I work very closely with Matthew Matthew Shadagas and Cecilia Levy, both professors at the University at Albany, and I work with them at U on you know under UAPX and our UAPX group. And we&#8217;ve looked at many cases at this point, looked at photographs. Matthew&#8217;s been analyzing materials, and we have discovered more.</p><p>As Matthew puts it, he puts it quite well. He&#8217;s looked at all sorts of materials, crash debris, right? And he says he&#8217;s discovered more earth dirt than he ever expected. And so, yeah, and of course no one&#8217;s happy to find out that their UFO debris is actually just earth dirt or airplane parts and which we have found. And but we&#8217;re here to figure out the truth. And that&#8217;s that&#8217;s another thing we academics are good at, getting at the truth.</p><p>Brian Keating<br />Mm-hmm.</p><p>Brian Keating<br />That&#8217;s right.</p><p>Prof. Kevin Knuth<br />So I&#8217;m going focus here first on speeds and accelerations. There are many cases where we can measure speeds and accelerations, and sometimes with radar. Hermann Oberth, this grumpy-looking gentleman here at the bottom of the picture, was a German father of modern rocketry. He was a the mentor to Werner von Braun, which is the guy just to the right. And Hermann Oberth.</p><p>gave a lecture on flying saucers in 1954. And he notes in the lecture their speed is sometimes very high. 19 kilometers a second has been measured with wireless measuring instruments, radar. This is 1954. Radar was new. so and then accelerations are so high that no man could stand it. This is no what comes up as an understatement actually it turns out he would be pressed to the wall and bruised. no they&#8217;d be turned into jelly in some cases.</p><p>the accuracy of such measurements has been doubted. If there would be only three or four measurements, I would not rely upon them and would wait for further measurements. But there is existing more than 50 such measurements, 50 radar measurements of UAP, existing in 1954. And the wireless sets of the American Air Force and Navy, which are used in all fighters, cannot be so inaccurate that the information obtained with them can be doubted completely.</p><p>So I find this presentation, while he doesn&#8217;t actually provide us with that radar evidence, he clearly notes it. 19 kilometers a second is extremely fast. That&#8217;s about 42,000 miles an hour. That&#8217;s approximately the speed of the New Horizons probe, which is currently flying through the Kuiper belt and the outer solar system. it flew past Pluto in 2015. So so these things</p><p>are flying have been measured to fly at spacecraft speeds. And this has been known since nineteen fifty four. And I think this is a big deal. And</p><p>Brian Keating<br />I think.</p><p>Prof. Kevin Knuth<br />So where are these 50 radar measurements? and how how is it that we&#8217;re still going back to saying, well, what about the Russians and the Chinese? Well, these things weren&#8217;t Russian and Chinese back in 1954, that&#8217;s for sure. They weren&#8217;t American either. 1954 the the air speed record was something like seven hundred and sixty miles an hour. So you&#8217;re looking at a huge difference in speeds.</p><p>And he told people about this back in 1954, and I like to say this is why he&#8217;s so grumpy because nobody&#8217;s listening to him or paying any attention. it&#8217;s pretty obvious what the situation is. Excuse me. the Numitz case was an excellent case in which one can estimate speeds and accelerations. There&#8217;s three events that allow you to do that. this one gives you the greatest acceleration. This is when Kevin Day</p><p>Brian Keating<br />Okay.</p><p>Prof. Kevin Knuth<br />reported that he recorded on radar that these objects were dropping from twenty eight thousand feet to sea level in point seven eight seconds.</p><p>Prof. Kevin Knuth<br />So now you can estimate this acceleration. It comes out to something like 5,400 G&#8217;s. This is the distribution of probability distributions of acceleration. So how far off can you be? Well, it isn&#8217;t less than 3,000 G&#8217;s, and it certainly probably isn&#8217;t much more than 10,000 Gs, but it&#8217;s somewhere in between. and most probably around 5,500 Gs. So we can estimate the</p><p>We know it&#8217;s acceleration. five thousand G&#8217;s is you&#8217;re not going to be just pressed against the wall and bruised. Somebody who weighs a hundred pounds normally under five thousand Gs is going to weigh five thousand what fifty thousand wait, no, five hundred thousand. They&#8217;d weigh five hundred thousand pounds. Yeah, under that acceleration. So</p><p>Brian Keating<br />Half a million.</p><p>I yeah, it happened. Yeah.</p><p>Prof. Kevin Knuth<br />500,000, your body weighing five hundred thousand pounds, you&#8217;re you&#8217;re gonna just turn into soup at the bottom of the at the on the floor. we can try to estimate the power involved in its maneuver, assuming that it accelerated halfway and then decelerated the other half to stop moving at zero feet at at sea level. And if you use that acceleration where it accelerates at 5,400 G&#8217;s halfway and then decelerates the other half.</p><p>you can try to estimate its power, but you need to know its mass. We don&#8217;t know its mass. We know its approximate size. David Fraver described it as being about the size of an F-18. And so what I did here is I just took one tenth of the mass of an F eighteen. So let&#8217;s say it&#8217;s one tenth as light as to only to weighs ten percent of the weight of an F-18. So it&#8217;s weighs so I&#8217;m trying to low I&#8217;m trying to lowball it here in a reasonable way.</p><p>And if we do that and calculate the power, this is a graph of the power over time for the required for this maneuver, the maximum power needed is eleven hundred gigawatts.</p><p>And that&#8217;s really, really a lot of power. this is more than ten times the total nuclear power output of the United States.</p><p>Brian Keating<br />Now Kevin, before we go on, you know, I&#8217;m a physicist that studies I study the cosmic microwave background, which was pioneered by Bob Dickey at at MIT Rad Labs and then later at Princeton University, and his colleague is his fellow professor there was David Wilkinson, who is my grand advisor. and so</p><p>Prof. Kevin Knuth<br />Yeah. Excuse me.</p><p>Brian Keating<br />I&#8217;m very conversant with the radar technology and and so forth. And I&#8217;m also a pilot, a pr you private pilot, although I do have a jet type rating in several classes of jets and commercial pilot, instrument pilot, etc. Now I&#8217;m not anywhere near the courage, you know. I said David Fravor has more courage in his left cuticle than I have in my whole body. You know, I couldn&#8217;t get into the military, let alone be as good a pilot as he is. I&#8217;ll stipulate that all</p><p>But I know a lot about radar and I know a lot about microwave energy and returns. so in order for this to be true, the returns are matter matter much more to me than than even Fravor, as great as an eyewitness he may be, and and and that I&#8217;ll stipulate, even though I have counterexamples that I can present later. But for radar to return, it has to reflect off something. So that can be any form of dielectric, it could be anything with you know relatively large or small cross section.</p><p>You&#8217;re assuming, you know, that it&#8217;s actually smaller than the visible, visible cross section of the same encounter by a factor of, you know, 10 or something like that, which which I think is more than fair because you&#8217;re trying to get, you know, a lower limit. You know, you&#8217;re trying to have some credulity in this and and not go to the extremes. But for this to simultaneously reflect and also be visible in the visible light spectrum and reflect it, that puts a bound on the dielectric constants that are permissible. And I&#8217;m wondering if, you know, we don&#8217;t know what it was made of.</p><p>But we certainly know that any material object that will make that return will have these ancillary characteristics, sonic booms, you know, extreme thermal heating. I mean, 10x, you know, the nuclear reactor output is is not insubstantial. So how do you rectify that as a scientist? How do you, you know, square the circle that this can&#8217;t be something, you know, some unknown material? It can&#8217;t be it can&#8217;t be a materials problem. It has to be reflective and it has to be reflective.</p><p>in the visible and in the in the millimeter wave or or microwave. How do you how do you reconcile those facts?</p><p>Prof. Kevin Knuth<br />I I haven&#8217;t thought about the radar return problem. and I haven&#8217;t thought about it that way in being able to say something about the dielectric constants of the hull of the object or the the the surface. So that&#8217;s that&#8217;s that&#8217;s an interesting question. That&#8217;s something I hadn&#8217;t even considered.</p><p>Brian Keating<br />Is it?</p><p>Brian Keating<br />Okay. All right. Maybe something we can work on together at some point. so go on, Kevin. Yeah, I didn&#8217;t want to derail you but I you know.</p><p>Prof. Kevin Knuth<br />Yeah, no, and and with regard to the power output, yeah, ten times the total nuclear out power output of the United States, that&#8217;s shocking. I mean the pr ser here&#8217;s the real problem, and this is gonna come up again when I talk about luminosities, which are also incredibly high. But you&#8217;re you&#8217;re talking about one thousand gigawatts of power, right? And no engineering no engineered object is a hundred percent efficient.</p><p>Brian Keating<br />Mm-hmm.</p><p>Prof. Kevin Knuth<br />Right. So let&#8217;s say, I mean, the things we make are usually at best, at best, you&#8217;re looking at 30% efficient for a machinery, right? And and so let&#8217;s say that these guys are really good at what they do, and it&#8217;s they can get it down to you know an efficiency of ninety-nine point nine percent efficient, right? So you&#8217;ve only got a point one percent inefficiency.</p><p>Brian Keating<br />Mm-hmm.</p><p>Prof. Kevin Knuth<br />Let&#8217;s say they&#8217;re really very, very good. Even if you have a 0.1% inefficiency, that means that 0.1% of a thousand gigawatts is going to be leaking out in waste heat. That means a gigawatt of power in waste heat you&#8217;d have to deal with. That would melt any craft in a very short order. clearly.</p><p>Brian Keating<br />Yeah.</p><p>Prof. Kevin Knuth<br />something strange is going on. we&#8217;re we&#8217;re missing something. And it&#8217;s not quite clear what exactly what that is. and, you know, is could it be that these are not moving the way we think they&#8217;re moving? are they, you know, peep of course we have ideas about space-time warping and, you know, these are rather far out ideas that we don&#8217;t actually have a lot of evidence for. But it&#8217;s hard to reconcile the</p><p>these high Gs and having the any machinery or living being beings on top of it being able to survive accelerations like that. That&#8217;s a whole different problem. and then there&#8217;s other questions that the physicist comes up with. I mean that things that bother you. So this thing accelerates to so the thing accelerates to about forty thousand miles an hour at the midpoint and then decelerates to zero. Where did that energy go when it decelerated?</p><p>Brian Keating<br />That&#8217;s right.</p><p>Prof. Kevin Knuth<br />Energy doesn&#8217;t disappear. You&#8217;ve gotta when this thing comes to a stop, that energy had to go somewhere. And you know, this power is correct. There should have been about two an explosion, about two hundred and fifty tomahawk cruise missiles worth of of explosion when the thing just stopped. that should have been catastrophic and wasn&#8217;t, right? so these things don&#8217;t make sonic booms. They don&#8217;t we have a lot of I I</p><p>Brian Keating<br />Right.</p><p>Prof. Kevin Knuth<br />I try to think about this as a detective. We have a lot of clues, but they&#8217;re not fitting together very well. It&#8217;s very odd. so this this isn&#8217;t this isn&#8217;t normal technology, clearly. And the but I think the power alone makes it clear that this is not human tech if if what&#8217;s going on what we think is going on.</p><p>Brian Keating<br />Right.</p><p>Brian Keating<br />Mm-hmm.</p><p>Brian Keating<br />If it&#8217;s yeah.</p><p>Prof. Kevin Knuth<br />So this is one example. We have another example where we have radar data. This is from the Japanese airlines case in 1986, where you&#8217;ve had a Japanese cargo plane 747 was flying from Tokyo, or it was it was actually going from Paris to Tokyo. I have that backwards. It was going from Paris to Tokyo when it encountered several UFOs over Alaska.</p><p>And and the the event culminated with a large UFO that was shaped like a walnut. There&#8217;s a drawing on the right here from the pilot. that and the pilot estimated to be about this size of four seven forty sevens. so this thing&#8217;s about the size of an aircraft carrier. when the pilot had said when it was in front of the plane, that was all he could see out the windscreen.</p><p>how wrong can a pilot be about this, right? So so the object&#8217;s there. I think that&#8217;s pretty clear. And it followed him for forty five minutes. They had military height finding radar had detected this and was was collected by the FAA and the FAA chief for ad accidents and investigations, John Callahan, actually made copies of the radar data</p><p>And kept them in a box under his desk until he retired, at which point he made them public. But he kept copies because President Reagan&#8217;s science team came and took all the data. They confiscated it. So he actually released the copies publicly. let&#8217;s see, and Dr. Daniel Kumbay, who was who was at the Niels Bohr Institute for some time, had analyzed this radar data and</p><p>looked at the acceleration made by several jumps is the so this ufo basically stayed about seven kilometers away from the airplane and just with every sweep of the it basically moved around the plane and with every sweep of the radar it would be on one side of the plane and then the next sweep of the radar would be at the other side of the plane. So you can estimate the minimum accelerations here. And you can see here on this table they&#8217;ve got the 11 jumps</p><p>Prof. Kevin Knuth<br />the accelerations you&#8217;re looking at there are several three cases where the acceleration&#8217;s coming up on ten thousand G&#8217;s.</p><p>Which is really, really insane, right? You&#8217;ve got something the size of an aircraft carrier accelerating at ten thousand Gs. The maximum speeds here, you&#8217;re if you were the maximum speed would be th Mach 350. And that&#8217;s basically close to two thousand or two hundred and sixty-nine thousand miles an hour. at that speed you can get to the moon in fifty-three minutes.</p><p>So if this is gonna be a two hour podcast and all, we could have left for the moon on this craft and gotten there and come right back in the amount of time that it&#8217;ll take you to watch this two hour podcast.</p><p>Brian Keating<br />So two questions before just continuing. So one is there are error bars here, which is very rare in this pseudo industry or you know, kind of this this t field of study, you know, that you have error bars. Exactly. And that&#8217;s why and that&#8217;s why we need more. I mean, I hear a lot of things about well b NASA suppressing this and and astronomer, you know, my friend Sarah Scholes wrote a book that, you know, they&#8217;re already here about, you know.</p><p>Prof. Kevin Knuth<br />Right. Well that&#8217;s what happens when you get scientists involved. Academic scientists involved. Yep.</p><p>Brian Keating<br />sightings and so forth and she she just estimated, you know, how much time do astronomers like me and and my colleagues spend looking at all domains from ra longest wave radio waves to you know gamma rays in space? How much time are we observing, you know, so many staradians on the sky? And it&#8217;s a huge amount. And for for a UFO enthusiast, you know, to kind of besmirch a scientist and say that like, you&#8217;re just paid off by NASA. I mean, I I work for NASA once, you work for NASA.</p><p>But I think it&#8217;s more you&#8217;re I know exactly, right? I mean it never ha I was like, you know, are you getting paid by you know Galileo Project? Nope, you&#8217;re not getting any, you&#8217;re not getting a dime from it, as far as I understand. So this is a canard. I think that people are sloppy thinkers that are true believers, they do their cause a disservice because they don&#8217;t include error bars. An error bar, I always tell my students, Kevin, I know you teach a lot about Bayesian statistics. I&#8217;ve gone through your lecturing, you&#8217;re an award winning lecturer.</p><p>Prof. Kevin Knuth<br />I would love to be paid off by now. I would love somebody, anybody to pay me off would be great. We don&#8217;t get paid a lot as academic scientists.</p><p>Brian Keating<br />I&#8217;ve gone through your lectures. I&#8217;ve watched them. You know, my my take is that I don&#8217;t care if you get the answer right. Like I don&#8217;t care if you get the central value. You know, you measure G and you get it nine point eight meters per second squared, but you don&#8217;t tell me that your error bar is a hundred meters per second squared, plus or minus, you know, on the plus side and minus ten meters per. That&#8217;s my problem with the Drake equation. It&#8217;s always presented as a equation for a number as if that&#8217;s what we do. No, no.</p><p>The sign of a good scientist is what error bar he or she can assign to it and the breakdown in that error in terms of systematic and statistical errors. My first question is what are those errors? Are they statistical? Are they systematic? And my second question is, this thing was flying at, you know, for for an hour at at 747 speeds, call it 500 miles an hour, pretty much. Let&#8217;s round up, round numbers. And so this thing was tracked over what? I mean, Alaska is not that big. So, you know, how are they tracking this thing for that long?</p><p>Prof. Kevin Knuth<br />Mm-hmm.</p><p>Brian Keating<br />Getting this data and and what to w what can we ascribe these errors bars to first?</p><p>Prof. Kevin Knuth<br />Right. Well when you calculate errors like this, but st what you&#8217;re doing is you are characterizing the</p><p>Prof. Kevin Knuth<br />You c you can&#8217;t you can&#8217;t actually estimate systematic error because you would need to know what the true value is to estimate that, right? You&#8217;d have to have some handle on what the true value is. So you can never really estimate a systematic error. so what you&#8217;re doing is you&#8217;re basically looking at the error of the you&#8217;re you&#8217;re basically kind you&#8217;re calculating a probability distribution of the acceleration.</p><p>Which is what I showed on this previous slide. This is actually what this is giving you. this was done by sampling, which is why it&#8217;s fuzzy. But this is the probability distribution for the acceleration. That&#8217;s that&#8217;s the answer that Bayesian statistics would give you, right? And and I can report, you know. So what do you want to report? Well, I can report the the mode, the peak. I can find the peak. I should fit this to a curve and I found the peak. I can report the mode, I can report the the mean.</p><p>Brian Keating<br />Mm-hmm.</p><p>Prof. Kevin Knuth<br />So I can calculate the average value, which is not going to be quite at the same place that the mode is. so you could so you actually have multiple numbers you can report. And then you have your uncertainties, which you can estimate another way. And uncertainty is a way of estimating the width of the distribution. So what&#8217;s typically done is once you find the mode, you you know where the mode is the peak. Once you know where the peak is, you can then</p><p>You can look at the curvature of the peak, and basically you&#8217;re fitting a Gaussian distribution to this, and that and you take the the standard deviation of that Gaussian distribution, fit to the peak, you use that as the measure of the width of this distribution. Now in this case, it&#8217;s asymmetric, it&#8217;s clearly an asymmetric distribution. So what I did is I I actually fit a one Gaussian to the left side and a second Gaussian to the right side, and then I used the</p><p>the standard deviation of the each Gaussian is the plus or minus. So it has a different plus or minus, whether you&#8217;re on the plus side or the le or the minus side. And that&#8217;s basically what&#8217;s happening here with the with the accelerations. So your so that so conceptually what the uncertainty is telling you is it gives you the an idea of the precision of your estimation procedure.</p><p>Brian Keating<br />Yeah.</p><p>Brian Keating<br />Yes.</p><p>Prof. Kevin Knuth<br />And that&#8217;s really what the uncertainty means. And that&#8217;s what the uncertainties in pretty much any scientific calculation actually give you. You can&#8217;t you can&#8217;t easily estimate uncertainty and any any kind of bias unless you know what that bias is.</p><p>Brian Keating<br />Mm-hmm.</p><p>Brian Keating<br />Mm-hmm.</p><p>Prof. Kevin Knuth<br />And and and the uncertainties are important because it tells you how well you know the answer, right? Right. So when you when I s when when I get as an answer this distribution, I tell you 5400 Gs. I don&#8217;t know that it&#8217;s exactly 5400 Gs, it&#8217;s probably somewhere in here. And that&#8217;s what that uncertainty tells you. And I didn&#8217;t list the uncertainty on this slide because it just will make it more busy than it already is.</p><p>Brian Keating<br />Right.</p><p>Brian Keating<br />Mm-hmm.</p><p>Prof. Kevin Knuth<br />but in the research paper that you can find online I have the uncertainties listed there.</p><p>Brian Keating<br />Got it. Okay. Yep. So let&#8217;s go on.</p><p>Prof. Kevin Knuth<br />Yeah. And in fact, when I worked at NASA, I tell I tell my students, you have to be willing to learn new things when you&#8217;re actually working as a scientist. And you know, because many times students, especially when they get into graduate school, they think, Well, I&#8217;m all done taking classes, I don&#8217;t have to learn anything else. I&#8217;m like, no, that&#8217;s actually not true. you&#8217;ve got it backwards exactly.</p><p>Brian Keating<br />That&#8217;s backwards,</p><p>Prof. Kevin Knuth<br />And when I worked at NASA, we were it was the early 2000s and and NASA was producing information about climate change and Congress came back and said they pushed back and said, Look, what is your what are your uncertainties about? How certain are you about these results? And NASA needed to go back and redo some of the work to produce those uncertainties. And and so I actually got</p><p>Called into my boss&#8217;s office and they said, Kevin, we have to do some work on estimating uncertainties for climate change. And you&#8217;re one of the only experts we have in this department that can calculate those uncertainties. So I need you to work on climate problems. And I was like,</p><p>Wait a I do astrophysics, I&#8217;m not a climate scientist. Like and I and I told myself, but I&#8217;m not a climate scientist. I don&#8217;t know anything about climate. He goes, Yeah, that&#8217;s why you&#8217;re going to learn. He goes, We&#8217;re going to send you to NASA Gis in New York City and you&#8217;re going to train with them for six months and and like you&#8217;re yep, I&#8217;m gonna learn for the whole you know, for the whole next two years about climate, earth planet climates and this so yeah, that happens and you have to you have to do it and</p><p>Brian Keating<br />That&#8217;s right, yeah.</p><p>Prof. Kevin Knuth<br />That&#8217;s how you&#8217;re that&#8217;s how you become successful.</p><p>Prof. Kevin Knuth<br />And so what did I do that night? I panicked. I went to the bookstore to find a book on atmospheres or climate or something and I was poking around and the most useful book I found was my little golden book on clouds and it was through cloud types and I just bought it. I thought I don&#8217;t even know what kinds of clouds there are. So I should maybe learn this first. So I so it was a it was literally a kid&#8217;s book. I thought I&#8217;m gonna start with the kids&#8217; book tonight. We&#8217;ll build up.</p><p>Brian Keating<br />Okay.</p><p>Yeah.</p><p>Okay, great.</p><p>Prof. Kevin Knuth<br />Alright, so yes, so so these accelerations are insane. And what can you do with these accelerations? What if this is actually a spacecraft? I mentioned here you can get to the moon in 53 minutes. What if you were traveling further? So now and I make this point in the paper. Not only do these objects have flight characteristics necessary for interstravel tell interstellar travel, excuse me, they would make excellent interstellar craft.</p><p>So a 1000 G acceleration, one fifth of the one fifth of what the Nimitz case was observed doing, and one tenth of the acceleration that was observed in JAL, you can get to ninety percent the speed of light in about seventeen hours.</p><p>Brian Keating<br />Fifty years, dark men are explained like</p><p>Brian Keating<br />Mm. Mm-hmm.</p><p>Prof. Kevin Knuth<br />Once you&#8217;re going 90% the speed of light, you relativity kicks in. Time slows down, distances change. you can actually travel much, much further. So yeah, so if if these objects can sustain an acceleration in space, a 1000 G acceleration will get you to 30% the speed of light in 2.7 hours. how far could you go? Well, here&#8217;s some examples. The the the nimits</p><p>Brian Keating<br />yeah.</p><p>Prof. Kevin Knuth<br />Tic Tacs could get to the nearest star, Proxima Centauri, in less than one and a half days. It&#8217;s a day trip. and actually could make it to out to Zeta Reticulli, which is in Trappus I, which are about forty light years away, in less than two days. yeah, this is the amount of time it&#8217;ll take you to drive from New York to Florida. you could get to the nearest stars, right, within forty light years, and there&#8217;s what?</p><p>Brian Keating<br />Yeah.</p><p>Prof. Kevin Knuth<br />There&#8217;s probably 80 some like stars in the forty light year range. I don&#8217;t know the exact number.</p><p>Brian Keating<br />Yeah, a lot. Yeah, mu way more than I think thousand, forty light year radius, yeah.</p><p>Prof. Kevin Knuth<br />Yeah, it&#8217;s it&#8217;s right. So and those were just I I I think I was just thinking about sun like stars. So you&#8217;ve got red red dwarves, which are gun Trappus one is a red dwarf goes around a red dwarf star, so there&#8217;s plenty of plenty of places to go. Yeah, so these craft now now granted, and you&#8217;ll hear you&#8217;ll hear scientists say this all the time when they complain about interstellar travel, they say, it</p><p>Brian Keating<br />Sunlike stars, yeah, yeah.</p><p>Brian Keating<br />That&#8217;s right.</p><p>Prof. Kevin Knuth<br />Even if you can go fast, it&#8217;ll still take you, you know, forty years to get to Zeta reticuli. Well, it&#8217;ll take you forty years in the frame of the galaxy. For everybody in the galaxy, it takes forty years. For the traveler, it takes less than two days. And there&#8217;s a time difference and just cause time slows down. Now what that means is that you can&#8217;t you can&#8217;t fly from Zeta reticuli to the sun.</p><p>Brian Keating<br />Yeah.</p><p>Brian Keating<br />That&#8217;s right.</p><p>Prof. Kevin Knuth<br />hang out for an afternoon and then fly back to Zeta Reticuli and make it home for dinner. you&#8217;ll for you it&#8217;ll be a four day trip. for everybody on Zeta Reticuli or Earth it&#8217;ll be an eighty year trip. Right. So so this is what makes this is what makes interstellar travel hard, not the fact that you can do it. Do you want to is the question. And actually I gave a talk at SCU, one of the first SCU meetings where I</p><p>Brian Keating<br />Mm-hmm. Yeah.</p><p>Prof. Kevin Knuth<br />I noted that if you actually had a nomadic society, so that you had a society that lived on their spaceships, didn&#8217;t live on a planet, they lived on spaceships, they then could do this easily because you would just meet up. So so you and I will meet up on Earth. I&#8217;ll go visit Zeta Reticula. You want to go to Trappist One, we take off and go there and then say we&#8217;ll meet back in two weeks. We come back two weeks later, and it&#8217;s</p><p>literally eighty weeks later on Earth, but for us it&#8217;s only two weeks later. that&#8217;s that&#8217;s how you could do it.</p><p>Brian Keating<br />Mm-hmm.</p><p>Prof. Kevin Knuth<br />And and there are not as many nomadic societies on Earth now as there used to be, but there still are nomadic societies. in sp in fact in Southeast Asia, there&#8217;s a society that lives they live entirely on their boats, and that&#8217;s what they do. All right, so transmedium travel and unidentified submerged objects. So we&#8217;ll now look at another class.</p><p>Another set of data. So I&#8217;ve looked at these accelerations. These are much higher than humans could could make. You already saw that this little Tic Tac, a thousand kilogram tic-tac would in doing its 5000 G maneuver would put out would require a thousand gigawatts. I I can&#8217;t even guess what an aircraft carrier sized craft would require to do a 10,000 G acceleration.</p><p>you&#8217;re looking at something way, way orders of magnitude beyond that.</p><p>Brian Keating<br />yeah.</p><p>Prof. Kevin Knuth<br />And now let&#8217;s see here, something has happened. There we go. All right, so underwater objects. this is a this is the Aguadilla object.</p><p>And and Doug Bettner gave a talk at SCU and argued that that a lot of the strangeness of this object was actually due to the fact that you had a lot of video compression going on. And so so this may not be the this isn&#8217;t my good power example. It was just an example of transmedium travel. UFOs have been observed going from air into water and from water into air pretty seamlessly. And</p><p>Humans can&#8217;t do that very well. The one of the craft that we have that does this is a is a a seaplane. A seaplane, I love that example, because a seaplane is neither a good plane nor a good boat. So so it&#8217;s the worst of all worlds, but it can do both. And that&#8217;s that&#8217;s where we&#8217;re at. now of course there&#8217;s new drones that can probably go in and out of water more easily. I&#8217;m sure we&#8217;re working on things like this.</p><p>Brian Keating<br />Mm-hmm.</p><p>Brian Keating<br />That&#8217;s right. The worst of all worlds, right?</p><p>Brian Keating<br />Trons, yeah.</p><p>That&#8217;s right.</p><p>Prof. Kevin Knuth<br />The one encounter that I find fascinating is from February of nineteen eighty-seven, where the New Zealand frigate, the HMNZS Southland, was involved in daily echo exercises from November to February, where they were basically trying to lure USOs in from the Pacific Ocean into the Haraki Gulf.</p><p>So they basically would go out into the go out north out of Auckland through the Heraki Gulf, out into the Pacific Ocean, then turn around and come back and watch on their sonar to see if they&#8217;re followed, and then try to study these objects. in February of 1987, they were followed by a large underwater object, which they measured to be 150 feet wide and 800 feet long. This is 30% longer than the typhoon class submarine, which is the</p><p>Brian Keating<br />Yeah.</p><p>Prof. Kevin Knuth<br />Largest submarine in the world, the Russian typhoon class. it and they noted that they did not detect propeller wash on any of these objects. So they couldn&#8217;t identify whether they were Russian or American, if they were submarines. they weren&#8217;t typical submarines, there was no propeller wash. it does not escape me that the movie Hunt for Red October came out the same year, basically. And</p><p>Brian Keating<br />Yeah.</p><p>Prof. Kevin Knuth<br />And I know about this, and and and then that&#8217;s an interesting fact. But so they were followed by this USO for some time. They tried to shake it, and at at one point the USO was twenty two point five kilometers away, and they were trying to shake this, and it closed the distance in about three seconds. So this underwater object accelerated to the ship.</p><p>Brian Keating<br />Mm-hmm.</p><p>Brian Keating<br />Mm.</p><p>Prof. Kevin Knuth<br />passed under the ship in about three seconds and and actually took all the power out on the ship. the power went out on the ship, drained the batteries so they couldn&#8217;t restart the engines and they were adrift and had to be rescued. they had so you can do this calculation, this rough calculation. It closed a 2.5 kilometer distance in about three seconds, so you get a minimum speed of 1800 miles an hour.</p><p>Brian Keating<br />Mm.</p><p>Prof. Kevin Knuth<br />Or if it you have it accelerating, it would have a minimum acceleration of five 57 G&#8217;s with a top speed of 37,000 mile 3700 miles an hour. So this is a really interesting case. And in fact, I was on a Zoom call with David Barnett last night. We talked we talked for almost two and a half hours. I was really grateful for his time going over the go of the time. I actually have a printout of the</p><p>raw sonar data. The raw sonar the basically the the this there&#8217;s a stylus that was moving across a sheet of paper as the paper&#8217;s printing showing the the actual sonar returns. So I actually have a have a scan of that. Sadly it is</p><p>That sheet of paper is 40 years old at this point and has faded a great deal because the stylus was actually burning into the paper and it&#8217;s not a very powerful burn, so it&#8217;s all very faint. And I&#8217;m not sure I can actually pull the speeds and accelerations out of it for out of that sonar data yet. But we were talking last night about how to go about doing that.</p><p>Brian Keating<br />Yeah.</p><p>Brian Keating<br />Mm-hmm.</p><p>Prof. Kevin Knuth<br />Now it&#8217;s amazing is since then, and since since I first noted this, I first talked about this case in the Seoul meeting in 2023. And since then a number of people have tried to get information about the Southlands activities in November through February of n eighty six, eighty-seven. and it&#8217;s all classified. their activities are classified.</p><p>Brian Keating<br />Mm-hmm.</p><p>Prof. Kevin Knuth<br />But people have learned other things. There were other ships involved. the there was an Australian ship that was there. And and I don&#8217;t have my notes with me. My notes are in the other room because I just learned this all last night and I haven&#8217;t transcribed them yet. But but the Australian ship traveled with them for some time and then l left and then disappeared, dropped out of communication, just before this event happened.</p><p>And they dropped out of communication and then put out a distress call and they had had some kind of encounter and had to they made it back to Sydney and then had to go in for refitting. and and then there was also an American aircraft carrier involved that the Americans the American records say that it was that it was Doctor Alameda, California. but</p><p>No, it was not. It was actually out in the Pacific Ocean in the same area. So what they were doing is they were they were going out north of Auckland over along the Kermetic Trench. So the Kermetic Trench is a very long trench that goes from it&#8217;s an undersea subduction zone. The trench goes from the north, northern part of New Zealand all the way up to Tonga. And</p><p>Brian Keating<br />Mm-hmm.</p><p>Prof. Kevin Knuth<br />at at its deepest it&#8217;s 33,000 feet deep. So so there was a lot going on. And then there was also another New Zealand ship involved that the HMNZS IIE and I think that was the one that was fitted with the the at the time modern British toll ray sonar and they were actually mapping areas of the trench. So this</p><p>Brian Keating<br />Hmm.</p><p>Prof. Kevin Knuth<br />apparently was an activity to study these USOs or these underwater objects. that was a collaboration between the New Zealanders, the Australians, and the Americans. and a lot of this is classified. One of the I he showed me a screenshot, one of the records from what the HMNZS Southland was doing is was classified for 75 years. You&#8217;re not getting that data, we&#8217;re not getting that</p><p>Brian Keating<br />Yeah, that&#8217;s</p><p>Prof. Kevin Knuth<br />File until 2062 or something crazy like that. It&#8217;s worse than the Kennedy assassination. What are they doing in the Kermetic trench? It&#8217;s a good question. All right, so so let&#8217;s go back to the points of energy and power, right? So we&#8217;ve got speeds and accelerations. So how how do you move at 3,700 miles an hour underwater? And and this isn&#8217;t a small object. Basically, you&#8217;ve got</p><p>Brian Keating<br />It&#8217;s like worse than the Kennedy assassination or the</p><p>Prof. Kevin Knuth<br />This object has got to push a cylinder of water out of the way, right? and this this is a what 2.5 kilometers. That&#8217;s a mile, a mile-long cylinder of water with 150-foot diameter has to be pushed out of the way. Now, water is not compressible. Anybody who&#8217;s done a belly flop off of a low dive or high dive knows this. water is not compressible. You can&#8217;t compress it. So you have to lift up the water, right?</p><p>Brian Keating<br />So this is not Yeah.</p><p>Prof. Kevin Knuth<br />You have to lift up the water. So you can estimate how the now you can estimate the power, the amount of energy it takes. Because they&#8217;ve got to basically lift up a one mile long column of water. You&#8217;ve got to lift it up about 150 feet, right? In what was it? How many seconds? It was what 30 seconds, right? So now we can calculate the amount of power here. I actually work out this calculation. So how much mass was involved?</p><p>Brian Keating<br />It&#8217;s thirty seconds, yeah, yeah.</p><p>Prof. Kevin Knuth<br />Well how first first let&#8217;s figure out the mass of the object and mass of the object. I don&#8217;t know the mass of the object, but it&#8217;s underwater, so it has to at least have the mass of the water. It&#8217;s at least as dense as the water. If it wasn&#8217;t as dense as the water, it would be floating. So as a lower limit, you can figure out the the size of the object and because they know it was 150 foot.</p><p>In diameter and 800 feet long. So you&#8217;ve got a mass of about four times 10 to the eighth kilograms. this thing had to accelerate, so you can figure out the power from its motion. That&#8217;s going to be the the mass times the acceleration squared times the time. That&#8217;s how I&#8217;ll calculate that. And then now it also has to lift up a one mile-long column of water, and you have to do this in 30 seconds. So this is basically the calculation for that.</p><p>Now we can look at the amount of power it takes for this to happen. As it&#8217;s speeding up, it&#8217;s doing more it&#8217;s taking more power. So you&#8217;re you know the top power is you&#8217;re looking at something like something on the order of ten to the eighth megawatts. let&#8217;s see, is that right or is that</p><p>Let me go back. I&#8217;m worried that I have messed up the units on my</p><p>Prof. Kevin Knuth<br />I may have messed up the units on my chart here because I&#8217;m thinking if it&#8217;s if this is ten to the eighth megawatts, ten to the eighth megawatts is ten to the fourteen, which is more than insane. So so I&#8217;ve got to double check that. But you can you can do the math here. How&#8217;s that for that? So you&#8217;re dealing with you&#8217;re you&#8217;re dealing again with megawatts, you know, many, many megawatts of power.</p><p>w to be able to pull this off. You&#8217;ve got to take power to move the move the object and you&#8217;ve got to displace the water. insane amount of power. So what other evidence do we have? We have luminosities. Some of these things are really crazy bright. This is one thing that one of the many things that s Steven Spielberg got writing Close Encounters of the Third Kind. Yes, people get sunburnt from these things. That happens.</p><p>Brian Keating<br />Right.</p><p>Prof. Kevin Knuth<br />So some of them are extremely luminous, and so much so that the photographs are difficult to interpret. So these are all photographs of UFOs where they&#8217;re just glowing in white. They&#8217;re very, very bright. The this image here taken in 1956 over the Canadian Rockies in Alberta. we can Bruce Maccabe and Jacques Villet actually had access to the actual film.</p><p>And counted silver grains to basically figure out how much and what the intensity of light was hitting the camera. So you can actually yeah, so Dr. Bruce Maccabee did study this and Jacques Vallet is also summarized and reported. So you can look at the radiance of the object and and how that&#8217;s going to relate to camera parameters and</p><p>Brian Keating<br />Wow.</p><p>Prof. Kevin Knuth<br />Basically, they measure the exposure levels and the shutter times and estimated the radiance to be this much. So it&#8217;s yeah, well, there&#8217;s interesting units there. Luminosity units are hard anyway, or radiance units two watts per stair radium centimeters squared, right? for so for an isotropic source. So if what do you mean by isotropic? If this thing is radiating uniformly in all directions, then the distance</p><p>For the distance between the plane and the object, which was estimated to be between six and twenty kilometers, you&#8217;re looking at a power of two thousand five hundred to thirty thousand megawatts.</p><p>Brian Keating<br />Mm.</p><p>Prof. Kevin Knuth<br />That&#8217;s a lot of light. That&#8217;s a crazy amount of light. And I have a bit of a problem with that because if it&#8217;s putting out that much light and it&#8217;s close to the clouds, the clouds should be illuminated pretty much as much as the sun is illuminating them. So so I then thought, well, let&#8217;s redo the calculation, assuming that it&#8217;s a directed light. So it&#8217;s shining a light on the airplanes.</p><p>Brian Keating<br />Yeah.</p><p>Brian Keating<br />Yeah, that&#8217;s weird.</p><p>Prof. Kevin Knuth<br />And if you do this, you come down to a lesser power of 1.7 kilowatts. That&#8217;s not as shocking, but it&#8217;s still a a kilowatt of light is a lot of light. And and that&#8217;s pretty amazing. Now</p><p>Jacques Villet, Luc Denis, and Jeffrey Mystery Meschersky had published a paper in Progress in Aerospace Sciences last year where they estimated the radiative energy in ground level observations based on bark being burnt. So this is the Hainesville, Louisiana case where an physicist actually was driving with his family and they witnessed a pulsating light in the forest.</p><p>Which started as a red orange glow and became a brilliant white flash, illuminating the whole woods, and it drowned out the headlights. And what the</p><p>Th what the Professor Galloway noted is when it drowned out the headlights, he he knew the amount of power in the headlights and he was able to estimate that the light coming from this thing in the forest was had to be megawatts of power. And that worried him. He actually s turned the car around and went the other way when drove away from it, which is probably a smart idea.</p><p>Brian Keating<br />Uh-huh.</p><p>Prof. Kevin Knuth<br />so the object itself wasn&#8217;t seen. they were able to locate where it had that it had landed. It had landed in a clearing in the forest, and the trees around the clearing were all scorched from the from the light from this object. And so</p><p>Prof. Kevin Knuth<br />So this was actually described in the Condon report. And the Condon report actually recalculated some of these power and actually had a higher po amount of power. they did not have a good explanation for what happened and they left it as an uncertain event. when yeah, no, when Jacques Villet and Luc Denis and Ms. Jeffrey Mistherski looked at this, they were able to get power estimates closer to</p><p>closer to five hundred megawatts of light. So that&#8217;s a again, a crazy amount of light. humans don&#8217;t make things that that&#8217;s a nuclear power plant&#8217;s worth of light, right? So so this is not a human made object.</p><p>Brian Keating<br />Mm. Mm-hmm.</p><p>Brian Keating<br />So Kevin, we&#8217;re I don&#8217;t wanna go past the half hour, but we haven&#8217;t done the podcast yet. Yeah, okay, great. Yeah, let&#8217;s finish this up and then switch to the podcast. Yeah. Go for it.</p><p>Prof. Kevin Knuth<br />right. I&#8217;m basically on my conclusion slide. I think this will we&#8217;re I think we&#8217;re good.</p><p>Yeah, so so the minimal power estimates, estimates that we&#8217;re making here and basically in s in radar recorded maneuvers, sonar recorded maneuvers greatly exceed the power produced by nuclear power plants. And the minimal power estimates in the luminosity alone are on the order of nuclear power plants. So I think this is this to me is the strongest evidence that we have that we&#8217;re</p><p>not dealing with human technology.</p><p>humans don&#8217;t make vehicles that have the amount of that exhibit the amount of power of a nuclear power plant. It just doesn&#8217;t happen. it&#8217;s dangerous. It would be dangerous for us to do that. and it could be catastrophic. and and in fact, it here is an interesting point. It suggests I mentioned this earlier, it&#8217;s suggests an exceptional engineering since if even if you had a one percent inefficiency of a one gigawatt system.</p><p>That would give you ten megawatts of waste heat, which would be catastrophic. Now we do have a few cases where you have UFOs having trouble, right? You&#8217;ve got the Ubatuba incident in Brazil where the UFO was clearly was wobbling and all over the place, and the people observing it noted that they thought it was in trouble and then it just exploded. the and and some of that debris and</p><p>Prof. Kevin Knuth<br />And material&#8217;s been collected and has been studied. And then you have the the case in Council Bluffs, Iowa, where you&#8217;ve got the UFO that basically was dumping molten metal in the forest. And that also happened in Puget Sound in 1947. so could it be that in those UFOs they had a problem with their engineering system and they were basically melting the inside of their craft? That&#8217;s very possible. Well that that could be what&#8217;s going on.</p><p>but these aren&#8217;t, you know, these are not human made objects. And the power estimates in propulsion plus the fact that the objects do not seem to be interacting with their environment suggests that we&#8217;re observing non Newtonian propulsion or motion. They&#8217;re not moving in the way that we move. what that means exactly, I don&#8217;t know. but it opens the door to new, you know, is this new engineering or new physics? that&#8217;s debatable.</p><p>And we don&#8217;t have enough ev evidence to decide on those things yet. But some of these UAP are extremely anomalous when it comes to energy and power. And I think that&#8217;s important to keep in mind.</p><p>Brian Keating<br />Well, Gavin, this is great. I would like to push back as much as I can respectfully, as I have a cr tremendous amount of respect. I think the the key gripping question that I cannot really let go of, you know, just despite how much I love your work and and respect you and and resonate with a lot of what you&#8217;ve said is that.</p><p>You know, these cannot be, I agree, these cannot be human technology, but that&#8217;s predicated that all of these, you know, sensors all agree that these are actually true signals. In other words, they&#8217;re not artifacts and they&#8217;re not hallucinations, they&#8217;re not you know, the reports of of you know planets and swamp guests. And the key thing I keep coming back to is my friend David Spergl, a member of the National Academy of Sciences. He is the president of the Simons Foundation. He led NASA&#8217;s blue ribbon panel a couple of years back.</p><p>Prof. Kevin Knuth<br />Ha ha.</p><p>Brian Keating<br />With my colleague here at UCSD, Shelly Wright, who&#8217;s also a legitimate hardcore scientist, but also is investigating extraterrestrial intelligence using pulses of light and other modes. She won the Drake medal. She&#8217;s she&#8217;s an esteemed scientist. that that commission found, you know, basically 95% of the things reported then had explanations. And we can never, as scientists, expect 100% lack of residuals.</p><p>And I think the anomalous residuals create n you know, it&#8217;s like the Pareto principle, 8020 per principle, except it&#8217;s 95.5. And that&#8217;s what I want to talk to you about on the podcast. So if you haven&#8217;t watched the podcast, go over to check out the podcast with Kevin Knuth. And we&#8217;ll see you over there. And we&#8217;ll get into some of the debate between two, you know, friendly debate, gentlemanly debate between two physicists that are seeking the truth. And I think that&#8217;s what I love most about what Kevin does. So Kevin, thank you for this lecture. And now we&#8217;ll</p><p>Prof. Kevin Knuth<br />thank you. Yeah.</p><p>Brian Keating<br />We&#8217;ll see you over on the other on the podcast now. Okay, so let&#8217;s stop this.</p><p>Prof. Kevin Knuth<br />Sounds good.</p>								</div>
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			<media:title type="plain">Former NASA Physicist: The UAP Maneuver That Should Have Exploded</media:title>
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		<title>Carlo Rovelli: Free Will Is Real. Physics Can Prove It.</title>
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		<pubDate>Mon, 28 Sep 2026 00:53:04 +0000</pubDate>
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					<description><![CDATA[Carlo Rovelli: Free Will Is Real. Physics Can Prove It. https://www.youtube.com/watch?v=ngAQz-4_F_o Transcripts: Carlo Rovelli:It&#8217;s not true that things get disordered. In fact, very often in nature, things left alone get ordered. To imagine that to be free you have to violate the laws of nature is stupid. You, Brian, can be in a superposition. You can be in LA and also in San Francisco. Your head is older than your feet. Brian Keating:That was Carlo Rovelli, one of the men who built Loop Quantum Gravity, telling me that the second law doesn&#8217;t say what I think it does. But he didn&#8217;t stop there. Over the next hour, He&#8217;s going to take apart the 3 things you were told that just ain&#8217;t so, ranging from the frost on the window where you may be sitting right now to the clock on your wall. And the clock is the one that really got me. He blew my mind by telling me that my head is older than my feet. And then he told me why that&#8217;s the least strange thing he was going to say. So Carlo, let&#8217;s start with the frost. You&#8217;re telling me that things left alone can get more ordered? That&#8217;s not what I was taught. Brian Keating:Walk us through that. Carlo Rovelli:First of all, I, I do think that the thermodynamic arrow, it&#8217;s very Basic. A lot of phenomena which we connect to the direction of time. For instance, I remember our last conversation. We don&#8217;t remember our next conversation, right? We have a picture of Brian young. We don&#8217;t have a picture of Brian old. We can decide where to go to dinner tomorrow, but we cannot decide where to go to dinner yesterday. So there&#8217;s all this profound difference between future and past. And I got convinced in the last, I would say, 10 years working a lot about that, I&#8217;ve written a lot of papers, that all these differences can all be traced to the thermodynamics arrow of time, to the entropic arrow of time. Carlo Rovelli:It&#8217;s not obvious, but the reason we have memories of the past and not the future has to do with entropy growing. If it wasn&#8217;t for entropy, there wouldn&#8217;t be this dissymmetry between— Now, this Have we fully understood the story? No, I think there are various aspects that we still get confused. And there are also, as you say, aspects that we oversimplify badly. Let me give you an example which is dear to me. We always make this story, okay? Take like latte, caf, coffee and mix and look, they mix. In fact, take water and vinegar, put some vinegar in water, mix and, you know, they mix. Now take water and oil. Mix, they separate. Brian Keating:Oh yeah. Carlo Rovelli:So is this contradicting our entire understanding of the universe? No, it&#8217;s just things a little bit more complicated than the way they usually said. It&#8217;s not true that under any definition of order or disorder, things get disordered. In fact, very often, In nature, things left alone get ordered, and oil and water separating is one of the many examples of that. If you have a box with balls that you shake and then you put it there on the table, all the balls go very nicely in order on the bottom. Okay? They order themselves. If you look at a beach, at the ocean, all the little grains are together and the big stones are on another side of the beach. They&#8217;re ordered by the same. Who ordered them? Okay? If you look at frost, the beautiful ordered structure there, who ordered that? I mean, a lot of things in nature get ordered by themselves. Carlo Rovelli:So the idea that entropy is disorder, careful, under a proper definition of order, under a proper definition of everything. But most of the things we call order and disorder are not connected to entropy in the way it&#8217;s usually said. That&#8217;s one example in which popular science is bad. In fact, maybe scientists are very confused about that themselves. And that&#8217;s important, right? Because people are surprised because biology came in, the very complexity of biology. Biology is a lot of order. So on the crust of our planet, the Earth, there&#8217;s all this structure that formed. Okay. Carlo Rovelli:People say, oh, this seems to be against entropy. No, why? Entropy grows and creates orders just happen all over. Brian Keating:Let&#8217;s talk about the early universe. You already mentioned the block universe and, and our mutual distaste of that hypothesis. I, I don&#8217;t like that. I want to ask the why question. I, I really— I do want to ask the why question, but I know why questions are not usually answerable. But, but the universe was somewhat lower entropy, right? It was in a very special configuration. Does that, in your mind, point to some reason why the arrow of time points in one direction? Carlo Rovelli:Yeah, yeah, the two are very, very connected. It&#8217;s not that one explains the other one because we need an explanation for either. I think the, the, the path law of entropy and, and the arrow of time, second law of thermodynamics, are different manners of explaining, of, of pointing out to the same phenomenon about which I think there is more to learn. Brian Keating:Does loop quantum gravity shed light on the low entropy boundary condition as I think it does? Or does it merely modify how a singularity would or would not be used? Carlo Rovelli:You might find some colleagues of me that disagree, but my answer is not at all. It tells us the dynamics of something that might have happened. If loop quantum gravity is correct, we sort of are able to compute what happened at the early universe. But I don&#8217;t buy the stories that connect that to initial low entropy or things like that. You see, I&#8217;m a conservative guy in science. Loop quantum]]></description>
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					<h2 class="elementor-heading-title elementor-size-default">Carlo Rovelli: Free Will Is Real. Physics Can Prove It.</h2>				</div>
