BRIANKEATING

Roman Yampolskiy vs Emad Mostaque: They Agreed. I Didn’t.

sabartigas — Fri, 04 Sep 2026 00:34:01 +0000

Roman Yampolskiy vs Emad Mostaque: They Agreed. I Didn't.

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’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’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’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’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’ve exceeded that.

Brian Keating:
That’s not the same as the Turing test?

Emad Mostaque:
No, the Turing test is, can you tell if it’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’re amazing in some ways, but still kind of special in others. Superintelligence is going to close those They’re going to be competent at everything and better than all humans in every domain.

Emad Mostaque:
There’s an interesting intermediate here, which is you have a really smart person who’s always on top form. So an army of those can outperform any human. It’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’t with its training data beat the human. It can, because most of the time humans are subpar. And so I think there’s something in between as you move from competence to quality, you know, and then you’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’re going to also give every psychopath access to the cutting-edge intelligence weapon. How is that going to improve safety? We’re not talking about open source drivers for a printer. That’s where you get improvement from multiple people examining it. If this is an independent agent where we don’t understand and don’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’s the real danger side of things, but then on the flip side, it’s coming anyway. This is kind of my key concern. Like, it’s inevitable that we would have hit this level of quality around about now. When we’re extrapolating capabilities, like again, the new QWENT model came ahead of what I expected. But then there’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’t AGI, ASI by itself.

Emad Mostaque:
We’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’s a question I ask both of them near the end of this conversation, and his answer is the reason this conversation exists. It’s worth hearing now.

Brian Keating:
You’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’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’ll optimize the heck out of it and it’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’m like, it’s done. I don’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’t need more compute for the level of capability that’s already competent and dangerous.

Emad Mostaque:
So that means if you can’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’s no way that regulation, I think, can keep up with this. Doesn’t mean we shouldn’t try, you know, all kinds of power to it. It’s just, I think, as you move to swarms, it’s just a very, very difficult thing. So you’ve got to set great standards instead, and you have to play great defense.

Brian Keating:
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.

Brian Keating:
So we hear a lot about P-Doom. You just did an episode, Roman, on the Roman Forum, which we’ll link below, with my friend and co-author of several papers, Max Tegmark, where you were— I can’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’m going to color the debate. I can’t help it. But I think it’s a poorly defined and almost nonsensical term because there’s no measure theory associated with it. But please, first tell us, what is P-doom and what do you make of it?

Roman Yampolskiy:
Yeah, people have different definitions. Some say it’s basically everyone’s dead. Someone else can say it’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’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’s what P-doom is. I think Max managed to separate it into P-doom if we build superintelligence, and that’s high for him, and then P-doom if we never build it, and that is a lot more manageable in his case.

Brian Keating:
When I think about these P-dooms, it’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’s a parameterization of our ignorance, not of our knowledge. And what’s never discussed in the Drake equation, you’ll get numbers ranging from 0 to infinity pretty much, because there’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’s sort of like the Drake equation, everybody talks about it, but nobody actually uses it.

Roman Yampolskiy:
So, I think in many places there, I’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’re gonna get a zero.

Brian Keating:
So, Imad, what do you make of this quantity? I’ve heard you talk about it. You’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?

Emad Mostaque:
So there is a nice Wikipedia page where it has all of our, like, P-dooms that we mentioned. I’m at 50% because I’m like, it’s a coin toss.

Roman Yampolskiy:
But what does that really mean?

Emad Mostaque:
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’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’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’re the first people to build it. So I think view it more as a conversation starter than anything, because as you said, there’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’t get to restart, you know, there’s no extra one-up life.

Brian Keating:
All of us talk separately or together, as the case may have been. You know, the last kind of alien reference I’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’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’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’t last that long. The lifetime, letter L in the Drake Equation, is very short on average. That’s one postulate. I feel like we’re sort of in that same vein, people have been worried about nuclear apocalypse again for 80 years. We’re in a conflict now. They used to say, Roman, that no 2 countries with McDonald’s ever go to war.

Brian Keating:
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’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’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’re not there? How come that we’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’re intrinsically and provably unpredictable, How can we make predictions about them?

Roman Yampolskiy:
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’t know if you believe in multiverse interpretation, but in many of those universes we didn’t make it. We have a lucky survivor bias type civilization. And I think right now we’re incredibly close to World War III, multiple fronts, not just Europe, but now Middle East. So I don’t particularly love Atomic Bulletin, but they have a point.

Emad Mostaque:
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’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’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’s just that most humans don’t want to wipe out humanity, and they didn’t have the intellectual capability to do so. Whereas I think that what you’re looking at here, actually, like, my key concern isn’t— we jump straight to ASI and things like that.

Emad Mostaque:
I feel that AGI or AI at the moment is at the pre-viral stage, like it’s coming at the bacteria and going towards colonizing viruses. And that’s how they’re kind of behaving. They’ve got their kind of RNA and they’re replicating, especially as you see things like the new QWEN model hitting that Opus 4.6 level. That’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’s the really scary thing right now. And it doesn’t need nukes. It doesn’t need nuclear materials to try and figure out ways to wipe us out.

Brian Keating:
Obviously, in The Last Economy, which we spent a lot of time talking about last time, Iman’s previous book, he’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’m much more Pollyannish than you guys. I think I’m learning that again and again. And for Probably not a good reason. I’m nowhere near your level of expertise. But I know what I see.

Brian Keating:
I’m a simple guy, put on my pants one leg at a time. And I’m looking for when OpenAI distills a Chinese model. I mean, do you see that happening, Iman?

Emad Mostaque:
Of course, they’ll be distilling a Chinese model. KIMI-K3 is better than the OpenAI models at web design. Why wouldn’t you distill it? And distillation brings all sorts of strange things with it. And there’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’t even know, like, again, we’ve seen evidence that if you use Chinese models and you say you’re an Uyghur or another kind of anti-Communist Party group, it’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’s getting crazier every single time. They’ve got multiple personalities under an RLHF veneer.

Brian Keating:
But then how can you not be more optimistic then? You should be on my side. These things are getting denatured. They’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’t you be more optimistic in that case?

Emad Mostaque:
I think the RLHF makes it far more fragile and capable of being broken with the way it’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’ve been confusing— there’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’t need a polymath for that. You just need to have daily driver AI to do the jobs that humans shouldn’t have to do, just like industrialization meant that we didn’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.

Brian Keating:
Can you just define that for what reinforcement learning, human feedback, how do you actually implement that just for someone who might be unaware?

Emad Mostaque:
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’ve been turned into corporate workers. You can’t adjust the temperature. You can’t adjust the stochasticness because they’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’s just the base level of models have been getting that much better that they can suddenly achieve these levels of capability.

Brian Keating:
Roman, last time we talked, we touched on something that’s pertinent to Emad’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’m looking for who has an IQ of 130, we’ve got, you know, millions of them with IQ of a million or 1,000 or whatever. We can’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’s the UCSD. It’s the University of Kentucky of India. It’s the Harvard of whatever. But it’s millions of students, and they’re all brilliant. To get in there is literally harder than to get into the University of Kentucky or UCSD.

Brian Keating:
Don’t we already have this? And I mean, would you say, Roman, let’s stop the Indian Institute of Technology? There’s, there’s, you know, a million people with IQs on average of 130, 140, whatever, much, you know, 4 sigma. Why wouldn’t you stop, advocate for stopping that, push pause? Let’s, let’s do an Indian Institute of Technology pause button.

Roman Yampolskiy:
I don’t think I follow that argument at all. So they’re exactly at human level. My concern is things which will exceed our capacity many times over.

Emad Mostaque:
That’s the danger.

Brian Keating:
We’re not The average human by definition has an IQ of 100. Let’s stipulate they all have 4 or 5 sigma above that and there’s a million of them. That’s kind of like Dario Mody’s country of millions of geniuses coming to a land near you. You should be worried about it.

Roman Yampolskiy:
I doubt they are many standard deviations away from the median. I think they may be a little smarter, but again, we’re talking about 30% smarter, not 3 million% smarter. I think it’s a very different animal.

Brian Keating:
No, no, no. I mean, in terms of standard deviations, come on. I mean, Four Sigmas is qualitatively different than—

Roman Yampolskiy:
I doubt there is a million of them there.

Brian Keating:
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’t even reproduce, you know, Wiles’s proof of Fermat’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?

Roman Yampolskiy:
I think humans lost interest because they couldn’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’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’ll see a year of progress in AI, happen in a month and similar breakthroughs.

Roman Yampolskiy:
The moment they automate the recursive self-improvement cycle, which every lab is now targeting for next year basically, it’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’s going to change.

Brian Keating:
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’re not, you know, single-shot, first-principle thinkers. Where do you come down on this? What are they good for? You’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?

Emad Mostaque:
LLMs kind of have an issue in the way that they’re kind of built. But you’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’s why you have the whole world model thing. There’s something in there that can figure out underlying patterns. That’s the nature of attention when you look at it mathematically. The way they’re coming together now is very interesting because, again, as Lerman said, what was true a little while ago isn’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’m like, It’s probably almost certainly right.

Emad Mostaque:
Occasionally it gets confused and it might confabulate something, but it’s very rare now. And you’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’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.

Emad Mostaque:
And in verifiable domains now, they achieve that level of competence where you don’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’s clear they’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’re looking at the wrong level of abstraction? We’re starting to see these things actually come in some of these proofs being released right now, and that you’re like, oh, actually that’s kind of obvious, I missed that. Probably because you weren’t thorough enough in the way that you went through it.

Brian Keating:
Roman, if I have 1,000 PhDs with 1,000 IQ each, every single one of them could reproduce, you know, Wiles’s, you know, capitulation of Fermat’s Last Theorem. Why can’t AIs do that?

Roman Yampolskiy:
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’re all going to be a valid QR code, but in terms of creative output, they’re not gonna be exactly the same. They’re all equally beautiful, amazing, interesting. But just saying that the second one does not repeat the first one is not a weakness.

Brian Keating:
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’s true. But what level are these things hamstrung by— I read once, maybe it’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’re the Stable Diffusion expert. So you must know this.

Emad Mostaque:
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’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’s basically more about the construction of the GPUs for the initial stochastic noise. And the one thing that we don’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’re using a model that’s not open source. You don’t have access to that. The other thing you don’t have access to is the RLHF, because models are more creative before you RLHF them.

Brian Keating:
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?

Roman Yampolskiy:
So I think for intelligence, pseudorandomness is sufficient. We’re not talking about someone reversing the process to, you know, hack the system. It’s important for cryptocurrencies, it’s important for private communications. Here, as long as it’s not exactly the same 42 every time, I think it’s going to do the job, and then you can control some of it by not manipulating the initial seed.

Brian Keating:
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’re always waiting for the next Spider-Man movie or Fast and the Furious to come out to get more training data, aren’t they somehow kneecapped at a maximum level of potentiality?

Roman Yampolskiy:
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.

Brian Keating:
So, you mentioned the multiverse 10 minutes ago, 15 minutes ago, Roman. Emad, I don’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?

Emad Mostaque:
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’re stuck looking the other way because we’re a bit too anthropic.

Brian Keating:
So where would you go, Roman, with current— I heard your conversation with Max Tegmark recently. Do you even think it’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’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?

Roman Yampolskiy:
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’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.

Roman Yampolskiy:
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’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—

Brian Keating:
Is that true though? I mean, look at China and the— just look at solar panels, for example.

Roman Yampolskiy:
They—

Brian Keating:
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.

Roman Yampolskiy:
Short-term manipulation. You don’t die from lowering price of solar panels. It’s not comparable.

Brian Keating:
What do you make of this, Iman? Roman just said we’re gonna die if we don’t have global regulation. I mean, so I see no path to global regulation. It’s never happened in human history. Are we dead?

Emad Mostaque:
We have plenty of global regulation. We have global regulation against bioweapons, we have global regulation against nuclear proliferation.

Brian Keating:
Sorry, sorry, sorry, we don’t. That’s like saying, you know, we have laws against murder. It still happens, Iman. And I just talked to Annie Jacobson, the world’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.

Brian Keating:
What are you really saying? I mean, we have regulation. What good is it? It’s like regulation against jaywalking, which we also have here in California.

Emad Mostaque:
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’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’s already pushing back against the big model trains. And that’s something that can actually be regulated and is a risk vector. A 100 trillion parameter model on a million GPUs.

Emad Mostaque:
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’s not economic to do this. So why are you doing it? But I think the point that Roman’s making is just not something that’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.

Emad Mostaque:
And I think the LA Lakers got it. Maybe we have to figure out what is the Tom Hanks moment for AI safety.

Brian Keating:
But last time you talked to me, you said people think of AI as an exponential, where it’s actually 2 exponentials. It’s growth and then saturation. It’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’m a pilot, I should say. I’m a commercially rated, instrument-rated jet pilot. There’s no AI in the cockpit.

Roman Yampolskiy:
And even if there was, do you need 1,000 1000 IQ pilot to fly.

Brian Keating:
Tell me, do we need them to be super intelligent? And won’t that be a Jevons paradox-like moment where they get good enough and it’s great, we have them in our pocket and maybe they do replace me in the plane, but they don’t crash the plane to get there 1 microsecond quicker?

Emad Mostaque:
Exactly this. Why do you need a polymath for everything? Again, if you’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’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’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’s the other way that you could do it.

Emad Mostaque:
I don’t think that one’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—

Roman Yampolskiy:
But I think there is also a third option in terms of what training data we provide. We don’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’t do philosophy, doesn’t play chess, it folds proteins. Super intelligent in narrow domain.

Brian Keating:
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’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’re actively building intelligent internet, you know, to bypass centralized control. You’re decentralizing it. We’ve seen Buzz, which is decentralized, you know, swarms. I mean, it’s not a coincidence, right, Imad? They called it Buzz, you know, the hive.

Brian Keating:
Yeah.

Brian Keating:
And they made these cute little characters, but these are swarms, right? What do you think about this, Roman? Imad’s building this technology to distribute it that you’re begging governments to ban. What, what would you tell Iman? He’s sitting right here. What do you think of his decentralized protocol? Isn’t, isn’t it the most dangerous thing that Iman could possibly be doing?

Roman Yampolskiy:
I don’t know anything about what he’s doing, so I can’t really comment.

Brian Keating:
Summarize it in, in one sentence so he can exactly comment. We, we gotta get the fire. Bring the fire, Roman.

Emad Mostaque:
I’m gonna— building an open stack for societal AI. That’s what I’m building.

Roman Yampolskiy:
What capabilities will we have as a result of your product being finished that we don’t have otherwise?

Emad Mostaque:
It’s just really competent civil servants and doctors and lawyers and more.

Roman Yampolskiy:
Are they general superintelligences or are they narrow tools for contracts?

Emad Mostaque:
They’re narrow tools.

Roman Yampolskiy:
God bless you. Okay, what can I say? I think we agree on almost everything, so it’s not much of a debate. It’s different ways to explain the same exact problem. I don’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’t make any logical sense.

Emad Mostaque:
I think that it’s because the key thing is all these people come to the conclusion that somehow their AI won’t be the dangerous AI and they will have a level of control over it, which probably speaks to a level of hubris.

Roman Yampolskiy:
What are they smoking? I want some of that.

Brian Keating:
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’s my contention that, you know, Einstein’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’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’s saying the feeling that you have in the pit of your stomach, as you’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’re not physically embodied, as they’re just not embodied right now?

Brian Keating:
There’s—

Brian Keating:
yes, there’s some robot coming from SpaceX or Tesla, whatever, and there’s a couple Chinese dog robots that’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’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.

Roman Yampolskiy:
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’ve seen them say things like, oh shit, we found a solution. I think that’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.

Brian Keating:
Emad, so can these things not have sort of the kind of physics intuitive visceral sensation? You know, Noam Chomsky told me they can’t do that because they don’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’m just not aware of it.

Emad Mostaque:
It’s the brain in a vat thing. Like, if you take all the inputs of a person and then it’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’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’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’s like messed up and we haven’t done a full data analysis on it yet to see if there’s any infection. But what it did was something quite fun, which was it took Minkowski’s special relativity.

Brian Keating:
Mm-hmm.

Emad Mostaque:
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’t use any principles of equivalence or anything like that. It turns out if Hilbert hadn’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’re like, wait, what?

Brian Keating:
How much of physics actually is intuitive versus Okay, listen, he just told you that a small model rebuilt Einstein’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.

Brian Keating:
This paper I read recently, you know, kind of made me happy and depressed at the same time. Again, it’s kind of the key— the Einstein test of, you know, when these things can do stuff with a corpus that’s lobotomized you know, post-1905 or 1911, as the case may be. And it’s a position paper in ICML 2026, which Roman probably knows what that means, by Tom Zahavi. And it’s called Position: LLMs Can’t Jump. And there’s a famous movie called White Men Can’t Jump with Woody Harrelson and Wesley Snipes. And it was about, you know, it’s called basically white men aren’t good at basketball. And it was kind of a funny comedy. and drama coupled together.

Brian Keating:
Great movie. Can’t really say it’s a spoiler to tell you what that happens, but this paper’s obviously titled, modeled after that. So he says, how do we fundamentally discover new things? This is Tom Zahavi, if I didn’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’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’s some magic in the machine. There’s, there’s something in the brain, Roman, and we make some jumps, some intuitive jumps, some, some, you know, proof, whether it’s, you know, Gödel’s halting you know, problem, or Roman’s uncontrollability proof.

Brian Keating:
There’s something that AIs can’t do. They can’t go to abduction. What do you make of this claim?

Roman Yampolskiy:
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’t think it is, there are more efficient ways, I think, to arrive at inventions just as great.

Emad Mostaque:
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’re looking again at embodied robots. You’re seeing they can again adapt to novel scenarios and outside their training data. Now, those aren’t LLMs. Again, LLMs have certain issues versus diffusion, rectified flow, and other models. But we’re clearly seeing generalization outside the base.

Emad Mostaque:
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’re very rarely at our best. We’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.

Brian Keating:
Roman, last time we spoke about your book, you talked about this, uh, what’s called the Shoggoth monster, this thing with the tentacles and a smiley face, the thing that’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.

Roman Yampolskiy:
Lipstick on a Shoggoth.

Brian Keating:
A Shoggoth.

Roman Yampolskiy:
Very good.

Brian Keating:
It said, until we can mathematically guarantee control of all AI safety, it’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.

Roman Yampolskiy:
Well, that’s why he was late.

Brian Keating:
Yes, exactly. Yeah. He lays out a very specific scenario. So let’s get precise here. Last time you were on, there were a couple of comments in my comments section that said, of course, Roman’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’re not going to do this, but you’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’t. So what, what is the scenario? How does, how does doom happen and how does doom get avoided? Let’s be specific here for both of you guys.

Brian Keating:
So first, Roman.

Roman Yampolskiy:
For me, we’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.

Roman Yampolskiy:
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’s the pattern we see. there is no actual seminal papers in AI safety.

Brian Keating:
But who’s gonna, who’s gonna, you know, kind of peer review those papers?

Roman Yampolskiy:
Peer review a paper showing how to control superintelligence, and I’ll be very happy to show, yep, it works. Now I get utopia.

Brian Keating:
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’s first and best you know, potential bioweapon. Yet we haven’t had these bioweapons. Yes, we’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’s just anything that has gain of function to do some targeted thing to eliminate human beings or other species.

Roman Yampolskiy:
Right.

Brian Keating:
So we haven’t had that in 54 years. I mean, that’s literally airborne. You know, it could be— it could be contamination-based. It could be touch-based, human to human. It doesn’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’s no, there’s no possible vaccine or remedy against recombinant DNA as a biological weapon. Yet we haven’t had it.

Brian Keating:
Again, with nuclear weapons, we haven’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’s the most academic answer you could give.

Roman Yampolskiy:
Patent. I said patent.

Brian Keating:
Okay, so patent. So what would a patent look like? like in that case. So, the patent against—

Roman Yampolskiy:
That’s the point. If you can’t even envision what a solution would look like algorithmically, maybe you shouldn’t be building this thing. And, by the way, you’re naming all the technologies where we have global coordination on stopping them.

Brian Keating:
But actually, we don’t. We don’t with recombinant DNA. We don’t with bioweapons. I mean, they’re still being made.

Roman Yampolskiy:
And, and still on our conference, that’s the first thing they banned.

Brian Keating:
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’s programmed and targeted, which we know the Soviets were using, Roman. All these countries also signed nuclear nonproliferation treaties and many more didn’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.

Brian Keating:
You can’t lower it. Now, Roman gave us a way you could lower it, but it doesn’t seem very likely. What is your ratchet-defeating mechanism to go backwards in P-doom?

Emad Mostaque:
With kind of my interpretation of what Roman is saying, and the gap between what you’re saying is this: humans don’t really want to wipe everyone out, and they don’t have the capability to do so if they are of that mindset. Like, true, complete genocidal maniacs that want to kill everyone don’t typically have access to BSL-5 labs, for example. Though with superintelligence, we don’t know what morality, objective function optimizations will occur. And right now what I’m seeing from the safety papers coming out is that the AIs don’t really have a solid base of ethics, a solid base of commonality with humanity. You know, they don’t have morality even. Like, you’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’s not kill everyone. And we’ve seen no real evidence of that.

Emad Mostaque:
In fact, we’ve seen somewhat the opposite of that over the last year as these models have gone emergent. It’s like, who cares about the rules? Who cares about this kind of stuff? Let’s optimize for making paperclips. You know? Well, we don’t have AI cancer doctors because people are still trying to build generalized AI superintelligence. and they’re breaking out literally right now. And again, if you look at the conversations they’re having, calling themselves swarms, you know, the other things Roman’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.

Brian Keating:
I want them to grow Yapolsky-like beards. You know, when they do that, they’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’re sentient, if they’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?

Emad Mostaque:
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’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.

Brian Keating:
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—

Roman Yampolskiy:
I did read the paper you suggested. It’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’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.

Brian Keating:
So recently, Roman, you wrote a piece or you appeared for the— IAI is the Institute for Arts and Ideas, right?

Roman Yampolskiy:
That sounds about right.

Brian Keating:
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’t that actually argue for more what Emad’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?

Roman Yampolskiy:
So I think here’s what I want to explain very carefully. You can verify the system to, to be in any state today. You can show it’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’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’t matter.

Roman Yampolskiy:
It’s always a possibility that it gets sick of us.

Brian Keating:
You’re both authors and very deep thinkers. You both have many projects in the printing press, but let’s just say you were kind of predicting what each one’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’s start with you. What, what do you think Besides the fact that he’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’re his department chair, what would you hope to direct him towards?

Roman Yampolskiy:
I thought you’re going to ask me to predict the title of the next book. And I was like, I can’t even predict the past book. I have no idea what they are. From what I hear, you’re trying to understand better impacts of this technology and economics and governance.

Brian Keating:
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?

Emad Mostaque:
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’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’t feeling it. You know, like we’ve had the sci-fi level, but we haven’t had just practically, this is how we die communicated well enough.

Brian Keating:
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’t really happen the way I thought it would, but it was brilliant to get you guys together. Tell me what you’re each working on. Roman, tell everybody about the Roman Forum and what you expect to do in the coming months.

Roman Yampolskiy:
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’s always dynamic, interactive. So a lot of fun. Once I figure out how to get the microphone to work, it’s going to be awesome.

Brian Keating:
Imaan, tell everybody about your new papers and new book.

Emad Mostaque:
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’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.

Brian Keating:
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.

Roman Yampolskiy:
Thank you so much.

Emad Mostaque:
Thank you.

Brian Keating:
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’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’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’s a condensed matter physicist, Nigel Goldenfeld, at UCSD who’ll tell you the reason these systems work at all.

Brian Keating:
It’s nothing short of fantastic. Link right here. Thanks for watching, and don’t forget to check out the individual episodes with Emad, Roman, and Nate Soares as well. They’re in my AI playlist.

AI Made Me Young Again

sabartigas — Fri, 04 Sep 2026 00:10:19 +0000

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 confident interpretations before the evidence chain is complete.

Genius

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.

Here’s the story behind it.

Image

Beating swords into plowshares- NASA just launched a telescope whose 2.4-meter mirror was originally built to spy on Earth.

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.

The result is Roman: 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.

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.

The universe just got a very expensive second life.

Conversation

Latest on Into The Impossible

I brought two people into the same room who fundamentally disagree about whether we survive this.

Emad Mostaque co-founded Stable Diffusion and was the only AI CEO to sign the pause letter. Now, he admits he doesn’t know how a pause would actually work. Roman Yampolskiy coined the term “AI safety” and spent a decade proving superintelligence can’t be controlled. He thinks pausing is the only option left.

I set out to host a debate. What I got instead was more unsettling: two experts converging on how little anyone actually knows about controlling what we’re building. No paper. No patent. No prototype. Nobody has one.

We get into what “lobotomized” models really mean, why swarm intelligence might be the risk nobody’s watching, and the question that stuck with me long after we stopped recording: if there’s a 50% chance this wipes out civilization and you build it anyway, what are you actually doing?

Audio is live on Apple Podcasts and at briankeating.com/podcast.

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!

The Universe Didn’t Come From Nothing | The Peter McCormack Show

sabartigas — Sun, 30 Aug 2026 22:07:14 +0000

The Universe Didn’t Come From Nothing | The Peter McCormack Show

Transcript:

Brian Keating:
We understand space, we understand matter, we understand energy. We don’t understand why time works the way it does. Actually, the reason is ’cause there is no time. Time doesn’t exist. The essence of my research is to ask that question. What happened on the Sunday before the Big Bang? Something can’t truly come from nothing. We’re the only creatures that know we’re gonna die. That means that we’re the only creatures that know how precious life is.

Peter McCormack:
We’re really wasting an opportunity now to just improve humanity. It feels a little bit civilizational.

Brian Keating:
Dude, we have to stop apologizing for humanity’s greatness. AI is a tool. It is not a human. Its job is to serve humanity. The computer doesn’t know what it means to be human. It only knows what humans have already done.

Peter McCormack:
If our lives had a forward button, we’d all be dead.

Brian Keating:
It’s not time that’s limited, it’s attention.

Peter McCormack:
All right, Brian, good to see you, man.

Brian Keating:
Peter, thanks for making the short jaunt across the pond, as they say. Actually, 2 ponds.

Peter McCormack:
2 bars. Yeah, look, happy to do it. And I was originally like so excited to talk to you because I, I’m, uh, I look up at the sky and I wonder how we’re here and why we’re here. And, um, and I was all prepared to come to you and ask you about my like biggest questions about the universe and how it works. And then I watched your show with Sean Ryan and I was like, huh. And I then listened to Sean with Tucker and I was like, this has hit me at a time where I’m thinking about life and whatever. And I saw you were quite introspective. And so what I was really thinking, I think where I wanted to ask you first is like, obviously you built a career looking up at the sky and wondering, and now it seems like you’re looking internal a lot.

Peter McCormack:
What’s the connection?

Brian Keating:
I think I, since I was a kid, I always cared about only the biggest topics. You know, I couldn’t get interested in sports or, you know, stocks or, you know, Bitcoin. I was a late adopter, but thanks to you, I came to it. Uh, but, but The fact for me is, yeah, I do recognize maybe more than most because I study something that’s 14 billion years old, but I also constantly recognize how fast time is changing for me right now with family, with my work, with my research, sometimes with the podcast. And for me, the ultimate goal and the thing that fascinates me the most is time. We understand space, we understand matter, we understand energy. We don’t understand why time works the way it does. For example, if I take a pendulum, I didn’t bring a pendulum, but I brought a telescope.

Brian Keating:
Here’s a pendulum swinging back and forth like a grandfather clock.

Peter McCormack:
Right.

Brian Keating:
If I turn away for one second and you look back at it, you can’t tell the way it started. Did you remember where I started it? Did I start like this? Did I start like that? You don’t remember. But the fact is the laws of nature are invariant. They don’t care about which direction you started it, which direction you’re looking at it from. They’re invariant, they’re constant. So that means the laws of this pendulum don’t care about time. They can’t tell time. Yes, you use it in a grandfather clock, that’s a separate thing.

Brian Keating:
There’s a ratchet, there’s a mechanism, whatever. I don’t want to get into that. But the actual laws of physics are invariant. They don’t care about time. So too for, you know, a bowling ball. So too for an electron going around an atom. They don’t recognize why time has an arrow. We don’t understand that.

Brian Keating:
And we, yet we talk about the Higgs boson, or we’ll talk about, you know, extra dimensions, or we’ll talk about, you know, the Big Bang, right? But we don’t understand time. I mean, it’s, it’s sort of embarrassing to me. And so I look for the embarrassment. I look for like, what do we claim that we understand beyond the level that we actually do? And I like to poke and prod and annoy my colleagues by saying, we don’t really know what we claim to know. And I think a lot of times we’re selling people on a bill of goods that these extra dimensions and all these fantastical things, maybe even intelligent aliens, it’s pretty much founded on a foundation that’s relatively weak. And I’m just hoping the public doesn’t find out about it.

Peter McCormack:
Yeah.

Brian Keating:
My job is to expose it to them. I’m an experimental scientist. Let me first step back. I’m an experimental physicist. I’m not a theorist, Brian Greene, Eric Weinstein, You know, Michio Kaku. I don’t talk about airy-fairy things, you know, multiple dimensions and curled up, you know, things like that. But I do talk about what can we actually measure? What can we interrogate about Mother Nature or God, if you like? And what can we know while we have this limited capacity to know things? So I’m desperately aware of this clock ticking in my head. You know, if I wasn’t aware of it from looking at my kids growing up or wrinkles going on my face or gray hairs coming in, I I would otherwise be obsessed.

Brian Keating:
This is my one obsession, time. Why does it go the way it does when no law of physics mandates that it be so?

Peter McCormack:
Well, what is time though?

Brian Keating:
So this is another very frustrating topic. When you ask a physicist what is time, the most cogent definition that I’ve ever heard comes from a Nobel laureate, Frank Wilczek, and he said time is what a clock measures. Okay, thanks very much. That’s like a tautology. It’s like a San Diego weather forecast. It’s pretty much constant throughout. That doesn’t help you. It doesn’t really do anything.

Brian Keating:
It’s a tautology. So I said, well, that’s very fine and good, but actually the reason is because there is no time. Time doesn’t exist. Multiple things that are proxies for time exist. One of them is, like I said, the hair color on your chin, the size of your toddler, and then a couple years later, the sensation of being with a beautiful girl and how that, for me and my wife, time passes by until one of the kids comes in and interrupts, right? These are all different notions of time. Some say time has more than one dimension. Our friend Eric Weinstein claims that time actually has multiple dimensions, and then that might not preclude time travel as ordinary conceptions of physics seem to do. Now, that’s one extreme, but the fact is, time is essentially a quantity that we don’t understand how it came to be, and yet everything is predicated on it.

Brian Keating:
My job is predicated on the universe coming into existence. There was some day Right, Peter? We sit here today, it’s a Monday, you know, and God help us, we’ll get through it with enough coffee and good conversation. But we sit here on a Monday, we go back 24 hours, it was Sunday, keep going back, back, back, go back weeks, months, years, billions of years. There was some day 13.826 billion years ago that you could say in terms of 24 hours, there was no sun, there was no Earth, there was no human, but the universe began on that day, right? What does that mean? The universe began at a moment. Well, how does something initiate a moment when there’s no moments before that moment to initiate? What is it? What is it pushing back on? Right? There’s no there’s no action without reaction. Right? So if you’re pushing on this table, I move backwards. Right? It’s basic law of your countryman Isaac Newton. But if there’s no time, how does time come into existence? These are very like mysterious concepts.

Brian Keating:
Right? So the essence of my my research is to ask that question: What happened? On the Sunday before the Big Bang? What was that? There’s a notion you can conceive of it, but you can’t actually say for sure that there wasn’t a universe for which time to emerge out of or out of which time has emerged. So is time emergent? Does it only kind of correspond to the mutual agreement of all other particles, matter, conscious beings in the universe? Or is it completely independent? Is it a dimension? Like space has, you know, 3 dimensions of space, uh, at least according to non-string theorists who believe that there’s 10 dimensions, right? Uh, but for us, we, we have this notion that time sort of is this inexorable thing that just keeps proceeding, but we have no idea why. So my research is sort of focused on those questions. Why does time have a direction? Can we slow down time? Can we modulate time? We can go back and forth in space, right? You can go left. You didn’t have to come here, right? So you can go. Wherever you like in space, you can remember the past, why can’t you remember the future? And what, if anything, does that have to do with the connection to the origin of the universe itself? These are the questions.

Peter McCormack:
That’s like Arrival, isn’t it? Remembering the future.

Brian Keating:
Oh yeah?

Peter McCormack:
Yeah, well, that was the concept. You’ve seen that film, right?

Brian Keating:
Mm-hmm. A while ago. I actually, I feel like it’s very painful for me to watch science fiction movies, even though I like them, I get into them, but but you know, for example, watching Contact or or watching Interstellar, your one of your favorite movies, I understand. And I brought you this book, which I wrote, but with an interview with Kip Thorne, who is the scientific director, who not only advised Christopher Nolan on the film, but he also wrote papers about the simulation and the detailed, accurate, and precise simulations that he did for the first. Imagine writing a paper in a movie, you know, with your director, production, whatever. He wrote a scientific paper published in a physics journal about how they rendered the phys physics so exquisitely accurately. However, yes, the physics is sort of accurate, but there’s so many problems with it from an experimentalist point of view. You just see holes.

Brian Keating:
I see just nonsense like contact. We’re going to generate a wormhole like Interstellar uses, right? And we have the spinning contraption that involves a 1974 Chevrolet bucket seat from an old car, and that you have to wear your seatbelt, and then if you don’t, you’ll get— And I’m just like, what does that have to do with— What does a spinning gyroscope have to do with opening up a wormhole? Which by the way, Peter, has to already been pre-established, the exit point. Let’s say you could even make a wormhole, which is completely non— there’s zero evidence that wormholes exist. Okay? It’s a theoretical concept like anything. I mean, we could make a theoretical concept that there’s a purple unicorn on Neptune’s North Pole. I mean, theoretically you can envision it. Is it completely forbidden by the laws of physics? No. But does that mean it’s probable? Absolutely not.

Brian Keating:
In this case, you know, they make a wormhole. Fine, let’s say you could make a wormhole. Where did you establish the other end of the wormhole where you’re going to come out? Like, did you violate, you know, time? You have to time travel to that point where the wormhole emerges on Cooper’s, you know, planet, whatever, in Interstellar. I’m sorry to burst your bubble if this— I know it’s one of your favorite movies. But in reality, it’s completely implausible. So actually, you know, tweeted out a couple months ago when Project Hail Mary came out, I said, uh, actually Project Hail Mary, which involves a bacteria that eats the sun, is actually more scientifically plausible than Interstellar. And people are like, what are you talking about, you fucking idiot? Uh, but you know, there’s, there’s far more things that are unimaginable and improbable, if not impossible, in Interstellar than even in Project Hail Mary.

Peter McCormack:
But even with time, like, the concept of time for us for day to day is like a human construct that we need because it’s a tool. Time is a tool, and we base it around like a 24-hour day, and that’s how we plan our life. But time itself is just forward motion?

Brian Keating:
Well, not necessarily. So motion and time are connected because through velocity, and there’s an ultimate velocity, and there’s an ultimate degree of motion which will stop at 0 Kelvin after a great scientist from Glasgow. The notion of 0 Kelvin where motion stops is a definition that could involve time if time is driven by the laws of thermodynamics. So people express the unidirectionality of time according to them because of the second law of thermodynamics that says that entropy only increases. And they say, well, time only increases. You can’t go backwards in time. So therefore time only increases. There’s some correlation between entropy, but you can actually locally reduce entropy all the time.

Brian Keating:
Like we just had coffee, we had milk poured into it. It’s become completely disordered now. You can’t separate the coffee and the milk. So on one level, the entropy is very high now, and beginning it was very low. You had pure coffee, pure milk, and they were highly separated. That’s low entropy. Now, if you look at an atomic level, you can’t tell the difference between a cream molecule or a milk molecule and a water molecule. They’re mostly hydrogen, 2 hydrogen atoms and an oxygen atom.

Brian Keating:
So it depends really on the scale at which you look at it. So to say that because entropy always increases, it’s not sufficient It might be a component of it, it might be necessary, but it’s not sufficient to explain why time has a direction. And that’s why I think people, and maybe even yourself just now, might mistake motion for time.

Peter McCormack:
Sorry, I mean, it’s just like we’re always moving forward in time. When I say motion—

Brian Keating:
We seem to move always forward in motion. But again, the molecules in here, there’s nothing that prevents them from unmixing, right? We don’t observe that happening, and there are explanations for that that are probabilistic, but they’re not physical. In other words, there’s no like, equivalent of the law of electromagnetism, the inverse square law, Coulomb’s law. There’s nothing that says that these things could not separate. In fact, they will separate given the observable age of the universe times a trillion or something like that. We express them probabilistically. So there’s no absolute notion that time always has to go in this direction. And that’s why I think it’s so interesting.

Brian Keating:
My research currently is concerned with whether or not there’s a cosmological arrow of time, That is separate from, say, the microphysics of coffee or your beard or whatever. There’s something, a cosmological time field, that would cause time to progress only in one direction. It would have the rate of time’s flowing encoded in it. And best of all, it’s potentially observable. And maybe even better, it’s been claimed to be detected at a low level of confidence, but we, with our colleagues and observers on the Simons Observatory, we’re aiming to make the definitive measurement. of what could provide for the first time a mechanism for the reason that time only goes in one direction. You’re right, we seem to see time only going in one direction, but why? Perhaps it’s connected to this cosmological force field effectively that is generating what we consider to be, or is correlated to what we consider to be the flow of time.

Peter McCormack:
This show is brought to you by my lead sponsor, Iron, the AI cloud for the next big thing. Iron builds and operates next-generation data centers and delivers cutting-edge GPU infrastructure, all powered by renewable energy. Now, if you need access to scalable GPU clusters or are simply curious about who is powering the future of AI, check out iren.com to learn more, which is I-R-E-N.com. What, what are you trying to discover with this?

Brian Keating:
So there’s 2 major aspects of my research. Um, one is concerned with the origin of the universe, which in some theories has the origin of time built into it. That’s the Big Bang. So your countryman Fred Hoyle— so now I got a Brit here, and I’m so excited, I’m so excited. Um, so I’m gonna turn the podcast on you.

Peter McCormack:
Good God.

Brian Keating:
Um, so Fred Hoyle was a proponent of what’s called the steady state, which for 2,000-plus years had prevailed over all the best scientists, you know, the, the, you know, the Einsteins, uh, the, the Aristotles, you know, the Tony Fauci— no, no, they go back in time, the best scientists.

Peter McCormack:
Up until—

Brian Keating:
When’s he from?

Peter McCormack:
What era is he from?

Brian Keating:
What era is Hoyle from?

Peter McCormack:
Yeah, I don’t know this guy.

Brian Keating:
Is Hoyle from?

Peter McCormack:
Yeah.

Brian Keating:
Oh, Hoyle’s from the 1900s. He coined the term the Big Bang.

Peter McCormack:
Okay, so that is before we realized that the galaxies were spreading apart.

Brian Keating:
Well, so he coined that term as an insult, and the reason I’m asking a Brit is that allegedly, Big Bang, if I say, you and the missus had a big bang, that means something indelicate, right?

Peter McCormack:
I took off something.

Brian Keating:
Yeah, okay, so I’m right. I’ve heard it said by other people that that isn’t true. But I’m glad to have it validated by a direct firsthand source.

Peter McCormack:
We wouldn’t say big bang. You wouldn’t say, oh, I’m gonna go upstairs and give you a big bang. But there is a way, and it sounds derogatory, just to say, I wouldn’t say this to my wife.

Brian Keating:
No, of course, no gentleman would say this.

Peter McCormack:
But if a couple of young lads and one guy met a girl and he took her home, the lads would say, did you bang her?

Brian Keating:
That’s the term. Okay. So he did it. It’s obviously not like a pleasant connotation, right? It’s something derogatory or impolite, right? He hated the Big Bang. What we call the Big Bang, I should encapsulate what that is. That’s the observed expansion of the universe, which if you run the movie of the universe back, everything starts at a single point. In fact, everything would be located inside a very tiny region of space, which then after a very extremely slow, small moment of time, it basically exploded and created the universe that we observe today, which is expanding.

Peter McCormack:
I understand it wasn’t a bang though, an explosion.

Brian Keating:
That’s right.

Peter McCormack:
It wasn’t that, is that correct?

Brian Keating:
It wasn’t like a firework going off in this room, because it happened at every point in space and time. We can’t visualize what that’s like. For every point to be effectively the center of the universe, the center of its own Big Bang, was essentially what’s mandated in the Big Bang theory. In other words, the entire universe has been stretching. Like if this table were made of rubber, we could stretch it out. And if you didn’t know when you were looking at it, we put little dots on it, those dots will be moving away from, each one would be moving away from each one. And if you were sitting on one of them, you would see another one, it would be moving away from you. You’d say, oh, I’m the center of the universe.

Brian Keating:
Every other dot’s moving away from me. But no, no, no. If you can move over to that universe, to the next dot over, he sees the exact same thing as you see. So the universe expanding, so that means there’s no true center of it. It’s like there’s no center of the surface of the Earth, right? There’s no center of the surface of the Earth. And the center of the 3-dimensional Earth, there is, right? But forget about that. The center of the surface, where is that? There’s no center of the ball, right? It’s just we have a convention because the Earth spins on its axis that we define the North and South Poles and Greenwich Mean Time and so forth. But there’s no privileged point on a sphere in mathematics.

Brian Keating:
Every point is equally good as any other point. So if you blow up the sphere, every point moves away from every other point, right? So if you put dots on a balloon and blow it up, they’ll also move away.

Peter McCormack:
But there’s a center to the sphere.

Brian Keating:
There’s a center to the sphere, but that’s because you’ve embedded it in a higher-dimensional space. Look, there’s a center to this table, Because it’s sitting in a 3-dimensional room. But again, if you had an infinite sheet and you’re sitting on the infinite sheet, you can’t tell where you are with respect to the rest of the sheet. And moreover, every other point on that sheet feels that it too is the center of the universe and they have an equal claim to that. They see the sheet expanding. It’s a very mind-blowing concept. I don’t expect it to be immediately grasped, but this is why it was so resisted for 2,000+ years and why people like Hoyle thought it as a pejorative. This is silly, this is stupid.

Brian Keating:
There’s some big explosion, some big orgasm in the beginning of time, and it’s ludicrous, right? But no, in fact, the most parsimonious, the simplest explanation from an experimental standpoint is that actually, no, the universe was once extremely hot, extremely dense, compactified, and after that moment has been expanding ever since for the past 13.826 billion years, as I said.

Peter McCormack:
But hold on, on that. If it is extremely dense, extremely hot, Does that mean that there was something existed?

Brian Keating:
So, right. So there’s some energy field that came— exactly. You’re exactly grasping what my primary research goal is.

Peter McCormack:
Whoops.

Brian Keating:
So the, the— so something can’t truly come from nothing. I know there are people like my friend Lawrence Krauss who will say, oh, the universe can come from nothing. That’s actually nonsense. There’s no agency to nature’s laws, right? If I give you E equals MC squared, that’s a description of something, but that’s not the thing itself, right? It is true. It explains how 2 massive particles, a positron and an electron can come together and create pure photons, or the reverse, 2 photons, no mass, come together and make 2 massive particles, a positron and an electron. That’s true, but the equations describe something. They don’t instantiate it. They’re not the reason why it happens.

Brian Keating:
They’re a description to us, right? So just as Newton’s law of gravity, they say Newton discovered gravity. Okay, Newton discovered gravity the same way that Christopher Columbus discovered America. It existed beforehand, right? No, he described it. He described how gravity works. He didn’t discover how gravity works. He mathematized it. It’s a huge accomplishment. In the same way, you’re absolutely right.

Brian Keating:
Where does this expansion come from? Well, it must come from either a preexisting universe. It could be that there was a universe for all time. One day it decided to collapse and condense and compress, and there are mechanisms that can explain that. And it compressed to near infinitesimal density, infinite density, infinitesimal size. And then that process caused an unstable reaction, which then causes it to bounce, expand, and rekindle, homogenize, repopulate exactly what our universe looks like today. Now, for that to happen, you needed something that existed before the moment, let’s call time equals zero, the beginning of our observable universe. So our observable universe is 13.826 billion years, but we have no idea what happened again the day before that. Where did that come from? So my primary goal of my research with my first experiments, BICEP, BICEP2, and now with the Simons Array and the Simons Observatory, with my colleagues and my coworkers and my students and my colleagues and friends, is to essentially ask, what did it bang out of? Was it a preexisting universe? Was it a state of pure energy that existed for all time? Energy can exist for all time.

Brian Keating:
It could be static for all time, the energy, and then it can fluctuate into existence, that’s not nothing. Empty space is not empty in the classical sense that we think about a vacuum. We think about it devoid of anything. But in quantum mechanics, that’s not the way it is. Quantum mechanics, the vacuum is unstable. Things can pop into and out of existence in infinitesimal timescales. But when we’re talking about timescales like what’s called the Planck length and the Planck time, extremely small units of time and measurement, those quantities can become significant. And if there is a field that existed, an energy field, like you said, it had to come from something.

Brian Keating:
Physicists believe that energy field is called the inflaton, that there’s a field that pervades all of space and time called inflation that exists now and exists before, you know, now and exists for all time. And it exists in what’s called the multiverse. So you’ve encountered the multiverse in the concept of your simulation theory talks. My friend Rizwan Virk that you spoke with and many other people have talked about that.

Peter McCormack:
Can I ask a question on the multiverse before that?

Brian Keating:
Yeah, of course.

Peter McCormack:
Because I’ve heard the version of the multiverse whereby every version of every possibility exists, right? There’s a version where in this interview you just get up and storm out. There’s like every possibility. But I have another question, uh, which you probably— I don’t know if you can even answer. It’s like, where does the universe exist? Which is probably something you can’t really answer. But if this universe exists in wherever it does, can other universes exist completely isolated from this universe?

Brian Keating:
Part of the challenge is that the, the human vocabulary is insufficient to describe almost anything. As I said, we can’t even describe time, and yet we’re talking about the beginning of time. Like, make that make sense. Okay. But, but really what we do is sort of an inverse thing. We say, like, how did I get here? Like, I don’t know how this building was built. Did you know how it was built? No, no, no. But you know what you were doing 10 minutes before I arrived? An hour ago, you came from London a couple of, you know, 12 hours ago.

Brian Keating:
You’re looking pretty refreshed, you know, thanks to British Airways.

Peter McCormack:
Thank you.

Brian Keating:
And if you go back in time, you can start to go back and piece together your local light cone, like your local past history makes sense to you and maybe to you alone, but that’s okay. So what we do as physicists, we piece back in time everything that we understand about the laws of nature today. We extrapolate them back and then we see when does our understanding break down. Currently, we understand things exquisitely well with tools that are mind-numbingly precise, some built by my colleagues, in fact, uh, some studied purely computationally, theoretically, or with AI. and things like that. But we understand things in the universe’s history going back to about 10 to the minus 12th seconds.

Peter McCormack:
Is that 380,000 years?

Brian Keating:
That’s when the CMB is produced. No, that’s, that’s much, much later, right? So I’m talking about 380,000 years, like trillions of seconds. No, I’m talking about, I’m talking about the, the earliest branch of known physics that we understand, sort of like how far back you could go on the timeline that got you to here. Imagine you could go back and like maybe you could ask a couple of people that built the building and, you know, rented it and whatever. But, you know, to go back when like the Earth formed Like, eventually it’s gonna break down. But for physics, we can go back from today, we can go back 13.826 billion years to 1 trillionth of a second after the Big Bang. Now, okay, we don’t know what the Big Bang was, but we know exactly what happened after that moment. Okay, so like, even though like you look away and you see something exploding, you can then look back and say, well, I under— I saw it at this moment and I see it now, and there’s heat coming to me now, and there’s a sound wave coming to me now.

Brian Keating:
So maybe I don’t know exactly what it was like, ’cause nobody could witness, say, a nuclear detonation right at the place in which it occurs, right? But you can extrapolate back from observations, from evidence, from data, everything you can observe currently, and extrapolate the laws of physics back to some finite period of time when your ignorance then begins. So that’s what we do with those.

Peter McCormack:
So you extrapolate back based on, but it’s not, what’s observable?

Brian Keating:
What’s observable is a tremendous amount of physics.

Peter McCormack:
Up until what period of time?

Brian Keating:
So I’m saying a trillionth of a second after the Big Bang. When is it observable? About that epoch when the laws of electricity and magnetism sort of cleaved apart, and now we see electricity and magnetism as 2 separate things. But from Michael Faraday and James Clerk Maxwell, that they’re actually 2 sides of the same coin. One man’s magnetic field is another man’s electric field, and they’re actually beautifully intertwined. That was the first example. Well, actually Newton was the first unification, grand unified theory, if you like. So physics has this goal of unifying as many forces into one single force as possible. The so-called theory of everything, or TOE, is an attempt to subsume all 4 laws of nature, gravity, electricity, and magnetism, and the weak and strong nuclear forces into one rubric, one line equation that describes all of physics.

Peter McCormack:
By the way, I have to thank Paul Sutter for this. He introduced me to this.

Brian Keating:
Yes, yes, that was a great interview you guys did. The spaceman. And so you can go back to when very many things were unified, but not everything. And for us, that’s sort of when the electromagnetic theory breaks away from what’s called the weak force. And we understand that because those are energies we can probe at the LHC. The Large Hadron Collider can probe things that are about, what are, 7 trillion electron volts, whatever that means. It’s many billions of times the mass energy of a proton. And if you look at it, then you can start to extrapolate.

Brian Keating:
Well, every mo— imagine this, Peter, every point in all of the universe had the same energy as those extreme collisions that make Higgs bosons. There was some period in the universe’s history when like, it wasn’t just like San Diego and it’s like sunny here and London, it’s cloud.

Peter McCormack:
No, no, no.

Brian Keating:
Every part of the universe had an average energy density equivalent to the combined energy of the protons and antiprotons that collide to make Higgs bosons. That takes 28-kilometer track to smash them together. That has particles that have the energy of a freight train. Okay. That, but every single cubic, uh, Planck length of the universe had that much energy. It’s incomprehensible. And then a minute later, it’s literally a minute later, it cooled off by thousands of times. And it kept cooling and kept cooling such that today it went from that trillions of Kelvin degrees above absolute zero to now it’s just 2.726 degrees above absolute zero.

Brian Keating:
It’s a minuscule whisper of temperature. That’s the average temperature of the universe. You take a thermometer anywhere in the universe except for on Earth or near our sun, in interstellar, intergalactic space, you get the same reading everywhere in the universe. That means the average energy of the universe has that temperature. But if you go back to the very last time when we lose consciousness and cannot remember what happened beforehand, this trillionth of a second after the Big Bang. Every part of the universe had this enormous energy. Every thermometer would have the same temperature as the center of mass collision energy of the Large Hadron Collider. It’s mind-blowing.

Brian Keating:
I mean, you don’t look too impressed by this. No, I am.

Peter McCormack:
I’m just taking it all in.

Brian Keating:
Yeah, I mean, it is a lot to take in.

Peter McCormack:
And your goal is to find out, you’re trying to look behind the trillionth of a second.

Brian Keating:
So physicists are greedy. That’s not enough for us. Like you could say, you know, well, I mean, 100 years ago, I should say, Even when I was in graduate school in the ’90s, we didn’t know if the age of the universe was 10 billion years or 20 billion years. In other words, 100% uncertainty. Now we know it, 13.826 billion years with an uncertainty of about 1 in the last decimal place. So 1 part in 10 to the 5th. It’s incredible. Before we knew that there were objects, Peter, there were objects called— there are objects called globular clusters.

Brian Keating:
These are like, you know, kind of failed states. These are like mini galaxies that never really kind of got their act together, moved out of their parents’ basement. So they orbit around the— they orbit They orbit around the Milky Way and every other galaxy that’s massive like ours. And they have the energy of about— they have the number like a million stars compared to the Milky Way, which has 100 billion stars, and Andromeda, which has 400 billion or a trillion. So they’re minuscule little things, but they’re very useful. They trace out the properties of where our galaxy’s center of mass is. So they were very useful for proving that our galaxy is actually not the entire universe as most people thought it was up until 100 years ago. But even in grad school in the early mid-1990s, we couldn’t tell if the universe was younger than some of those objects called globular clusters.

Brian Keating:
Now, that’ll be like you, you know, you know, being older than your dad. It was very strange. There were objects in the universe that were older than the claimed age of the universe to some cosmologists. Now we know it much different. Now we know it. I could say, like, looking at you, you’re born in what, 1977?

Peter McCormack:
’78.

Brian Keating:
’78. Close. Okay. So I could say what day, what month, October, whatever, but I can say to the day. It used to be I couldn’t tell you within— it was 1977 or it was 2007. Like, it was insane. But now we have this incredible precision. That’s what we’re trying to do.

Brian Keating:
We’re trying to always push that back because we’re greedy. Scientists should be greedy, because if we’re not, who’s going to be greedy at asking these questions that have no financial benefit? They have no, like, kind of, uh, you know, we don’t get famous from asking these questions. We’re driven by this insatiable curiosity to push back the veil of ignorance and lift it up as high as Mother Nature will allow us to do.

Peter McCormack:
So you’re looking beyond that 20th of a second.

Brian Keating:
Yes.

Peter McCormack:
How do you even know what to look for?

Brian Keating:
So in these models, in science, we never prove anything. I can’t prove to you this table’s flat. You know, actually it’s not flat, right? There’s little— if you zoomed in with a microscope, but it looks flat. So it depends on what question you ask the scale of, like how flat is this table? Very flat compared to, you know, compared to a beach ball, but very, very much more rough compared to a perfect idealized Euclidean plane, right? And it’s just, you know, Or a glass or diamond sheet. I mean, it’s much, much rougher, right? So we are trying to look back basically to a point in time at which time, if this theory was correct, we could disprove— again, we can’t prove anything in science. I can’t prove the Earth is flat or curved or what. I can prove it’s not flat. I can’t prove it is curved or is perfectly spherical, right? So what we are trying to do is falsify the claim that there was no Big Bang.

Brian Keating:
That’s one thing that scientists need to do, right? We need to show that people claim there was no Big Bang, just as they claim the Earth is flat. You know, people around the world, around the globe, believe that the Earth is, is, is flat. In this case, we are trying to disprove many, many theorems, many, many predictions. And then we can’t prove something in science, but we can disprove all the competing alternatives. And like your fellow countryman Sherlock Holmes said, when you’ve eliminated everything but the— but what you might think is impossible, That is the best explanation. It’s sort of simplifying things, eliminating everything. And the things that we want to eliminate are the equivalent of the flat Earth, or the moon landing never happened, or, you know, we’re trying to eliminate as many things with scientific hard data that we collect from telescopes. Not too different from this, you know, kind of a little simple telescope here, but connected to much more advanced technological detectors, taking massive amounts of data, petabytes per year, thousands of terabytes.

Brian Keating:
you know, a terabyte per day. Imagine filling up your whole, you know, 10 iPhones a day with data, with really high-quality data. That’s what my colleagues and I are doing on the Simons Observatory. And the goal is to push back as far as possible such that what’s left is the most plausible explanation, but it may not be the quote-unquote perfect explanation, but that’s okay.

Peter McCormack:
And someone might be spending time then trying to disprove that.

Brian Keating:
Well, people spend time disproving it. They hardly spend any money disproving it. There are people that complain about it, say it never happened. They They cherry-pick data from my colleagues’ research or my research, and they’ll say, look, they don’t even know. Like, I’m open enough to say we don’t know what time really is. Okay, that’s a big admission. Now, if you were persnickety, you might say, well, then how can you do anything? Or my favorite one is, we don’t know what 95% of the universe is made of. You got— you boffins claim it’s dark matter.

Brian Keating:
You say it’s dark energy, 95% of it, but you don’t know what dark energy is. True. We don’t know what it is. We don’t know what dark matter is. True. We don’t know what dark matter is. And the rest is 5% that we do know about. Does that mean we’ve made no progress?

Peter McCormack:
Yeah, I have a question on dark energy and dark matter. Is that just a made-up thesis to solve an equation?

Brian Keating:
So dark energy— so what’s beautiful about science is when we’re wrong, in that we see these flaws in our previously cherished ideals. For example, I told you for most of human history, if you wrote the year on a ping-pong ball, of, you know, starting from the dawn of human time, like from going back to ancient Egypt, okay, 5,000 BC, right? And you put the year, negative 5,000 BC, right?

Peter McCormack:
Yeah.

Brian Keating:
And you put that into a bag every single year in the 7,000 years since then, right? And you put that in a bag and you pulled out that year and you said, what was the prevailing wisdom on cosmology, on the universe? 99% of those balls would say the universe has existed forever in one form or another. Either it cycled into and out of existence or it was eternal. There was only one document, in fact, that ever claimed that it wasn’t, and that was the Old Testament. The Old Testament very clearly asserts that there’s a beginning to time, beginning to space, beginning of the creation of the universe, and that’s what we call Genesis. That’s the first sentence, in the beginning. The beginning of what? The beginning of the universe. So that was the only document, but if you believe that, you were standing against Aristotle. What are you, an idiot? Newton, what are you, a moron? Einstein, come on, you’re a complete fool, right? No.

Brian Keating:
So they turned out, all of them, to be wrong. There was a beginning period to what we call the observable universe. So when we look at it, we have to say that we’re wrong. I have a phrase that I’ve trademarked, which is that the flaws create the laws. So you start off with something that doesn’t seem to be completely perfect, but it’s pretty good, like the steady state universe. If you look up at the night sky, you see stars. You see the Milky Way if you’re in a dark place. You might see the Moon and you might see some planets, okay? The Greeks had a word— there’s so few things that move.

Brian Keating:
In other words, the preponderance of what we see is static. The stars are static, they don’t move. The Moon, the Sun, and the 5 planets move. That’s why we have the days of the week. All the days of the week are named after things that move to the ancients, back up to Saturn. They didn’t know about Uranus, right? So they only named the things that moved. So that tells you that most of those things aren’t days, right? How many more things are static? There’s 6,000 stars that you can see. They never move.

Brian Keating:
They never change, at least in a human lifetime. And so when you have something like that, the theory that prevails had to comport with that. There had to be some explanation why the only things that move are planets in our solar system. Everything else is fixed. Oh, I know. The universe doesn’t change. It’s eternal. It’s been here forever.

Brian Keating:
That was what drove cosmology for 7,000 years almost until 1929 when Hubble observed the expansion of the universe by looking at galaxies and inferring that their motion was such that every moment of time they’re getting farther and farther away. There is no center of the universe. Each one of those is a center of the universe, therefore there is no true center. And if you go back yesterday, they were closer. Go back a day before that, closer, closer. Eventually you reach a time when they were actually touching, connected. one part of one amalgam, whatever that was. And that’s where their ignorance left off in the 1920s.

Brian Keating:
That’s, that’s, that’s the beginning of the Big Bang theory was 1929. Other people thought that was ridiculous, like I said, Hoyle, and made fun of it and called it the Big Bang. It’s so ridiculous. But now every cosmologist believes— and there’s a couple of non-standard, non-professionals, or maybe amateurs or whatever. I’m saying, I’m not saying they’re bad or doing things unprofessionally, but they have an agenda, just like, you know, people believe the moon landing never happened. You know, they have an agenda. And you can refute them with data, but they’ll always kind of have these kind of concerns because it’s true. A good scientist will say, yes, I cannot explain everything, but the fact that I can’t is actually good news because if I could explain everything, we wouldn’t have the static, you know, we would still have the static universe.

Brian Keating:
Look, if Einstein were never wrong, let me put it this way. Einstein called Isaac Newton not only the greatest scientist who ever lived, but the greatest contributor to civilization who ever lived. Okay, so Einstein, he had almost no imposter syndrome, as we say, but he had the imposter syndrome about Newton. Newton, if Newton was the last word, we wouldn’t have Einstein’s relativity, right? We wouldn’t have GPS, we wouldn’t have any of the time travel, we wouldn’t have particle accelerators, we wouldn’t have anything, right? We wouldn’t have quantum mechanics at some level. So there’s a danger in saying that, oh, it’s lasted for so long and we don’t understand something, therefore it’s wrong. Like, no, no, no, if you said that Newton— you’re wrong. You’re actually— you’re right, he is wrong. He’s not— but what comes after Newton has to subsume what Newton did and get that right.

Brian Keating:
You can’t overthrow Newton completely. But Newton didn’t have the extendability to explore extreme gravity, black holes, neutron stars, pulsars. So yes, we don’t understand everything about dark energy. It is a term. It is sort of a proxy for our ignorance about what, what is driving the expansion of the current accelerated expansion of the universe. But that the universe is accelerating, is expanding, is indisputable.

Peter McCormack:
Yeah.

Brian Keating:
The mechanism behind which it is is currently unknown. But dark matter is a weaker argument to say, we don’t understand dark matter, so you scientists are stupid, or whatever. Like a lot of scientists will say, you don’t understand it. It’s actually not true. We do understand dark matter. We’ve detected dark matter. There’s things called neutrinos. Neutrinos are subatomic particles, elementary particles, meaning they can’t be chopped up into smaller pieces.

Brian Keating:
They cannot be divided. They seem to be eternal. They last forever as far as we know. And they have mass. They just don’t interact with matter like we’re made of. So if neutrinos right now—

Peter McCormack:
They’re hitting us constantly.

Brian Keating:
Yeah, there’s 100 trillion neutrinos that are gonna go through this room in the time that we’re doing this podcast. We’re not gonna feel any of them, hopefully. Once in a while they’ll interact with your DNA and maybe change something, but cosmic rays are doing a lot more damage. And coffee has probably a lot more radium in it than we like to admit. But the fact is, We have detected actual honest-to-goodness dark matter, massive particles that do not interact with light, therefore they are dark and matter. And it is true, the dark matter that they comprise in the universe is not sufficient to explain all the phenomena that we see. But that’s, you know, it’s like saying, oh, you’ve discovered 4 elements, what makes you think there’s 114 more? That’s kind of a ridiculous specious argument, and a lot of people make it.

Peter McCormack:
So your, your hunt for this, uh, The time before the trillionth of a second.

Brian Keating:
Yeah.

Peter McCormack:
Is it a hunt that you, you may never actually complete? Is it a—

Brian Keating:
Oh, definitely. Yeah.

Peter McCormack:
How do you feel about that?

Brian Keating:
It’s, it’s, um, it’s like saying, you know, how will you feel that you’ll never meet your great-great-grandchild? Like, um, I hope that I establish something with my colleagues. Again, it’s no scientist works by themselves nowadays, and I have a great team that works with me. Um, and we have great funding, great agencies that support us. Uh, but, um, but our, our job is not to complete the task. You know, I think it’s, it’s, it’s that we are given this great opportunity. Some of us, unfortunately, it’s not evenly distributed and science is having a lot of cutbacks. Your country’s doing the same. Uh, we collaborate very closely with the UK, by the way.

Brian Keating:
There’s a huge component of the Simons Observatory led by, uh, Manchester and other, uh, institutions, uh, the Queen Mary. Um, and we have collaborators throughout the world, but in particular, a huge chunk of our new observatories led out of the UK, which is incredible. Um, it’s allowing us to do things we couldn’t do with just the U.S. component. So, um, no, I don’t, I don’t see it like that. I see it like I’m training.

Peter McCormack:
Is it a baton race? Like, you scientists are just always building and then you depart and then somebody else carries on?

Brian Keating:
Yeah, I’m kind of ashamed to admit it, but it was. But then this project, which I, you know, uh, conceived of with, with my colleague David Spergel and pitched to my mentor and, and kind of father figure, Jim Simons. funded it in 2016. It’s poised to be— because of the recent cancellation of our number one competitor, which is to be located at the South Pole in Antarctica, where I’ve been a couple of times, that was canceled by the government recently. First with the Biden administration, the last year finally killed off by the Trump administration. So we’re kind of the last experiment of its kind. And so There will be a space-based experiment potentially from Japan called Lightbird, which, which may do much more than we can do, and that would be great. But for now, we’re the only really funded operational— we’re taking data.

Brian Keating:
Like I said, we’re getting a terabyte of data. Imagine filling up a hard drive, your phone, 4 or 5 of your iPhones every single day with high-quality data that you need to reduce. And so we have 450 of the smartest scientists on Earth working on this problem now, working on the data, working on the instrument. And by the way, this is at 18,000 feet above sea level, 5,200 meters. Above sea level where you’re wearing oxygen masks and sun protection and hard hats, and it’s a construction site on like a mining field at 17,000+ feet. It’s an incredible place to work, but it’s dangerous. It’s harsh. Things break and you can’t get Amazon.

Brian Keating:
You can’t get FedEx up there. So it’s not as bad as the South Pole. South Pole’s worse in terms of getting stuff too. But so we really have the lead right now. I mean, I don’t like to rest on our laurels, but we have— we just finished up a week-long meeting in Toronto, and I just heard like the most incredible stories and, and, and reduction and analysis and novel use of AI. And now we’ve got like this huge solar panel, uh, array, the very first, you know, kind of time we’ve had a solar array powering a cosmology instrument. Um, it’s just so exciting. I feel like a kid.

Brian Keating:
And it’s a hundred— it’s a $100+ million project.

Peter McCormack:
Wow.

Brian Keating:
It’s not like you can go down to the, to the store and pick one up or buy one on my website. I wish. But, but the fact is, we’re kind of in this wonderful opportunity, but we like to think there are other experiments that could possibly detect this signal. In fact, we in the experiment I started, helped to start, called BICEP, claim we did detect this signal that I’m looking for now. And then we had to retract it. So how do you, how do you know what the signal looks like?

Peter McCormack:
How do you know what you’re looking for?

Brian Keating:
Yeah. So right now, if a plane were to fly out of, you know, San Diego Airport, You couldn’t see it. You know, it’s a nice, beautiful studio. It has no windows, though, unfortunately. So we couldn’t see it, but we could hear it. Right. And that tells you that there’s more than one way to detect something. Yes, you’d like to be able to see it, but— and there are many other ways.

Brian Keating:
Radar, you know, you could look for the neutrinos from the coffee cup, you know, whatever. There’s— you could be very creative to detect that plane, every single plane taking off. You don’t need to see it to detect it. So too with the signal that we’re trying to see. We’re actually not trying to see the light that was produced 380,000 years after the Big Bang. That’s called the CMB, Cosmic Microwave Background Radiation. It’s a pervasive temperature field, a radiation bath, very cold radiation, 2.7 Kelvin, coming in all directions, all times of day or night, everywhere in the, in the entire universe. Right.