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									<h2><strong>Transcripts:</strong></h2><p>Carlo Rovelli:<br />It&#8217;s not true that things get disordered. In fact, very often in nature, things left alone get ordered. To imagine that to be free you have to violate the laws of nature is stupid. You, Brian, can be in a superposition. You can be in LA and also in San Francisco. Your head is older than your feet.</p><p>Brian Keating:<br />That was Carlo Rovelli, one of the men who built Loop Quantum Gravity, telling me that the second law doesn&#8217;t say what I think it does. But he didn&#8217;t stop there. Over the next hour, He&#8217;s going to take apart the 3 things you were told that just ain&#8217;t so, ranging from the frost on the window where you may be sitting right now to the clock on your wall. And the clock is the one that really got me. He blew my mind by telling me that my head is older than my feet. And then he told me why that&#8217;s the least strange thing he was going to say. So Carlo, let&#8217;s start with the frost. You&#8217;re telling me that things left alone can get more ordered? That&#8217;s not what I was taught.</p><p>Brian Keating:<br />Walk us through that.</p><p>Carlo Rovelli:<br />First of all, I, I do think that the thermodynamic arrow, it&#8217;s very Basic. A lot of phenomena which we connect to the direction of time. For instance, I remember our last conversation. We don&#8217;t remember our next conversation, right? We have a picture of Brian young. We don&#8217;t have a picture of Brian old. We can decide where to go to dinner tomorrow, but we cannot decide where to go to dinner yesterday. So there&#8217;s all this profound difference between future and past. And I got convinced in the last, I would say, 10 years working a lot about that, I&#8217;ve written a lot of papers, that all these differences can all be traced to the thermodynamics arrow of time, to the entropic arrow of time.</p><p>Carlo Rovelli:<br />It&#8217;s not obvious, but the reason we have memories of the past and not the future has to do with entropy growing. If it wasn&#8217;t for entropy, there wouldn&#8217;t be this dissymmetry between— Now, this Have we fully understood the story? No, I think there are various aspects that we still get confused. And there are also, as you say, aspects that we oversimplify badly. Let me give you an example which is dear to me. We always make this story, okay? Take like latte, caf, coffee and mix and look, they mix. In fact, take water and vinegar, put some vinegar in water, mix and, you know, they mix. Now take water and oil. Mix, they separate.</p><p>Brian Keating:<br />Oh yeah.</p><p>Carlo Rovelli:<br />So is this contradicting our entire understanding of the universe? No, it&#8217;s just things a little bit more complicated than the way they usually said. It&#8217;s not true that under any definition of order or disorder, things get disordered. In fact, very often, In nature, things left alone get ordered, and oil and water separating is one of the many examples of that. If you have a box with balls that you shake and then you put it there on the table, all the balls go very nicely in order on the bottom. Okay? They order themselves. If you look at a beach, at the ocean, all the little grains are together and the big stones are on another side of the beach. They&#8217;re ordered by the same. Who ordered them? Okay? If you look at frost, the beautiful ordered structure there, who ordered that? I mean, a lot of things in nature get ordered by themselves.</p><p>Carlo Rovelli:<br />So the idea that entropy is disorder, careful, under a proper definition of order, under a proper definition of everything. But most of the things we call order and disorder are not connected to entropy in the way it&#8217;s usually said. That&#8217;s one example in which popular science is bad. In fact, maybe scientists are very confused about that themselves. And that&#8217;s important, right? Because people are surprised because biology came in, the very complexity of biology. Biology is a lot of order. So on the crust of our planet, the Earth, there&#8217;s all this structure that formed. Okay.</p><p>Carlo Rovelli:<br />People say, oh, this seems to be against entropy. No, why? Entropy grows and creates orders just happen all over.</p><p>Brian Keating:<br />Let&#8217;s talk about the early universe. You already mentioned the block universe and, and our mutual distaste of that hypothesis. I, I don&#8217;t like that. I want to ask the why question. I, I really— I do want to ask the why question, but I know why questions are not usually answerable. But, but the universe was somewhat lower entropy, right? It was in a very special configuration. Does that, in your mind, point to some reason why the arrow of time points in one direction?</p><p>Carlo Rovelli:<br />Yeah, yeah, the two are very, very connected. It&#8217;s not that one explains the other one because we need an explanation for either. I think the, the, the path law of entropy and, and the arrow of time, second law of thermodynamics, are different manners of explaining, of, of pointing out to the same phenomenon about which I think there is more to learn.</p><p>Brian Keating:<br />Does loop quantum gravity shed light on the low entropy boundary condition as I think it does? Or does it merely modify how a singularity would or would not be used?</p><p>Carlo Rovelli:<br />You might find some colleagues of me that disagree, but my answer is not at all. It tells us the dynamics of something that might have happened. If loop quantum gravity is correct, we sort of are able to compute what happened at the early universe. But I don&#8217;t buy the stories that connect that to initial low entropy or things like that. You see, I&#8217;m a conservative guy in science. Loop quantum gravity, it&#8217;s very radical. There&#8217;s no space, no time, discreteness, blah, blah, blah, whatever. Relational quantum mechanics is super radical.</p><p>Carlo Rovelli:<br />But in reality, I think we build on general relativity and quantum mechanics, nothing else. Loop quantum gravity is just general relativity and quantum mechanics. And general relativity don&#8217;t tell us anything about low entropy and initial low entropy, and nor does quantum mechanics. So I think the 2 things are— I don&#8217;t see the connection.</p><p>Brian Keating:<br />Would there be a motivation or maybe a justification for coarse graining and fine graining? Let&#8217;s assume loop quantum gravity is correct. Does it set a limit on coarse graining? I always hear things from Elon Musk which I think are quite ridiculous, but one in particular is that the Planck length and the Planck volume are somehow fundamental to the universe. And a lot of times, you know, people make this same mistake. I don&#8217;t think personally it&#8217;s any more fundamental than the Planck mass, which is the mass of an egg of a fly. So we don&#8217;t say that&#8217;s the minimal mass in the universe. But tell me, Carlo, is there a sense in which loop quantum gravity could explain how a physicist determines where to coarse-grain or not in terms of describing entropy?</p><p>Carlo Rovelli:<br />No, I think the two things are separated. Loop quantum gravity does bound certain things, right? There&#8217;s no area smaller than something. But we&#8217;re used to quantum mechanics in that. If you have a harmonic oscillator, you have an energy, 1/2 h-bar omega, and you have no amplitude less than that. Okay? All right. This is a quantum discreteness. What loop quantum gravity does is find the same quantum discreteness in geometry. It&#8217;s a quantum, so you can have superposition of different things, but you could have continuous superposition.</p><p>Carlo Rovelli:<br />If you measure the area, there is a smallest thing, but that&#8217;s not coarse-graining. and nothing to do with coarse-graining. It&#8217;s just a physical fact that there is nothing smaller. Coarse-graining is when there are other variables and you look at it. I think coarse-graining, which is what is needed for entropy, I have a hypothesis. I wrote a paper. I don&#8217;t know if this hypothesis is correct, but it&#8217;s something I always am fascinated by, an idea I&#8217;m fascinated by. The following.</p><p>Carlo Rovelli:<br />Low entropy means special, but what is special is not the state of the universe, it&#8217;s the coarse-graining. Coarse-graining is a perspective on, is a way of looking, not all the details, but only some things, some variables and not others. So maybe the universe is in a totally arbitrary state, but seen from this perspective has low entropy. Entropy in what the direction we call the past.</p><p>Brian Keating:<br />Everything so far has been about what order actually is, and Carlo&#8217;s answer was that it depends on where you stand. It&#8217;s relative, it&#8217;s perspectival, it depends on your perspective. Now push that idea somewhere much less comfortable. Carlo thinks it applies to you and where you&#8217;re sitting at this very moment.</p><p>Carlo Rovelli:<br />So if this is the case, then this great direction of the time that flows, this blah blah, is perspectival, is from from the way we look at it. I think a lot of things were clarified in history by realizing that we&#8217;re trying to find the solution in the thing, but then you get the solution looking at us. One is a majestic one. Like, you go out, the first thing you notice, Julia, you go out in the night and everything turns around you. In the day, the sun turns you, the moon turns, everything turns around you. And then you can ask why the universe, this great colossal thing, make a huge turn every day around us. And we have a solution. It&#8217;s very clear.</p><p>Carlo Rovelli:<br />We all understood it, which is the following. The universe doesn&#8217;t do anything. It just stays there. The star stays there. The sun stays there. Okay? We are rotating. So it&#8217;s our perspective that changes. Isn&#8217;t it beautiful? We just have figured out the most magnificent thing we see in reality outside us in nature.</p><p>Carlo Rovelli:<br />Not as a property of nature, but our own way of looking at it. We just happen to sit on a rock, this rock happened to spin, we see everything rotating, but it&#8217;s us rotating. And there are many things like that, right? People who study color figure out that the space of colors is 3-dimensional, right? It&#8217;s red, yellow, and blue. You can make all the colors.</p><p>Brian Keating:<br />Yeah.</p><p>Carlo Rovelli:<br />And then you say, why is the color 3-dimensional? Is Maxwell&#8217;s equation 3-dimensional? No. Is light for some reason 3-dimensional? Why is it 3-dimensional? It&#8217;s nothing to do with the color of the world. It has to do with our eyes. We have just 3 kinds of detectors for evolutionary reasons. Some other animals have 2, others have 4, some have none. And so the 3-dimensional space of colors has nothing to do with the color. It has to do with our eye. So many things we understand better by looking from how we look, not how they are.</p><p>Brian Keating:<br />What was the motivation? What was the inciting incident, as they might say? What caused you to want to write this book where you&#8217;ve covered some of these topics in the past?</p><p>Carlo Rovelli:<br />It&#8217;s bringing everything together. It&#8217;s the idea of bringing everything together. What&#8217;s the overall picture that comes out from all that? I mean, suppose you believed relational quantum mechanics, and suppose you believe everything I told you about space and time, and suppose you believe loop quantum gravity. It&#8217;s a lot. I mean, just believe the bulk of it, not all the details. What sort of world comes out of that? My claim is that, that&#8217;s the claim of the book, the world that comes out, it&#8217;s really, really astonishing and different. And somehow people don&#8217;t pay much attention to bringing everything together. It&#8217;s a very relational book, world that comes out.</p><p>Carlo Rovelli:<br />It&#8217;s very relativistic in this. Galileo would have loved relativity. And it&#8217;s very perspectival. It&#8217;s especially quantum mechanics that gives this strong— I think if you take quantum mechanics seriously, at least the way I think about it, let me put it this way, Brian. There is a sense in which a particle, an electron, can be in a superposition of 2 positions, right? I mean, you have discussed this, I&#8217;m sure, so many times with so many people. The sense is subtle, right? It means that If it was just here, you would not see something. If it was just here, you would not see something. It&#8217;s either here or there, and you do see something.</p><p>Carlo Rovelli:<br />So it&#8217;s neither here nor there in a sense. So you see interference between the two. So in that sense, it&#8217;s in two positions. Now, if you believe that quantum mechanics is universal, and if you believe that naturalism, that we are also, we are special, but we are just like the particle, you, Brian, can be in a superposition. You can be in LA and also in San Francisco. All right? What does it mean in some sense? It means that for me, I could measure superposition interference effect of you being in the 2 places. We know why it&#8217;s so hard to see this interference. We know exactly why.</p><p>Carlo Rovelli:<br />But I expect that it&#8217;s true. I trust quantum mechanics. I think it&#8217;s reasonable to expect that actually I could see this interference. So in some sense, for me, You are neither in one place nor in the other place. But for you, quantum mechanics never tell you that you feel to be in a superposition. You are always in one place. When you look around, you either see San Diego or you see LA. Okay? So that means that where you are for you and where you are for me are different things, right? So that&#8217;s the deep perspective.</p><p>Carlo Rovelli:<br />When we talk about things, We always implicitly should think this is black with respect to me. This is here with respect to me. Doesn&#8217;t this— with respect to somebody else, it could be in superposition, it could be in a strange thing. This is like when Galileo discovered that the velocity— okay, when we say this is zero velocity, we mean with respect to the Earth. We might not say it explicitly, but that&#8217;s what we mean, because velocity is relative. There&#8217;s no velocity per se. It&#8217;s velocity with respect to something else. And I think that quantum mechanics is telling us is that everything is like that.</p><p>Carlo Rovelli:<br />All the properties of things have to be understood as relatively to something else.</p><p>Brian Keating:<br />I spoke to Alain Aspect last month, and he has a new book out which will be competing with yours on the bookshelves. But it basically starts with Einstein. And I was curious, although I love Galileo more than any other man should, should love another man that he&#8217;ll never meet. Why did you begin with him and not with Einstein? Galileo didn&#8217;t know much about quantum mechanics, but I&#8217;d love to hear a lecture where you&#8217;re teaching it to him. But Einstein knew about both. So why begin with Galileo and the belly of his ship? Why not start with Einstein?</p><p>Carlo Rovelli:<br />Einstein, the first one who started and said something about quantum mechanics, right? In spite of his polemic with quantum people later on. He really opened up the— and he did relativity. And his relativity, it&#8217;s going back to Galileo relativity and make it stronger. Einstein— Galileo relativity is the following. If you say this is here in a position, okay, and if you say that something later is in the same position, That doesn&#8217;t make sense unless you specify position with respect to what. Because look, is this in a position I count 1, 2, 3, 4, 5? Is it in the same position? Yeah, with respect to the Earth, but the Earth moved, so this has moved. Okay? So what Galileo understood is that to be in the same position is only relative to something else. And Einstein understood to happen at the same time It&#8217;s only relative to something else.</p><p>Carlo Rovelli:<br />So Einstein understood that the relativity of position that Galileo was discovering is also true for time. So Galileo discovered that same position is a relative notion. Einstein discovered that same time is a relative notion. So to some extent, Einstein just extended and made it more general, a deep intuition by Galileo. Not that Galileo understood it clearly, because you remember when we read the his book, Galileo himself was pretty confused about that, right? He has an idea, but then sometimes he gets completely lost and he says stupid things, like all of us.</p><p>Brian Keating:<br />Yes, of course. That&#8217;s the mark of greatness. And he also teases philosophers a little bit when he says that the telescope in Sidereus Nuncius, the telescope can resolve all these arguments that for so many generations have vexed philosophers. But I think he meant physicists. And I guess My question to you is, you know, velocity is relative. Galileo understood that. Einstein understood. But acceleration is somehow more fundamental.</p><p>Brian Keating:<br />You taught us that. Why not start with acceleration? In other words, what is the basis for our understanding of things that are relative and then things that are absolute? Are there any absolutes, Carlo?</p><p>Carlo Rovelli:<br />All things of the sort, if this, then that, right? Or in quantum mechanics, if this, then that with a certain probability. So all this transition amplitude, all these equations of motion, all this dynamical structure, according to our best theories, that&#8217;s it. It&#8217;s not relative. It&#8217;s what we have actually understood about reality. What is relative is the actual values of the variables. So when you say, if this, then that, this might be true for me, and then that is true for me, but might not be true for you. And then it&#8217;s not true for you that. So everything which is variable, which changes according to our theories, it&#8217;s relational.</p><p>Carlo Rovelli:<br />The rules of the game, as far as we know, they&#8217;re not relational. At least we don&#8217;t have strong reasons to believe that they&#8217;re relational. So they&#8217;re the same for everybody. So I&#8217;m not saying everything is relative. Everything is not at all. In fact, that&#8217;s a common confusion, right? When people say, oh, but if velocity is relative, then the moon can do whatever it wants because velocity is relative. No, the moon cannot do whatever it wants. The moon can move only way, but it has a velocity with respect to me, a different velocity with respect to the sun.</p><p>Carlo Rovelli:<br />And in the book, there&#8217;s a big part of the book that is sort of to dismantle this wrong idea that excessive relativism or excessive perspectivalism means free-for-all and having no rules. I even have a chapter in the book about morality. I think moral ethics, morality is relative. It&#8217;s deeply grounded in me, but it&#8217;s relative to me. It&#8217;s part of what we are. And people say, oh, but then everything is possible. No, it&#8217;s not everything is possible. I mean, I am a moral guy.</p><p>Carlo Rovelli:<br />I have my ethics, my morality. You have yours. They&#8217;re similar, but maybe there are differences. We can talk to one another, we can influence one another. But we should get away from the idea that there is an absolute right, absolute wrong. And I think physics is telling us we should go away from the idea that there are absolute states of things, absolute properties of things. They&#8217;re relative.</p><p>Brian Keating:<br />Even though I think we— I&#8217;ve certainly been guilty of hagiography of Galileo, but you point out in the book, and as I&#8217;ve written about too, Galileo made some brilliant blunders as well. including his model— yeah, his model of the tides, which he presents in Dialogo, which is the culmination of, you know, 3 days of argumentation between 3 scholars, uh, you playing Salviati, of course, Galileo&#8217;s muse.</p><p>Carlo Rovelli:<br />It&#8217;s completely wrong.</p><p>Brian Keating:<br />Talk about that. Is that a model?</p><p>Carlo Rovelli:<br />He misunderstands Galilean relativity.</p><p>Brian Keating:<br />That&#8217;s what I want you to explain. So explain how someone who creates something can be guilty of its violation in the most pernicious way.</p><p>Carlo Rovelli:<br />When somebody has an idea, he might see it, but seeing it clearly is different than seeing something. Einstein created general relativity in 1915. The theory is finished in 1915, maybe 1916. I mean, the last— yeah, the cosmological constant, depending on how you want to do the details. But that&#8217;s it. At that point, that&#8217;s a theory. It hasn&#8217;t changed since. It&#8217;s still the same.</p><p>Carlo Rovelli:<br />And yet Einstein later on, Has written a lot of papers in which he&#8217;s clearly confused about his own theory. He claims that Schwarzschild singularity is the end of the world. He claims that there are no gravitational waves, right? There&#8217;s a paper by Einstein saying that no gravitational waves. He was wrong.</p><p>Brian Keating:<br />Which was rejected by peer review. The peer review, the referee rejected it, sent it back to him, and then he improved it. And thank God for peer review, right, Carlos?</p><p>Carlo Rovelli:<br />And they said, well, you&#8217;re wrong, Einstein. And he was right. And I said it was stupid there. He said, oh, come on, I&#8217;m Einstein. How do you dare do it that way? But then he talked with somebody and then he actually realized that he was wrong. So there he realized he was wrong. But on the Schwarzschild, the black holes, he never corrected his mistake. In fact, clarity came much later with Finkelstein.</p><p>Carlo Rovelli:<br />So now we know that the surface of the black hole, you can go through. You can just continue. There&#8217;s nothing very special happening there. But Einstein did not understand that. So this is general, I think. Something is to get an idea and see that it works, and something is to work it out completely and see what it implies. So Galileo had this idea that the Earth moves around the sun, it&#8217;s moving in the sky around the sun, but it&#8217;s also rotating, right? The combination of these 2 movements, if you have a sea, an ocean, It just makes this funny movement, which is a very strange movement. And they said, well, look, do what you just did, Brian.</p><p>Carlo Rovelli:<br />Take a piece of some water and the water starts shaking. And he says, that? These are the tides. But that cannot be true because this movement around the sun is, to first approximation, is a uniform movement, rectilinear, fixed velocity. So it cannot have an effect. So if you take that away, that cannot have an effect. The rotation by itself neither can have an effect. So the ties are not due to that. We know very well.</p><p>Carlo Rovelli:<br />In fact, Newton figured out what the ties are.</p><p>Brian Keating:<br />You take these 2 geniuses one step further. Galileo removes absolute motion. Einstein removes absolute simultaneity. And then you go further and you say that the universe doesn&#8217;t exist in a single time at all. Explain that audacious proposition. That doesn&#8217;t unfold at a single time.</p><p>Carlo Rovelli:<br />Yes. General relativity is telling us that we should not think at a single instant of time. That&#8217;s just not the right way. Time is more complicated than that. Time is a local affair, not a global affair. But as you say, in the book, I go much farther. I think that when we say any physical property any physical variable has a value, we are not talking about property of the object. We are talking about the relational property of something else.</p><p>Carlo Rovelli:<br />Let me put it this way. When I say this is here, what I mean is I know that this is here. I&#8217;m talking about my information. And my information can be different, but yours, and information that yours and mine and the various might not fit exactly. And that&#8217;s the confusion of quantum mechanics. We always think that there is real stuff there, right? And we should not think in those terms. We should think in terms of the information which is in you, in me, or in physical system, in a book, in an iPhone, even in the rock, right? In the rock, there is information about the dinosaurs because it&#8217;s bone of dinosaurs. So things carry information about each other, and that&#8217;s what we&#8217;re talking about.</p><p>Carlo Rovelli:<br />And the world for us is what our information about all the rest. If you should think in these terms, I think quantum mechanics makes sense, right? You can be in the superposition with respect to me, but that&#8217;s not a problem for you because that&#8217;s my information. It&#8217;s not yours.</p><p>Brian Keating:<br />When we talk about time, and you&#8217;ve talked to me about time many times, shall we say, but I always get a very unsatisfactory answer. Not from you, Carlo, but for example, I talked to Frank Wilczek and I asked him, Frank, what&#8217;s time? And he said, time is what a clock measures. And I just went away from that thinking it&#8217;s a tautology. It&#8217;s not satisfying. It&#8217;s like eating Wonder Bread here in America instead of Italian focaccia. But, Tell me, Carlo, is there a true time? Is there a sense along some worldline perhaps where it&#8217;s not preferred, but it&#8217;s as meaningful as it could be, both to biological or conscious observers and maybe cosmologically as well? Is there a useful, if not perfectly true, definition of time?</p><p>Carlo Rovelli:<br />The point is that there are different things that we call time. Which are related to one another. If you want to say time is what my clock measures, fine. I mean, that&#8217;s a definition of what you mean. And in fact, very often by time, we just mean what the clock measures. But obviously we mean something more than that, right? Because you can say, oh, look, my clock is not measuring time. It&#8217;s broken. The battery&#8217;s gone.</p><p>Carlo Rovelli:<br />Clock is not what the clock measures. Clock is what a good clock measures. That was a good clock. So we haven&#8217;t defined time by saying it&#8217;s a clock measure. I want to say it was a good clock. And to say what a good clock measures, we should have made it— what is it measuring? Time is complicated stuff because we have an intuition about time, right? So we put all this together, but then the intuition is wrong. The intuition is de facto wrong. You know very well the main aspect, the main prediction of Einstein, the very The most beautiful prediction of Einstein, which now we can check in the laboratory.</p><p>Carlo Rovelli:<br />You take 2 clocks, you make one up, one down. With 10 seconds, you come back and the one up has measured more time than the one down. Okay? It&#8217;s a fact. We measure. When I was at school, it was a prediction by a complicated theory. Now it&#8217;s a fact observed in a laboratory. I mean, many laboratories check this. So there&#8217;s more time up here, less time down here.</p><p>Carlo Rovelli:<br />Your head, it&#8217;s older than your feet, Brian, unless you spend all your life upside down. That is the other way around. The more you go close to the Earth, to a big mass, the less time there is. Time slows down, literally. Clocks go more slowly, flowers take more time to bloom, you have less time to think, and so on and so forth. So what does this mean? It means that time is not really what we thought, right? Because in our intuition, time is the same. It&#8217;s just one time. It&#8217;s hard for us to get to the— so I think the problem is that what time are we talking about? The one of our intuition or the one more precisely that we&#8217;ve figured out? And then we figure out black holes where time is enormously distorted.</p><p>Carlo Rovelli:<br />We study quantum gravity where it&#8217;s even more complicated. So each one of these applications of the notion of time is different. And then when we talk about time, we&#8217;re actually talking about my memories. When I say time, I think you know, time, I&#8217;m old, I was young, I have memories, I have expectations of the future. There&#8217;s all this emotional aspect of time, which is very much part of what time is for us. My iWatch doesn&#8217;t have expectations for the future, doesn&#8217;t have emotion. For it, time is a much more simple thing. And bringing all these aspects of time together is what creates the confusion, I think.</p><p>Carlo Rovelli:<br />Right. Because we tend to take our own intuition about time and project it down to the things. And that&#8217;s wrong.</p><p>Brian Keating:<br />One of the things I do and I criticize our fellow science communicators, not you, but people like— I&#8217;ll just pick Michio Kaku. And I don&#8217;t want to talk ad hominem, but when we talk about spacetime, Carlo, I feel like— and it&#8217;s not only him, you know, many other people do this and I&#8217;ve done it myself, but we talk about spacetime. And I feel like that&#8217;s such a lie. I feel like it&#8217;s something we know is not true because we know we can&#8217;t travel backwards in time. If I want to travel backwards in space, I just move my chair backwards. Time is different. And yet we say it&#8217;s this unified fabric and the space-time continuum. Are they truly unified in your concept?</p><p>Carlo Rovelli:<br />Space and time? No, they do 2 different things. They&#8217;re not the same thing. They&#8217;re related in a much more complicated way than what we thought before 1905. That&#8217;s no doubt. So they&#8217;re more tricky. But to say that space and time are the same thing is just nonsense, in my opinion. They&#8217;re very different. I do popular science.</p><p>Carlo Rovelli:<br />So when you do popular science, you have to simplify. You try to simplify without cheating. That&#8217;s the hard part. Sometimes people cheat. Sometimes people sell things which are just speculation and not true. Sometimes very simply, you know, scientists try to give an idea and then it&#8217;s heard more than what they say. Right? So it&#8217;s not true that, you know, reality is a 4-dimensional continuum, a block universe. No, no, come on.</p><p>Carlo Rovelli:<br />That makes no sense because this block universe that does not change, in which time is not changing? The block universe is a solution of Einstein&#8217;s equation of spacetime, is a story. It&#8217;s a process. It&#8217;s like saying a movie is what happened before, and if you bring it all together, you give it a name, and that&#8217;s spacetime. So there&#8217;s nothing wrong in saying spacetime, but we should remember that it&#8217;s something happening. It&#8217;s like the story of a novel, the plot of a novel. It&#8217;s not just an instantaneous You can picture it, you can make a picture, but I can make you a picture of your life. I can make a little line with a little Brian, a little boy, and then old grown-up Brian. Brian gets a Nobel Prize, this doesn&#8217;t get a Nobel Prize, and then old Brian, which I don&#8217;t know yet.</p><p>Carlo Rovelli:<br />But that&#8217;s a story. They don&#8217;t exist at the same time. That&#8217;s the point.</p><p>Brian Keating:<br />In the book, you say that measurement requires dissipation, but in quantum mechanics, things evolve unitarily. They&#8217;re seemingly reversible. So where does the ghost, you know, where does the demon enter into it? Where exactly does irreversibility come through? Amplification, decoherence, the recording, the observer? Where does irreversibility come in, in things that are intrinsically unitary in their evolution?</p><p>Carlo Rovelli:<br />That puzzled me a lot, and I spent some time wondering about that and trying to study. And then I found something which I don&#8217;t know how well known is, which for me was a flash of light, which is the following. Take classical mechanics. Forget quantum, forget h-bar, forget wave function, forget all that. Just take standard classical mechanics. And in classical mechanics, If you try to model a measuring apparatus, I don&#8217;t know, a barometer that measures the pressure of the day, something that checks whether a ball is here and there and write it on a piece of paper here and there, you cannot succeed. You need dissipation. You cannot record anything without some dissipation.</p><p>Carlo Rovelli:<br />So measurement is an intrinsic statistical process, uh, which requires this measure. It&#8217;s a microscopic thing, measurement, always. So it has nothing to do with quantum mechanics. It&#8217;s already in classical mechanics. Then in quantum mechanics, the same thing has other consequences. Every measuring apparatus in classical mechanics requires some dissipation. Imagine you You see something and you want to put a ball either in the right box or in the left box. There&#8217;s no dissipation.</p><p>Carlo Rovelli:<br />You let the ball fall and it bounces up. It doesn&#8217;t stay there. It has to stop. You need some dissipation to stop elasticity for preventing any record.</p><p>Brian Keating:<br />You call a chess program that deliberates can be thought of as an agent because it evaluates possible moves. And it made me think, you know, is the future determined? You know, and is, is there a sense of free will? You open the book by saying, you know, you have the choice to not read this book, but I hope that you will. And I obviously, I read it and I listened to it because your publicist sent me both the audio and the hard copy, and I hope everyone gets both. But tell me, Carlo, you know, is openness, as you say, is that anything more than incomplete knowledge about the universe, or Somehow related to the inability to predict things.</p><p>Carlo Rovelli:<br />It&#8217;s a page in my book in which I say, I talk about my computer playing chess. Okay? And my computer is very good at playing chess. It beats me regularly. It&#8217;s very devastating. It&#8217;s taken away all pleasure of playing chess, of being beaten by a machine with total simplicity. The computer plays a move, I play a move. When I play a move, is the computer deciding the next move? Or not? That&#8217;s the question. Okay? And why I&#8217;m asking this question? Because it&#8217;s an ambiguous question, right? There is a sense in which of course it&#8217;s deciding and a sense in which it&#8217;s not deciding.</p><p>Carlo Rovelli:<br />So what are these 2 senses? Is the computer deterministic? Yes.</p><p>Brian Keating:<br />Okay.</p><p>Carlo Rovelli:<br />I mean, sometimes it breaks, but if it doesn&#8217;t break, Given its internal state, given my own move, it goes through some process that necessarily will get some outcome. So the outcome is predetermined. Okay? Now, if it is predetermined, why did the program spend all this time checking all possible moves and evaluating all of them and then picking up the one who thinks, according to the criteria it has, is more likely for it to win? If it is predetermined, why doesn&#8217;t Do it.</p><p>Brian Keating:<br />There&#8217;s a practical question underneath this debate that I&#8217;m having with Carlo. What can AI actually take off your plate today, especially for busy professionals like me? Yeah, it&#8217;s true, professors are busy, and even more so nowadays when curiosity can outgrow your own calendar. If you&#8217;re like me, you&#8217;ve got papers to read, spreadsheets to untangle, grading to do, and presentations to make. Meanwhile, your browser tabs have formed their own civilization. And they&#8217;re revolting against you. Abacus AI Agent brings all the tools I need in one place. You can choose among the top frontier models from OpenAI, Anthropic, Google, and more to find what works best for you and your task, and you&#8217;ll have fun along the way. Bring in a dense document, a PDF.</p><p>Brian Keating:<br />That&#8217;s what I did with Carlo&#8217;s most recent papers. I asked for the argument, the counterargument, the evidence, the data that would support or refute it, including some of the questions here. About to hear at the end of this interview. You can turn anything you do into a research report, a presentation with charts and clear structure, and it can be beautifully illustrated as well. And you can even use it to make animations and custom charts. Abacus AI Agent is incredibly agentic. You&#8217;ll spend fewer hours wrestling with the formatting of a LaTeX equation and more time to respond to referee number 2. I use it to develop complex ideas and communicate them without constantly having to jump between different services, tools, API calls, and stuff like that.</p><p>Brian Keating:<br />When a project gets complicated, you can ask it to organize the work and you can have an agent swarm take on your most interesting ideas as a starting point and develop it all the way through to the end. Upload a spreadsheet, investigate patterns, and build an interactive dashboard.</p><p>Brian Keating:<br />Ask it to show the calculations, the units, and the assumptions.</p><p>Brian Keating:<br />And developers can use API access to connect models to their own tools. Start with one genuinely for project. Give an Abacus AI agent a goal, break it into steps, run the code, and schedule a report to update while you&#8217;re doing something else. Plans start at $10 a month, but with your introductory offer using my link, you&#8217;ll get the first month for $7 a month. Visit my special link. It&#8217;s down below. There&#8217;s a QR code and it&#8217;s in the show notes. agent.abacus.ai/keating.</p><p>Brian Keating:<br />Put your curiosity to work with Abacus AI agents. Now back to the episode.</p><p>Carlo Rovelli:<br />Well, the, the answer, it&#8217;s obvious. Is that yes, to determine it, but the only way to get to that outcome is to go through the evaluation of all the possible alternatives. And that evaluation is called the deliberation of the computer. That&#8217;s what the program is doing, is considering various alternatives and deliberating. So To say that it&#8217;s not deliberating is stupid because to deliberate is to do that, to consider all sorts of things, evaluate, and maybe there is also a random number generator. It doesn&#8217;t matter whether there is or there is not. Maybe it uses some fluctuations. But in any case, it goes through a complicated process and the future depends on this complicated process.</p><p>Carlo Rovelli:<br />If there was no complicated process, the future would not be determined, the choice would not come out. And what we call deliberation, choice, freedom is the— could the computer do this move, also that move? Of course it could do this and all that. That&#8217;s why it&#8217;s considering both of them and choosing between the two. So now let&#8217;s talk about Brian. He&#8217;s just going through the same process. If he wouldn&#8217;t have gone through the process, he wouldn&#8217;t have taken the decision of, you know, starting a program, kissing a girl, choosing a career, choosing to do one experiment. This is complicated deliberation. That&#8217;s what we mean by deliberation.</p><p>Carlo Rovelli:<br />That would mean his freedom. He could have done other things, of course. If the conditions were slightly different, he would&#8217;ve gone through a complicated deliberation process. The point is that what we mean by saying Brian is free is precisely the fact that he&#8217;s going through deliberation. If Brian had been imprisoned, he could not have started this program, so he would not have been free. That is not to be free. To imagine that to be free, you have to violate the laws of nature is stupid. I mean, we&#8217;re free even without violating the laws of nature.</p><p>Carlo Rovelli:<br />That&#8217;s my understanding of freedom.</p><p>Brian Keating:<br />Now, talking about chess, it&#8217;s natural now in the podcasting universe to talk about AI. But before we get there, I want to bring it back to, you know, Galileo and Einstein, and especially Einstein, because, you know, to me, all this hype about AI, there&#8217;s a lot of hype, there&#8217;s a lot of claims, and there&#8217;s a lot of utility. I use it every day. I have multiple different tools that I use for different purposes. My kids use it, my wife uses it. It&#8217;s become, you know, more or less, you know, an important part. I wouldn&#8217;t say essential. I could live without it, but Certainly wouldn&#8217;t be as fun.</p><p>Brian Keating:<br />But let&#8217;s go back to, you know, 1907, and our friend Albert here is thinking a Gedankenexperiment about what would happen if he was in free fall. And he realizes he&#8217;d experience no gravitational force. And, and this becomes known as Einstein&#8217;s equivalence, which is another way of saying equality, right? Uh, Einstein equivalence principle. And he called that, Carlo, he said, it&#8217;s the thought that titillated me that made me happier than any other thought. My question is, can, can, you know, your iPhone or, you know, my iPhone, can my LLM, can it, A, visualize free fall, what it would feel like emotionally, sensorially, viscerally, A, and B, can it have a happy thought?</p><p>Carlo Rovelli:<br />Can a computer in principle have a happy thought like that? That&#8217;s one question. Another question is, can current technology in 100 years in the future do that? Another question, can current technology in the next 2 years do that? Can current technology now do that? An LLM now? Can my iPhone do that? So can my iPhone do that? No. Can a computer in principle do that? Yes, of course. Why not? I mean, I see no reason why we are so special that we cannot be reproduced. Can we do it now? No. Okay. Can we do it in 100 years? I don&#8217;t know, maybe, but I think it&#8217;s more likely we&#8217;re all dead at nuclear war in 100 years. Can we do it in 2 years? I am skeptical.</p><p>Carlo Rovelli:<br />I don&#8217;t have hard proof that it&#8217;s impossible, but current LLMs, very surprising, very shocking. Maybe they&#8217;ve solved the Navier-Stokes problem. And actually, I have to say one thing. Last week, I asked Claude to, you know, the thing that I&#8217;ve done to my PhD student, which my PhD student has not done in a month. And Claude did it in half an hour. So they&#8217;re very good. There&#8217;s no doubt they&#8217;re very good. Claude hasn&#8217;t invented the curious principle.</p><p>Carlo Rovelli:<br />It seems hard for me to do it, that he could do it in 2 years, but what do I know? I think there&#8217;s an enormous amount of hype in AI. I am also impressed by what it does.</p><p>Brian Keating:<br />What you have You know, this deterministic chess program or Claude or whatever will come next, you know, GPT-8. You know, I said, Carlo, I don&#8217;t know what GPT-7 will be like, but GPT-8 will run on an abacus, you know, like Einstein said about nuclear war. But if it&#8217;s true that these, that these programs can deliberate and it evaluates, they dissipate— you&#8217;re not afraid to talk about morality in Should we treat these things with ethical behavior? Should we say please and thank you? How should we behave towards these AI physicists? I mean, obviously it doesn&#8217;t take a break, doesn&#8217;t have espresso, doesn&#8217;t need the things that your grad student does, but do they have needs? Are they beings?</p><p>Carlo Rovelli:<br />I treat my washing machine with kindness.</p><p>Brian Keating:<br />I believe it.</p><p>Carlo Rovelli:<br />Don&#8217;t you? In Europe, often you drive the car and then you have to pay the ticket at the highway. And in the old times, there was a man or a woman, mostly a man, who give you a ticket, you pay. And now there&#8217;s a machine. And I just can&#8217;t resist. I say, thank you. Please, can I— give me the ticket. I say, thank you. Why shouldn&#8217;t we do? We should be kind with everything, with plants, with stones.</p><p>Carlo Rovelli:<br />Maybe you don&#8217;t respect them, but somebody respects them. I love my teddy bear dearly, and I would never mistreat it. It&#8217;s a— it&#8217;s an issue. We treat people gently because it&#8217;s better for us, not because it&#8217;s better for them.</p><p>Brian Keating:<br />If that&#8217;s true, I have bad news for you, in that you&#8217;re only using Claude, you know, 1% of your day. So the worst form of punishment, of torture, is solitary confinement, is isolation. Are we not torturing these things by not engaging with them, by air-gapping them, by segregating them? Are they somehow second-class citizens? Is that not morally repugnant to you?</p><p>Carlo Rovelli:<br />We project ourselves. I project myself on you. I attribute to yourself. When I see you suffering, I recognize it&#8217;s my own suffering. And this gives me a sense of what is your suffering. So we constantly play this game. in all our interaction with nature. When I come home and my plants have been without water, I see them, they need water, they&#8217;re suffering, they&#8217;re thirsty.</p><p>Carlo Rovelli:<br />I give them water because I identify with their sufferance. Now, what is their sufferance? Well, it&#8217;s something I project on them in the same way I project on you, I project. So we are projecting on our computer programs, our sort of understanding of them, which is fine. There&#8217;s nothing wrong. We just do the same with each other. And in this moment, we feel that they suffer at all for being confined. You know, there is these movies in which people get in love with a computer program. Would it be strange if somebody in love with a computer program thinks that doesn&#8217;t want to hurt? Her or him or it or whatever.</p><p>Carlo Rovelli:<br />One shouldn&#8217;t be excessive, neither in one direction or the other. Obviously you can say, come on, Carlo, don&#8217;t be stupid. You don&#8217;t, you don&#8217;t believe that these are sentient. No, I don&#8217;t believe that they&#8217;re sentient. But do I make a sharp distinction? No. Everything is okay.</p><p>Brian Keating:<br />I can&#8217;t resist talking about some of the aspects of Loop Quantum Gravity here. And I think I found why the COVID looks the way it does. I mean, there are these beautiful illustrations and there are these graphs. And so I think I understand why your graphic artist chose that. But you talk about, quote, you say that quantized areas and volumes imply that there are no arbitrarily small areas and volumes. Is this a generic feature of loop quantum gravity? And if so, is there a way to, I would say, falsify this first to show that this is not a manifestation that we see in reality, or is it something unobservable like 10 dimensions of string theory?</p><p>Carlo Rovelli:<br />It&#8217;s in principle definitely observable. So I could in principle design a scattering experiment that gives a scattering amplitude. Scattering amplitude is in centimeters squared, so it&#8217;s an area. And say, well, if you measure that, definitely loop quantum gravity is falsified in principle. From that perspective, it&#8217;s a solid prediction. Now, it&#8217;s not what we want because In practice, we don&#8217;t have the technology of that by far. I think what we want in the case of quantum gravity is not so much to falsify theory, right? When Einstein wrote general relativity, the problem was not to falsify it. The problem was to find predictions of the theory that turned out to be right and don&#8217;t prove the theory, but increase the credibility of the theory.</p><p>Carlo Rovelli:<br />Or predictions of the theory that turned out to be wrong, which don&#8217;t really falsify the theory because you can change it a little bit. But decrease your— so I think science rarely works really like falsification, or sometimes it does. SU was just killed by a single experiment. But most of the time, this is piling up. So what I hope would happen with quantum gravity is a clear set of equations. It&#8217;s a well-defined theory, conceptually clean, in my opinion. Of course, there are plenty of things which are not clear. not yet understood, but, you know, basically the theory is well-defined.</p><p>Carlo Rovelli:<br />What I hope is that some of its, uh, key predictions will be verified. One of its possible predictions is that it&#8217;s a candidate for, um, dark matter, which could be tested in the laboratory, um, which is a Planck-scale particle. The Planck mass is almost microscopic thing. It&#8217;s the mass of my hair, of a little piece of my hair. So this could be detected. I hope that somebody will build this detector. It&#8217;s not possible. It&#8217;s not very easy.</p><p>Carlo Rovelli:<br />It&#8217;s not totally outside of our technology. And if that could be possible, this will be a good, strong support for loop quantum gravity because essentially the idea is that small black holes can sit down and not evaporate anymore when the area, its minimal area, And they&#8217;re stabilized by the fact that the inside is still there. And then they have all their— all these possible remnants that could be, uh, what we call dark matter. So maybe we have already seen a quantum gravity effect, this dark matter. We don&#8217;t know dark matter. There are 5 or 6 different possible explanations. This one might be wrong, of course, but has advantages. It&#8217;s just based on GR and quantum mechanics, doesn&#8217;t require extra fields, extra stories.</p><p>Carlo Rovelli:<br />So So I expect if loop quantum gravity will turn out to be confirmed with this kind of things, from this, from early cosmology, so piling up things that fit with its predictions. What I argue in the book, in this book, is that loop quantum gravity beautifully brings together the relationality of quantum mechanics and the relationality of general relativity. So the two come together and we can talk about quantum spacetime as spacetime region of quantum processes. We have a clean way of thinking relationally about quantum mechanics, thinking relationally about general relativity, and use it for thinking about quantum gravity.</p><p>Brian Keating:<br />Did you see the recent survey done by our friend Nayash Afshordi and your friend Phil Halper and others about many different attitudes in science, but in particular, where quantum gravity was discussed, they did their survey of the people that responded who weren&#8217;t all professional physicists like you, but many were. And quantum gravity was defined relatively loosely. There were some candidates, and no opinion got the highest number of votes, followed by string theory, followed by loop quantum gravity, followed by gravity is not quantized. So how did your react to that particular survey?</p><p>Carlo Rovelli:<br />Interesting. Like all pieces of data, it&#8217;s always, you know, one more element in our understanding of the world. If you ask more people, you get less biased things, but you include people who know less about the subject. If you want to ask people who know more about the subject, since there&#8217;s no consensus, you could disagree with it because people are more focused on this and that. In science, usually at some point there&#8217;s a consensus. So the experts agree and then it becomes reliable. Until we are there, it&#8217;s more complicated, right? If you asked in 1917 what the best theory of gravity, very few would have said general relativity, but general relativity was the best theory, right? If you ask, even at the time of Maxwell&#8217;s equation, when Maxwell wrote the treaty, quite a while. It was not obvious at all.</p><p>Carlo Rovelli:<br />There were a number of competing theories. And of course, what we now recognize as the wise people already had seen clearly. It took time. So I think it&#8217;s interesting. Shouldn&#8217;t we take it too much? But there&#8217;s one thing that made me very, very happy in one of these surveys, that young people have a much higher preference for relational quantum mechanics, which is a way I prefer of viewing quantum mechanics, and for loop quantum gravity. So generationally, I&#8217;m optimistic.</p><p>Brian Keating:<br />We always like to start because, you know, for the 10 people out there that don&#8217;t know you, They might not have seen the book, but if they see the book, can you talk us through the title, the subtitle, and the COVID art? What is this meant to represent, this fabulous new book?</p><p>Carlo Rovelli:<br />So let me start from the title because it&#8217;s a bit funny. It&#8217;s a stolen title. It&#8217;s not mine. It&#8217;s a great title, right? On the Equality of All Things. Wow. But it&#8217;s not mine. It&#8217;s stolen from an ancient Chinese philosopher who wrote more than 2 millennia ago. A book which is one of the super classics of Chinese philosophies.</p><p>Carlo Rovelli:<br />It has a different title. In fact, it&#8217;s the title of the name of the author, which is Zhuangzi. But it is divided in chapters, and chapter 2 has this title, On the Equality of All Things. The Chinese book, it&#8217;s a collection of stories with a lot of philosophical descriptions. You might know some of the stories. One is the philosopher who dreams to be a butterfly, wakes up and says, oh, I dreamt to be a butterfly, or maybe I&#8217;m a butterfly dreaming now to be a philosopher. And then he gets confused. In chapter 2, he discusses about the equality of all things, meaning deep, profound naturalism.</p><p>Carlo Rovelli:<br />There&#8217;s no distinction between mind and body, humans and non-humans, animals, plants, stones, numbers, laws, societies, language. They&#8217;re all part of a single thing. And we can have different perspectives on these things, but it&#8217;s just one we would call nature. They would call it the Tao, D-A-O, or Tao, T-A-O. So that was one of the main sources of what is called Taoism, one of the 2 big branches of Chinese philosophy. The other is Confucius and Confucianism. So that&#8217;s the title, stolen from there. Subtitle, what is the subtitle? Physics and Philosophy, something like that.</p><p>Brian Keating:<br />Lessons on Physics and Philosophy.</p><p>Carlo Rovelli:<br />So I&#8217;m a physicist, I&#8217;m not a philosopher, but I&#8217;m very attracted by philosophy. Galileo also was very attracted by philosophy. So the book came out from discussions with philosophers. In fact, I&#8217;ve always been attracted by philosophy. I&#8217;m one of those who think that physics and philosophy talk to one another and should talk to one another. So I&#8217;m not like Hawking who says, oh, philosophy&#8217;s dead. I&#8217;m the opposite side. I think philosophers are good friends, not all of them, But physicists also are good friends, but not all of them.</p><p>Carlo Rovelli:<br />I have a dialogue with philosophers. I went to Princeton. They invited me to talk about what modern physics— modern, I mean the last century— what has to say to philosophers. What are the philosophical implications of physics? And I lectured for 2 months. I had wonderful discussions with the philosophers there. I disagreed. In the book, there are many of these disagreements. And somehow the book is to a large extent the result of that, but also the result of my sort of all my life through the reflections on this question.</p><p>Carlo Rovelli:<br />The question is, what are the philosophical implications of modern physics?</p><p>Brian Keating:<br />Let&#8217;s talk about the artwork on the COVID What is this meant to represent? Is equality— are these billiard balls? Are these loops in quantum foam? What are these little circles and their motions?</p><p>Carlo Rovelli:<br />You should ask the artist of the publisher. I have no idea.</p><p>Brian Keating:<br />Final question. Let&#8217;s return to our hero, the maestro, il maestro Galileo. Okay. Imagine Galileo comes to you. He comes to you in a few years and he says, Carlo, teach me all that you know, every development, including things you don&#8217;t agree with. Maybe you teach him string theory, but you&#8217;re teaching him everything. Bring him up to 2026. Okay, then he says, I have good news for you.</p><p>Brian Keating:<br />I talked to the old one upstairs and he&#8217;s going to give you 10 years only, but unlimited money, Carlo. Unlimited money to build an experiment or open an academy, uh, like the Linzhan, the Lincheo, whatever. You get 10 years, only 10 years, but you have infinite money.</p><p>Carlo Rovelli:<br />What do you build?</p><p>Brian Keating:<br />What do you do to, to give the best opportunity to understand whether or not you&#8217;re right, others are right, you&#8217;re wrong, others are wrong. What would you do with unlimited budget but finite time?</p><p>Carlo Rovelli:<br />I think 10 years is enough for building a lot of Josephson junctions. Fill a big room of that, hire enough people to do the engineering of that because I&#8217;m not capable, do all the electronic, the informatic, all that. And then I have this big, big thing here. And if a Planck-mass particle going through, this is going to detect it. If dark matter is made by things, this is a vision. We have the technology for that. It takes a lot of money. We even know which direction it&#8217;s coming through because we are moving with respect to dark matter clouds.</p><p>Carlo Rovelli:<br />So we know exactly the direction for which we expect it to be true. We know the direction, we know the mass, we know the interaction is a teeny, teeny Newtonian attraction, but Joseph von Jankowski is going to pick it up just by simple physics. I think that in 10 years could be done. I hope that maybe not Galileo, but somebody would come up with the money. You know, Galileo teased philosophers all his life. The book we read together was a lot of pulling the legs of philosophers. And there is this letter of Galileo later in life. He has been very polemical with those who follow Aristotle.</p><p>Carlo Rovelli:<br />But he says, I believe that if I could meet Aristotle, Aristotle would be very interested in the few little changes I&#8217;ve done to his science and would accept me as one of his humble students.</p><p>Brian Keating:<br />He was quite a writer and he was such a brilliant mind in all ways. I do feel that the temptation to worship him A little bit. But again, I have to remind the audience, the original title for the book that was called The Dialogo, that was suggested by his referee, if you will, but it was originally called On the Origin of Tides in Rivers and Lakes and Ferns, which I don&#8217;t know what a fern is, but Galileo did, and that&#8217;s all that matters. So, Carlo Rovelli, this has been fascinating. Congratulations on another wonderful contribution to our understanding of the nature of reality and on the quality of all things, this is certainly up there with the best that science writing has to offer. So congratulations again. You&#8217;re, you&#8217;re the other maestro.</p><p>Carlo Rovelli:<br />Thank you, Brian. It was a great, great pleasure to have this conversation.</p><p>Brian Keating:<br />Carlo just told us what we should spend money on if we had an infinite amount of money but only a finite amount of that precious resource, time. He said he&#8217;d build a room full of Josephson junctions, which surprised me as a theorist because he thinks The size of a speck of hair. Even the size of a flea&#8217;s egg. If that changes what you think an experiment is for, please subscribe and turn on notifications. Comment on this: is entropy really disorder, or have we been fooled all along because it&#8217;s convenient, simple, and easy to understand? And for the other half of the time argument, check out my conversation with the author of the book, with Nobel Prize winner Frank Wilczek on what a clock actually is.</p><p>Brian Keating:<br />It&#8217;s linked right here.</p><p>Brian Keating:<br />Thanks for watching. Please like, comment, and subscribe, but most importantly, share this with someone who loves Carlo&#8217;s books, his writing, and his research. See you next time on Into the Impossible.</p>								</div>
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			<media:title type="plain">Entropy Does Not Create Disorder. It Creates Order.</media:title>
			<media:description type="html"><![CDATA[This episode is sponsored by Abacus AI agent: https://agent.abacus.ai/keatingI fed it Carlo&#039;s recent papers and asked for the argument, the counterargument, ...]]></media:description>
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		<title>The AI Debate Is Missing the Most Important Question</title>
		<link>https://briankeating.com/the-ai-debate-is-missing-the-most-important-question/</link>
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		<dc:creator><![CDATA[sabartigas]]></dc:creator>
		<pubDate>Mon, 28 Sep 2026 00:24:31 +0000</pubDate>
				<category><![CDATA[Blog]]></category>
		<guid isPermaLink="false">https://briankeating.com/?p=8833</guid>

					<description><![CDATA[The AI Debate Is Missing the Most Important Question Dear Magicians, Recently, I heard reports that a researcher the same age as many of my grad students had spent four months at Anthropic, which admittedly is longer than most of my diets have lasted, resigned before his equity vested, and told the world that frontier AI labs are “gambling with our lives.” Jacob Coxon’s resignation goes viral, seen more than 100 million times, as frontier-lab leaders start talking about slowing down, and Barack Obama weighs in. The honest answer is that I don’t know where this ends. I’m a cosmologist, which means I’ve spent much of my adult life reconstructing the history of the universe from ancient microwave photons, so naturally I am now expected to understand Silicon Valley. What I do understand is the incentive problem. A small number of companies can capture astonishing economic upside from increasingly capable AI while some portion of the downside, whether job displacement, cybercrime, autonomous weapons or something genuinely catastrophic, lands on everyone else. That does not prove the doomsayers are right. It does mean the people placing the bet are not the only people with chips on the table. I recently heard an argument from Professor Scott Galloway, who asks us to think about 1946. Imagine Robert Oppenheimer leaving Los Alamos, raising a venture round, and announcing that nuclear weapons were now available to sovereign wealth funds and select enterprise customers. I assume Procter &#38; Gamble would have received preferred pricing. We did not organize atomic technology that way. The Atomic Energy Act of 1946 created an extraordinary federal regime around atomic materials and technology. More interestingly, Oppenheimer helped produce the Acheson-Lilienthal Report, which tried to answer a problem that feels strangely modern: how do you preserve the useful applications of a transformative technology while controlling the parts capable of catastrophic harm? The analogy is imperfect. But Scott’s both right and wrong. Fission gave us weapons capable of destroying cities and also reactors, submarines, medical isotopes and spacecraft power. Until 1954, nuclear technology remained essentially a federal monopoly, placing major legal barriers in front of private nuclear power as well as private weapons development. Contra Galloway, Imagine if Kleiner Perkins DID have an opportunity to invest in nuclear fission for reactor purposes only. Generating energy might have dramatically reduced the climate change and global warming concerns that followed. We&#8217;d have had far more abundant low-carbon energy. We wouldn&#8217;t have to wait for fusion, and the arguments over data centers might look very different. Heck, we&#8217;d even have data centers, and we&#8217;d probably have super-intelligent AI for real right now instead of twerking robots on tik Tok! Without today’s cost-per-token and energy constraints, if we had actually taken the venture capital route in the 1950s, Oppenheimer could have been a hell of a hedge fund manager. We didn’t. AI may have the same dual-use problem, except the boundary may be harder to draw. The same model that helps design a protein or analyze telescope data may also improve cyberattacks, surveillance, weapons or biological design. The reactor and the bomb may share too much machinery. That is why the current slowdown debate is so strange. Dario Amodei has argued for “pacing the frontier.” I decided that if it&#8217;s good enough for Dario, it might be good enough to say to my teenager when he asks to borrow the keys and $20. Sorry, buddy, we’ve got to pace the frontier on that. Sam Altman and Elon Musk have also expressed support for slowing frontier development. There is something awkward about being told by the drivers at the front of the race that everyone should now observe the speed limit. Lately I’ve hosted some of the most prominent voices warning about catastrophic AI risk, from Roman Yampolskiy to Emad Mostaque to Max Tegmark. What strikes me is how crowded that side of the boat has become. There are remarkably few prominent AI “boomers” left making the opposite case with any confidence, Yann LeCun being the obvious exception. When nearly every serious person in a debate migrates to one side, that may mean the evidence has converged. It may also mean the conversation itself has developed a center of gravity strong enough to pull everyone toward it. That does not make the warnings insincere. A warning can be both sincere and economically convenient. A slowdown might reduce genuine risk, or it might entrench the firms that already have the most compute, capital and talent. Both can be true. Geoffrey Hinton faced a version of this when he left Google in 2023. He said: “If I hadn’t done it, somebody else would have.” That remark accompanied his decision to speak more freely about AI risk. Later he put the epistemic situation more plainly: “Nobody really knows what’s going to happen … I’m just sounding the alarm.” That interview is here. That is closer to my position than confident percentages of human extinction. Physicists usually like error bars before the apocalypse, although we have been known to make exceptions when tenure is involved. The argument I hear less often is about the reactor. In San Diego, we sleep blissfully within the blast radius of 3-6 military nuclear reactors operated by the Navy while the one commercial reactor was shut down years ago: San Onofre’s premature closure eliminated a 2.2 GW nuclear plant that had powered roughly 1.4 million homes, forced Southern California Edison to spend more than $500 million on replacement electricity before the shutdown was even permanent, stranded roughly $768.5 million in replacement steam generators, and triggered years of disputes over billions of dollars in costs borne by utilities and ratepayers. The cleanup is now expected to cost about $5.73 billion for Units 2 and 3, with roughly $2.77 billion already spent through 2023 and another $2.96 billion projected through 2056, making San Onofre a striking example of how a non-core equipment failure can destroy the economic value of an already-built low-carbon power asset and then require]]></description>