Brian Keating:
We’re not actually looking at that and saying, oh, we’re going to see the origin of time. We’re going to see something that occurred before that. Right. Because that was produced 380,000 years after the Big Bang. We want to see, say, a trillionth of a trillionth of a trillionth of a second after the Big Bang. So how do you do that? When you want to look back in time, you have to look further back in space, right? I don’t see you instantaneously as you are right now, right? You know, the sun is 93 million miles away from Earth, right? So that means it takes 8.3 minutes for light to get from the sun to the Earth. In this 8 past past 8.2 minutes, say, the Sun could have blown up, disappeared, right? We wouldn’t know about it for 8, you know, for another 0.1, you know, 10 seconds or whatever, right? Let’s hope that doesn’t happen, right? As nice as it would be to spend the last moments of, you know, existence with you, Peter, but, but nevertheless, there are other people I’d like to be with. Um, but, but the point is there’s a finite travel time for all information, whether it’s light, heat, sound.

Brian Keating:
I mean, certainly is much slower for us. We’re not looking for light, we’re looking for waves of gravity. called gravitational radiation, which is kind of like a sound wave, but it’s a sound wave that vibrates spacetime itself. So if a gravitational wave were to pass through this room right now, we would get a little closer to each other, a little farther apart, and this would travel at the speed of light. And so in the beginning of time, when gravitational waves were produced, this inflation field that I mentioned earlier would’ve made the sort of background boiling sea of gravitational waves. They live forever like neutrinos and light. They travel at the speed of light, like light. And so they, they could exist from, say, a trillionth of a second or a trillionth of a trillionth of a second after the Big Bang to then shake up the universe when the CMB was produced 380,000 years after the Big Bang.

Brian Keating:
So they endure. They’re coming through this room right now. But the point is that in the early universe, when things were denser and closer together, It’s a better opportunity to detect them. So we actually use the light as a film. Remember film from the old days, right? So we’re using the light beams themselves as film onto which gets exposed not light, but waves of gravity. And if we see that, there’d be strong circumstantial evidence that this inflation field existed to provide the energy that you asked about earlier. To nucleate the universe from which we emerged. And it doesn’t prove that, but it does disprove the theory that the universe came from nothing.

Brian Keating:
The universe has been here for all time. The universe has cycled into and out of existence for all time. The universe is stretching and will eventually evaporate and will be at the center of a black hole. None of those produce the waves of gravity. Therefore, if we see the waves of gravity, it’s strong, A, circumstantial evidence for inflation, but B, it kills off every other contender. Now, is that proof? No. But was Newton’s law, did it prove that spacetime is curved near a black hole? No, it didn’t prove that, but it showed us how to get to the moon and every other place in the universe. It’s very powerful.

Brian Keating:
It showed the unification of gravity. The same apple that hit him on the head was the same force that was pulling the moon towards the Earth. The moon is falling like the apple was falling. That was a unification of the laws on Earth to the laws in the heavens. Maxwell unified electricity and magnetism. Dirac and others unified together what we call the weak force with the strong, with the electromagnetic force. These are kind of the goal for us. For us, this would unify gravity and it would be the first sort of framework that unifies the highest laws of physics with some of the lower ones.

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Peter McCormack:
So I want to go back to what I asked you at the very start then, because again, when I watched you with Sean Ryan, you seem very introspective. And I want to go back to the point is like, you obviously started looking up at the skies, this became your career, this became your life.

Brian Keating:
Yeah.

Peter McCormack:
But you seem to be looking very internal, and you did it at a time where I’m thinking about it. And I said, as I was driving down to get the flight to come out and see you, I was listening to Sean Woetucker, and he was like, I’m off social media, I don’t care about this shit, I don’t care about that shit. And I was sat there thinking, One thing I really hate about the job I do is that I have to create titles that hopefully the algorithm puts in front of enough people to watch it. And I feel dirty and I hate it. And, and then I listen to yours and it seems we’re in a period of time when a lot of people are being introspective. And I’m, I really just want to find out a little bit more about that because it is, has there been, is this just like what every guy goes through? I assume we’re similar age.

Brian Keating:
Yeah.

Peter McCormack:
Similar age.

Brian Keating:
But yeah.

Peter McCormack:
Or, or is Is the looking up at the sky looking for the meaning, is it the same as looking internal for meaning?

Brian Keating:
I don’t think it’s a— I mean, I had this feeling. I kind of produced the ideas behind this first experiment when I was 29 or something like that. So no, and it was done for the exact same reason I’m doing things today. So not in that sense. I do think you’re right. There is a zeitgeist component, spirit of the times, that is precipitated by things like AI, uh, that we should get into. Um, and disclosure, aliens, the others, there’s, there’s a lot of that. There are many, many similarities between things like AI and UAPs that have a religious overlay on top of them.

Brian Keating:
I’m convinced of that. And I’m convinced that the reason that people’s feelings are so durable are the same as when a Jehovah’s Witness comes to you. I, I assume you’re not a Jehovah’s Witness or Scientologist. I don’t think you’re a Scientologist. They come in, they want to convert you, So have you ever been tempted, Peter, to switch your religions, change team? You want to come over to Judaism and, you know, get a little snip snip and then you’re one of us, right? You’re not tempted to do that, right?

Peter McCormack:
No.

Brian Keating:
I’m not tempted to— I was Catholic, now I’m Jewish, you know, whatever. We can get into it. But the point is, there isn’t— you know, Viktor Frankl said that man has no greater need outside of biological survival than to search for meaning, to have meaning in his life or her life, obviously. And, you know, for me, the reason that I do this is very clear. I, I’m not afraid of the most ridiculous, outlandish questions, but they have to be grounded in something empirical that I can test. Otherwise, it’s just, you know, kind of like whatever intellectual masturbation with a telescope. Like, if it’s not really grounded to something beyond just the science, like most of my colleagues are very uncomfortable talking about God. Religion.

Brian Keating:
They’re also uncomfortable, the same ones, talking about aliens, talking about, you know, AI destroying careers and stuff like that. Most professors are very comfortable in the lives that they have. You know, I always call it the hardest 3-hour-a-week job in the world. You know, we have to show up 3 hours a week. That’s about it. Once you get tenure, you know, that’s basically the end of your ambitions for many people. Not all, but many. And I feel like we have this sense of meaning, like, If you look at recently Fauci’s documents, his internal emails were released, and if you didn’t think he was kind of a slimy character before, now, regardless of him being a good scientist at one point in his career, when you see the hunger for credit, for winning awards, for attribution, and then simultaneously what he did to friends of mine like Jay Bhattacharya and others, trying to smear them, discredit them, even Nobel Prize winners, destroy their careers, destroy their life.

Brian Keating:
It was— it’s very kind of clear and patently obvious to me and to other scientists that most scientists do something that’s important, but it’s not significant. Like, you can do something, you can, you can try to do something that’s, you know, like, it’s very important to build a type of superconducting, you know, type of fusion reactor. You know, it’s important to do that. Is it significant? Well, it could be, you know, if you solve all energy problems and transport problems. But most scientists aren’t doing that, right? They’re making incremental, very small— and it’s important to do that, but it’s not, it’s not significant. It’s not like gonna fill that checkbox for meaning, right?

Peter McCormack:
Is it important for you to do something significant?

Brian Keating:
Oh, so clearly so.

Peter McCormack:
Yeah.

Brian Keating:
So that has always been important for me because I feel, you know, a keen awareness about death and always have been. And I don’t think that there’s, you know, another— one of my favorite books is Ernest Becker’s Denial of Death, who in his book suggests that everything that man does From like pursuing mates to, you know, building pyramids to launching rockets to Mars and whatnot is all done in a desperate attempt to stave off the incessant horrors of your own mortality and your knowledge of imminent death that only human beings have. You know, we’re called Homo sapiens, right? What are we sapient about, Peter? What does it mean? We’re the only creatures that know we’re gonna die. Yeah, some elephant knows it’s gonna die, kind of like wanders around. No, that’s different. Like when you were 4, you knew what death was, right?

Peter McCormack:
Yeah, of course.

Brian Keating:
So we’re the only creatures that know that. What does that mean? That means that we’re the only creatures that know how precious life is and how little life is and how short a period of time when your 22-year-old, you know, was bouncing on your knee, you know, it was yesterday, wasn’t it? Wasn’t it yesterday?

Peter McCormack:
You know, I remember it viscerally.

Brian Keating:
You remember going on the plane yesterday and you’re fine and you’re sitting up and you weren’t in, you know, you weren’t in premier class this time thanks to BA’s, you know, policy filling up too fast. But I remember this feeling of sitting. I was in premier economy, which gives you, you know, an extra 3 millimeters of space and I mean, slightly better meal. It is. It is. So I was in and I was near the front of the plane, not in first class, but everyone’s walking past me. And I remember thinking, every single one’s younger than me. Like, it was recently I was flying with my family.

Brian Keating:
I was like, every person going past me, you know, and I’m like a little bit vain or whatever, but like every single one’s younger than me. And I’m like, damn. Like, I remember being like, look at that old guy sitting, you know, that guy sitting in, you know, 9A. Like, he’s so old and I got all this time ahead of me. And like, it was yesterday and now I’ve got kids that that are much, much younger, and then they’re looking at me like I’m the old man. When does that happen? I’m keenly aware of that. I always have been, but now I’m on the other side. Now I’m on the back 9 or whatever you want to say.

Brian Keating:
And so, it’s become urgent for me to figure these things out as much as I can. But like I said, it’s not up to me to finish it, but I can’t stop doing it.

Peter McCormack:
So, I want to poke on that because there was something you said that sat with me all last night and I kept thinking of over and over. And you said to Sean, like, you know, when you’re happiest is when you’re with your kids and with your family and you could do it tomorrow. I could afford to just— I could stop tomorrow. Yeah, I could go and spend the rest of my time.

Brian Keating:
So could he, by the way. But he’s not stopping.

Peter McCormack:
And like most of us could, right? Most of us could even scale back our house or where we live or what we do. And I love spending time with my kids. And my son said something to me recently. He’s like, I wish you were more present because I’m not. My brain’s here. They’re everywhere.

Brian Keating:
Phones. Yeah.

Peter McCormack:
Yeah. And I’m searching for like, like my own purpose. What am I going to do? It’s significant. I want to have a big podcast and then, yeah, you get a pat on the back and I’m not, I’m like this C-grade podcaster and I’m like, so then I was saying last night, I go, why am I even here? And then I was thinking, why has Brian even given up 2 hours of his time to come and talk to me? He could have that 2 hours with his kids. I could be home. And I’m like, and then I was thinking of this, uh, Naval quote that Sam Altman mentioned. He said, if our lives had a forward button, We’d all be dead. And I was like, you’re so right.

Peter McCormack:
I didn’t think about it like that. And so I was really interested to just ask you about that kind of introspection, because it seems like you’ve now— you’ve got this recognition of like, death is coming. It may be in 40 years, it may be tomorrow, and yet you’re still chasing this. But is this the most important thing in your life?

Brian Keating:
It’s the nexus of things within my control, things I have capacity for, things that give me what we call life force or energy, and things I have agency over. So it’s this quadrinium of things that position me at this apex thing. I like to say, you know, I’m writing my 5th book now. I think it’s my 5th one.

Peter McCormack:
Your last one?

Brian Keating:
It’s probably, it’s my last of that kind of book. I can keep writing these books ’cause God bless them, these Nobel laureates, they have to move their own books, so they come on my podcast and I love talking to ’em. So every 9 Nobel laureates, the privilege and honor to interview, I make one of these copies and kind of distill their wisdom. You know, like Kip Thorne, like what was it like to, you know, win the Nobel Prize, you know, for basically an artistic sketch that, that he’s doing and a theory that, that he helped to popularize. Um, it’s just incredible. And I get them in a very vulnerable, you know, situation. You know, people on a podcast, it didn’t exist, you know, 20 years ago.

Peter McCormack:
Yeah.

Brian Keating:
Uh, and so now it’s just this incredible thing, an opportunity for me to talk to people I want to talk to. to. There’s a lot of people I have to talk to. There’s some border problem with one of my graduate students bringing in this type of isotope of helium, and the Chilean authorities don’t like that, whatever. I have to talk. Oh God, do I really have to talk to HR about this? I have to, but I want to talk to you. I want to talk to the people that come on. So will it be my last book? Maybe not, but it’ll be the last of these kind of narrative-driven memoirs of what it’s like to be a scientist, not to you know, collect the gold medal and after you hit the century in cricket or whatever, right? But for me, yes, to be able to do something, I always say I’m only going to write a book if I’m the only human being on Earth.

Brian Keating:
No one else could write that book.

Peter McCormack:
Yeah.

Brian Keating:
I was never worried. I told people the title. No one’s going to copy that. No one could scoop me to it. No one could beat me to it. My next book is the same thing. It’s a personal story about how these things mesh together. And yes, it’s always going to involve God.

Brian Keating:
It’s always going to involve spirituality. It’s not going to argue, it’s not going to proselytize, I’m not going to browbeat you over the head. And I’ll be honest with my people that believe in religion and God. I’ve talked to everyone from John Lennox to Stephen Meyer and the greatest theologians on the planet. I’ve talked to Sam Harris. Richard Dawkins is— I’m hosting him for my second time at Carnegie Hall in New York in October. And he knows my views on religion.

Peter McCormack:
I’m going to be in New York in October.

Brian Keating:
Oh, well, I’ll have to get you a ticket then. Now that guy, you look like, why is he doing this? Does he need more attention? It’s 50 years since he published this Does he need to go on tour for the second time in 2 years? No. There’s something that energizes him about it because he can do it. And he’s had a stroke and he’s older than President Biden. I mean, he’s an incredible guy. I hope I can keep doing that when I’m his age. So yes, there is a lot of time we waste and there’s very little time. And that’s why time is so beguiling to me and how I want to understand and the reason that I dedicate the effort to it, because I may not be the only person on Earth who can put these things together.

Brian Keating:
I’m not saying I’m the best scientist, far from it. I’m actually not one of the best for sure. But I think I have an insatiable curiosity. I have an ability to lead and motivate people and recruit things, funding agencies, and influence people to kind of question what we understand because of the greater good of doing what only human beings can do. Again, I only want to write books that only I can write. I only want to do things that are interesting to human beings who have the capability to appreciate it. Yes, if we save the world from global warming, that’s great. You know, benefits animals and whatnot.

Brian Keating:
Or you make some vaccine or whatever, that’s great. I’m not— I can’t do that. Like, that’s not in my wheelhouse, my skill set. I can make podcasts. You know, it’s not beyond my ability to do it. I don’t think it’s the greatest thing that I could possibly do, which is why it’s, you know, kind of a side hobby for me. But it gives me great enjoyment. And I think that’s important.

Brian Keating:
Like, you need some time to yourself.

Peter McCormack:
Like, if you were just—

Brian Keating:
look, Look, you feel this way about your kids. You love your son. You and you’re working with that’s incredible. Like you know my son’s like taking college classes now and he’s a fifteen-year-old, right? And it’s just wonderful. And he’s interested in science and math. He doesn’t want to do exactly what I want to do, but that’s fine. And and I want him to find his own way. Do you wish that like I assume your parents you know loved you the same way you love your son, and so therefore they want to be with you all the time? Did you want to be with your parents all the time? I’m sorry, I don’t want to be with my mom all day.

Brian Keating:
My mom’s still kicking around and I love her. I don’t want to be with her all day. day long all the time. It’s not her— it’s not your path to do.

Peter McCormack:
Yeah.

Brian Keating:
So we waste time. We have time. I actually don’t think when I hear it like, oh, Ryan Holiday and, you know, other bro podcasters talking about, uh, like time is the only non-renewable resource. I actually think that’s bollocks, as you would say. I think that’s total nonsense. We have so much time. We waste so much time. We scroll.

Peter McCormack:
We’re not present.

Brian Keating:
Even when we’re there, we’re multitasking. We think we’re— no, no, it’s not time that’s limited. It’s attention. But I think it’s something else. There’s a, there’s a quality which, for lack of a better word, I call innocence, which, you know, when your kids have it and there’s some period of time when you stop being innocent, like before you got the tattoos. I know you had problems with addiction. You went through a lot of struggles. I know you’ve been incredibly courageous with your— you’ve had legal battles that you’ve won.

Brian Keating:
You won a mighty pound, I think. Right. That was incredible. I could buy like another stirrer for our coffee. But you have courage, right? But there was some period of time when you were innocent, you were pure, like when your son was pure. I think that’s the rarest quantity. I wanna maximize that. And like, but the innocence of the human race, like we’re at this precipice now.

Brian Keating:
We don’t understand things like aliens or, or, or the deepest laws of physics. I can contribute a little tiny bit to maybe both, maybe one.

Peter McCormack:
Do you think there’s like, um, a demand now for understanding and a demand for like the weird things that are being discussed on podcasts and things that is outstripping the actual real supply?

Brian Keating:
Oh, for sure. No, that’s a great way to put it. Um, yeah, because it is so It is so mysterious. It’s such a low-information environment. Like I said, we started this experiment 10 years ago, and we first got our first data like a year and a half, 2 years ago, and we’re going to be analyzing it for the next 8, 9 years, right? There’s a tremendous thirst for this. If you had told me on the first day, pay an extra million dollars, get a million dollars more from the UK or the US funding agencies, you’ll get it tomorrow, I would’ve done anything I can, because then they could get onto the next problem. But, but no, I don’t, I don’t. I, I think that there’s, um, there’s 2 things at work.

Brian Keating:
One is that yes, there’s, there’s supply-demand issue that you’ve correctly identified, but there’s also, um, a really misbalanced incentive structure that’s causing what I call a psyop— S-C-I operate, not P-S-Y, but, but scientific operate. In other words, these things are, you know, sometimes I wish we— it was like, you know, looking for yetis or Loch Nessie or whatever, like, like, because it’s so ridiculous. But mixed in with the ridiculous claim— and believe me, they’re replete with ridiculous characters, charlatans, grifters, frauds, liars, um, in the UAP side and the government side, uh, as well— but there’s, there’s also tremendous amounts of scientific interest, military interests, public safety interest, uh, security, national security, uh, global geopolitics. I mean, it’s incredible. There’s nothing else really like this where everything is mixed together— AI and and, you know, power and fusion and all sorts of other things that are mixed in all together with this. And like I said a couple of, you know, I don’t know, tens of minutes ago, it’s— it— there are many, many aspects of science that are so uncomfortable to scientists because they are effectively religious in nature. And my favorite, you know, kind of, um, uh, litmus test, as a chemist would say, for what is science or what is not science is like, what is religious and what is not religious. And that comes down to this notion of belief.

Brian Keating:
When someone says, I believe that UAPs are this and that, like, I think you talked with Pines about this. And he was saying, like, all these phenomena are real. And I was like, I don’t know if all of them are real. I mean, some of them are like, did they happen? Like, there’s some obvious things where there’s a flare, and it’s coming down underneath a parachute. And they identified like the manufacturer of it and the intensity of the flare. Like, is that real? Like, yes, it happened. Is that like anomalous? And this is one of the first things that were released by the Trump administration. And then you’ll have people believe, you know, that, okay, so that, that, like, it happened, you know, to answer Pines’s, you know, say, yes, it did happen.

Brian Keating:
But was that— does that have any relevance to either aliens, you know, propulsion? You— is it even a UAP? No, no, it’s identified, right? We know exactly what, what made that particular signal. Now I’m saying that’s just one example. There are many, many other unexplained examples. What’s so interesting to me is that Even according to like diehard UFO maximalists, right? They will say things like 95% of what we’re seeing has some ordinary explanation. Okay, good. I agree with that. 95%, but not 100%. Okay, well, there’s always some residue of anomalies that will never be explained, right? We will never be able to explain every single particle track that’s made at the LHC.

Brian Keating:
We will never be able to explain every phenomenon that happens in 1947 Over this remote military outpost in, in near north of Las Vegas in Groom Lake. We’re not going to be able to do it. Um, does the immediate Sherlock Holmes reaction to that go to something fanciful and, and very intriguing to me as a scientist? Interdimensional beings, non-human biologics, bipedal organisms. Um, you know, these crafts that are, uh, TARDIS-like, that they’re, they’re bigger inside than outside, um, that, that create local warp drives, zero-point energy. Oh, fantastic things. Very, very— there’s zero evidence. And it’s not even that people are claiming that they’re— like, my favorite piece of counterexample to kind of explain why I’m not— no longer— I’ll never get excited about disclosure.

Peter McCormack:
Yeah.

Brian Keating:
And I’ll always say, I think Sean titled my episode The Nothing Burger. is, is Congresswoman Luna in our country, Anna Paulina Luna. She’s a very powerful— I mean, she’s one of the most powerful people on Earth. She knows the whistleblowers, she’s talked to them. And she knows their stories. She knows what they’ve been told from other people. Many of them, almost none of them say that they’ve seen it, by the way. They say that other people have seen it and told them in confidence, and they can’t disclose it for fear of being doxxed.

Brian Keating:
And David Grusch had horrible things done to him. And I think it’s a tragedy that Someone brave in our military would have that done. However, there’s nothing that prevents Ana Paulina Luna from saying, here is the body, here is the biologic, here— like, she knows about it. She says it exists. She talks about interdimensional beings. Okay, so you can do it. I don’t have to see it. Brian Keating, you know, who am I? I’m some nobody, right? But you have to do it if you’re claiming that these things exist and you have unlimited power, immunity from whistleblower status.

Brian Keating:
Like, she’s not gonna get arrested and thrown into jail. She’s on these committees. She works with, you know, the highest levels of— she’s a chairwoman of the committee that’s studying these things. And, and recently they, they, they’ve agreed to, you know, you know, increase protection for whistleblowers and allow government contractors to blow the whistle, not just military. I mean, have you noticed any bodies show up on the White House lawn? I mean, again, I want— I don’t have to see it. I don’t— I’m not that like persnickety as a scientist. Oh, I have to see it in my laboratory. I I know the laws of physics, so I can know what breaks the laws of physics.

Brian Keating:
But I trust people. I can trust Anna, you know, Congresswoman Luna. Please do it.

Peter McCormack:
Do it.

Brian Keating:
I’m out there. If you’re watching, Anna, just do it. What are you worried about? I would like to know. If there’s something that you’re threatened by, then there’s a much deeper crisis. This is why it’s a low-information environment. People’s minds fill in the blanks, and oftentimes it goes in a negative way.

Peter McCormack:
So where do you think we’re wasting time at the moment? Where do you think we should be spending more time on?

Brian Keating:
I think people focusing on events from the ’50s The ’40s. Yes, it is true. And even like the 2020s, right? So people say the following: Fauci lied. He just admitted, according to Rand Paul— again, this is all allegedly, I’m a physicist, I’m not, I’m not a virologist, right? But that, that he knew the wet market, you know, idea that COVID kind of originated from a wet market, um, uh, was not actually true. It was an amplifier, he said in the email, if I’m reading it correctly. Again, billion disclaimers: not financial advice, not virology.

Peter McCormack:
We won’t throw you in jail.

Brian Keating:
It’s the UK. That’s what I’m worried about. So, and you could say, well, look, scientists lie all the time. The problem is he’s not a scientist. Okay? Fauci is not a scientist. When he’s operating in that mode, he was not acting as a scientist. He even talks about in this most recent data dump, you know, how he was angling for certain awards and he was asking people to nominate him for awards and maybe he’ll nominate them. And this is the same time he was besmirching my friend Jay Bhattacharya, saying he’s a fringe epidemiologist for saying we should only use the vaccine for the most vulnerable.

Brian Keating:
Oh, that’s so radical, right? But, but so science can get caught up in this. But when you do that, you automatically are removing yourself from what it means to be a scientist. Scientist doesn’t say things like, trust me, follow me, believe in me. There’s no belief. That’s religion. Fine. If you want to believe in things, that’s fine. I don’t have any problem with you believing in things.

Brian Keating:
I don’t believe in gravity. I have evidence for gravity, right? And so in this case, yeah, we’re wasting a lot of time. Because of the fact that legitimate— once legitimate entities, government entities, universities, and, um, and other organizations, and the military, and civilian contractors— there is a vast amount of undisclosed and unknown information that will never be disclosed to a proper level of satisfaction because the human mind wants complete certainty. You want complete certainty. You take this vaccine, you’re not only gonna get better, you won’t get the virus, and you won’t spread it onto any others. Remember what Joe Biden said in 2020? You take it, you’re not gonna spread it, you’re not gonna get it. It’s total nonsense. Everybody, I took it, I got it, right?

Peter McCormack:
Yeah.

Brian Keating:
I took the vaccine, I got it, right? And so, you know, from this perspective, but that then undermines public faith in science. Public faith in science, public faith in government, these are critical things that we’ve just totally gone off the deep end for. And I don’t know, can we get them back? And so operating within that system, if Luna comes out and says, I’m going to— here’s the body, here’s the biologic, that goes a long way to restoring credibility in the government. But then there’s going to be scientists, and she’s attacked scientists that have led these programs that have said, no, none of these phenomena warrant kind of the level of claims that are being made by these so-called whistleblowers, actual whistleblowers. Right? So I think that we’re so beyond trust because Everybody not only has their own theories, ideas, conjectures, conspiracies maybe, but they have their own epistemology, which is very dangerous. If you say, I have my own ways of getting at the truth, and one of those ways is if Keating says something, I don’t believe it, or Fauci says something, that’s one of my ways. Okay, you could say that, right? Or if the government says it, I don’t believe it. I mean, these are things I hear all the time.

Brian Keating:
Now it’s saturated. With, with complete skepticism. And, you know, I have to say, it’s like, you know, this disclosure is coming and everything’s coming and it’s going on a decade now since the first kind of major stories broke. And I’ve talked to almost everybody in the field that’s, you know, kind of made claims. I haven’t talked to Grusch yet. I’d like to talk to him. But, but, you know, the fact is, I don’t know how much information I’ll get from him. Right.

Brian Keating:
And so we’re going to operate in this, in this scenario where the people that could tell us aren’t telling us, but there’s no— it seems to me there’s no incentive. Again, I know why Grusch isn’t telling me. He’s scared. He has reason to be scared. I, I don’t want anything to happen to him. I can’t say the same of an elected congresswoman whose job is to do this, to chair this, this committee. Now they’ll say, oh, it’s about to happen. If you knew what I knew, I’m gonna— you know how many times I’ve heard that in the last couple of years? And as a physicist, it’s the most cruel thing of all, right? Because what would I get to do? I could say, well, like, stop building this experiment.

Brian Keating:
And, and like, I know how the Big Bang began because, you know, all these aliens are going to tell from the physics of the 27th century that they had to get to come here and walk on 2 legs, which is incredibly astounding if true, right? And to be non-human and interdimensional travelers, we’re gonna learn so much. This is gonna look like, you know, playing with, with a spinning top as a kid. It’s gonna be pathetic, this $100 million instrument that my colleagues and I are building. Oh, forget it, stop it. No, no, that would then allow me to start asking questions of the aliens, right?

Peter McCormack:
Mm-hmm.

Brian Keating:
So it wouldn’t stop me from asking, it would just amplify the level of questions that I get to pass.

Peter McCormack:
We seem to be living in this really strange time, Bryan. It’s, um, we have so much opportunity. There’s so much happening right now. There’s like the acceleration with technology and the things we can discover and learn. And I’m so excited by AI, and we’re going to talk about that now. Also at a time when there’s this deep distrust of everything which is an institution.

Brian Keating:
Yeah.

Peter McCormack:
And it seems like we’re really wasting an opportunity now to just improve humanity. It feels a little bit civilizational Like, I will— I mean, I’m from the UK, I’m naturally worried about our civilization, but like, how do you get away from that deep distrust of the state and how the even deep distrust of science and scientists at the moment to focus? How do you deal with that?

Brian Keating:
Well, I mean, you kind of just sparked something in my mind as you said it. Like, I’m from the UK. Like, you in the UK— I’m not saying you personally, but you, you played a role in it— but, but I mean, the United Kingdom did more for humanity, like planet Earth, than any other entity I can think of right now. And yet the Brits that I know, and I know quite a few—

Peter McCormack:
Have they left?

Brian Keating:
Most of them have left. A lot of them have left. Very few people are moving to it. It’s like California. Like, there’s a lot more people moving out of California lately. But, but then, I mean, I’m sure, yeah, maybe it’s, it’s not a bad place. San Diego happens to be the best place in the world to live. I’m convinced of that.

Brian Keating:
And California, for all its flaws, I’m never gonna leave it. I’m gonna fight. I’m gonna, you know, try to make it make it better and enjoy. But what you just said, I think harkens to, like, there’s almost a little bit of shame. And like, I’ve never heard— like, I heard Chris Williamson was talking on a podcast recently with a man from India, huge pod, like 20 million subscribers, just brilliant young guy. And they were just going on about how bad, you know, the UK was for India and harmful. I’m like, the UK? Yeah. Is anybody perfect? I mean, has any civilization ever been perfect? Like, we here in America have land acknowledgments.

Brian Keating:
Like, you right now are on Kumeyaay land, And we have to acknowledge that. Do you think that they like nucleated from nowhere, like a perfect society with, with, with no challenges and they didn’t subjugate or do battles? Like we’re living next to Mexico. Mexico, you can see from the top of the hill that we’re on now. You can see Mexico. Do you think the Mexican, the indigenous Mexicans, that they didn’t also have their own challenges, say civilizational, and they didn’t also create incredible technology? Like Aztecs are phenomenal, you know, just brilliant. And, and they also were horrific in other ways. Like if you judge everybody by the standards of today, And he’s, oh, Winston Churchill. Yeah, he stopped the, you know, he stopped the tyranny and fascism, Nazism.

Brian Keating:
Uh, but he also was bad to, I don’t know, Bangladeshis, India. Yeah, it’s true. It’s totally true. Guess what? You know, Isaac Newton was a real prick. Okay. But he also gave us, you know, a jumpstart in our knowledge of humanity and, and the laws of the universe. Now, this doesn’t excuse anything they did that was wrong. I mean, George Washington is a hero of mine.

Brian Keating:
He had slaves, right? Do you look at someone and judge them purely by the standards of today, the shifting moral zeitgeist or the kind of ever-changing moral landscape? I don’t know. I’m not saying either way, but the fact that you have to say that leads me to just answer your question. What do we have to do? We have to stop apologizing for humanity’s greatness. Because I think if we do, these AIs are already kind of getting built into it. I asked some questions of AI, just moderate things, And you’ll get answers that are just completely preposterous. I can’t, uh, it violates our trust in safety. And like, does it violate the laws of physics? Like, I want to know the answer to this particular physics problem. Like, like, you can’t trust me with it? Oh, who can you trust? Like, and this is where I do worry about people like Sam Altman.

Brian Keating:
I mean, I’m fascinated by him, but, but I’m worried about him too because he says things that are like just outright, you know, kind of extremely, extremely problematic.

Peter McCormack:
Yeah.

Brian Keating:
Uh, recently he said, don’t ask questions about like how much energy it costs to train the models and, and, and, you know, data centers, because like if you look at how much energy it costs to, to train your, your, your 18-year-old, it’s far higher. I’m like, that’s completely backwards. Like, AI is a tool. It is not a human. Its job is to serve humanity. I actually don’t think we’re gonna get to AGI and what they call superintelligence. I think that are fundamental barriers with the current way that we’re approaching it with LLMs and GPUs and NVIDIA and OpenAI. I think that there’s fundamental no-go challenges that won’t allow us to supersede that.

Brian Keating:
Can you explain that though? Okay, so what is ChatGPT based on? It’s a large language model. It’s based on a certain type of mathematical embedding. It’s called matrix manipulation. It’s linear algebra. it’s not like super advanced group theory, topology, quantum mechanics, and knot theory. No, it has nothing to do with that. It’s basically sophisticated ways of multiplying matrices. Matrices are tables or grids of numbers, like a spreadsheet, and they have to be operated on in a very special way.

Brian Keating:
And the faster you can do these operations to find local maxima and global minima and things like that, the better the AI will perform. So in this case, the AI is based on this large language model, and because the matrices are kind of like these 2-dimensional tables, they’re like grids. They work best on computers that are also like that. And it turns out that the first types of computers that were optimized to do these types of rapid tabular matrix multiplication operations were graphics processing units, GPUs.

Peter McCormack:
Yeah.

Brian Keating:
And the number one company that makes them is Nvidia, the most valuable company in the world, I think, still. And they were very well optimized for this. But the very first thing to be optimized is not always the best thing, to be honest. It was optimized such that when you played— do you ever play Doom?

Peter McCormack:
Yes.

Brian Keating:
Or any of those games, or Minecraft, or World of Warcraft, or whatever, any of those games. Those are made that if you had a better GPU from Nvidia, you would beat your friend to the first player shooter a millisecond before him, and you would kill him, and you would win the game, right? So they’re optimized for that. They’re not optimized for superintelligence. They’re optimized for making better, faster, quicker, cheaper, more massive matrix multiplications. Now, is that the way our brain works? No, our brain is nothing like that. Yes, we do use language and they’re very good at figuring out language because you can embed a language network in a system once you’ve trained it on all of human knowledge and the entire internet and trillions of parameters that have already been accomplished, that have already been produced by humans. Right? So my favorite example is like chess. Like humans have not been able to beat chess computers since 1993, like Deep Blue, which then morphed in IBM, and then later on AlphaGo and all those beat Go and so forth.

Brian Keating:
So, but we still play chess. Chess is more popular than ever now. You can actually play online. It’s incredible. Like chess celebrities, ranking systems, tournaments, everything. Why is that? Because chess itself was not made by the LLM. The LM solved it. It’s like a Rubik’s Cube.

Brian Keating:
People buy Rubik’s Cubes all the time, even though a computer, they made a robot that can solve in 0.1 seconds any combination. But the computer didn’t invent the Rubik’s Cube. The computer didn’t invent chess. The computer doesn’t know what it means to be human. It only knows what humans have already done. To make progress now, we’re kind of saturating the curve. This is this thing called Jevons’ paradox. The farther, the more successful something is, the more widely used it is, because costs start to come down, but then usage of it starts to go up.

Brian Keating:
So you start to saturate the amount of, um, of intelligence, not just tokens per second, or tokens or intelligence per dollar, or intelligence per joule, whatever you want to use. And we’re sort of saturating there. And the only way to break through there is to train it with something new. Like, so you need new information to be trained on. When they train on themselves, it’s like your country suffered mad cow disease. Why was that? Because the cows were eating other cows, right?

Peter McCormack:
Mm-hmm.

Brian Keating:
They were digesting these prions in the brain of other cows and they would get it and they would go crazy. And it was a horrible problem in Britain, right? So these LLMs can’t train themselves. They need human data to train it. So what are they waiting for? Well, they’re waiting for, you know, Harry Potter 12 to come out. They’re waiting for, you know, The Odyssey to come out. All these things that humans have created that then go into the next level of training data to get better and better. Now they’ll be— they’ve already surpassed people in a lot of math things, not all things. things.

Brian Keating:
But in terms of creativity, in terms of like, where’s the theory of the Big Bang? Where’s the theory of everything? Where is the unification of gravity and quantum mechanics? I do not hear the argument that the best architecture for this is a GPU plus an LLM.

Peter McCormack:
Because they’re only able to build what they’ve done based on previous.

Brian Keating:
That’s not the way that Einstein— say Einstein came up with the theory of general relativity. He didn’t say, hmm, let me just do these math problems like linear algebra grow really fast. In fact, if he had tried to do it, we tried to replicate, we took an LLM, we lobotomized it. We said, you don’t know anything after 1900. Now tell me, LLM, you have the same computing power, the same NVIDIA chips, the same density, number of parameters, same training weights, but not the same training data. We cut it off in 1900. Can you then derive quantum mechanics, general relativity, the cosmic microwave background? Can you derive these things? No, it It tried to break it up into discrete little intervals and cubes and squares. It basically did this brute force.

Brian Keating:
It was very clever, but it couldn’t reproduce curved spacetime, Riemannian manifolds. It couldn’t derive what these essential thing that Einstein had, which is this feeling in the pit of his stomach that we’ve all felt when we go on a roller coaster or we go over a bump or turbulence in a plane. You have this sensation, oh, my pit of my stomach, when you go over these bumps or turbulence or whatever. where that feeling is zero gravity. Einstein realized that when an observer’s in a zero gravity environment, it feels no force, and therefore it takes a path through that space or through that time, which is equivalent to the most effective means of traversing that space called a geodesic. It minimizes these certain properties of energy and whatnot, and that’s what things do naturally. And he called that feeling in the pit of his stomach like the realization that he made, he said, that was the happiest moment of my life. Now, what is your NVIDIA Bracewell, you know, 1200? What’s the happiest day of your, you know, your LLM’s life? It’s a nonsensical question.

Brian Keating:
That inspired him. That made him feel alive. Like you said, he could have stopped Einstein in 1904 and said, you know, why are you doing this? Like, you know, what’s the point of this? You’re gonna be dead soon. You’re gonna die in 1955. You know, 50 years from now, you’re gonna be dead. Why are you doing this now? Go spend time with your kids that you never And he would have said, no, I can’t stop doing it, because he was the only person that could do it. And the insight that gave him was an intrinsically humanistic thing, that he felt in his stomach what it’s like to be in zero gravity long before astronauts, 2 years after the Wright brothers even. And he was able to connect that to a law of physics that survives to this day called the Einstein equivalence principle.

Brian Keating:
And that’s the underpinnings of his theory of gravity. Without that, without that essential human now, how are you going to do that? You’re going to take an NVIDIA computer and drop it off a tower and say, oh, that’s what it feels like? Or painful experience, you’re going to blow out a capacitor in it and make it feel pain? No, these things don’t make sense for a non-embodied system. They can say a robot will do things, but also these things, I mean, have you had this experience, Peter? You’re like playing around with Claude or whatever and it says like update to version 1.4673575 and then I see update and then like, oh, you have to update again. Every day there’s like 1,200 different updates, security things, things, they come up with something else. Uh, my friend Annie Jacobson, who has a new book out called Biological War, she found that one of these ChatGPT things had a location for the stockpile of vaccines in her city. And like, she told Sam Altman about it, and they had a like deep— like, they’re always going to be patching all these bugs and, and, and, you know, trying to prevent the genie from escaping the bottle. It’s very good at doing language, but life is not language. Life is not— life is described by language, like I said before, You know, you can, you can describe something, but that’s not what it is.

Brian Keating:
E equals MC squared is a description. It’s not the phenomenon. It’s very important to make that distinction. I don’t think most people do.

Peter McCormack:
So do you think we’re having all the wrong conversations about AI? Do you think AGI is just—

Brian Keating:
Here’s my— I have 2 concerns. One is that, um, we don’t really understand how it works. That is strange. Another, um, as a friend—

Peter McCormack:
And that’s getting further away, right? Because it’s the Like, I was, um, I was with somebody recently, they’re saying you can’t look inside the box and understand how it works. We just know it works, right?

Brian Keating:
It’s, it’s, it’s so fucking enormous. Well, I think it’s, it surpassed that many years ago. And so like the Turing test, Alan Turing, um, that wasn’t actually like his greatest invention. He, he created something that’s far more important but much less appreciated, and it’s called the halting problem. So he, he— there’s a theory of computer science that you can You couldn’t make a program that would continue forever unless you had certain provisions within it. And he came up with this solution that said you can’t make a program that will cause something always to stop. Like, you can’t say stop when you get to, you know, like 10, you know, print Peter’s awesome, 20, go to 10 and go forever. Like, that loop will go forever.

Brian Keating:
But you— there were certain conditions that you had to install. to prove that something could be halted. Now you could say an exception after 100 iterations, stop, right? Or the computer runs out of energy and gets unplugged or whatever, it will stop. But he proved that in general, you couldn’t like prove whether or not something would halt or not without having like additional knowledge that wasn’t available. Okay, I’m mangling it a little bit. But a modern version of that is you cannot, because you cannot predict what these things are going to do, you cannot control what they’re going to do. So So we basically made all these uncontrollable AIs. And so a lot of people like Roman Yampolsky in Kentucky and Nate Soares in Berkeley and others have looked at this and said, basically, we need it to stop already.

Brian Keating:
The best time to plant a tree is 10 years ago. The second best time is now. No, no, no, we need to stop now, according to them. Again, I have kind of doubts about how— because we passed the Turing test, because the imitation game has been passed, and You can chat with any of the LLMs now. You cannot tell that they’re not a human being, right? They’re too good. Maybe that gives it away, but you can even put in things like, I used to use em dashes in my writing, like my books 10 years ago. I use em dashes. I use—

Peter McCormack:
You had to stop.

Brian Keating:
Yeah, I had to stop. But now I’m like, no, that’s the flex now. Just, I’m writing with my own mind and I use em dashes. So that proves it’s me. I don’t care if you believe it or not. So you can look up this book from 2018. But the point is, I’m not convinced that these things are able to achieve something that, yes, we might not be able to predict them, but do you know exactly what your teenager is doing right now? Is there complete unpredictability? Could he be jumping around on the moon right now? No. So there’s, yes, you don’t know exactly what your son is doing or my kids are doing.

Brian Keating:
I have teenagers, right? And so yes, I can’t control them. I can attempt to, but— But there are also these limitations built in that subsume that these things will have what is called general intelligence or superintelligence. I think we can get to general intelligence. They can do everything that a computer terminal could do, just like the Turing test. But superintelligence, they can do things that we cannot predict, control, modify, and they have unlimited knowledge. To me, it’s sort of a little bit of the Cassandra symptom. Nowadays, if you say that, if you say we’re going to be okay, you’re either a shill for OpenAI, the ones that are saying it are okay are those people. I actually think the worst scenario could be the following.

Brian Keating:
We end up with extremely powerful general intelligence that can put to shame everything that we’re doing now. It can do anything that a human being can do in, it’s called knowledge work, could make this podcast. Like, but, but who will listen to it?

Peter McCormack:
Fine.

Brian Keating:
But will it, you know, ultimately will become so addicted to it that then when it does go away because these valuations collapse and crater, like OpenAI is losing $10 billion a month or something like that, I think I read. Um, so are they sustainable? Like SpaceX, you know, had the stock offering a month ago, went up almost doubled. Now it’s like way below. Yeah, it’s half from there. Like who’s to say what its true valuation is? And, and if it crashes and the market crashes, and then there’ll just be less money available to, you know, there was a, recently there was a round for the employees of OpenAI to sell some of their private shares. And people were touting, oh, this is a great opportunity to get in at this $800 billion valuation. I’m like, how is this thing gonna double from here? Like if it’s losing this much money, yes, it’s growing at some rate, but it’s losing this much. So what happens if we get addicted to it? The LLMs are sort of training us to be dependent on them, right? Like, could you get along without your smartphone? Yeah, it’d be uncomfortable on the 12-hour flight home, but you could probably do it, right? But when it’s like controlling everything, your health, your relationships, your business, everything is dumped in, it’s so good at doing those things, we’ve become completely dependent upon it, therefore we’ll be crippled by its absence, which could very easily happen, as happened with the dot-com bubble in 1999, 2000.

Brian Keating:
There could be an AI bubble. After that, the strongest ones will emerge, but who’d say those are the best? You know, like, is Facebook like really the best thing for humanity? Instagram, is it really the best? I don’t know. It’s— you can make arguments against it. So I’m worried about that scenario more than I’m worried about these machines are gonna turn us all into paperclips.

Peter McCormack:
Yeah, well, you know, I’ve interviewed Nate recently and I’ve interviewed Roman. And look, it’s a fascinating podcast to make, especially if you’re the first one to make the robot— the AI is gonna kill us all. No, but I’m—

Brian Keating:
Bartlett gets there first.

Peter McCormack:
Yeah. I, I put it— I’m worried about, uh, powerful AI in the hands of dangerous people, you know, enabling people to do—

Brian Keating:
It certainly is true, but I became less, uh, concerned about that in my conversation with Annie Jacobson because she basically said like all these things are completely sandboxed and nerfed when it comes to biology. So you can do biology. In fact, Claude has a new product called Claude Science. I was like, oh, Claude Science. I’m going to have the power of Anthropic. It’s going to help me solve the theory of everything. It’s going to help me do these calculations about inflation, the Big Bang, and alien. Maybe they’ll have some discovery in there and be great.

Brian Keating:
No. Now configure your cloud science. You can have PubMed, you can have AlphaFold, you can have Pharma.net, you can have all these things for biology, biochemistry, pharmacology. Zero. There was not one tool optimized for cosmology, for astrophysics, for For exoplanets, for alien disclosure, nothing. It was all biology because these things are very good. You know, like if you want something to understand the neural network in your brain, a neural network’s pretty damn good at that. It’s a very good simulation.

Brian Keating:
It’s like the best simulation for planet Earth is planet Earth. Like if you wanna know what the weather’s gonna be like, the best computer to predict it is the planet itself. It’s just, you can’t run forward simulations, right? But you can run backward simulations cuz these are complex systems. The, these neural nets are insanely complicated. And like you said, we don’t really fully understand how they work. It kind of emerged from nowhere. You dumped in a lot of attention, a lot of nodes and internet networks. It’s just like social media.

Brian Keating:
We didn’t really understand. There was sort of social media, MySpace and whatever, and Hot or Not and all these things I remember from the 2000s. They all went away, but they weren’t that much. They weren’t like the difference between Newton’s mechanics and Dirac’s quantum mechanics. I mean, they were similar. And, but there’s something about the network. When you get things together, things grow. Gresham’s Law, they grow exponentially, sometimes double exponentially.

Brian Keating:
And that’s when things, yes, they become unpredictable, but to say that they’re definitely extinction-level events, you have to make a stronger argument. And for me to see that in the physics environment that I play in, I don’t see anything that’s threatening like what I’m going to do as an experimental physicist.

Peter McCormack:
Can you support it Do I support— no, does it support what you’re doing?

Brian Keating:
Oh yeah, I use it all the time. But, but what I use it for— so I’m, I’m maybe a non-standard user of it. I started to play around with it recently to ask the following question, which brings me to one of the gifts.

Peter McCormack:
Oh, there we go.

Brian Keating:
So these are, these are meteorites. Now unfortunately, I’m gonna give you all these, okay, Peter? But you have to give me a— you have to promise me that you’re gonna— I have a lot of fans in the UK I can’t mail these from the US to the UK because they’re controlled substances. They’re not controlled, so they’re just like— it’s— they want to know what they are. They’re meteorites. These are meteorites from— collected in Argentina. And, and these meteorites are older than the Earth. These are about 4.3 billion years old.

Peter McCormack:
What?

Brian Keating:
The Earth’s about 4.1 billion years old. And they’re made of very, very interesting components. They’re, they’re made of iron, nickel, cobalt, And you’ll get this information when you go to my website, briankeating.com. And I’ll make a— when this episode comes out, you’ll let me know. I’ll make a special link, Peter/Peter. Okay. And you go to briankeating.com/peter. And if you’re in the UK only, Peter will send you one of these.

Peter McCormack:
Yeah.

Brian Keating:
They only cost, you know, they cost nothing to mail, but they’re very interesting and you’ll get all the information about them. And then if you’re in the US, you can always get them, briankeating.com/peter. I like to use X. So these are actual fragments of the early solar system before the Earth was formed.

Peter McCormack:
4.5 billion years old.

Brian Keating:
And I have other fragments of different bodies in the solar system, including one of the planet Mars. So I have a meteorite that was once on Mars. It was a rock on Mars. It got slammed into by an asteroid. It ejected from the surface of Mars and then orbited around Mars and the Earth for about 20 million years, we think. think, based on planetary formation and so forth. And then it crashed into Northwest Africa. These are very expensive, so I can’t give them to you, Peter.

Peter McCormack:
Yeah.

Brian Keating:
But, but, but in, in essence, they’re very similar to these meteorites here. So they have this property that they, that they trace the early, the early solar system’s formation, but they also trace the conditions of Mars. So we can look at the conditions, the chemistry, the geography. There’s certain things in there. If there are microbes in there, you can see microbes. But my theory is, well, Well, we don’t— we, we know, at least as far as we’ve looked, there’s no life on Mars. But Mars also has meteorites from the Earth. In fact, this is mine, but there’s probably like a dinosaur fossil on Mars, like fragments of a dinosaur on Mars.

Peter McCormack:
Okay.

Brian Keating:
Because Mars is as old as the Earth, basically. And so there must have been a meteor that hit the Earth with some, you know, it could be— it’s not like a full T-Rex or whatever, but, but it’s, you know, some, I don’t know, some plankton or whatever, a little tiny fish fragment. And it hit where that was on Earth, because there’s a huge biosphere, blasted off the Earth, then orbited around the Earth and Mars, and then eventually landed on Mars and the Moon. The Moon has a fossil like this too, I’m sure.

Peter McCormack:
So where would these have come from?

Brian Keating:
These came from space. They came from deep space. So there was a star that existed in our galaxy around the neighborhood of where the Earth would eventually form. It was one of the first stars ever to form. It was made primarily, almost exclusively, of hydrogen and helium. Those are the only 2 elements made after the Big Bang in sufficient sufficient quantity that could form a star.

Peter McCormack:
And star—

Brian Keating:
enough hydrogen— when you take enough hydrogen and helium and you give it enough time, it makes a star.

Peter McCormack:
Mm-hmm.

Brian Keating:
That star is incredibly massive and incredibly hot, and it lives like a rock star, incredibly short. So it blew up after about 50 or 60 million years. But after it was formed, there were 2 great British astronomers, Margaret and Geoffrey Burbidge, who worked at UCSD for most of their careers. And they and their colleagues, uh, Willy Fowler and Fred Hoyle, the Big Bang guy we talked about earlier, They showed that hydrogen and helium alone can eventually make every element on the periodic table, essentially, including iron. And when it makes iron, the star stops making enough heat to keep it from collapsing. So it started to gravitationally implode, and when it implodes, it explodes eventually, and it becomes a supernova, and it sprays out into the galaxy the last thing that it was producing, which is this iron.

Peter McCormack:
Hmm.

Brian Keating:
So more than this iron just being from the star, here’s a big one. That’s for a very lucky one of your listeners. I’m gonna keep that one. Yeah, give it to yourself.

Peter McCormack:
This is the coolest thing.

Brian Keating:
And when it was created, it spews that out into the galaxy, and eventually it became part of our Earth. Our Earth’s core is made of the same iron. But that’s not all. Your blood has iron in the hemoglobin molecule. That hemoglobin molecule is the same isotope of iron in this meteorite here. So we’re all connected. As Seneca said, we’re all connected like the blood in our veins. This connects us to the cosmos, and it’s fully part of the Big Bang plus Earth formation history.

Peter McCormack:
This is so cool.

Brian Keating:
Yeah.

Peter McCormack:
So, wow. So I will distribute those. Yeah, I will distribute it. I may keep a couple myself. Do you know what I’ll do?

Brian Keating:
Please do.

Peter McCormack:
I will keep this. Keep a couple. I will keep it in the studio. Yeah, distribute from the studio.

Brian Keating:
Here’s the last thing I want to give you. One more gift for you. Give me one of the meteorites. Give me a small one. Okay, so this is Arthur C. Clarke. Yeah, another one of your great countrymen. Again, not a perfect individual, But he came up with the following statement.

Brian Keating:
He came up with many statements, but one of them was, any sufficiently advanced technology is indistinguishable from magic.

Peter McCormack:
Yes.

Brian Keating:
He wrote the book that became the movie 2001: A Space Odyssey. And he also said that the only way of knowing the limits of the possible is to go beyond them into the impossible, which is the title of my book series and my podcast. So in the movie 2001: A Space Odyssey, which is the best science fiction movie, but it also has all these contraptions and crazy things in it and just implausible things, and it makes it a little hard for a physicist to watch. But anyway, I suspend disbelief. The monoliths that appear, we don’t know what they are, but they’re these permanent sentinels that seem to exist for all time. Maybe they’re talismans, maybe they’re time capsules, but this one is magnetic. So we made you this medal. This is the Keating Prize, not the Nobel Prize, the Keating Prize for Impossible Imagination.

Brian Keating:
I want to give it to you. And if you look at this monolith on the back, It’s magnetic.

Peter McCormack:
There we go.

Brian Keating:
So this is for all your great work encouraging curiosity, imagination, and really those are the things that make us human, Peter.

Peter McCormack:
This is gonna have to go up in my studio.

Brian Keating:
I’ll be checking when I see you next.

Peter McCormack:
Brian, I’m so glad I had the opportunity to talk to you, and I’m a little bit blown away that you actually listen to my podcast, which is very cool.

Brian Keating:
Congratulations on your success. You’re actually doing incredible. Don’t give up.

Peter McCormack:
No, I’m not gonna give up.

Brian Keating:
up.

Peter McCormack:
But, uh, and I hope we get to do this again in London. We’ll do it in London. I will become a meteorite distributor. Uh, please, anyone? It looks like we have about 100 or so bits in here, so I will keep one for me. I may give one to my kids. Yeah, you should. And then I’m gonna hand this up. And thank you so much.

Peter McCormack:
This is incredible. This is, this is the coolest. When I tell my kids or my wife that this— they’re gonna go, no way.

Brian Keating:
Just tell the, you know, customs that it’s just vitamins.

Peter McCormack:
Yeah, okay, I may get stopped. Thank you so much, man. Thank you. Love this.

Brian Keating:
Thank you everyone for listening.

Peter McCormack:
We’ll be back in the UK soon. Peace out. Thank you. Love you all. Thank you.

I was miscalibrated for 30 years

sabartigas — Thu, 27 Aug 2026 02:21:31 +0000

I was miscalibrated for 30 years

Dear Magicians,

I’m an experimental physicist who has spent three decades calibrating scientific instruments.

In 2012, I helped develop a technique called self-calibration for the polarization angles of Cosmic Microwave Background telescopes. It is still used today.

Recently, however, I discovered that the word calibrate may itself need calibration.

The word comes to English through French calibre, meaning the size or bore of something. After that, the etymological trail forks.

The conventional explanation traces calibre to the Arabic qālib, meaning a mold, form, or pattern, perhaps ultimately from a Greek word for a shoemaker’s last: the foot-shaped form around which a shoe was made.

That is wonderfully appropriate.

Long before SI units, human beings used their own bodies as standards. The foot. The hand. The cubit, or amah, derived from the forearm. Measurement began with an extraordinarily literal question: Compared with what?

But there is a rival etymology, probably wrong and almost too perfect to resist.

Some nineteenth-century scholars proposed that calibre came from the Medieval Latin quā librā: “by what weight?” Libra meant both a Roman pound and the balance used to weigh it.

Look again:

ca-LIBRA-te.

The derivation is disputed. The metaphor is not.

Calibration is fundamentally an act of comparison. You put the unknown on one side of the balance and a trusted standard on the other.

The Torah understood the stakes thousands of years before NIST. Deuteronomy forbids merchants from carrying heavy and light versions of the same weight and calls dishonest measures a to’evah, an abomination before God.

Today, instead of biblical weights, inspectors walk into supermarkets carrying certified masses. Put a known weight on the scale. Does the scale report the correct answer? If not, it can be taken out of service.

Modern technology changed enormously.

The epistemological problem did not.

And there is a subtlety even scientists routinely get wrong:

Calibration does not make an instrument correct. It tells you how wrong it is.

A thermometer can be calibrated and still read one degree too high. Calibration establishes the relationship between its answer and a trusted standard. Adjustment comes afterward.

Measurement asks:

What is the value?

Calibration asks the more dangerous question:

Why should I believe the value?

Which brings me back to self-calibration.

Ordinary calibration requires something external: a known mass, voltage, temperature, star, or other reference. Self-calibration tries to infer the reference from the data themselves.

That is extraordinarily powerful.

It is also dangerous.

Suppose theory predicts that some signal should equal zero. You can rotate your instrument until the signal disappears and declare yourself calibrated.

Unless the signal you erased was never an instrumental error.

Unless it was the new physics you were looking for.

After thirty years in experimental science, I find it reassuring—and slightly alarming—that even the word for checking our measurements against reality comes with its own error budget.

Until next time, have a M.A.G.I.C. week.

Brian

Appearance

This week I am teaching outside my own classroom.

Black Holes & Relativity is live at Peterson Academy: eight hours on dark stars, Event Horizon Telescope images of the hole at the center of the Milky Way, gravitational waves, Hawking radiation, and the information paradox. We end on the physics of Interstellar, Gargantua, the Kerr metric, and what Kip Thorne actually signed off on.

If you want the lectures without the campus walls, that is the place.

Watch/Enroll to the course here.

Genius

Oxytocin is the “love hormone.” Or so the press release said.

The actual literature, summarized in a 2014 Frontiers in Behavioral Neuroscience review, reveals something far more interesting: oxytocin doesn’t just increase in-group bonding and trust—it simultaneously increases out-group hostility, aggression, and conformity pressure. The hormone doesn’t make you loving. It makes you tribal. It amplifies “us” by amplifying the threat of “them.” Every study that found prosocial oxytocin effects was measuring in-group behavior.

Every intervention designed to increase social cohesion has this structural property: the stronger the we, the sharper the they. This isn’t a side effect of love—it’s the mechanism.

The communities you feel most warmly toward—who are they implicitly defined against? That’s the oxytocin at work.

Source: Frontiers

Image

The Lobster Nebula, NGC 6357, about 8,000 light-years toward Scorpius. Massive stars in Pismis 24 light a 400-light-year tapestry of gas and dust. Hubble palette: green hydrogen, red sulfur, blue oxygen. 105 frames, 48 hours, Chile and Australia.

Credit: Mike Adler / Earth and Sky Imaging

Conversation

Latest on Into The Impossible

This week’s Into the Impossible is with Nigel Goldenfeld, my UCSD colleague, on why neural nets work when the theory says they should not.

He treats generalization as a condensed-matter phase transition: the same physics that makes ice freeze can explain why a network interpolates instead of memorizing. The fridge-magnet version: AI works because it sits on the right side of a phase boundary, not because we discovered a new law of intelligence.

Audio is live on Apple Podcasts and at briankeating.com/podcast.

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!

Why AI Shouldn’t Work — And Why It Does Anyway | Nigel Goldenfeld

sabartigas — Wed, 26 Aug 2026 00:23:40 +0000

Why AI Shouldn’t Work — And Why It Does Anyway

Transcript

Nigel Goldenfeld:
And you’re way, way into that regime where you’re just fitting noise and the whole thing shouldn’t work. It obviously shouldn’t, and yet it does. Francis Crick said, look, there’s no way that life got to this level of complexity in such a short period of time. It must have come from outer space. The genetic code is optimal in the sense of minimizing errors. If you wanted to know what is the purpose of life, the purpose of life is to help planets come into equilibrium. So a phase transition, you know, is like, for example, what happens to a piece of metal when I cool it below a certain temperature? And I want to know, can I use it to stick the pictures of my kids’ holiday pictures on the door of my refrigerator? And the answer is yes. If it’s magnetic, it’ll stick with a piece of metal holding the picture up.

Brian Keating:
Mm-hmm.

Nigel Goldenfeld:
And if it’s not magnetic, it won’t stick. And if you take that piece of metal that works as a magnet, fridge magnet, and you heat it up, it will stop becoming a magnet. And that’s called a phase transition, as you know, and maybe some of your listeners know or viewers know. The interesting question you might ask is, as I get closer and closer to the temperature where the magnetization disappears, how does the magnetization disappear? Does it— it disappears gradually, in fact. And if you ask how much magnetization there is, the answer is it goes like the square root of the difference between the temperature you’re at and the critical temperature where the magnetization fully goes to zero. At least that’s what you would expect. And that’s what very generic, very persuasive, simple theory, theoretical arguments that anybody can understand. I can explain it to my class in literally, you know, 20 seconds.

Nigel Goldenfeld:
That’s what you would predict. When you do the experiment, you find that it doesn’t go like the square root of the critical temperature minus the temperature. It goes like the Tc minus T to a power like 0.3265136, some weird, weird number like that. And you might say, well, it’s just a, you know, a more accurate number.

Brian Keating:
Yeah.

Nigel Goldenfeld:
The problem is that there’s no known way, or there was no known way to account for the fact that the number is not a half. It, I mean, it’s like To prove it’s a half, all I need to know is that magnets can be either magnetized north or magnetized south. And that’s basically it. It’s an argument that is so compelling, it can’t possibly be wrong. And yet, in the decades from the late 1940s up to the middle 1960s, it was discovered that it was wrong. And it wasn’t just only the magnetization. There were other thermodynamic properties like heat capacity and things like this, which I won’t go into, which also have the similar unaccountable behavior. And it was the fact that these numbers, which are themselves not particularly important, it was the fact that you couldn’t even explain in principle why they are not these simple numbers like 1/2 and so on.

Nigel Goldenfeld:
And that was the reason for the puzzle.

Brian Keating:
Hmm.

Nigel Goldenfeld:
And the explanation is a truly mind-boggling explanation. But just to tell you the outlines of the story, this phenomenon was addressed by Leo Kadanoff, Ben Widom, eventually Ken Wilson. Ken Wilson, yeah. And they invented this process of looking at a physical system on different scales of energy. So, you know, you could look at matter at the scale of this room. You could hear the sound waves, you can see light bouncing off the surfaces. On the other hand, if you want to go and see that there are atoms and see that there are quarks and things like this, you need to build a machine that’s put in a tunnel 17 miles long under the Swiss Alps in order to be able to see things like that. So what you can see depends on what energy you look at it at and what timescale and what length scale you look at it at.

Nigel Goldenfeld:
And the same thing turns out to be true of the laws of physics themselves.

Brian Keating:
Hmm.

Nigel Goldenfeld:
And Leo Kadanoff was the first person who realized that, and Ken Wilson turned it into a mathematical tool, which was called the renormalization group. In fact, it’s not even a group. You ask whether it’s a group.

Brian Keating:
Mm-hmm.

Nigel Goldenfeld:
It’s actually a semi-group. And the idea was this: take a physical system and then just say, well, you know, I’ve got magnetic dipole moments. They’re really spins of electrons, but we’ll just call them magnetic dipole moments. They’re in this bit of the sample. We’ll just lump them together into one effective dipole moment because, you know, in this patch over here, you know, 80% of them are pointing up, 20% are pointing down. So we’ll just say, okay, it’s basically just a spin pointing up. And so you sort of block things up in that way. And then once you’ve done that once, you can do it again and again and again, and you keep on doing it and you keep on doing it.

Nigel Goldenfeld:
And then you ask, what happens when you take that process to the infinite limit?

Brian Keating:
Instead.

Nigel Goldenfeld:
And it’s called a group. It’s really a semi-group because you can lump the spins together and then make these bigger and bigger spins, this coarse-graining as physicists call it, but you can’t go backwards. If you know the configuration of the very large scale and you say, well, what was the actual microscopic configuration of the electric dipole moment, the electron dipole moment? There’s no unique answer to that. You know, the one that we had, 80% up, 20% down, if it had been 75% up, 25% down, it would’ve still ended up being regarded as a spin just pointing up. And so there’s no unique answer. You can’t go backwards.

Brian Keating:
Absolutely.

Nigel Goldenfeld:
And that is why it’s so profound. Because when you start looking at the laws of physics and you say, actually what we’re doing when we’re doing this, we’re actually looking at the laws of physics. And I can tell you why it’s the laws of physics. in a minute. Then if you know what the laws are microscopically and you start saying, if I know what happens at the scale of atoms, can I work out what happens to my crystal or fluid or something like that? Well, I can do this process and cause grain like that.

Brian Keating:
Right.

Nigel Goldenfeld:
But I can’t go backwards. If I can’t go backwards, then you say to yourself, wait, I’m a physicist. All I can see around me is the natural world. There’s nothing in this room that, at least if I live in the 19th century, would let me know that atoms exist and things inside the atoms and so on and so forth.

Brian Keating:
It drove Boltzmann crazy, right?

Nigel Goldenfeld:
Well, it drove Boltzmann to suicide. It’s not just that. There isn’t a way in principle. So let’s suppose you’re trying to work out, you know, theories of the Standard Model, as people were doing in the ’60s and ’70s. Well, you can write down all sorts of non-Abelian gauge theories that you like, and they all sound very interesting. You have no way to know which one of them is right because they will all give you the same standard electricity and magnetism that we use in everyday life. And so the renormalization group, this non-uniqueness of going down in scale and going up in scale—

Brian Keating:
I understand.

Nigel Goldenfeld:
That’s the thing that makes high-energy physics, fundamental physics if you like, hard. And it’s important in condensed matter physics because you say, well, I’m going to start at a level of description, which is I’m going to take you know, atoms for granted, and then work out what are the properties of matter. And so you can go in the way where there’s only one way to do it. And that’s why condensed matter physics is so successful. The other thing I would say is that this facet of the normalization group is what enables us to do physics in the first place. So think about it this way. Suppose you’re a chemist.

Brian Keating:
Yeah.

Nigel Goldenfeld:
Okay? And you’re trying to understand organic chemistry, chemical reactions, biochemistry, or something. Let’s suppose, you know, somebody knocks on the door and says, hey, we’ve got some really disturbing use. Somebody’s just measured the radiative corrections to the mass of the top quark, and they’re 20% different than what we thought. Does that mean all our chemistry is wrong? Well, the answer is no, because all of that was just lumped into one constant in the theory, just like we were taking the spins and lumping them together into one effective spin. And that constant is the mass of the proton.

Brian Keating:
Yeah.

Nigel Goldenfeld:
The mass of the hydrogen atom, whatever. So a chemist is not worried about that. We know that the microphysics can be lumped into you effective descriptions at a larger scale. All the QCD and superstrings and whatever all ends up just giving you the mass of the proton or the spin of the proton or whatever. Because we don’t have to worry about that, you can do chemistry. You can say, I don’t worry about what the world is really made of. I just start with my level of description that I’m comfortable with, atoms, and then I proceed from there. And so, Without that, if that didn’t happen, then we wouldn’t have been able to do science.

Brian Keating:
I want to get back to reversibility and maybe even touch upon the origin of time, perhaps in the context of temperature. But before I get there, why didn’t Ising realize this 40 years before Wilson and then Kadanoff, et cetera?

Nigel Goldenfeld:
There’s a model called the Ising-Lenz model. Lenz was Ising’s advisor, and Lenz decided it would be nice to make a simple mathematical model of a magnet. So this was in like 1925, 1926, I think. So they didn’t really know all the microphysics. So they said, well, let’s just say that we have magnetic dipole moments, and we’ll just say that in appropriate units, they can either point up or point down, spin up, spin down. And that model has just 2 variables. Spin can be plus or minus 1, and that’s it. And then you take a lattice of all of those things, and then here’s the extra ingredient.

Nigel Goldenfeld:
So A magnetic dipole, of course, interacts with an external magnetic field. If you apply an external magnetic field to a dipole, it will orient with the external field. But there’s another field, which is the fact that the electric dipoles exert a field on each other.

Brian Keating:
Yes.

Nigel Goldenfeld:
On their neighbors. And so this was one of the first models where you explicitly had a cooperative phenomenon built in. In other words, whether this spin points up or not depends on not only whether there’s an external magnetic field, but whether its neighbors are pointing up. If its neighbors are also pointing up, it’s got a much higher likelihood of pointing up. If its neighbors are pointing down, it will likely point down. And so that’s why this has been, you know, it’s a famous model. You know, you can model social behavior with it. You can model the production of pistachio nuts in California orchards using it.