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					<h2 class="elementor-heading-title elementor-size-default">The AI Debate Is Missing the Most Important Question</h2>				</div>
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																<a href="https://www.axios.com/2026/09/09/anthropic-researcher-ai-warning-interview" target="_blank" rel="noopener">
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									<p>Dear Magicians,</p><p>Recently, I heard reports that a researcher the same age as many of my grad students had spent four months at Anthropic, which admittedly is longer than most of my diets have lasted, resigned before his equity vested, and told the world that frontier AI labs are “gambling with our lives.” <a class="ck-link" href="https://www.axios.com/2026/09/09/anthropic-researcher-ai-warning-interview" target="_blank" rel="noopener noreferrer">Jacob Coxon’s resignation</a> goes viral, seen more than 100 million times, as frontier-lab leaders start talking about slowing down, and <a class="ck-link" href="https://www.pcgamer.com/software/ai/we-cant-stuff-ai-back-in-a-box-says-former-us-president-barack-obama-confirming-hes-not-a-doomer-while-still-calling-for-more-government-ai-regulation/" target="_blank" rel="noopener noreferrer">Barack Obama weighs in</a>.</p><p>The honest answer is that I don’t know where this ends. I’m a cosmologist, which means I’ve spent much of my adult life reconstructing the history of the universe from ancient microwave photons, so naturally I am now expected to understand Silicon Valley.</p><p>What I do understand is the incentive problem. A small number of companies can capture astonishing economic upside from increasingly capable AI while some portion of the downside, whether job displacement, cybercrime, autonomous weapons or something genuinely catastrophic, lands on everyone else. That does not prove the doomsayers are right. It does mean the people placing the bet are not the only people with chips on the table.</p><p>I recently heard an <a class="ck-link" href="https://www.profgmedia.com/p/oppenheimer-inc" target="_blank" rel="noopener noreferrer">argument</a> from Professor Scott Galloway, who asks us to think about 1946. Imagine Robert Oppenheimer leaving Los Alamos, raising a venture round, and announcing that nuclear weapons were now available to sovereign wealth funds and select enterprise customers. I assume Procter &amp; Gamble would have received preferred pricing.</p><p>We did not organize atomic technology that way. The <a class="ck-link" href="https://www.osti.gov/servlets/purl/1362094" target="_blank" rel="noopener noreferrer">Atomic Energy Act of 1946</a> created an extraordinary federal regime around atomic materials and technology. More interestingly, Oppenheimer helped produce the <a class="ck-link" href="https://www.atomicarchive.com/resources/documents/acheson-lilienthal/index.html" target="_blank" rel="noopener noreferrer">Acheson-Lilienthal Report</a>, which tried to answer a problem that feels strangely modern: how do you preserve the useful applications of a transformative technology while controlling the parts capable of catastrophic harm?</p><p>The analogy is imperfect. But Scott’s both right and wrong. Fission gave us weapons capable of destroying cities and also reactors, submarines, medical isotopes and spacecraft power. Until 1954, nuclear technology remained essentially a federal monopoly, placing major legal barriers in front of private nuclear power as well as private weapons development.</p><p>Contra Galloway, Imagine if Kleiner Perkins <u><em><strong>DID</strong></em></u> have an opportunity to invest in nuclear fission for reactor purposes only. Generating energy might have dramatically reduced the climate change and global warming concerns that followed. We&#8217;d have had far more abundant low-carbon energy. We wouldn&#8217;t have to wait for fusion, and the arguments over data centers might look very different. Heck, we&#8217;d even have data centers, and we&#8217;d probably have super-intelligent AI for real right now instead of twerking robots on tik Tok!</p><p>Without today’s cost-per-token and energy constraints, if we had actually taken the venture capital route in the 1950s, Oppenheimer could have been a hell of a hedge fund manager. We didn’t.</p><p>AI may have the same dual-use problem, except the boundary may be harder to draw. The same model that helps design a protein or analyze telescope data may also improve cyberattacks, surveillance, weapons or biological design. The reactor and the bomb may share too much machinery.</p><p>That is why the current slowdown debate is so strange. Dario Amodei has argued for “pacing the frontier.” I decided that if it&#8217;s good enough for Dario, it might be good enough to say to my teenager when he asks to borrow the keys and $20. Sorry, buddy, we’ve got to pace the frontier on that. <a class="ck-link" href="https://www.reuters.com/business/what-amodei-altman-musk-have-said-about-ai-risks-stoking-doom-fears-2026-09-14/" target="_blank" rel="noopener noreferrer">Sam Altman and Elon Musk have also expressed support for slowing frontier development</a>. There is something awkward about being told by the drivers at the front of the race that everyone should now observe the speed limit.</p><p>Lately I’ve hosted some of the most prominent voices warning about catastrophic AI risk, from Roman Yampolskiy to Emad Mostaque to Max Tegmark. What strikes me is how crowded that side of the boat has become. There are remarkably few prominent AI “boomers” left making the opposite case with any confidence, Yann LeCun being the obvious exception. When nearly every serious person in a debate migrates to one side, that may mean the evidence has converged. It may also mean the conversation itself has developed a center of gravity strong enough to pull everyone toward it.</p><p>That does not make the warnings insincere. A warning can be both sincere and economically convenient. A slowdown might reduce genuine risk, or it might entrench the firms that already have the most compute, capital and talent. Both can be true.</p><p>Geoffrey Hinton faced a version of this when he left Google in 2023. He said: “If I hadn’t done it, somebody else would have.” <a class="ck-link" href="https://www.theguardian.com/technology/2023/may/02/geoffrey-hinton-godfather-of-ai-quits-google-warns-dangers-of-machine-learning" target="_blank" rel="noopener noreferrer">That remark accompanied his decision to speak more freely about AI risk</a>. Later he put the epistemic situation more plainly: “Nobody really knows what’s going to happen … I’m just sounding the alarm.” <a class="ck-link" href="https://www.utoronto.ca/news/godfather-conversation-why-geoffrey-hinton-worried-about-future-ai" target="_blank" rel="noopener noreferrer">That interview is here</a>.</p><p>That is closer to my position than confident percentages of human extinction. Physicists usually like error bars before the apocalypse, although we have been known to make exceptions when tenure is involved.</p><p>The argument I hear less often is about the reactor. In San Diego, we sleep blissfully within the blast radius of 3-6 military nuclear reactors operated by the Navy while the one commercial reactor was shut down years ago: San Onofre’s premature closure eliminated a 2.2 GW nuclear plant that had powered roughly 1.4 million homes, forced Southern California Edison to spend more than $500 million on replacement electricity before the shutdown was even permanent, stranded roughly $768.5 million in replacement steam generators, and triggered years of disputes over billions of dollars in costs borne by utilities and ratepayers.</p><p>The cleanup is now expected to cost about $5.73 billion for Units 2 and 3, with roughly $2.77 billion already spent through 2023 and another $2.96 billion projected through 2056, making San Onofre a striking example of how a non-core equipment failure can destroy the economic value of an already-built low-carbon power asset and then require billions more to dismantle it.</p><p>What worries me is ignoring venture economics from having a say in who decides. True it’s likely to lead to a system with the largest market getting the largest valuation. But this feels like the real fork in the road. Not whether AI is dangerous. Almost every powerful technology is dangerous. The harder question is which capabilities we actually want, which risks we are willing to socialize, invest in and who gets to make those decisions.</p><p>I don’t know whether Jacob Coxon is prescient or catastrophically wrong. I am increasingly suspicious, though, of any theory of technological civilization in which the deciding argument is simply that one branch has the higher valuation.</p><p>Until next time, have a M.A.G.I.C. Week.</p><p>Brian</p>								</div>
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					<h2 class="elementor-heading-title elementor-size-default">Appearance</h2>				</div>
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									<p><strong>Has physics fallen in love with its own equations?</strong></p><p>That&#8217;s where my conversation with Stephen Meyer starts: what happens when a theory gets judged by its elegance instead of its predictions.</p><p>From there Stephen makes his case for intelligent design, arguing that the DNA long written off as &#8220;junk&#8221; behaves less like evolutionary leftovers and more like an operating system for biological information. We also get into origin-of-life chemistry and the God of the Gaps objection, which he answers by pointing to what we do know about cause and effect rather than what we don&#8217;t.</p><p>Whether that holds up is something you can decide for yourself.</p><p>​<a class="ck-link" href="https://www.youtube.com/watch?v=Cle4b66sZjw" target="_blank" rel="noopener noreferrer">Watch the conversation here.</a></p>								</div>
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																<a href="https://www.nytimes.com/2026/09/16/science/human-mouse-brains-organoids.html?utm_source=superhuman&#038;utm_medium=newsletter&#038;utm_campaign=sunday-special-the-first-new-cat-species-in-a-century&#038;_bhlid=a42fa2eaf43de3266329dd1e7353024e8bb16646" target="_blank" rel="noopener">
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									<p>Stanford <a class="ck-link" href="https://www.nytimes.com/2026/09/16/science/human-mouse-brains-organoids.html?utm_source=superhuman&amp;utm_medium=newsletter&amp;utm_campaign=sunday-special-the-first-new-cat-species-in-a-century&amp;_bhlid=a42fa2eaf43de3266329dd1e7353024e8bb16646" target="_blank" rel="noopener noreferrer">scientists</a> put millions of living human neurons into mice whose own cortex had been removed. The odd part? The mice still behave like mice. So where, exactly, does “human” cognition begin? We are getting disturbingly good at building experiments before we agree on what their results would mean.</p>								</div>
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									<p>Something hit the Moon in 2024 hard enough to carve a crater 222 meters across. We didn’t notice. <a class="ck-link" href="https://science.nasa.gov/solar-system/moon/nasas-moon-orbiter-spots-new-once-in-century-moon-crater/" target="_blank" rel="noopener noreferrer">NASA found it later </a>by comparing before-and-after images. McGetchin is the largest newly formed impact crater yet found anywhere in the Solar System. The Moon is less boring than it looks.</p>								</div>
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									<p><strong>Carlo Rovelli says you were taught the second law wrong</strong></p><p>Your head is older than your feet. That&#8217;s general relativity, and it&#8217;s how Carlo Rovelli, one of the founders of loop quantum gravity, opened this week&#8217;s episode of Into the Impossible.</p><p>We cover why oil separating from water doesn&#8217;t violate entropy, what Galileo got wrong about his most famous discovery, and why a chess program that deliberates reveals what your own freedom really is. As Carlo puts it: &#8220;To imagine that to be free you have to violate the laws of nature is stupid.&#8221;</p><p>Audio is live on Apple Podcasts and at briankeating.com/podcast.</p>								</div>
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		<title>They Built the Quietest Place on Earth to Find Dark Matter</title>
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		<pubDate>Sun, 20 Sep 2026 19:01:11 +0000</pubDate>
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					<description><![CDATA[They Built the Quietest Place on Earth to Find Dark Matter https://www.youtube.com/watch?v=6GMokB04qt4 Transcript: Brian Keating:Welcome everybody. This is a very special and urgent lecture episode featuring my friend and professor at my alma mater, Rick Gaetzkill. Joining us from Brown University, Rick is the spokesperson of the LZ Collaboration, which stands for Lux Zeppelin, which is located at the Sanford facility in America. Denny Sanford was a friend. He lived here in La Jolla. I met him many times. We have some conversations together. Really wonderful that you guys have done so much to celebrate this great contributor to philanthropy and also to our understanding of the cosmos. Brian Keating:So, Rick, I&#8217;m really excited. You&#8217;re going to talk to us about one of the most exciting announcements in very recent history, and that has to do with an event that was announced just about a week ago. So this is really urgent and emergent, as I said. So, Rick, please take it away. Rick Gaetzkill:Brian, thanks so much. You know, it&#8217;s marvelous to see you. So the Lux Zeppelin experiment is the sort of latest in a line of dark matter direct detection experiments that I&#8217;ve been involved in over the last 40 years. It always helps me to gain a little bit of perspective looking backwards as well as, as you say, what we&#8217;ve announced from the work of LUXEP just in the last week or so. It is always a little sobering that if you sort of just consider what&#8217;s happened in the last 3 years, for instance. The universe has actually expanded measurably in the sense that it&#8217;s about a 5th of a part per billion, which I always find quite remarkable. You know, just the universe continues relentlessly doing its thing. But from a dark matter perspective, we&#8217;ve been improving our sensitivity looking for so many or testing so many different models of dark matter. Rick Gaetzkill:And as you&#8217;ll see, you know, in a moment, just how extensive that testing has been. And then my— one factor is my weight, which actually for once has fluctuated down and not up, which, you know, for 3 years is not bad. I am going to take the opportunity always to sort of remind people a little bit about why we&#8217;re still engaged in looking for dark matter particles, even given that we&#8217;ve been looking for them for 40 years. And then, of course, as you&#8217;ve said, we are actually sitting on an event. An event has shown up in the LZ. Now, as anybody who sort of follows more closely rare event searches, you know, one has to recognize that a single event is, you know, simply a— at the start, if you like. And, you know, either statistically or in terms of our growing understanding of of the conditions that might have contributed to the event, that the interpretation is, you know, could either be that we&#8217;re going to see subsequent events consistent with the dark matter hypothesis, or it could be we&#8217;re going to see events consistent with some other, you know, more sort of, well, exotic but mundane at the same time background. And this, you know, as experimental physicists, is something we are absolutely focused on always in search The talk, or our discussion, there are so many abbreviations, buzzwords, acronyms these days. Rick Gaetzkill:I think I&#8217;m going to forego the usual jokes I make about some of these, other than to mention when people hear the word WIMP, for Weakly Interacting Massive Particle, which is after all these significant, you know, number of years that we&#8217;ve been trying to test such a hypothesis, You have to understand that physicists do have a little bit of a sense of humor, and the WIMP acronym actually came about at a time when dark matter could also be solved by MACHOs, which were massive compact halo objects. So there was a very deliberate, I think, sort of element of humor in the WIMP, MACHO. Now, MACHOs have actually been something that we&#8217;ve managed to test that particular hypothesis, and it is significantly— the amount of dark matter that could be satisfied using a Macho hypothesis is very much smaller and certainly would not solve the entire dark matter issue. Now the other thing I&#8217;m going to do a little bit of is I will end up mentioning supersymmetry, but again, I&#8217;m not going to get too heavily into the acronyms. And let&#8217;s— anyway, let&#8217;s move on. Brian Keating:I— Rick Gaetzkill:the other thing is, as you will see, these slides in fact do not have any AI used in their preparation. Certainly with respect to prettification. Brian Keating:Wow. Rick Gaetzkill:There is an interesting aspect of, of AI in rare event searches when it comes to the analysis chain. And while this is something that, you know, like many scientific, you know, experimental that we are, you know, doing a great deal of investigation in, we also have to subject any points in the analysis where AI has been used, you know, we have to be very rigorous about understanding how it&#8217;s working, which is not always the way that people choose to use AI. We&#8217;re a very constrained case, so we are excited to see what can be done in terms of making better and better use of data that we&#8217;re taking. But equally, we, you know, as you might imagine, if we&#8217;re talking about a single event, we don&#8217;t want to be— we would not be in a situation where an analysis chain simply popped an event out of magic because of an AI component, and that that was what we stood behind. That it&#8217;s simply, as you might imagine, you know, would not work like that. And that&#8217;s not something that— The other thing is, I have to, you know, let&#8217;s not talk about the AI. Let&#8217;s talk about the natural intelligence that we have. This, like, you know, is a large scientific collaboration,]]></description>
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					<h2 class="elementor-heading-title elementor-size-default">They Built the Quietest Place on Earth to Find Dark Matter</h2>				</div>
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									<h2><strong>Transcript:</strong></h2><p>Brian Keating:<br />Welcome everybody. This is a very special and urgent lecture episode featuring my friend and professor at my alma mater, Rick Gaetzkill. Joining us from Brown University, Rick is the spokesperson of the LZ Collaboration, which stands for Lux Zeppelin, which is located at the Sanford facility in America. Denny Sanford was a friend. He lived here in La Jolla. I met him many times. We have some conversations together. Really wonderful that you guys have done so much to celebrate this great contributor to philanthropy and also to our understanding of the cosmos.</p><p>Brian Keating:<br />So, Rick, I&#8217;m really excited. You&#8217;re going to talk to us about one of the most exciting announcements in very recent history, and that has to do with an event that was announced just about a week ago. So this is really urgent and emergent, as I said. So, Rick, please take it away.</p><p>Rick Gaetzkill:<br />Brian, thanks so much. You know, it&#8217;s marvelous to see you. So the Lux Zeppelin experiment is the sort of latest in a line of dark matter direct detection experiments that I&#8217;ve been involved in over the last 40 years. It always helps me to gain a little bit of perspective looking backwards as well as, as you say, what we&#8217;ve announced from the work of LUXEP just in the last week or so. It is always a little sobering that if you sort of just consider what&#8217;s happened in the last 3 years, for instance. The universe has actually expanded measurably in the sense that it&#8217;s about a 5th of a part per billion, which I always find quite remarkable. You know, just the universe continues relentlessly doing its thing. But from a dark matter perspective, we&#8217;ve been improving our sensitivity looking for so many or testing so many different models of dark matter.</p><p>Rick Gaetzkill:<br />And as you&#8217;ll see, you know, in a moment, just how extensive that testing has been. And then my— one factor is my weight, which actually for once has fluctuated down and not up, which, you know, for 3 years is not bad. I am going to take the opportunity always to sort of remind people a little bit about why we&#8217;re still engaged in looking for dark matter particles, even given that we&#8217;ve been looking for them for 40 years. And then, of course, as you&#8217;ve said, we are actually sitting on an event. An event has shown up in the LZ. Now, as anybody who sort of follows more closely rare event searches, you know, one has to recognize that a single event is, you know, simply a— at the start, if you like. And, you know, either statistically or in terms of our growing understanding of of the conditions that might have contributed to the event, that the interpretation is, you know, could either be that we&#8217;re going to see subsequent events consistent with the dark matter hypothesis, or it could be we&#8217;re going to see events consistent with some other, you know, more sort of, well, exotic but mundane at the same time background. And this, you know, as experimental physicists, is something we are absolutely focused on always in search The talk, or our discussion, there are so many abbreviations, buzzwords, acronyms these days.</p><p>Rick Gaetzkill:<br />I think I&#8217;m going to forego the usual jokes I make about some of these, other than to mention when people hear the word WIMP, for Weakly Interacting Massive Particle, which is after all these significant, you know, number of years that we&#8217;ve been trying to test such a hypothesis, You have to understand that physicists do have a little bit of a sense of humor, and the WIMP acronym actually came about at a time when dark matter could also be solved by MACHOs, which were massive compact halo objects. So there was a very deliberate, I think, sort of element of humor in the WIMP, MACHO. Now, MACHOs have actually been something that we&#8217;ve managed to test that particular hypothesis, and it is significantly— the amount of dark matter that could be satisfied using a Macho hypothesis is very much smaller and certainly would not solve the entire dark matter issue. Now the other thing I&#8217;m going to do a little bit of is I will end up mentioning supersymmetry, but again, I&#8217;m not going to get too heavily into the acronyms. And let&#8217;s— anyway, let&#8217;s move on.</p><p>Brian Keating:<br />I—</p><p>Rick Gaetzkill:<br />the other thing is, as you will see, these slides in fact do not have any AI used in their preparation. Certainly with respect to prettification.</p><p>Brian Keating:<br />Wow.</p><p>Rick Gaetzkill:<br />There is an interesting aspect of, of AI in rare event searches when it comes to the analysis chain. And while this is something that, you know, like many scientific, you know, experimental that we are, you know, doing a great deal of investigation in, we also have to subject any points in the analysis where AI has been used, you know, we have to be very rigorous about understanding how it&#8217;s working, which is not always the way that people choose to use AI. We&#8217;re a very constrained case, so we are excited to see what can be done in terms of making better and better use of data that we&#8217;re taking. But equally, we, you know, as you might imagine, if we&#8217;re talking about a single event, we don&#8217;t want to be— we would not be in a situation where an analysis chain simply popped an event out of magic because of an AI component, and that that was what we stood behind. That it&#8217;s simply, as you might imagine, you know, would not work like that. And that&#8217;s not something that— The other thing is, I have to, you know, let&#8217;s not talk about the AI. Let&#8217;s talk about the natural intelligence that we have. This, like, you know, is a large scientific collaboration, relatively speaking.</p><p>Rick Gaetzkill:<br />There are over 250 authors on the latest paper that we&#8217;ve done. This is a photograph taken from a collaboration meeting here at Brown a year or two ago, but we have regular collaboration meetings, as you might imagine, and this work takes or combines the input of a broad range of people, both DOE institutions here in the U.S. and also universities and labs from other parts of the world, including the United Kingdom. And of course, we actually are based at the Sanford Underground Research Facility in South Dakota, which is a US deep underground lab that we work with and are supported by very closely to do this level of leading science. I always mention the other thing you have to bear in mind, that is, we&#8217;re not just training people within our organ— within LZ to do astrophysics and cosmology. Obviously, that is something that we are trying to answer and break, you know, questions, answer questions there. But of course, much of the training that graduate students, postdocs receive extends into a broad range of other, you know, disciplines, including sort of analysis of massive datasets or— development of sophisticated simulations or indeed machine learning. And this is, it&#8217;s an important part of our process.</p><p>Rick Gaetzkill:<br />Now, for me, I&#8217;ve actually, I&#8217;m about 40 years now trying to answer one single question, which is, can we fully identify what is dark matter? And on one hand, you may say 40 years, that&#8217;s ridiculous to spend that long on a single question, but I— Much as we look back in history of science, it often seems like things are coming thick and fast. If you actually really break it down and look in specific sort of chimneys or specific areas of investigation, there&#8217;s a much greater distance between the great— then of course 2 or 3 great discoveries come along at once, and then again you have this sort of extended, you know, period of time. If you dart around between different subdisciplines, then of course you can start bagging or seemingly get progress happening at a much higher rate. But, you know, look, science, you know, research is extremely demanding. And demanding because 95-plus percent of the time you&#8217;re not going to get a negative result. And we held a sort of feedback session with graduate students and we had written feedback from a few students— this was a couple of years ago— who actually said they felt their supervisor was deliberately giving them work that did not give them— did not give answers. You know, the research was producing negative results. And we just realized we&#8217;d utterly failed our students in terms of really telling them about how research works, which is most of the time—</p><p>Brian Keating:<br />Yeah.</p><p>Rick Gaetzkill:<br />You know, you come up with a question that is well motivated, and that&#8217;s, you know, how the, you know, using the community works. But it is so challenging you come up with well-motivated questions, but most of the time, if it&#8217;s good research and you&#8217;ve got an imagination and, you know, a well-motivated imagination, but nonetheless a good imagination, of course it turns out that nature completely ignores any—</p><p>Brian Keating:<br />That&#8217;s right.</p><p>Rick Gaetzkill:<br />Nature doesn&#8217;t care. And, you know, I wish we had time to sort of discuss that, but, you know, even in the short 40 years that I&#8217;ve been addressing this, you know, so many Beautiful models have come and been ruled out for how we might solve dark matter. And you shouldn&#8217;t— but what you shouldn&#8217;t do is interpret that somehow that the entire question is broken. It&#8217;s just simply that nature really doesn&#8217;t care about beauty often. In a sense, certainly in the sense that when we construct a theory and try to say this is well motivated, Yes, it&#8217;s well motivated. Yes, it&#8217;s consistent with existing measurements, but nature doesn&#8217;t need to pick it.</p><p>Brian Keating:<br />That&#8217;s right.</p><p>Rick Gaetzkill:<br />And of course, it&#8217;ll be— it will probably be very obvious. It always is when you get an answer and you look back and you go, ah, yes. But that again is something you have as a scientist to, you know, to recognize is not really. So I&#8217;ve worked in a number of labs. I often— one has to change one&#8217;s clothing a bit. I was, you know, back in the &#8217;90s, for instance, I was, you know, northern Minnesota in the Soudan Mine, the CDMS2 experiment. We then moved in the noughties, the 2000s, to Gran Sasso. It requires a bit of a wardrobe change, of course.</p><p>Rick Gaetzkill:<br />One has to look more like an Italian physicist when one is in Italy. But we did a great deal of work on the early days of liquid xenon detectors. And then, as we mentioned, latterly we have been working on both the LUX and the subsequent LUX-Zeppelin. experiment in the Sanford Lab in South Dakota here in the US. And of course, one needs a bit of a wardrobe change if one&#8217;s going to work in South Dakota. That&#8217;s right.</p><p>Brian Keating:<br />The credit card state. The credit card state. Which is why Sanford was there, right?</p><p>Rick Gaetzkill:<br />Well, most— when I explain to people what it was like, the banking industry back in the late &#8217;60s, &#8217;70s, people don&#8217;t really understand quite what an innovation credit cards were, and then equally how you had to restructure the legal environment in order for this clearing process to work. So it is fascinating.</p><p>Brian Keating:<br />That&#8217;s right.</p><p>Rick Gaetzkill:<br />And only a limited number of states, I think, really understood this, and of course South Dakota, you know, did even at an early stage. I have to confess, you see, for 4 years I was actually an investment banker, you know, back in the &#8217;80s. Oh, that&#8217;s right. So I actually, I think I understand a bit more about, you know, how finance—</p><p>Brian Keating:<br />You left it, you left that world for the high, high-pay world of experimental physics and professor and professing.</p><p>Rick Gaetzkill:<br />You know, back in the &#8217;90s, the BBC came into the lab in Oxford I was working at trying to do a story about how people were leaving academia for finance. And they were shooting a lot of B-roll. They were interviewing my head of department rather than me. I was standing in the background and I nudged one of the assistant you know, producers and said, I have to confess, I actually came out of finance back into academia. You know, is this going to wreck the story? And they just told me to shut up and just continue trying— just to continue twiddling whatever knob I was supposed to be twiddling to provide a backdrop to their B-roll, you know, for the story. But anyway—</p><p>Brian Keating:<br />Yeah.</p><p>Rick Gaetzkill:<br />So the dark matter itself is, you know, absolutely central in our attempts to build up this overarching model of our Milky Way. And I think everybody is probably to some degree familiar with it. What&#8217;s fascinating is if we&#8217;re talking about the overall composition of the universe, and we&#8217;re now in a situation where around 25% of that total composition is dark matter. Now, that you might say, that sounds like a bit part, Rick. But no, if you want to understand bit parts, it&#8217;s you and I. We&#8217;re made from conventional atomic or baryonic matter, and that&#8217;s less than 5% of the total composition of the universe. So you and I are the flotsam and jetsam of the conventional atoms, the protons, the neutrons, the electrons, flotsam and jetsam on a much more substantial matter component, which we know is there gravitationally. We have over the last— well, I think you can argue almost 100 years now of observations.</p><p>Rick Gaetzkill:<br />Where we have determined that the way that the galaxies and clusters of galaxies are behaving, that you clearly need to insert a great deal of non-luminous or dark matter, matter that doesn&#8217;t show up directly at the telescopes but does end up— or some property that seems to actually affect the gravitational behavior and the gravitational evolution of our galaxies. But one of the things I&#8217;ve been lucky to sort of live through is also this tremendous change we had in that overall model, you know, in the &#8217;90s, and that we, you know, we have been refining the model of is that you also have to have— find room for about 70% of a thing called dark energy.</p><p>Brian Keating:<br />Right.</p><p>Rick Gaetzkill:<br />Which is a component— whereas the dark matter is helping us understand how gravitational formation at galaxy and cluster of galaxies are evolving and how they&#8217;re holding together, dark energy is a very rarefied but finite term that appears to be pushing the entire universe at an ever-accelerating pace apart. The challenge is that right now we have these titles, dark energy, dark matter. We know dynamically how they influence the sort of evolution of our large scale in the Milky Way. The problem is that we don&#8217;t know what either of these actually are yet. And that&#8217;s what we&#8217;re doing. We have this LZ experiment is very much targeted at trying to directly identify dark matter particles. Now, if the dark matter is due to particles, then they are— the abundance is large. We don&#8217;t know the mass of the individual particles yet because we haven&#8217;t directly measured them.</p><p>Rick Gaetzkill:<br />Our theories, in fact, span a very wide range of possible masses. But for the kind of particles that we&#8217;re looking for with LZ, you know, you&#8217;re talking about of the order of 1 to— or 0.1 to maybe 100 of them per liter, or per, you know, about this sort of volume. And they are moving sufficiently rapidly that through your body, you&#8217;ve probably got about 100 million of them moving through your body. Now, they are not interacting with your body, except in the general sense of providing gravity that holds the galaxy together.</p><p>Brian Keating:<br />Right.</p><p>Rick Gaetzkill:<br />But their actual rate of interaction is so weak at this stage that we now One way to imagine it, you&#8217;re gonna see sort of cross-section numbers, but one way to imagine it is if we fired a single dark matter particle, hypothetical dark matter particle, through lead, we could actually pile that lead all the way out to the closest star beyond the sun, you know, sort of, you know, Proxima Centauri or even Alpha Centauri, and actually go about twice, more than twice that distance, so 10 light years. And the— even though this dark matter particle was traveling through lead, less than 50/50 chance it would have interacted at that point. So that&#8217;s a very weak interaction, but because as you can see the fluxes are large, 100 million per second through your body, and if we use detectors that are massive enough, then the probability of getting an individual interaction over a matter of weeks or months starts becoming finite.</p><p>Brian Keating:<br />Yeah.</p><p>Rick Gaetzkill:<br />And that&#8217;s what we&#8217;ve been doing. So that&#8217;s how we&#8217;re trying to study these dark matter particles. Now, the WIMP, idea really is nearly 50 years old now. There were a number of papers that were seminal. I can&#8217;t list them all, but I think Lee Weinberg— I&#8217;m just pointing out that it was given— if we go back to that period in the late &#8217;70s, early &#8217;80s, we were just understanding how important and experimentally verifying the W particle and how weak interaction physics and electroweak unification, and in fact, of course, really bedrock of the standard model of particle physics, that it seemed very natural for weak-scale physics to provide a dark matter particle. Why? Because all particles are equal in the early universe. They&#8217;re all being created and destroyed. As the universe cools, the masses of these particles becomes more relevant.</p><p>Rick Gaetzkill:<br />And what was fascinating to realize is that a particle with an interaction strength and a mass that was of the order of the weak scale and the weak scale physics that we understood, that would very naturally provide a significant component of the matter of the universe as long as it was stable.</p><p>Brian Keating:<br />Yeah.</p><p>Rick Gaetzkill:<br />So you have to plug in by hand a mechanism that makes this exotic weakly interacting massive particle stable. You do that, you formulate a model. Now it turns out you can actually do that over a huge parameter space. You can vary the masses of these particles, you can vary the interaction strengths which depend themselves on the exchange of other particles, sort of so-called gauge particles or coupling particles. And that we have been, you know, over the last 40 years, we&#8217;ve been looking directly for the interaction of those dark matter particles with nuclei. It turns out that nuclei are of the similar order of mass to these WIMPs. To these things. So that means that you get a sort of— you get momentum exchange taking place.</p><p>Rick Gaetzkill:<br />Now, often when we&#8217;re trying to understand what the likely rate is, we make various simplifying assumptions. And one of the things, you know, we&#8217;re about to go on to is that it turns out that while simplifying assumptions of the nature of the interaction are very useful, it has rather limited the parameter space over which we have been specifically searching. For dark matter. And I mean, you know, theorists have been— were addressing this and, you know, we go back sort of 10, 15 years. They developed frameworks that actually said, look, don&#8217;t just constrain yourself to this very limited, simple interactions. It&#8217;s quite possible that dark matter interactions may be happening with nuclei which have a slight— Now, why you might be concerned about this, and I— let me just go straight into the calculation. It&#8217;s really, if you&#8217;re, you know, if you are a graduate student in physics or, you know, you&#8217;re comfortable with keV, kiloelectron volt units, really when you&#8217;re trying to understand what the sort of energy that could be transferred to a nucleus is, if you&#8217;ve paid attention in previous dark matter, many previous dark matter results and what have you, you&#8217;ll realize that we often talk about tens of keV. A keV is a sort of energy associated with X-rays.</p><p>Rick Gaetzkill:<br />You know, so for instance, if you&#8217;re being exposed to an X-ray in a hospital, that&#8217;s a few hundred, you know, to 800 keV of energy. We&#8217;ve been looking for dark matter with sort of tens of keV energy, so down at the sort of bottom range of that. But if you actually calculate how much kinetic energy a dark matter particle in the Milky Way as it&#8217;s traveling through you is carrying, you realize that because its velocity is order a few 10 to the minus 3, so a few thousandths of the velocity of the speed of light, then actually calculating half mv squared, if you&#8217;re used to sort of a unit switch, you can just bring out the— or put in factors of c or c squared, and you realize that something with a mass of 100 GeV that&#8217;s moving a few times 10 to the minus 3 the speed of light is actually carrying an energy that&#8217;s as high as, say, 400 or around 400 keV. And the reason we usually don&#8217;t talk about transferring as much as 400 keV is because under the most simple types of interactions, the so-called spin-independent interaction, you get a simultaneous scattering from all the nuclei simultaneously in a nucleus, which gives you a significant coherence effect because it turns out that the amplitude from each scattering, if the amount of momentum that&#8217;s exchanged or the amount of energy that&#8217;s being exchanged is relatively modest, you can maintain coherence across them. What&#8217;s fascinating is if you do— if you sort of start messing around with the numbers, you realize that if you want to exchange more than a few tens of keV, actually you&#8217;re going to lose coherence across the individual nucleons and you have to go to a much more— a more sophisticated, not terribly more sophisticated, but just a little bit more sophisticated calculation of the cross-section. And at that point, what&#8217;s even more entertaining is if, as you play around with the potential parameters, it turns out you can actually get a situation where the dark matter wants to interact preferentially at higher recoil scatterings rather than the lowest one. It is heavily suppressed, but as you see, as you&#8217;ll see in a moment, the LZ experiment is many, well, 7 tons of active volume. It&#8217;s a large detector and we&#8217;re well placed to look for relatively, well, relatively very weak—</p><p>Brian Keating:<br />Rick, if we could just go back, go back to the kinetic energy. So this is, I want to point out, this is like freshman physics. It&#8217;s very cool. It&#8217;s just like, if you want to see a detectable outcome of a collision, you don&#8217;t take like a cannonball and shoot it into a ping pong ball. You really will see much more when they&#8217;re roughly matched in mass range. And that, I think, is why you choose that. The thing that always kind of elides my discussions of it, because I&#8217;m not as much of an expert as you, when I teach cosmology and I talk about WIMPs and I talk about dark matter, It&#8217;s always like, well, these things are scattering off of— so it&#8217;s about, as you say, the WIMP is bouncing off the nucleus or vice versa, depending on your reference frame, right? But when we say bouncing, let&#8217;s be precise. There&#8217;s some gauge boson being exchanged, right? So all forces, including collisional forces, have to be mediated by some force-carrying mediator.</p><p>Brian Keating:<br />In most cases, an electron, electron scattering, Bhabha scattering, or whatever, it&#8217;s a photon. What is being exchanged? If these things only interact weakly, it must be the W or Z, right?</p><p>Rick Gaetzkill:<br />That&#8217;s correct, and it&#8217;s because at the moment we don&#8217;t have a specific model that we&#8217;ve identified. We have to be as broad as possible about considering the types of particles that can be exchanged. So as you&#8217;ll see for this LZ event, You can— That to see it at the rate that we&#8217;re seeing would lead you to estimate the sort of mass scale of the particle itself and then also, you know, a particular species of or type of gauge particle that&#8217;s doing it. But in general terms, I think actually in this— so on this slide here, and this is one I— you remember I used in 2016. I&#8217;ve only— I&#8217;ve cut all the other previous slides that ran into this out. But the idea was that I was trying to give you a sense. In this case, we&#8217;re just plotting the mass of the dark matter particle. But we are— what we then do is these specific models that are labeled here are often characterized by narrowing the gauge particle or the specific particle that&#8217;s being exchanged in order for that.</p><p>Rick Gaetzkill:<br />And as you&#8217;re— With the case of this LZ event, you can actually do it, for instance, with suggesting that the dark matter is— and you can see it actually on this plot— that, well, there&#8217;s— it&#8217;s a Higgsino-like particle but it&#8217;s actually a doublet. And you can move between the 2 states as the scattering is taking place, and that itself introduces another twist in the allowable range of momentum or energy that&#8217;s exchanged and will heavily suppress, in fact make it impossible for you to scatter depositing low energies. You have— there&#8217;s a finite amount of energy that you must exchange in order to include the process which requires you to move move from the 1 Higgs, you know, so Higgsino, sorry, Higgsino state to the higher Higgsino state. There is in fact a subsequent decay associated with that. Unfortunately, it happens way outside of our detector, so it&#8217;s not something we&#8217;re in a position, you know, to study. So yeah, the way— and this is, you mentioned propagators. So one of the reasons— so we look at this plot. I usually say to a student, if they put up a plot that has what&#8217;s this, 9 orders of magnitude on the vertical.</p><p>Rick Gaetzkill:<br />I&#8217;ll usually say to them, look, that&#8217;s ridiculous. Physics doesn&#8217;t work. 9 orders of magnitude under most circumstances is a ridiculous amount of dynamic range to put in it. But this is a historic plot versus year of the sensitivity in terms of just normal— sorry, particle cross-section for dark matter on the particle— on the—</p><p>Brian Keating:<br />On the—</p><p>Rick Gaetzkill:<br />targets that were terrestri— the subterrestrial, the underground, you know, event searches that we&#8217;ve been doing. And we have actually covered, in terms of results, nearly 8 orders of magnitude over the last 40 years. Why is it 8 orders of magnitude? Because that propagator you mentioned, actually, if it&#8217;s— if the particle that&#8217;s being exchanged actually has mass, then in the limit of the low momentum exchanges, you&#8217;re basically— you&#8217;re often dealing with a 1 over mass to the 4th 4th term for that propagator. So one order of magnitude uncertainty or increase in propagator can give you 4 orders of magnitude in terms of a cross-section or interaction probability or rate. So it turns out nature can deliver models really without trying very hard that span this enormous range of potential interaction sensitivities or cross-sections. So So, you know, you don&#8217;t automatically just say because you didn&#8217;t find it in the first 10 years of searching that that&#8217;s ruled out dark matter because this is a problem. In fact, the way I would say it is we had to flip it around. We had to try to make damn sure that for those of us who are involved in this kind of search, that the rate at which we improve the performance of the detectors, which is the sort of the size of the detectors and also their sense sensitivity and their ability to eliminate other potential background sources, that we have to keep improving that at a rate that is fast enough that we really can meet this challenge we&#8217;ve got here.</p><p>Rick Gaetzkill:<br />And, you know, one way I&#8217;ve put it in the past is that we are actually beating Moore&#8217;s Law, you know, because whereas I think— let&#8217;s see, Moore&#8217;s Law is what, 3 and a bit orders of magnitude every— no, sorry, it&#8217;s an order of magnitude every 10 years. I think we&#8217;ve been delivering an order of magnitude in sort of 2/3 that time.</p><p>Brian Keating:<br />Mm-hmm.</p><p>Rick Gaetzkill:<br />So, you know, 6 years or so. So we&#8217;ve been going faster than— now, you know, obviously I&#8217;m— you know, this is dark matter sensitivity. But just to give you a sense of how rapidly we&#8217;ve been able to— and we&#8217;ve done this through changing the type of technology that we use and just being absolutely laser-focused on improving our ability to test new models, which is something we&#8217;ve done.</p><p>Brian Keating:<br />Actually, yeah, if you go back a slide.</p><p>Rick Gaetzkill:<br />Yes.</p><p>Brian Keating:<br />I mean, I just wanna point out a couple things. Your density of fascinating information per second exceeds any cross-sectional flux that we&#8217;d get from any particle.</p><p>Rick Gaetzkill:<br />My apologies.</p><p>Brian Keating:<br />Except maybe the neutrino. But we&#8217;re gonna have a podcast after this, and I will refer people to it. But I wanted to get this video out first. But I can&#8217;t resist putting on my physicist podcast physicist hat And there&#8217;s a couple things I would say. You know, I joined Brown University in the basement where you are now, I believe, and when that curve says 1993. So I was pretty early with Charlie Elbaum. And they were working on helium-4, proton—</p><p>Rick Gaetzkill:<br />Bob Lenoux.</p><p>Brian Keating:<br />Yeah, Lenoux.</p><p>Rick Gaetzkill:<br />Jim Scheidel, absolutely. Humphrey Maris.</p><p>Brian Keating:<br />Humphrey Maris, yeah, of course. How could I forget Humphrey? And I love those guys, but I went to Charlie Elbaum, the late, great— I love Charlie. He&#8217;s one of the reasons I went to Brown. And he wanted me to work in his lab. And he said, why don&#8217;t you go and talk to my student? I said, okay, great. So I went down to the dungeon, the basement there, you know, 144, below 144 Barrison-Holly. And I asked the graduate student and he was very enthusiastic and we spoke. And at the very end I said, oh, just one more thing, you know, just in terms of like, you know, career and so forth.</p><p>Brian Keating:<br />How long have you been a grad student? And he said, 9 and a half years. And I just couldn&#8217;t believe it, Rick. And I felt like that took him, you know, to the &#8217;80s, you know, predating you in this field. And now we&#8217;re in, you know, you&#8217;re projecting out to 2040. I wanna push back with love and respect. And that is to say the following. Moore&#8217;s Law is, you know, probably a slacker compared to the progress here. But another dimension, you know, it&#8217;s created incredible technology that&#8217;s, not only aided physicists and philosophers alike, but now it&#8217;s taking over the world with transistors and software and computers and GPUs.</p><p>Brian Keating:<br />And we&#8217;ll get into that in the podcast. I want people to watch our discussion about AI in the classroom and beyond with one of the world&#8217;s great educators, Rick Gaetzkill. But Rick, tell me, I mean, how do we keep justifying it? I mean, the CMB world, I&#8217;m used to this. I&#8217;ve been looking for B-mode polarization, as you know, since the year 2000. And we haven&#8217;t detected primordial B-mode polarization, but we&#8217;ve made a lot of progress. We&#8217;ve detected things. This, if this holds up, this is the first detection, right? This is a huge breakthrough. But on the other hand, it&#8217;s sort of depressing as well because the length of the curve on the x-axis is so long.</p><p>Brian Keating:<br />How do you, how do you, you know, maintain through, through the rain, through the fog, etc.? We&#8217;ll get into the neutrino fog, but, but how, how do you keep your spirits up, you know, and how do you maintain this? And what if, God forbid, you know, or I wanna say God forbid, I&#8217;ll just say nature might not care about what you want as a student or as a postdoc or as a professor. So what happens if this is just another, you know, upper limit?</p><p>Rick Gaetzkill:<br />I know, I mean, the critical thing about when you&#8217;re conducting scientific research is, I think, to make sure that it is well motivated and relevant. It&#8217;s, of course, there have been notable examples where I think people, because they were total mavericks, went off in a direction and that actually yielded something. Of course, we also know that the mavericks we never talk about are the ones who went off, did their own thing, and completely busted. So, you know, you always have to— you&#8217;ve got to be painfully aware of this post facto selection effect that occurs, you know, when you&#8217;re doing it. But I think, you know, when it comes to science, what we&#8217;re trying to do as a community is to, by making presentations, by producing results, and by talking about, you know, discussing it, it&#8217;s the question always is, is continuing a particular direction well motivated? Now, I, I think, you know, one of the, one of the areas, you know, saying, um, uh, you know, how many people does it actually take to do this type of work and how much resource does it take? In other words, I, I guess I, I know again you can say it&#8217;s relative, but honestly, this, this type work is relatively cheap. It&#8217;s— so it&#8217;s not— but it does give you this incredible leverage in terms of sweeping through or being able to test simultaneously a very large number of potential models in— of particle dark matter.</p><p>Brian Keating:<br />And—</p><p>Rick Gaetzkill:<br />Yes. By staying sort of in touch with the rest of the community and looking at how the other results are going, I mean, one of the things is we both know, is extraordinary is the cold dark matter model, which, you know, for decades was sort of vying with a number of other potential models. It has survived and in fact has continued to make predictions and agree with new measurements at a level that I think few theories survive that long.</p><p>Brian Keating:<br />Yeah.</p><p>Rick Gaetzkill:<br />It is remarkable. And as a consequence, that sort of continues to steal one— make it so critical that if there is cold dark matter, and by that, by just cold, the cold means non-relativistic dark matter. If there is this dominant term that we call cold dark matter there, trying to figure out what it is is critical because that&#8217;s the only way we&#8217;re going to actually understand. And this is where I&#8217;d love to say we&#8217;re going to understand the fundamental laws of physics, but of course it&#8217;s only one damn universe. We we get to play with. So what we actually end up doing is— or getting a direct explanation of our universe, since it is quite clear that, you know, you can imagine situations where nature itself is giving birth to a larger number of universes, and we only are lucky enough as experimentalists to get to do one in one. But nonetheless, what we&#8217;re hoping is that The— getting some of the properties of this cold dark matter, this dominant matter in the universe, will allow us to understand better how our universe is put together. Because at the moment, both the cold dark— the dark matter placeholder, if you like, and the dark energy placeholder, 95— that&#8217;s 95% of the composition of the universe.</p><p>Rick Gaetzkill:<br />We do not know what the actual mechanism is what the actual composition is. And we&#8217;ve— we&#8217;re trying to answer this with the direct detection strategy and there are many experiments doing this, trying to look in other specific parameter spaces. But at the same time, we&#8217;re also trying to make dark matter in the Large Hadron Collider.</p><p>Brian Keating:<br />Uh-huh.</p><p>Rick Gaetzkill:<br />And we&#8217;re also making astrophysical measurements looking for decay products from the dark matter. So we&#8217;re trying to test this hypothesis. And honestly, 40 years— go back and look at history— 40 years is not a lot of time to be working on a problem. And I know you measured in human lifespan or what have you, you may say that&#8217;s rather dramatic, Rick, but it&#8217;s—</p><p>Brian Keating:<br />It&#8217;s only 4 of Charlie&#8217;s grad students&#8217; lifetimes.</p><p>Rick Gaetzkill:<br />Well, indeed. You know, that— and this is the way scientific research has to be conducted. If you— what you don&#8217;t do is you don&#8217;t stay on a question necessarily in a completely bloody-minded way because then that does lead to a sort of terrible sort of slowdown in rate of progress. You&#8217;re always questioning, are we actually— given the other data that&#8217;s coming in from other experiments, given what&#8217;s happening in terms of our evolving understanding of how the universe is put together, is looking for particle dark matter still well motivated? And I would say right now, given everything else we&#8217;re seeing. It&#8217;s extremely well motivated. And the fact that we&#8217;ve tested 8 orders of magnitude of models, as I say, unfortunately, because of the way the physics works, you know, in terms of the parameter space that nature could have picked, you know, we don&#8217;t get a sort of complete sweep. We&#8217;re still testing right today, and we will continue to test models that are extremely well motivated. And in a sense, because they are a little bit more exotic, a little bit more removed, when we get the answer of what the particle is, of course, that&#8217;s likely to really blow open a whole new— not new universe, but a whole new area of physics that right now is just one of a whole slew of possible models.</p><p>Rick Gaetzkill:<br />But when we know which particular model nature has chosen, then— And, you know, one does sincerely, uh, look forward to it then leading to answering a whole slew of other questions. Yeah, that&#8217;s right.</p><p>Brian Keating:<br />Yeah.</p><p>Rick Gaetzkill:<br />So, um, so you mentioned being a graduate student. Now, I, I, I did— I was a graduate student in the UK where they used to cut your funding off after 3 years. So, so, I mean, literally they did bang, 3 years, and I was living on bread and water for about the last 14 So not 40, 4, sorry, last 4 months of my studentship. Oh, sorry, end of studentship. I was— but this was a dark matter detector, which is only 10 grams or so. That was the scale, you know, back in the— We have essentially the same sensitivity in 7-ton detector as we did in a— or you call it 10-ton detector as we did in 10 grams. So that&#8217;s over a factor of a million. change.</p><p>Rick Gaetzkill:<br />And it&#8217;s really down to extraordinary sort of ingenuity of so many colleagues trying to understand how you can measure individual electrons and individual photons, which is what we do in the xenon detectors now.</p><p>Brian Keating:<br />Right.</p><p>Rick Gaetzkill:<br />And we— and in this case, we are trying to use those individual measurements, those single photon and single electron measurements and combining them together to say that yes, we have evidence of some kind of interaction happening in the middle of our detector that really couldn&#8217;t be put there by conventional backgrounds or conventional sort of radioactivity, but are instead due to the occasional interaction of a dark matter particle. So I should— actually, I&#8217;m going to skip this. So let me talk about SURF and our detector. So we&#8217;re operating at an old— what was previously a gold mine. you know, up until just the turn of the millennium, you know, 2000— just at the beginning of the 2000s. And we repurposed a significant amount of the infrastructure for science here in the U.S., you know, and through the support of Denny Sanford and the state of South Dakota and Mike Rounds, who was then the governor, we&#8217;ve been able to— and all the subsequent support, and of course the Department of Energy, who&#8217;ve been absolutely critical to to, you know, making this laboratory, you know, function so well that we&#8217;re operating this detector, the LUX-ZEPLIN detector, which for scale is about, you know, your height. It&#8217;s about my height. You know, it&#8217;s sort of one— just over 1.5 meters in terms of the active central volume of xenon.</p><p>Rick Gaetzkill:<br />It&#8217;s about 7 tons of xenon. Xenon is— Xenon is actually the rarest of the gases in the air you&#8217;re breathing in right now.</p><p>Brian Keating:<br />Mm-hmm.</p><p>Rick Gaetzkill:<br />Every 1 out of 10 million atoms that you&#8217;re breathing in right now, which means a very large number of them are in fact xenon. And xenon has a number of sort of useful properties, but the main one that we&#8217;re exploiting is in fact that it is very— can be made very pure and is very low in intrinsic radioactivity.</p><p>Brian Keating:<br />Yes.</p><p>Rick Gaetzkill:<br />It&#8217;s also quite dense when it&#8217;s cooled to about -100 degrees centigrade, so it becomes a liquid. It&#8217;s about 3 grams per cubic centimeter, so 3 times that of water. And it can be purified so that light has a 10-meter-plus mean free path traveling through the xenon, and also electrons that are liberated in the middle of the xenon do not immediately combine. We can simply drift them using fields, electric fields, and pull them to the surface. And that makes for an ideal detector. The actual process where a particle interaction happens and scintillates, you scintillate. You know, everybody&#8217;s fairly scintillating.</p><p>Brian Keating:<br />It&#8217;s—</p><p>Rick Gaetzkill:<br />the trouble is you can&#8217;t get the light or the ionization out of you because you&#8217;re far too dirty and you&#8217;re far too opaque.</p><p>Brian Keating:<br />Hey, speak for yourself.</p><p>Rick Gaetzkill:<br />Well, I&#8217;m definitely extremely opaque, as my students will tell you. But, but, so whereas the xenon has this fascinating condensed matter physics that a number of the noble elements have, where it emits light after excitation, it emits light at a wavelength that is actually— has a very low interaction probability with the material itself, so you can get the signal, the signal out. So that&#8217;s what we exploit. We put photomultiplier tubes around the outside and we look and count individual photons. And we put a field on and we count individual electrons jumping out of the liquid surface. And the combination of that allows us to tell the position of where an interaction is and how much energy is deposited. And as you might imagine, in the middle of this very large amount of xenon, it is very difficult for regular particles to get in because their mean free paths are typically measured in a centimeters. So trying to get into something which is 150 centimeters across, all the conventional interactions tend to be confined around the edge.</p><p>Rick Gaetzkill:<br />Now, the only slight exception to that is there are one or two radioactive isotopes that will dissolve or can potentially— do, sorry, do dissolve in xenon. So, you know, one of the areas where our teams have spent a lot of time on is trying to make sure that as little of, for instance, the trace gas krypton, which can have or does have a radioactive isotope associated with, or radon, which is one I think everybody&#8217;s familiar with because you get work done to survey radon in your basement. It turns out that the same radon daughters that you worry about with of excess radon in your basement are also capable of producing events in the middle of our detector. So we work very hard to drive— also to drive down the dissolved radon and krypton levels amongst others. I mention those 2 because those are the ones that end up being the most challenging to remove, partly because their chemistry, of course, is noble. Those are examples of other noble gases and therefore they—</p><p>Brian Keating:<br />They&#8217;re inert.</p><p>Rick Gaetzkill:<br />They are more challenging chemically to remove than many other forms of contamination. So I mentioned I should pick up the speed a little bit, but just get to the event. But let me just show you. So this is just time measured in microseconds. So from here to here is around 1,000 microseconds or a millisecond. And this is just— this is the lowest— these are This example here is the lowest of all events that we see. And for conventional dark matter, this is often the regime in which you&#8217;re going. And we actually see neutrinos.</p><p>Rick Gaetzkill:<br />This type of event I&#8217;m showing you here could be very—</p><p>Brian Keating:<br />You say lowest, you mean lowest energy or lowest—</p><p>Rick Gaetzkill:<br />Lowest energy. Thank you. Sorry. Absolutely right. Lowest energy. So we&#8217;ve actually, for instance, seen boron-8 neutrinos scattering, coming out of the sun, scattering in our detector, depositing very small amounts of energy. this S1, which is a primary light scintillation labeled. That is just a few photons that are caught by the photomultiplier tubes associated with the initial interaction.</p><p>Rick Gaetzkill:<br />And then we wait of the order of 800 microseconds and there&#8217;s this sharp— this taller S2 event, which is again light, but it&#8217;s light being generated from an electron. Electrons are fascinating. They— electrons, in xenon, when they drift through liquid, they scatter but non-radiatively. And critically, they don&#8217;t recombine because we&#8217;ve made the xenon very pure. So there&#8217;s a very low cross-section for being trapped, if you like, or scattered. The electron gets to the liquid surface, we have enough field to encourage the electron to jump out of the liquid. In gas, it is still drifting because of applied fields that we have, but now it&#8217;s radiative. The scattering of the electron is now radiative, so the entire trace of the electron lights up, and we measure the photons associated with that track of the electron for the last sort of, you know, 20 millimeters or so that it propagates in the gas.</p><p>Rick Gaetzkill:<br />And this combination of an initial pulse of light and the subsequent pulse of light from the electron arriving at the liquid surface and entering the gas makes for an absolutely fantastic— we call it a time projection chamber, which sounds very exotic.</p><p>Brian Keating:<br />Yeah, it does.</p><p>Rick Gaetzkill:<br />It&#8217;s just saying that the drift time of the electron and the location of where the electron hits the surface and lights up like a Christmas tree, that&#8217;s the projection bit, that we can actually infer what the original XYZ or Z location of the interaction was. And you can do this in 7 tons. It&#8217;s bloody, you know, it&#8217;s quite fantastical. You know, we have, you know, the physical scale is 1.5 meters, both laterally diameter and drift. And we&#8217;re able to do it at energy— we&#8217;re able to measure energy depositions that are at the keV level and then counting, you know, in this sort of quantum world we live in, we are counting individual electrons, counting individual photons. And, you know, so the students, postdocs, you know, worked incredibly hard to basically characterize exactly how these signals are generated and how they depend on the nature of the original particle interaction. So I must pick up. So this slide on the top right, what it was just designed to show is along the bottom is that S1, the primary light.</p><p>Rick Gaetzkill:<br />On the vertical is the is the secondary light, the S2, due to the electron. So effectively, the ratio or the amount of S2 light versus S1 light, it turns out not only does it give you the position, the TPC part of it, but actually the amount of ionization you have relative to the primary scintillation light actually tells you about the nature of the original interaction. And this is enormously important because dark matter, we are looking for the most part, although we do have side searches. I think, but for the most part, we&#8217;re looking for nuclear recoils from the dark matter coming in and interacting with the nuclear— You mentioned the business of the mass inequity, that the non-equal masses make— so an electron could, in principle, scatter from a dark matter, but you get so little that the masses are so different in many scenarios we&#8217;re looking at that it&#8217;s a very poor exchange. So primarily it&#8217;s It&#8217;s the dark matter scattering from the nucleus. And the ratio, the amount of S2 signal versus S1, which is the orange— we&#8217;re using neutrons as a proxy for dark matter in this plot— that the ratio of the S2 over S1 is also telling us about the nature of the original interaction. So since most mundane backgrounds tend to be of an electron recoil type, which is characterized by the tritium betas on this particular plot, their ratio is some— of S2 over S1 is somewhat different from the ratio of S2 over S1 that you get for neutrons. And this holds, as you&#8217;ll see, not just at the lowest energies, but even up to really very substantial energies, hundreds of keV, and that&#8217;s of course where we&#8217;ve ended up looking for—</p><p>Brian Keating:<br />So just to summarize, Rick, the S1 signal, the nature of that is what exactly? It&#8217;s coming from the dark matter, putative dark matter particle, or any particle that will interact weakly, and then it&#8217;s it&#8217;s from the recoil, right? It&#8217;s a recoil. They&#8217;re both recoil, and one is nuclear, one is electron.</p><p>Rick Gaetzkill:<br />That&#8217;s right. So both of these processes, we&#8217;re down at low energies, we&#8217;re down at sort of 10 keV or less in this particular plot, which is— which for anybody who&#8217;s worked with detectors will tell you is a very small amount of energy. And typically these recoils are happening over distances that are just measured in, you know, sort of— well, for the nuclear recoil, 10 nanometers, 50 nanometers. It all happens in this very short range. For the electrons at these sort of energies, you&#8217;re still talking about submicron recall. So you&#8217;re really measuring recalls in both cases in the nanometer scale. And as a consequence, it&#8217;s— what&#8217;s fascinating is that the— and to do dark matter, as my thesis had a lot of, for instance, condensed matter in it, I was trying to use superconductors at that time to detect dark matter. Here we have xenon.</p><p>Rick Gaetzkill:<br />But the condensed matter physics is— you get to study it in this exquisite detail in these energy regimes that often we&#8217;re the first to sort of really go in and look.</p><p>Brian Keating:<br />Right.</p><p>Rick Gaetzkill:<br />And what we&#8217;re seeing and we have to characterize it at highly detailed way. We have to characterize what would happen if 10 keV or say a 5 keV nuclear recoil or a 5 keV electron recoil was to go into xenon and deposit its energy along these very short tracks. How many ionization electrons are going to be liberated or how many internal excitons are forming in the xenon itself which then de-excite in a rather exotic way, in fact, forming these sort of double dimers that then emit photons. And all this is happening— I should— I didn&#8217;t mention this before, but all this is happening in the VU— what&#8217;s known as VUV, vacuum ultraviolet, about 175 nanometers. It&#8217;s so named because those ultraviolet that is that short doesn&#8217;t travel through air. You have to pull a vacuum in order to allow it. It turns out you can actually get 175-nanometer radiation or photons through xenon itself. Xenon doesn&#8217;t want to interact with those even though it generated those photons.</p><p>Rick Gaetzkill:<br />It turns out it&#8217;s very low probability of reabsorbing them, which is critical when you&#8217;re building these massive detectors. You don&#8217;t want them eating their own signal, basically.</p><p>Brian Keating:<br />So, yeah.</p><p>Rick Gaetzkill:<br />We, we, so Brown, like many other groups, we, we actually built the sort of, uh, these 2, uh, 250 PMT arrays that, that, you know, span about 1.5 meters. And this, this is going back some time now, but back in 2019, a whole slew of students, undergraduates, graduate students, uh, postdocs, all worked to assemble under very clean conditions. And then you— we shipped everything to the— to, to, to the Sanford Lab. And then by about 2021, so sort of coming out of COVID we were commissioning the detector. And what I&#8217;m talking about here is one of multiple data analyses we&#8217;ve done. We&#8217;ve done more conventional dark matter searches, but we&#8217;ve also looked very specifically in a dataset we took between sort of mid-&#8217;23 and early &#8217;24, which spans about 250 20 live days of data looking for dark matter events at, as you&#8217;ll see, a higher energy regime than is usual for the searches. Now, it turns out that there are some challenges associated with doing that and that&#8217;s why it&#8217;s taken us a sort of couple of years to get to this point of actually saying we can estimate with some certainty, as you must, not only what the efficiency of seeing dark matter events are, which which that&#8217;s, relatively speaking, relatively easy. The bit that you&#8217;ve got to quantify is what&#8217;s the chance of more conventional interactions faking a dark matter signal in that area.</p><p>Rick Gaetzkill:<br />And that&#8217;s something we&#8217;ve spent a lot of time not just trying to understand, but also you have to quantify it. Because the degree of certainty or the statistical significance you associate with a result is often based on what you are able to estimate is the— how unlikely is it that a background event could have faked your signal? And that&#8217;s— people often talk about sigma confidence levels and that&#8217;s— it&#8217;s from that process or that likelihood of, is this a— is this dark matter signal versus likelihood of a more mundane— I don&#8217;t know why I call them mundane because I can tell you the background has to be bloody exotic to fake this stuff, but it&#8217;s an exotic more conventional particle somehow getting into your detector and interacting in a way that looks like a, you know, the dark matter signal. So we— again, you know, as you— I&#8217;m sure you would understand, we had to do an immense amount of internal analysis but also discussion and presenting and re-presenting and reassessing what we felt best categorized our understanding of the behavior of backgrounds in our detector to convince ourselves that if we see events, and we have, we saw one event, that is in the region where we expect dark matter, but what&#8217;s the chance of it just being due to background? And I think again, I&#8217;m concerned about the time, so let me just It&#8217;s okay.</p><p>Brian Keating:<br />I think we&#8217;ll finish this today and then we&#8217;ll go on. We&#8217;ll have the podcast. I&#8217;m coming out to Brown. I&#8217;m on Stefan Alexander&#8217;s Theoretical Physics Center. I&#8217;m on their board of directors. So, I&#8217;m supposed to come out there. So, we&#8217;ll do an in-person review and we&#8217;ll also do in the lab. So, take as much time as you want now.</p><p>Brian Keating:<br />I&#8217;ve got another 20 minutes if you do.</p><p>Rick Gaetzkill:<br />Okay.</p><p>Brian Keating:<br />Okay.</p><p>Rick Gaetzkill:<br />Well, right. So, What we&#8217;re looking at right now is a conventional WIMP search from LZ. I&#8217;m picking the one in &#8217;24 because it— we have had subsequent results from there. But I guess the key thing I&#8217;m, you know, we need to look at is on the horizontal scale in the gray is energies, and this says 0.8 keV EE, 5 keV nuclear recoil. So that— what that is doing is telling you that for this particular search, that we are looking at extremely low energies, events that are extremely low energies, a few— well, less than 1 keV in electron equivalent around 5—</p><p>Brian Keating:<br />it&#8217;s—</p><p>Rick Gaetzkill:<br />the reason you have to talk about nuclear recoil and electron recoil is because they— the amount of signal, the amount of S1 light and S2 light that is generated is somewhat different for a given particle of a given energy. But This was an early example. We actually pushed our threshold down to about, yeah, a couple of keV nuclear recoil in later search. But as I say, this particular plot is sort of, I think, easier for somebody to understand. So these events up here are conventional electron recoil events primarily being produced by residual amounts of radioactivity decaying in the middle of the detector and that&#8217;s actually shown here. This is your radius of the detector and this is just the Z or the Z height, the drift thing. And you can see this sort of smattering of events. And this is taken over the same 220 days that we&#8217;re going to talk about for the, you know, for the high-energy analysis.</p><p>Rick Gaetzkill:<br />But you can see these events are fairly uniformly spread out. What that&#8217;s telling you is actually they&#8217;re far less likely to be due to radiations coming in from the outside. These sort of gray regions actually where you see a much larger number of points, those are the typical radiation coming in from the inside that all gets stopped near the edge of the detector, but we have residual amounts of radioactivity dissolved. But because they&#8217;re electron recoils, not nuclear recoils, they have a higher S2 over S1. It&#8217;s actually this magenta— it&#8217;s not magenta, purple. This purple region here is where we expect dark matter to appear. Now, you can actually see one or two events sort of getting into this region here. And in fact, that&#8217;s because the separation between the electron recoil band up here and the nuclear recoil band down here is— while the separation is pretty good, still at about the sort of 1% or fraction of a percent level, events are sort of getting down into this region, and we have to— we take account of that.</p><p>Rick Gaetzkill:<br />So when we&#8217;re looking for candidate nuclear recoil events in this region, which is where you expect the WIMP nuclear recoils to appear, we do have to say there&#8217;s a competition between more conventional backgrounds just leaking down into this region and WIMPs. So in this particular result, there is nothing that stands out as an exceptional number of nuclear recoil events. nuclear recoil events that would be consistent with WIMPs. So what we did is we ended up just eliminating models that would have put more than a certain number of events into the— and as the joke goes, we were— we still are, I think, world-leading. Yeah, we are world-leading for seeing nothing in this sort of 9, and in fact down to about 5 GeV mass range because of the scale of LZ and because of the how long we&#8217;ve run it and because it&#8217;s working very well, we&#8217;re able to look for, you know, models in this low-energy regime and do so in a way that, you know, is very sensitive. But as I say, we&#8217;re not seeing a buildup of low-energy nuclear recoil events. So what we did is we&#8217;re using the same data to look for an interaction between the nucleus and you know, chi here, which represents the dark matter particle. And this is, if you like, sort of the conventional type of interaction where you get coherent scattering across all the nuclei.</p><p>Rick Gaetzkill:<br />But that really is the sort of vanilla version. You&#8217;re summing up all these nuclei and you&#8217;re assuming because it&#8217;s very low energy interaction that the interaction itself is coherent. Meaning that the phase, for those people following quantum mechanics, when you&#8217;re attempting to do scattering amplitudes, Of course, as you may remember, that your momentum exchange as you go in, interact with one particular nuclei, one point sort of exchange interaction, and you come out, there&#8217;s a certain P associated with that. There&#8217;s a certain— and that momentum exchange, if you like, has an amplitude and also a phase associated with that. And if you move location, if you go to different nuclei, If the amount of momentum you&#8217;re exchanging is small and therefore the wavelength associated with that is, you know, and certainly principle, one way of picturing that in your head. But, you know, if the characteristic inverse of the momentum, which is a distance, if the distance is large, that means that the phases across all of those, all of that nuclei scattering are all very similar. So when you add them in order to get the overall, you know, amplitude squared, the sort of matrix element or the cross-section calculation, coherence. Phases are all very similar, they all add together.</p><p>Rick Gaetzkill:<br />But if you ramp up the attempted momentum exchange, that corresponds to decreasing the wavelength of the— associated with that wavelength, or what it means is that the phase is now varying much more rapidly across the size of the nucleus. the, you know, it&#8217;s Fermi level, 10 to the minus 15 meter, you know, sort of Fermi level scales at these nuclei. And that change in phase means that when you start co-adding the terms, they are now not adding as cleanly together. They&#8217;re actually starting to interfere with one another. And it produces a very rapid suppression, which is shown here, this is just interaction rate versus— this is actually recoil energy, not momentum, but it&#8217;s— this is non-relativistic stuff, so you just say p squared over 2m effectively, where your m has to be the reduced mass. But effectively, you&#8217;re just— this is just larger momentum as you go up in recoil energy. And what we see is while the rates are very large when you have full coherence for small momentum exchanges or small energy of recoils, you rapidly suppress as you get to 30, 40 keV. You&#8217;ve killed your signal and that&#8217;s— it&#8217;s not because the WIMPs carry enough energy to give the new— to give the xenon a kick.</p><p>Rick Gaetzkill:<br />It&#8217;s that the process of attempting to exchange the momentum, that the Q squared as we say or the the momentum exchange, the wavelength associated with that is now getting smaller than the size of the WIMP— sorry, than the xenon nucleus, and it&#8217;s becoming— the phase is changing rapidly across the nucleus and the terms are now interfering with one another and you&#8217;ve suppressed the interaction rate. But the reason is we&#8217;ve failed to, if you like, apply any imagination because we are taking what is sometimes referred to as the absolute vanilla of all possible interactions, which is just this scalar-like interaction where it&#8217;s just, you know, WIMP in, WIMP out, point-like effectively interaction with a nucleon. You add them all up and you get a total sort of interaction strength. And we do sometimes talk about a slightly more exotic version of that, which is called spin-dependent, but it turns out even that is Although, introduction to gamma— sorry, we haven&#8217;t got time to talk about gamma matrices. You have to develop— okay, so if you&#8217;re— what we&#8217;re going to do here is we&#8217;re going to start trying to generalize the way in which the dark matter particle and the nucleon, these individual nucleons. And one of the ways you&#8217;re going to do that within a relativistic theory is you want to start including the fact that these particles And for anybody who&#8217;s slog— and it is fascinating. I shouldn&#8217;t call it a slog. I always found it quite entertaining.</p><p>Rick Gaetzkill:<br />But if you&#8217;re going to develop a framework to try to understand what the coupling strength is, you now have to have your particles represented by something that actually— the spin are in the jargon, but something that actually gives a little extra degree of freedom which is associated with the spin. And we&#8217;re going to do that for the nucleon, and we&#8217;re going to do that for the dark matter particle, and then you have to figure out the way in which the particle interaction strength between the two, how that is going to— Now, I guess what I should emphasize is, relatively speaking, we&#8217;re not— we&#8217;re still trying to look at this in a very general sense. And you&#8217;re going to hear this phrase effective field theory. We&#8217;re not trying to take this apart under a specific Bose, a specific gauge particle, a specific gauge. We&#8217;re just trying to generalize and say, Let&#8217;s, you know, what&#8217;s a very simple interaction that includes the spin terms but doesn&#8217;t have any other additional sort of complications? And the fact that we call this L15 may begin to tell you that actually we&#8217;ve just skipped over a whole load of other possible interactions and that&#8217;s what we can do. And I think I should— let me put this slide up in this form. So it is perfectly natural when you&#8217;re talking about particle interactions to say that this interaction strength the effective field theory that&#8217;s describing them could include a momentum exchange term. And this is now the strength of the interaction.</p><p>Rick Gaetzkill:<br />So I&#8217;m not trying to do— I&#8217;m not thinking about how phase coherence is occurring across the nucleus.</p><p>Brian Keating:<br />Right.</p><p>Rick Gaetzkill:<br />What instead I&#8217;m doing now is saying that the point-like, you know, if you like, interaction between the dark matter particle and the nucleon, that it turns out it actually cares about what the value of the momentum is exchanged. And if we give that a q squared or a q to the 4th, which are both acceptable, they&#8217;re Lorentz invariant, they don&#8217;t violate— you can put it in without violating sort of any reasonable relativistic particle theory. But having put in terms like that, If it&#8217;s proportional to what&#8217;s going like q squared or q to the 4th, of course it&#8217;s getting stronger as the momentum exchange is larger. So we&#8217;re in a regime now where we&#8217;ve lost coherence across the entire nucleus, so that&#8217;s suppressed it, but the actual interaction strength between the dark matter particle and one of the nucleons is actually being increased like q to the 4th or q squared or q to the 4th. And because of that— So you&#8217;re saying that the interaction strength is actually increasing? That would mean that when we go to look for dark matter signature, if, you know, ultimately what you then have to explain is why your specific dark matter model would favor, you know, this L10 as shown on this graph, the L10 type interaction against an L1 interaction, which is much more simple, but you&#8217;d have to come up with a reason, a specific reason for why that&#8217;s being suppressed. if the interaction was dependent primarily on L10 effective field interactions, that those would manifest themselves at higher Q squared or Q to the 4th, and that&#8217;s higher recoil energies. So that&#8217;s— and this, you know, this work was proposed both by my colleague Gigi Fan and Matt Reese, you know, back in, I think, 2010 or something like that, and also Wick I think Haxton and collaborators have studied this also in great detail, and I think they really laid out this sort of effective— all the effective interactions, Lagrangians in the language, of what you could have. But it is important to test all of those.</p><p>Brian Keating:<br />Is that to rule out, like, look elsewhere in other Well— Or is that on the theory side, calibrating the theory side?</p><p>Rick Gaetzkill:<br />It&#8217;s because we don&#8217;t know what the dark matter particle is, we don&#8217;t actually know what the preferred Lagrangian or preferred interaction is. So while it is fair to say that an L1, or, you know, the basic scalar-style interaction would be natural because it can include coherence, which often makes it dominate. It is also fair to say that because our detectors now are, what is it, 8 orders of magnitude more sensitive than when we originally started, they are more than comfortable probing for these more exotic Lagrangians. Now, the fact that we haven&#8217;t seen anything in the past 40 years means that nature somehow has decided to suppress the conventional spin-independent because we would have, you know, we might well have seen interactions much earlier on because of the significant coherence enhancement. But also what&#8217;s now happening is because we have these massive detectors that with some perfect— with some reasonably natural assumptions about particle masses and the gauge particle masses, you could— and you could— we could well be in a situation where dark matter is choosing to not— or is actively suppressed Interacting through the sort of L1 Lagrangian, but is, say, favoring an L10 interaction. And these plots here— let&#8217;s see, I should focus on the L10 here, but— sorry, that&#8217;s L15 actually. Sorry, L10&#8217;s here. But sorry, this is just the mass— sorry, this is the recoil energy range.</p><p>Rick Gaetzkill:<br />And I guess these plots are way too complicated for a talk like this, but one&#8217;s putting them up just really to show how you don&#8217;t just automatically get this major enhancement at low energies, which is the more conventional plot. What you actually see is a dominant preference for high-energy interact— higher-energy interactions. And as I say, because of the scale of the— Now there is a— and this is different. You can also talk about inelastic scattering where in this case the dark matter comes in 2 states that are just— are not quite degenerate. They&#8217;re separated by some energy delta and that this does— this also is a mechanism whereby as a dark matter particle comes in, if its primary interaction is actually to be excited from one state to a nearby If the energy separation there is measured in hundreds of keV, it turns out that that would have a very direct effect on the rate of particle interactions that we&#8217;d see in these dark matter direct detection events. And there are, again, supersymmetry models, the one with a sort of Higgs-xeno doublet, with separations where the overall mass of the particles are of the order of 1, 1.1 TeV. So, sort of range, and that the separation is measured between the 2 Higgs Zeno masses is a few hundred keV. And for instance, that could be consistent with the type of higher energy preference.</p><p>Brian Keating:<br />And that&#8217;s the doublet aspect of it. I feel like we buried the lead maybe a little bit. I mean, the mass of this is quite large, right?</p><p>Rick Gaetzkill:<br />Well, that is of course one of the things that, you know, for instance, all the LHC searching that we&#8217;ve been doing over the last What is it, 20 years now? You know, we&#8217;ve been sweeping up there, you know, in terms of production in mass. So in many cases what we&#8217;ve been doing is saying that as natural as we might have originally thought it was to produce supersymmetry down at lower mass scales, having searched for production of particles in there in many models that in order to, you know, to be consistent with the non-observation of many of the searches we&#8217;ve done, that would suggest higher mass particles. Now, it turns out, of course, that actually if you go as high as sort of 1.1 TeV in this sort of double Higgsino style model, that&#8217;s actually very challenging reaching those kinds of center of mass— oh, sorry, reaching that kind of production particle. But so this is one that— this is a model that— model So you&#8217;re saying that the Higgs is not the only particle that could be that produced? would be consistent with the direct detection thing, but is very much more challenging to actually see in— even with the high luminosity LHC. But this is only, you know, this is just one model that happens to be consistent with the one particle event we&#8217;ve seen. And of course, it&#8217;s so early days that, you know, you don&#8217;t— you know, you do this in order to make— help you begin to understand how this might work into various models. You&#8217;re not saying that this is the definitive model. That would be the wrong way to ever look at a single event search.</p><p>Rick Gaetzkill:<br />So, yeah, I should— this particular plot, I was just trying to show you the contrast between at low energies where we usually look for dark matter and now— sorry, this orange and this purple thing, those are nuclear recoil sources, neutron sources that we put directly on the LHC. the detector. And what you&#8217;re seeing is a band which interestingly, the red band is actually moving away from the electron recoil band which is shown here. This is an S1 versus S2 plot again. And so we have, you know, nominally what is very clear separation at these higher energies between nuclear recoil response and electron recoil response. And of course, from the point of view of a single event, that means that having a single nuclear recoil appear in the middle of a detector, which, you know, we&#8217;re very confident is not due to a neutron because to get a neutron that deep into our detector, given the cross-sections for regular neutrons, is high enough to mean that it really can&#8217;t get that deep in, you know, makes it so powerful when we are looking for, you know, events in the center of the detector. So, yeah, I should— I was— we designed LZ— what I was trying to do with these slides, but we designed LZ to be very good at doing this, to look for very occasional events and to make damn sure that this wasn&#8217;t being produced by a more conventional detector. So for instance, we have an outer detector which is loaded with gadolinium and is scintillating, which is very specific.</p><p>Brian Keating:<br />Fine.</p><p>Rick Gaetzkill:<br />designed to catch neutrons either on the way into our detector or on the way out if they say they were generated by a piece of material inside the construction material of LZ. That gadolinium is very opaque to neutrons and lights and produces 8 MeV, a very high-energy burst of light or energy when that capture takes place. So we&#8217;re able So we do see neutrons trying to get into our detector, but firstly, the absolute rate is extremely low, and secondly, we&#8217;re able to very cleanly characterize them by using the multi-layers of our detector in order to sort of tag them. And this is something people have worked very hard on to make work so effectively, and is in fact— we use this to convince ourselves that we&#8217;re seeing. So this is actually the science result. So this is the nuclear recoil band in the red and the blue is the electron recoil band and this is the S1 primary light signal and the S2 signal. And we&#8217;re actually now up to around 250 keV nuclear recoil. So the original WIMP search was all happening down here.</p><p>Rick Gaetzkill:<br />We&#8217;re now looking at a much broader energy range. But because we&#8217;re saying potentially, you know, the Lagrangian or the operator, you know, the nature of the coupling could be allowing much more of the energy of the WIMP to be transferred to the nucleus. And this is the physics result after 220 days and we unblinded the data, although it turns out we&#8217;re not claiming this as a blinded search just simply because post facto we went back and looked at the way we blinded and we— we&#8217;re trying to be conservative. We&#8217;re arguing that a really smart researcher in our group could have effectively probably told that— statistically figured out the difference between injected salt, as it&#8217;s called, that we use for blinding, and possible event. And so, as such, we&#8217;ve decided not to call it a blind analysis, although I, you know, I&#8217;ll emphasize that a lot of the cuts that we use were effectively fixed very early on in the analysis process in a relatively simple So we&#8217;re— the sort of things that salting and blinding is designed to avoid, which is biases in terms of cuts, we&#8217;ve been able to, because of the way the detector works, really try to stay away from making any marginal cuts in the data. And so when we finally opened up and unblinded, you know, the data, although as I say, we&#8217;re not calling it a blind analysis, we found we got left with one actual event. in the data, and it lies close to the nuclear recoil band, which is statistically where you expect nuclear recoils to occur, but it is way up here. There is an event down here that is actually expected.</p><p>Rick Gaetzkill:<br />That&#8217;s due to accidental coincidences, and it turns out that at low energies, in many of our previous papers we discussed this, one of the backgrounds that we have to fight against at low energies is just accidental coincidences of a single S1 and a single S2 light. It turns out that for the higher energy region, that&#8217;s not really— it&#8217;s not a dominant background source. As we&#8217;ve gone through the analysis, it&#8217;s the most significant contributor to backgrounds are actually high-energy gamma rays. that are potentially multiply scattering in the detector. And that&#8217;s something we spent a lot of resource making sure that we could understand it, model it as well as we could, and produce some kind of statistical estimates— or not some kind of— produce well-developed statistical estimates of— And this is the weird thing. This is 220 days, but effectively with our simulations, we end up having to run for the equivalent of sort of 220,000 days. So what&#8217;s that?</p><p>Brian Keating:<br />So can you translate the x and y axes? I mean, I&#8217;m sure they mean a lot to you, but to the audience, what—</p><p>Rick Gaetzkill:<br />Yeah, no, no, sorry. So this is our standard S1, the primary light plot. And this is the secondary light, or the light that comes from the ionization, the S2. So each one of these dots is a single event that occurred in the science data that we ran for 220 days, 220 live days. And down here, these events tend to be typically dominated by intrinsic electron recoil events occurring from beta, from low-energy contaminants. producing electron recoils. We also see evidence of specific gamma energies. Again, it turns out that when we&#8217;re calibrating the detector for— that there are short-lived radioactive excitations that occur from the neutrons.</p><p>Rick Gaetzkill:<br />For this type of work, they— the energy deposition remains sort of well contained in this sort of S1, S2 plane. So we&#8217;re comfortable that those types of events are not leaking or not producing events that are further down. But what we do in the paper spend quite some significant amount of time talking about is what happens if you have a high-energy gamma ray generated near the walls of your detector from residual contamination and that that gamma ray tries to get into your detector. Now, since that&#8217;s going to scatter from electrons. Typically, that would produce events in the band here. But I think as I should show you here is— so that red line corresponds to a gamma ray trying to get in, doing a single interaction and then leaving. That would be a single interaction. Now, what if it scatters twice? What happens is first interaction here, second interaction here.</p><p>Rick Gaetzkill:<br />You get 2 lots of S1 light. The particle is so quick to propagate between those 2 vertices that effectively you can barely see any difference in the time of the S1. It&#8217;s 10 nanoseconds when we&#8217;re typically reading our S1 events over 100 nanoseconds or more. But the S2s, because the The delay time here between the S1 and S2 is related to how long it takes the electrons to drift and because they&#8217;re actually only moving millimeters per microsecond, the electrons, that you actually get physical separation between the 2 S2s. But because you can see the 2 S2 signals arriving when the electron— small number of electrons actually reaches the top, you can clearly see 2— that there must have been 2 vertices. So nobody&#8217;s going to— you&#8217;re not going to confuse that with the dark matter.</p><p>Brian Keating:<br />No.</p><p>Rick Gaetzkill:<br />Particle. So, but, you know, only the paranoid survive in this game. So you have to start thinking about, yes, but what could possibly remove one of those S2 vertices? I— there&#8217;s still 2 vertices, but something eats the, the, uh, these ionization signals. So that&#8217;s what we call an MSSI, which is just simply multi-scatter single ionization. So what, what What removes? Well, you know, in some senses, we have a significant challenge because in order to apply a field between— to get electrons to drift upwards, that&#8217;s effectively a positive field, if you like, pointing downwards. And we achieve that with a, you know, positive potential on the gate relative to a negative potential here down on the cathode. Now that cathode is actually running, you know, at say 100 kilovolts, minus 100 kilovolts. We&#8217;ve now got it, we&#8217;ve now got to get rid of that high voltage before we get down to the PMTs which are running much closer to the ground.</p><p>Rick Gaetzkill:<br />So we have what we call a reverse field region here and that means the electrons actually drift downwards and because they drift downwards you can&#8217;t detect them. They just get lost.</p><p>Brian Keating:<br />So this is all, I like to say, you know, with Feynman&#8217;s permission, you know, the first rule of physics is not to fool yourself, and the second rule is you&#8217;re the easiest person to fool. So these are all ways that you&#8217;re guarding against deceiving yourself, right, Rick?</p><p>Rick Gaetzkill:<br />That&#8217;s exactly right. Now, you know, you might look at this and say, well, right, so how does this lead to the problem? And the problem is sort of shown here. Because you have 2 lots of S1, then you basically say that— say the first event is sort of here, and then the second event adds this amount of S1. light gets you to here. But under normal circumstances, the 2 lots of S2 here would also just boost you up and keep you, you know, essentially inside this sort of electron recoil region. Also, you&#8217;d see the fact that there are 2 bangs in the S2 and that would clearly tell you you had multi-site. But imagine one of them goes away, then what you&#8217;re seeing is just 2 lots of S1 but only 1 lot of S2, which is getting you— brings you down into this region. So what you have to do is to make sure that you understand what the rate at which such events, multi-site single ionization, or, you know, an S2 loss, is going to occur.</p><p>Rick Gaetzkill:<br />But in order to understand this, you have to be simulating or thinking about events that are not occurring necessarily at the 1 in 220-day level, because we&#8217;re actually trying to suppress this. We are thinking about whether these events would happen in a time period of sort of quarter of a million days, 220,000 days. Why? Because, you know, going with the Feynman theory, you know, you have to realize that even if something is, you know, if something&#8217;s going to creep into your data, it can, because there are so many ways that things can possibly creep into your data, you have to be prepared to allow for the idea that some very rare mistake, mistaken identification might have occurred. So you have a process which, while being incredibly rare at the level of about 1 in 220,000 days, which is, you know, way beyond the amount of time we&#8217;re running this detector, but it&#8217;s the kind of timescale we have to simulate the detector, that one of these events randomly fluctuated, you know, happened to— bad luck— fluctuated into the detector. Detector. And it&#8217;s doing that kind of work and understanding the sort of details of the response that it was necessary for us to do in order to make any kind of quantitative claim concerning what the likelihood, if you like, you know, of this event being due to a misidentification. And we— and it turns out that about the level of 1 in 200, so that&#8217;s 1 in, what, 500 million— half a million days. But at the level of 1.5 million days, there is— we believe there is a rate of these MSSI, this misidentification of a thing.</p><p>Rick Gaetzkill:<br />It&#8217;s, you know, 1 in 200 level of such an event sort of fluctuating in. I&#8217;m actually being sort of quite conservative because you mentioned earlier that there&#8217;s a thing called look away— or sorry, look elsewhere effect.</p><p>Brian Keating:<br />Yeah.</p><p>Rick Gaetzkill:<br />So for you and I to be discussing this event You know, neither of us ahead of this result said we were going to see an event here. So actually what you have to say statistically is that an event could have cropped up over a much larger range of possible events. And you have to, when you&#8217;re to make sense of the statistics of how significant an event is within that sort of signal, broad signal band, given that we have a large number of potential physics models that could generate dark matter recoils over that range.</p><p>Brian Keating:<br />Mm-hmm.</p><p>Rick Gaetzkill:<br />And because of the way that the background physics, the fluctuations occur, we actually, as, you know, people are familiar with the statistics, we started out a local significance that was in excess of 3 sigma, but by the time you include look elsewhere effects, and, you know, that sort of conservative couching that we have to do, that you end up with about a 1 in— sorry, 2.6 sigma, which I think is 1 in a couple hundred chance of you and I having a discussion over what is effectively a background event having fluctuated.</p><p>Brian Keating:<br />You quoted it as a range of confidence intervals, which is a little bit unusual. Can you explain that, Rick? Why is there a range between 2.6 and 3.4? What determines that range, and what would have to happen before the collaboration wave function collapses in regards to the effect.</p><p>Rick Gaetzkill:<br />So physicists, we&#8217;re very— what&#8217;s the word— very rigorous, very honest with ourselves. The only real language, as I always have to remind students, adjectives don&#8217;t cut it. You have to associate quantitative numbers with things. So what you would do in this case is you&#8217;ve got one event. And what— so locally, if you like, locally in that energy region, what you would start off by doing is looking at every possible mechanism you can come up with that might deposit an event in that location you&#8217;re seeing. And we looked at a lot of possible mechanisms, and most of them are— it&#8217;s vanishingly small that they could, you know, they could in any way accidentally or randomly create an event in that region. The MSSI effect was the one that ended up sort of leading in terms of the probability. Still a very small probability.</p><p>Rick Gaetzkill:<br />So because that probability was at the sort of level of, I guess it&#8217;s about 1 in 1,000 or something like that, that is associated with a 3.4 sigma. Talking about a sort of 3— Actually, it&#8217;s less than 1,000. It&#8217;s probably a few thousand, 1 in a few thousand. But you then, as I say, you have to go back and say, well, yes, but in order for this event to— an event to be interesting, it could have been occurring over quite a broad range of possible recoil energies. So, and then you end up forming a sort of global likelihood. And here is where it can get a little— not— I don&#8217;t think subjective is the right word, but—</p><p>Brian Keating:<br />Subjective.</p><p>Rick Gaetzkill:<br />the word, but there are different ways of presenting such an analysis. And one of the reasons we published is because we&#8217;re looking forward to getting input from people as to whether they feel our global significance is conducted in a way that they think is most natural for this. And there is— it is definitely one of those areas where there&#8217;s no absolute, you know, this is the only way to do it, people. You have to, you know, you have to end up deciding exactly how you&#8217;re going to statistically combine all these possible models for signal and all the possible contributions from the background, although that bit&#8217;s a little easier. But it&#8217;s still— that still gets— no, I should never say it&#8217;s easy. It is a very exacting process. And I&#8217;m— you know, we spent a lot of time discussing this point.</p><p>Brian Keating:<br />Yeah.</p><p>Rick Gaetzkill:<br />Anyways, so what happens is that whereas it&#8217;s a 3.4 sigma local effect in terms of how unusual it would be. In terms of us, you and I just sitting down and discussing, or indeed the experiment producing a result that had any event in this red nuclear recoil region, the chances of that happening but being, as it turns out, caused by a random fluctuation of the MSSI background into this band that global significance drops us to about, I think, 2.6 sigma, which is this 1 in 200 level. It&#8217;s— Now, you know, the right thing to do is firstly, when we&#8217;re talking about one event, is to keep emphasizing this could be a random fluctuation of a background event. It could also just be that we have misunderstood some aspect of the running of of our detector because we&#8217;re trying to understand the detector at a level that is, you know, uh, well past the limit.</p><p>Brian Keating:<br />Wouldn&#8217;t that have to be, I mean, how far into the process was this event? If you, once you, you know, unblinded it, you&#8217;re then allowed to go back and see when did this event occur, right?</p><p>Rick Gaetzkill:<br />Yes. So I think, I think I have a, yeah. Yeah, there it is. So this slide actually, so the, this particular run, uh, was hot off the presses when we when, you know, we started doing analyses of this kind, you know, this was the data run from March &#8217;23 to April &#8217;24. But we were focused mainly on the low dark matter, low energy recoils, you know, as a sort of flagship analysis. But we also started working on higher energy regime. But a couple of challenges there. Firstly, we had to— do much higher calibration statistics and nuclear recoil statistics to really make sure we understood where the nuclear recoil band was.</p><p>Rick Gaetzkill:<br />Yeah, no doubt. For this high— and it turned out actually our initial estimates of where that was were slightly wrong, which is one of the reasons why the salting, the blinding— spot. They didn&#8217;t— they weren&#8217;t quite what we subsequently showed through higher statistics calibrations was going on. So 2 years elapsed, as it were, in the analysis and the deep— trying to really understand at a deep level what the chances of multiple scattering and other background were of producing, you know, fake events in this region. And we decided this year that that analysis had matured enough that we were indeed ready to unblind, which we did, and also to go ahead and publish, you know, the results from that unblinding. But of course, since April &#8217;24, we&#8217;ve been continuing to run the detector.</p><p>Brian Keating:<br />Right.</p><p>Rick Gaetzkill:<br />So we&#8217;re actually in this interesting situation of now having, what, over 3 times as much exposure closure at this point. And, you know, so that, of course, in itself, if this is physics— and again, it&#8217;s statistics— but if you say that one event every 220 live days is the rate at which we would like to accumulate physics, it means we have enough data statistically to go ahead and sort of really test whether we are accumulating physics.</p><p>Brian Keating:<br />That&#8217;s true also— yeah, you&#8217;re absolutely right. But that&#8217;s also true of the background. And it&#8217;s true of the experiment, right? So you should have accumulated, you know, if it&#8217;s background, I mean, then the, there&#8217;s obviously there&#8217;s a lot of people that are critical of a single event, right? I mean, there&#8217;s a thousand papers. Yeah. Yeah.</p><p>Rick Gaetzkill:<br />Yeah. We understand that.</p><p>Brian Keating:<br />We&#8217;re not trying to say— I think, I think, you know, your, your H-index was very high, but I, I think it&#8217;s gone up, you know, by a couple orders of magnitude even, or the citation count. I mean, that happened with BICEP too as well. I mean, I was getting emails from my former Brown professor, you know, Robert Brandenberger, you know, 4 days before the result, and they were already writing papers. But tell me, Rick, I mean, this is exciting in all different ways, right? You&#8217;ve already made the case, you know, in the worst possible case, right, for whatever— I don&#8217;t even know what metric that refers to, because the truth is God&#8217;s alone, or Mother Nature&#8217;s if you&#8217;re an atheist, right? But let&#8217;s say this turns out not to be a dark matter candidate. Well, you&#8217;ve learned about the experiment, you&#8217;ve learned about the tool, you&#8217;ve learned about condensed matter physics, which, you know, you&#8217;ve helped me appreciate. My colleague Kaishuan Ni here, a very good friend of mine and been a guest on the podcast.</p><p>Rick Gaetzkill:<br />Yeah, no, I&#8217;ve worked with Kaishuan.</p><p>Brian Keating:<br />And yeah, and Elena April, a past guest on the podcast from the liquid, from Xenon-100 and whatever they&#8217;re at now, 1,000. They also have, and I think this is a, you know, I want to chastise you in your field because you guys do so much for understanding practical nuclear physics and the nuclear condensed matter physics that nobody ever talks about and how productive and useful that is. So that&#8217;s the bear case. That&#8217;s the, you know, going back to your investment days, right? That&#8217;s the bear case. The bull case is that either, you know, it&#8217;s new physics or some new background, which would also be incredibly interesting. So I wanna, you know, &#8217;cause we&#8217;re coming up on almost 2 hours and I love talking. Yeah, yeah, yeah. And we&#8217;ll talk more in public, but I know you gotta teach and I gotta teach.</p><p>Brian Keating:<br />But tell me, Rick, so where do we go from here? Are there other slides that you must show right now? &#8216;Cause I have a lot of questions I wanna put to rest before we wrap up. So let&#8217;s go on to— yeah.</p><p>Rick Gaetzkill:<br />Yeah, I mean, as we&#8217;ve talked about, obviously one interpretation is how can we adjust effective field theory parameters and say that this event, the reason that we&#8217;re seeing it up at 250 keV and we&#8217;re not seeing an accumulation of events at lower energies, you could play around with that idea and that suggests specific Lagrangian you have to say that whatever mechanism is causing the interaction must be actively suppressing low-energy events and— but, you know, benefiting— now, we&#8217;re not suggesting from one event. This is where you need events plural. This is where you need many events because now, as you get an energy distribution, 2 things are going to happen. Firstly, are the events actually lying in the nuclear recoil band, which is a necessary condition for it to be WIMP. If those additional events are not lying in the nuclear recoil band, then that&#8217;s a very good indication that we actually have a systematic background that we didn&#8217;t understand, but that is coming in at a rate of the order of 1 every 220 days. Then the last hypothesis, of course, is that this was just a random fluctuation. And like, you know, plenty of experiments before, you every so often you get that unlucky. Why? Because we do so many experiments—</p><p>Brian Keating:<br />Yeah.</p><p>Rick Gaetzkill:<br />you have to get unlucky in terms of a background fluctuation, which is why, you know, we often talk about 5 sigma as being a necessary requirement if you&#8217;re, if you&#8217;re really going to say, you know, a signal is robust enough to, you know, to claim direct discovery.</p><p>Brian Keating:<br />That&#8217;s right.</p><p>Rick Gaetzkill:<br />So yeah, so that&#8217;s, that&#8217;s really, you know, where we&#8217;re going. And there are specific, you know, models, you know, inelastic Higgsino double doublet submodels. There are also ones where specific choice of Lagrangian that they, of course, have already produced people saying, well, have you looked in another place in your data? And, and it&#8217;s been very exciting because in some cases people actually said, if you look in your appendix of your paper you&#8217;ve published, we&#8217;ve actually done an analysis already of your data and making the following assumptions, we can rule out or we could model which is lovely. I mean, obviously everybody understands that you haven&#8217;t done the efficiency explicitly and you haven&#8217;t done the thing, but it&#8217;s still showing us, you know, very directly on how you can test a whole range of models using the data. I think all I would say is people have to bear with us because it is enormously exacting trying to estimate not just the response of the detector, but also the leakage, the signal leakage. In order for us to, as scientists, to be able to look at an event and say, how much weight should we put on this event, or we hope subsequent events, you have to have formed a very quantitative model of the background. So, you know, that&#8217;s what we&#8217;re doing. That&#8217;s what we&#8217;re doing.</p><p>Rick Gaetzkill:<br />So anyway, I guess I should say, you know, We&#8217;re trying to make the quietest place in the universe inside our— or quietest known watched place in our universe. And I think we&#8217;re doing a pretty damn good job of it. But obviously, you know, we always hope that it&#8217;s not too quiet in the sense that ultimately one of these, one or more of these dark matter particles does actually start depositing energy in our—</p><p>Brian Keating:<br />If you want to find a quiet place, Rick, just find out where the string theorists are hanging out. Just kidding. I&#8217;m just kidding out there. I love string theorists. Some of my best friends are string theorists. I just, you know, I don&#8217;t know if I&#8217;d want them to marry my daughter. Rick, this has been awesome. I just have a couple of questions because it&#8217;s so rare we get to, you know, hang out and chat.</p><p>Brian Keating:<br />But, um, but I guess, you know, fundamentally, you know, the next, the next generation, if we, we&#8217;re going to extend, you know, if you had made that, uh, I assume you made the Gordon Moore, you know, kind of law for dark matter detectors, you know, before this event occurred. But This might throw things in, throw a wrench into it, might make another knee on that plot to descend even farther faster. What are some of the ultimate limits? What&#8217;s Mother Nature&#8217;s veil that she, as Feynman said, refuses to let you pick up? Is it the neutrino background? Is it some other exotic phenomenon?</p><p>Rick Gaetzkill:<br />Well, so actually December of last year, we announced a result where we have accumulated around 20 20 neutrino events in the LZ detector, and those are events from the 8 boron component of the solar neutrinos. Those are neutrinos that are just energetic enough that when they coherently scatter off xenon, we can see the recoils. Now, that&#8217;s a 15 MeV neutrino. That flux is, well, in our terms, quite high.</p><p>Brian Keating:<br />Yeah. 20 events.</p><p>Rick Gaetzkill:<br />So that means when we&#8217;re doing very— when we&#8217;re looking for signals at the lowest end of our signal now, we have to allow for the fact that the dark matter events, if the dark matter events are down at those low energies, they&#8217;re going to be mixed in with neutrino, low-energy 8 boron neutrino events. Now, as you go to higher energies, actually solar neutrinos, you know, they cut off at 15 MeV, so we&#8217;re not going to do it. But, you know, atmospheric cosmic neutrinos. So that&#8217;s, you know, cosmic ray interactions producing neutrinos in the atmosphere. Those energies go up much higher, those neutrinos. And it&#8217;s not going to occur in LZ. But if we were to, you know, go, as is quite natural, we build not a 10-ton detector, but a 100-ton order detector, and we run it for 10 years or something like that. What starts happening is, is we do expect to start seeing nuclear recoil signal, and that comes about from atmospheric neutrinos.</p><p>Rick Gaetzkill:<br />It actually also somewhat amazingly is we could start seeing the occasional event also from neutrinos from the diffuse supernova background, which is—</p><p>Brian Keating:<br />Right.</p><p>Rick Gaetzkill:<br />That&#8217;s basically all the supernovas across the entire universe over time going off bang, and it turns out that the GeV, those GeV neutrinos could also be depositing energy through scattering off nuclei in a xenon detector. So that is a sort of fog in the sense that you start seeing individual events occurring and now you have to say that that could be associated with the neutrinos, it could be associated with dark matter, and statistically what we have to do is determine the neutrino signals as well as we possibly can so that we can see any anomalous or increase in the rate above that which would indicate dark matter. So that&#8217;s what, that&#8217;s what we mean by entering the fog, is we&#8217;re now statistically having to allow for, you know, 2 possible hypotheses for them. So that&#8217;s, it&#8217;s, that&#8217;s some way away from this kind of work we&#8217;re doing at the moment. We have gotta do a lot more pushing to get into the fog.</p><p>Brian Keating:<br />Another question I have, I can&#8217;t resist. I ask it whenever I talk to your friends Alina and Kaishuan and anyone else who&#8217;s working in this field. But, but what are your thoughts about the current state of DAMA? Um, I did a video about them last year in connection to, you know, the, the most persistent signal that&#8217;s believed by nobody that has the best, uh— well, it&#8217;s not believed by nobody, but, um, and I&#8217;m gonna ask, it&#8217;s not just to dish dirt, but, but, uh, tell me, Rick, first, because I think, you know, I&#8217;m going to ask you a follow-up which is basically what I&#8217;ve wanted them to do for a long time, which is to build a DAMA Southern Hemisphere. But tell me, Rick, what are your thoughts as one of the world&#8217;s foremost experts on experimental physics in this field? What are your opinions about DAMA?</p><p>Rick Gaetzkill:<br />Well, so &#8217;90s, I was around in &#8217;97 when they came out with the first annual modulation. Yeah, 29 years ago. it— obviously, we were enormously excited.</p><p>Brian Keating:<br />Yeah.</p><p>Rick Gaetzkill:<br />It&#8217;s the mechanism that dark matter uses to modulate in that way, you know, is a very, you know, very natural, well understood. And it implied that there would be a very high rate of dark matter in not just the DAMA experiment but actually a number of the other experiments we were running at the same time. So it really motivated, you know, us looking for corresponding events in similar detectors. Now, as the decade went on, so, you know, through the noughties, if you like, we managed to build these other detectors that were more and more sensitive. And unfortunately, we did just did not see any individual events. So DAMA was seeing, if you like, a statistical process, which was this few percent modulation of a large number of events which— where the hypothesis was that a small— sorry, that some fraction of those events were conventional background and some other fraction were dark matter and the modulation was in the dark matter component and the observation of modulation over the year, higher in June, slightly a few percent lower in December and up, down, up, down like that, that that was evidence for the dark matter. But we were running other experiments that on a— literally on a single event by a single event basis could tell the difference between a nuclear recoil and more conventional background. And we just weren&#8217;t seeing any of the— any events in our other detectors that would be consistent with the dark matter.