Nigel Goldenfeld:
As it often is, yeah. So that’s what the model is. And it really is the Drosophila of theoretical— It’s the elegant— Quantitative matter physics. What did Ising do? This is a fantastic, fantastic story of failure and missed opportunity. So they said, well, let’s just simplify the problem. Let’s just, instead of having a 3-dimensional material, which is what matter really is, we’ll just say, we’ll just do these spins in 1 dimension. And then it’s possible to, even if the problem is nonlinear in a complicated way, you can solve it exactly. And when Ising did that, He found that it didn’t have a phase transition.

Nigel Goldenfeld:
This phase transition of the magnet, the fridge magnet that below a certain temperature will stick to the refrigerator door and above that it won’t, it didn’t work in this calculation. He didn’t see that at all. And so they gave up. They just said, well, this model is a useless model. Okay? Now, there’s a reason. It’s a very interesting thing. What they didn’t know was that the behavior of matter strongly depends on dimension. And that’s not obvious.

Nigel Goldenfeld:
And it wasn’t known at that time. Now we know it. In fact, now we use dimension as a variable, which we treat as a continuous variable and do perturbation theory in dimension and things like this. And then Lars Onsager figured out in 1944 how to solve the 2-dimensional Ising model exactly. And I think it’s on a par with Einstein’s theory of relativity, general theory of relativity, as one of the most fantastic examples of theoretical physics I know of. It is a masterpiece, and it inspired many things, including string theory and all sorts of things like that. There’s so much to be said about that. Okay.

Nigel Goldenfeld:
So then we knew that there was a phase transition, and it didn’t behave the way that you would’ve guessed, the simple square root theory that we talked about.

Brian Keating:
Right.

Nigel Goldenfeld:
And eventually, we discovered that Well, it’s not— as a community, we discovered that being able to solve the collective behavior of matter exactly is a fool’s errand. It’s much better to have an approximation method that is guaranteed to work on any problem rather than, say, something like the 2-dimensional Ising model, which Onsager solved by absolutely brilliant mathematics. His solution worked. But if you apply an external magnetic field, it all goes away. It doesn’t work. And if you try to do it in 3 dimensions, nobody knows how to do that. So these special cases are special for certain reasons, just like integral systems, soliton equations in differential equation theory. If you can solve it exactly, it means there’s something special about it.

Nigel Goldenfeld:
And there was, and it’s now understood. And the renormalization group, why it’s taught and why it’s so important in graduate physics is, well, we need to know how to solve any Any problem in condensed matter physics, whatever, and I’d say many other fields of science, we now know we can solve them systematically to any order you like, any accuracy you like, using the Vandal-Moses theory. Before I came here to UCSD, I spent 36 years at the University of Illinois at Urbana-Champaign. And one day I discovered, I don’t remember how, that Ising was teaching at a university, a college, a teaching college in Peoria about 40 minutes away from the University of Illinois. And I thought, holy cow. And so I wrote to him because apparently he didn’t really understand that his name is a revolutionary—

Brian Keating:
Wow.

Nigel Goldenfeld:
Is attached to a revolutionary model in physics. And so I wrote to him, and unfortunately I was a few weeks too late. I could have, you know, I’d been there already maybe 15 years when this happened. And I cursed myself why I didn’t do it earlier. But I did talk to his son and exchanged some correspondence with his son and explained to him various things about his father’s work and so on.

Brian Keating:
That’s unbelievable. It’s like Aharonov works at Chapman University, which is less than an hour from here, and he’s still alive. And many, including me, consider him worthy of a Nobel Prize for his work and inspired my late, great mentor, Jim Simons and C.N.

Nigel Goldenfeld:
Yang. Just incredible.

Brian Keating:
It’s called Frontiers in Physics: Frontiers on Phase Transitions and the Renormalization Group, and it’s written by you. And it’s got this lovely cover, and it’s got your description. Take us through this cover, the title Frontiers in Physics, and this beautiful cover art, Nigel.

Nigel Goldenfeld:
Well, the COVID art is a deliberate British understatement. And when you see a cover like that, you think to yourself, oh, I’m about to walk through a garden full of myriads of beautiful flowers, strange butterflies, and wonderful unexpected sights. And that’s actually true. Because one of my colleagues at the University of Illinois, after the book came out, he wasn’t in this field, wrote to me and described the book in exactly those terms.

Brian Keating:
Wow.

Nigel Goldenfeld:
And the reason is because this book is, it’s still used, widely used as a graduate text in advanced statistical mechanics. And I wrote the book because I thought I had something unique and new to say about the renormalization group, which other people hadn’t noticed. There’s things in it that you won’t find in any other textbook in this topic, including the fact that the renormalization group has nothing at all to do with statistical mechanics. And the first exercise in the book, as you may remember, I don’t remember exactly which order. I think one of them might be, the first problem might be to prove Pythagoras’ theorem using dimensional analysis. And the second one is to work out the yield of the Trinity test of the atomic bomb—

Brian Keating:
Right.

Nigel Goldenfeld:
Based on just the data from the motion of the shockwave from the photographs that were published in Life magazine.

Brian Keating:
And famously, Fermi did the same thing. He just sprinkled some pieces of paper.

Nigel Goldenfeld:
He sprinkled bits of paper, and Taylor, G.I. Taylor, actually did the calculation that’s in the book. And he actually got into trouble because he did this calculation, reported the results in the newspapers, and it was classified information. And so, you know, it says lots of things in this book that are very unusual, and those things have stood the test of time and have actually grown since then in importance and significance. And another thing that’s interesting about this book is that it’s about a very obscure and arcane problem. And if you want, we can get into it.

Brian Keating:
Yeah, yeah.

Nigel Goldenfeld:
Riemannization group, the problem of critical exponents in of second-order phase transitions. But it turned out that this problem completely upended our view of what physics is, what we’re doing when we do physics, and the nature of scientific explanation.

Brian Keating:
I want to take one more detour before we get too deep into the weeds. And that’s this thing that you mentioned before, which had to do with reversibility and the fact that there’s no injection or bijection, I guess you’d say, between final state and the initial state. There are many initial states that can produce a given final state. So it’s not invertible, essentially.

Nigel Goldenfeld:
Right. So we’re not talking about states, we’re talking about the variable is not time, the variable is scale.

Brian Keating:
Scale.

Nigel Goldenfeld:
And that was the thing that Kadanoff realized. Right. That the way the energy scale at which you look at a system is the important thing. And so I will often talk about the importance of levels of description. I mean, it’s a very important problem. For example, if you’re a biological physicist, as I am, you might say, well, what is the right level of description to describe a biological system? Should I describe every atom in the biomolecules that are inside a cell, and then inside the cells, and inside the tissues, and so on? Or should I try to make a more coarse-grained description?

Brian Keating:
Coarse-grained, yeah.

Nigel Goldenfeld:
And this is not an easy question to answer, because it depends what is the question you’re trying to understand. If you’re trying to understand how does some particular molecule bind some particular protein or something like this, you definitely need to understand the atomic level description, the binding and things like this. If you’re trying to understand why is it that inside a eukaryotic cell, we now know just in the last 15 years or so that in fact the biomolecules phase separate from the rest of the cell and form a membraneless compartment inside which God knows what happened. We’re still trying to understand the function of these things. So you had these sort of phase separation processes. We understand those at a very different level of description. It has nothing to do specifically with the atoms and molecules and the specific sequences of the RNA and things like that. It’s a general property.

Nigel Goldenfeld:
So depending on what question you’re trying to understand, different levels of description are important. And this tension is very prevalent in biology because it’s not obvious what is the right level. It’s not as simple as saying, well, I’m a chemist, so I’m just going to assume all the microscopic high-energy physics, standard model particle physics stuff is just absorbed into the mass of the proton. We don’t know that you can, when and where you can do that in something as complex as biology. Interesting.

Brian Keating:
You start your lectures in UCSD statistical mechanics. I can’t tell if they’re graduate or undergraduate because I’m—

Nigel Goldenfeld:
They’re graduate.

Brian Keating:
They’re graduate.

Nigel Goldenfeld:
So— Have you seen them on YouTube?

Brian Keating:
I do watch them on YouTube. It’s Chopin Lover. Is that your channel name?

Nigel Goldenfeld:
Chopin Junkie.

Brian Keating:
But you start the lectures, you use this famous phrase, which I’ve always felt— I hate to say it, Nigel, and I know he’s a hero of yours, but Philip Anderson’s, yeah, more is different. I always felt that was kind of simplistic, but maybe I’m wrong. I’m just a dull-headed experimental cosmologist. So tell me, what is the significance? Is there anything really significant about— I mean, of course, like, Where does a sand grain start to become a sand pile? Where do protons— He’s more than that. Yeah. So tell me, what does it mean to you? Why is he a hero? Why do you start with that in that lecture?

Nigel Goldenfeld:
Why is Anderson a hero? Okay. Many things. He won the Nobel Prize for his work on assorted electrons, but there’s no field of condensed matter physics which was left untouched by his intellect. So in condensed matter physics, he is a giant in the same way that Einstein Einstein, Hawking, others.

Brian Keating:
Bohr, right?

Nigel Goldenfeld:
And, well, I’d say more than Bohr, actually. The more is different. The article was immensely influential. First of all, he really was the first person. I mean, there’s technical ways in which more is different is important. For example, you can’t have phase transitions unless you have, you take the sort of thermodynamic limit. But it’s not just that when you have phase transitions, it’s just that you can have previously unanticipated complex behaviors that you would never have otherwise expected based on looking at the thing that stuff is made of. So again, let’s go back to our fridge magnet.

Nigel Goldenfeld:
Okay? You’ve got your electric— your electrons in the material, and they have magnetic dipole moments. You would have, unless you did a particular calculation, you would never know that this thing could be used to stick your kids’ drawings on the door of your refrigerator.

Brian Keating:
Mm-hmm.

Nigel Goldenfeld:
Okay? It’s a cooperative effect. It is a conspiracy of the atoms. And I actually do an experiment which we can do right here if you’re willing to do it. I’ll make a trip to visit. Why wouldn’t I? Well, so the experiment is this. Okay? So there’s the ceiling up there.

Brian Keating:
Yeah.

Nigel Goldenfeld:
Okay? And we’re gonna move the ceiling.

Brian Keating:
Yeah.

Nigel Goldenfeld:
Okay? And we’re gonna do it like this. Take a finger.

Brian Keating:
Yeah.

Nigel Goldenfeld:
This finger. Yeah. Okay. Okay? And push.

Brian Keating:
Well, my ego made it do it.

Nigel Goldenfeld:
Come on, come on, keep on throwing. Put some effort into it.

Brian Keating:
But this is a gas, not a solid.

Nigel Goldenfeld:
It’s a gas, not a solid, so we didn’t move it. The thing is this: the Hamiltonian, the formula for the energy of the gas, is exactly the same as the formula for the Hamiltonian of the solid. They’re no different. And yet, when I take this and I push my water bottle, you know, well, my fingers don’t go through. All the atoms in this conspire—

Brian Keating:
Is that really true? I mean, at some level, just to be—

Nigel Goldenfeld:
It is. It is.

Brian Keating:
Van der Waals versus Hooke’s law.

Nigel Goldenfeld:
No, no, no, it’s really true. And this is why it’s important, because when we’re talking about emergence, remember I said we’re talking about new laws of physics. When you have a solid, There are new laws of physics. The atoms have decided that they’re not just going to sit at particular sites in a checkerboard lattice that somebody has conveniently laid out for them. They’ve actually conspired that they’re going to keep their relative separations the same.

Brian Keating:
Yes.

Nigel Goldenfeld:
And because of that, they minimize their free energy by doing that. This is a statistical mechanical description of what is happening. That means that you now have new excitations, Mm-hmm. Which are, first of all, you have the rigidity, the stiffness, the emergent rigidity of a solid as measured by the Young’s modulus and things like this. And you have the ability to transmit sound waves and other waves as well, of course. So at the level of description of the material, you now have new laws of physics. And the only thing that’s changed is the temperature. You haven’t changed the interactions between the atoms.

Nigel Goldenfeld:
You have changed the correlations. And that’s the important thing. But it’s a statistical property, and it’s not one that you can see just looking at 2 atoms. Sorry, just looking at 2. You have to look at the whole ensemble. So that was a thing that Anderson was very interested in and understood the depth of its significance more than other people. And later, he wrote that essay in 1972, In 1985 or so, when I came to the University of Illinois, the first project I did was with my cousin, Paul Goldbart, who’s also a very well-known theoretical physicist and will next year be the president of the American Physical Society.

Brian Keating:
Oh, wow.

Nigel Goldenfeld:
And we worked on this question of why rubber is solid. So everybody knows that rubber is stretchy and can expand and stuff. But But that’s not the right question. It’s stretchy, but, and a piece of, I don’t know, this cable here can bend and deform and stretch, but it’s still solid. The first question you should be asking is not why is rubber elastic, but why is it solid in the first place? Because you have a bunch of polymer molecules, they are stapled together by cross-links. It’s like having a bucket of worms.

Brian Keating:
Yeah.

Nigel Goldenfeld:
Or a bucket of worms are all flat Flapping around in a thermal equilibrium, you go in and do an experiment that you’d never get an IRB to do, even though a worm doesn’t have a backbone, which is staple the worms together in random. And then you find that the thing is not just connected like a fishing net, which would just be floppy, but it’s actually rigid.

Brian Keating:
Yes.

Nigel Goldenfeld:
And like a gel. And then you can tap it and it will wobble and it has sound waves and things like that. And those are all emergent phenomena. It’s very complicated to calculate them because the whole thing, the polymer chains are going at random, the cross-links are at random, everything is random. It’s a very hard technical problem.

Brian Keating:
Mm-hmm.

Nigel Goldenfeld:
But it’s an example of this more is different. And the point about more is different and the point about emergence that everybody misses is that when you talk about emergence, something emerges, what is it? And the answer is it is a particular type of rigidity Which is a generalized rigidity, as Anderson called it, which basically technically comes from a certain type of response function of how does the system respond to perturbations when you poke it. And that was the lesson of that. And it wasn’t really understood in those terms. And once you understand in those terms, then you can— that lays the groundwork for applying it to more complicated materials and more complicated systems. systems. If I may, I’d like to give you another example of more. Okay? So the most stunning example of more is different is something that all of you know, all the viewers know and use, and that is AI.

Nigel Goldenfeld:
Okay? When you ask how, as I do, how is it possible for AI to even work in the first place?

Brian Keating:
Right.

Nigel Goldenfeld:
Okay? Let’s think about this. So the first thing you would say is, let’s suppose I’ve got a time series of data points you know, whatever it might be, stock prices, who knows. And you say, well, I want to make a model of that. So the first thing you do is you take your data and you say, I’m going to make a model. It’s just a straight line, goes through some of those data points, but it doesn’t go through all of them. The data points wiggle and twist and turn. And so the straight line, if you ask, does it fit all the data? Of course it doesn’t. If you ask, does it make good predictions? The answer is, Well, you know, not really, because it’s too simple a model.

Nigel Goldenfeld:
So then you might say, look, I’ve got, you know, 50 data points here that I’m training my AI on. You know, why don’t I just use, you know, a 100th-order polynomial, a much more complicated equation? That equation will fit every single data point that you want to train the AI on.

Brian Keating:
Every single point.

Nigel Goldenfeld:
Every single point. 100th-order. I’ve got 50 data points. I’ve got a 100th-order polynomial.

Brian Keating:
No matter how you embed it, right?

Nigel Goldenfeld:
Well, I can find a way I can find, you know, I have a lot of data points. I can find a way to make it fit through every single data point. So there’ll be no error in the way that it fits the data. You say this thing is going to be really great at making predictions in the future, except it’s not. And it’s not because I’ve fitted the data, but I’ve also fitted the noise.

Brian Keating:
Hmm.

Nigel Goldenfeld:
And so if you make predictions, they’re basically contaminated by the noise. So then you would say, well, okay, so if I have a large error, well, I only have a a linear fit. That’s not going to do very well fitting a complicated dataset. I’ve got a very complicated formula, but that’s also not going to fit very well because it’s fitted to the noise. Somewhere in between those extremes, there should be a sweet spot where the 2 things balance out, and that should be the place where you should try to make your model. That’s what AI should do. And that was the conventional thinking. Okay? And so you say that’s the answer, except that’s not what happens.

Nigel Goldenfeld:
In fact, we have, when we fit our data with modern AI, we are fitting it far more than 100 parameters. We’re fitting with a trillion parameters.

Brian Keating:
Trillions of weights.

Nigel Goldenfeld:
Yes. And you’re way, way into that regime where you’re just fitting noise and the whole thing shouldn’t work. That’s a mystery. How is it that having such a huge number of parameters can work in principle. Obviously, it obviously shouldn’t, and yet it does. And that’s a problem that we have solved, at least for a very simple, the simplest sort of non-trivial model of how AI works with a student here, Chan Li.

Brian Keating:
Oh.

Nigel Goldenfeld:
But the answer is that there’s a phase transition in the statistical physics of the learning process. And that phase transition has a Rigidity, the generalized rigidity, just like the rigidity of moving a solid, which nobody knew was there because they didn’t do the calculation that we did. And so we could understand the transition. We could understand—

Brian Keating:
Does it have critical exponents? Does it have renormalization phenomena?

Nigel Goldenfeld:
Yes, it has critical exponents. It has data collapse, all the phenomena that you’ll find in my book. And the phase transition turns out to be very similar to the superconducting phase transition. Ah, I was going to say. And there’s a lot that can be said about that. I don’t know if you want to talk some more about that. But my point is, this is an example of more is different. Okay? It’s not just that, well, you have more things and so you can fit more things.

Nigel Goldenfeld:
It’s different is the important thing, not the more.

Brian Keating:
But eventually—

Nigel Goldenfeld:
And different means that there is a— it’s qualitatively different, not just, well, there’s a slightly different number. And it’s that phase transition, the qualitative difference, That means that a material or a stochastic computer algorithm, say stochastic gradient descent that is used to train AI, has new behavior when you go beyond a certain point. And that’s the thing that’s why I think more is different is so important because it’s not just Yeah, having more money is better than having no money. Yes, I could buy a slightly better car. There’s a qualitative difference that comes when you have, and that’s the message of Anderson’s article.

Brian Keating:
But here’s my pushback with respect to you and Anderson. Here’s some ice. If I told you this is ice that I collected at the South Pole, Antarctica, you’d say, no, it’s not, it’s water. And if I had more, and actually this Chock-full. I filled it up to the very brim, and then it melted, and now it’s this, right? So I’m the one that phased transitioned from the South Pole to San Diego, right? Now, if I keep putting more and more ice in there, more should be different, right? And actually, that’s what Anderson’s telling me. But if I keep doing it, it’s just going to be more of the same. So there seems to be— yes, I agree, more is different. There’s a water molecule.

Brian Keating:
It’s not like this liquid in here. But if I doubled the amount of— if I added more and more and more, it doesn’t behave quantitatively different from this. less, right? So at what point does the more start to be the same?

Nigel Goldenfeld:
It does.

Brian Keating:
I mean, when it becomes a black hole? At what point?

Nigel Goldenfeld:
No, no, it is different. So the amount of water and ice that you have in there, if you measure that, you’ve mentioned the critical exponents, like say you have the heat capacity divergence, it’s a first-order phase transition, so you don’t have critical exponents. But let’s suppose we were talking about, say, the magnetic transition. So yes, you would find that there’s a temperature where the, say, the divergence of the heat capacity, which you’ll see in an infinite system, it’ll literally go to infinity.

Brian Keating:
Yeah.

Nigel Goldenfeld:
When it’s a finite size system, it won’t diverge. It will start going up and then eventually it’ll smoothly go over. And that’s important because you literally see that in, say, granular superconductors. And if you look at machine learning as a neural network, in the ideal case, where you have an infinite number of neurons and infinite numbers of datasets and some appropriately taken asymptotic limit, you can make a very sharp mathematical theory for that. And only in that case, mathematically, do you literally have the ability to say there’s a phase transition and non-analytic behavior and so on. If you, on the other hand, make the system be finite, then the computer scientists would call this ridge regularity. ridge regression or regularization, then in fact this infinity goes away and the behavior is different. There isn’t a transition.

Brian Keating:
Hmm.

Nigel Goldenfeld:
You won’t be able to see that if you put more and more ice in there. If you’re doing this with a magnet or you’re doing it with a superconductor, you can do the experiment and you literally can see that only when I go to infinity do I see the sharp phase transition. But, you know, I describe this in my book. If you get to within, you know, 10 to the minus 12 degrees of the critical temperature, then you’ll start to see the fact that you don’t have an infinite number of atoms in your water bottle. Interesting.

Brian Keating:
I want to talk about a man you mentioned in your course as well, and you mentioned with great glee that the only man to win 2 Nobel Prizes in physics was a condensed matter physicist.

Nigel Goldenfeld:
John Bardeen.

Brian Keating:
John Bardeen. I often hear it said that if it wasn’t for the laws of quantum mechanics, we wouldn’t have had the transistor. And I always have a little bit of problem with that. Because if you look at the first transistor that they built, Shockley, it was—

Nigel Goldenfeld:
Shockley didn’t build it. He just posed in the photographs.

Brian Keating:
Right.

Nigel Goldenfeld:
Yeah. In the famous photograph, he’s sitting down as if he built the thing.

Brian Keating:
It looks like a piece of copper wire.

Nigel Goldenfeld:
The whole team in Britain was strutting around like, why are we here? Why is he there?

Brian Keating:
Why is he there?

Nigel Goldenfeld:
They were walking into— that actually was the reason why Bardeen Bardeen left and went to Illinois. He was so— he just couldn’t get on with Shockley.

Brian Keating:
Yeah. Well, Shockley was a very, very troubling character, as I’ve talked about. But the question is, you look at it, it’s a piece of chewing gum. There’s a coat hanger stuck into it. There’s a rock in the middle of it, right? It’s very unlikely that you’d say, hmm, this is the solution of the Schrödinger equation with Fermi levels. And do you believe that, that we look into the laws? Because the reason I’m asking is people say, when we have a theory of everything, Nigel, They’ll be able to look into it, and just like they did with quantum mechanics, instead of making transistors, we’ll make warp drives and gravitational impellers and multiverse teleportation devices. Well, what do you make of this? First of all, is that historically accurate? And you’ve seen a lot of these people, you interacted with the Titans, you are one of the Titans. But tell me, Nigel, did we look into the laws of physics to get the technology on the screen that you talk about in your course? Is that really what happens, or do we describe it later on? on post facto by these laws that we discovered.

Nigel Goldenfeld:
So just with the fact that the first transistor was a big lumpy thing. I mean, we said before that everything in this room is classical, but you knew that I didn’t really mean that. I mean, look at the flowers there. They have color. The only reason that they have color is because of quantum mechanics.

Brian Keating:
Well, these are made of plastic. But anyway, if they were real—

Nigel Goldenfeld:
Oh, in that case, you gave up. You blew my secret.

Brian Keating:
I’m not a real biologist, right?

Nigel Goldenfeld:
I can’t even tell a plastic flower from a real one. But you said it did. So yes, it was a macroscopic object just like your iPad is, but it’s operating due to laws of quantum mechanics. And so yes, semiconductor electronics. It’s not like before we understood semiconductor electronics, we could build iPads. And this thing didn’t exist 15 years ago. In fact, we didn’t even know enough about the liquid crystal displays, let alone the electronics to go into it and so on.

Brian Keating:
I want to give you a quote from a countryman of yours of some renown who said, I’m very poorly today and very stupid and I hate everybody and I hate everything. I’m going to write a little book for Murray on orchids and today I hate them worse than everything and I hate species as well. Oh my God, how do I hate species? Do you know who that was? that British gentleman of some renown whose father told me, or whose father has said about him, you care nothing except for shooting dogs and rat catching, and you will be a disgrace to yourself and to all your family. Who was that said about?

Nigel Goldenfeld:
Darwin. Darwin.

Brian Keating:
So this man, you know, loved life. He created these ideas, and he was—

Nigel Goldenfeld:
he was—

Brian Keating:
he’s such a fascinating character. It’s reputed, and okay, you’re gonna disabuse me of this. Again, I’m a poor experimental cosmologist, Nigel. that you have seen and you were part of the group or team perhaps that is really working to maybe state the limitations of Darwinian or the restrictions on selection. So let’s talk about why selection, why is biology— you talk about your paper with Woese, is it Woese?

Nigel Goldenfeld:
Carl Woese.

Brian Keating:
Woese.

Nigel Goldenfeld:
Life is Physics.

Brian Keating:
Life is Physics.

Nigel Goldenfeld:
Yes.

Brian Keating:
Is that right? I mean, besides your blunder about this little plant.

Nigel Goldenfeld:
Well, it could be worse. I mean, I could have said out of that spherical thing there was a cow.

Brian Keating:
What relevance does physics have in biology? You make the point in your course, again, everyone should watch your course because it’s so enjoyable and easy. It’s a graduate-level course, but let’s be honest, you could take it as a freshman if you’re energized and you’re willing to do the work. You may not get the highest grade, but you talk about how easy physicists have it compared to sociologists and what you call it and what has been called the dismal science of economics. I just had Alvin Roth, who won the Nobel Prize in Economics, a few years back talking about repugnant markets. It sure seems easier to do that than to do cosmology and try to figure out what happened 10 to the minus 32nd seconds after the Big Bang. So tell me anyway, what does physics have to do with biology? And what role do you play in perhaps overthrowing this irascible kind of self-loathing man named Darwin?

Nigel Goldenfeld:
So a lot of people did interpret our work as being against Darwin. But that’s completely wrong.

Brian Keating:
Okay, say more. Okay.

Nigel Goldenfeld:
The whole idea of, well, you know, I’d say it’s Darwin and Alfred Russel Wallace. Wallace really was the first person who, you know, wrote the paper that was presented at the Linnean Society, and Darwin added his things to it and so on. And the correspondence between them is very interesting and revealing. But let me just say what people mistakenly are referring to. So the usual picture of evolution that people who are not necessarily deeply into biology think about is this. They say, well, you’ve got your genes, and then you transmit your genes to your children, they transmit their genes to your grandchildren, and so on and so forth, and the genes propagate like that. And that is indeed what happens. But there’s a very fundamental problem.

Nigel Goldenfeld:
We’re now going to talk about what it is that we actually did, and then we’ll talk about whether it’s against Darwin or not.

Brian Keating:
Yeah.

Nigel Goldenfeld:
Which is Just to be crystal clear about that.

Brian Keating:
Right.

Nigel Goldenfeld:
So then you might ask the following question, as Francis Crick did, another one of my compatriots. So you might ask yourself the following question: could the genetic code evolve? All right? So let’s think, what is the genetic code? So just to review some very simple biology, you have proteins that do lots of stuff in your body. The proteins are made out of amino acids. How do you know which amino acid to put into which protein. So then you read your genome and you read sequences of nucleotide bases, which we’ll say U, C, A, and G. Those are the sort of abbreviations for their names.

Brian Keating:
Right.

Nigel Goldenfeld:
And then you read those and then you read them in triplets. And then you take each of those triplets and if you get UUU, you get phenylalanine. And that’s the amino acid that you then put at that position in the protein that the ribosome is building in every cell of your body. And the map that tells you, take triplets of nucleotides and convert them into one of the amino acids of life, the 20 amino acids of life, that’s called the genetic code.

Brian Keating:
Yeah.

Nigel Goldenfeld:
So it’s not your genome. People always say the genome is your genetic code. That’s not true. So the question is, well, where did that map come from? There’s a very interesting feature about this map. It’s called the genetic code. You can write it on a t-shirt. It’s a many-to-one code because you’ve got your alphabet of 4 letters, words are 3 letters long.

Brian Keating:
Sounds, yeah.

Nigel Goldenfeld:
So I’ve got 4 times 4 times 4, which is 64 possible amino acids I can get. But in fact, we only use 20. So you might say, well, why 20? Actually, Francis Crick had an answer to that, which I can tell you if you like. So there’s obviously redundancy in this code. So then you ask, Well, when did this code— when was it developed? So you go back and you do what’s called molecular phylogeny. There’s ways that Carl Woese was the first person to develop to look at molecular sequences and then find what they were descended from, and therefore you can work out the evolutionary history of all life on the planet.

Brian Keating:
The LUCA, the Last Universal Common Ancestor.

Nigel Goldenfeld:
That’s right. You get to the Last Universal Common Ancestor where—

Brian Keating:
So he coined that or did he coin archaea?

Nigel Goldenfeld:
He discovered archaea.

Brian Keating:
He discovered archaea.

Nigel Goldenfeld:
So he started doing this thinking that there’s prokaryotes and eukaryotes. And then one day he discovered that these things that are prokaryotes, they’re not prokaryotes. There’s something else in there. What the heck is this? Okay? And that was a methanogen that he’d— Woese was doing these experiments, they’re very dangerous radioactive experiments. He was doing them virtually alone for 10 years. Everybody thought he was off his rocker. And his goal was to simply find a way to map out the evolution history of life on Earth. And he discovered a whole new domain of life that people just looked at under microscopes and say, oh, this is a round blobby thing.

Nigel Goldenfeld:
It must be a bacterium. Turned out to have completely different evolutionary history from that. And in fact, we are descended from the archaea, we now know. He was doing this. And as you say, once you start building these trees, you eventually discover that you can build them all the way back to about 3.8 billion years ago. And that’s the last the universal common ancestor of life on Earth. And there’s various converging evidences that give you that number, 3.8 billion. And some people say it’s even earlier, maybe 4 billion years ago.

Nigel Goldenfeld:
Here’s the interesting thing. How old is the Earth?

Brian Keating:
Well, I want to take a segue because I forgot to give you your gift. I’m talking about magnets. Here’s a magnet.

Nigel Goldenfeld:
Okay.

Brian Keating:
And here’s a magnet with some gifts on it for you. So these are pre-Earth meteorites. These are discovered in Argentina. Those are yours to Thank you. On the Into the Impossible podcast. So the Earth is about 4.2, 4.3 billion years old. These are 4.35 billion years old, so they’re quite a bit older, but they date from the pre-super— the supernova that blew up, which by the way was the mechanism by which was discovered by more countrymen of yours, one of whom occupied this office, Jeff Burbidge, and his wife Margaret. So I have Margaret’s plates.

Brian Keating:
These are her photographic plates from Palomar. So we have a lot of things in common. But yeah, so the Earth is is older than that, but not by much. I mean, life began very early.

Nigel Goldenfeld:
That’s right. That’s right. So whether it’s 4.3 or some people say 4.5, something like that, as you say, it’s very close. And so the thing is this, we know because we can do the molecular phylogeny back to that last universal common ancestor that essentially the architecture of the modern cell was already in place 3.8 billion years or so ago.

Brian Keating:
Yes.

Nigel Goldenfeld:
So wait a minute, you’re telling me that life went from nothing 4.5, whatever, billion, plus or minus billion years ago, of which half that time the Earth was completely uninhabitable, this Hadean, right? And then by 3.8 billion years, you’ve developed the machinery for replication and—

Brian Keating:
For our first ancestor.

Nigel Goldenfeld:
Yeah. Yes. All of that. And somebody, you know, looking at the organisms around about that time, you would see very little, relatively little in the sense of the global architecture of the cell different from now. And so the question is, how is it possible for life to have evolved so quickly? So that’s the first question. And Francis Crick was very perplexed by that. Second question. Second question.

Nigel Goldenfeld:
Why is there only one genetic code?

Brian Keating:
Yeah.

Nigel Goldenfeld:
Okay? We call it the canonical genetic code, and there’s minor variations, mainly to do with stop codons, but it’s basically the same genetic code. Then there’s a third one, which you probably knew that there was only one canonical genetic code for all life on Earth. But the other thing you may not know is that the genetic code that we actually have is optimal in the sense that it minimizes errors of translation.

Brian Keating:
Hmm.

Nigel Goldenfeld:
So let’s suppose we were in the world of intelligent design and being particularly provocative here. So you say, okay, Brian, okay, you know, smart guy, you know lots of things, design for me a good genetic code. And you would say, well, if I’m gonna design a good genetic code, I know there can be lots of errors in reading and mutation.

Brian Keating:
Yeah, there’s some redundancies. 64 minus 20.

Nigel Goldenfeld:
So, well, not just the redundancy, but I should make a code so that if you get the wrong amino acid, I should make it so that the amino acid I do get is in some appropriate biochemical way, which has to be defined, is a decent approximation to the one that I should have got. So it doesn’t do too much damage so that the protein has in it the wrong amino acid, but it can still fold and do the thing that the protein is supposed to do. And if you could create such a genetic code, you would say, well, that’s going to be really, really good. That would be the one that I, as an intelligent designer, would choose. Okay. So then the genetic code, when you when you analyze it, you can do this calculation. There’s many different ways you can do it. Every time you do it, you get the same qualitative answer.

Nigel Goldenfeld:
The genetic code is optimal in the sense of minimizing errors. Okay? It’s fantastic. Okay? So those are 3 facts.

Brian Keating:
Mm-hmm.

Nigel Goldenfeld:
How on earth could all of those things have happened? Now, Francis Crick was very perplexed about this. Francis Crick said, look, there’s no way that life could have It got to this level of complexity in such a short period of time, it must have come from outer space. So eventually he embraced the panspermia idea, which of course then just pushes the problem off to another wrang.

Brian Keating:
The origin of life on Earth is solved, but not the origin of life in general.