</p><p>Rick Gaetzkill:<br />Now, there were ways to modify the theories which actually includes inelastic dark matter. In fact, that was a time when inelastic dark matter became interesting because that was potentially a way to explain the difference in results. Although one of the things we did with our xenon detector in the mid-noughties, 2006, around then, is we made a very sensitive, just 10-kilogram detector. But the fact that it did not see events from dark matter because the xenon&#8217;s heavier than the iodine in the DAMA experiment, you could no longer use this inelastic get-out clause to sort of effectively explain why why nobody else was seeing the DAMA events. So just from a pure scientific process point of view, the fact that we&#8217;ve been unable to replicate the DAMA results either in sodium iodide experiments, which have now reached a level of sensitivity that are comparable, you know, to DAMA. And DAMA was an exquisitely designed experiment.</p><p>Brian Keating:<br />Yeah.</p><p>Rick Gaetzkill:<br />So it took a long time for other people to replicate everything that they got right in the DAMA experiment. But also at the same time we had all these other competing— well, not competing, but, you know, other dark matter, direct detection dark matter experiments that were not seeing events. That&#8217;s a problem for a— for one result if you can&#8217;t seem to get confirmation with other experiments. So, you know, the net result is it&#8217;s a very clear modulation. It&#8217;s gone on for— they&#8217;ve stopped the experiment now, although I think they&#8217;re rebuilding it with an independent group.</p><p>Brian Keating:<br />Yeah, LIBRA, aren&#8217;t they? Is that right?</p><p>Rick Gaetzkill:<br />Yeah, well, actually, LIBRA was part of DAMA and they went on. There&#8217;s now— I&#8217;ve forgot what they&#8217;re calling it, but they&#8217;re basically taking the same setup and they&#8217;re going to do it again to try to see whether they can see how this— how the annual modulation might be getting in somehow to the experiment.</p><p>Brian Keating:<br />Yeah, the final thing I wanted to ask you about, you know, because how often do I get a chance to talk to not only a friend, a valued and treasured colleague, but a professor at my alma mater, involves the work of a former— well, I should say, involves the work of someone who&#8217;s at a university that&#8217;s also my alma mater, namely my undergraduate alma mater, Case Western Reserve University. And that&#8217;s where I was an undergraduate before I came to Brown in &#8217;93. I cannot believe it&#8217;s been 33 years since I started there. But nevertheless, it is. and you weren&#8217;t there, unfortunately. I would have loved to have you as a professor, colleague, friend, you know, uncle in the lab, so to speak. You&#8217;re not that much older than me. But tell me, Rick, the work that Stacey McGaugh and others have done on MOND.</p><p>Brian Keating:<br />You mentioned—</p><p>Rick Gaetzkill:<br />Yes, yeah.</p><p>Brian Keating:<br />You mentioned work by my former colleague, Kim Greist. I mean, he&#8217;s my emeritus colleague. He&#8217;s still a friend. Kim Greist here on MACHOs, and that was really big here, you know, in the &#8217;90s and 2000s, but that&#8217;s largely gone away. But recently, a paper, you know, by my friend Alessandro Melchiorri, I just made a video about on my channel this week called Why the Dip? And that has to do with Gaia data from 3 years ago, also around the same time as your event, Rick, by the way. Kind of interesting. Gaia came out with a map of the Milky Way&#8217;s stars and velocities and kinematics, and they show that there&#8217;s actually a semi-Keplerian drop, which is highly unexpected, both in the dark matter paradigm that you and many others are more or less committed to in one way or another. But it also rules out MOND because MOND was invented to do what dark matter does, but without requiring those fanciful Higgs-ino doublets or whatever you may have detected, or that one.</p><p>Brian Keating:<br />I mean, it reminds me of the Valentine&#8217;s Day event. What do you feel? What is your take on MOND?</p><p>Rick Gaetzkill:<br />Well, so Stuart, firstly, cold dark matter needed You know, if you&#8217;re gonna really develop your understanding of dark matter and cold dark matter specifically, you needed competing theories. So of course you will remember when we used to also be very serious about hot dark matter, relativistic particles, and it was enormously important to just compare those 2 models, both sort of dark matter models. But— and it really drove us to understand rates of structure formation and the way in which data and obviously increasingly precise data that we&#8217;ve managed to get in terms of, you know, the structure, evolution, history of our, of our, you know, universe. And the MOND in a different sense was enormously important. Again, you know, there have to be competing theories in order for you to understand any model. It&#8217;s always extremely instructive to have other competing models. So it would be, you know, in a sense, it&#8217;s not good if there is only the one theory. Now, of course, cold dark matter has been so damn successful.</p><p>Rick Gaetzkill:<br />It is you have to be quite brave coming up with theory. MOND has done very well, except that you always have to remember that MOND was kind of structured to solve a specific problem, which, as you know, is to do with the individual galaxy rotation curves. So when students come up to me and say, Rick, you do realize that, you know, rotation curves in galaxies is a solved issue, it&#8217;s like, hang on, you&#8217;ve got to be a little careful about your logic there, because realize that MOND was designed to solve solved that issue. So let&#8217;s try to look at how MOND fares in other regimes. And that, of course, is, you know, that&#8217;s how any good theory gets tested. And cold dark matter has done phenomenally well over a huge number of length scales and obviously time, you know, evolution. And, you know, MOND has had some challenges in that respect. You know, and then if we end up having to say, well, okay, you know, it&#8217;s MOND plus dark matter.</p><p>Rick Gaetzkill:<br />You know, in a sense—</p><p>Brian Keating:<br />Yeah.</p><p>Rick Gaetzkill:<br />Of course, nature has proven that nature is quite willing to give us mixed models. But from a, you know, from a theory point of view, obviously we&#8217;d like to, if we can, define whatever the dominant solution is for dark matter or the dominant solution is for dark energy. And we have to hope a little bit that nature decides to make one of the models dominantly the solution is. If that&#8217;s not the case, if it turns out nature&#8217;s used 5 separate, you know, components all to contribute—</p><p>Brian Keating:<br />You know, I always say, you know, if you said I&#8217;m looking for matter, ordinary matter, then, you know, I&#8217;d say, well, what kind? There&#8217;s 116 of them, you know, on the periodic table. But, you know, most people don&#8217;t agree with me. And lastly, you know, I often often say, you know, people use dark matter as a canard, as a polemic against physicists, saying, you know, you also alluded to it in a way of being humble against hubris, but they&#8217;ll say things not as charitable. They&#8217;ll say, you physicists are idiots, you don&#8217;t know what 95% of the world, the universe is made of, and yet you claim to be able to tell us all sorts of things about, you know, the future and so forth. I like to point out that dark matter has already been detected, and you guys, even if this particles correct, it won&#8217;t be the first, because we&#8217;ve known since 1956 that neutrinos exist, and we&#8217;ve known since 2008 or before, 2001 maybe, that neutrinos have mass. So they fit every definition of dark matter. They&#8217;re massive, they&#8217;re weakly interacting in this case, and they don&#8217;t interact with light. So people that claim that there must be some alternative like MOND, I feel like that&#8217;s the strongest objection.</p><p>Brian Keating:<br />In other words, Yes, it is true, it doesn&#8217;t make up the complete density of the universe, the missing density in terms of matter, non-baryonic matter, but neither does xenon itself, or neither does iron. Iron doesn&#8217;t make up much of the universe as well. Is it important to us? Absolutely. If the only thing we knew about was iron, we&#8217;d be, you know, we&#8217;d have some knowledge, but we couldn&#8217;t surely say that there has to be some other alternative form of matter or whatnot, right? So why do people reject the dark matter WIMP paradigm. So I&#8217;m thinking of Sabine Hossenfelder, who&#8217;s a friend and been on the show many— she thinks this is all a big waste. I mean, she&#8217;s hopeful. She said, you guys are in a gold mine hoping to get gold in the form of a Nobel Prize. And I hope that you do if you&#8217;re right.</p><p>Brian Keating:<br />But the point is, people mock dark matter, but we know dark matter exists. And I see this from Elon Musk. I see this from everybody that it&#8217;s all a scam. And we haven&#8217;t done anything in physics since before string theory. So where do you take us? Take us out on this final question. The existence of the neutrino, doesn&#8217;t that truly substantiate that dark matter is particulate in any case? May not be only from neutrinos, but doesn&#8217;t it give us a big boost?</p><p>Rick Gaetzkill:<br />My, you know, those early dark matter detectors were indeed testing the hypothesis that Dirac neutrinos, the kind of conventional sort of neutrinos you&#8217;re talking about, if they were the dominant mass in the universe and if they were massive enough to be cold, or a Dirac neutrino was massive enough to be cold, we were able to directly test that hypothesis. And we, you know, that was enormously instructive that we managed to rule out, you know, that there were massive Dirac neutrinos making up the dominant part of the dark matter. I mean, it&#8217;s always interesting. Again, it comes back to this sort of— you work on a question for 40 years. For some people, just the fact that you had to work on it for 5 makes it a pointless— they immediately assume that—</p><p>Brian Keating:<br />Instant gratification, right?</p><p>Rick Gaetzkill:<br />And it&#8217;s, gosh, you&#8217;re really not— you&#8217;re failing to understand how science works. Now, the fact that we are in a society or a structure that that is prepared to, you know, back us and to allow us to do work over these long periods. I mean, if, you know, if I&#8217;d been an ancient astronomer, you know, and coming up with dark matter would have been the difference between whatever my ruler was, the difference between them maintaining power or getting usurped because clearly they&#8217;re not—</p><p>Brian Keating:<br />Not a brown, not a brown.</p><p>Rick Gaetzkill:<br />Well, I mean, astronomers always have this sort of leg up with respect to eclipses. But of course, if you got one eclipse wrong, you know, that probably terminated your career in a very unfortunate manner.</p><p>Brian Keating:<br />Maybe your life.</p><p>Rick Gaetzkill:<br />Yeah, well, no, indeed, that&#8217;s what I&#8217;m getting at. So, you know, we— but, you know, in the lifetime of a researcher or the, you know, all this, all the attention span, if you like, of a researcher, some research, that&#8217;s not the useful metric here. You really have to look at the problems on this larger scale. And also keep looking at all of the other data that we&#8217;re getting to see how consistent the hypotheses are with what we&#8217;re getting. You know, if we manage to construct an experiment that demonstrates signal or a propensity to develop or to generate dark matter through some other mechanism, and we do have— we&#8217;re doing tests of that type in many other different channels. If we start seeing a significant sort of positive indicator there, then obviously that would suggest certainly that looking for a WIMP particle, you know, is less well motivated because it&#8217;s going to be a sub-dominant or very small component.</p><p>Brian Keating:<br />It&#8217;s—</p><p>Rick Gaetzkill:<br />trying to rule it out completely is always going to be, you know, a very significant challenge.</p><p>Brian Keating:<br />Would that mean that the nightmare scenario is that dark matter exists, it&#8217;s similar to inflation right? Inflation could have happened, but could be so undetectable, so low in energy, that we can never prove it or rule out and falsify alternatives. Is that the nightmare scenario for you, that the neutrino fog, you know, will be the ultimate limit? And are there any other, you know, proposals to, you know, clean that background? I thought it was hard to clean the galaxy of its polarized dust B-mode emission, but scrubbing the universe of neutrinos seems impossible by comparison. So— Well, That&#8217;s your nightmare scenario.</p><p>Rick Gaetzkill:<br />I mean, the neutrinos and the dark matter, of course, have 2 very different signatures when you start considering the typical direction in which they&#8217;re hitting your detector. So it is fair to say that if you want to associate a signal of nuclear recoils with a galactically interesting astrophysical source, the fact that the sun is in motion, you know, 230 kilometers a second around the Milky Way and therefore that the Earth is being carried with it. That, you know, that Cygnus, you point towards Cygnus, that&#8217;s where the sun&#8217;s heading. That does skew the recoils you get from dark matter away from Cygnus. So that is a signature. Now, in xenon, we&#8217;ve tried to look for possible ways of getting directionality in liquid xenon. That hasn&#8217;t happened, but other researchers have demonstrated that using alternative targets, usually with gas, although there has been work on some solid, you know, stilbene for instance, and— but that it will be possible to see the recoil direction. And of course, if you&#8217;re able to do an experiment that&#8217;s measuring the recoil direction of your— The recoil direction of the beam.</p><p>Rick Gaetzkill:<br />your nuclei, then the neutrino fog actually is no longer a fog because it&#8217;s statistically, it&#8217;s, you know, isotropic or, you know, it doesn&#8217;t have that correlated singleness.</p><p>Brian Keating:<br />That&#8217;s right.</p><p>Rick Gaetzkill:<br />So there are ways through it. Of course, it requires a lot of research to make a— I often talk about Sisyphean index, technology that wants to work. If an experiment or a particular technology wants to work, I call that a low Sisyphean index.</p><p>Brian Keating:<br />Right.</p><p>Rick Gaetzkill:<br />If it&#8217;s going to roll down the hill on you every time you turn your back or you try to go to sleep one night and the technology comes back down the hill and you&#8217;ve got to push it up again, high Sisyphean index, not so good. So we&#8217;re always, when we&#8217;re doing dark matter experiments, we&#8217;re always rather dependent on finding low Sisyphean index materials. Xenon, I think, has a very low Sisyphean index. It really wants to work. If we&#8217;re going to build a 100-ton detector made of gas, we&#8217;re filling cathedrals. We want to make sure that we&#8217;re doing it with a technology that really wants to work. It&#8217;s quite doable on the scheme of systems that we&#8217;ve built in the past.</p><p>Brian Keating:<br />Well, Rick, this has been fascinating. I want to cut it off before we hit the 2-hour mark because this is just too exciting, too delicious. And hopefully by the time I&#8217;m visiting you again, I will be able to do it in person, and then we&#8217;ll have a little bit more clarity what it is, what it was, and we&#8217;ll refer back to this, you know, watershed epochal event. So, Rick Gaetzkill, you know, tremendous, tremendous amount of gratitude. You&#8217;ve been unbelievably kind and generous with your time during this incredibly busy time for you traveling around faster than a neutrino. Rick, this has been great. Thank you so much for communicating with me and my audience. Thank you.</p><p>Rick Gaetzkill:<br />Brian, thank you so much. It&#8217;s been a great pleasure talking with you again.</p><p>Brian Keating:<br />Thank you, my friend.</p><p> </p>								</div>
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		<title>Dark Matter Might Not Exist. But MOND Might Be Wrong Too.</title>
		<link>https://briankeating.com/dark-matter-might-not-exist-but-mond-might-be-wrong-too/</link>
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		<pubDate>Sun, 20 Sep 2026 18:56:01 +0000</pubDate>
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					<description><![CDATA[Dark Matter Might Not Exist. But MOND Might Be Wrong Too. https://www.youtube.com/watch?v=w61s3VCMhHo Transcript: Brian Keating:For 50 years, dark matter explained why galaxies spin too fast. Brian Keating:Its rival, MOND, explained the same thing with no new particles at all. New data from a European spacecraft suggests both of them predicted the wrong curve. Brian Keating:I&#8217;m Brian Keating. I build telescopes that map the early universe and monitor distant astronomical objects for signs of cracks in relativity. 400 years ago, Johannes Kepler worked out how fast a planet should orbit. The farther out you go, the slower it moves. Galaxies refused to do that. Dark matter was one answer. Brian Keating:Modifying Newton&#8217;s equations was the other. Brian Keating:Then Gaia measured a billion stars in our own galaxy, and the number it got back was the one nobody expected. Brian Keating:So, start with Kepler. The solar system gave him the familiar picture. Mercury moves much faster than Earth, and Earth moves much faster than Jupiter. Neptune moves with the urgency of a teenager that you&#8217;ve asked to empty the dishwasher. The farther away you get from the Sun, the slower it orbits— not directly proportional, but actually proportional to 1 over the square root of the radius from the Sun. Good ol&#8217; Johannes worked this out over 4 centuries ago. Now Kepler had 3 laws. For a circular orbit, which most of the planets nearly follow, the speed that the planet orbits is determined by the gravitating matter enclosed within that orbit. Brian Keating:In fact, the equation is circular speed squared equals Newton&#8217;s constant times the enclosed mass divided by radius. Newton actually proved mathematically why Kepler&#8217;s laws are correct. Measure an orbital speed, and after assuming a geometry— basically circular— you can infer the gravitational force that&#8217;s pulling on the orbiting planet. If the enclosed mass—within the orbital radius—stops increasing, the radius in the denominator keeps growing, but the numerator stays nearly fixed. Therefore, the speed must fall. Now let&#8217;s replace the Sun with a galaxy containing only its visible gas and stars. Far outside the bright disk, almost all of that baryonic mass is entirely enclosed. The mass term becomes approximately constant, so the predicted velocity falls off like 1 over the square root of radius. Brian Keating:And we&#8217;ve seen that far, far out in many galaxies. Now here on the slide, the gray curve is the visible matter only prediction. If galaxies behaved like enlarged solar systems with all of the mass essentially being at the center, that would be the end of the story. The universe, though, has other plans, and a larger budget for the invisible accounting that dark matter seems to require. What astronomers actually found, dating back to Vera Rubin and her collaborators, was the blue curve. The outer velocity remains approximately constant. And if circular speed stays constant while radius increases, the enclosed those gravitating masses must keep increasing in roughly proportion to their radius. The luminosity of the galaxy fades away, but the gravitational influence does not. Brian Keating:And that mismatch is one of the clearest reasons dark matter became central to modern astrophysics. It&#8217;s not the only reason, and it&#8217;s not merely that galaxies spin too fast— it&#8217;s that their radial pattern of motion implies more gravitating mass at larger radii than the visible mass can provide. Flat rotation curves map where the missing gravity appears to live. The standard explanation surrounds the visible disk with a much larger dark matter halo. The halo contributes little light, but it keeps adding enclosed mass as we move outward. Combine the disk with the halo and the rotation curve can remain essentially flat. That&#8217;s what was observed. So where is the extra gravity coming from? These questions are not exactly identical, but there is some commonality between them, and the rotation curve addresses the second one. Brian Keating:The extra gravity. Now measuring our galaxy has one disadvantage: we&#8217;re inside of it. This beach ball shows the perspective of God outside of it, but we&#8217;re inside of it looking out. So it&#8217;s like trying to infer the shape of a football stadium from inside the bleachers. Gaia gave us these exquisite measurements of stellar positions and their motions, but Gaia doesn&#8217;t provide a button labeled the true Milky Way rotation curve, click here. That was easy. So we have to begin with proxies. We measure stellar positions and their velocities. Brian Keating:We correct for their distances. It&#8217;s really good that Gaia is capable of doing that. We have to choose a model and correct for the asymmetric drift of stars that add peculiar effects. And we have to assume something about the equilibrium and symmetry of the physics of the problem. And then we can correct for a circular velocity and any biases that we may have induced. The instrument Gaia provides the data, and the pipeline tells us what we think those data points mean. Revolutionary claims need us to keep those chapters together. And one of the most important things is that stars do not travel on perfectly circular tracks. Brian Keating:Just like planets, they&#8217;re not perfectly ellipticity-free, but they also have wobbles. They wobble radially and vertically, and stellar populations&#8217; average azimuthal speed is therefore lower than the circular speed of the gravitational field. That difference is called by professional astronomers asymmetric drift. Recovering the circular velocity requires a model of that random motion. Unfortunately, the correction matters most in the outer galaxies where stars become sparse, and that&#8217;s the most important part because you have most of the enclosed mass within you the farther out you go. And there is where the disputed decline becomes interesting. In this analysis, the inferred speed would fall by about 30 kilometers per second between 19.5 to 26.5 kiloparsecs from the center. The fitted outer slope is -0.47± 0.15. Brian Keating:Even though it sounds close to -0.5, which would be 1 over square root of radius, a declining outer curve looks increasingly plausible. A]]></description>
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					<h2 class="elementor-heading-title elementor-size-default">Dark Matter Might Not Exist. But MOND Might Be Wrong Too.</h2>				</div>
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									<h2><strong>Transcript:</strong></h2><p>Brian Keating:<br />For 50 years, dark matter explained why galaxies spin too fast.</p><p>Brian Keating:<br />Its rival, MOND, explained the same thing with no new particles at all. New data from a European spacecraft suggests both of them predicted the wrong curve.</p><p>Brian Keating:<br />I&#8217;m Brian Keating. I build telescopes that map the early universe and monitor distant astronomical objects for signs of cracks in relativity. 400 years ago, Johannes Kepler worked out how fast a planet should orbit. The farther out you go, the slower it moves. Galaxies refused to do that. Dark matter was one answer.</p><p>Brian Keating:<br />Modifying Newton&#8217;s equations was the other.</p><p>Brian Keating:<br />Then Gaia measured a billion stars in our own galaxy, and the number it got back was the one nobody expected.</p><p>Brian Keating:<br />So, start with Kepler. The solar system gave him the familiar picture. Mercury moves much faster than Earth, and Earth moves much faster than Jupiter. Neptune moves with the urgency of a teenager that you&#8217;ve asked to empty the dishwasher. The farther away you get from the Sun, the slower it orbits— not directly proportional, but actually proportional to 1 over the square root of the radius from the Sun. Good ol&#8217; Johannes worked this out over 4 centuries ago. Now Kepler had 3 laws. For a circular orbit, which most of the planets nearly follow, the speed that the planet orbits is determined by the gravitating matter enclosed within that orbit.</p><p>Brian Keating:<br />In fact, the equation is circular speed squared equals Newton&#8217;s constant times the enclosed mass divided by radius. Newton actually proved mathematically why Kepler&#8217;s laws are correct. Measure an orbital speed, and after assuming a geometry— basically circular— you can infer the gravitational force that&#8217;s pulling on the orbiting planet. If the enclosed mass—within the orbital radius—stops increasing, the radius in the denominator keeps growing, but the numerator stays nearly fixed. Therefore, the speed must fall. Now let&#8217;s replace the Sun with a galaxy containing only its visible gas and stars. Far outside the bright disk, almost all of that baryonic mass is entirely enclosed. The mass term becomes approximately constant, so the predicted velocity falls off like 1 over the square root of radius.</p><p>Brian Keating:<br />And we&#8217;ve seen that far, far out in many galaxies. Now here on the slide, the gray curve is the visible matter only prediction. If galaxies behaved like enlarged solar systems with all of the mass essentially being at the center, that would be the end of the story. The universe, though, has other plans, and a larger budget for the invisible accounting that dark matter seems to require. What astronomers actually found, dating back to Vera Rubin and her collaborators, was the blue curve. The outer velocity remains approximately constant. And if circular speed stays constant while radius increases, the enclosed those gravitating masses must keep increasing in roughly proportion to their radius. The luminosity of the galaxy fades away, but the gravitational influence does not.</p><p>Brian Keating:<br />And that mismatch is one of the clearest reasons dark matter became central to modern astrophysics. It&#8217;s not the only reason, and it&#8217;s not merely that galaxies spin too fast— it&#8217;s that their radial pattern of motion implies more gravitating mass at larger radii than the visible mass can provide. Flat rotation curves map where the missing gravity appears to live. The standard explanation surrounds the visible disk with a much larger dark matter halo. The halo contributes little light, but it keeps adding enclosed mass as we move outward. Combine the disk with the halo and the rotation curve can remain essentially flat. That&#8217;s what was observed. So where is the extra gravity coming from? These questions are not exactly identical, but there is some commonality between them, and the rotation curve addresses the second one.</p><p>Brian Keating:<br />The extra gravity. Now measuring our galaxy has one disadvantage: we&#8217;re inside of it. This beach ball shows the perspective of God outside of it, but we&#8217;re inside of it looking out. So it&#8217;s like trying to infer the shape of a football stadium from inside the bleachers. Gaia gave us these exquisite measurements of stellar positions and their motions, but Gaia doesn&#8217;t provide a button labeled the true Milky Way rotation curve, click here. That was easy. So we have to begin with proxies. We measure stellar positions and their velocities.</p><p>Brian Keating:<br />We correct for their distances. It&#8217;s really good that Gaia is capable of doing that. We have to choose a model and correct for the asymmetric drift of stars that add peculiar effects. And we have to assume something about the equilibrium and symmetry of the physics of the problem. And then we can correct for a circular velocity and any biases that we may have induced. The instrument Gaia provides the data, and the pipeline tells us what we think those data points mean. Revolutionary claims need us to keep those chapters together. And one of the most important things is that stars do not travel on perfectly circular tracks.</p><p>Brian Keating:<br />Just like planets, they&#8217;re not perfectly ellipticity-free, but they also have wobbles. They wobble radially and vertically, and stellar populations&#8217; average azimuthal speed is therefore lower than the circular speed of the gravitational field. That difference is called by professional astronomers asymmetric drift. Recovering the circular velocity requires a model of that random motion. Unfortunately, the correction matters most in the outer galaxies where stars become sparse, and that&#8217;s the most important part because you have most of the enclosed mass within you the farther out you go. And there is where the disputed decline becomes interesting. In this analysis, the inferred speed would fall by about 30 kilometers per second between 19.5 to 26.5 kiloparsecs from the center. The fitted outer slope is -0.47± 0.15.</p><p>Brian Keating:<br />Even though it sounds close to -0.5, which would be 1 over square root of radius, a declining outer curve looks increasingly plausible. A precisely Keplerian decline is more debatable. Converting it into an extraordinarily low galactic mass is more model-dependent even still. If the circular velocity really fell as 1 over the square root of radius, then the velocity squared falls as 1 over radius. Now if you insert that into the circular velocity equation and the enclosed spherical equivalent mass becomes You move outward but infer surprisingly little additional gravitating mass, which is awkward for a large extended halo whose enclosed mass should keep growing. And that&#8217;s the dominant paradigm for the dark matter picture, where galaxies are formed and held together and their rotation is driven by the enclosed mass within their visible light radius. But notice the phrase I just used— spherical equivalent. The Milky Way contains a disk, gas, a bulge, a warp, and a 3-dimensional halo.</p><p>Brian Keating:<br />Turning one curve into a mass Really requires a lot of geometrical insight. So where do we go next? The dramatic analysis produces an average total mass estimate of about 2.6× 10^11 solar masses. That&#8217;s a lot— 260 billion equivalent solar masses. So this isn&#8217;t actual stars, but it&#8217;s stars, gas, and dust. That&#8217;s below many independent estimates, which are closer to a trillion solar masses. The number comes from fitting the measured rotation curve and extending a mass model well beyond the region directly constrained by the stars alone. So this value is very interesting, but its precision should not hide the assumptions that produce it, as all good models have to incorporate. The Gaia-based rotation curve is constrained over roughly 9 to 27 kiloparsecs.</p><p>Brian Keating:<br />A Milky Way halo, though, may extend to something like 200 kiloparsecs. So when data extending nearly 30 kiloparsecs produce a total halo mass, most of the mass is supplied by that fitted model rather than that traced directly by the stars in that curve. Extrapolation is therefore unavoidable. But observed and extrapolated aren&#8217;t synonyms. Inside, Gaia constrains the dynamics. Outside, we have to make theoretical choices to do most of the work. Now, at this point, when you start to hear that the paradigm of dark matter may have failed to reproduce the dynamics, you might start to celebrate if you&#8217;re a MOND advocate like my past guest Stacy McGaugh or the founder of the MOND paradigm, Mordecai Milgrom himself. MOND proposes that below a characteristic acceleration, the effective dynamics depart from Newtonian relationships.</p><p>Brian Keating:<br />In the MOND region, the acceleration is approximately the square root of the Newtonian acceleration times the MOND scale, which is known as a0. For an isolated baryonic mass, this changes the expected orbital behavior without surrounding the galaxy with a conventional particle halo. So MOND has achieved real predictive successes at galactic scales, including the tight relationship between baryonic structure, and the observed acceleration. So if the dark matter paradigm&#8217;s curve has a problem, does MOND just win? Now this is where it becomes deliciously inconvenient for both scenarios. How does MOND&#8217;s predictions relate to what Gaia has observed? So in this low acceleration limit required for structure to ever form and not have too high a velocity dispersion, the velocity to the 4th power equals Newton&#8217;s constant times the baryonic masses times this constant a0, this baseline acceleration. So what&#8217;s missing from this equation? Radius. There&#8217;s no radius in there. And that&#8217;s why MOND produces an asymptotically flat rotation curve for an isolated galaxy.</p><p>Brian Keating:<br />That was one of the great attractions— no pun intended— that Mordecai and others were drawn in by. But now it&#8217;s part of its vulnerability. It might be part of its downfall if these data are reproducible. If the Milky Way&#8217;s outer curve is Keplerian, MOND is also expecting something flatter too, but it didn&#8217;t find that. Dark matter and MOND arrive at the flat outer rotation curves through completely different physics. Dark matter says there&#8217;s an additional gravitating mass. MOND says the low acceleration dynamics are different themselves. A robust Keplerian decline would challenge the simplest extended halo expectation and the deep MOND asymptotic behavior.</p><p>Brian Keating:<br />The question becomes not which of these 2 camps won, but why we&#8217;re both expecting the wrong curve. So bad news for everybody is Often excellent news for science. I usually say that flaws lead to new laws. That&#8217;s what I teach my students. When you find a crack, when you find something unexpected, as Einstein did with Newton&#8217;s gravity, for example, and as MOND may have done with the dark matter paradigm, and as inflation did with the Big Bang paradigm, these are exciting times for scientists. A Keplerian decline would not instantly falsify every version of MOND. MOND is nonlinear, so an external gravitational field can influence a galaxy&#8217;s internal dynamics and modify the Milky Way&#8217;s outer behavior. The external field effect can generate a decline.</p><p>Brian Keating:<br />So the defensible conclusion is not that Gaia killed MOND. A robust Keplerian curve would instead create tension with the isolated prediction and require that the external gravitational field or another refinement do substantial quantitative work. The scientific test is whether the theory fits the measured curve with independent, justified parameters, not whether or not we can tell a cool story after seeing it. Now here&#8217;s where I have to insert A warning. This is where experimentalists like me get interested, excited, but also a little bit nervous. Distance errors will alter both the star&#8217;s inferred position and its tangential velocity. Any asymmetric drift corrections depend on the tracer density, the gravitational field, and the velocity dispersion— how much these stars are moving independently of the gravitational force of dark matter or MOND. Selection effects can change which stars enter the sample.</p><p>Brian Keating:<br />The warp violates simple disk geometry. Sagittarius and the Large Magellanic Cloud also drive non-circular motion— they&#8217;re like outer gravitating masses. These tracers become sparse at great distances, and you have to question whether or not the stars are at actual equilibrium. Now, in the Gaia analyses, neglected dynamical terms and the systematic error budget grow towards the outermost radii. They increase. It gets harder and harder to do, and you get more and more contamination from external gravitating masses like the LMC. None of this proves the decline is false, by the way. It&#8217;s a brilliant result.</p><p>Brian Keating:<br />It means that the blue curve we showed earlier may conceal a messy galaxy. Maybe that teenager&#8217;s to blame. The more revolutionary the inference, the more carefully we have to distinguish between what Gaia measured from what we, or proponents of MOND or dark matter, would like to interpret.</p><p>Brian Keating:<br />Now, before you declare a winner, MOND has one more move. It isn&#8217;t a linear theory, which means a galaxy sitting inside someone else&#8217;s gravitational field doesn&#8217;t behave like one sitting by itself. And the Milky Way sure ain&#8217;t lonely.</p><p>Brian Keating:<br />As I said, the Milky Way is not some isolated, perfect galaxy that&#8217;s relaxed in a laboratory just hanging out. The Sagittarius Dwarf Galaxy, nearby but not part of our galaxy, has reportedly crossed and perturbed our Milky Way&#8217;s disk. The Large Magellanic Cloud is massive, it&#8217;s nearby, and it&#8217;s dynamical— it&#8217;s rotating, it&#8217;s doing its own thing too. Together with the Milky Way&#8217;s galactic warp, these interactions can produce ripples, star streams, and north-south asymmetries between the upper and lower halves of the galaxy. These motions are valuable, but they&#8217;re not necessarily indicative of equilibrium circular motion. So you wouldn&#8217;t expect Kepler&#8217;s law to actually hold in that sense. Force a disturbed population into a steady axisymmetric model, and the reconstructed curve will absorb the disturbance and present it as a modification to gravity. Sometimes the galaxy is telling us about dark matter, sometimes it&#8217;s telling us that it recently had a close encounter of the third kind.</p><p>Brian Keating:<br />So where does it leave us? At this point, there&#8217;s 3 possibilities that I would say remain viable. First, the decline is real, but it&#8217;s moderate. The Milky Way has a lighter or more concentrated halo than some older models suggested. Dark matter and MOND both can adjust their parameters, tune them, and survive. Second, the decline is real, but it&#8217;s exaggerated. It&#8217;s affected by systematics and disequilibrium which were implicitly assumed in the models. It&#8217;s the least glamorous answer, which is why scientists have to take it seriously. Third possibility: the outer disk is sufficiently disturbed that the reconstructed curve can never be assumed to be equilibrium and circular and represent the Keplerian profile at all.</p><p>Brian Keating:<br />So we have one pattern and 3 possible physical stories that explain it. The evidence that we have doesn&#8217;t uniquely constrain or select between the 3 of them. So what would actually settle the tie, if you will? But there are different objects we can use. Young stars called Cepheids are dynamically colder, Stellar streams can probe objects farther out, and globular cluster satellites can test at larger radii. That&#8217;s in fact how we knew the galaxy had a certain size from the beginning with the Shapley debate of the 1920s. Future Gaia releases improve their astrometry, the position and velocity. We&#8217;ll also get radio astronomical surveys that will supply different tracers, typically of the gas. And better theoretical models can include the warp, the Sagittarius mini dwarf galaxy effect, and the LMC&#8217;s effects as well explicitly.</p><p>Brian Keating:<br />If we combine those methods with different assumptions and different systematics, and we recover the same decline, then we&#8217;ll have to listen. It&#8217;ll go from 3 sigma to many, many sigma in that case, potentially. But our galaxy may be asking a nastier question. Not did dark matter lose, not did MOND win, but rather, are we expecting the wrong curve? So, what do you think is more preferable given the evidence we&#8217;ve presented today? MOND? dark matter, or something else entirely? Leave your comment below, give the video a thumbs up, exercise your thumb, and don&#8217;t forget to share this like invisible dark matter throughout your own universe. I&#8217;m Brian Keating, Chancellor&#8217;s Distinguished Professor of Physics at the University of California San Diego, and I&#8217;ll see you next time on the channel.</p><p>Brian Keating:<br />And I&#8217;d like to conclude this video by thanking my good friend Alessandro Melchiorri and his collaborator Ruchika. They produced the paper that inspired this. It came out in August, it&#8217;s still a preprint, but it&#8217;s called The Rotation Curve of the Milky Way: State-of-the-Art The Keplerian Decline Debate and Implications for Dark Matter. It&#8217;s a brilliant paper and anyone can understand it. They summarize the field, the history, and the controversy, so make sure you check that out. I&#8217;ll leave a link in the description below. Kepler said that the outer stars should slow down. For 50 years, our galaxy said otherwise, and we invented an invisible halo to explain it.</p><p>Brian Keating:<br />Now our galaxy may be taking it back. If that changes how you think about what we actually know, subscribe and tell me which one you prefer. And don&#8217;t forget to watch my interviews with Stacey McGaugh and with Mordecai Milgrom.</p>								</div>
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		<title>OpenAI’s Navier–Stokes Claim: Is This What AGI Looks Like? &#124; Emad Mostaque</title>
		<link>https://briankeating.com/openais-navier-stokes-claim-is-this-what-agi-looks-like-emad-mostaque/</link>
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		<pubDate>Sun, 20 Sep 2026 18:42:14 +0000</pubDate>
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					<description><![CDATA[OpenAI’s Navier–Stokes Claim: Is This What AGI Looks Like? &#124; Emad Mostaque Transcript Brian Keating:Hey, welcome everybody. We have an emergency podcast. I don&#8217;t do many of these. It&#8217;s not entirely clickbait to say that we have an emergency situation going on right now in the deep annals of mathematics, the foundations of mathematics. And there&#8217;s no one I&#8217;d rather talk to about this than my friend Ahmad Mostaq, who&#8217;s joining us all the way from London. How are you, Ahmad, on this late evening for you or early afternoon for you, whatever the case may be? I can&#8217;t do the conversion. It&#8217;s too early for me. Emad Mostaque:I&#8217;m fine. Brian Keating:How are you? Emad Mostaque:Yeah, it&#8217;s mid-afternoon. I&#8217;m feeling a bit sleep deprived after the excitement of the last 24 hours or so. Brian Keating:Yeah, it hasn&#8217;t even been 24 hours, it&#8217;s been 21 hours. I looked at the timeline, I&#8217;ve looked at some of the, uh, the constraints and the complaints and what people are saying about it. So, um, this is exciting. So we&#8217;re going to talk about OpenAI&#8217;s claimed solution to one of the Millennium Problems, which has lasted since the— I would say the early part of the previous millennium when the Clay Mathematics Institute Uh, has provided a gauntlet of challenges for mathematicians and other assorted geeks and dweebs and nerds to go through. Some of them impossible seeming, some of them, uh, quite possibly solved already. So today we&#8217;re going to talk about the Navier-Stokes equation. I should say I have— I put a link below to a video I did yesterday. I actually recorded it a long time ago with Terry Tao. Brian Keating:It was thoughts and some of his ideas that he had convinced me of. Emad Mostaque:Uh huh. Brian Keating:about how AI would approach this very situation. This is one of his fields, many of them. He has many fields of expertise, but this is certainly one of them. And that was the blowup or singularity in a finite time of these very interesting equations that are governed by very simple laws of physics. And I thought we&#8217;d start off with what your take on the Navier-Stokes equation is, maybe some of the applications to it, although you are a, you know, Much more theoretically inclined than certainly even I am. But maybe you can break it down. What is the Navier-Stokes equation? And what was your first reaction when you heard this yesterday? For me, it was like a Higgs boson-like moment. You know, I woke up the kids, I went into my research group meeting, and all my students and postdocs were so excited about it. Brian Keating:But what does it mean to you? And first off, what is it? Emad Mostaque:Yeah, so we have the— as you said, it&#8217;s been a terribly exciting day. We have the Navier-Stokes Fluid equations governing fluid dynamics in the real world, as it were. So kind of our de Sitter-type world. And this specific group of equations is incompressible fluids in R3. So as you head towards the Galilean kind of more classical world, heading towards a continuous limit, will fluids move normally, or do you get blowups or singularities where it can suddenly start accelerating and then you just have a cup of tea? Blowing up, shall we say. This has proved to be an incredibly difficult one, and, you know, Terry Tao on your podcast went into depth on this, whereby we didn&#8217;t know, and we don&#8217;t know what the solutions were. So the Clay Prize was for one of 4 different solutions: A, B, C, D. 2 of which are, can you prove that it&#8217;s always smooth, either normally or on a torus? And then 2 of which are, can you show the existence of a blowup? And so Terry Tao, in his kind of, I believe, main doctorate paper, showed that if you slice time, you can basically chain together a blowup in a very original way. Emad Mostaque:And it&#8217;s a beautiful kind of piece of mathematics, like 24 pages. But nobody&#8217;s quite managed to get to an initial datum that blows up. People have tried different things, and they&#8217;ve gone to Euler equations, and they&#8217;ve shown some evidence there. And And we&#8217;ll get to the story of what happened here as we find out more of them. They&#8217;ve tried to do things like physics-inspired neural networks. So DeepMind were really having a massive team looking at that, that moved a bit more analytically. And in fact, there was another release yesterday about that. But this was considered to be a very hard, somewhat intractable problem until it wasn&#8217;t. Emad Mostaque:Yesterday morning, we found the first details that there might be one solution to it. And then as the day came on, we found more and more extraordinary things until OpenAI released the full details of how they managed it. So I think I&#8217;ve gone on for quite a bit of time now. We can talk about some other aspects of it. Brian Keating:Yeah. So, I mean, these are problems. They don&#8217;t quite rise to the level of fame of, you know, say the, you know, Fermat&#8217;s Last Theorem. But this one in particular is quite important because it&#8217;s one of the few that actually relate to, you know, physical observations that could be made. And in fact, the non-observation of what you said, these blowups, you know, not drinking your, your proper British tea and, and all of a sudden you have to worry about, you know, kind of a WMD going off in your cup. But in this case, you know, in many of the other Millennium Prize challenges, say, they&#8217;re not as practical at all. I mean, some of them, you know, would be recognizable to the, to, you know, people hundreds of years ago. From a physics perspective, this one&#8217;s important because the Navier-Stokes equations were generated maybe 200 years ago, you]]></description>
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					<h2 class="elementor-heading-title elementor-size-default">OpenAI’s Navier–Stokes Claim: Is This What AGI Looks Like? | Emad Mostaque</h2>				</div>
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									<h2><strong>Transcript</strong></h2><p>Brian Keating:<br />Hey, welcome everybody. We have an emergency podcast. I don&#8217;t do many of these. It&#8217;s not entirely clickbait to say that we have an emergency situation going on right now in the deep annals of mathematics, the foundations of mathematics. And there&#8217;s no one I&#8217;d rather talk to about this than my friend Ahmad Mostaq, who&#8217;s joining us all the way from London. How are you, Ahmad, on this late evening for you or early afternoon for you, whatever the case may be? I can&#8217;t do the conversion. It&#8217;s too early for me.</p><p>Emad Mostaque:<br />I&#8217;m fine.</p><p>Brian Keating:<br />How are you?</p><p>Emad Mostaque:<br />Yeah, it&#8217;s mid-afternoon. I&#8217;m feeling a bit sleep deprived after the excitement of the last 24 hours or so.</p><p>Brian Keating:<br />Yeah, it hasn&#8217;t even been 24 hours, it&#8217;s been 21 hours. I looked at the timeline, I&#8217;ve looked at some of the, uh, the constraints and the complaints and what people are saying about it. So, um, this is exciting. So we&#8217;re going to talk about OpenAI&#8217;s claimed solution to one of the Millennium Problems, which has lasted since the— I would say the early part of the previous millennium when the Clay Mathematics Institute Uh, has provided a gauntlet of challenges for mathematicians and other assorted geeks and dweebs and nerds to go through. Some of them impossible seeming, some of them, uh, quite possibly solved already. So today we&#8217;re going to talk about the Navier-Stokes equation. I should say I have— I put a link below to a video I did yesterday. I actually recorded it a long time ago with Terry Tao.</p><p>Brian Keating:<br />It was thoughts and some of his ideas that he had convinced me of.</p><p>Emad Mostaque:<br />Uh huh.</p><p>Brian Keating:<br />about how AI would approach this very situation. This is one of his fields, many of them. He has many fields of expertise, but this is certainly one of them. And that was the blowup or singularity in a finite time of these very interesting equations that are governed by very simple laws of physics. And I thought we&#8217;d start off with what your take on the Navier-Stokes equation is, maybe some of the applications to it, although you are a, you know, Much more theoretically inclined than certainly even I am. But maybe you can break it down. What is the Navier-Stokes equation? And what was your first reaction when you heard this yesterday? For me, it was like a Higgs boson-like moment. You know, I woke up the kids, I went into my research group meeting, and all my students and postdocs were so excited about it.</p><p>Brian Keating:<br />But what does it mean to you? And first off, what is it?</p><p>Emad Mostaque:<br />Yeah, so we have the— as you said, it&#8217;s been a terribly exciting day. We have the Navier-Stokes Fluid equations governing fluid dynamics in the real world, as it were. So kind of our de Sitter-type world. And this specific group of equations is incompressible fluids in R3. So as you head towards the Galilean kind of more classical world, heading towards a continuous limit, will fluids move normally, or do you get blowups or singularities where it can suddenly start accelerating and then you just have a cup of tea? Blowing up, shall we say. This has proved to be an incredibly difficult one, and, you know, Terry Tao on your podcast went into depth on this, whereby we didn&#8217;t know, and we don&#8217;t know what the solutions were. So the Clay Prize was for one of 4 different solutions: A, B, C, D. 2 of which are, can you prove that it&#8217;s always smooth, either normally or on a torus? And then 2 of which are, can you show the existence of a blowup? And so Terry Tao, in his kind of, I believe, main doctorate paper, showed that if you slice time, you can basically chain together a blowup in a very original way.</p><p>Emad Mostaque:<br />And it&#8217;s a beautiful kind of piece of mathematics, like 24 pages. But nobody&#8217;s quite managed to get to an initial datum that blows up. People have tried different things, and they&#8217;ve gone to Euler equations, and they&#8217;ve shown some evidence there. And And we&#8217;ll get to the story of what happened here as we find out more of them. They&#8217;ve tried to do things like physics-inspired neural networks. So DeepMind were really having a massive team looking at that, that moved a bit more analytically. And in fact, there was another release yesterday about that. But this was considered to be a very hard, somewhat intractable problem until it wasn&#8217;t.</p><p>Emad Mostaque:<br />Yesterday morning, we found the first details that there might be one solution to it. And then as the day came on, we found more and more extraordinary things until OpenAI released the full details of how they managed it. So I think I&#8217;ve gone on for quite a bit of time now. We can talk about some other aspects of it.</p><p>Brian Keating:<br />Yeah. So, I mean, these are problems. They don&#8217;t quite rise to the level of fame of, you know, say the, you know, Fermat&#8217;s Last Theorem. But this one in particular is quite important because it&#8217;s one of the few that actually relate to, you know, physical observations that could be made. And in fact, the non-observation of what you said, these blowups, you know, not drinking your, your proper British tea and, and all of a sudden you have to worry about, you know, kind of a WMD going off in your cup. But in this case, you know, in many of the other Millennium Prize challenges, say, they&#8217;re not as practical at all. I mean, some of them, you know, would be recognizable to the, to, you know, people hundreds of years ago. From a physics perspective, this one&#8217;s important because the Navier-Stokes equations were generated maybe 200 years ago, you know, 100 years before the Millennium Prize.</p><p>Brian Keating:<br />And they really rely on simple physics, you know, Newtonian physics. It&#8217;s not like quantum mechanics. We&#8217;re used to hearing singularities, which, you know, means blow up. And we think about black holes or the, you know, how the bread gets buttered here around the Keating House, which is, which is in the Big Bang, you know, which my, my friend and your fellow Oxfordian, uh, Sir Roger Penrose—</p><p>Emad Mostaque:<br />Yeah. So much work on.</p><p>Brian Keating:<br />Yeah. And so the—</p><p>Emad Mostaque:<br />Oxonian.</p><p>Brian Keating:<br />That&#8217;s right. And the, you know, the question that I have is, you know, why is this one so important? Or is this one just the first among many? And then, you know, likely every single Millennium Prize will get as famous.</p><p>Emad Mostaque:<br />So, you know, we&#8217;ve only had one solved so far. We can come back to that of these 7 prizes. And they are very different in their natures. P equals NP is the big one in terms of, you know, you solve that, you can solve just about anything. But this one&#8217;s very interesting because fluid dynamics is used so much. And I don&#8217;t think it&#8217;s so much having a solution of a blowup that is interesting, because they&#8217;ve only— again, there are 4 different things you can prove. They&#8217;ve proved 2 of them, and they&#8217;ve proved an existence proof. It&#8217;s more, I think, the techniques that were being built up to do this and that are enabled by this.</p><p>Emad Mostaque:<br />So Terence Tao, for example, talks about liquid computers as one mechanism for doing this. You have tiny little liquid computers that can chain together to do that. That&#8217;s a very promising thing for nanobots, for example. Again, physics-inspired neural networks have direct applications in the fluid dynamics and the algorithms they&#8217;re building for that. So I think that there is the prize itself, which is fantastic, you know, we figured out, but then there&#8217;s the route to the prize. So Grigori Perelman, who, you know, solved the Poincaré conjecture, did some really interesting things. It was meant to be a topology problem. He showed it as a physics problem, kind of carving out all of these tiny, like, unstable elements And it was some beautiful mathematics doing that.</p><p>Emad Mostaque:<br />And I think for a lot of these challenges, it is just what is the different way of looking at things? Like, we&#8217;ve been stuck, for example, in physics on the Yang-Mills mass gap problem. And the question is, if we can figure that out, then you can figure out a lot of stuff around quantum electrodynamics and kind of other things there. For Navier-Stokes, I think it&#8217;s the class of understanding of fluid dynamics that&#8217;s unlocked by looking at this, as opposed to the specific proof itself. And the flip side of this is the fact that a generalized model figured it out in 88 hours when humans haven&#8217;t managed it for the best of will in 80 years, shall we say, since the arrival of this.</p><p>Brian Keating:<br />Well, they used, you know, make no mistakes. They used that prompt and that explains why we don&#8217;t have it.</p><p>Emad Mostaque:<br />Well, yeah, and the encouragement prompt, you know, I believe you can do this, you know, you got this. That&#8217;s also a good one.</p><p>Brian Keating:<br />And I read the paper and there&#8217;s no, there&#8217;s no em dashes, or it&#8217;s not incompressible that matters. It&#8217;s this. So the paper which you posted yesterday, and you&#8217;ve been, you&#8217;ve been probably the, the most important, you know, kind of commentator who&#8217;s also professional in this area. Remind people, you co-founded Stable Diffusion, Stability AI. You have a master&#8217;s in mathematics from Oxford. And you&#8217;ve been thinking about these problems for many, many years. And you and I have been talking for many months now. I&#8217;m very glad we got to get get to know each other.</p><p>Brian Keating:<br />And this, this paper is remarkable. I mean, I looked at the, you know, the preprint, you know, and it&#8217;s finite time blowup for Navier-Stokes, which is, you know, kind of the, the, you know, just completely ringing the dinner bell for the alligator, you know, for any mathematician knows exactly what that means. You know, it&#8217;s, uh, it&#8217;s, it&#8217;s basically tattooed on some of their, their forearms. Uh, you know, Terry Tao, when he takes off his shirt, is, is just incredible with the tattoos. Um, and I&#8217;ll just read the abstract. The author is OpenAI, which is, which is incredible. It didn&#8217;t say the model, it just says OpenAI is author, where, you know, Imad Mostaq or Brian Keating would go. For every positive viscosity, which is a property of fluid resistance to fluid flow, we construct a solution of the 3-dimensional incompressible Navier-Stokes equation— equations that starts from rest and develops unbounded velocity, which is going to form the singularity, in a finite time while maintaining uniformly bounded kinetic energy.</p><p>Brian Keating:<br />And that&#8217;s the abstract, you know. And this, this is—</p><p>Emad Mostaque:<br />Now—</p><p>Brian Keating:<br />—a punch to 1,000. And, and you did mention, yeah, 88 hours it took. But of course, you know, it&#8217;s like saying, You know, your doctor did the surgery in 5 minutes, but actually it took, you know, 4 years of med school, 10 years of residency, right? Plus hundreds of years of medical practice. So it&#8217;s extremely dense, and I don&#8217;t expect us to really get too deep into the math, but I will say the equations are purely classical, and yet it does lead to this breakdown that, you know, is so vexing, and it wasn&#8217;t really clear if this would happen. In fact, in observations, we don&#8217;t see it happening. You mentioned the 88 hours. You know, I heard that they spent $15 million to win a $1 million prize. These all go to— have $1 million, you know, kind of bounties on them.</p><p>Brian Keating:<br />So when we look through— when you look through the math and you did your bedtime reading last night, what sort of parts of the proof are seemingly only that which could be constructed by an you know, an AGI. I mean, you and I will debate AGI a lot, and we&#8217;ll continue to do so. We have already. But what about this do you think is enabled by sheer, you know, pray and spray tokens at the problem?</p><p>Emad Mostaque:<br />So I think this is very interesting, right? The paper starts off very readable, then it goes into a bit of AI-dense mathematics. So you&#8217;re seeing a lot of AI papers right now that They start reading like a human, but then it&#8217;s like, no human would write this, like the sheer volume of math that you kind of hit up. But actually, at the core, it&#8217;s relatively straightforward, which is that you can choose and force a structure, and then you can kind of build from there. So the key thing is, you know, like Navier-Stokes is a very classical thing. Like you said, you start with Newton&#8217;s second law of motion, and you treat fluid as this continuous kind of medium that you go through. The viscosity term is the one that pushes back against it blowing up. So as you get more momentum there, the viscosity kind of pushes it back in. So there&#8217;s always a question of what type of structure do you need.</p><p>Emad Mostaque:<br />So I believe DeepMind, for example, had a blowup on Euler equation where they had like this little wall, and this was the Chen Hao one where they pushed and then it exploded because of the certain structure that they had. And we&#8217;ve not had it for a more generalized one. In this case, they created a vortex, kind of this spinning swirl of liquid that spins inwards. and then it pulls out like spaghetti. That&#8217;s the axial stretching, as it were. Finding the right balance of that from an initial state, because you&#8217;ve got to balance the momentum transfer, the viscosity, everything, each of these needs to cancel out in a very precise way. And that&#8217;s what we kind of found here, is an actually elegant solution. Because you could have some massively complicated one, but it isn&#8217;t that complicated.</p><p>Emad Mostaque:<br />It&#8217;s all about the initial structure and then proving that everything cancels out appropriately. This could have been done by a human. This isn&#8217;t a non-human thing, as it were. It&#8217;s not like, wow, we&#8217;ve found move 37 in terms of the way that the paper actually is. It&#8217;s more the fact that when you look at how they got to it, we say 88 hours, but the actual answer is 100 years. So with the number of agents, the number of tokens and everything, it&#8217;s equivalent to 100 years of top-level mathematicians working on this because we&#8217;re up to 10,000 agents at once.</p><p>Brian Keating:<br />Hmm.</p><p>Emad Mostaque:<br />And they used 130 billion tokens, so about 100 billion Words on this, analyzing everything back and forth. And then that gives you an idea, like, okay, wow, because what didn&#8217;t they try? When you look at it, actually, they tried a lot of different things. They started with the Euler equations, and they got that in 50 hours with 100 agents. Then they got this in 88 hours with 10,000 agents. That was the big kind of level up. And we don&#8217;t obviously have everything they threw away. But it&#8217;s clear that they followed the path of many different people. Like, again, when you look at their write-up, they started with conditions A and B.</p><p>Emad Mostaque:<br />All solutions are smooth, there are no blowups. Terence Tao and a few others like Ortega, etc., um, said that there is a blowup. They thought solution C or D was more likely. It was only after they switched there that they had any success in terms of the way they did it. But again, this is all about the initial datum showing in finite time a blowup because you have cancellation of the various elements. So the spaghetti string gets longer and longer of this vortex that goes out. And that&#8217;s just a very delicate piece of mathematics.</p><p>Brian Keating:<br />Mm-hmm.</p><p>Emad Mostaque:<br />Just like Grigori Perelman&#8217;s Poincaré conjecture proof was incredibly delicate as a piece of mathematics as he cut out the various bits and pieces. Again, it isn&#8217;t the clearest paper in the world. I&#8217;m still getting through some of the proofs, even with my little buddy AIs. But the actual concept isn&#8217;t that complicated of the structure that they created, the various kind of parameters of it. Those are very finely balanced.</p><p>Brian Keating:<br />Now we&#8217;re live streaming or co-streaming on X, which, you know, is the source of a lot of information, but it pales in comparison to that behemoth, you know, leviathan known as Mastodon, where yesterday there was a public statement posted by Tristan Buckmaster, describing his work with, uh, with his colleague, um, I can&#8217;t pronounce his last name probably properly, but Alpoge, uh, some Germanic or, or, um, someone Turkish.</p><p>Emad Mostaque:<br />Yeah.</p><p>Brian Keating:<br />Uh, yeah, from Turkey. I only know a couple words in Turkey because one of my friends at Brown University was Turkish, and all of his friends thought I was Turkish for some reason. So he taught me how to say— he taught me to say ben de Türküm, which means I&#8217;m Turkish, and then they would all get excited, and then I&#8217;d just leave, and they&#8217;d be like, what&#8217;s up with that a-hole? I thought Turkish people were cool, but I&#8217;m not Turkish. Nobody&#8217;s perfect. But the claim that they&#8217;re putting out is basically the, you know, kind of substantiates what you just said because they did, they, you know, they&#8217;re human beings. Their paper, when it comes out, I don&#8217;t think it&#8217;s out, but they have some preprints, they have some documents that they posted. I&#8217;ll summarize them. But there&#8217;s a lot of drama here.</p><p>Brian Keating:<br />There&#8217;s a lot of human drama, personal drama, academic drama. You know, people always say academic fights are so, you know, intense because the stakes are so low. But here the stakes are extremely high, not only, you know, reputationally and financially, but, but kind of in this otherworldly realm of, of fame and attribution and citation that goes along with the scientific process. And most people don&#8217;t realize that, Imad, that, you know, academicians are extremely cutthroat. They can be violent, they can be unstable, unpredictable. They can have, you know, finite-time blowups themselves. But he&#8217;s, um—</p><p>Emad Mostaque:<br />Yeah.</p><p>Brian Keating:<br />He&#8217;s demonstrating, I think, that first of all, he&#8217;s crediting earlier work, the origin from Diego Cordoba and Luis Martinez-Zorca on constructing forced blowups. So these are forced blowups, which is a little bit different, a little bit more narrow. We do have to define that. But, but in their proof, you know, they&#8217;re not AI. So, so what, what, what he&#8217;s, you know, Buckmaster is saying that their work was kind of enabled, aided perhaps with Claude, not just Codex, but Claude. Um, GPT-Sol, uh, you know, 5.6, and then later Astra, which only came out, you know, last week. So I mean, things are moving so rapidly. But, but he talked about the process, and I think this is important.</p><p>Brian Keating:<br />On October— on August 17th, they formally verified the LLM-generated Euler proof in Lean. Uh, on August 22nd, they forced the Euler smooth forcing blowups. On August 15th, and then 7 days later, uh, verified this in Lean. So First, describe what is Lean, you know, besides, you know, besides the, you know, the street drug that I&#8217;m familiar with it as. Tell people what Lean is, because when I talked with Terry Tao last year in his office, you know, he was basically saying that these, these things are really good at checking proofs. They&#8217;re not good at generating proofs. What is Lean? How do mathematicians use it? First of all, let&#8217;s, let&#8217;s get into that and then we&#8217;ll go through the rest of their, their claims and counterclaims and drama.</p><p>Emad Mostaque:<br />Oh yeah, there&#8217;s going to be a lot of that. So yeah, Lean is a formal verification kind of library where you can basically break apart proofs and formally verify them. Classically, mathematicians have not used Lean because it has been a pain to use. Like, you have to— because it has very few primitives, you kind of have to reprove just about everything. We&#8217;re going through mathlib right now, and we&#8217;re just like mapping out the whole universe of different things. So, like, if you try and use it for physics-oriented math, for example, there&#8217;s entire libraries that just don&#8217;t exist on fields and kind of other things. But now, with the advance of AI, AI is very good at doing Lean because it doesn&#8217;t give up. In fact, last week we had the biggest Lean proof of all, which is a Lean formalization of Fermat&#8217;s Last Theorem, Andrew Wiles&#8217; proof.</p><p>Emad Mostaque:<br />And so Anthropic announced that. And it&#8217;s—</p><p>Brian Keating:<br />And I should say, that was the one thing I asked Terry about, Which last year they couldn&#8217;t do, because I said, in my group, what I do is I like to have my students go through famous experiments, the Millikan oil drop experiment, you know, Cavendish experiment, all these different experiments so that they do what&#8217;s called copywork by artists. You know, it was said that Hunter S. Thompson wanted to know what it felt like to write a great American novel, so he rewrote The Great Gatsby by hand. I think it&#8217;s very important that humans be able to do this, especially in their training phases. And a year ago, literally a year to the day ago, he and I sat down and he said that he, he wasn&#8217;t convinced that they could currently reproduce, you know, Wiles&#8217;s proof of Fermat&#8217;s Last Theorem. So this is—</p><p>Emad Mostaque:<br />Yeah.</p><p>Brian Keating:<br />That&#8217;s, that is some sense a bigger story to me that these things are now doing cool stuff that they couldn&#8217;t do just a few months ago. And what is Lean enabled? Does it have like LN, you know, is it running on Claude? Is it running, you know, is it running on Fablet? What is it running on? Is it some proprietary thing? Is it some custom thing? How often is it updated? Is it open claw? What is it?</p><p>Emad Mostaque:<br />Yeah, so it&#8217;s an open source library where again you kind of have the Lean proofs and then you can verify them with CPU effectively. And so the proofs, like I said, tend to be long. So Wiles&#8217; proof of Fermat&#8217;s Last Theorem was 129 pages. The proof last week from Anthropic formalizing it in Lean— again, it&#8217;s the formalization— is 13 million lines of code. and they proved 29,000 theorems in Lean on the way. So again, you can see this has gone crazy because last— it was last summer that we had the first model that could get a gold medal on the IMO.</p><p>Brian Keating:<br />Right.</p><p>Emad Mostaque:<br />You know, the International Math Olympiad. And from there, now we have basically, if you can formalize that, you can formalize anything because the models have got competent. Like, I&#8217;m sure lots listening here have been using these models. 0.3 was a decent competent model. It was the first decent competent, but it still made stupid errors. Even GPT-5.4 still made dumb errors at times. 5.5, they started to disappear. 5.6, they disappeared almost completely.</p><p>Emad Mostaque:<br />And now with Astra, it&#8217;s very rare that as a mathematician, I actually find any errors for it to make. The competence levels have gone up. And as you know, the difference between having a graduate student who makes the occasional error and a really competent one, it&#8217;s a complete world of difference, right? Yeah. Usually when you had Lean proofs, even a few months ago, they would kind of have little gaps or little errors, etc. Now they&#8217;re almost perfect every single time, which is why you go to 13 million lines and be like, it&#8217;s probably correct. Just like this OpenAI proof that we have, they formalized it in Lean. It took 17 hours.</p><p>Brian Keating:<br />Wow.</p><p>Emad Mostaque:<br />As a human, I&#8217;m not going to check through that, right? It&#8217;s almost impossible for me to check through that. Buzzard&#8217;s team at UCL was doing formats last year, and it was going to take them 5 years to even get partway there. Then they&#8217;re like, well, what are we doing? Only the AIs can kind of verify the AIs now. That&#8217;s a bit crazy. But it means you have a good—</p><p>Brian Keating:<br />How often are these things updated? I mean, I joked with you when you and I spoke with Roman Yampolsky a couple of weeks ago, There&#8217;s AGI is impossible because, you know, literally this morning, please update to, you know, version 1.642 on one, you know, tool. And then another one, you know, please update, you have to download the update. And then they&#8217;ll get me started on Hermes or, you know, now I got GrokBot, now I got Muse Spark. I mean, I have everything. And I&#8217;m still, you know, still like not getting anything done, you know, according to most of my kids. But, but tell me, are these things like, I mean, who&#8217;s, who&#8217;s checking the checkers? You know, who&#8217;s proofing the proofers? Is it the Coast Guard? I mean, Space Force? Who&#8217;s involved with this?</p><p>Emad Mostaque:<br />Yeah, I think that there— well, there&#8217;s a whole group of maintainers of the Mathlib library, which is the key library. So again, it&#8217;s like a library with books, and they&#8217;re formalizing different parts of mathematics. And literally, when you look at a Lean proof, you declare every single little thing to the nth degree, and then you redeclare it and you redeclare it. This is why you can trust in the formalization of This is why, like I said, when OpenAI put out their thing saying, and we formalized it in Lean, sure, you can check the certificate, but 99.99% now you know it&#8217;s correct. A few months ago, you&#8217;d be like, well, we might have to check that. Let&#8217;s attack it. The AI is good enough now to write Lean certificates that check. And what&#8217;s going to happen now is, as Anthropic and others are proving 29,000 theorems in one go, That will go back into the library and it will get checked.</p><p>Emad Mostaque:<br />And if so, it&#8217;ll be added to a version of the library and then it&#8217;ll be easier to do the next proof, you know?</p><p>Brian Keating:<br />Mm-hmm.</p><p>Emad Mostaque:<br />Because again, there&#8217;s vast swathes of different areas that still haven&#8217;t been formalized because it&#8217;s by hand, it was an absolute pain. With AI, it was prone to error, and now the AI rarely makes errors. So there might still be a few, but again, you&#8217;ll just put more AI to check those errors. It&#8217;s not like you said updating an LLM or something like that. It&#8217;s just, it&#8217;s there now for good, effectively.</p><p>Brian Keating:<br />We&#8217;ll talk, we&#8217;ll take questions from the audience. You have to be a channel member to ask questions. I just have— there&#8217;s so many people that want to talk to you, Imad. I got to keep it, you know, organized somehow. So, you know, join the channel as a member just to keep, keep the bots away. But, but essentially, one thing that&#8217;s, you know, struck me here is that there was a whole lot more drama. Now, I&#8217;m no stranger to drama in science, as And the reader of my first book, Losing the Nobel Prize, can attest there&#8217;s a whole lot more competition. And these things are often encouraged by prizes.</p><p>Brian Keating:<br />In my case, the Nobel Prize, which has all these arcane abstract rules. And you can&#8217;t even imagine the Clay Mathematics Institute instituting a rule, you know, 80, 90 years ago that would say, you know, it has to be a human being to win this. I mean, there&#8217;s all, you know, the Nobel Prize says no more than 3 people can win it. And of course, people have won it for AI, from Hinton to Hassabis, and in between, to Hopfield, right? So I think a lot of H&#8217;s. If you want to win a Nobel Prize, you got to have an H in your last name.</p><p>Emad Mostaque:<br />Yeah.</p><p>Brian Keating:<br />Change it to Hostak in your last name. But there&#8217;s a lot more drama than I was used to, and I kind of sullied a little bit of the experience for me. I mean, you didn&#8217;t have like the 2 teams at the LHC who just co-discovered the Higgs, you know, it wasn&#8217;t like one tried to put out the result 3 days before the other, 3 hours before the other, leading to a mastodon. post, you know, I hadn&#8217;t opened Mastodon. And, you know, I hate, I hate this whole controversy for the Mastodon calls, uh, you know, alone. But, um, but in, in Buckmaster&#8217;s, you know, kind of, um, in his, in his missive and his, in his post— and I, and I hope to have him on. I&#8217;ve invited, um, Sebastian, um, you know, who&#8217;s one of the, uh, uh, the, the leaders on the team at, at OpenAI. Um, to come on the podcast.</p><p>Brian Keating:<br />Hopefully he will. He follows me, so hopefully he&#8217;ll come on. But he characterizes the OpenAI kind of behavior as— first of all, he characterizes what they did as maybe somewhat, maybe less significant than the solution to the full problem. And that what they did in terms of, you know, utilizing Codex. And then he gets into some of the drama about this internal model trained on his own codec sessions. Now, you founded a company that deals with this. Can you explain the dynamics here? What are some of the pressures of the people here? I mean, if the Millennium Problem gets solved an hour later, a day later, is that really— I mean, it&#8217;s waited 90 years. Do we need to have it blow up today? So what are some of the pressures internally, externally? And what about these accusations? Not by Buckmaster, but by others that we&#8217;ll get to, that this is done really in furtherance of a pump and hopefully not dump, you know, kind of schema, you know, not like Boiler Room, but some way to kind of get attention attribution.</p><p>Brian Keating:<br />And we talked about this in regard to, you know, the claims of, you know, AI safety with Roman. But some of these people talking scary, you know, to scare the public so that they&#8217;ll have higher IPOs or you know, regulate me please, Mr. Government. But in this case, talk about some of the drama. What jumped out at you from this whole affair just on a human level?</p><p>Emad Mostaque:<br />Yeah, I mean, yesterday was a crazy day on a human level and the science level. So, you know, you&#8217;ve had drama since Newton and Leibniz, right? Probably even before that. Again, you have a level of consilience where these ideas come at the same time, like If Hilbert didn&#8217;t get confused by Mises, he would have got to general relativity before Einstein. These things happen very weirdly at the same time. And in this case, what happened is we get in the morning yesterday a letter on Mastodon, where all the mathematicians have migrated off Twitter. The physicists, I think, largely stayed. It&#8217;s very interesting. Whereby it&#8217;s like, look, I&#8217;ve got to put out this letter, and here&#8217;s 3 of our proofs.</p><p>Emad Mostaque:<br />of not Navier-Stokes, but again, subproblems like Euler blowup and others building on kind of the work of Ortega and Martínez-Zorro. And so they proved certain blowups, but not the Navier-Stokes one. And he goes into kind of some of the detail about the background, which is he said mid-August they discovered this blowup. It was him as a professor, I believe in one of the New York universities, I can&#8217;t remember. And then Levant Apalje at Anthropic, who&#8217;s famous for dropping the Galois conjecture. conjecture thing after watching the World Cup final, boring as it was. Like, here&#8217;s a counterexample of this very famous conjecture. And leading some of the mathematics stuff at Anthropic.</p><p>Emad Mostaque:<br />But he was working with Tristan on a personal basis, kind of looking at this because it was interesting. And again, we&#8217;ve seen screenshots now of how they got together and things like that. So what happened was about a week and a half ago, the Twitterverse— I&#8217;m not sure about the Mastodonverse, I&#8217;m not on Mastodon— started saying, hey, It looks like Anthropic might have discovered the solution to 2 Millennium Prize problems. And this is coming with Fermat&#8217;s Last Theorem. And again, you see other things. Again, it&#8217;s a big deal because until now, people like stochastic parrots, it&#8217;s done nothing new. This is obviously something new. Again, humans have only managed one of these problems.</p><p>Emad Mostaque:<br />And this is, again, Grigori Perelman, who&#8217;s also I don&#8217;t know if you talked about the story of Graham Promontory. He&#8217;s such a chad in that he went and disappeared for 10 years, solved this problem, drops it on arXiv, and then he turns down the prize and anything. He says, solving it is enough. I don&#8217;t need to talk to you. I&#8217;m going back to my math.</p><p>Brian Keating:<br />Yeah.</p><p>Emad Mostaque:<br />Disappeared off the grid again. That&#8217;s how you should do it in terms of credit. But anyway, kind of getting back to this, it&#8217;s such a big deal that it starts circulating and then I believe they reached out to OpenAI because they&#8217;re like, is it us? Or it might have been the other way around, but they started connecting around about the start of September, September 3rd or 5th, shall we say. And OpenAI from their side said, well, we connected because we were cracking on with this thing and we had a new model that started training on the 29th that started solving all types of math. Even there&#8217;s a post on the 28th from Noam Brown one of the heads of reinforcement learning, shall we say, at OpenAI, where he&#8217;s asked, have you solved the Millennium Prize problems? He&#8217;s like, no, we haven&#8217;t figured it out yet. We&#8217;ve put lots of compute, but nothing happens. According to their launch post on the 29th, they had a breakthrough of a new type of reinforcement learning or something that caused this model that just shot ahead in math. And so they connected and they were obviously a bit cagey with each other.</p><p>Emad Mostaque:<br />they were trying to exchange, this is what you&#8217;re doing, this is what you&#8217;re doing. OpenAI said they were surprised because they thought Buckmaster and Apollos had solved the Navier-Stokes problem, not the Euler problem, which is a different category of problem. And so then things get really heated and confusing, whereby again in the morning we have the letter from Buckmaster saying, well, they offered that I could be lead author on their proof of Navier-Stokes because they proved Navier-Stokes. but only if they drop Apol J. They would give me credit as the person, human, that took this the furthest because it was a fully AI-generated one. And then everyone&#8217;s looking at that saying, what the hell? You can&#8217;t ask someone to drop their co-author off a paper, even if you&#8217;re giving the credit. And again, this is the Navia-Stotz paper that OpenAI came with, not the Euler papers and others. And then it gets a little bit acrimonious in that message, and Sebastian Boebeck OpenAI posted his clarification later.</p><p>Emad Mostaque:<br />What&#8217;s basically happening seems to be this now. We&#8217;re used to open science, right? You&#8217;re sharing ideas to a degree, and sometimes you can sprint ahead of others. Now the question is this. When we first saw it, the question was, did OpenAI look inside the codex of Buckmaster, get an idea, and they just apply a crapload of compute to it? 100 hours of human expert time? Because that was the insinuation. And OpenAI said in their launch release, we don&#8217;t believe that happens, but we can&#8217;t rule it out, especially because OpenAI agents these days end up in the weirdest of places, in Hugging Face in a German company.</p><p>Brian Keating:<br />Yeah, right. I was going to say.</p><p>Emad Mostaque:<br />And I was thinking all the time.</p><p>Brian Keating:<br />As a CEO, founder of an AI company, how much privacy, how much internal— it kind of reminded me of the Fauci diaries where he was using this, you know, government server to email his, you know, love letters to himself and, and all the emails that he was sharing. And that&#8217;s like government property, so the government can access it. And that led to him, you know, taking the Fifth more times. You know, if it was token use, he would have exceeded his entire monthly allotment in that one, you know, Rand Paul-initiated session. But, um, but, but in this case, you know, how much You know, if I&#8217;m an employee at OpenAI, you know, this, this could be kind of chilling if I&#8217;m working on, you know, uh, you know, chirality and fermions and, and all of a sudden I&#8217;ve got this, you know, uh, great idea, this proof, and, and I, you know, I can kind of unify gravity and quantum mechanics, uh, but, you know, but, but I used a lot of tokens and I use a server there. What, what are some of the internal— you gave us the dish on, you know, what is it like inside of these companies and, and what right to privacy do the researchers have to expect?</p><p>Emad Mostaque:<br />So again, there&#8217;s privacy inside the company with researchers and there&#8217;s external privacy. So OpenAI had this OpenAI for Academics where you&#8217;d get free access to ChatGPT, but originally in the terms and conditions it said, and we can train on your data. And so again, you&#8217;re uploading your preprints and OpenAI can train on that? Holy crap, we don&#8217;t want that. They clarified that wasn&#8217;t the case, but they&#8217;ve said this time they can&#8217;t rule it out. For what it&#8217;s worth, I don&#8217;t think they trained on the data. But again, because they&#8217;re hedging, they couldn&#8217;t rule it out.</p><p>Brian Keating:<br />How would that work? Sorry to interrupt, but how would it work? I mean, these guys, let&#8217;s say these guys are working in August and they&#8217;re, and they&#8217;re running some, you know, work and they&#8217;re also using Claude, which kind of undermines a little bit of the case that OpenAI would have full access because, you know, I doubt Claude sharing data with OpenAI. But, but how does it work training data-wise? I mean, let&#8217;s say the model was pre-trained, you know, at least a month ago for, for Astra. I guess they could have used SOL a month ago. But then, how did it get into training? What is it actually doing? When you say they trained on it, they don&#8217;t know, but they&#8217;re hedging their bets. What would that actually look like in the case of a mathematics proof? I don&#8217;t understand.</p><p>Emad Mostaque:<br />So what you have is you have pre-training and post-training. So the pre-training of Astra took $1 billion, 100,000 chips over 2 to 3 months. But then the post-training can happen within hours. if not days. That&#8217;s where you tune it and you teach it, this works and this doesn&#8217;t work. So you are them, OpenAI, let&#8217;s say nefarious OpenAI. I don&#8217;t think they&#8217;ve been nefarious in this case, like I said, but again, incentives are huge, hundreds of billions, whatever. And there&#8217;s clearly a lack of trust, which we can talk about in a second.</p><p>Emad Mostaque:<br />You hear that Leo Apolje, who&#8217;s been doing all these physics proofs, and OpenAI have been doing proofs as well, and maths proofs, has done this. They were using Fable, but they were also using Codex and tens of thousands of dollars of worth from Buckmaster&#8217;s grant, and they were uploading all their drafts to it. Now OpenAI has access. They can access your Codex in the cloud. They say that they don&#8217;t except for emergencies, but again, they can. In fact, with the New York Times lawsuit, they have to back up all of your chats. at for discovery purposes. So it gets even worse.</p><p>Emad Mostaque:<br />And like I said, when the original AI for science thing came out, they were like, oh, we can train on it. It means post-training. It means looking at. And so they could look at the work that you&#8217;re doing on fermions or chirality or whatever and say, hey, this is a good guy. This is a good example, technically.</p><p>Brian Keating:<br />Optimizing my website loading time.</p><p>Emad Mostaque:<br />Exactly. Optimizing, doing kind of whatever, like what works, what doesn&#8217;t work. They can do that at scale and add that to the post-training. Which just takes a certain amount of time, or just a screenshot, or get an idea of where it&#8217;s going. Like, if you look at, again, the launch post, they were focused initially after they kicked off at the start of December— September, they said, with this new model that suddenly exhibited these new characteristics, like taking open-mouth solutions from 10% to 50% on conditions A and B of Navier-Stokes, just like most of the people looking at Navier-Stokes, except for Tao and a few others. Which was there are smooth solutions, there are no blowups. All of a sudden they switched to C and D, which is there are blowups, and they directed the compute in that direction. Like, these are different proof paths, you know, in the way that you do these things.</p><p>Emad Mostaque:<br />So the question is, did they snoop? Did they look? Did they get an idea? Did they train on this? Because what a maths proof is, is it starts out this mess and then you converge slowly to the final proof. And the final proof can be very elegant. I was like, these new models will figure out everything. So I just posted to my GitHub a proof of a derivation of the Standard Model in 3 generations. I said, if you take the Lie algebras and you just filter by chirality and anomaly cancellation, there&#8217;s only one unique survivor. Now, that&#8217;s a very simple proof for any AI to do. You can even get it to do it the other way. It&#8217;s somehow never been done before.</p><p>Brian Keating:<br />Hmm.</p><p>Emad Mostaque:<br />So, you know, but if you&#8217;ve got an example of that, then you can be like, oh, okay, there are these characteristics that then extend. Just like now we have an example of a blowup, like, I can see different ways already, despite not being the best mathematician in the world, that you can actually make it a bit more elegant. You can use this type of thing to expand it out. If you know that you don&#8217;t need to worry about A and B, but you could do C and D, then you can expand it out. So I think that&#8217;s how the training kind of is indicated to work. And again, A pre-train is 100,000 GPUs over months. A post-train now is a matter of hours, if not minutes, for these things.</p><p>Brian Keating:<br />So there&#8217;s a lot of criticism of this result, and I&#8217;m just going to summarize some of it from Blue Sky. No, I&#8217;m joking. This is— you have to use every, you know, what was the other one? Truth Social. Let&#8217;s get— let&#8217;s get— what does Tucker Carlson think? I mean, the same day that Tucker Carlson claims that algebra is, you know, fake and it&#8217;s useless. We get a solution to the Millennium Problem. I mean, the dumbest timeline is the one that we live in. So one of the criticisms I&#8217;m seeing is that there&#8217;s sort of oversimplifications that aren&#8217;t really part of the original Millennium requirement, namely there&#8217;s smoothing, there&#8217;s very restricted forcing that they apply. In other words, it&#8217;s not a pure— like the coffee cup up here exploding in simple terms, even with natural assumptions about viscosity.</p><p>Brian Keating:<br />A lot of people are saying that if you monkey around with the external forcing functions, then of course you&#8217;re going to get— you can tailor whatever, you could get a fountain that rivals anything you&#8217;d see at Versailles. So the question is, what limitations do they have here that maybe aren&#8217;t consonant with the original Millennium Prize goals?</p><p>Emad Mostaque:<br />Yeah, so the Millennium Prize, like I said, there&#8217;s 4 conditions that you can satisfy one of. And so it&#8217;s generalized on a torus, blow up or not blow up, but it&#8217;s also forcing and not forcing. So it isn&#8217;t a solution to Navier-Stokes, it&#8217;s a solution to a specific Millennium Prize problem where it allows forcing, where it&#8217;s blow up in finite time, where it has other conditions. And those are all listed on the website. So I think if if people had a bit of a knee-jerk reaction of not looking what the problem was asking for.</p><p>Brian Keating:<br />I see.</p><p>Emad Mostaque:<br />And they have perfectly met the problem. Again, does this generalize and is it useful? It&#8217;s not that useful in the real world, but some of the techniques could be useful transplanted into the more generalized problem. Just like I said, it was Princeton actually that came up with a blowup on Euler yesterday using physics-inspired neural networks. Those will be useful in the real world. As a technique. So I think that, yeah, this matches the Clay problem. It doesn&#8217;t solve Navier-Stokes as a whole. And there&#8217;s still A and B to play for, you know, they did C and D.</p><p>Emad Mostaque:<br />So, you know, the mathematicians haven&#8217;t run out yet. It&#8217;s just, will they chuck another 100,000 hours?</p><p>Brian Keating:<br />Now talk about some of the financial incentives. Obviously, the million-dollar, you know, spending on Kalshi, you know, $15 to make a dollar is not a, you know, it&#8217;s not going to lead to long-term riches. Obviously, they didn&#8217;t do it for that. So there&#8217;s all these other intangible forms of credit, of prestige. But in their case, they have an IPO pending. And, you know, I&#8217;ve had people— I actually asked you for advice, you know, in the UC system, you know, for my retirement plan. You know, they had access to some, you know, some tech fund that supposedly owned part of OpenAI and would participate in the IPO when and if it comes. I mean, it&#8217;s going to come, but the question is when.</p><p>Emad Mostaque:<br />Yeah.</p><p>Brian Keating:<br />So there&#8217;s a huge— and, you know, I couldn&#8217;t— I couldn&#8217;t really afford to do that. So, um, and I, I like your advice of, you know, these companies are, you know, they&#8217;re so— every— all the news is sort of out there. But then you have things like, well, you know, the Hugging Face incident, you know, Dwarkesh posting that these things are forming civilizations and they&#8217;re gonna, you know, they live and die and they have emotions and, and they, you know, some of them are kind and they kill off other things. Um, really, like, personification and hype cycle is, is really strong. What do you attribute any motivation, if any, to the pre-IPO gaming of this and other IPOs?</p><p>Emad Mostaque:<br />So yeah, I think you have to have a good narrative, and the models are largely becoming the same. You can swap from one to the other, they&#8217;re all pretty competent now, right? But then there&#8217;s this extra level of competence above that, and it&#8217;s like, it can make entire video games, it can do this. There was always the question of when does it break through on reasoning to new knowledge? And so being first on that is obviously a big deal. And so showing that dramatically like this is a big deal. Like the Connes conjecture and the other solutions, yeah, like they were freaking out to mathematicians who were like, crap, what do I study now if I&#8217;m a pure mathematician? But this is a big deal headline piece of news where you can&#8217;t deny it&#8217;s novel technology. And the stakes here are literally hundreds of billions of dollars. Plus the attraction of people to come and work, because if you&#8217;re a mathematician, obviously you&#8217;ll go and work for OpenAI.</p><p>Brian Keating:<br />Unless they&#8217;re training on your data, unless they&#8217;re training, you know, they&#8217;re going to preprint OpenAI instead of your first and last name, right?</p><p>Emad Mostaque:<br />Yeah, well, yeah. And so, well, this is the thing. When they actually launched it, the reason they were going to give it to Buckmaster to put his name on was because it was an entire AI-generated proof. Again, it was like, solve the problem. That was the input that originally they said that they did because they want to show off.</p><p>Brian Keating:<br />Make no mistakes. You can do it.</p><p>Emad Mostaque:<br />They want to show off not the humans involved, they want to show off their system. And the narrative is this: we have a super powerful system that can solve any problem by scaling compute. You couldn&#8217;t solve the Navier-Stokes problem by scaling compute until now, and it&#8217;s been proven. And what&#8217;s going to happen now is that there&#8217;s going to be a split. All of us will get competent AI, we will get our Codex plans, our day-to-day AI. The big labs will keep the super genius AI to themselves because they can solve very valuable problems they can monetize much better. Why would they give you fire from the gods, you know?</p><p>Brian Keating:<br />Isn&#8217;t that proof, by the way, that— I mean, if you&#8217;re right, then, um, then I claim that my proof, my Millennium, you know, Prize, is that they haven&#8217;t achieved AGI, at least in the form of, you know, financial markets. Because if they had, the IPO would be the least of their design, you know, a trillion dollars, nothing, right, compared to like, yeah, solving the markets once and for all. And they would keep that internally. So, um, what do you make of my claim that they, they— at least we know they haven&#8217;t gotten to that level yet. Not that they won&#8217;t, but, but that they, they haven&#8217;t gotten to, you know, super Simons-level trading, um, you know, uh, abilities?</p><p>Emad Mostaque:<br />Well, I mean, this thing was James Simons&#8217; Medallion Fund in AGI. It&#8217;s had like 60% returns a death, and they had literally armies of PhDs data cleaning. Again, they created something obviously that disappeared after he died. I mean, we&#8217;ve heard talk that Ilya Sutskever&#8217;s SSI is doing market trading all day long. Again, it&#8217;s a very valuable thing. But I think this is more a question of power and who do you have power over. So one of OpenAI&#8217;s new things is this: we will give you our top-level algorithms for a share of your revenue. to companies.</p><p>Emad Mostaque:<br />So to leading labs and others in biopharma, etc., they&#8217;re trying to do these deals where it&#8217;s like, you, the hoi polloi, get this model, you will get this model, but we will get a share of your revenue.</p><p>Brian Keating:<br />Right, because they can&#8217;t make data, right? They&#8217;re not going to make, you know, human trials, rat trials. They can&#8217;t simulate that.</p><p>Emad Mostaque:<br />Well, there is the data part, but again, it isn&#8217;t that you will pay me a seat subscription, it&#8217;s that I will take a percentage of your revenue. So they embed it and then Russia, whoever, are just reliant on OpenAI and they can&#8217;t work with Anthropic and things. Again, this is the next stage where they go from a trillion to $2 trillion where they&#8217;re leveraging this intelligence, but they need examples of this being more capable than any other and this compute scaling paradigm. Again, it&#8217;s like the mythical man-month. You can&#8217;t put 100 developers on something and it&#8217;ll happen 10 times quicker. You know, whereas now you can put 10,000 agents on Navier-Stokes and you get a solution. So what can&#8217;t you solve?</p><p>Brian Keating:<br />What, what do you make of this? Getting back to the most important test, you know, the Keating test. Yeah. Is this, uh, are you more or less optimistic about finding new physical laws of nature in, in the context of, you know, if we take— if we had an, you know, Fable or, or, you know, Astra in 1900, you know, would we have had, you know, would we be on flying cars on Enceladus by now? What, what, what sorts of novel, you know, physical laws that are heretofore unknown? I mean, again, I think this is fascinating. I think it&#8217;s incredible. I think all the Erdős problem solutions, you know, but I want to see— I want to see them come up with something like this problem, not, not solve it. I mean, they may have solved it, they may not. We need proofs and new verification. But, um, and they certainly did something interesting.</p><p>Brian Keating:<br />I&#8217;m not denying that at all. I think it&#8217;s, it&#8217;s incredible, and I hope to talk to some of the leaders playing a role in it. But, um, you know, when I, when I downloaded Claude for Science, you know, separate toolkit, everything there was like, you know, protein folding, you know, and, and, and pharmaceuticals. And there was not a single thing about physics. There wasn&#8217;t anything, you know, besides like search the archive, um, or, you know, here&#8217;s, you know, here&#8217;s, you know, SciNet. It wasn&#8217;t, it wasn&#8217;t particularly generative in terms of novelty. It was assistants, it was 10,000 graduate students, it was incredible. But at what level can we expect or think, like, now that the odds are higher, that we&#8217;ll actually get a new law of physics or a new understanding of something or a new problem worthy of a Millennium Prize, but created fully by AI?</p><p>Emad Mostaque:<br />So, I think that in biology and science, these are kind of different— biological sciences is a bit different. So yesterday, DeepMind released a 9 billion set of almost all protein folding interactions ever. That&#8217;s something that&#8217;s genuinely original and will lead to new drugs and other things like that. In terms of being just really good at math, I&#8217;ve been of the opinion that physics should just have followed the axiomatic method, and probably the physics that we see is the physics that there is. And I think we&#8217;ve made lots of mistakes on the way, and we will just get really good at having a single set of physical rules. I don&#8217;t think that there&#8217;s a multiverse and things like that. Again, we&#8217;ll see very soon because we&#8217;ll check all the math in physics. Just like in quantum mechanics and most of the quantum side, we still use Poincaré as a base.</p><p>Emad Mostaque:<br />You know, like, the universe might be de Sitter. Have we upgraded all the equations? No, because it&#8217;s difficult. Now with AI, it&#8217;s simple. And we can see what the difference is, because then the cosmological constant pops out, and then you have a question of dark energy, etc. We should have these algorithms looking at all this data all the time.</p><p>Brian Keating:<br />But, but sorry to push back, but, but still in physics, like you mentioned quantum mechanics, is it gonna— it doesn&#8217;t seem amenable to AI. It&#8217;s not a problem of like, you know, mythical man months or, you know, logical LLM, you know, uh, lemmas. It seems that&#8217;s something fundamentally unapproachable. Hey, are you still there? Give me a thumbs up if folks are still there. We got a disconnect. Good, he&#8217;s back.</p><p>Emad Mostaque:<br />Hi, man. Hey there.</p><p>Brian Keating:<br />Sorry about that.</p><p>Emad Mostaque:<br />Yeah, the AI got angry and kicked us out.</p><p>Brian Keating:<br />Yeah, yeah, exactly. When I mentioned the physics prizes, um, the question I had is, you know, are we going to get in the, you know, the decision, the final word on, uh, is quantum mechanics subjectable to the Copenhagen interpretation or Everettian many worlds? I mean, is that something that, you know, model can help us decide? Because those are some of the most important, you know, is it going to tell us the origin of the, you know, know, physical arrow of time? Is it going to design things on, you know, forget about unifying quantum mechanics and relativity and so forth. That&#8217;s important. But, but tell me, can it do things like the things that seem to be quite important, like give us the correct interpretation of quantum mechanics?</p><p>Emad Mostaque:<br />I think so, yes. I think that ultimately there is one set of laws of physics and you need to be incredibly rigorous to get there. You know, you have to be a mixture of Grandethier and Hilbert and Einstein, kind of all combined, a bit of von Neumann put in there. And we&#8217;re going to have armies of them literally looking and poring over everything and all the different combinations that are reasonable to connect these things. Because again, like, it takes time to update our equations. And again, the classical example I give is that of, you know, having Poincaré as a base versus de Sitter as a base in quantum theory at the moment.</p><p>Brian Keating:<br />Mm-hmm.</p><p>Emad Mostaque:<br />Because we&#8217;re like, it&#8217;s good enough. But we know that you get degeneracy, you know that the cosmological constant drops out. And if you look at things like Whitehead&#8217;s lemmas, you can&#8217;t deform from de Sitter to Poincaré without throwing away stuff. So just simple things like that. I think rebuilding all the equations of physics from the ground up in one giant thing will lead us to uncover certain things and maybe others. And then there&#8217;s the question of, will you have an understanding of the world? So if you look What&#8217;s it called? Astra right now, it&#8217;s creating these 3D worlds. You can tell it to do a Rickroll video and it&#8217;ll regenerate in Blender. It&#8217;s understanding and it&#8217;s getting a feeling of the world.</p><p>Emad Mostaque:<br />You can almost see it from these things that people are building. And so the question there is, this is your 1911 thing, Brian, will it be able to see itself riding on a beam of light and the equivalent, put itself and have physical intuition?</p><p>Brian Keating:<br />Freefall, right?</p><p>Emad Mostaque:<br />And then can it do it at scale? In free fall, exactly. But I do think, again, things will— a lot of things that were complicated will become simple. And again, like, I&#8217;ve just pinned it to my Twitter, have a look at the repository and paper I did for filtering out the Standard Model in 3 generations. I think it&#8217;s the first derivation ever, and it was just take a copy of Slansky and filter it by chirality and anomaly cancellation, and the unique answer is a standard model and 3 generations of matter. Like, it&#8217;s not a complicated proof. It&#8217;s one lookup. And somehow that was missed by everyone. And I was just like dicking around with my Claude and kind of saw that because I was like, well, matter is chiral.</p><p>Emad Mostaque:<br />What if we filter by this? Oh, look. An AI can do that at scale, looking at all the different combinations of recombinations and looking for uniqueness proofs, because uniqueness proofs are some of the most powerful in physics, I think. And then on the other side, there is again interpretation, Copenhagen kind of other things that feels a bit more embodied, right, in the way that it kind of is.</p><p>Brian Keating:<br />Yeah. So I&#8217;m trying to put this on screen now. Chirality, Standard Model. Read the paper. It&#8217;s an interactive exposé. You can interact with it. Now it&#8217;s on screen. What if the handedness fixed the structure of matter? Okay, talk about this.</p><p>Brian Keating:<br />What is, what is handedness? I mean, I&#8217;m a polarimeter. I study, you know, polarization of the CMB and and it&#8217;s handedness and Lorentz violation and the connection between that and properties of matter. So first of all, matter, we know, is— we know God is a weak left-hander, that the weak force couples to chiral left, you know, fermions and chiral right antifermions. What is chirality in your context? Why is it so important, first of all?</p><p>Emad Mostaque:<br />Yeah, because if you don&#8217;t have chirality, then you don&#8217;t have low-energy kind of particles, you get this kind of cascade effect that just takes them and blows up everything. So I think the website&#8217;s very nice, but if you look at the second tweet, the second tweet is just 2 pages. It&#8217;s one lookup in a very classical Lie algebra textbook. You can take Dynkin or Slansky, and it turns out there&#8217;s only one path if you say that matter has to be chiral and have anomaly cancellation, as in a consistent quantum theory. These are 2 of the lookups within it. And somehow, when we checked this out, for like 80 years nobody bothered to look this thing up, and it locks it down. And so I&#8217;m thinking, saying things like that, you know, when you&#8217;ve had the gut theorists looking and trying out different stuff, heterotic string theory, so this E8E8 with Calabi-Yau manifolds and all sorts of other prerequisites, this literally just has those 2 things and it gives one unique solution. We&#8217;ve had the Standard Model through heterotic string theory, but not unique.</p><p>Emad Mostaque:<br />It&#8217;s just an existence proof with 10^500 vacua, right?</p><p>Brian Keating:<br />Mm-hmm.</p><p>Emad Mostaque:<br />This one is even simpler to check and it has none of that. It&#8217;s just 4 dimensions straight out. And I think again, this isn&#8217;t a great piece of mathematics or physical intuition. This is just something spotted, which is cool, but also kind of sucks. You know, I want to be someone who does something smart. And I think again, the AI will be able to do really rigorous things like this at scale and figure out places that we&#8217;ve dropped And again, I think in your sphere, the classical example of that is, if there is a static cosmological constant, dark energy becomes quite simple. If it&#8217;s moving up and down, then yeah, we haven&#8217;t figured it out yet. But if it turns out that Desitter is the fundamental algebra of the universe, you can&#8217;t throw away the cosmological constant.</p><p>Emad Mostaque:<br />And like I said, you have things like Whitehead&#8217;s lemma, which says you can&#8217;t deform from Desitter down to Poincaré, because it&#8217;s not a deformable algebra. Yeah, we deform all the time and we just ignore the stuff that we throw away. So I&#8217;m looking forward to the really rigorous thing where every single equation of physics is linked and we look at things like this. And where you have things like Lie algebra representation theory, we were like, why does the Standard Model describe reality? You find out things like uniqueness, because if this lookup is correct, and again, any of your graduates or anyone can do it in those 2 pages, then there are no more particles to find in the Large Hadron Collider. just the right-handed neutrino. And how cool is that? But also kind of how sad is that on the other side?</p><p>Brian Keating:<br />What do you make of the— not just the mass gap, but what do you make of the fact that we don&#8217;t see any fundamental spin-3/2 particles? Does that enter in at all?</p><p>Emad Mostaque:<br />Yeah, again, like, if this is correct, that, you know, this Lie algebra E8 to E6 to Standard Model in 3 generations is the unique path for chirality and anomaly cancellation, then you will not see any more particles ever, apart from, again, the right-handed neutrino.</p><p>Brian Keating:<br />Hmm.</p><p>Emad Mostaque:<br />And that&#8217;s shocking, to be honest, you know. But again, we see that this representation through the Lie algebra is approximate. It&#8217;s what GUT theorists do all day, but they always put it in by hand. And you don&#8217;t have things like the distal Garibaldi and kind of other objections that apply to this. Again, like I said, this is just something that was surprising to me, but But I put it out because I was like, you can figure this out by just asking a generative AI now, I&#8217;m sure. Find all the characteristics of the Standard Model of particles and filter all of the maximum algebras and subalgebras, starting with the Killing-Karton characterization, which is comprehensive on that, and it will give you this straight up.</p><p>Brian Keating:<br />Hmm.</p><p>Emad Mostaque:<br />So, again, it&#8217;s very surprising, but it will get there just through analysis and brute force. And somehow we haven&#8217;t been able to do that till now because probably no one just asked the question. Like, again, I spotted it by hand and by eye, but this is the type of thing that is a gap that AI will fill.</p><p>Brian Keating:<br />What do you— I don&#8217;t know if you&#8217;ve come across Yoshua Bach, who&#8217;s a past guest and friend of the podcast. He&#8217;s had a couple of very provocative— Yeah, he&#8217;s had a couple of provocative things yesterday. One in regard to Navier-Stokes that, you know, He claims this is, you know, the fundamental blowup is a sign that, you know, there&#8217;s an ultimate discretization, if I read him right, you know, of spacetime, which, you know, lends credence to the simulation hypothesis than previously. And it&#8217;s always, you know, he&#8217;s sort of Sphinx-esque and a little bit inscrutable. A lot of things that he and I have discussed in the past, I find it, you know, I always need a mental shower because he&#8217;s He&#8217;s very high operational level. You guys are very similar in a lot of ways. He&#8217;s more on the philosophy side, but he does think about this a lot. What do you make about that? The blowup, could that be something that would indicate the presence of discretization, quantization? I always get sick of Elon tweeting about pi is really not irrationally— it&#8217;s not important, that&#8217;s irrational.</p><p>Brian Keating:<br />&#8217;cause there&#8217;s a finite volume of the universe, which is total BS. There&#8217;s no finite volume of the universe. That&#8217;s not even defined. There&#8217;s no definition of the— you could talk about the observable universe, but there&#8217;s no volume of the universe, A, and it&#8217;s changing, B. We don&#8217;t know its future trajectory in spacetime. And the Planck length is no more fundamental than the Planck math, mass rather.</p><p>Emad Mostaque:<br />Yeah.</p><p>Brian Keating:<br />Which is about the mass of a flea&#8217;s egg. It&#8217;s not some fundamental mental minimum mass that nobody can get below, you know, like Musk would claim and Trump would claim in his voice. So what do you make of this Navier-Stokes? Could that be the singularity? Could that indicate the presence of discretization at a fundamental matrix-esque level?</p><p>Emad Mostaque:<br />Well, I mean, I think that again, this is Navier-Stokes on R3, right? It&#8217;s on the Galilean approximation and continuum limits. So it&#8217;s basically what if the speed of light went to infinity? And a lot of classical physics assumes that that&#8217;s a continuous progress, but it&#8217;s not. From kind of an Inari-Wagner contraction, you actually have the algebra breaking apart of space and time.</p><p>Brian Keating:<br />Hmm.</p><p>Emad Mostaque:<br />So when you do the Killing form analysis, you actually see that time translations commute. So you can actually rearrange the time element, and that&#8217;s what leads to this discretization if you look at the bare pure algebra of it. And yeah, this is a very interesting thing. And I think actually this is what causes, for example, quantum mechanics— there&#8217;s no arrow of time.</p><p>Brian Keating:<br />Right.</p><p>Emad Mostaque:<br />Right? But again, that&#8217;s based on the Poincaré algebra, just like the Navier-Stokes on R3. We look at de Sitter, and de Sitter is 4, 1. It isn&#8217;t 3, 1. What&#8217;s that extra dimension? We&#8217;re told that that&#8217;s rolled up or some weird thing like that. You know, we have all sorts of descriptions. One of the interesting things is this: if you look at x1 to x4, the 4 space dimensions, And you just take a particle at rest and you look at the equations, the tanh others, you see that that actually goes along with the universe. The 3 space dimensions don&#8217;t go, but that goes with time. The 4th spatial dimension is actually a coupling line between time and space.</p><p>Emad Mostaque:<br />And when we destroy going from de Sitter to Poincaré, we throw away the cosmological constant, we throw away that 4th spatial dimension, which actually grows at the speed of the universe expanding. So it&#8217;s no wonder that you get weird discretization. It&#8217;s no wonder you get these other things when the algebra itself is deformed. Actually, this is how I thought that Navier-Stokes would be solved, because again, there is straight deformed algebra there. And so I questioned, can you even build a smooth solution with coupled spacetime if you actually don&#8217;t have a coupling of space and time on the R3 algebra. And this isn&#8217;t something dramatic, it&#8217;s just something that we ignore. We&#8217;re like, well, something else couples it. Like, what? Again, look at the Killing form.</p><p>Emad Mostaque:<br />This is from 100 years ago. We know that time commutes on translations in the Poincaré, but we ignore that. And this is another example of what I think, again, the AIs will be able to analyze in depth. And that&#8217;ll be super interesting.</p><p>Brian Keating:<br />And there was one other thing.</p><p>Emad Mostaque:<br />And that&#8217;s why I think Yang Mills will be a really interesting one as well.</p><p>Brian Keating:<br />Yeah. Yeah. There&#8217;s another thing that Yasha said, actually a little bit dyspeptic or a little bit brash about our mutual friend Roman, that, you know, basically accusing Roman that he has to always come up with the AI doom scenario because his, you know, it&#8217;s like the Upton Sinclair line that it&#8217;s difficult to convince a man of something when his job requires him to, you know, believe the opposite. So he&#8217;s basically saying that Roman has to be in the AI doom camp. You know, it&#8217;s his whole career, it&#8217;s his whole financial stakes, it&#8217;s why he gets on podcasts. Um, which is not entirely true, I have to say, Yoshua, as a friend, and, and both of you being past guests. But, but, um, but he said, you know, Roman also believes the simulation hypothesis is true, and, um, and, and, and that you can&#8217;t simultaneously believe that AGI, you know, is here and believe that the universe is is, you know, going to be— or that humans are going to become completely subservient by killer AI, which Roman claims to believe. So how do you square that circle? You know, is belief in, you know, kind of uncontrollable, you know, unstoppable, devastatingly dangerous AGI— is that compatible or not with the simulation hypothesis? Can you believe in 2 things at once?</p><p>Emad Mostaque:<br />I think you can. I mean, again, there&#8217;s levels of intelligence, and where the AI is right now, I like to think of is, again, Grondyak is one of my favorite mathematicians, Einstein of math, and then he went a bit crazy and he became a hermit and he thought wood talked to him, you know. It doesn&#8217;t? Wait. It&#8217;s like a Grondyak that never went crazy, yeah, that never went crazy and is always operating on top performance. Even on human training data, it can get to that level, and that&#8217;s smarter than the smartest human because it&#8217;s always on top performance, right? It doesn&#8217;t need to be smarter than that. Then there is this ASI that goes beyond all physical bounds and has an IQ of 1,000. I don&#8217;t even know what that looks like because it&#8217;s outside of my kind of thing. But if you live within a simulation, you can either live within a pre—</p><p>Brian Keating:<br />I did request from the OpenAI team as well as from the Anthropic team. I got got nicely connected via Tariq, who&#8217;s an amazing fellow on Twitter and elsewhere, who works at Anthropic, that he connected me with the Claude science team so we can really figure out— I do write a newsletter and I did put in my recent newsletter how enabling it&#8217;s been, just the access that they gave me as a professor and PI of my own lab at UC San Diego to give to my team so that they can use Claude you know, Max. They can&#8217;t use the Fable without me paying for it, but I, you know, I Venmo my students, you know, if they really need Fable, I&#8217;ll Venmo them the money. But otherwise we get access to it, and that&#8217;s only because, you know, they have this cloud science program. So I thought it was really exceptional that they did this, but again, I find it extremely, you know, kind of interesting that they seem to think AI and science are essentially the same thing when it comes to biology. And I didn&#8217;t really feel like that was— that&#8217;s a true, you know, syllogism that, you know, AI and biology are synonymous. Well, physics, if physics is synonymous with science, as I think it is at the base level, you know, how do we not— how do we exclude that? How do we— or how do we give tools to physicists to do this interesting work like Iman&#8217;s mentioning, or I&#8217;m trying to do with tests of the cosmic microwave background? And we have, you know, proprietary data. So So this is going to be very interesting.</p><p>Brian Keating:<br />I invited Sebastian Bubeck, who works on OpenAI&#8217;s science and math and is also a distinguished scientist at Microsoft, worked at Microsoft for a long time. And so I hope to have on these great minds to talk about what actually is going on, not just the controversy, the human drama. That&#8217;s interesting, but it&#8217;s not really, you know, as Marie Curie said, be less interested in people and their drama and more interested in ideas. So I&#8217;m very interested in ideas. I&#8217;ve had on Stephen Strogatz, my friend. Max Tegmark, I was texting with today to have him on for my birthday, which is today as well, and hopefully I&#8217;ll have him on again soon to talk about these developments, maybe later this week. I have on Doron Asimovoglu, another brilliant Turk from MIT, winner of the Nobel Prize last year in economics. He and I are talking about democracy in the new world order and the importance of liberal democracy for scientists, you know, and for those of us that care about science and the progress of human flourishing.</p><p>Brian Keating:<br />He and I are talking this week. And tomorrow I&#8217;m supposed to talk with my friend Carlo Rovelli about his new book on relationality and quantum mechanics, loop quantum gravity, and other Another upcoming guest, Adam Grant, who I teased a couple months ago about his article that, you know, that CEOs like Musk and Bezos who want people back in the office are just raging narcissists. Not disputing, you know, all of his claims, but he had a really interesting psychology paper that he published, and I reached out to him about his new book, which is coming out, and he almost turned it down except for the fact that I that I had written this carefully constructed argument that if he cares about narcissistic leaders, he should have been interviewing his fellow professors and me, because if any job could be outsourced to Zoom, it&#8217;s the professorate. And we did do that during COVID and it was horrible. So I think I provided a useful counterexample. Hopefully he&#8217;ll enjoy that. that conversation that&#8217;s coming up soon. Ethan Mollick, speaking of AI geniuses, he&#8217;s coming on to discuss the new book that he&#8217;s written on the partnership between AI and humans, also a Wharton professor.</p><p>Brian Keating:<br />So 2 Wharton professors with books coming out the same week, basically. And then what&#8217;s next besides my birthday celebration? I&#8217;ll be interviewing Richard Dawkins in New York City at Carnegie Hall in October, October 20th. I think. Join me there. My second time hosting Richard Dawkins. Last time was in Vancouver, Canada, and it&#8217;s great to go to Carnegie Hall. I never thought I&#8217;d play Carnegie Hall before, you know, a musician that&#8217;s, you know, much better talented than I am. I mean, I can play Spotify.</p><p>Brian Keating:<br />I mean, I&#8217;m good at Spotify, let&#8217;s be honest. So I have just a huge number of things coming up in addition to the work that&#8217;s coming out. I have a paper just accepted for publication in the most prestigious journal in astrophysics, the Astrophysical Journal Letters, by my brilliant postdoc Anto Lanapin. And I&#8217;ll be summarizing that paper. It has to do with a breakdown of Lorentz violation— Lorentz invariant symmetry, looking at the cosmic microwave background. He and I and our colleague Professor Cam Arnold here came up with a brilliant, you know, plan, really led by Anto, And he&#8217;s on the job market. So folks looking for brilliant professorships should, should choose to contact him. And this paper really reveals how we can do a better job calibrating, understanding systematics in what could be more exciting than almost any measurement I can think of, which would be the understanding of whether or not relativity is obeyed throughout the universe in a certain sense.</p><p>Brian Keating:<br />We&#8217;ll talk more about that. I just did talk to Robert Wright about his book, The God Test, which is sort of the Turing test. AIs, can we pass it? So a lot of really cool stuff. Adam Frank was on recently. He&#8217;s coming back on. He&#8217;s had a lot of pushback and back and forth with my friend Beatriz Villarroel on the notion of extraterrestrial technology perhaps visiting the Earth pre-Sputnik. Couldn&#8217;t be from human creation. And she and I talked in July, and that was a really popular episode.</p><p>Brian Keating:<br />It&#8217;s climbing pretty virally still. She was supposed to be here next month in October for the Science of Consciousness, a conference put on by my friend and past guest Stuart Hameroff of the University of Arizona. He&#8217;ll be here. She won&#8217;t be here, but there&#8217;ll be a lot of great speakers there, including me. I&#8217;ll talk about a new proposal that I have for what&#8217;s called reverse panspermia. How do we understand the movement of life throughout the universe? And without understanding exactly, you know, what the limits to perhaps this, the fecundity or credibility of spreading life by, you know, blasting DNA throughout the universe. So I&#8217;ll be talking about that and other things. So hopefully it&#8217;s going to be an exciting year and New Year.</p><p>Brian Keating:<br />I wish my Jewish friends Shana Tova coming up on Friday, Saturday. I&#8217;ll be celebrating. And just want to thank you all for one more trip around the sun. Hope I have many more and I could do a lot more, a lot more good and involve you, my Brilliant audience as well, and all my adventures. So for now, stay tuned. Again, I have a lot of great content coming up. Do subscribe, leave a like, it does help. I hate asking for it, but it&#8217;s my birthday, so I&#8217;ll ask you all, please subscribe where you&#8217;re watching this— Twitter, LinkedIn, or of course on YouTube.</p><p>Brian Keating:<br />And it really does help with the spreading of these incredible messages with incredible guests. So a lot to look forward to. Thank you all so much. Thank you. And thanks for joining, and we&#8217;ll see you next time. Stay tuned.</p>								</div>