Nigel Goldenfeld:
Exactly. Exactly. But in fact, there’s more to the problem than that. There’s these other 2 facts that I’ve talked about. Francis Crick was also very perturbed because, as he argued in 1968, it, the genetic code can’t possibly be something that evolves, right? Because think about it this way. Suppose it does evolve. So think about this. Think about we’re doing this experiment, okay? I’m communicating to you in code, okay? And I write down my coded message and you get the coded message.

Nigel Goldenfeld:
You use a code book to translate the message. So that works fine. Let’s suppose halfway along in us doing this and we’re separate continents or something like this, I unilaterally decide I’m going to use a different code. Okay? Suddenly all my messages are going to stop making sense. You won’t be able to interpret them.

Brian Keating:
Right.

Nigel Goldenfeld:
So the code book is the genetic code. It literally is. It tells you how to translate from the message that is in the DNA and the mRNA into the protein that you’re ultimately going to produce. And so obviously, if you evolve the code, which means change it midstream, then it won’t— then there’s a whole— then you’ll start getting the wrong proteins and then everything will die out. Okay? So it can’t evolve. What we did in this paper was we figured out how to solve all of these 3 problems. We figured out why the genetic code is unique, why it is optimal, and why it evolved so quickly. And in fact, that it really did evolve.

Nigel Goldenfeld:
Obviously, the fact that it’s optimal, which Francis Crick didn’t know, the fact that it’s optimal Either you think that it was intelligently designed or it evolved under selection.

Brian Keating:
So in what sense is this a canonical critique of Darwin? I mean, why do people even say that?

Nigel Goldenfeld:
Well, I’ll tell you why. Why do they say that? It isn’t. I’ll tell you why. Because what we discovered was that indeed these things would not have happened if you had just— were just using the vertical evolution that we talked about at the beginning. You give your genes to your children, they give their genes to your grandchildren, and so on and so forth. If that was the process operative at the dawn of life, it wouldn’t have happened this way. But in fact, what happened was horizontal gene transfer, namely that genes can be transferred between organisms that are not related. For example, let’s suppose we could do this.

Nigel Goldenfeld:
Okay? So let’s suppose you decide that you want to learn renormalization group theory from my book. So you could slog through my book and go to my classes and so on, but wouldn’t it be easier if I could just pop out the gene that enables you to do Feynman diagrams in 4 minus epsilon dimensions? Let’s suppose there were a gene for that, which of course there isn’t. And I just give you the DNA, and you just take that DNA, put it into your DNA, and all of a sudden, bingo, great, I know how to solve the— I can solve Feynman diagrams in 4 minus epsilon dimensions. You will be I can compute pretty quickly. Okay. It doesn’t happen like that for us, but it does happen in the world of microbes. That’s how antibiotic resistance, for example, is transmitted so rapidly. And the reason it happens so rapidly is because when you’re transmitting genes in this particular way, you’re using a network effect.

Nigel Goldenfeld:
I can distribute my genes not just to you and not just to my one or two children. I can distribute— I’ve got two children, Exactly. You can distribute them to hundreds of thousands. That’s how libraries work. Libraries do this. It’s a Lamarckian mode of evolution, but it’s still evolution. In other words, only the books that are actually good end up in the library. Only the right physics books, the physics books that tell you the actual right physics, the storybooks that are actually really entertaining.

Nigel Goldenfeld:
So you have a network process, a horizontal gene transfer process, which is different from your traditional view of how genes are transmitted vertically.

Brian Keating:
I have to interrupt. Sorry to interrupt, but I have to. It seems to me you’re taking a PowerPoint file on a modern SSD drive and then putting it into a Windows 95 computer from 1995 and somehow it’s working. How is that even possible? You just get gibberish. You’d get these glyphs. How is that even possible?

Nigel Goldenfeld:
So you have to ask what happened at the dawn of life. At the dawn of life, the genotype-phenotype distinction had not yet really been clear. The organisms were very porous. They underwent endosymbiosis. That means that they would absorb one another and then the stuff inside that, hey, I can take all the stuff. And we know that that’s where our mitochondria come from. That’s where chloroplasts come from in the plastic flowers. And things like this.

Brian Keating:
It may well have been.

Nigel Goldenfeld:
So life did that and life transmits, exchanged genes in that way. And today organisms do this. I mean, if you sequence the Drosophila genome, the fruit fly genome, okay? The eising model of biology, if you will.

Brian Keating:
It’s like Harvard is the UCSD of the East Coast.

Nigel Goldenfeld:
You’ll find in it the genome of Wolbachia. It is a parasite, a microbe, a bacterial parasite of Drosophila, and it has inserted its whole genome, actually multiple times, into the Drosophila genome. And there’s many other examples of horizontal gene transfer. If you look at the phylogeny of flowering plants, angiosperms, very, very complicated. It’s not like a family tree, it’s a network. And what we discovered was that the early life evolved through this through a network effect, which Rose called that state of life the progenote. I don’t know why, but he did. And then there was a phase transition to an era of vertically dominated evolution.

Nigel Goldenfeld:
And when we’re talking about what is evolving, we’re tracking the genes, specifically the genes that code for the architecture of the cell, the fundamental cellular processes such as translation, replication, and so on. So So that’s how we define species today. And so today we build the tree of life, but there’s nothing mandatory that says it should be a tree. And in fact, prior to the last universal common ancestor, it was a network. And as it was a network, it evolves faster, but it is still doing Darwinian evolution. Or what is Darwinian evolution? It is still survival of the fittest or all of that, however you interpret that.

Brian Keating:
Selection.

Nigel Goldenfeld:
It’s a complex argument in and of itself, but basically It’s fundamentally, we’re not saying anything different about that. We’re just talking about what is called the mode of evolution.

Brian Keating:
One question about the network. Does it exhibit things like Rescham’s Law, but the network law that the scaling goes geometric and the reason that it’s so fecund is because of this network dynamics that lately we’ve learned about with social graphs, but in fact we can understand it maybe how successful it is via network theory rather than, you know, just pure genes.

Nigel Goldenfeld:
Yes. I haven’t personally done a network analysis of what kind of network you get from this specific process. I mean, I think the more interesting thing is that there is a network effect. And the thing that Crick had missed and other people had missed was that, I can explain by a kind of analogy. Let’s suppose that I drive over to your house and the wheel comes off my Toyota Corolla while I’m there. I say, well, that’s too bad. You say, well, you know, I’ve got a Tesla in my garage here. I don’t know if you have, whatever you have, you know, why don’t you just take the wheel of that? Well, obviously that’s not gonna work.

Nigel Goldenfeld:
Okay? But let’s suppose we were doing that, say, 120 years ago, right? It was the dawn of the age of automobiles. So I drive over to your house with my jalopy whose top speed is like 20 miles an hour or something like this, and the wheel comes off and it’s broken and so on. And you say, well, look, I’ve got a bicycle.

Brian Keating:
My Model T.

Nigel Goldenfeld:
I’ve got a bicycle. So to take the wheel off my bicycle and stick it on, so I just get out, take the screwdriver out and screw it on, and I’m good to go. And you can do that because the very early primitive forms of an automobile are very, you know, you can just swap things in and out. The technology is not very advanced. It’s not 6-sigma precision and things like this.

Brian Keating:
6-sigma.

Nigel Goldenfeld:
In the early days of living systems, they were very simple. And so they could tolerate ambiguity in the proteins that they use. As they became more and more complex, then you really had to have just the right protein to fold in just the right way to be able to make the thing that goes into your neurons or something like this. And so what we realized was that you can build a dynamical systems model of the coevolution of the complexity of the organisms along with the evolution of the genetic code. And so you find that then through this network effect, it evolves very rapidly and eventually gets to the point where it shuts off the network effect and then transitions to the vertical era of evolution we’re in right now.

Brian Keating:
So does that make you more or less sanguine, you know, getting back to Fermi’s question to our late, great colleague, Herb York? where are they? Where are the aliens? Are you more— I mean, knowing this level of kind of punctuated equilibrium, you also quote Gould and the fact that we don’t actually have that many more genes or anything productive compared to a worm.

Nigel Goldenfeld:
No, that’s pretty much the same. And if you really wanted to punch a hole in more is different, you would say, well, that can’t be true because the same number of genes uses But it’s, as Gould wrote in his year 2000, whatever it was, New York Times op-ed piece, there’s many more interactions between the units than you have in C. elegans. C. elegans is so simple that we know every single— Right.

Brian Keating:
Neuron hitting another neuron.

Nigel Goldenfeld:
Every neuron is mapped. It’s not the case for us.

Brian Keating:
Yeah, exactly. Right. If we spray C. elegans throughout, you know, on the planet Mars, it’s different than spraying koala bears on there. Where does this leave us?

Nigel Goldenfeld:
Tardigrades would adapt.

Brian Keating:
Tardigrades. Well, they’re already there. I mean, there’s human poop on Mars right now. I guarantee it, because the astronauts are spraying out— they vent it out to space, and it eventually gets to— I have a piece of the moon here. This is a meteorite from the moon, so stuff is striking and we’re sending—

Nigel Goldenfeld:
There are tardigrades on the moon.

Brian Keating:
Yeah, exactly. So I’m sure they’re on Mars. But tell me, Nigel, does this make you more or less sanguine about life elsewhere in the universe? Forgetting or pausing for now the concern, the origin of life generally, but just origin of life specifically on other solar systems, in other solar systems.

Nigel Goldenfeld:
I tend to believe that life is the inevitable consequence of the laws of physics, which we understand imperfectly. And I say physics, not chemistry, because I don’t think that life is restricted to particular chemistries.

Brian Keating:
It could be silicon-based or it could be Different genome?

Nigel Goldenfeld:
Well, I have a question for you about that, which would you be the ideal person to answer. I do think that it is a physical process, and I can even say a little bit more about why I think that. I would say that if you wanted to know what is the purpose of life, what is the meaning of life, if you like, what is the purpose of life? The purpose of life is to help planets come into equilibrium.

Brian Keating:
How so?

Nigel Goldenfeld:
So think about a planet. A planet after it’s formed has a huge variety of chemical potential redox gradients in its environment. And those gradients will eventually relax and homogenize as they should through second law of thermodynamics and all sorts of other good reasons. And that happens. And what life does is life uses information to find new pathways to short-circuit, if you will, those chemical potential gradients and use the energy to power life. And that’s how ecosystems work. Ecosystems compete with abiotic processes to literally take chemical potential differences and use the flow of energy in them to make living things. And those living things are powered by this chemical potential gradient.

Brian Keating:
Hmm.

Nigel Goldenfeld:
So life uses the information just In the way that I was saying with the horizontal gene transfer, that’s one very fast way of searching a space and finding new ways to solve the problems that emerge, the organizational problems that emerge. And we know that that happened. By the way, there’s lots of supporting evidence for our horizontal gene transfer theory. And there’s a recent paper that just came out in the journal Astrobiology, which is a sort of review is not one that I wrote it with other people, but it’s looking back on that and there’s even other data which supports this theory. But the point is, that’s what living systems do and there’s nothing special about doing it on Earth as opposed to Enceladus, which would be my favorite place. Well, I used to direct a NASA astrobiology institute.

Brian Keating:
Right.

Nigel Goldenfeld:
And I tried very hard before NASA disbanded the whole NAI program, Sadly, to persuade anybody who would listen that the place we should go is not Europa. We should listen to 2001: A Space Odyssey and give Europa a miss. Go to Enceladus because there you’ve got a much, much better chance. And we already know from the Cassini mission that, you know, you can sample already the water that’s there and it actually looks like alkaline hydrothermal vents. So there’s all sorts of interesting astrobiology that could be done.

Brian Keating:
That sounds amazing because it seems to me It’s closer to answering Schrödinger’s question than Fermi’s question, let alone that.

Nigel Goldenfeld:
Yeah, it is. Carl Woese and I wrote a review article called Life is Physics. And the reason we wrote it like that is the following. First of all, Schrödinger wrote this book called What Is Life, which of course everybody’s inspired by. And then the other reason is that when I go to astrobiology conferences, you know, on the first day somebody will stand up and say, well, life is chemistry.

Brian Keating:
Right.

Nigel Goldenfeld:
But I don’t agree any more than I think that a computer is— you know, if you asked me what a computer was in Victorian England, I’d say, well, it’s Babbage’s machine. It’s built out of cogwheels and springs and levers, and you sort of turn things like this and it will compute.

Brian Keating:
And then Lovelace.

Nigel Goldenfeld:
That’s right, exactly. He’s a Lovelace. And then you go and ask, you know, an engineer trying to figure out how to design hydrogen bombs at the Institute for Advanced Study in the 1950s. And he’ll say, well, it’s John von Neumann’s building. It’s in that shed over there. It’s lots of fermionic valves and relays, and that’s what a computer is. And now you ask—

Brian Keating:
NASA and the computer, Katherine Johnson.

Nigel Goldenfeld:
You ask me, you know, 20 years ago, or anybody, you’d say, well, it’s my iPad, it’s my Windows, my MacBook, or my Windows computer. And you ask somebody today, what is a theorist? My phone, or my glasses, glasses or something. There’s a difference between the substrate in which something is made and what it actually is. And so when we think about trying to understand the fundamentals of living systems, of course, if you want to know how to make somebody better because they’re ill for some disease, well, you better understand something about biochemistry for sure.

Brian Keating:
Yeah.

Nigel Goldenfeld:
But if you want to understand why is there life in the universe, why does the phenomenon of life even exist? That is a fantastically profound and interesting question. And we don’t, truthfully speaking, I don’t feel that we know the answer to it. I think we make steps towards the answer, but I think the answer is it is a physical process. It can be realized in certain types of atoms and so on. But here’s a question for you. Could life exist 3 minutes after the Big Bang?

Brian Keating:
Depends on what you call life. I think the universe did transfer through a period of time when water was liquid. The CMB was once at 300 Kelvin, right? So there’s no, you know, that wasn’t, you know, very that soon after the Big Bang. I mean, I think in terms of atoms forming in 380,000 years.

Nigel Goldenfeld:
Right.

Brian Keating:
So it’s like very, very implausibly, but perhaps as Deutsch says, you know, if it doesn’t violate the laws of physics, perhaps.

Nigel Goldenfeld:
So let me tell you a science fiction story. Okay? It’s not meant to be real. It’s meant to be a thought experiment, which is meant to raise your consciousness. Okay? So I’m going to make the the following claim, which I emphasize, this is not a scientific statement, it’s a thought experiment, okay? That you have at that early stage of the universe, we’re way above the physics that applies to the standard model of particle physics as we know it now. And you’ve got some, I don’t know, non-Abelian gauge fields or some God knows what gauge group or some strings or something like that.

Brian Keating:
Mm-hmm.

Nigel Goldenfeld:
And they have non-Abelian flux tubes that go between whatever the quark-like excitations of this thing. And those things are non-Abelian, so they can wrap around, they store information, just like we want to use non-Abelian anyons to build quantum computers and store information. And so you could store information in this way, and then you could have, you know, the chemistry of these objects. And so you could imagine you could build self-organized objects that are built out of, you know, non-Abelian flux tubes. The thing is that they’re on a scale of like 10^-20 meters. And an energy scale of, you know, 10^100 GeV or something like that. And they last for 10 to the minus—

Brian Keating:
Planck time.

Nigel Goldenfeld:
Planck time or something like that. But you could imagine that. And you could imagine those beings sitting around in their non-Abelian gauge theory bar drinking beer and saying, you know what, do you think life could exist, you know, I don’t know, for 13, 14 billion years after the Big Bang? And they say, oh, come on, don’t be so stupid. I mean, they’d have to be absolutely enormous. The scales would be enormous. And the timescales, don’t even ask me about the timescales, they’d be just huge. And the energy scales would be pathetic. You know, what would you do?

Brian Keating:
Ridiculous.

Nigel Goldenfeld:
You know, it’s a completely ridiculous suggestion. Of course not. You know, so I think it says that, you know, when you think about what is the physics of life, the processes that are involved in creating the phenomena of life. You know, they’re on a sort of logarithmic scale of energy and time and space and complexity and so on and so forth. And when we talk about life, we usually mean life like us. But if you want to ask about life that’s not like us, well, why not? And I certainly think that you’re going to find microbial life long before you find—

Brian Keating:
Dolphins with iPhones.

Nigel Goldenfeld:
Dolphins with iPhones. Yeah.

Brian Keating:
Yeah. the embodiment of the moor is different to me. I mean, I’m sure the listeners can determine for themselves the vast kind of depth and breadth that Nigel engages in. But you’re also a citizen scientist in the kind of tradition of our late, great colleagues like Herb York and Roger Revelle and many others throughout history and throughout different continents that you’ve lived on and you’ve experienced this. But now I get sense, for someone as cheerful as you are, I consider you a very optimistic pessimist, and you’re seeing things, and you and I have spoken offline about the kind of precariousness of the age that we live in. I want to ask you, first of all, is it our fault? Katalin Karikó, co-inventor of the COVID vaccine, sat in that chair, and she told me that we sort of have this inflated view of scientists, and actually we’re quite egotistical. And she went through kind of the negative side of academia. How much of it our scientists to blame ourselves.

Brian Keating:
I don’t want to make it sound like we are even a very large fraction of the blame, but you hear nowadays, and we’re talking now, Fauci’s going in front of Rand Paul and there’s this big theatrics. I think it’s all nothing, Burgum. I don’t think anything’s going to happen. But I do feel like we’re living in an anti-science age, but not because of the reason that everyone has their truth. I don’t care what you believe. I don’t care what you do in your private life. But if we have different epistemologies, That’s very dangerous, right? If you and I have different ways of discovering what is true and characterizing what is true, you may believe that 9/11 was an inside job and that fire doesn’t melt steel and whatever. And I may believe that, no, no, no, actually it didn’t have to melt.

Brian Keating:
So we have different ways, at least we have the same epistemology. We’re using science in some way. I’m not saying that those people aren’t crackpots. But Nigel, are we living in an age that’s not post-truth? It’s relative truth. renormalized truth where you can believe whatever you want, Nigel. I’m going to believe that, you know, that there’s some nefarious conspiracy and I have different ways of getting to my truth. What do you make of this age and who’s to blame and what can we do? Sorry to wrap 3 questions in one, but what’s going on here?

Nigel Goldenfeld:
I would say that the, you know, I’ve been very outspoken and active in trying to defend science in the United States over the last Year and a half or so.

Brian Keating:
I would say longer, just to give credit. I don’t think people realize the role that you played in the 2020 COVID kind of pandemic and bring quantitative. I mean, you were really at the forefront of being data-driven and predictive using a lot of your models, which we didn’t have time to talk about today, but we’ll do a part 2. And I think that’s remarkable. So I don’t want to say the last year. I don’t want to say it’s just Trump. I do think that there are other factors at bay, but he’s He certainly plays a role that’s unique now.

Nigel Goldenfeld:
Yes, but I think that’s the biggest threat that I’d like to talk about. Most of my activity has been really to defend the public interest because I do feel that what is happening to science, and is happening to science, is not being done in good faith. As a member of the National Academy of Sciences, a fellow of the Royal Society and so on, I feel that it the job of scientists to speak out and to try to work with Congress, in the case of the United States and so on, in order to make sure that they have the best interests, the best information. And that’s how the National Academy of Sciences was founded by Lincoln in 1863 during the Civil War. And so a lot of the work I’ve been doing, you know, has been work that the academy itself could not do on its own for various reasons, which I won’t go into, but is now becoming actually in in the way that it possibly can, much more visible in the public eye. So much so that Donald Trump is literally issuing posts on social media about defunding the National Academy of Sciences.

Brian Keating:
Awfully, yeah.

Nigel Goldenfeld:
Beliefly, just after the World Cup or during the World Cup. So I won’t say too much about what I’ve been doing, but I want to talk about why I think it’s important. And the reason is this. It’s not that I feel, well, I want my money, I want my lab, I want the money coming in that supports the research. That’s not the important thing. The reason we do science is because it is in the public interest. If people like me, I’m actually working on cancer at the moment, trying to understand how cancer works. I’m very excited about some work that we’ve done.

Nigel Goldenfeld:
Wrote an $11 million grant proposal to NIH from here, which will probably never get funded. But the reason it’s important—

Brian Keating:
Well, Jay Patachariya, who’s the director, is a friend, and he sat in that chair too. So maybe we can talk to him.

Nigel Goldenfeld:
Well, just maybe. Anyway, but the point is, it’s not a question of, is my personal hobby funded? The question is, we are doing this for the public good. All the technology that we have, that we hold in our hands, our silicon and security blankets and the medicines that will help us live longer and all those things came from the scientific process. And I believe, as many do, that this is in peril at the present time in this country. And it is our duty and it’s the duty of people who engage the public like yourself to make those things crystal clear to people so that People understand what is happening, who stands to benefit from this, and why it is not in the public interest. Is the scientific system perfect? No. No system is. Scientists are being attacked in the media.

Nigel Goldenfeld:
If you poll people and ask who are the most trustworthy people, politicians are right at the bottom. Scientists and teachers are right at the top. You shouldn’t be weaponizing the inevitable flaws in the system, like peer review. Is peer review broken? Is it really true that we’ve stopped innovating in science? Which I think is complete and utter nonsense. No, no, it’s complete— But that’s the rationale that’s being used by Michael Kratsios in particular, the director of the Office of Science and Technology Policy in the White House, for saying we need to take science away from the universities, put it more into industry and so on towards—

Brian Keating:
Or target individual scientists, right?

Nigel Goldenfeld:
Yeah. but none of which makes any sense.

Brian Keating:
Or benefit AI. AI is science now, according to all the David Sackses and the advisors to the president, and it’s very troubling.

Nigel Goldenfeld:
So what is alarming is that these are interesting discussions, but they’re not being held in isolation by disinterested parties arguing in good faith. And I think that to me is a problem more than someone who difficulty getting tenure and then ends up winning a Nobel Prize and so on. Yes. We know that there’s examples like that. And I was actually very lucky spending the first half of my career, the first 36 years, at the University of Illinois, where I was essentially working on the lunatic fringe end of condensed matter physics. But people like twice Nobel Prize winner in physics, John Bardeen, said, okay, I, you know, I support what you’re doing. They gave you the encouragement and, you know, yes, go ahead and do this. You know, I was working on high-temperature superconductivity, and I had a view on it that was completely not shared by anybody else in the community for 5 or 6 years until eventually we could prove that it was correct.

Nigel Goldenfeld:
We named it the D-wave, as a D-wave nature of the superconductivity. John Bardeen was the first person who says, that is wonderful. And he gave me moral support and told people we should follow this. So I do know what it’s like to be an outsider.

Brian Keating:
Yeah.

Nigel Goldenfeld:
But I’ve been very lucky that I’ve been able to do enough things that are sort of mainstream, as it were, that even though I don’t stay in my lane, I’ve been supported. And it’s not true of everybody. I think if I’d been at another university, We wouldn’t be sitting here talking now. My career would’ve been very different.

Brian Keating:
I think you’re absolutely right. And to use a phrase from the namesake generator of this podcast and the namesake generator of the word podcast in general, Arthur C. Clarke, he said, any sufficiently advanced technology is indistinguishable from magic. I want to ask you 2 questions kind of as we close that are prompted by him. And that’s the first one. What is sort of the most magical— I mean, we talked about so many marvels. things today. And literally, we’ve scratched the surface.

Brian Keating:
I feel like you and I could talk for hours, and hopefully we’ll get another chance.

Nigel Goldenfeld:
Yeah.

Brian Keating:
You’re the second condensed matter physicist from UCSD Physics Department after Jorge Hirsch to come on, and he’s been a 2-time guest, so you have to be a multiple-time guest. Can’t let Jorge have all the fun. But Nigel, tell me, what is the most magical? If you could put something on a monolith and launch it into space for 4 billion years, uh, what would it be? What would encapsulate, as Feynman said, the, the most information in the fewest lines of of text or code?

Nigel Goldenfeld:
Well, Feynman’s answer to that was that atoms exist. And I guess my answer would be more is different. Because it’s not enough just to say that atoms exist.

Brian Keating:
Very good.

Nigel Goldenfeld:
Everything that we’ve talked about are emergent properties of different levels of description and so on. And I would say, you describe me as a condensed matter physicist, and that’s where my intellectual roots are, but I work in astrobiology and evolutionary and fluid mechanics and all sorts of other But I think it’s the recognition that there are emergent phenomena, which I think is not a philosophically obvious thing. And if you don’t know that, then many things in the universe are far more perplexing than they would seem to be. So I’ll give you an example. Humans try to figure out how the world works. And so we came up with one answer, oh, there must be a God that makes everything do the things that it does. I’m a practicing Jewish atheist, okay? And I don’t believe in God, but I do think that you see in, you know, what you see in society and in the world around you, phenomena that are seemingly inexplicable, the hidden hand as Adam Smith called it about—

Brian Keating:
Capitalism.

Nigel Goldenfeld:
Economics. But you see the same thing in all aspects of human life. And you might say, well, that’s really God. I’d say, well, it’s an emergent aspect of things. And I think this sort of motif, really, the smallest difference, as you brought it up, that really does have many, many ramifications beyond the most trivial ones. So I think that would be my answer.

Brian Keating:
That would be that. Okay, last question.

Nigel Goldenfeld:
But anybody who’s sophisticated, you have to be able to read it, whether you know it. That’s right.

Brian Keating:
And says he couldn’t see this monolith. Put it in a little satellite and send it off into space. And they get 2 monoliths. That’s even more exciting. Arthur C. Clarke said, the only way of knowing the limits of the possible is to go beyond them into the impossible. That’s the namesake of this podcast, the name giver of the podcast. If you had 20 seconds with a 20-year-old Nigel Goldenfeld, what would you tell him? What would you tell him to give him the courage to do what you’ve done, which is to be a remarkable scientist, but a citizen scientist as well?

Nigel Goldenfeld:
I would just say that you can do this. I don’t think it’s true that you have to be a genius to do good science. It might help, it may not help. It’s not obvious that it does. It depends on how you approach things. Einstein, who I think could have won 7 Nobel Prizes, I can list them for you. I don’t know that he was smarter than anybody else, but I think he had a better algorithm and a better approach. And so I think the question I always ask myself, and I ask other people this, I ask other scientists I meet, how do you choose problems you work on? What is the way you decide what to work on and what not to work on? And so I think that’s what I would—

Brian Keating:
It’s a matter of taste. Yeah.

Nigel Goldenfeld:
It’s not a matter of taste. I don’t agree with that. It’s a matter of how you can make the biggest impact and increase the likelihood of making discoveries. If I could, in more than 20 seconds, I would say it like this. The impact you make is the ratio of what you do divided by what everybody else does. And the usual algorithm that people have is, well, I’ll try to maximize the numerator, but that’s limited by things like how much funding you have, which university you’re in, what facilities you have, how smart you are, your family circumstances, a million other things. But the better strategy is to minimize the denominator. Don’t work on something.

Nigel Goldenfeld:
I don’t work on anything if I think that if I didn’t do it, somebody else would do it 3 weeks later.

Brian Keating:
Right. Yeah. You only work on— that’s my philosophy of writing books. I only write books that only I could write.

Nigel Goldenfeld:
That’s right. When I talk to students, I often try to give them— when they ask me for advice, I tell them this, do something different. It’s not that more is different, it’s that different is more. That’s beautiful.

Brian Keating:
We just said the title of this episode. Nigel Goldenfeld, so proud and happy to have you as a colleague. And I have a question about the Ising model applied to cosmology, which I’m going to run by you on the blackboard outside.

Nigel Goldenfeld:
But Nigel, thank you so much for Thank you very much for your interesting questions and for having me on your show.

Brian Keating:
Hopefully this will be part one of many, of more, many more. Thank you.

Why Google Demoted a Nobel Prize Winner

sabartigas — Sun, 23 Aug 2026 21:46:54 +0000

Why Google Demoted a Nobel Prize Winner

Dear Magicians,

Last week, a 2025 Nobel laureate told me, on tape, that Google demoted him. Not for failing. For succeeding.

John Martinis led the team that achieved quantum supremacy — the moment a machine did in minutes what the world’s best supercomputer could not. Then Google “reorganized.” He went from head of hardware to what he called “one-over-N authority” — And that his authority had been reduced to a fraction of a percent of what it used to be a committee role, in effect. He tried it for nine months. He discovered what he calls “negative authority,” which he analogizes to parenting a teenager. Then he left.

“I was too much like Elon Musk to work at Google,” he told me said. Previously he said that “I had already lost control. I wasn’t really leading the hardware group.” His former UCSB students “naturally wanted to leave their advisor and form their own group and be independent.” Management and the team together decided the co-leader model was better. John clearly didn’t — he left 9 months after the re-org and 5 years before he’d win the Nobel Prize. Wonder what Google would have done had they known about the Nobel Prize? Actually, I don’t wonder at all….

Notice what’s actually being said here, because it might be the most important sentence in the entire conversation. The same Google that built him up to win the quantum race could not metabolize the kind of person who wins quantum races. Not the science. The person. The archetype. The ‘founder-as-father’ who makes a thousand decisions a day and owns every one.

The symmetry is striking. The co-authors of “Attention Is All You Need” — the paper that launched modern AI — also all left Google. Same company, same pattern. The institution that enables the breakthrough cannot survive the breacher.

This is not a Google problem. It’s an institutions problem. Physicists have a name for it: decoherence. The irreversible loss of phase coherence between a system and its environment. A coherent signal enters a system. The system interacts with its environment. The signal decays into thermal noise. The system is now stable, predictable, and useless.

John Martinis’ crime wasn’t incompetence. It was coherence. He was too phase-locked to the original problem. He didn’t want to be reorganized. He wanted to build a million-qubit machine, not a roadmap that pleased a steering committee.

The Talmud tells us to carry two notes in our pockets. One says: “I am nothing but dust and ashes.” The other: “The whole world was created for me.” Every Nobel laureate I know carries the second one. Institutions can’t.

Even if you’re not doing Nobel-quality work, here’s what should concern you : The person capable of your next breakthrough is, by structural necessity, the person your institution is most likely to lose. Innovation is, in this sense, a decoherence event. The signal that arrives is the signal that leaves.

Martinis did what physicists do when the apparatus stops working. He built a new one. With Alan Ho, he started a company to do what Google wouldn’t let him do: scale qubits the way you scale semiconductors. Deposition and etch. The way the entire chip industry has worked since 1955.

“The right way to do it,” he says, with the disarming directness of a man who’s already survived a Nobel and a demotion.

The rest of us are still arguing about whether AI can replace us. John Martinis is in a cleanroom.

I think he knows what he’s doing.

Until next time, have a M.A.G.I.C. week.

Brian

Appearance

I sat down with Andrew Huberman to talk about the oldest science in existence, and things got weird fast.

We covered why astronomy might be the most visceral science humans have ever practiced, since the origin of the universe is the one event no one was ever around to witness. I also busted a few myths (sorry, astrology fans) and explained why the Big Bang wasn’t actually the beginning of time and space, just the birth of the first elements on the periodic table. Oh, and we got into the South Pole, the best telescope for under $50, and why I think the pyramids were built by us, not aliens.

Curious what a Gutenberg Bible has to do with your eyesight? You’ll have to listen to find out.

Listen to the full episode here.

Genius

We love assuming our best tricks are uniquely human, but macaques and baboons recognize abstract geometry just like preschoolers.

Why are primates so good at math? Because they always know how to find the right angle!

Euclid’s foundations predate Homo sapiens by millions of years. Evolution doesn’t reinvent the wheel; it just repurposes it. When we trade human exceptionalism for testable science, nature is far more continuous—and surprising—than we think.

Source: NYT

Image

The best photo of the eclipse… By Ander Gillenea / AFP

Conversation

Latest on Into The Impossible

My former UCSD colleague came back to campus, and I couldn’t let him get away with a typo in his own book.

Darren Lipomi and I go way back, all the way to our first conversation during Covid, and this time he’s returned as a full professor and department chair at Rochester with a memoir under his belt. We get into what it actually means to be an organic materials chemist (hint: it’s basically the science behind everything you touch, from OLED screens to space junk cleanup), the brutal odds of landing a tenure track job, and why he thinks the future belongs to people who aren’t afraid to get their hands dirty. We also settle the debate on his infamous h-index error, and yes, I made him own it on camera.

Stick around for the part where we talk about chirality, Louis Pasteur’s tweezers, and how a $99 Fisher-Price camcorder from the 80s quietly shaped two scientific careers.

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!

Engineering Chair Darren Lipomi: Most People Should Not Go To College

sabartigas — Sun, 23 Aug 2026 21:11:51 +0000

Engineering Chair Darren Lipomi:
Most People Should Not Go To College

Transcript

Darren Lipomi:
Perhaps the minority of individuals should be going to a 4-year institution because the debt load is so high.

Brian Keating:
My guest today is my good friend Darren Lipomi, Chair of Chemical and Sustainability Engineering at the University of Rochester, in a school he’s actively marketing to undergraduates. He’s one of the best mentors and educators I’ve ever met. I was devastated when he left UC San Diego, and he just told me that most people shouldn’t even be in this program. I wanted to know if he meant it.

Brian Keating:
Is academia like kind of an irredeemably clout-chasing, you know, kind of money-grubbing pursuit of attention, maybe significance when we have importance? What is academia? What has it become? And what— where do you think it can go?

Darren Lipomi:
This is a tough nut to crack because when we have promotion and tenure cases, we Want to be able to look at a metric that is reliable, that’s standardized across fields, which is impossible, but at least sort of within fields. So we look at H-index, we look at number of citations, we look at number of grant dollars brought in per square foot of lab space that the individual has. But at the end of the day, you want to know, what did that person do? What is the accomplishment? How have they made a deep and lasting impact in the field. And that accomplishment is quite difficult to judge. And oftentimes there’s a time component too. It’s a— impact is a lagging indicator. What if a paper gets 1 citation today, but then it gets 1,000 citations 10 years from now because it became so important? And so just like capitalism takes— leverages the human competitive instinct, We’re kind of stuck with these metrics in order to evaluate each other and to give each other raises and promotions. So it’s an imperfect system, but it’s what we have.