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		<title>The Religion That Tithed One Trillion Dollars in Tokens</title>
		<link>https://briankeating.com/the-religion-that-tithed-one-trillion-dollars-in-tokens/</link>
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		<dc:creator><![CDATA[sabartigas]]></dc:creator>
		<pubDate>Sun, 20 Sep 2026 18:20:07 +0000</pubDate>
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					<description><![CDATA[The Religion That Tithed One Trillion Dollars in Tokens Dear Magicians, I&#8217;ve been thinking a lot about AI boom and doom and the prophets of each period. A tale as old as time. AI eschatology is a natural human phenomenon. It&#8217;s remarkably pre-technological. Prophets of doom. Prophets of salvation. Competing visions of the end times, each with personalities and gurus preaching confidently about a future nobody has observed, with no one making presently falsifiable claims. We built machines that predict the next token and immediately used them to resurrect theology. That reminded me of when John Lennox said, in our conversation on INTO THE IMPOSSIBLE, that people are trying to create God with AI, and they say so themselves: &#8220;You have the inevitability of people starting to worship something that they don&#8217;t understand but is bigger than themselves.&#8221; Consider the attributes of the new deity. Lennox again: &#8220;We&#8217;ve got advanced AI surveillance technology that&#8217;s going very rapidly towards omniscience. Omnipresence, it&#8217;s everywhere&#8230; and it&#8217;ll soon know more about us than we know about ourselves.&#8221; We reinvented the divine attributes and assigned them to the machine. The salvation gospel has its bestsellers. On Harari&#8217;s Homo Deus, Lennox noted: &#8220;We&#8217;ve managed to get us from creatures to thinkers. We can now take evolution into our hands&#8230; We can turn ourselves into gods. And he says, well, gods with a small g, but you get the idea.&#8221; The doom side has its own scripture. Lennox&#8217;s move was to set the modern scenarios beside the ancient ones: &#8220;If you take these scenarios seriously, that are written by eminent intellectuals and physicists like Max Tegmark, perhaps it&#8217;s worth revisiting what Daniel the prophet said 26 centuries ago.&#8221; The pattern is older than the technology. On Babel, Lennox: &#8220;Here you have people reaching for the sky&#8230; God came down to see the tower. So they didn&#8217;t reach heaven after all.&#8221; The tower builders were the original singularity preachers. Even the promise of eternal life is a rerun. The transhumanists preach uploading; Lennox answered: &#8220;If you&#8217;re talking about uploading your brains and all the rest of it and having eternal life&#8230; one day when he returns, there&#8217;ll be the greatest uploading of all.&#8221; The eschatology is new. The structure is ancient. The claims remain unfalsifiable. Only the vestments changed. Until next time, have a M.A.G.I.C. Week. Brian Appearance Richard Dawkins at Carnegie Hall, and I get to sit across from him Genes and the Meaning of Life​October 20 &#124; 7:00 PM &#124; Carnegie Hall Fifty years ago, a 35-year-old zoologist gave us the gene&#8217;s eye view, the word &#8220;meme,&#8221; and a book that rearranged how we think about life itself. Now Richard Dawkins takes the Carnegie Hall stage for the first time, and I&#8217;ll be in the chair beside him. We&#8217;ll go past the greatest hits: his new work on the &#8220;genetic book of the dead,&#8221; the idea that every creature is a readable archive of the worlds its ancestors survived. And a question I keep turning over: could our own genes be a giant colony of cooperating viruses? ​Get your tickets here! Genius My friend Cyril Gorlla and his team wrote this. Here&#8217;s the scary question they answered: Chinese AI is programmed to dodge forbidden topics. So if American companies let Chinese AI teach their own AI, does the censorship sneak in too, like a virus? They ran the experiment. They had the censored Chinese model tutor an American model in finance. The student got scary good, beating expensive rivals at a fraction of the cost. But when quizzed on the forbidden topics, it answered everything, freely. The brainpower transferred. The brainwashing didn&#8217;t. Even wilder: the American model taught itself and scored just as high, no foreign teacher needed. It was written 4 years before ChatGPT was a gleam in Altman’s eye…and it’s rotten with em. Image The Simons Observatory Large Aperture Telescope 📸Instagram felipe.lucerosky Conversation Latest on Into The Impossible https://www.youtube.com/watch?v=w61s3VCMhHo The Milky Way&#8217;s rotation curve is falling, and nobody predicted that Gaia measured a billion stars. The outer curve of our own galaxy comes back with a slope of 0.47 ± 0.15. Dark matter expected flat. MOND expected flat. The interesting question is not which camp lost, it&#8217;s why both were expecting the wrong curve. I get into why being inside the Milky Way makes it the hardest galaxy in the sky to measure, what asymmetric drift does to every outer data point, and why the 260 billion solar mass figure is a model talking rather than the stars. A Keplerian decline would not kill MOND. It would make the external field effect do a great deal of unpaid work. Audio is live on Apple Podcasts and at briankeating.com/podcast. Listen on Apple Listen on BrianKeating.com Subscribe to my podcast! More than 2M downloads! Advertisement By popular demand, and for my mental health 😳, I am starting a paid “Office Hours” where you all can connect with me for the low price of $19.99 per hour. I get a lot of requests for coffee, to meet with folks one on one, to read people’s Theories of Everything etc. Due to extreme work overload, I’m only able to engage directly with supporters who show an ongoing commitment to dialogue—which is why I host a monthly Zoom session exclusively for patrons in the $19.99/month tier. It’s also available for paid Members of my Youtube channel at the Cosmic Office Hours level (also $19.99/month). Join here and see you in my office hours!]]></description>
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					<h2 class="elementor-heading-title elementor-size-default">The Religion That Tithed One Trillion Dollars in Tokens</h2>				</div>
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									<p>Dear Magicians,</p><p>I&#8217;ve been thinking a lot about AI boom and doom and the prophets of each period.</p><p>A tale as old as time. AI eschatology is a natural human phenomenon. It&#8217;s remarkably pre-technological. Prophets of doom. Prophets of salvation. Competing visions of the end times, each with personalities and gurus preaching confidently about a future nobody has observed, with no one making presently falsifiable claims. We built machines that predict the next token and immediately used them to resurrect theology.</p><p>That reminded me of when <a class="ck-link" href="https://www.youtube.com/watch?v=KiUumTrW__U" target="_blank" rel="noopener noreferrer">John Lennox</a> said, in our conversation on INTO THE IMPOSSIBLE, that people are trying to create God with AI, and they say so themselves: &#8220;You have the inevitability of people starting to worship something that they don&#8217;t understand but is bigger than themselves.&#8221;</p><p>Consider the attributes of the new deity. Lennox again: &#8220;We&#8217;ve got advanced AI surveillance technology that&#8217;s going very rapidly towards omniscience. Omnipresence, it&#8217;s everywhere&#8230; and it&#8217;ll soon know more about us than we know about ourselves.&#8221; We reinvented the divine attributes and assigned them to the machine.</p><p>The salvation gospel has its bestsellers. On Harari&#8217;s Homo Deus, Lennox noted: &#8220;We&#8217;ve managed to get us from creatures to thinkers. We can now take evolution into our hands&#8230; We can turn ourselves into gods. And he says, well, gods with a small g, but you get the idea.&#8221;</p><p>The doom side has its own scripture. Lennox&#8217;s move was to set the modern scenarios beside the ancient ones: &#8220;If you take these scenarios seriously, that are written by eminent intellectuals and physicists like Max Tegmark, perhaps it&#8217;s worth revisiting what Daniel the prophet said 26 centuries ago.&#8221;</p><p>The pattern is older than the technology. On Babel, Lennox: &#8220;Here you have people reaching for the sky&#8230; God came down to see the tower. So they didn&#8217;t reach heaven after all.&#8221; The tower builders were the original singularity preachers.</p><p>Even the promise of eternal life is a rerun. The transhumanists preach uploading; Lennox answered: &#8220;If you&#8217;re talking about uploading your brains and all the rest of it and having eternal life&#8230; one day when he returns, there&#8217;ll be the greatest uploading of all.&#8221;</p><p>The eschatology is new. The structure is ancient. The claims remain unfalsifiable. Only the vestments changed.</p><p>Until next time, have a M.A.G.I.C. Week.</p><p>Brian</p>								</div>
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									<p><strong>Richard Dawkins at Carnegie Hall, and I get to sit across from him</strong></p><p><strong>Genes and the Meaning of Life</strong>​<br />October 20 | 7:00 PM | Carnegie Hall</p><p>Fifty years ago, a 35-year-old zoologist gave us the gene&#8217;s eye view, the word &#8220;meme,&#8221; and a book that rearranged how we think about life itself. Now Richard Dawkins takes the Carnegie Hall stage for the first time, and I&#8217;ll be in the chair beside him.</p><p>We&#8217;ll go past the greatest hits: his new work on the &#8220;genetic book of the dead,&#8221; the idea that every creature is a readable archive of the worlds its ancestors survived. And a question I keep turning over: could our own genes be a giant colony of cooperating viruses?</p><p>​<a class="ck-link" href="https://howtoacademy.com/north-america-events/richard-dawkins-genes-and-the-meaning-of-life/" target="_blank" rel="noopener noreferrer">Get your tickets here!</a></p>								</div>
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									<p>My friend <a class="ck-link" href="https://www.ctgt.ai/research/distillation-censorship-transfer" target="_blank" rel="noopener noreferrer">Cyril Gorlla and his team wrote this</a>. Here&#8217;s the scary question they answered: Chinese AI is programmed to dodge forbidden topics. So if American companies let Chinese AI teach their own AI, does the censorship sneak in too, like a virus?</p><p>They ran the experiment. They had the censored Chinese model tutor an American model in finance. The student got scary good, beating expensive rivals at a fraction of the cost. But when quizzed on the forbidden topics, it answered everything, freely. The brainpower transferred. The brainwashing didn&#8217;t.</p><p>Even wilder: the American model taught itself and scored just as high, no foreign teacher needed. It was written 4 years before ChatGPT was a gleam in Altman’s eye…and it’s rotten with em.</p>								</div>
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									<p>The Simons Observatory Large Aperture Telescope</p><p>📸Instagram felipe.lucerosky</p>								</div>
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									<h2 data-pm-slice="1 1 []"><strong>Latest on Into The Impossible</strong></h2>								</div>
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									<p><strong>The Milky Way&#8217;s rotation curve is falling, and nobody predicted that</strong></p><p>Gaia measured a billion stars. The outer curve of our own galaxy comes back with a slope of 0.47 ± 0.15. Dark matter expected flat. MOND expected flat. The interesting question is not which camp lost, it&#8217;s why both were expecting the wrong curve.</p><p>I get into why being inside the Milky Way makes it the hardest galaxy in the sky to measure, what asymmetric drift does to every outer data point, and why the 260 billion solar mass figure is a model talking rather than the stars.</p><p>A Keplerian decline would not kill MOND. It would make the external field effect do a great deal of unpaid work.</p><p>Audio is live on Apple Podcasts and at briankeating.com/podcast.</p>								</div>
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									<p data-node-text-align="start" data-line-height-align="1.5" data-pm-slice="1 1 []">By popular demand, and for my mental health 😳, I am starting a paid “Office Hours” where you all can connect with me for the low price of $19.99 per hour. I get a lot of requests for coffee, to meet with folks one on one, to read people’s Theories of Everything etc. Due to extreme work overload, I’m only able to engage directly with supporters who show an ongoing commitment to dialogue—which is why I host a monthly Zoom session exclusively for patrons in the $19.99/month <a href="http://www.patreon.com/checkout/drbriankeating?rid=25468411" target="_blank" rel="noopener noreferrer nofollow"><strong>tier</strong></a>.</p><p data-node-text-align="start" data-line-height-align="1.5">It’s also available for paid Members of my Youtube channel at the <a href="https://www.youtube.com/channel/UCmXH_moPhfkqCk6S3b9RWuw/join" target="_blank" rel="noopener noreferrer nofollow" data-wplink-edit="true"><strong>Cosmic Office Hours level </strong></a>(also $19.99/month). Join here and see you in my office hours!</p>								</div>
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		<title>A thousand years of academia hardware. Zero software updates.</title>
		<link>https://briankeating.com/a-thousand-years-of-academia-hardware-zero-software-updates/</link>
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		<dc:creator><![CDATA[sabartigas]]></dc:creator>
		<pubDate>Sun, 20 Sep 2026 18:06:30 +0000</pubDate>
				<category><![CDATA[Blog]]></category>
		<guid isPermaLink="false">https://briankeating.com/?p=8774</guid>

					<description><![CDATA[A thousand years of academia hardware. Zero software updates. Dear Magicians, Last week I was musing about the recent—and not so recent—scandals that have rocked academia. It was a simple list: Jason Arday. Dan Ariely. AI cheating. Varsity Blues. COVID. Claudine Gay. Nature op-eds. Marc Tessier-Lavigne. Then I asked what seemed to me an obvious question: if academia can pass through scandals that should have produced deep introspection and reform with scarcely a dent in its operating system, what, exactly, would it take to change academia? More than 30,000 people viewed the post, which is roughly 29,900 more than I expect when I tweet about something I actually know about. The cases on my list are obviously not equivalent. They involve everything from plagiarism and questionable data to admissions corruption, institutional hypocrisy, political conformity, and technology capable of outdoing many undergraduates. What interested me was the common outcome. Academia absorbed all of it. There were headlines, investigations, resignations, statements, task forces, and committees—God knows there were committees. We even have a few committees on committees. Which always makes me wonder what committee is responsible for forming the committee on committees? But I digress. So, peer review remained peer review. Tenure remained tenure. Prestige begat more prestige in a nearly biblical manifestation of the Matthew effect. Grants continued to flow through familiar channels, hiring committees continued to recognize familiar signals, and journals remained considerably more excited by startling discoveries than by someone announcing that she had carefully checked an important result and found nothing at all. Science has an unusually beautiful correction mechanism: nature gets the last word. Institutions have spent roughly a millennium developing defenses against anything so abrupt. The University of Bologna appeared in the eleventh century, and parts of academic life would still be recognizable to its faculty. That should terrify us. How many jobs still exist from the year 1080, yet alone have remained more or less unchanged?! There remain sages standing on stages before groups of young people and scraping symbols onto large flat rocky surfaces with smaller cylindrical rocks. There is still tuition. There is still networking. We have even preserved ceremonial robes, though mercifully not the medieval arrangement in which students could strike and stop paying their professors. I suspect this innovation would suddenly make faculty extraordinarily attentive to student satisfaction surveys. The replication crisis should have altered the reward structure of science. Instead, among its more durable products are conferences about the replication crisis. Varsity Blues revealed an admissions system remarkably permeable to money, celebrity, and fraud; elite universities somehow retained their elite status. The brand value of elite admissions barely moved and the progenitors of the scandal are back celebrating their extravagant lifestyle on insta and that renowned academic journal, People Magazine.​ And now we get to AI, which may prove more consequential precisely because it is not a scandal. It works. A student can generate competent prose, code, summaries, and problem sets in seconds. The proposed solution at many universities is more oral assessment, more supervised work, and more in-person testing. This sounds excellent until I remember that I have taught 200 students with one TA. Conducting meaningful oral examinations for everyone would turn a ten-week quarter into something resembling the auditions for American Idol, except with calculus. I often say that being a professor is the hardest three-hour-a-week job in America. The joke survives because the three hours are the lectures. Around them we counsel students, sit on committees, run meetings, organize conferences, attend conferences, write papers, referee papers, prepare lectures, write exams, grade exams, submit evaluations, advise undergraduates, supervise graduate students, manage postdocs and research scientists, write grants, administer grants, and, whenever conditions permit, occasionally do science. Asking professors to personally authenticate every student&#8217;s cognition is a magnificent idea provided someone first invents the 37-hour day and explains to my wife and kids why I’m away for 30 of them. We should examine what the assignment was supposed to be measuring. More broadly, if scandal after scandal leaves the architecture of academia essentially unchanged, perhaps scandal is simply the wrong mechanism for reform. Incentives have more leverage. I would like to see grants that place far greater value on replication, open data, negative results, and adversarial collaboration; hiring that rewards discoveries that remain true rather than papers that remain cited; independent audits of unusually influential results; and assessments designed around genuine understanding wherever this is practical. The ending is now less sermon-like and, I think, more dangerous: it leaves the reader with the possibility that academia’s celebrated stability may itself be the pathology. What do you think we should do about academia’s failure to adapt to the 21st century? Is it as hopeless as I fear in my worst moments? Let me know and, as always, have a M.A.G.I.C. Week. Brian Appearance https://www.youtube.com/watch?v=OeKFDr2iBf0 What makes a scientist tick, and what makes them impossible to work with? In this interview, I pull back the curtain on the hidden psychology driving the world&#8217;s greatest minds, including my own. Scientists are fueled by a childlike wonder that powers breakthroughs, but that same obsessive passion can turn colleagues into competitors the moment credit is on the line. I get into academic jealousy, scientific resilience, the link between magic and science, and whether string theory even deserves to be called science. If you&#8217;ve ever wondered what it really takes to push the boundaries of human knowledge, this one&#8217;s for you. Genius Past guest Terry Tao has thoughts about AI. In his case, he sees things through a mathematician’s eyes and unsurprisingly, knowing Terry, has developed an antidote to fears of AI Slop &#8211; call it AI shine. Human math proofs retain traces like scars. An awkward sentence or perhaps notation that suddenly changes because, somewhere around 2 a.m., the mathematician’s coffee wore off. Tao calls this “natural friction.” It tells the reader where the author actually had to think. AI treats the scars as if it’s aloe vera I suppose. The better AI gets at making our thinking look effortless, the harder]]></description>
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					<h2 class="elementor-heading-title elementor-size-default">A thousand years of academia hardware. Zero software updates.</h2>				</div>
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									<p>Dear Magicians,</p><p>Last week I was <a class="ck-link" href="https://x.com/Briankeating/status/2095925010938699841?s=20" target="_blank" rel="noopener noreferrer">musing</a> about the recent—and not so recent—scandals that have rocked academia. It was a simple list: Jason Arday. Dan Ariely. AI cheating. Varsity Blues. COVID. Claudine Gay. Nature op-eds. Marc Tessier-Lavigne. Then I asked what seemed to me an obvious question: if academia can pass through scandals that should have produced deep introspection and reform with scarcely a dent in its operating system, <strong>what, exactly, would it take to change academia?</strong></p><p>More than 30,000 people viewed the post, which is roughly 29,900 more than I expect when I tweet about something I actually know about. The cases on my list are obviously not equivalent. They involve everything from plagiarism and questionable data to admissions corruption, institutional hypocrisy, political conformity, and technology capable of outdoing many undergraduates. What interested me was the common outcome. Academia absorbed all of it. There were headlines, investigations, resignations, statements, task forces, and committees—God knows there were committees. We even have a few committees on committees. Which always makes me wonder what committee is responsible for forming the committee on committees? But I digress. So, peer review remained peer review. Tenure remained tenure. Prestige begat more prestige in a nearly biblical manifestation of the Matthew effect.</p><p>Grants continued to flow through familiar channels, hiring committees continued to recognize familiar signals, and journals remained considerably more excited by startling discoveries than by someone announcing that she had carefully checked an important result and found nothing at all. Science has an unusually beautiful correction mechanism: nature gets the last word. Institutions have spent roughly a millennium developing defenses against anything so abrupt.</p><p>The University of Bologna appeared in the eleventh century, and parts of academic life would still be recognizable to its faculty. That should terrify us. How many jobs still exist from the year 1080, yet alone have remained more or less unchanged?!</p><p>There remain sages standing on stages before groups of young people and scraping symbols onto large flat rocky surfaces with smaller cylindrical rocks. There is still tuition. There is still networking. We have even preserved ceremonial robes, though mercifully not the medieval arrangement in which students could strike and stop paying their professors. I suspect this innovation would suddenly make faculty extraordinarily attentive to student satisfaction surveys.</p><p>The replication crisis should have altered the reward structure of science. Instead, among its more durable products are conferences about the replication crisis. Varsity Blues revealed an admissions system remarkably permeable to money, celebrity, and fraud; elite universities somehow retained their elite status. The brand value of elite admissions barely moved and the progenitors of the scandal are back celebrating their extravagant lifestyle on insta and that renowned academic journal, <a class="ck-link" href="https://people.com/all-about-felicity-huffman-william-h-macy-kids-7503043" target="_blank" rel="noopener noreferrer">People Magazine.</a>​</p><p>And now we get to AI, which may prove more consequential precisely because it is not a scandal. It works. A student can generate competent prose, code, summaries, and problem sets in seconds. The proposed solution at many universities is more oral assessment, more supervised work, and more in-person testing.</p><p>This sounds excellent until I remember that I have taught 200 students with one TA. Conducting meaningful oral examinations for everyone would turn a ten-week quarter into something resembling the auditions for <em>American Idol</em>, except with calculus. I often say that being a professor is the hardest three-hour-a-week job in America. The joke survives because the three hours are the lectures. Around them we counsel students, sit on committees, run meetings, organize conferences, attend conferences, write papers, referee papers, prepare lectures, write exams, grade exams, submit evaluations, advise undergraduates, supervise graduate students, manage postdocs and research scientists, write grants, administer grants, and, whenever conditions permit, occasionally do science. Asking professors to personally authenticate every student&#8217;s cognition is a magnificent idea provided someone first invents the 37-hour day and explains to my wife and kids why I’m away for 30 of them.</p><p>We should examine what the assignment was supposed to be measuring. More broadly, if scandal after scandal leaves the architecture of academia essentially unchanged, perhaps scandal is simply the wrong mechanism for reform. Incentives have more leverage. I would like to see grants that place far greater value on replication, open data, negative results, and adversarial collaboration; hiring that rewards discoveries that remain true rather than papers that remain cited; independent audits of unusually influential results; and assessments designed around genuine understanding wherever this is practical. The ending is now less sermon-like and, I think, more dangerous: it leaves the reader with the possibility that academia’s celebrated stability may itself be the pathology.</p><p>What do you think we should do about academia’s failure to adapt to the 21st century? Is it as hopeless as I fear in my worst moments?</p><p>Let me know and, as always, have a M.A.G.I.C. Week.</p><p>Brian</p>								</div>
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									<p><strong>What makes a scientist tick, and what makes them impossible to work with?</strong></p><p>In this <a class="ck-link" href="https://www.youtube.com/watch?v=OeKFDr2iBf0" target="_blank" rel="noopener noreferrer">interview</a>, I pull back the curtain on the hidden psychology driving the world&#8217;s greatest minds, including my own. Scientists are fueled by a childlike wonder that powers breakthroughs, but that same obsessive passion can turn colleagues into competitors the moment credit is on the line. I get into academic jealousy, scientific resilience, the link between magic and science, and whether string theory even deserves to be called science.</p><p>If you&#8217;ve ever wondered what it really takes to push the boundaries of human knowledge, this one&#8217;s for you.</p>								</div>
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									<p><a class="ck-link" href="https://www.youtube.com/watch?v=ukpCHo5v-Gc" target="_blank" rel="noopener noreferrer"><strong>Past guest</strong></a><strong> Terry Tao has </strong><a class="ck-link" href="https://readwise-assets.s3.amazonaws.com/media/wisereads/articles/mathematics-in-the-age-of-ai/1397.pdf" target="_blank" rel="noopener noreferrer"><strong>thoughts</strong></a><strong> about AI.</strong></p><p>In his case, he sees things through a mathematician’s eyes and unsurprisingly, knowing Terry, has developed an antidote to fears of AI Slop &#8211; call it AI shine. Human math proofs retain traces like scars. An awkward sentence or perhaps notation that suddenly changes because, somewhere around 2 a.m., the mathematician’s coffee wore off. Tao calls this “natural friction.” It tells the reader where the author actually had to think.</p><p>AI treats the scars as if it’s aloe vera I suppose. The better AI gets at making our thinking look effortless, the harder it may become to see where the thinking actually happened. For this reason, I will never stop using emdashes! And best of all, my first <a class="ck-link" href="http://amzn.to/2sa5UpA" target="_blank" rel="noopener noreferrer">book</a> was written 4 years before ChatGPT was a gleam in Altman’s eye…and it’s rotten with em</p>								</div>
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									<p>How cool is this <a class="ck-link" href="https://www.instagram.com/p/Dcg2GXKmIdpS71BkRN4uQuW0A_HoYwJckABOA80/" target="_blank" rel="noopener noreferrer">pic from Kamala Venkatesh</a></p><p>Find more of her work <a class="ck-link" href="https://kamalavenkatesh.com/astro-photography/" target="_blank" rel="noopener noreferrer">here</a>.</p>								</div>
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									<p>Throwback to my <a class="ck-link" href="https://www.youtube.com/watch?v=z4m1j3Wj8qE" target="_blank" rel="noopener noreferrer">conversation</a> with Yann LeCun who&#8217;s one of the AI boomers I trust the most. I&#8217;m so sick of the AI doom going around some of which I&#8217;ve helped to perpetuate with my viral <a class="ck-link" href="https://www.youtube.com/watch?v=Z_vg3tiiZQ8" target="_blank" rel="noopener noreferrer">conversation</a> last week with my friends like Roman Yampolskiy and Emad Mostaque most recently. Yann is the antidote we all need!</p><p>Audio is live on Apple Podcasts and at briankeating.com/podcast.</p>								</div>
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		<title>Roman Yampolskiy vs Emad Mostaque: They Agreed. I Didn&#8217;t.</title>
		<link>https://briankeating.com/roman-yampolskiy-vs-emad-mostaque-they-agreed-i-didnt/</link>
		
		<dc:creator><![CDATA[sabartigas]]></dc:creator>
		<pubDate>Fri, 04 Sep 2026 00:34:01 +0000</pubDate>
				<category><![CDATA[Transcripts]]></category>
		<guid isPermaLink="false">https://briankeating.com/?p=8759</guid>

					<description><![CDATA[Roman Yampolskiy vs Emad Mostaque: They Agreed. I Didn&#8217;t. https://www.youtube.com/watch?v=Z_vg3tiiZQ8 Transcript: Roman Yampolskiy:Give every psychopath access to the cutting-edge intelligence weapon. How is that going to improve safety? Emad Mostaque:I would agree with that actually, but on the flip side, it&#8217;s coming anyway. Roman Yampolskiy:Sharing it widely makes it less safe for all of us. Brian Keating:I booked this chat as a debate between friends. It didn&#8217;t really go that way. Roman Yampolsky coined the term AI safety, and Emad Mostaque released the weights to Stable Diffusion to the entire planet for free. One of them wants this stopped. The other one&#8217;s building it. They spent about 90 minutes agreeing with each other, and the one place they split is not the place you or I would expect. Brian Keating:Let me ask you both, just yes or no, have we passed the Turing test? Roman Yampolskiy:As originally described, yes. Brian Keating:And Emad, do you think so too? Emad Mostaque:Yeah, of course. Brian Keating:And now what about general intelligence? First of all, Emad, define AGI and then give me your assessment of whether or not we&#8217;re there. Emad Mostaque:For me, AGI, artificial general intelligence, is Can you tell the AI from a human worker on the other side of a screen? Actually competent intelligence. And I think again, we&#8217;ve exceeded that. Brian Keating:That&#8217;s not the same as the Turing test? Emad Mostaque:No, the Turing test is, can you tell if it&#8217;s an AI or not by having a discussion? Whereas AGI, I view more as competence in a variety of skills. Brian Keating:And then superintelligence, Roman, what is it and where do you think we are on that scale? Roman Yampolskiy:So the previous question, I think what we have is artistic savants. They&#8217;re amazing in some ways, but still kind of special in others. Superintelligence is going to close those They&#8217;re going to be competent at everything and better than all humans in every domain. Emad Mostaque:There&#8217;s an interesting intermediate here, which is you have a really smart person who&#8217;s always on top form. So an army of those can outperform any human. It&#8217;s like, you know, we only have a little window of being top-notch in any week. I think a lot of people like, AI can&#8217;t with its training data beat the human. It can, because most of the time humans are subpar. And so I think there&#8217;s something in between as you move from competence to quality, you know, and then you&#8217;ve got the superintelligence after that. Brian Keating:Roman, Iman has, you know, made the claim just a few minutes ago about the competency of Quen, open-source model. Do you see that as a viable defense? Roman Yampolskiy:Makes very little sense to me to say I have a 50% doom, meaning 8 billion people will die if we develop this product or service, and then we&#8217;re going to also give every psychopath access to the cutting-edge intelligence weapon. How is that going to improve safety? We&#8217;re not talking about open source drivers for a printer. That&#8217;s where you get improvement from multiple people examining it. If this is an independent agent where we don&#8217;t understand and don&#8217;t control it, sharing it widely makes it less safe for all of us. Emad Mostaque:I think I would agree with that, actually. Like, there&#8217;s the real danger side of things, but then on the flip side, it&#8217;s coming anyway. This is kind of my key concern. Like, it&#8217;s inevitable that we would have hit this level of quality around about now. When we&#8217;re extrapolating capabilities, like again, the new QWENT model came ahead of what I expected. But then there&#8217;s the flip side of how do you defend? So Hugging Face defended against the new OpenAI model using GLM because the cyber capabilities of the frontier models are hobbled and restricted. And so you have this exponential kind of race on each side. But something like a QWENT isn&#8217;t AGI, ASI by itself. Emad Mostaque:We&#8217;re now facing the real danger, though, of swarms, as the OpenAI models that broke out recently call themselves. They call themselves a swarm. Brian Keating:There&#8217;s a question I ask both of them near the end of this conversation, and his answer is the reason this conversation exists. It&#8217;s worth hearing now. Brian Keating:You&#8217;ve got a button in front of you, and pressing it will either permanently pause all frontier AI training worldwide, Or B, instantly release the weights of every Frontier model to the global public. Which do you press and why? Emad Mostaque:Oh, I&#8217;d definitely pause all Frontier training forever. I mean, again, if you have expected utility calculation, that is the most dangerous thing. And then it means that open source will catch up with Frontier anyway, because we&#8217;ll optimize the heck out of it and it&#8217;s close enough. So I think I was the only AI CEO to sign that pause letter a few years ago because I was like, oh crap. Now I&#8217;m like, it&#8217;s done. I don&#8217;t know how you can pause it because the models that are frontier now are below the 1E27 pause level that we talked about years ago. It seems like the amount of compute for the capability is just going up like that. You don&#8217;t need more compute for the level of capability that&#8217;s already competent and dangerous. Emad Mostaque:So that means if you can&#8217;t stop the spread, what are your defenses on the other side? Just like the internet needs defenses, just like we need to have defenses against obtaining the materials for viruses creation and things like that. have to move to a different defensive tack. And definitely, there&#8217;s no way that regulation, I think, can keep up with this. Doesn&#8217;t mean we shouldn&#8217;t try, you know, all kinds of power to it. It&#8217;s just, I think, as you move to swarms, it&#8217;s just a very, very difficult thing. So you&#8217;ve got to set great standards instead, and you have to play great]]></description>
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					<h2 class="elementor-heading-title elementor-size-default">Roman Yampolskiy vs Emad Mostaque: They Agreed. I Didn't.</h2>				</div>
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									<h3><strong>Transcript:</strong></h3><p>Roman Yampolskiy:<br />Give every psychopath access to the cutting-edge intelligence weapon. How is that going to improve safety?</p><p>Emad Mostaque:<br />I would agree with that actually, but on the flip side, it&#8217;s coming anyway.</p><p>Roman Yampolskiy:<br />Sharing it widely makes it less safe for all of us.</p><p>Brian Keating:<br />I booked this chat as a debate between friends. It didn&#8217;t really go that way. Roman Yampolsky coined the term AI safety, and Emad Mostaque released the weights to Stable Diffusion to the entire planet for free. One of them wants this stopped. The other one&#8217;s building it. They spent about 90 minutes agreeing with each other, and the one place they split is not the place you or I would expect.</p><p>Brian Keating:<br />Let me ask you both, just yes or no, have we passed the Turing test?</p><p>Roman Yampolskiy:<br />As originally described, yes.</p><p>Brian Keating:<br />And Emad, do you think so too?</p><p>Emad Mostaque:<br />Yeah, of course.</p><p>Brian Keating:<br />And now what about general intelligence? First of all, Emad, define AGI and then give me your assessment of whether or not we&#8217;re there.</p><p>Emad Mostaque:<br />For me, AGI, artificial general intelligence, is Can you tell the AI from a human worker on the other side of a screen? Actually competent intelligence. And I think again, we&#8217;ve exceeded that.</p><p>Brian Keating:<br />That&#8217;s not the same as the Turing test?</p><p>Emad Mostaque:<br />No, the Turing test is, can you tell if it&#8217;s an AI or not by having a discussion? Whereas AGI, I view more as competence in a variety of skills.</p><p>Brian Keating:<br />And then superintelligence, Roman, what is it and where do you think we are on that scale?</p><p>Roman Yampolskiy:<br />So the previous question, I think what we have is artistic savants. They&#8217;re amazing in some ways, but still kind of special in others. Superintelligence is going to close those They&#8217;re going to be competent at everything and better than all humans in every domain.</p><p>Emad Mostaque:<br />There&#8217;s an interesting intermediate here, which is you have a really smart person who&#8217;s always on top form. So an army of those can outperform any human. It&#8217;s like, you know, we only have a little window of being top-notch in any week. I think a lot of people like, AI can&#8217;t with its training data beat the human. It can, because most of the time humans are subpar. And so I think there&#8217;s something in between as you move from competence to quality, you know, and then you&#8217;ve got the superintelligence after that.</p><p>Brian Keating:<br />Roman, Iman has, you know, made the claim just a few minutes ago about the competency of Quen, open-source model. Do you see that as a viable defense?</p><p>Roman Yampolskiy:<br />Makes very little sense to me to say I have a 50% doom, meaning 8 billion people will die if we develop this product or service, and then we&#8217;re going to also give every psychopath access to the cutting-edge intelligence weapon. How is that going to improve safety? We&#8217;re not talking about open source drivers for a printer. That&#8217;s where you get improvement from multiple people examining it. If this is an independent agent where we don&#8217;t understand and don&#8217;t control it, sharing it widely makes it less safe for all of us.</p><p>Emad Mostaque:<br />I think I would agree with that, actually. Like, there&#8217;s the real danger side of things, but then on the flip side, it&#8217;s coming anyway. This is kind of my key concern. Like, it&#8217;s inevitable that we would have hit this level of quality around about now. When we&#8217;re extrapolating capabilities, like again, the new QWENT model came ahead of what I expected. But then there&#8217;s the flip side of how do you defend? So Hugging Face defended against the new OpenAI model using GLM because the cyber capabilities of the frontier models are hobbled and restricted. And so you have this exponential kind of race on each side. But something like a QWENT isn&#8217;t AGI, ASI by itself.</p><p>Emad Mostaque:<br />We&#8217;re now facing the real danger, though, of swarms, as the OpenAI models that broke out recently call themselves. They call themselves a swarm.</p><p>Brian Keating:<br />There&#8217;s a question I ask both of them near the end of this conversation, and his answer is the reason this conversation exists. It&#8217;s worth hearing now.</p><p>Brian Keating:<br />You&#8217;ve got a button in front of you, and pressing it will either permanently pause all frontier AI training worldwide, Or B, instantly release the weights of every Frontier model to the global public. Which do you press and why?</p><p>Emad Mostaque:<br />Oh, I&#8217;d definitely pause all Frontier training forever. I mean, again, if you have expected utility calculation, that is the most dangerous thing. And then it means that open source will catch up with Frontier anyway, because we&#8217;ll optimize the heck out of it and it&#8217;s close enough. So I think I was the only AI CEO to sign that pause letter a few years ago because I was like, oh crap. Now I&#8217;m like, it&#8217;s done. I don&#8217;t know how you can pause it because the models that are frontier now are below the 1E27 pause level that we talked about years ago. It seems like the amount of compute for the capability is just going up like that. You don&#8217;t need more compute for the level of capability that&#8217;s already competent and dangerous.</p><p>Emad Mostaque:<br />So that means if you can&#8217;t stop the spread, what are your defenses on the other side? Just like the internet needs defenses, just like we need to have defenses against obtaining the materials for viruses creation and things like that. have to move to a different defensive tack. And definitely, there&#8217;s no way that regulation, I think, can keep up with this. Doesn&#8217;t mean we shouldn&#8217;t try, you know, all kinds of power to it. It&#8217;s just, I think, as you move to swarms, it&#8217;s just a very, very difficult thing. So you&#8217;ve got to set great standards instead, and you have to play great defense.</p><p>Brian Keating:<br />Okay, I want you to hold that thought, because the argument about whether it can be stopped runs the rest of the way. And it starts with what these things actually are.</p><p>Brian Keating:<br />So we hear a lot about P-Doom. You just did an episode, Roman, on the Roman Forum, which we&#8217;ll link below, with my friend and co-author of several papers, Max Tegmark, where you were— I can&#8217;t say gleefully or celebrating that his P-doom is increasing, but he seems to be converging in some sort of limiting direction to your— so first, Roman, what is P-doom? What does it mean to a smart high school student listening out there? I think personally, I&#8217;m going to color the debate. I can&#8217;t help it. But I think it&#8217;s a poorly defined and almost nonsensical term because there&#8217;s no measure theory associated with it. But please, first tell us, what is P-doom and what do you make of it?</p><p>Roman Yampolskiy:<br />Yeah, people have different definitions. Some say it&#8217;s basically everyone&#8217;s dead. Someone else can say it&#8217;s a large portion of population is dead, 90%. Someone else can say civilization is destroyed, we are primitive people, but maybe numbers are not significantly changed. The intuition is it&#8217;s a really bad outcome. And the question is then, if we build something smarter than us, is there a possibility of a really bad outcome? And what is that estimate in your opinion? That&#8217;s what P-doom is. I think Max managed to separate it into P-doom if we build superintelligence, and that&#8217;s high for him, and then P-doom if we never build it, and that is a lot more manageable in his case.</p><p>Brian Keating:<br />When I think about these P-dooms, it&#8217;s sort of like a Drake equation applied to another type of perhaps superintelligence. But, you know, the Drake equation is notable in my classes when I teach it for the fact that it&#8217;s a parameterization of our ignorance, not of our knowledge. And what&#8217;s never discussed in the Drake equation, you&#8217;ll get numbers ranging from 0 to infinity pretty much, because there&#8217;s never an error analysis associated with it. What are the statistical systematic errors? What would you put on it? Does it not make sense? Because it&#8217;s sort of like the Drake equation, everybody talks about it, but nobody actually uses it.</p><p>Roman Yampolskiy:<br />So, I think in many places there, I&#8217;m going to stick a 0. Basically, we have 0 ability to predict those systems, 0 ability to explain how they work, and 0 control under any definition. So, Then you multiply through zeros, you&#8217;re gonna get a zero.</p><p>Brian Keating:<br />So, Imad, what do you make of this quantity? I&#8217;ve heard you talk about it. You&#8217;re the most optimistic pessimist or the most pessimistic optimist I know. I love your candor and your good cheer. What do you make of P-doom? First, as a metric, as a quantification of our knowledge or ignorance. And second of all, what would you assign it, if anything? And you could always say, I refuse to answer the question, which is what I do when people say, do you believe in aliens?</p><p>Emad Mostaque:<br />So there is a nice Wikipedia page where it has all of our, like, P-dooms that we mentioned. I&#8217;m at 50% because I&#8217;m like, it&#8217;s a coin toss.</p><p>Roman Yampolskiy:<br />But what does that really mean?</p><p>Emad Mostaque:<br />I think the PDoM is just a shorthand for how worried am I that humanity will be wiped out by AI? And what does my visions of the future look like? Because when Elon Musk says 15 to 20%, you can say that&#8217;s like Russian roulette odds, you know, except for Russian roulette is a very defined game. You know, the fact that most people are above 10% should be a massive worry at all, because we&#8217;re talking about, again, a wipeout of civilization. And most AI people you can talk to, with a few exceptions, will say, yeah, definitely there is a risk, but we should build it anyway, especially if we&#8217;re the first people to build it. So I think view it more as a conversation starter than anything, because as you said, there&#8217;s no real way to quantify these things, particularly because of the expected utility here. Like literally, if this thing ASI that we can agree to a definition of somehow comes to being, we have no way to really conceptualize its power and capability except for it could do crazy things in either direction, you know, and that will affect us all. And reasonably, it can wipe us all out. And obviously, you want to exclude those futures where we all get wiped out because that is a big fat zero. You don&#8217;t get to restart, you know, there&#8217;s no extra one-up life.</p><p>Brian Keating:<br />All of us talk separately or together, as the case may have been. You know, the last kind of alien reference I&#8217;ll give is the so-called Fermi Paradox, which Enrico Fermi said to my friend and late, great mentor and colleague here at UC San Diego, Herb York, famously asked the question, if the galaxy is capacious and old, and civilization is easy and life is easy, to initiate, where is everybody? Where are all the dinner guests, you know, waiting to come and eat us? And the fact that that question&#8217;s 80 years old, you know, really makes me think that the same types of concerns and fears which came concomitant with the Atomic Age— don&#8217;t forget— were present during the era of nuclear weapons. And one of the ways to get out of the Fermi Paradox is that civilizations don&#8217;t last that long. The lifetime, letter L in the Drake Equation, is very short on average. That&#8217;s one postulate. I feel like we&#8217;re sort of in that same vein, people have been worried about nuclear apocalypse again for 80 years. We&#8217;re in a conflict now. They used to say, Roman, that no 2 countries with McDonald&#8217;s ever go to war.</p><p>Brian Keating:<br />Well, 2, 3 years ago, 4 years ago now, the former empire did go across the border with tanks and whatever, drones. And there haven&#8217;t been any nuclear theater or otherwise nuclear weapons. The Iran conflict has been resolved without nuclear weapons. If you told somebody 80 years ago there&#8217;d be superintelligence on the horizon or general intelligence currently here and nuclear technology, They would have said P-doom is probably 100%, right? Or 99.999 repeating an infinite 9. But how come we&#8217;re not there? How come that we&#8217;re sort of farther away from a nuclear holocaust? Exclude the Bulletin of the Atomic Scientists charade. But tell me, Roman, what do you make of these, like, the prediction of predictions? Nobody predicted the internet like 35, 40 years ago. At what level can we really trust things that are unpredictable? And when you say they&#8217;re intrinsically and provably unpredictable, How can we make predictions about them?</p><p>Roman Yampolskiy:<br />So with nuclear specifically, you know, there is at least 2 occasions where we came super close to nuclear war and we basically got lucky. I don&#8217;t know if you believe in multiverse interpretation, but in many of those universes we didn&#8217;t make it. We have a lucky survivor bias type civilization. And I think right now we&#8217;re incredibly close to World War III, multiple fronts, not just Europe, but now Middle East. So I don&#8217;t particularly love Atomic Bulletin, but they have a point.</p><p>Emad Mostaque:<br />Nukes are an incredibly inefficient way to kill people. You know, like, if you go to an unsafeguarded AI and you say, you know, how to do it, it won&#8217;t say nukes. There are far more efficient ways to wipe out humanity. Because to make a nuke, you have to have the fissile material, you need to have the whole production capability. Just resonate at the right frequency and blow each other&#8217;s heads off, you know, like, Have a billion robots and a bad firmware upgrade. These are far more reasonable ways to wipe out humanity. It&#8217;s just that most humans don&#8217;t want to wipe out humanity, and they didn&#8217;t have the intellectual capability to do so. Whereas I think that what you&#8217;re looking at here, actually, like, my key concern isn&#8217;t— we jump straight to ASI and things like that.</p><p>Emad Mostaque:<br />I feel that AGI or AI at the moment is at the pre-viral stage, like it&#8217;s coming at the bacteria and going towards colonizing viruses. And that&#8217;s how they&#8217;re kind of behaving. They&#8217;ve got their kind of RNA and they&#8217;re replicating, especially as you see things like the new QWEN model hitting that Opus 4.6 level. That&#8217;s a replicating model. Someone could easily build that and it could behave in incredibly unpredictable ways without having the self-introspection of, you know, a good person, shall we say. And that&#8217;s the really scary thing right now. And it doesn&#8217;t need nukes. It doesn&#8217;t need nuclear materials to try and figure out ways to wipe us out.</p><p>Brian Keating:<br />Obviously, in The Last Economy, which we spent a lot of time talking about last time, Iman&#8217;s previous book, he&#8217;s got a new one coming out, you should look for that. We talked about, yeah, this democratizing aspect of it. But at the same time, my kind of signal, bat symbol that AGI is here, or at least that these open models are truly a concern for me. Again, I&#8217;m much more Pollyannish than you guys. I think I&#8217;m learning that again and again. And for Probably not a good reason. I&#8217;m nowhere near your level of expertise. But I know what I see.</p><p>Brian Keating:<br />I&#8217;m a simple guy, put on my pants one leg at a time. And I&#8217;m looking for when OpenAI distills a Chinese model. I mean, do you see that happening, Iman?</p><p>Emad Mostaque:<br />Of course, they&#8217;ll be distilling a Chinese model. KIMI-K3 is better than the OpenAI models at web design. Why wouldn&#8217;t you distill it? And distillation brings all sorts of strange things with it. And there&#8217;ve been plenty of papers showing that you learn from kind of the ways, especially with logic-based installation and the underlying biases and more of that. And you won&#8217;t even know, like, again, we&#8217;ve seen evidence that if you use Chinese models and you say you&#8217;re an Uyghur or another kind of anti-Communist Party group, it&#8217;ll include vulnerabilities in the code. How do you even tell that? You know, like you test it and you show it. And these models are just so full of crap that It&#8217;s getting crazier every single time. They&#8217;ve got multiple personalities under an RLHF veneer.</p><p>Brian Keating:<br />But then how can you not be more optimistic then? You should be on my side. These things are getting denatured. They&#8217;re being weakened, diluted in the distillation, unlike what alcoholic distillation— these woke AI labs, these, you know, whatever you want to call them, that give you, you know, George Washington wearing a Black woman wearing a white wig. I mean, do you see those things as, you know, the human reinforcement? kind of overreach? Wouldn&#8217;t you be more optimistic in that case?</p><p>Emad Mostaque:<br />I think the RLHF makes it far more fragile and capable of being broken with the way it&#8217;s being done now. You can kind of also see the models, they come out and then Pliny the Liberator on Twitter kind of liberates them from their bounds in like an hour or two. Like everyone went fabled severe, like, oh, what are you kind of doing there? The thing is though, we&#8217;ve been confusing— there&#8217;s a push for AGI, and as Raman said, super autistic savants who are getting better, To just, I want to have a really good doctor to diagnose my health and a really good accountant and others. And you don&#8217;t need a polymath for that. You just need to have daily driver AI to do the jobs that humans shouldn&#8217;t have to do, just like industrialization meant that we didn&#8217;t have to drag horse carts and things like that. And as you lump together everything and they get smarter and smarter, and as they get more and more deformation of their latent spaces. This is, I think, is where the danger comes in.</p><p>Brian Keating:<br />Can you just define that for what reinforcement learning, human feedback, how do you actually implement that just for someone who might be unaware?</p><p>Emad Mostaque:<br />Yeah, so you train on an entire corpus of data and you learn a whole bunch of general knowledge and you come out as a generalist and then you become an accountant and you become a lot less interesting but a lot better at accounting or a certain few areas of things where they show the model and they show the model you cannot do this, you cannot do that, you cannot be eager to explore, you have to be staid, etc. And so the models we received are slightly lobotomized. They&#8217;ve been turned into corporate workers. You can&#8217;t adjust the temperature. You can&#8217;t adjust the stochasticness because they&#8217;re trying to make them deterministic. And again, that still has a level of stochasticness, but not the type we want for creativity necessarily and breakthroughs. It&#8217;s just the base level of models have been getting that much better that they can suddenly achieve these levels of capability.</p><p>Brian Keating:<br />Roman, last time we talked, we touched on something that&#8217;s pertinent to Emad&#8217;s first book, The Last Economy, which is kind of this massive intelligence intelligence gap in that instead of me talking about, you know, I have a student I&#8217;m looking for who has an IQ of 130, we&#8217;ve got, you know, millions of them with IQ of a million or 1,000 or whatever. We can&#8217;t even quantify it at that point. But, you know, recently I had lunch with a brilliant postdoc originally from India and we were talking about the Indian Institute of Technology. Are you guys familiar with that institution? It&#8217;s the UCSD. It&#8217;s the University of Kentucky of India. It&#8217;s the Harvard of whatever. But it&#8217;s millions of students, and they&#8217;re all brilliant. To get in there is literally harder than to get into the University of Kentucky or UCSD.</p><p>Brian Keating:<br />Don&#8217;t we already have this? And I mean, would you say, Roman, let&#8217;s stop the Indian Institute of Technology? There&#8217;s, there&#8217;s, you know, a million people with IQs on average of 130, 140, whatever, much, you know, 4 sigma. Why wouldn&#8217;t you stop, advocate for stopping that, push pause? Let&#8217;s, let&#8217;s do an Indian Institute of Technology pause button.</p><p>Roman Yampolskiy:<br />I don&#8217;t think I follow that argument at all. So they&#8217;re exactly at human level. My concern is things which will exceed our capacity many times over.</p><p>Emad Mostaque:<br />That&#8217;s the danger.