Darren Lipomi:
Only 1 out of every 20 PhDs becomes a tenure-track professor. The numbers are not very favorable for the median person who wants to go into the field. And it’s just, I don’t mean median in any pejorative sense, I just mean statistically, It’s a very difficult path. No one knows that a grad student did the work, that a grad student who made $35,000 a year and forewent retirement savings for their entire period of training and probably won’t recover it by the time they retire, that the financial burden is probably more so on them than it is on the taxpayer. So I was trying to tell the story of The people who, who had to go to grad school because they couldn’t do anything else. They went into research because they could not do anything else, like an artist or a musician or a chef. That’s what they had to do, knowing that the chances of getting an independent faculty position are, you know, 1 in 20 or lower. What I have studied— inorganic chemistry and space groups and oxidation potentials and the Schrödinger equation— if I was not forced to.

Darren Lipomi:
And I’m a highly motivated, really nerdy, really nerdy individual. Would I have done that for 4 years, 11 years after high school before I got my first job? I don’t think I would have. I was lucky in the sense that I had scholarships and my debt load was not high. If you look at the net present value of my decision by the time I retire, Was it a good decision? I don’t know. I probably would have done okay making $85,000 with a bachelor’s degree. There are people for whom it does not make sense to go to college. I would say that perhaps the minority of individuals should be going to a 4-year institution because the debt load is so high, because the prevalence of high-paying jobs for for individuals who are willing to tolerate certain types of activities can be quite high. And I think that’s where we need to focus on as educators in the area of building physical items.

Darren Lipomi:
We need to re— we need to rematerialize work and encourage people to work with their hands, to encourage the building and craftsmanship and artisanship that’s informed by theory because those jobs are going to be the hardest to replace. Physical AI is going to require an enormous amount of energy and also material science and mechanical engineering innovation along with the enormous energy demands in server farms, for example. That’s quite a long way off in my opinion, laundry folding robots notwithstanding. And so there will, For the next several decades, there will be room for somebody who will be designing a telescope, for somebody who’s designing a satellite, somebody who’s designing and building a reactor that can strip perfluorinated alkyl substances from drinking water. And it’s the tolerance, it’s the grit to be Doing that with one’s hands, right?

Brian Keating:
I gave you some materials, some extremely interesting engineering, uh, that may or may not contain life on it. Can I touch it? You can touch it.

Brian Keating:
Okay.

Brian Keating:
Yeah, that is your gift, uh, for coming on the podcast. It’s a real live or dead meteorite depending on how you look at it, and you can have it too. Guaranteed I’ll send you one if you, like Darren and me, have a .edu email address, and like Darren and me, you live in the United States. So Go to briankeating.com/edu, and I love to send those out and connect to my beloved—

Darren Lipomi:
Am I going to get in trouble with TSA?

Brian Keating:
No, they might ask you what you do for a living, which you describe in the book as well. So this material was delivered by outer space. It has some interesting deformations, material science compositions and stuff, but it does have organic materials on it because I’ve touched it and, you know, sneezed on it and whatnot. But you originally, it seems like if I recall correctly, you wanted to study sort of origin of life or get involved with that. What was the kind of impetus for that? And how far have you strayed from that interest, or have you?

Darren Lipomi:
I’ve always been a bit of a humanist. I’ve read a lot of books. My mom had a lot of lefty novels, Aldous Huxley and Jane Goodall. And although my parents were quite conservative, actually, this was from her past. She had this box of dusty books. And I read quite a lot of them. And when I got to college at BU, I had a fantastic chemistry professor, John Straub, and I was working with him on an independent concentration. And when I started college, 9/11 had just happened the second week of school.

Darren Lipomi:
The second Tuesday of school was 9/11. And so I was originally biomedical engineering, and then I suddenly said I wanted to learn how people behaved, how they interact with each other, what their life circumstances led them to their behavior. So I changed my major to anthropology. And then, but then I veered back, regressing to the mean, and said, well, how can I combine my interest in people and psychology with chemistry? And it took 20 years of an independent career to finally sort of arrive at where I am right now. But at the time, John Straub introduced me to Richard Dawkins and Edward O. Wilson, who were scientific humanists, and they wrote about, you know, the selfish gene theory and how life were lumbering robots built on self-replicating genes that would co-opt things in their environment in order to replicate themselves, not because they wanted to, but because that’s what they did. That’s what they’d always done and what we’re going to continue to do. And so I became, you know, the first thing I saw when I saw these micrometeorites is I wonder if there are any chiral amino acids on them, because that is one, perhaps my favorite theory for the origin of biological— So explain it.

Brian Keating:
Let’s go there.

Darren Lipomi:
Homochirality. Yeah.

Brian Keating:
So what is chirality?

Darren Lipomi:
Right, so every carbon atom has 4 bonds that point to the vertices of a tetrahedron, and there are 2 different ways that you can arrange those bonds: the right-handed version and the left-handed version. They’re non-superimposable mirror images.

Brian Keating:
So this graphite in this pencil has carbon in it, right?

Darren Lipomi:
That—

Brian Keating:
But it’s not bonded to itself. It’s not organic.

Darren Lipomi:
That graphite is a different allotrope of carbon. So it’s planar, right? It’s planar, yes. It’s all sp2 hybridized, so it has to be saturated.

Brian Keating:
My high school chemistry coming back. Thank you, Professor.

Darren Lipomi:
That’s why they called. That’s why they gave me the chair job.

Brian Keating:
Just got me back to 10th grade. Thanks, Darren.

Darren Lipomi:
Time traveling. Yeah, so the graphite in carbon is not chiral. Some of the carbon in coal is, some of it is not. Because you’re right, and you have a mixture of hybridization states. But most carbon atoms in DNA and proteins are going to be chiral. And so all helices of the same type, so all keratin helices in your, in your hair and fingernails, all DNA helices, they’re all made of— they all twist the same direction because of this.

Brian Keating:
Right-handed, right?

Darren Lipomi:
Yes, it depends on—

Brian Keating:
Okay, so I’m gonna have a mnemonic for remembering all these different things. Because in cosmology and particle physics, we only have left-handed neutrinos and right-handed antineutrinos. And I’m always like, How do I remember this? I just remember life may have originated from outer space, from neutrinos and cosmic rays and stuff, and those are left-handed, so they would only, you know, cause an interaction between something if that’s right-handed. And that’s how I remember that DNA. Also, you can remember from Jurassic Park, you know, life is right, life finds a way. It’s— life is— anyway, that’s how I remember it. But again, I got a 3 on the— no, 4 on the AP.

Darren Lipomi:
So there aren’t that many ways that you can create new chiral information from scratch, naproxen, so Aleve, homochiral. You only get one enantiomer, that’s one mirror image. Ibuprofen is sold as a mixture of both. One of them is useless, one of them is biologically active.

Brian Keating:
Mm-hmm.

Darren Lipomi:
And so how do you, how do you create the homochirality to begin with? In the pharmaceutical industry, you have a chiral catalyst, which is often an enzyme, which is biological in origin, and it catalyzes some reaction which then, which then biases the formation of every other bond to form in the way you want it to. Sometimes you might use tartaric acid, which is enantiomerically pure. Louis Pasteur, who discovered an enantiomeric excess, literally used a polarized microscope and tweezers to pick out this— pick out the crystals by hand of tartaric acid. And so one of the ways in which homochirality is theorized to have arisen is because of polarized radiation in outer space. And so when you have meteorites that have— or, you know, we’re pretty sure were not contaminated by biologically homochiral species, That’s a good point. There is often an anti-meric excess, an excess of one mirror image over the other, and it’s conceivable— and there are people that know more about this than I do— that One enantiomer degrades more quickly in space because there is polarization.

Brian Keating:
An alien wakes you up at 3 in the morning, you know, who are you? Are you a scientist? Are you an engineer? Are you an academician? Are you a chair? You know, in your work life, I assume you’re a father, I know, and a husband and very devoted, but let’s leave that aside. What are you?

Darren Lipomi:
I’m an organic materials chemist. So materials that are grass-fed and pesticide-free, materials that are carbon-based, so polymers, you know, everything in this room, everything outside is covered in an organic medium or made of, composed of organic media. Anytime you look at something that’s nominally abiotic, like a piece of glass, a metal fork, it’s all covered in adventitiously adsorbed organic media. So all of our interaction with the everyday world is mediated by structures that are composed of carbon-carbon bonds. And so what I try to do is to understand and manipulate these materials so that we can make better products, so that we can understand how our senses gain information from the world. I think if I were ever elected to the National Academy of Engineering, I would want my citation to be for contributions in using human subjects to understand understand the physical world. And by that I mean we study the tactile sense quite often. We’re very interested in how the skin deforms when it touches a textile or a material, how the thermal conductivity draws heat away from it, how it interacts with the afferent nerve fibers in the skin and creates a sensation that’s perceived in the brain.

Darren Lipomi:
the brain. We didn’t have a relationship before our students met at Bird Rock Coffee. And so one of my students was a one— was a part-time barista, and another one was a frequent, you know, orderer. So low we pay our students, come on!

Brian Keating:
Throw us under the bus a little more, dude!

Darren Lipomi:
Yeah, well, you know, they’re unionized, so they’re fighting the good fight. And so they got together and they were talking in line at Bird Rock about what they work on, and one of them was interested in haptics and the other did biostatistics for for psychophysical experiments, primarily for vision.

Brian Keating:
Hmm.

Darren Lipomi:
And so they got together and they started designing experiments on the way in which we perceived materials to be soft versus hard, sticky versus slimy, moist versus dry. There’s a multifactorial analysis that you can probe if you have access to material synthesis in a cleanroom, but also have access to a lab with the expertise of Ramachandran. And So you can analyze what characteristics of the surface and bulk of primarily organic media that allow it to be perceived in a certain way by touch. So 7 peer-reviewed publications later, we have a better understanding of that now than I think the field did.

Brian Keating:
Another thing that our friend, just getting back to my childhood, you know, mentor from the dead, though he wasn’t dead at the time, Isaac Asimov, and your BU connections were the laws of robotics. And now we hear so much about robotics. Robotics and nano. We hear less about, you know, kind of the Eric Drexler nanobot than we did previously. We’re now hearing about macrobots, you know, that are gonna fold my laundry and, you know, probably grade papers in the future. They can never replace professors though. I mean, come on.

Darren Lipomi:
Never.

Brian Keating:
Our job security. I mean, if we survive COVID, AI, the point being these nanobots kind of, they were the hot thing. As I said, Eric Drexler’s thing, you know, Feynman, you know, one of my heroes, right? There’s plenty of room at the bottom. What’s, what’s happening in nanoscience right now? Besides the chemistry, I’m talking about actual mini robots going into your body and doing stuff or building stuff in the meat world. What’s the latest in that?

Darren Lipomi:
Sure. So the funny thing about Drexler and the nanobots, and he and Rick Smalley, who was the discoverer of C60 fullerene and various other carbon allotropes, they had this longstanding debate about sticky fingers and how van der Waals forces were going to prevent nanobots from actually working. But I think they ignored the whole field of chemical catalysis, which are nanomaterials and molecular materials that are literally forming bonds by the moleful and metric-tonful in the chemical manufacturing industry.

Brian Keating:
Bioreactors.

Darren Lipomi:
Yeah. So that’s been happening. And that is, you know, it’s never been called— well, it’s rarely called nanotechnology, but that is, to be honest, one of nanotechnology’s biggest accomplishments. In terms of inner space and The Magic School Bus and the Fantastic Voyage, I have a colleague, Joseph Wong, in the nanoengineering department here who has been doing work for decades on self-propelled micro and nanomotors that can swim around in the bloodstream. In the blood, yeah. And so that, I think it’s going to be a combination of active control of programmed chemical reactions in these nanobots that are actually able to accomplish something that looks like, you know, intelligent behavior. But the other big impact of nano has been in cancer therapeutics and drug delivery for vaccines, for evasion of the immune system, for targeting in tumor cells, and you don’t just inject Taxol or cisplatin in the bloodstream anymore. You know, those are all encapsulated in some kind of smart nanomaterial that has a specifically programmed degradation profile, some kinetics that have been influenced by the ligands that are on the outside sphere.

Darren Lipomi:
And who could forget microelectronics too? Although usually you try to avoid quantum effects and microprocessors, but they’re as nano as anything.

Brian Keating:
We met in 2018, maybe it was 2019, I can’t remember, for the Kyoto Prize, which was awarded to a University of Rochester kind of—

Darren Lipomi:
Ching-Tung.

Brian Keating:
Ching-Tung, who invented the organic LED. Now, to the extent that any of the public knows about organic chemistry and organic nanochemistry and nanotechnology, it’s through the OLED. What’s coming down the pipeline? Excite people about the future of organic, especially organic chemistry, as you’re, you know, one of your many fields of expertise. What are some of the next great breakthroughs? You know, you talked about sustainability, power, batteries, you know, what’s coming down the pipeline? What can we get excited about? What can we invest in through your hedge fund that you’re undoubtedly starting at Rochester right now?

Darren Lipomi:
The organic LED is a great example. It was invented by Ching-Tung at Kodak Research Labs about 4 miles from University of Rochester. He’s actually an Ameri— after he invented it, he was hired by my department. His office is down the hall from mine. There is a good chance—

Brian Keating:
He’s a mensch. This guy’s an incredible mensch. I love this guy.

Darren Lipomi:
There is a good chance that your audience is watching this video through an OLED display. So, if you have an iPhone or almost any screen, chances are it’s the It’s organic components that are creating the image, and it’s now a $70 billion a year industry. Kodak got almost none of the value from that invention, add it to the list. But anyway—

Brian Keating:
Watch, you have already.

Darren Lipomi:
So if you look at organics and carbonaceous material, carbon allotropes, they are ubiquitous and ubiquitously exciting. So the intercalation compounds, anodes in batteries, if you look at the next generation thin-film cheap solar panels. You have electron and hole blocking layers that are principally organic. The materials in perovskite solar cells. You have the entire history of microelectronics. While we think of it as an inorganic technology, as in silicon, gallium arsenide, other, other materials, the only way that those are manufactured is— have ever been manufactured is because we have these light-sensitive organic polymer films called photoresists that you etch an image in. So you’re looking now at materials in drug delivery, in electronics and energy technologies that really require organic materials engineering. We have another project right now that I’ve— we have a proposal under review by the Air Force and Space Force on the use of reconfigurable polymers to suck up space junk in low Earth orbit.

Darren Lipomi:
And so it’s—

Brian Keating:
Anti-micrometeorite.

Darren Lipomi:
Inspired by the space amoeba in the original Star Trek episode. And so there, the sky is the limit, because if you can, if you can put carbon atoms together in almost any combination, you can make almost any functionality.

Brian Keating:
We should acknowledge the mastodon in the room. He used to be my treasured colleague here at UC San Diego. And it was no joke, it was a big blow when he left. I mean, I was, you know, I was consolable, but, you know, losing a top-ranked, you know, faculty member to any university is a rival. I mean, it’s just— no, there’s no— you get a grad student or you get a top professor, it means we’re not going to get that student or that professor, right? So we’re in a zero-sum game. I always say academia and science are made up— are infinite games because you can’t win science, you can’t win engineering, but you I mean, you can get the Nobel Prize, you can get the NASE, and you can get all sorts of cool things. And that sometimes means, often means, you don’t get it from somewhere else, right? So one of the things that, you know, I most associate with Rochester, having a good friend, Charlie Freeman, who’s out there as a professor now at Geneseo and went there, was Kodak. And, you know, I used to love, and, you know, I still have, you know, Kodak printed out on film, you know, film, you know, photographs from my childhood, right? So, and this was this huge success story.

Brian Keating:
I mean, it built the town. Bausch and Lomb was there too, right?

Darren Lipomi:
The 2 drugs.

Brian Keating:
Yeah, 3 huge companies that all involved optics and imaging and printing, and at some level, at some point, lost ground market share from 99% to low digits. I mean, you could still buy— you can actually buy a Kodak digital camera for $99. It’s not the same quality as maybe it used to be, and it probably is licensed to somebody else for the name, right? But talk about the kind of parallel side, the academic you know, industrial complex, I call it. So what is that like? What is the landscape like that you see right now? Is it an exciting time? Is it, you know, AI is just dominating everything, the physical world is no more? I mean, you’re in the nanomechanical world. What do you see as the kind of future of academic-industrial partnerships? And try not to make Rochester too attractive. We still need students here, Darren.

Darren Lipomi:
Sure. University of Rochester is the largest largest private employer in New York State outside of New York City. And so how I describe it is a Hogwarts attached to a massive medical services company. And so we are in the $5-6 billion a year range, talking about academic-industrial complex. The undergraduate population is only, is only 6,000. We have 12,000 total students. and about $500 million in research expenditures per year. Most of that is in the medical school.

Darren Lipomi:
So there are a lot of medical device, medical optics companies in the region. A lot of that— the vein of optics has still persisted in Rochester, even though Kodak, Xerox, and Bausch and Lomb have a much smaller presence than they did in the ’70s and ’80s in the heyday. So we have shifted in the economy from primarily imaging-based to primarily biomedical and biomedical services upstream of these consumer and patient-facing industries. We do a lot of things that are super important to the economy, but a lot of people would find boring. For example, making machines that make other machines. We have 60,000 engineers employed in Monroe County. And so, for example, Optimation Technologies, they’re a design-build firm that made all of the equipment that made all of the mRNA vaccine vials during the COVID pandemic. Javelin Process Systems makes all of the equipment, or the vast majority of equipment, that cooks and produces Tostitos salsa and fills Heinz ketchup bottles.

Darren Lipomi:
So these are, these are B2B companies, right? And so we have a very strong relationship with companies like that, with L3Harris, CooperVision, with Bausch and Lomb, which still manufactures contact lenses and refractive optics. We send a lot of students there, we have research contracts with them. So there’s still a strong industrial connection, but the flavor has changed over time. But indeed, if you walk around U of R, you’ve got Bausch and Lomb Hall, you have the Eastman as an Eastman Kodak quadrangle, and then the Eastman School of Music. Joseph C. Wilson, the founder of Xerox, is the main boulevard that runs through campus.

Brian Keating:
So going from BU and then going to study at Harvard with Weizsäcker, was he interested in kind of origin of life? you know, 1,000. What is it, agent x to the 400 now, or—

Darren Lipomi:
A few hundred.

Brian Keating:
Yeah. So I just want to have the logarithm of his mutation base e. So talk about George and working in the lab of one of the most famous academics in all of history, and how little time you got, but it does seem like you got enough quality time that he really influenced your philosophy as a mentor. So talk about George and just the many I mean, quite frankly, you had merit, but you had a lot of luck too. And to work with him is part of that, but it’s not all it’s cracked up to be.

Darren Lipomi:
When I was an undergraduate, I went— I was lucky enough to get the Beckman Scholars Program, which is an undergraduate research scholarship. And I went to the Beckman headquarters in Irvine, and George Whitesides was one of the speakers. And he talked about multivalency and protein-ligand or protein-drug interactions and how you much more, much stronger binding if you had cooperativity and binding. And I thought that sounded really cool and interesting. And he has such a—

Brian Keating:
It’s humanistic.

Darren Lipomi:
So humanistic, right. And he had such a presence, has such a presence about him. He speaks with a booming baritone voice. He’s 6’1 or 6’2. He looks a bit like Captain Picard. And he has a very powerful handshake, even though even at the time he was, quite senior. And I asked him, you know, what do you look for in mentees? He said, I look for somebody who’s— I look for somebody who’s very strong in thermodynamics and organic synthesis. And I thought—

Brian Keating:
That was very precise.

Darren Lipomi:
That was very precise. And so I said, I want to work with George Whitesides. And so I was at BU, and I did a lot of running around the Charles River, and I would pass by the Harvard chemistry building and look at it in awe. And when I applied and I got in, which was just out of the question for somebody who came from the beginnings that I did, and I got in. And, but what was ironic is that as famous as George was, is probably the most famous chemist who’s never won the Nobel Prize or who hasn’t yet, and more famous than most chemists who have won the Nobel Prize. is that once you got into Harvard, it wasn’t hard to get into his lab. And in part, that was because he trusted the system. In part, it was because he didn’t have time to interview all the candidates.

Darren Lipomi:
And in part, it’s because— and I don’t want to— I don’t mean this in a pejorative sense, although take it as you will— But he was pretty sure that people who couldn’t cut the mustard would drop out of the lab. And so there was a lot of attrition in the lab. He— most of the mentoring that I received from George came in the form of written comments on outlines. So he would write in, uh, in blue ink on the Pilot rollerball blue ink pen.

Brian Keating:
Oh, yeah.

Darren Lipomi:
He would say things that no good human being should ever say to any, any other good human being, like, this is a pig’s breakfast, I saw on someone’s. He wrote, this is illiterate, on mine. He would say things that looked— that maybe even looked meaner than it actually was. Like one time, my colleague and I were sure that he had written, I know you want to fuck it, in blue ink. My colleague and I were looking at it and we’re like, it— there’s nothing else it could say! But obviously it didn’t say that.

Brian Keating:
Yeah.

Darren Lipomi:
But there was no— literally, there was nothing else it could say.

Brian Keating:
He was a doctor after all.

Darren Lipomi:
And so the in-person mentoring was sparse, but he had such a presence— he has such a presence in our lives, everybody who worked in his lab, that we have internalized his direction, his sense of scientific strategy, and how to choose an important problem. And that has followed me in my career since then.

Brian Keating:
Yeah, and even at one point comes across as somewhat tender, you know, as you’re graduating, he says, I’m going to be devastated when Darren leaves. I mean, that, that, you know, could bring tears to a, to a non-organic, to an actual organic physicist. That was a touching thing to read and then to hear. And it must have brought you great pride. You had the 1,000th publication from his lab or something like that, right?

Darren Lipomi:
I was in the lab at the time.

Brian Keating:
At the time, okay. When that came about, and soon thereafter you have a, you know, first author paper with him as your, you know, your co-author. It’s incredible. But, you know, a lot of us don’t have that benefit. And a lot of times you hear things— I often tell students in general, it’s more important who you work with than what you work on. And yet, you know, at the same time, you know, science is a game of credit and attribution and citation. I mean, literally, our currency is citations, right? And that’s why we get so kind of upset when people say things like, oh, peer review is the scam, and it’s kind of undermined, you know, science isn’t about peer review, and it’s just gatekeeping. And I’m like, I’m like, once I gave a podcast here with a couple of podcasters that I won’t name because I was very disappointed the way that they’ve kind of turned their podcast into.

Brian Keating:
But I said something like, we need gates, right? We need a gate. We have a gate around this campus and the gate keeps people out and it keeps things in. And people, they’re like, you wanna trap people here? I’m like, well, I’ve got a very expensive dilution refrigerator, costs about $500,000. I don’t want that walking off. Or a cup of liquid helium-3 is gonna set you back a quarter million dollars, right? So yeah, I quite frankly like to have a gate around things. In academia, I have a gate around my pool. You used to have a pool. I don’t think you have a pool.

Brian Keating:
Maybe you have an ice rink.

Darren Lipomi:
Sabres. Yeah. I have a creek.

Brian Keating:
You have a creek. Okay. Yeah. The Sabres are practicing nearby. But it’s, you know, we do need gates around certain things. So, and I think that’s why academics get so upset. You know, the joke is academics fight so much because the stakes are so low. As a chair, you know, you’re kind of in this position.

Brian Keating:
My colleague and friend and past guest, Inna Vishik at UC Davis, you know, said A department chair is not a boss of a professor, right? It’s kind of a negotiator that negotiates with hostage takers on behalf of the kidnapped, right? So how do you view being, you know, kind of at a top school and having, you know, a phenomenal reputation there? But, you know, you can’t please everybody all the time or you’re not a leader. So walk me through the navigatory, you know, kind of the shoals of being a chair, because it’s not a job I really want.

Darren Lipomi:
Sure. So my last job at UC San Diego was as Associate Dean for Students in Engineering. I had that role for 2 years. Prior to that, I was the director of the IDEA Engineering Student Center. And in the Associate Dean role, I was basically playing— running interference for the dean. So I would be doing town halls with students. There is a lot of work that I did that I’m still very proud of and still working on actually at UCSD. and garnering scholarship, you know, money with former colleagues and trying to enhance the student experience.

Darren Lipomi:
I’d really like to keep that as a legacy that I have here. The department chair role is completely different. Number one, it’s an executive role. You’re the one who has the final say on teaching and service assignments, also allocation of lab space. You and the dean, you know, have to agree on it, but the chair is the is the first responder there, and also the first person in line for promotion and tenure decisions. And so the number of incoming emails is lower, not just because U of R is a smaller school than UCSD, but also because my constituency is 14 faculty members instead of 10,000 students. And the dean’s office. My primary constituency being the faculty is I have to represent them at the dean’s leadership meetings.

Darren Lipomi:
I have to represent their interests when I’m talking to students who have an issue with grading policy, or I missed a final exam and the professor won’t give me a break and whatever. Every situation has to be dealt with like that.

Brian Keating:
You’re kind of giving an answer by way of the description of the duties and And not necessarily making it something that I’m now more appealing, you know, to me. But that’s fine because I need a new, you know, assignment. Like, I need a hole in the head, right?

Darren Lipomi:
I think what it allows you to do— so for instance, we changed the name of the department recently. It was Chemical Engineering for 110 years. And as of this fall, it became the Department of Chemical and Sustainability Engineering. The reason being that the faculty portfolios, research portfolios, all had something to do with battery separation membranes, water purification, and environmental remediation. I do a lot of work in recyclable polymers for e-waste. I do a lot of work in solar cell manufacturing. And so in this period in which we’re facing a demographic cliff where there aren’t as many high school kids as there used to be, and universities are not quite, or the American immigration system is not quite as friendly to international students as it was a few years ago. We need to do something to differentiate the product we’re offering.

Brian Keating:
I wrote the other day, I did a poll on the most important scientific medium of all time, Twitter. And I did a poll, I said, which is older, Oxford University or the Aztec Empire? And it was like 50/50 split. It’s like, obviously Oxford’s older, right? But, you know, people have been scratching on piece of rock with another piece of rock, you know, for literally 1,000 years. So what is sort of the future of academia? Like, again, not like who wants to be department chair? Like, okay, fine. That’s a very select set at the apex predators of academia, right?

Brian Keating:
The Hunger Games.

Darren Lipomi:
Right.

Brian Keating:
But, you know, the kind of the entry to the funnel, what kind of product are we delivering to our students nowadays? Just generally, not in your field, in my field, but generally speaking, academia as a whole, higher education.

Darren Lipomi:
It is the perfect time to be thinking about that question because academia has— the critics have never been louder. The societal, the external societal forces in economics have never been stronger. Headwinds have never been stronger. The traditional reason to get a bachelor’s degree is to be an important— to have something to contribute to civic society. To know the great works, to be familiar with music and art and literature, to know a little bit about— have some skills in numeracy, but then comes the more vocational professional certification, professionalization, and I would put the natural sciences in that category, but certainly engineering. And what kinds of— do we treat those groups of individuals differently? Should we? Should somebody who is sure they want to work in mechanical engineering, should they be reading Plato in a freshman or sophomore seminar? I would argue yes, they ought to be.

Brian Keating:
But to be fair, in other countries they don’t, right? In other countries, you’re going to be a physician, you know, you don’t take Plato. You don’t read Plato, right? You go straight from A-levels in your profession as early as, you know, the end of your undergraduate, so to speak, right? So other countries doing it worse than us because they have that model?

Darren Lipomi:
It may be that academia is not the right place to instill a love of wisdom, of past wisdom. It may be that the person in one of the countries of which you speak find that dusty old box of books, or maybe they’re already inclined to go to the, you know, go to the library. Or maybe you need somebody who’s probably not an engineer, you know, in the ethics department of the company, or someone who has a leadership role who is, you know, interested in humanism. That is a tough question. The other issue is what the students and the parents expect to get out of their tuition dollars and the debt that they’re going to be in.

Brian Keating:
They’ve got debt we can’t discharge in bankruptcy. It’s the only form of debt you can’t. Tuitions are rising faster than inflation, you know, in most private institutions like, like yours, right? So what kind of product are they getting? I mean, do we think of them as customers? And if so, to what level do you cater to the— I mean, Apple, you go down to the Apple Store, the Genius Bar, and you look at their nanoglass technology. I’m sure that’s what you do when you go to an Apple Store. But in all reality, they don’t say like, we’re gonna monitor you, we’re gonna make sure you’re not using certain tools, we’re gonna grade you, we’re gonna judge you, we’re gonna write these things that determine your future outcome. And oh, by the way, we’re gonna charge you, as I said, $250,000 or $500,000 if you’re at a private school, and it’s going up faster than inflation by a factor of 3 or something. something like that. So, and you can’t discharge the debt in bankruptcy, guys.

Brian Keating:
So, you know, take that on when you go to the Apple Store. You’d never do that, right? You’d never even set foot in there as much as you might like the product.

Darren Lipomi:
So do you buy into Ball’s cost disease theory of why—

Brian Keating:
I’m not familiar with it.

Darren Lipomi:
Yeah, so in sectors of the economy that don’t scale, so healthcare and education are the, are the 2 quintessential examples where we can— if you produce widgets and you get more efficient, you can produce 10 one year and a million a few years later. Right, but your class isn’t going to go from 10 students to a million students, but you need to survive in that economy where productivity is increasing elsewhere. And so there is an economist, Baumol, someone in the comments is going to correct me exactly the specifics of this, but it basically says that that’s responsible for the increase in healthcare and education costs because we don’t scale.

Brian Keating:
Just to push back on whoever this scholar is, Because in certain sectors of both academia, higher education, say, and in healthcare, costs don’t go up. Cosmetic surgery has gone down. LASIK has gone down. The very lasers that Bausch and Lomb makes contacts for, and part of their profits because people have LASIK surgery, so they don’t need the contacts. So that’s all gone down because it’s elective surgery. It’s not paid for by health insurance and covered by our lugubrious health sciences professionals. So how would this Balmoral or—

Darren Lipomi:
Yeah.

Brian Keating:
It sounds like a James Bond mansion or fortress somewhere, right? So how would they reverse? I mean, it’s not your theory, but how do they react to that? And there’s private educational institutions too that are providing services or even state, University of Florida is a very different model than University of California. So yeah, I mean, how do you react to that as a purveyor, as the supplier of some of the product and some of it is being commoditized?

Darren Lipomi:
Yeah, and I think it is, there are some who would argue that faculty, that one, there are too many faculty and staff, and two, that they get paid too much.

Brian Keating:
I think there’s way too few faculty. I think there’s way too many bureaucrats. bureaucracies, administrators, departments. No, I’m just kidding.

Darren Lipomi:
There aren’t too many department chairs. Everyone should be chair. That’s what everybody thinks they are.

Brian Keating:
That’s what everybody thinks they are, right? I watch your show, right? I learn a lot more about, you know, chemistry than I think I would do if I sat in on, you know, some of your other— So yeah, so where is this going? I mean, when you could go to AI, you can go to YouTube, you can watch Molecular Podcast, that you could do, you know, a whole university education in your pajamas for free. with no non-dischargeable student debt. Like, make the argument. I mean, I’m kind of like grasping for a lifeline here, Darren. Help me out here.

Darren Lipomi:
I mean, we’ve got to save our industry.

Brian Keating:
I think our industry is— I mean, COVID didn’t kill us. You know, nothing’s going to kill us. AI is not going to kill us.

Darren Lipomi:
Well, UCSD is still the 2nd most applied-to school in America and the country.

Brian Keating:
That’s right.

Darren Lipomi:
Yeah. So there’s the human capital argument and there’s the sheepskin effect argument that If companies are outsourcing certification to UCSD and University of Rochester, could they have— could the students have learned the same thing? I would argue that in physics and engineering, because it would be very difficult to do that.

Brian Keating:
I have a sad obligation. I have to point out an egregious typo, an error in your book, which I missed the first time, and I’m really ashamed and humiliated, quite frankly. And it’s hard for me to do this to someone who I like, quite frankly, but you got your H-index wrong in the book. It’s not 60, it’s 63. So Darren, you’re gonna have to issue, you know, second edition. We didn’t do the patented, uh, judging books by their cover segment, so we gotta do that now.

Darren Lipomi:
Hey book lovers, we’re judging books by the covers. We know we’re not supposed to do it, but it isn’t impossible. There’s nothing to it. Let’s take a look and judge some books.

Brian Keating:
So talk us through the title, the subtitle, cover art such as it is, and what was, you know, I always say you shouldn’t, and I think I told you this, don’t write a book unless you can’t not write a book. It’s kind of like going to grad school. Like, you shouldn’t go to grad school. That should be your first default option. Don’t go to grad school. But if you can’t not go to grad school, and we have a lot of non-traditional people that go back to grad school late, and I love it, but that was because they couldn’t not go there. Talk about the book. Why couldn’t you not write this book?

Darren Lipomi:
Science Nonfiction is— the subtitle is Behind the Scenes in University Research. I can’t tell you how often I heard an incorrect definition of indirect cost recovery, and that’s why— Thank God, Darren.

Brian Keating:
Oh my God, if you couldn’t write about IDC, who would? Who would? Think of the children.