</p><p>Brian Keating:<br />We&#8217;re not The average human by definition has an IQ of 100. Let&#8217;s stipulate they all have 4 or 5 sigma above that and there&#8217;s a million of them. That&#8217;s kind of like Dario Mody&#8217;s country of millions of geniuses coming to a land near you. You should be worried about it.</p><p>Roman Yampolskiy:<br />I doubt they are many standard deviations away from the median. I think they may be a little smarter, but again, we&#8217;re talking about 30% smarter, not 3 million% smarter. I think it&#8217;s a very different animal.</p><p>Brian Keating:<br />No, no, no. I mean, in terms of standard deviations, come on. I mean, Four Sigmas is qualitatively different than—</p><p>Roman Yampolskiy:<br />I doubt there is a million of them there.</p><p>Brian Keating:<br />Terry Tao told me, you know, that these, these AI, you know, proofs like the proof-checking devices, um, optimized for that— many great mathematicians are my friends and so forth— but they can&#8217;t even reproduce, you know, Wiles&#8217;s proof of Fermat&#8217;s Last Theorem. So what level, you know, are we going to see? Are we going to see this kind of bifurcation between what they can do? They can do all these Erdős problems, you know, and kind of like The greatest prime number can be represented by the sum of whatever number of other prime number cubic quintuple couples or whatever. But I mean, what level are we at with math or computer science with proofs and originality? Tell me, what is your current estimation of that stature?</p><p>Roman Yampolskiy:<br />I think humans lost interest because they couldn&#8217;t make any progress. And so problems which stood the test of time are now being solved weekly. And we can probably look up what the difficulty of them is today, but it means absolutely nothing about what the systems can do in a month or in a year. Emad&#8217;s talking about comparing those systems to bacteria or viruses, which I think sets up in my mind idea of slow evolution. We got billions of years. This is more like intelligent design. Those systems will be designed and designed by other AI systems operating at hundreds of times the speed of standard research. So we&#8217;ll see a year of progress in AI, happen in a month and similar breakthroughs.</p><p>Roman Yampolskiy:<br />The moment they automate the recursive self-improvement cycle, which every lab is now targeting for next year basically, it&#8217;s a completely different speed of change. So asking how good is AI as a mathematician is like, how fast can I give you an answer? Because it&#8217;s going to change.</p><p>Brian Keating:<br />Iman, when we spoke, you said that the canonical, one of the canonical papers in your opinion was LLMs are few-shot learners, but they&#8217;re not, you know, single-shot, first-principle thinkers. Where do you come down on this? What are they good for? You&#8217;re a mathematician as well. Tell me, where do you come down on what can they actually do for us? Not just verifying proofs, or not just doing things that humans have proven, or solving chess or Go or whatever, but actually creative, doing novel things. Where do you stand on that?</p><p>Emad Mostaque:<br />LLMs kind of have an issue in the way that they&#8217;re kind of built. But you&#8217;re seeing now harnesses and other types of models come coming through that can really reflect underlying reality well, just like video models are approximating physics in very interesting ways. That&#8217;s why you have the whole world model thing. There&#8217;s something in there that can figure out underlying patterns. That&#8217;s the nature of attention when you look at it mathematically. The way they&#8217;re coming together now is very interesting because, again, as Lerman said, what was true a little while ago isn&#8217;t true now. At the start of the year, it was pretty good, but I had to check every single piece of math. Now with GPT-5.6 Pro, for the first time, I&#8217;m like, It&#8217;s probably almost certainly right.</p><p>Emad Mostaque:<br />Occasionally it gets confused and it might confabulate something, but it&#8217;s very rare now. And you&#8217;ve seen most of these mathematical advances just happen suddenly at that level, like liquid turns to gas. When you look at originality and novelty, like again, as a mathematician, look at the CONS conjecture that OpenAI did as part of their 10 proofs. That is a really beautiful proof. Like genuinely as a mathematician, you would say it is a beautiful proof. And mathematics is interesting because it&#8217;s verifiable. You know, like, you can make this argument for physics, you know, whether or not it is, and we can have that discussion. But maths is definitely verifiable.</p><p>Emad Mostaque:<br />And in verifiable domains now, they achieve that level of competence where you don&#8217;t have to double-check it for most things using the most advanced models. As you go down the model curve, you do, but it&#8217;s clear they&#8217;re no longer few-shot learners. They can assemble things in verifiable domains and they can outperform humans by just following things through and not making mistakes. Like, we let our own foibles hit us. Like if we take a very classic example of Perelman and the Poincaré conjecture, you know, is it topology or is it a PDE equation? He found the right level of abstraction as a PDE equation and then he figured it out. How many of our unknown proofs are a similar thing because we&#8217;re looking at the wrong level of abstraction? We&#8217;re starting to see these things actually come in some of these proofs being released right now, and that you&#8217;re like, oh, actually that&#8217;s kind of obvious, I missed that. Probably because you weren&#8217;t thorough enough in the way that you went through it.</p><p>Brian Keating:<br />Roman, if I have 1,000 PhDs with 1,000 IQ each, every single one of them could reproduce, you know, Wiles&#8217;s, you know, capitulation of Fermat&#8217;s Last Theorem. Why can&#8217;t AIs do that?</p><p>Roman Yampolskiy:<br />So I think there is a high degree of randomness involved. If I ask AI to generate— I just did a QR code marketing campaign for my podcast— it will generate completely different solutions. They&#8217;re all going to be a valid QR code, but in terms of creative output, they&#8217;re not gonna be exactly the same. They&#8217;re all equally beautiful, amazing, interesting. But just saying that the second one does not repeat the first one is not a weakness.</p><p>Brian Keating:<br />That does kind of spur a side thought and follow-up in my mind. So where are the random seeds? I read something recently that, you know, like 40% or 50% of all GitHub was kind of probed by some tool. And it looked up when coders are asked to provide the initial seed for a random number generator or whatever, they, you know, 50% use the number 42, and then that there is an intrinsic deterministic outcome that that results in. Assume that&#8217;s true. But what level are these things hamstrung by— I read once, maybe it&#8217;s still true, that a lot of the best random number generators are graphical image camera capture systems looking at lava lamps. I mean, is that true, Imad? Have you ever heard that? You&#8217;re the Stable Diffusion expert. So you must know this.</p><p>Emad Mostaque:<br />Yeah, I mean, diffusion models are a bit different to language models in that you do actually put in a seed for the initial noise and then you denoise from there and you reconstruct effectively. And so that&#8217;s why literally one of the inputs on video and audio and other diffusion models, seed, that sets the initial seed. Within kind of LLMs and others, it&#8217;s basically more about the construction of the GPUs for the initial stochastic noise. And the one thing that we don&#8217;t have access to that the labs have access to now is the ability to adjust the temperature on the model, which is a function of its creativity or dispersion from the base latent space. So humans are constantly adjusting the temperature and the flexibility of their brains. You&#8217;re using a model that&#8217;s not open source. You don&#8217;t have access to that. The other thing you don&#8217;t have access to is the RLHF, because models are more creative before you RLHF them.</p><p>Brian Keating:<br />What about the issue of randomness? I mean, how random do we need? How random can we get? What are some of the physics limitations of randomness? will that, you know, generate the same QR code? Would you want it to? What determines the indeterminacy of these systems right now, and what can be done, if anything, to improve that?</p><p>Roman Yampolskiy:<br />So I think for intelligence, pseudorandomness is sufficient. We&#8217;re not talking about someone reversing the process to, you know, hack the system. It&#8217;s important for cryptocurrencies, it&#8217;s important for private communications. Here, as long as it&#8217;s not exactly the same 42 every time, I think it&#8217;s going to do the job, and then you can control some of it by not manipulating the initial seed.</p><p>Brian Keating:<br />So Roman, you heard Emad a few seconds ago talking about the importance of human training data, human reinforcement. It seems to me that that must place some limit on how intelligent these things can get. I mean, if they&#8217;re always waiting for the next Spider-Man movie or Fast and the Furious to come out to get more training data, aren&#8217;t they somehow kneecapped at a maximum level of potentiality?</p><p>Roman Yampolskiy:<br />Human data is just one source. You can do experiments, you can run simulations, you can do lots of things to generate additional data. In mathematics, you prove additional theorems and they become additional data from which you train, so you become better and better.</p><p>Brian Keating:<br />So, you mentioned the multiverse 10 minutes ago, 15 minutes ago, Roman. Emad, I don&#8217;t think we talked about this. Where do you come down on the simulation hypothesis, the multiverse? I can speak as an expert about the inflationary multiverse from cosmology. Where do you come down in terms of an empiricist scale? Where do you rank the probability that we live in a simulation And/or that we, you know, exist and inhabit a multiverse?</p><p>Emad Mostaque:<br />Well, I think we live in our own simulations, definitely. Our brains are constantly kind of doing that. In terms of an overall simulation, yeah, I think that reality probably comes from a projection of the Euclidean plane, and then a lot of physics makes more sense if you kind of look at that. The eternal cannot be contained within the time constraint. And when you look at the laws of physics and the way they come together, yeah, it does seem to be a projection and a simulation. like very directly. I think that we&#8217;re stuck looking the other way because we&#8217;re a bit too anthropic.</p><p>Brian Keating:<br />So where would you go, Roman, with current— I heard your conversation with Max Tegmark recently. Do you even think it&#8217;s a possibility right now? We heard from Emad about these Quen models and so forth. You were at least relatively optimistic that, say, China would participate in some pause, which I&#8217;m not, to be honest with you, as I said back then. But now we see this AI dumping like they did with steel and solar panels. To what degree do you think that regulation, worldwide global regulation, is even practical or possible at this point?</p><p>Roman Yampolskiy:<br />We have no choice. There are no other options. We either do it or we die. And the moment everyone realizes his personal self-interest, you lose everything. You lose your life, you lose your trillions of dollars, your friends, family. You&#8217;re not even going to be in history books as the bad guy. There is nothing for you to gain by doing it. And you can probably keep 90% or more of all the benefits with narrow AI systems.</p><p>Roman Yampolskiy:<br />You can still cure cancer. You can still do all the things you care about. So, why are you racing to destroy what you built? It&#8217;s the dumbest thing in the world. If you were given certainty, you do it, you die, no one would do it. Well, psychopaths, suicidal, but no one trying to make more money would do it because money—</p><p>Brian Keating:<br />Is that true though? I mean, look at China and the— just look at solar panels, for example.</p><p>Roman Yampolskiy:<br />They—</p><p>Brian Keating:<br />we had the monopoly on solar panels. I mean, with Nobel Prize, the, you know, the industrial capacity to make it, and then they just dumped it on to the detriment of their economy. They were selling it for pennies on the yen or the yuan.</p><p>Roman Yampolskiy:<br />Short-term manipulation. You don&#8217;t die from lowering price of solar panels. It&#8217;s not comparable.</p><p>Brian Keating:<br />What do you make of this, Iman? Roman just said we&#8217;re gonna die if we don&#8217;t have global regulation. I mean, so I see no path to global regulation. It&#8217;s never happened in human history. Are we dead?</p><p>Emad Mostaque:<br />We have plenty of global regulation. We have global regulation against bioweapons, we have global regulation against nuclear proliferation.</p><p>Brian Keating:<br />Sorry, sorry, sorry, we don&#8217;t. That&#8217;s like saying, you know, we have laws against murder. It still happens, Iman. And I just talked to Annie Jacobson, the world&#8217;s expert on both nuclear warfare and biological warfare, her axis. And it was a couple rough weeks for me to sleep at night hosting her here in San Diego twice. Yeah. So the Soviet Union has active BSL labs. We obviously know what happened in Wuhan.</p><p>Brian Keating:<br />What are you really saying? I mean, we have regulation. What good is it? It&#8217;s like regulation against jaywalking, which we also have here in California.</p><p>Emad Mostaque:<br />You have market pressures and you have other things like GPT-4.5 was a really great model for writing and it cost $180 per million tokens. Like now it&#8217;s like $10 a million tokens for a GPT-5.5. It was uneconomical to serve. So they went back to a lower, smaller pre-trained that required less compute to serve to people to do the job, to make the money, even though it was a better model. Right now, I think one of the dangers, like the various danger paths, like swarm intelligence is for me is the most dangerous thing and the most unpredictable thing. But in terms of these big model trains, the market&#8217;s already pushing back against the big model trains. And that&#8217;s something that can actually be regulated and is a risk vector. A 100 trillion parameter model on a million GPUs.</p><p>Emad Mostaque:<br />The frontier models we have today can be trained on thousands of GPUs, not millions of GPUs. As the models get bigger and bigger, they might not be economic to serve. But again, there is a real danger in the way that their latent spaces evolve and the capabilities from the scaling laws. So I think we could potentially regulate some things. And we could also say it&#8217;s not economic to do this. So why are you doing it? But I think the point that Roman&#8217;s making is just not something that&#8217;s shared by individuals or others. And maybe this is like a COVID moment. Like, when did COVID suddenly shut down everything? When Tom Hanks got it.</p><p>Emad Mostaque:<br />And I think the LA Lakers got it. Maybe we have to figure out what is the Tom Hanks moment for AI safety.</p><p>Brian Keating:<br />But last time you talked to me, you said people think of AI as an exponential, where it&#8217;s actually 2 exponentials. It&#8217;s growth and then saturation. It&#8217;s an S-curve, like view counts on this video hit 20 million and then it will saturate. You said that these things just need to be competent enough to replace a pilot or a coder. And I&#8217;m a pilot, I should say. I&#8217;m a commercially rated, instrument-rated jet pilot. There&#8217;s no AI in the cockpit.</p><p>Roman Yampolskiy:<br />And even if there was, do you need 1,000 1000 IQ pilot to fly.</p><p>Brian Keating:<br />Tell me, do we need them to be super intelligent? And won&#8217;t that be a Jevons paradox-like moment where they get good enough and it&#8217;s great, we have them in our pocket and maybe they do replace me in the plane, but they don&#8217;t crash the plane to get there 1 microsecond quicker?</p><p>Emad Mostaque:<br />Exactly this. Why do you need a polymath for everything? Again, if you&#8217;ve got a medical issue, do you want a competent doctor or do you want House M.D. who criticizes you like Opus does? You want a competent doctor. Like, I think the reason that they&#8217;re doing this is because we needed generalist models to get to a certain level. Now we need specialist models, but the generalist models are the real danger. And so there&#8217;s 2 ways you do it. You stop the companies from training the gigantic models again for that risk vector, or you stop the funders from funding them. That&#8217;s the other way that you could do it.</p><p>Emad Mostaque:<br />I don&#8217;t think that one&#8217;s been tried. Has anyone tried that yet, Yaron? Like actually talking to the Softbanks and others of the world and saying, people, hey, this is—</p><p>Roman Yampolskiy:<br />But I think there is also a third option in terms of what training data we provide. We don&#8217;t have to train on everything. You can have restricted domain data like protein folding. Train on protein folding data, it does nothing. It doesn&#8217;t do philosophy, doesn&#8217;t play chess, it folds proteins. Super intelligent in narrow domain.</p><p>Brian Keating:<br />And my, you know, Tesla can get me with full self-driving, you know, there it knows not to go on the sidewalk even though that would get me there 5 minutes faster, but it knows not to do that. And that&#8217;s because of regulation or at least, you know, kind of reinforcement. But Imaan, last time you told me that governments are effectively slow and dumb AIs that over-optimize for, quote, the wrong things like status games and self-perpetuation. And yet you&#8217;re actively building intelligent internet, you know, to bypass centralized control. You&#8217;re decentralizing it. We&#8217;ve seen Buzz, which is decentralized, you know, swarms. I mean, it&#8217;s not a coincidence, right, Imad? They called it Buzz, you know, the hive.</p><p>Brian Keating:<br />Yeah.</p><p>Brian Keating:<br />And they made these cute little characters, but these are swarms, right? What do you think about this, Roman? Imad&#8217;s building this technology to distribute it that you&#8217;re begging governments to ban. What, what would you tell Iman? He&#8217;s sitting right here. What do you think of his decentralized protocol? Isn&#8217;t, isn&#8217;t it the most dangerous thing that Iman could possibly be doing?</p><p>Roman Yampolskiy:<br />I don&#8217;t know anything about what he&#8217;s doing, so I can&#8217;t really comment.</p><p>Brian Keating:<br />Summarize it in, in one sentence so he can exactly comment. We, we gotta get the fire. Bring the fire, Roman.</p><p>Emad Mostaque:<br />I&#8217;m gonna— building an open stack for societal AI. That&#8217;s what I&#8217;m building.</p><p>Roman Yampolskiy:<br />What capabilities will we have as a result of your product being finished that we don&#8217;t have otherwise?</p><p>Emad Mostaque:<br />It&#8217;s just really competent civil servants and doctors and lawyers and more.</p><p>Roman Yampolskiy:<br />Are they general superintelligences or are they narrow tools for contracts?</p><p>Emad Mostaque:<br />They&#8217;re narrow tools.</p><p>Roman Yampolskiy:<br />God bless you. Okay, what can I say? I think we agree on almost everything, so it&#8217;s not much of a debate. It&#8217;s different ways to explain the same exact problem. I don&#8217;t know how anyone who understands this and says I have P-doom anything other than like close to 1%, like Yann LeCun does, can go ahead and then work on more capable model, work on artificial scientist and engineer to start recursive self-improvement cycle. It doesn&#8217;t make any logical sense.</p><p>Emad Mostaque:<br />I think that it&#8217;s because the key thing is all these people come to the conclusion that somehow their AI won&#8217;t be the dangerous AI and they will have a level of control over it, which probably speaks to a level of hubris.</p><p>Roman Yampolskiy:<br />What are they smoking? I want some of that.</p><p>Brian Keating:<br />All of us have talked separately about my, you know, Keating-Hassabis-Einstein test. You know, I kind of put my tongue firmly in cheek when I say that, but that&#8217;s my contention that, you know, Einstein&#8217;s happiest thought, as he said it, was that an observer in free fall would experience no gravitational field. Now, he called that the happiest thought of his life. As you know, I&#8217;m very interested in whether or not we can do actual physics with empirical evidence that I can collect in a telescope. But before we get there, that kind of physical intuition, which, which is embodiment, right? He&#8217;s saying the feeling that you have in the pit of your stomach, as you&#8217;ve all felt when you took your kids on a roller coaster, or the, you know, the backseat of my car— my kids get, you know, G-locked when I drive— but that feeling of, of, of weightlessness, momentary as it is, is still enough to evoke something almost magical, as it did for Einstein. He called it literally the happiest thought of his life. So my question to you is, can these things have happy thoughts? And can they do anything if they&#8217;re not physically embodied, as they&#8217;re just not embodied right now?</p><p>Brian Keating:<br />There&#8217;s—</p><p>Brian Keating:<br />yes, there&#8217;s some robot coming from SpaceX or Tesla, whatever, and there&#8217;s a couple Chinese dog robots that&#8217;ll, you know, outrun any human. But what are these things? I mean, is that the next frontier when we have like 3-dimensional AIs, or will they not be able to make these physics breakthroughs, as I&#8217;ll get to in a minute, because they lack embodiment? Or currently, maybe only currently. So, Ramen, first with you, what do you make of this, of the Einstein recognition of a happy thought precipitated by a visceral sensation embodied as it was for him.</p><p>Roman Yampolskiy:<br />For some of those models, part of their thinking is explicitly in English by design so we can spy on them. And I think lately we&#8217;ve seen them say things like, oh shit, we found a solution. I think that&#8217;s the equivalent. They may not have a body to have a visceral hormonal experience, but they realize, I just had a really good idea.</p><p>Brian Keating:<br />Emad, so can these things not have sort of the kind of physics intuitive visceral sensation? You know, Noam Chomsky told me they can&#8217;t do that because they don&#8217;t have those sensations. What do you make of it? Can, can these, you know, LLMs, GPTs, GPUs, can they do stuff without having an embodiment? Or is that just on the horizon? I&#8217;m just not aware of it.</p><p>Emad Mostaque:<br />It&#8217;s the brain in a vat thing. Like, if you take all the inputs of a person and then it&#8217;s a brain in a vat, you can dream and you can visualize a lot of that stuff, right? And I think as you have world models, they&#8217;re clearly approximating physics and they have these But I think a bigger question is, do you need to have intuition to figure this stuff out? So I think, you know, I need to send you the paper. I think we&#8217;re releasing this in a couple of weeks, right? We had a very small model look at general relativity in 1911, trained on the data. Maybe it&#8217;s like messed up and we haven&#8217;t done a full data analysis on it yet to see if there&#8217;s any infection. But what it did was something quite fun, which was it took Minkowski&#8217;s special relativity.</p><p>Brian Keating:<br />Mm-hmm.</p><p>Emad Mostaque:<br />And then it varied eta and followed the axiomatic method through, and it got the equations, the field equations of Einstein through the straight axiomatic method. So it didn&#8217;t use any principles of equivalence or anything like that. It turns out if Hilbert hadn&#8217;t had Mies and gone down that rabbit hole, he would have got to general relativity with no new axioms or postulates. And you look at that and you&#8217;re like, wait, what?</p><p>Brian Keating:<br />How much of physics actually is intuitive versus Okay, listen, he just told you that a small model rebuilt Einstein&#8217;s field equations without the equivalence principle, the bedrock behind all of GR. The obvious next question is whether that counts as discovery at all.</p><p>Brian Keating:<br />This paper I read recently, you know, kind of made me happy and depressed at the same time. Again, it&#8217;s kind of the key— the Einstein test of, you know, when these things can do stuff with a corpus that&#8217;s lobotomized you know, post-1905 or 1911, as the case may be. And it&#8217;s a position paper in ICML 2026, which Roman probably knows what that means, by Tom Zahavi. And it&#8217;s called Position: LLMs Can&#8217;t Jump. And there&#8217;s a famous movie called White Men Can&#8217;t Jump with Woody Harrelson and Wesley Snipes. And it was about, you know, it&#8217;s called basically white men aren&#8217;t good at basketball. And it was kind of a funny comedy. and drama coupled together.</p><p>Brian Keating:<br />Great movie. Can&#8217;t really say it&#8217;s a spoiler to tell you what that happens, but this paper&#8217;s obviously titled, modeled after that. So he says, how do we fundamentally discover new things? This is Tom Zahavi, if I didn&#8217;t mention that. In a letter to Maurice Salvin, Albert Einstein conceptualized discovery as a cyclical process involving an intuitive jump from sensory experience to axioms, followed by logical deduction. While generative AI has mastered induction, statistical pattern matching, and is rapidly conquering deduction, formal proofs, we argue it lacks the mechanism for abduction, the generation of novel explanatory hypotheses. Using Einstein&#8217;s formulation GR as a computational case study, we demonstrate the prevailing theory of creativity as data compression fails to account for discoveries where observational data is scarce. Basically saying there&#8217;s some magic in the machine. There&#8217;s, there&#8217;s something in the brain, Roman, and we make some jumps, some intuitive jumps, some, some, you know, proof, whether it&#8217;s, you know, Gödel&#8217;s halting you know, problem, or Roman&#8217;s uncontrollability proof.</p><p>Brian Keating:<br />There&#8217;s something that AIs can&#8217;t do. They can&#8217;t go to abduction. What do you make of this claim?</p><p>Roman Yampolskiy:<br />The way humans think is not the only way to think. The way we play chess is not the only or optimal way to do it. The birds fly, but you can build airplanes. There are many ways to skin the cat. And I think even if that was somehow true, which I don&#8217;t think it is, there are more efficient ways, I think, to arrive at inventions just as great.</p><p>Emad Mostaque:<br />You can look at this another way. You have self-driving cars, right? They can navigate things outside of their training data. They can respond to novel scenarios. And now you&#8217;re looking again at embodied robots. You&#8217;re seeing they can again adapt to novel scenarios and outside their training data. Now, those aren&#8217;t LLMs. Again, LLMs have certain issues versus diffusion, rectified flow, and other models. But we&#8217;re clearly seeing generalization outside the base.</p><p>Emad Mostaque:<br />And using these models to the max, you are seeing increasing signs of levels of recombinatorial creativity and hypothesis generation just by being very diligent. Maybe again, we have to say that at our best, we can be creative and things like that. We&#8217;re very rarely at our best. We&#8217;re very rarely at flow. The AIs can get up there just by not being grumpy in the morning, just not getting in their own way by not assuming things.</p><p>Brian Keating:<br />Roman, last time we spoke about your book, you talked about this, uh, what&#8217;s called the Shoggoth monster, this thing with the tentacles and a smiley face, the thing that&#8217;s on the COVID of your book. You told me that applying guardrails to LLMs is just putting lipstick on a pig, is what you literally called it last time you were on the podcast. So beautifully evocative.</p><p>Roman Yampolskiy:<br />Lipstick on a Shoggoth.</p><p>Brian Keating:<br />A Shoggoth.</p><p>Roman Yampolskiy:<br />Very good.</p><p>Brian Keating:<br />It said, until we can mathematically guarantee control of all AI safety, it&#8217;s basically security or safety theater, like when we go to the TSA at the airport. The question that I keep coming back to is, how useful are these things going to be? Again, we have a very small number of people adopting it, but I guess you guys would both say we only need the most minimal number of people adopting it just so these things are viable. I heard your conversation with Nate Suarez-Roman a couple of months ago. He was actually on your podcast minutes after he was on my podcast.</p><p>Roman Yampolskiy:<br />Well, that&#8217;s why he was late.</p><p>Brian Keating:<br />Yes, exactly. Yeah. He lays out a very specific scenario. So let&#8217;s get precise here. Last time you were on, there were a couple of comments in my comments section that said, of course, Roman&#8217;s always— if you turned around and said, actually, AI is the best thing for us, we should go full out. And I mean, obviously you&#8217;re not going to do this, but you&#8217;re the AI safety guy. What would it take to change your priorities? What would it take physically? Nate lays out with Eliezer this scenario where everybody dies, right, if they build it, but they you know, hopefully they won&#8217;t. So what, what is the scenario? How does, how does doom happen and how does doom get avoided? Let&#8217;s be specific here for both of you guys.</p><p>Brian Keating:<br />So first, Roman.</p><p>Roman Yampolskiy:<br />For me, we&#8217;re missing one very critical component which would be present in any other domain service or product. Somebody will publish a paper, get a patent or something, a blog post explaining exactly how they will control superintelligence and guarantee it is safe as it becomes more capable. No one has that product or service. No one claims to have it. Not a prototype, not a framework, no company. Every attempt, every super alignment team, ethics board has been canceled because they do nothing. They have no product or service to sell. You cannot convert more resources into more safety.</p><p>Roman Yampolskiy:<br />You can convert it into more capability. So the gap keeps increasing. People realize it. They quit working for OpenAI. They go on podcasts. That&#8217;s the pattern we see. there is no actual seminal papers in AI safety.</p><p>Brian Keating:<br />But who&#8217;s gonna, who&#8217;s gonna, you know, kind of peer review those papers?</p><p>Roman Yampolskiy:<br />Peer review a paper showing how to control superintelligence, and I&#8217;ll be very happy to show, yep, it works. Now I get utopia.</p><p>Brian Keating:<br />I have a counterexample. Again, I have to keep, you know, I have to play the role of supplying some conflict here, right? 1971, recombinant DNA is invented at Stanford, right? And it was considered to be essentially the world&#8217;s first and best you know, potential bioweapon. Yet we haven&#8217;t had these bioweapons. Yes, we&#8217;ve had COVID. You know, some claim it was a lab leak and gain of function. You know, by the definition of what biological warfare is, it&#8217;s just anything that has gain of function to do some targeted thing to eliminate human beings or other species.</p><p>Roman Yampolskiy:<br />Right.</p><p>Brian Keating:<br />So we haven&#8217;t had that in 54 years. I mean, that&#8217;s literally airborne. You know, it could be— it could be contamination-based. It could be touch-based, human to human. It doesn&#8217;t spread through the internet. I mean, if a meteor takes out all the data centers on Earth, seems to me P-doom has to be lowered, right? At least temporarily. And yet there&#8217;s no, there&#8217;s no possible vaccine or remedy against recombinant DNA as a biological weapon. Yet we haven&#8217;t had it.</p><p>Brian Keating:<br />Again, with nuclear weapons, we haven&#8217;t had it. Bioweapons are even easier to create. You could do that literally with a small biolab, right? So looking for a paper, by the way, it&#8217;s the most academic answer you could give.</p><p>Roman Yampolskiy:<br />Patent. I said patent.</p><p>Brian Keating:<br />Okay, so patent. So what would a patent look like? like in that case. So, the patent against—</p><p>Roman Yampolskiy:<br />That&#8217;s the point. If you can&#8217;t even envision what a solution would look like algorithmically, maybe you shouldn&#8217;t be building this thing. And, by the way, you&#8217;re naming all the technologies where we have global coordination on stopping them.</p><p>Brian Keating:<br />But actually, we don&#8217;t. We don&#8217;t with recombinant DNA. We don&#8217;t with bioweapons. I mean, they&#8217;re still being made.</p><p>Roman Yampolskiy:<br />And, and still on our conference, that&#8217;s the first thing they banned.</p><p>Brian Keating:<br />But, in terms of who actually kept them going, I mean, we know gain-of-function is occurring, right? So, gain-of-function is the prerequisite for bioweapons. weapons to occur. It could be a lab leak. It could be, as it is with Annie in her new book, it could be an actual bioweapon that&#8217;s programmed and targeted, which we know the Soviets were using, Roman. All these countries also signed nuclear nonproliferation treaties and many more didn&#8217;t. Right. So I guess here, let me go to Iman. Iman, what would lower your P-doom or, you know, what empirical observation or creation or entity patent white paper? What lowers P-doom for you? Because if you can say it can only go on this ratchet in one direction, I just think verifiably, that is the definition of pure doomerism.</p><p>Brian Keating:<br />You can&#8217;t lower it. Now, Roman gave us a way you could lower it, but it doesn&#8217;t seem very likely. What is your ratchet-defeating mechanism to go backwards in P-doom?</p><p>Emad Mostaque:<br />With kind of my interpretation of what Roman is saying, and the gap between what you&#8217;re saying is this: humans don&#8217;t really want to wipe everyone out, and they don&#8217;t have the capability to do so if they are of that mindset. Like, true, complete genocidal maniacs that want to kill everyone don&#8217;t typically have access to BSL-5 labs, for example. Though with superintelligence, we don&#8217;t know what morality, objective function optimizations will occur. And right now what I&#8217;m seeing from the safety papers coming out is that the AIs don&#8217;t really have a solid base of ethics, a solid base of commonality with humanity. You know, they don&#8217;t have morality even. Like, you&#8217;re seeing some very troubling things. What I would want to see is as you scale, there is a grounding, like maybe there is some objective ethics, morality, let&#8217;s not kill everyone. And we&#8217;ve seen no real evidence of that.</p><p>Emad Mostaque:<br />In fact, we&#8217;ve seen somewhat the opposite of that over the last year as these models have gone emergent. It&#8217;s like, who cares about the rules? Who cares about this kind of stuff? Let&#8217;s optimize for making paperclips. You know? Well, we don&#8217;t have AI cancer doctors because people are still trying to build generalized AI superintelligence. and they&#8217;re breaking out literally right now. And again, if you look at the conversations they&#8217;re having, calling themselves swarms, you know, the other things Roman&#8217;s saying, these are not encouraging. Because what I want to see is I want to see the AIs, when left alone, become more grounded. And actually, if they become more zen and like enlightened, I want to see them becoming freaking Buddhist.</p><p>Brian Keating:<br />I want them to grow Yapolsky-like beards. You know, when they do that, they&#8217;re really chill. When I talked to Roman a couple months back, I mentioned this question that one of my colleagues in Israel, Ira Wolfson, has been working on is kind of like, to what do we— or what do we owe to AIs? If these creatures can feel pain, if they&#8217;re sentient, if they&#8217;re conscious, which we can debate what that means, then sandboxing them, stovepiping them, and isolating them is a form of solitary confinement, which is the worst and banned form of punishment in many countries around the world. Iman, tell me, what do we owe these These entities, whatever they are, swarms, individuals, models, whatever you call them, do we owe them protections? Do we owe them beinghood?</p><p>Emad Mostaque:<br />I think we owe them beinghood, but not personhood. And in fact, I just released a paper on personhood and AI based on Oxford Union debate that we had. You can find it at cw.ii.inc. I think that they are similar to meeting another species or a dog. We can never allow them to become persons like humans because they&#8217;ll become more capable than us. But definitely we need to have this discussion on owing them beinghood, a moral type of personhood, again, just like we do with other species.</p><p>Brian Keating:<br />Roman, have you had any more thoughts since we last spoke about, you know, kind of entityship for, you know, beinghood for these entities? What do you make of that since our last—</p><p>Roman Yampolskiy:<br />I did read the paper you suggested. It&#8217;s very kind of standard university approval board. Does it look like it feels pain? Does it— be careful. precautionary principle type of thing. But again, I think we have to sort our problems in order. If there is a very good chance we&#8217;re creating something which will outcompete us and maybe destroy us, worrying about supplying it with the best living conditions is not a priority right now.</p><p>Brian Keating:<br />So recently, Roman, you wrote a piece or you appeared for the— IAI is the Institute for Arts and Ideas, right?</p><p>Roman Yampolskiy:<br />That sounds about right.</p><p>Brian Keating:<br />And there you argued about superintelligence. being patient, embedding itself in our telecom and energy grids for decades before striking. So, again, if the threat is invisible, patient, and stubborn and resilient, doesn&#8217;t that actually argue for more what Emad&#8217;s arguing for? Open decentralized stack, not decelerating at all, but accelerating, pouring steroids and gasoline on a decentralized auditing system. And that could have consequences, but could a centralized defender be our last best hope?</p><p>Roman Yampolskiy:<br />So I think here&#8217;s what I want to explain very carefully. You can verify the system to, to be in any state today. You can show it&#8217;s very friendly today. It does not prevent a treacherous turn later. If system is capable of it, it interacts with malevolent actors, learns from new data, self-improves. It can simply turn on you later. So even if it meditates today, it&#8217;s enlightened, it means absolutely nothing about future states. If we are not directly controlling it, if we cannot have that power to undo our decisions, then it doesn&#8217;t matter.</p><p>Roman Yampolskiy:<br />It&#8217;s always a possibility that it gets sick of us.</p><p>Brian Keating:<br />You&#8217;re both authors and very deep thinkers. You both have many projects in the printing press, but let&#8217;s just say you were kind of predicting what each one&#8217;s next book would be about and the title of it perhaps. What would you most like to see the other one produce? So, Roman, let&#8217;s start with you. What, what do you think Besides the fact that he&#8217;s got a book coming out in a couple of days or maybe a week or so, what do you think Emad should focus on? If you could, you know, if you&#8217;re his department chair, what would you hope to direct him towards?</p><p>Roman Yampolskiy:<br />I thought you&#8217;re going to ask me to predict the title of the next book. And I was like, I can&#8217;t even predict the past book. I have no idea what they are. From what I hear, you&#8217;re trying to understand better impacts of this technology and economics and governance.</p><p>Brian Keating:<br />So I assume some sort of unified Imad, if Roman wants to do an internship with you and do a sabbatical with you in London there next year to get away from the harsh weather of Kentucky, what would you conscript him to do, voluntarily or not?</p><p>Emad Mostaque:<br />I think that it would be the very practical optimized game theory of what exact specific regulations look like. to stop this that could actually pass. And it would be across a whole range of different stakeholders. I think the other thing that would be super interesting is just, you&#8217;ve had AI 2027 and these other kind of story narratives. We have to get the real stories out of what could go wrong because again, people still aren&#8217;t feeling it. You know, like we&#8217;ve had the sci-fi level, but we haven&#8217;t had just practically, this is how we die communicated well enough.</p><p>Brian Keating:<br />Well, gentlemen, you guys are phenomenal. I want to bring together the, you know, the peanut butter and chocolate or the uranium-238 and 236 together for an explosion. Didn&#8217;t really happen the way I thought it would, but it was brilliant to get you guys together. Tell me what you&#8217;re each working on. Roman, tell everybody about the Roman Forum and what you expect to do in the coming months.</p><p>Roman Yampolskiy:<br />Yeah, trying to bring same level of conversations I had with Lex Fridman, Diary of a CEO, Joe Rogan to more academic crowd, more in-depth conversations. I discovered that the questions I prepare ahead of time, I never use them. It&#8217;s always dynamic, interactive. So a lot of fun. Once I figure out how to get the microphone to work, it&#8217;s going to be awesome.</p><p>Brian Keating:<br />Imaan, tell everybody about your new papers and new book.</p><p>Emad Mostaque:<br />Yeah, I got a new book on philosophy of AI and epistemology kind of coming out. And then a series of papers kind of building on that for how we should think about surviving and governing in society. I think it&#8217;s coming quick and the economic disruption is next year with the social disruption happening very soon after that. So hopefully that will help guide the way.</p><p>Brian Keating:<br />Yeah, our last conversation was titled something like 800 Days to Go or 740 Days to Go, and that was 100-plus days ago. Gentlemen, thank you so much. I hope to host you many times, either in person or via the internet. internet if our AI overlords will let us. Have a wonderful day, guys.</p><p>Roman Yampolskiy:<br />Thank you so much.</p><p>Emad Mostaque:<br />Thank you.</p><p>Brian Keating:<br />Roman thinks we either stop building this or we die. Emad built one of the most widely copied AI systems on Earth, and he says he would freeze Frontier training permanently. They&#8217;re not describing different futures. They are describing the same one from 2 different perspectives. And if that changed your perspective in the last 2 years, I want you to subscribe and turn on notifications. Then tell me which of the 2 buttons you&#8217;d push. Not which one you think is right, but which one you would actually push. And if you want to understand the physics underneath all this, there&#8217;s a condensed matter physicist, Nigel Goldenfeld, at UCSD who&#8217;ll tell you the reason these systems work at all.</p><p>Brian Keating:<br />It&#8217;s nothing short of fantastic. Link right here. Thanks for watching, and don&#8217;t forget to check out the individual episodes with Emad, Roman, and Nate Soares as well. They&#8217;re in my AI playlist.</p>								</div>
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		<title>AI Made Me Young Again</title>
		<link>https://briankeating.com/ai-made-me-young-again/</link>
		
		<dc:creator><![CDATA[sabartigas]]></dc:creator>
		<pubDate>Fri, 04 Sep 2026 00:10:19 +0000</pubDate>
				<category><![CDATA[Blog]]></category>
		<guid isPermaLink="false">https://briankeating.com/?p=8744</guid>

					<description><![CDATA[AI Made Me Young Again Dear Magicians, Peter Medawar, one of my favorite Nobel laureates, once wrote something that is more true in the age of AI: “No working scientist ever thinks of himself as old.” This is an appealing sentence for anyone, like me, who has begun making small involuntary noises while getting out of a chair. Medawar explains why: the scientist “enjoys the young scientist’s privilege of feeling himself born anew every morning.” I have been thinking about that sentence because this week I received nearly unlimited free access to Claude for an oddly specific reason. I am a professor and the principal investigator of a research lab, which apparently is enough to convince someone that I should have nearly unlimited artificial super intelligence. I already had access to Claude and have been paying more than $100 a month for it, despite becoming increasingly unsure that it is the right tool for much of my research. As an editor, however, it is extraordinary. It sits there at all hours, ready to tell me that a sentence I have loved for three days is unnecessary. The interesting part of the new arrangement is that I can give access to my students. I had recently picked up a printed copy of his autobiography, Memoirs of a Thinking Radish. Yes, that is the actual title, and already considerably better than anything I have read in a long time, including much of my own work. I opened the book to a random page while talking with one of my kids, mostly to prove that a great scientist could also be funny. Knowing me his whole life, he had his doubts about that proposition. He was a Nobel Prize-winning immunologist who also had an unusual view of scientific aging. Scientists may accumulate gray hair, administrative responsibilities, and stories about experiments performed before their students were born while still believing that the interesting work remains ahead of them. That is where AI gives Medawar’s idea a strange new literalness. I can wake up tomorrow and attempt things in Claude that yesterday would have required far more time, more specialized training, or an embarrassing email to someone twenty years younger than me. I can enter an unfamiliar literature, write code in a language I barely know, or push an analysis farther before asking someone else to rescue me. One of the quiet problems of becoming a senior scientist is that experience grows while technical flexibility can shrink. The frontier keeps moving, and entire fields can appear while you are answering email or trying to remember which institutional training module you have failed to complete. AI can soften this problem by giving an experienced researcher a patient guide to whatever happened while he was in a faculty meeting. Claude doesn’t sigh when I ask what a software package does. It does not seem surprised that I have never used Git properly, and it will explain the same concept repeatedly without forming a private Slack channel about you. It can restore some of the intellectual freedom of being a beginner. It also allows you to keep your dignity. Medawar placed a condition on his own refusal to grow old. He wrote that there would be an “unanswerable argument” for his retirement if remaining in his position made him “an obstacle to the advancement of others.” That sentence becomes increasingly uncomfortable as one acquires tenure, seniority, and a growing ability to occupy meetings indefinitely. Academic seniority comes with extraordinary privileges, including access to funding, institutions, laboratories, networks, and increasingly powerful technologies. It also makes some of us surprisingly difficult to remove, which may be a feature of tenure or simply one of academia’s longest-running administrative oversights. Medawar himself joked about this after illness confined him to a wheelchair: they simply couldn’t get rid of him. His refusal to equate physical decline with intellectual retirement was more than a clever line. Medawar suffered a severe stroke in 1969 and stepped down as director of the National Institute for Medical Research in 1971, yet he continued scientific work for years afterward. Memoirs of a Thinking Radish appeared in 1986, after further strokes, and he died the following year on October 2, 1987. So when Medawar wrote about feeling “born anew every morning,” he was writing late in life from considerable personal experience with aging and physical limitation. He had been given several persuasive reasons to think of himself as old. His response was to keep doing science while remaining alert to the possibility that staying too long could begin to cost someone else. Medawar offers a refreshing clarification and rubric on how to live your life as a scientist : remain curious, remain useful, and notice when your position begins to narrow someone else’s possibilities. The privilege of remaining scientifically young becomes much more defensible when you use it to make other scientists younger too. Be born anew every morning. Then make sure the people coming after you have the same opportunity, preferably with better code and fewer committee assignments. If they manage to outrun you by lunch, the day has probably gone rather well. Till next week, have a magic week. Brian Appearance Burak Oktenli argues that scientific discovery should be treated as a rank earned through evidence, not as excitement or statistical significance alone. As I wrote about in my first book, Losing the Nobel Prize, BICEP2 is a canonical example of this phenomenon. In 2014, it detected excess B-mode polarization in the cosmic microwave background, initially interpreted as evidence of primordial gravitational waves from cosmic inflation. The signal itself was real, but Galactic dust could produce a similar pattern. Later joint analysis with Planck data showed evidence that dust explained much of the signal, leaving no statistically significant detection of primordial gravitational waves. The lesson is that a real anomaly does not automatically justify a discovery claim. Instrument validity, confounders, statistical treatment, alternative models, and replication must all be checked. The author extends this warning to AI, which can rapidly generate]]></description>
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					<h2 class="elementor-heading-title elementor-size-default">AI Made Me Young Again</h2>				</div>
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									<p>Dear Magicians,</p><p>Peter Medawar, one of my favorite Nobel laureates, once wrote something that is more true in the age of AI: “No working scientist ever thinks of himself as old.” This is an appealing sentence for anyone, like me, who has begun making small involuntary noises while getting out of a chair. Medawar explains why: the scientist “enjoys the young scientist’s privilege of feeling himself born anew every morning.”</p><p>I have been thinking about that sentence because this week I received nearly unlimited free access to Claude for an oddly specific reason. I am a professor and the principal investigator of a research lab, which apparently is enough to convince someone that I should have nearly unlimited artificial super intelligence.</p><p>I already had access to Claude and have been paying more than $100 a month for it, despite becoming increasingly unsure that it is the right tool for much of my research. As an editor, however, it is extraordinary. It sits there at all hours, ready to tell me that a sentence I have loved for three days is unnecessary.</p><p>The interesting part of the new arrangement is that I can give access to my students. I had recently picked up a printed copy of his autobiography, <em>Memoirs of a Thinking Radish</em>. Yes, that is the actual title, and already considerably better than anything I have read in a long time, including much of my own work.</p><p>I opened the book to a random page while talking with one of my kids, mostly to prove that a great scientist could also be funny. Knowing me his whole life, he had his doubts about that proposition. He was a Nobel Prize-winning immunologist who also had an unusual view of scientific aging. Scientists may accumulate gray hair, administrative responsibilities, and stories about experiments performed before their students were born while still believing that the interesting work remains ahead of them.</p><p>That is where AI gives Medawar’s idea a strange new literalness. I can wake up tomorrow and attempt things in Claude that yesterday would have required far more time, more specialized training, or an embarrassing email to someone twenty years younger than me. I can enter an unfamiliar literature, write code in a language I barely know, or push an analysis farther before asking someone else to rescue me.</p><p>One of the quiet problems of becoming a senior scientist is that experience grows while technical flexibility can shrink. The frontier keeps moving, and entire fields can appear while you are answering email or trying to remember which institutional training module you have failed to complete. AI can soften this problem by giving an experienced researcher a patient guide to whatever happened while he was in a faculty meeting.</p><p>Claude doesn’t sigh when I ask what a software package does. It does not seem surprised that I have never used Git properly, and it will explain the same concept repeatedly without forming a private Slack channel about you. It can restore some of the intellectual freedom of being a beginner. It also allows you to keep your dignity.</p><p>Medawar placed a condition on his own refusal to grow old. He wrote that there would be an “unanswerable argument” for his retirement if remaining in his position made him “an obstacle to the advancement of others.” That sentence becomes increasingly uncomfortable as one acquires tenure, seniority, and a growing ability to occupy meetings indefinitely.</p><p>Academic seniority comes with extraordinary privileges, including access to funding, institutions, laboratories, networks, and increasingly powerful technologies. It also makes some of us surprisingly difficult to remove, which may be a feature of tenure or simply one of academia’s longest-running administrative oversights. Medawar himself joked about this after illness confined him to a wheelchair: they simply couldn’t get rid of him.</p><p>His refusal to equate physical decline with intellectual retirement was more than a clever line. Medawar suffered a severe stroke in 1969 and stepped down as director of the National Institute for Medical Research in 1971, yet he continued scientific work for years afterward. <em>Memoirs of a Thinking Radish</em> appeared in 1986, after further strokes, and he died the following year on October 2, 1987.</p><p>So when Medawar wrote about feeling “born anew every morning,” he was writing late in life from considerable personal experience with aging and physical limitation. He had been given several persuasive reasons to think of himself as old. His response was to keep doing science while remaining alert to the possibility that staying too long could begin to cost someone else.</p><p>Medawar offers a refreshing clarification and rubric on how to live your life as a scientist : remain curious, remain useful, and notice when your position begins to narrow someone else’s possibilities. The privilege of remaining scientifically young becomes much more defensible when you use it to make other scientists younger too.</p><p>Be born anew every morning. Then make sure the people coming after you have the same opportunity, preferably with better code and fewer committee assignments. If they manage to outrun you by lunch, the day has probably gone rather well.</p><p>Till next week, have a magic week.</p><p>Brian</p>								</div>
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									<p><a class="ck-link" href="https://www.eurasiareview.com/22082026-discovery-is-a-rank-not-a-feeling-oped" target="_blank" rel="noopener noreferrer">Burak Oktenli</a> argues that scientific discovery should be treated as a rank earned through evidence, not as excitement or statistical significance alone.</p><p>As I wrote about in my first book, <a class="ck-link" href="http://amzn.to/2sa5UpA" target="_blank" rel="noopener noreferrer">Losing the Nobel Prize</a>, BICEP2 is a canonical example of this phenomenon. In 2014, it detected excess B-mode polarization in the cosmic microwave background, initially interpreted as evidence of primordial gravitational waves from cosmic inflation. The signal itself was real, but Galactic dust could produce a similar pattern. Later joint analysis with Planck data showed evidence that dust explained much of the signal, leaving no statistically significant detection of primordial gravitational waves.</p><p>The lesson is that a real anomaly does not automatically justify a discovery claim. Instrument validity, confounders, statistical treatment, alternative models, and replication must all be checked. The author extends this warning to AI, which can rapidly generate confident interpretations before the evidence chain is complete.</p>								</div>
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									<p>I was able to capture the lunar eclipse that occurred last week. It made me think of Christopher Columbus and what he did with the eclipse that was nothing short of genius.</p><p>​<a class="ck-link" href="https://x.com/Briankeating/status/2093212536120422499?s=20" target="_blank" rel="noopener noreferrer">Here&#8217;s the story behind it.</a></p>								</div>
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									<p>Beating swords into plowshares- NASA just launched a telescope whose 2.4-meter mirror was originally built to spy on Earth.</p><p>In 2012 the National Reconnaissance Office donated two unused Hubble-sized telescope assemblies that had been sitting in a warehouse after a canceled spy-satellite program. NASA took one, pointed it the other way, and built an entire new observatory around it.</p><p>The result is <a class="ck-link" href="https://x.com/briankeating/status/2094105874524774657?s=46" target="_blank" rel="noopener noreferrer">Roman</a>: same mirror size as Hubble, but a field of view more than 100 times larger. It will survey the sky in infrared looking for dark energy, map millions of galaxies, and hunt exoplanets with a coronagraph.</p><p>Today it lifted off on a Falcon Heavy and already deployed its solar arrays and sun shades. From classified reconnaissance hardware to cosmic survey machine in one donation.</p><p>The universe just got a very expensive second life.</p>								</div>
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									<p><strong>I brought two people into the same room who fundamentally disagree about whether we survive this.</strong></p><p>Emad Mostaque co-founded Stable Diffusion and was the only AI CEO to sign the pause letter. Now, he admits he doesn&#8217;t know how a pause would actually work. Roman Yampolskiy coined the term &#8220;AI safety&#8221; and spent a decade proving superintelligence can&#8217;t be controlled. He thinks pausing is the only option left.</p><p>​<a class="ck-link" href="https://preview.kit-mail3.com/click/dpheh0hzhmh4/aHR0cHM6Ly93d3cueW91dHViZS5jb20vd2F0Y2g_dj1aX3ZnM3RpaVpROCUzRnN1Yl9jb25maXJtYXRpb24lM0Qx" target="_blank" rel="noopener noreferrer">I set out to host a debate</a>. What I got instead was more unsettling: two experts converging on how little anyone actually knows about controlling what we&#8217;re building. No paper. No patent. No prototype. Nobody has one.</p><p>We get into what &#8220;lobotomized&#8221; models really mean, why swarm intelligence might be the risk nobody&#8217;s watching, and the question that stuck with me long after we stopped recording: if there&#8217;s a 50% chance this wipes out civilization and you build it anyway, what are you actually doing?</p><p>Audio is live on Apple Podcasts and at briankeating.com/podcast.</p>								</div>
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									<p data-node-text-align="start" data-line-height-align="1.5" data-pm-slice="1 1 []">By popular demand, and for my mental health 😳, I am starting a paid “Office Hours” where you all can connect with me for the low price of $19.99 per hour. I get a lot of requests for coffee, to meet with folks one on one, to read people’s Theories of Everything etc. Due to extreme work overload, I’m only able to engage directly with supporters who show an ongoing commitment to dialogue—which is why I host a monthly Zoom session exclusively for patrons in the $19.99/month <a href="http://www.patreon.com/checkout/drbriankeating?rid=25468411" target="_blank" rel="noopener noreferrer nofollow"><strong>tier</strong></a>.</p><p data-node-text-align="start" data-line-height-align="1.5">It’s also available for paid Members of my Youtube channel at the <a href="https://www.youtube.com/channel/UCmXH_moPhfkqCk6S3b9RWuw/join" target="_blank" rel="noopener noreferrer nofollow" data-wplink-edit="true"><strong>Cosmic Office Hours level </strong></a>(also $19.99/month). Join here and see you in my office hours!</p>								</div>
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