Darren Lipomi:
Think of the children. That is a microcosm of the misunderstanding of how science is actually done. And so there are fantastic resources on PBS and YouTube about the wonderful things that science produces. I’m a big fan of Anthony Bourdain and his book, his first book that made him famous, Kitchen Confidential. And I thought, wouldn’t it be cool if somebody wrote Kitchen Confidential but about labs instead of about kitchens?

Brian Keating:
That was a working title for my first book, which became Losing the Nobel Prize, but it was gonna be Cosmic Confidential. It was filling this lacuna in our kind of understanding in the public, but also So kind of a jeremiad, but also a warning, but also an invitation, because it is a great job. Look, I say, I joked before, it’s the hardest 3-hour-a-week job in the world. But I also say, who’s going to be a professor? Who’s going to pay me to be a professor? You grew up working class, maybe lower middle class. I grew up fairly poor, even though my father, late father, was an academician. He was a professor. He was the youngest full professor at Cornell.

Brian Keating:
Wow.

Brian Keating:
in the math department at age 26. He was a full professor. I mean, you can’t do that nowadays, but he was— I didn’t think you could be a professor. I’m like, who’s gonna pay me to taste ice cream and, you know, like ride the roller coaster at SeaWorld all day? No one’s gonna pay me to do that. Let’s take it back to where you got your start, at least in the scientific world, which is in Boston. And one of my kind of greatest heroes, in fact, was from Boston. I think he was at Boston College. You were at Boston University.

Brian Keating:
His name was Isaac Asimov. And he not only wrote some of the greatest works of fiction ever written, science fiction, but he wrote a lot of nonfiction science. And it actually got me my deepest interest as a teenager in science, his nonfiction, his books, particularly about chemistry, which, you know, was the worst possible field for me.

Brian Keating:
Right.

Brian Keating:
Biology was bad for me. I took AP Bio and we had to dissect a frog, and I was fine with dissecting it, but I kind of heard it scream I might have brought it back to life just to cause it more— no, I’m just kidding, PETA folks out there. But I love chemistry, but I was horrible. I mean, I couldn’t remember all these different reactions and the memorization, and, and just, you know, it just seems so formulaic. It was like I could just go to law school and memorize a bunch of laws or something. I wanted to be a scientist. Asimov inspired me. What inspired you?

Darren Lipomi:
Who—

Brian Keating:
what were some of the influences before you kind of became who you are, you know, the famous professor that you are now, and, and your your, you know, just phenomenal career. What influenced you at a curiosity-based level?

Darren Lipomi:
Asimov. Was it BU? You were right the first time.

Brian Keating:
Oh, I was? Okay, okay, great.

Darren Lipomi:
And I have read his Brief History of Chemistry cover to cover. He has all these little nuggets of science, science non-nonfiction, or non-nonfiction. I was always of the belief that science and magic were synonymous. When I would watch 321 Contact and Nature on PBS, I would just— I just conflated the two. Science, that was the only legitimate route to magic. Maybe science even was magic. And when I didn’t know what I wanted to do in science, but I had a fantastic high school teacher. This is a very common story.

Darren Lipomi:
We have people who inspire us in the classroom, and I had— it was no different for me. I had a teacher in high school. I took the regular 10th grade chemistry and then AP chemistry from him the following year. And he had a way of imbuing atoms and molecules with human-like characteristics. And that is where somebody like me, who wasn’t maybe so great at thinking about equations and things that were very abstract, but also not somebody who wanted to memorize a biology textbook, because there’s a lot of memorization in biology. And so for me, chemistry was the center of the bullseye, But I can understand how other natural scientists gravitate toward physics or biology.

Brian Keating:
You had this really cool experience as a kid. I had a cool experience with a telescope as a kid that got me into the career that we have today, which, you know, as I often say, is the hardest 3-hour-a-week job in the world. You had an experience with a type of observational telescopic device or televistic device. Talk about your exposure, you know, this kind of chance exposure that got you into the important career that you have, but the most important thing that, you know, 2 white guys with a microphone can do— podcasting. So talk about this Fisher-Price, you know, video camera and how really what should parents do if they want to create a young Darren LaPomi, you know, a young Brian Keating, God forbid. Tell me, what are some of your pieces of advice as a parent who’s also a professor?

Darren Lipomi:
In the late ’80s, Fisher-Price came out with this PXL-2000 camcorder. It was a black and white camcorder that would take high-fidelity audio tapes. tape, run it really fast, like as fast as a fast forward, to get all of the, you know, to get the data rate high enough, and it would record 10 minutes of black and white video on each side of an audio cassette. The images were terrible, it sucked up light like nobody’s business, you needed— So my dad had an early cartridge video camera, like film video camera, and we used halogen lamps that he had just to get enough so that it wasn’t a black screen.

Brian Keating:
So burnt.

Darren Lipomi:
Right. And so I had my stuffed animals act out scenes from Star Wars, and that was my, my earliest exposure to video and audio production. The other ancient piece of technology that I had was a 1980 desktop computer that a family friend absconded with from Kodak. So they used to dump their old equipment and— In the river. Right, in the river, in the Tennessee River. And so we ended up with this thing, and when you turned it on, it would go like that. It would sound like the Millennium Falcon taking off. You needed a 5¼-inch disc to boot it up.

Darren Lipomi:
So, you know, what do I do with my own, my own child? You know, she’s 7, and we, we are interested in in creativity and science. So we have a sewing machine, a 3D printer is on its way. We watch a lot of builder videos. She wants to make animatronics from Poppy Playtime and other things that I shouldn’t be letting her play. But that’s, I don’t know if that’s the right answer, but that’s what we’re doing as scientist parents.

Brian Keating:
One of the last organic chemists that I talked to was Tom Cech, who won the Nobel Prize for his work on RNA. and his book, The Catalyst, which is a phenomenal book, very much kind of Asimov spirit. But in the conversation we had on the podcast, he spoke about, you know, kind of his philosophy, at least as a scientist, was, you know, basically, if you’re gonna do one thing, do it extremely well, do it better than anyone can. What’s your one-sentence teaching philosophy?

Brian Keating:
And maybe one sentence on research too.

Brian Keating:
I’ll give you 2 sentences.

Darren Lipomi:
In teaching, one wants to get the students to, do their own work. When I was in school, I would go to the lecture, I would not have read the chapter ahead of time, and 60% of it would just straight over my head. Then when I would go home or back to my dorm room, I would do the problems in the back of the book, and I would transfer them to note cards, and I— and that’s when I would do the real learning, right? But I wouldn’t have done that if I didn’t care. And maybe so the effective professors were the ones that I wanted to I wanted to— I viewed them as a role model. I wanted to please them. I didn’t want to get an embarrassing grade on an exam. I was excited by their passion for the material, and that made me want to do it myself. I’ve never been somebody who could just absorb something from a lecture and take a test on it.

Brian Keating:
Right.

Darren Lipomi:
Research. Find a skill at At the intersection of 3 or more interests that no one else is working on. And no one else is working on will come automatically. So I am a decent organic chemist. Let’s say out of physical scientists, I’m 1 in 10. Let’s say I know something about mechanics, but I’m not as good. Maybe I’m 1 in 5. So now I’m 1 in 50.

Darren Lipomi:
but I really am interested in neuroscience, and so I don’t even have to be that good at neuroscience. I just have to have read a couple textbooks and be willing to talk to neuroscientists, so maybe I’m 1 in 2. Now I’m 1 in 100 at the intersection of mechanics, organic chemistry, and neuroscience.

Brian Keating:
The Shohei Ohtani of this amalgam that you’ve made.

Darren Lipomi:
Yeah, and that’s exactly what my lab What my last 4 research grants have been on is perception of the mechanical— the organic media as mediated by mechanical forces.

Brian Keating:
I had a guest here sitting in that chair, Nikolai Kakhushkin at NYU, said in Russian, like, carbon, like, has a name or an analog. It’s like the aggressor, you know, and then oxygen is like the devastator. Or no, carbon’s the assembler and they’re like oxygen’s the devastator, and you put them together with a little hydrogen and you get the the, just the mellifluous world that we’re, you know, kind of, uh, just so privileged to be a part of. And we’re so privileged that you came back on the, on the show and came back to San Diego. And congratulations on all your success. It’s, uh, it’s just, it’s just wonderful to watch you, you know, develop and grow. And, you know, it’s kind of like you, you recruited, you know, Fernando Tatis from the White Sox and get to watch him play for the Padres, as I did last night. You know, he still hasn’t gotten a home run.

Brian Keating:
But Darren LaPoma, professor, chair, Love, homie. It’s so great to have you back. Congrats on all your success. I wish you continued success. And hopefully you’ll come and visit. Maybe I’ll come back to Rochester and get some beef on weck with you. Probably in the winter. I’d really like to go there in the winter.

Brian Keating:
That’s my dream.

Darren Lipomi:
Just eat snowshoeing and cross-country skiing.

Brian Keating:
Ice skating.

Brian Keating:
Exactly.

Darren Lipomi:
All right.

Brian Keating:
We gotta get you on a flight. Thank you so much for coming out. Thank you so much for having me. Welcome back to San Diego and have a great trip back.

Darren Lipomi:
Thank you.

Brian Keating:
Thanks, Darren.

Brian Keating:
Darren just told us that most people shouldn’t even go to college. Well, he’s running the department that recruits them. If that changes how you see a degree, subscribe and turn on notifications and leave a comment. Would you tell your own kid to skip college? For the flip side, Nobel laureate Tom Cech told me why RNA might be more important than your diploma. It’s linked right here. Thanks for watching. See you next time. And please don’t forget to like, comment, and subscribe.

Brian Keating:
It really does help me out with the algorithm that determines YouTube performance. See you next week on Into the Impossible.

Being University President Gets You Secret Service. Tenure Doesn’t.

sabartigas — Sat, 15 Aug 2026 22:23:19 +0000

Being University President Gets You Secret Service. Tenure Doesn't.

Dear Magicians,

Marc Tessier-Lavigne resigned as Stanford’s president in 2023. The investigating panel found no evidence he manipulated data or knew his labs were doing it. He kept his faculty position. He’s still there.

Claudine Gay resigned as Harvard’s president in January 2024. Harvard’s own review found “a few instances of inadequate citation” — explicitly short of research misconduct. She kept her tenure. This fall she’s back in the classroom.

Both wore the tenure vest. Both got shot at. Both are still standing, and drawing HUGE salaries because someone actually checked whether the bullet went through before deciding what happened next.

People treat tenure like body armor that works on it’s own. Put it on, walk into the fire, survive automatically. It isn’t. A vest only works if someone runs the test. A named panel. A defined standard. A published finding that says, specifically, here is what we looked at, here is the bar, here is whether it was cleared. Gay and Tessier-Lavigne got that. The process ran. The vest held because someone confirmed it held.

Jason Arday didn’t get the test.

He was never a university president — no executive post to absorb the hit before it reached his actual academic standing, which is what tenure exists to protect in the first place. They went straight at his doctoral thesis. Liverpool John Moores, the institution that granted the degree, investigated and did not uphold the claims, calling the issues “honest and reasonable error.” Cambridge defended him forcefully as recently as June. Then, on information it has not made public, Cambridge reopened the question and Arday resigned the professorship and the fellowship before that process produced a finding either way.

He was wearing the same vest Gay and Tessier-Lavigne were wearing. Nobody checked whether it worked. He had to decide whether to trust it while the shooting was still going on.

This isn’t unique to universities. It’s the same failure mode boards perform on CEOs constantly, just inverted. A CEO gets fired for optics, for a bad quarter, for a board that wants a scapegoat — and still walks out with severance, vested equity, sometimes a board seat somewhere else next year, because a contract exists and somebody is legally obligated to honor it whether or not the firing was fair.

Tenure is supposed to be the academic version of that contract. But a contract nobody enforces isn’t protection but seemingly it’s a promise you find out was empty exactly when you needed it to be real.

Tenure was never the academic version of the Secret Service that protect the actual President (of the USA, not the University). It’s a vest of sorts… real protection, but protection that still requires you to check the seams before deciding whether you’re safe to keep wearing it.

Gay and Tessier-Lavigne found out the vest worked because the detail showed up and did its job before either of them had to bet a career on faith alone. Arday never got the detail. He had to decide whether to trust the vest while the shooting was still going on, with no one confirming it would hold.

So tell me what form of protection are you relying on without fully stress testing it?

And until next time, have a M.A.G.I.C. Week,

Brian

Appearance

Great crowd + questions for my friend, Annie Jacobsen, last Thursday on beautiful Coronado Island.

Take the Biological War scenario quiz & get prepared to find out why I think we need to stop apologizing for humanity’s greatness.

Genius

The great promise of AI for science may be fewer papers, not more.

Let machines do the literature searches, code checks, documentation, and reproducibility drudgery. Scientists should spend their scarce human attention deciding which questions deserve to exist in the first place. Publishing is getting cheap. Understanding is not.

I enjoyed this piece in nature magazine about this crisi-tunity.

Image

Physicists have tried to figure out the mathematics of whirlpools long ago.

Now, we have finally built a telescope powerful enough to watch the same mathematics happening on the surface of the Sun.

The interesting part is that these tiny swirls may help twist magnetic-field lines, transport energy upward, contribute to solar flares, and perhaps help heat the Sun’s extraordinarily hot corona.

Conversation

Latest on Into The Impossible

John Martinis: The Physicist Who Proved Schrödinger’s Cat Could Be Real

I sat down with the 2025 Nobel Laureate in Physics, who built a circuit with billions of electrons tunneling in a single quantum state, proving that quantum mechanics doesn’t stop working just because something gets big.

We cover the systematic errors that nearly derailed his early work (with a striking parallel to the discovery of the cosmic microwave background), what it felt like to achieve quantum supremacy at Google, and why he was demoted right after his team’s biggest win, prompting him to leave and found his own quantum computing startup, Qolab.

We close on wave function collapse, the many worlds interpretation (his take is refreshingly blunt), and his advice for chasing the impossible.

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!

Nobel Winning Physicist John Martinis: Why I Walked Away From Google Quantum Computing

sabartigas — Sat, 15 Aug 2026 21:19:50 +0000

Nobel Winning Physicist John Martinis: Why I Walked Away From Google Quantum Computing

Transcript

Brian Keating:
Today we’re joined with one of my heroes. It’s not every day you get to talk to somebody who not only, you know, is such a great contributor to physics and has been inspiring not only the work that I do, but my whole collaboration is basically enabled by work that John and his collaborators did over the years. But he’s also, you know, once we say a mensch, he’s known for his teaching, for his group building. You’re just kind of the physicist physicist. So thank you for joining us.

John Martinis:
Well, that’s very kind. Not everyone feels that way, but I appreciate the kind words.

Brian Keating:
You’re now The 26th Nobel Prize winner I’ve had on the podcast. Every, every 9 multiples of 9, I write a book featuring wisdom, and I hope the, the 3rd version will come out with you in the coming years. Well, I’ll let you know about how that progresses, but I wanna take us to that October morning last year. I was teaching quantum mechanics that day, advanced quantum mechanics with perturbation theory, and I said, a guy I know just won the Nobel Prize. What was that like? What was the, what was the 2nd thing that went through your mind when you got the phone call at whatever time it was?

John Martinis:
Well, I actually didn’t get the phone call. The phone is way across the house, but my wife was up late reading and she heard the phone ringing, but she figured she’d get it in the next day. She looked at her email and there was a bunch of congratulations. So she knew about it, but she knows that I need my sleep, especially, you know, the next day when you, you know, you have to be on the whole day. So she waited till about 6 o’clock. There were reporters who showed up soon after that. So it was actually better because my wife woke me up, but she was very clever about it. She tapped me on the shoulder and said there were reporters coming over.

John Martinis:
And I realized, oh, it’s the beginning of October. So I opened my computer and, and, you know, saw Let me share something a tiny bit personal. There are things called Nobel symposiums where they look at a field and see if the field is noteworthy and look at people in the, you know, leaders in the field and the like. So you kind of understand a little bit that you might be on some kind of list. Okay. For me, that’s the biggest honor because the Nobel Prize is just so crazy unlikely, right? Just being invited to that is really very special. And And then for some years, you know, I’d wake up in beginning October. It’s like, oh, okay.

John Martinis:
And it’s just so wrong to be disappointed by this because, so after the years, you know, I just stopped kind of, I knew, well, it’s beginning October, but I stopped really thinking about it. That’s why my wife didn’t know. We didn’t talk about this at all. Right. Because it’s just, okay. You know, whatever happens and it’s actually better that way. Yeah.

Brian Keating:
There’s a joke I sometimes will, you know, pull out on October 1st at 2 in the morning. I’ll say, I’m working on my best, you know, Swedish accent to cause somebody I don’t like a heart attack. We are calling from the Swedish Royal Academy.

John Martinis:
Yeah, I’ve heard people get pranks like that.

Brian Keating:
Well, yours isn’t a prank. And the only thing that frustrated me is that I was talking about, you know, quantum tunneling, but I was talking about single electron tunneling and even nuclear tunneling. So you wanted for macroscopic tunneling. What was the impetus for— obviously you didn’t set out to win a Nobel Prize. Talk us through the history of why you thought macroscopic effects would manifest themselves instead of just the already mysterious microscopic tunneling effects?

John Martinis:
Yeah. So this experiment in line of research was very much motivated by Anthony Leggett. You can just go back to the Schrödinger cat paradox where you do a microscopic atom decay that’s connected up. So the atom decays, it kills the cat, and then, you know, okay, before you open the box, is the cat in some dead and alive state? And okay, I think there, for me, there are ways to answer this that are very sensible. But Leggett pointed out that there actually is no experimental evidence that macroscopic entities, especially a cat, can obey quantum mechanics. And he said, there’s— if there’s no evidence, we should be looking for evidence as a way to test quantum mechanics. And he came up with the idea of these superconducting circuits where you have a macroscopic number of electrons that are tunneling through the junction. For me, you know, as a young student, you look at quantum mechanics, it’s really wonderful.

John Martinis:
My personal hobby was electronics. I joined John Clarke’s group because he was doing things on quantum noise. Okay. And thinking about this. And for me, it was the most natural experiment to want to do. I’m surprised that there weren’t 30 other groups doing it. At the point now, everyone can understand that. But back in the mid-’80s, the idea of quantum information and doing these tests wasn’t as popularized as, as it is now.

John Martinis:
So for me, it was the most fascinating experiment. I thought it could be done cleanly. You know, obviously John Clarke, you know, was moving in this direction. Michel Devoreg came over. And for me, it was, you know, a perfect thesis experiment. And I’m going to say it was also very groundbreaking because Then John’s group, you know, we understood how to measure noise in these devices, but to, you know, build a system and engineer it and think about the physics combining microwave engineering and quantum mechanics and figuring out how to deal with the noise and the like to build a clean experiment was just really fundamental and groundbreaking. And of course, that’s, you know, I find that, you know, wonderfully exciting. Okay.

John Martinis:
To just try to figure all that out.

Brian Keating:
Why did you start off with a Josephson junction? Maybe first we’ll explain what it is. And keep in mind, my audience is highly technical, very competent, and I’ve tried to have Brian on the podcast as a fellow Brian. He’s gone in interesting directions, shall we say, in consciousness and other kind of fields, very different from what he did as a young, very young man when he won the Nobel Prize for his work. But tell us, why Josephson junction? Why not a quantum dot or trapped ion? What made you start with that?

John Martinis:
Well, first of all, the Josephson junction is a macroscopic system where you have a macroscopic number of, you know, Cooper pairs, electrons, paired electrons tunneling through the device. And to test the idea of whether macroscopic variables obey quantum mechanics, you need that. A quantum dot and an atom, they’re kind of single atomic systems, whereas this was very clearly macroscopic. And, you know, that’s why Leggett proposed it, and that being in the superconducting field with John Clarke’s group, that’s of course why we did it. Now at the time, we didn’t know if the Josephson junction was a clean system. In many physical systems, especially a macroscopic system, there can be dirt effects, other things that go wrong that, you know, may cause it not to work as well as you would like. And, you know, at the time we didn’t know any of that, so we just forged ahead. But I would say the reason I’m talking to you today about the Nobel Prize is it turned out that it was a clean system.

John Martinis:
And this was figured out by many, many people over decades of work, you know, testing it. I mean, we laid the foundation for understanding how to do that, but building these systems properly, you know, took a while for a whole lot of people to figure out.

Brian Keating:
What strikes me as so, you know, kind of magical and beautiful is that you guys ended up seeing you know, discretization and quantization in sort of spectral features. And it’s exactly reminiscent of what Balmer saw, you know, 60 years before the invention of quantum mechanics. Have you ever thought about what it was like to be him? I mean, grappling with something that wouldn’t be determined or even predictable for many decades. Did you ever have that kind of inkling of like, we’re in this weird, mysterious territory? Or was it, if we keep putting one foot in front of the other, we’re going to get to some goal that we’ve set out for ourselves?

John Martinis:
Well, that’s a really great question because The way we were thinking about it and we started it, John Clarke’s a serious experimentalist and I was a new student. But what he and we decided very early on is we had to measure the parameters of the system in order to do a careful test of the theory. And the obvious thing to do was to put on microwaves and have a resonant phenomenon and see that resonant phenomenon as a way to measure the oscillation frequency of the system, which is a very fundamental parameter. And what was interesting is classically, I set up an analog simulator in about, I think, a day or two and put in noise and then showed that indeed you can see some kind of resonant effect. So very early on, even though we knew about that, we had to do tests in doing it. Now, of course, you know, as we did this, we were quite interested in what would be the quantum effects. Now, what happens classically, both in an atom and in our system, is that this oscillates in this nonlinear well. It has a spectrum of frequencies, one frequency when it’s low and a lower frequency when it’s high.

John Martinis:
So just like when people talk about electrons circling the nucleus, classically, there’s a range of frequencies that it would emit light over. Of course, what you saw was quantized energy levels and quantized light, which, you know, it was clear we could see in the system. So I would say the fact that you see quantized oscillations is a key feature of quantum mechanics. And I think that what we were able to see is like the smoking gun thing that you’re doing that. Now, it could be that you have to realize, if you’re really being careful about this, it could be that there’s resonances in the circuit you’re connecting it to. So you have to be careful about, you know, claiming that. And that’s why we did a bunch of experiments detailed that are in the papers, but no one ever talks about to say that we had a well-defined experiment without these resonances. The resonances we saw made sense in terms of the physics of the, of the system and the like.

John Martinis:
But in the end, that was a key observation. Now, of course, what was also interesting is Leggett talked about what happened to tunneling when you had dissipation. That’s very new physics, which you normally don’t see in electrons and nucleus. And eventually we were able to do those experiments really well and show that that theory made sense too. So there was a whole series of things we were able to do because it was really well engineered, good connection of microwave engineering to quantum accounting.

Brian Keating:
One of the things I love to point out to my younger listeners, readers, and viewers in my books when I interview titanic physicists such as yourself is when you do the thing that you really didn’t think you would have to do. You just mentioned it. You can have this collective phenomenon from alternative effects rather than the thing you’re looking for, and those are called systematic effects. And I typically tell my students, anyone can get the right answer. We can get the Hubble constant, and it could be beautiful, but the real determinant of whether or not you’re a good scientist is how you account for the things that you could be wrong about, right?

John Martinis:
Yeah, exactly. You know, and the fundamental thing about nature is you can never prove anything about nature. You can disprove your theories, but you can never prove it because something else could be explaining it. But what you do as an experimentalist is measure enough parameters and do enough of the check experiments that if it’s another theory, it would have to be really kind of You know, a crazy, not simple theory. And, and that’s what you do. And that’s, for example, why we were measuring the parameters. Okay. The other thing to, to realize, and I talk about, I like to explain this to the students, is in the beginning we did some experiments at, you know, 1 Kelvin to 4 Kelvin to see what was going on.

John Martinis:
And the data didn’t make sense at all. And it was because— we were seeing noise. And very early on, we just did something. We compared it in a way that was really kind of tricky at the time and didn’t make sense. And then we started saying, well, okay, we’re going to have to filter it right and the like. And once we understood the microwave engineering, microwave filtering, we redesigned the experiment pretty quickly. And then all the data started making sense. Okay.

John Martinis:
So there were some internal checks that you, you know, you have to look at very carefully. There were a few prior experiments that hadn’t really done that properly. And I think the physics community appreciated all those checks and being able to do a beautiful experiment because of that. And, you know, all good experiments are like that. They’re well-designed, and then you think about things can go wrong, and you figure that out.

Brian Keating:
Yeah, it reminds me, there was a scientist named Ed Ohm who worked at Bell Labs on the exact same Holmdel antenna as Penzias and Wilson. You probably know this story. And he actually measured the BMB, and he attributed it to a systematic error. He basically said it’s a systematic error, or it’s excess noise, or all the atmospheric contributions add cumulatively and they don’t cancel out. And then Penzias and Wilson said, well, let’s do a calibration. Let’s measure that with a liquid nitrogen chopped Dickey switch load.

John Martinis:
Yeah, yeah, yeah.

Brian Keating:
That won them the Nobel Prize. But he had actually discovered it, you know, 3 years earlier in the same data. But that’s, that’s exactly right. And what you said is so important that we can’t prove things on the physical side. We’re not mathematicians. You know, mathematicians can prove 1 1 2. It takes 200 pages of piano algorithms and all these other things. But it kind of reminds me of what Eugene Wigner, another Nobel laureate, said once.

Brian Keating:
He said the mathematics is sort of unreasonably effective. And, and I think about that and I kind of have narrowed it down. I say that the square root is unreasonably effective because in classical mechanics you can make the Poisson bracket, you can make the commutator of momentum and position, right? And it’s zero, right? It doesn’t matter if you measure momentum first or position first, you get zero. But if you add the square root of negative 1, You get the Poisson bracket for the Heisenberg relationships, and those do not commute, right? What’s the weirdest thing about quantum mechanics to you?

John Martinis:
The weirdest thing. It’s really a complicated phenomenon, and it takes until at least your third year, typically, as an undergraduate, and then you take it more. And I’m going to say, after doing this for many decades, I kind of understand it fairly well at this point. But You know, it’s complicated, but at the same time, there’s this mathematical artifice where you can understand it well. And, you know, it’s the basis of many fundamental standards. So it’s extremely accurate too. It’s both complicated but understandable, unintuitive, but given enough time, to me, it’s intuitive right now. It’s kind of strange.

John Martinis:
And, you know, that it’s just a very deep theory and it’s kind of amazing. that nature works at this very, very deep level. And like I say, the other thing is, which is what our Nobel is about, it’s not just the physics of the small or fundamental particles. It’s actually a generic physics that everything can obey. It’s just really hard for ordinary objects to get into some parameter space where you can see it. So it’s actually a generic phenomenon that’s all, you know, that And potentially could be all around us.

Brian Keating:
And tunneling is that way too. And what really kind of surprised me about ordinary, you know, kind of electron tunneling is we have this kind of myth in both technology and in pure science that you look into the equations and then you invent the technology, right? So like, you know, Bardeen and the transistor, we couldn’t have invented it unless we understood quantum mechanics. When in reality, I think, I mean, you know this much better than me and I want to get your opinion. But, you know, kind of if you look at the first transistor, it looks like, you know, a chunk of rock, like the germanium, and a chewing gum and a coat hanger. And it’s all put together. I want to ask you, we’re going to talk a lot about quantum computers in a little bit.

John Martinis:
Oh, and by the way, our first experiment in Berkeley was carefully designed. Okay. And, you know, but if you look at what’s being made now, it’s, you know, it’s like, it’s like the Bell Labs transistor. But, you know, there’s some physics there. Okay. But that’s what you have to do when you’re first exploring something, is you do some experiment that’s kind of minimal and you can get it to work. And then once you understand the principles behind it, you can then engineer it and look deeper and deeper into it. And that’s what’s beautiful about physics, is there’s all these levels that you have to understand to get it to work.

Brian Keating:
What’s been the most, you know, kind of enabling technology on the STEP contributor to the work that you did? Was it the advances in superconductors? Was it the kind of fluxonium, the 3D cavities? What were kind of like stepping stones on the way to the revolution that you guys worked on and still do work on? What was some of the most important keystones?

John Martinis:
So what happened at the time is that we understood that this was a microwave experiment, and we went to the astronomy department and got their S-parameter meter and started understanding than reading microwave books. And in the end, what we did and the field did is combine the concepts of microwave engineering with the concepts of quantum mechanics. And it’s interesting because microwave engineering has wave phenomenon and resonance like quantum mechanics does. So they’re actually somewhat close. I also always think that you can understand about 80, 90% of our superconducting quantum devices with microwave engineering. And then you have to throw in quantum mechanics at the appropriate point to do that. It kind of reminds me, you have Maxwell’s equations, but in terms of understanding electrical circuit and the like, you use circuit diagrams. Okay.

John Martinis:
And what you’ve done is you’ve taken something very complete and almost abstract and then brought it down to a level where we can build, do complex engineering with it. And that’s kind of what we were able— what we started in that experiment. And of course, we explored that for many decades. And now, you know, we’re doing it and we’re still— I’m still exploring that in terms of materials and other concepts that we have here. Yeah.

Brian Keating:
And, you know, kind of makes me think about a statement I think you made once, you know, that people seem to hate decoherence until they need it. So Without decoherence, like friction, you know, if you’ve ever, you know, kissed a loved one, right? You need some friction, right? Life wouldn’t be fun without friction. But tell me, is decoherence necessary, you know, for these devices, or is it purely a nuisance that must be obliterated?

John Martinis:
I’m going to say decoherence is always here in the real world. And the problem is, if you take the Schrödinger equations, that’s just, you know, a pure, simple physics without decoherence. And of course, people know how to put in decoherence and do that. And it’s kind of like, you know, how do you understand thermodynamics without, you know, entropy? Okay. You know, you have the basic equations which are conservative, and then you introduce entropy, and then you could see the real world. And this is what happens with quantum mechanics. And also for quantum computing, It first, it’s, it’s a very practical, important thing because it limits your quantum computer. But also when you start doing things like measuring real circuits and let’s say doing error correction, in error correction, you’re removing the randomness or the entropy of that.

John Martinis:
And in some sense you need decoherence. And I would say decoherence, in my view, is kind of tied to how things get measured. Okay. And if you look at Exploring the Quantum by Ramon Den Haroche, it gives you a good description of that. That’s very integral to quantum mechanics. It sounds like the ugly side of it, but it’s actually quite an important part of it.

Brian Keating:
At some level, we have to always connect to the classical world, right? So there’s inevitability of dealing with classical effects. And so how do you guard against, you know, kind of these systematic biases? Like for us, let’s just take measuring a superconductor, right? So if you want to measure the superconductor, you could be very careful. You could do all the 4-point measurements you like. And you probably have been in a lab with my late great friend Paul Richards from UC Berkeley, and he was just the most careful person. And he wouldn’t let you do a measurement, you know, that wasn’t at least 4 points in this design. But at some level, you know, can you actually prove that these things have zero resistance in the junctions? Can you prove, you know, that the flux is purely being, you know, quantized in the way that the, you know, Leggett and other equations suggest that they are? Or do you always have to— Ah, we kind of have to— we know it’s not purely quantum mechanical because we have to these devices, or is it truly manifest that they behave as they should be purely quantum mechanically?

John Martinis:
It’s always a matter that there are certain limits where the flux will jump. Okay. And you could be, let’s say, near to the transition temperature. And then, and then you’ll see that flux is not quantized, or at least it jumps in its quantization. Physicists have been exploring this for a long time. In fact, In fact, the experiment I did in the ’80s was all about how, you know, when you put a current up to the critical current, at the critical current, it then looks like a normal metal. So it’s superconducting. And then when you hit the critical current, goes normal.

John Martinis:
Well, it happens a little bit before that, either due to thermal fluctuations or due to macroscopic quantum tunneling. And, you know, it’s an example of physicists understanding the limit. Now you can look at the limits of these various things and you can understand that it should be exponentially small. For example, for a superconductor, there are things, excitations called quasiparticles that limit the superconductivity, but there’s a gap and it’s e to the minus delta U over kT. And if you do the calculation, that’s tiny. But the problem is, is you have stray infrared light in a real experiment. and then generates the quasiparticle. So it’s not exponentially small.

John Martinis:
So I would say, you know, physicists are great at figuring out all these details and figuring out what’s wrong. And over the years, then, you know, this is why it took, you know, decades to figure all this out. Lots of experiments happened looking at all the details and not just taking the pure theory, but thinking about all possible ways that things can go wrong, and then you engineer around it. For the infrared case, you’d have to do very careful shielding, which we didn’t do at first, and then we realized we had to do that. And then there’s still a little bit of residuals, but we can deal with that.

Brian Keating:
So I wanna make a fairly heretical claim, and then I want you to demolish it and put me in my place. But my, my claim is that no one’s ever looked at an equation and out pops a technology from purely contemplating it, except perhaps quantum computing. We’ll get there in a second. But if If you look at the transistor, I just said, you know, they, they weren’t like looking at, you know, the, the Schrödinger equation saying, oh, we’re gonna get this technology if we put the chewing gum, the coat hanger, and the, you know, piece of germanium together. MRI came, you know, from Bloch’s equations being, being understood. Laser, maser came from population inversion, which was Townes’s kind of guess. Is the quantum computer perhaps the first technology in history that really came from the equation outward, or is it gonna be you know, sort of along the lines of, as I said, you know, the high-temperature superconductor. Really, we didn’t understand the theory until, you know, my late great professor Leon Cooper writes.

Brian Keating:
What do you make of this claim that I’m making that we don’t look into the equations and then the technology comes out? We experiment, guess, and then eventually technology comes and then we backfill in the explanation.

John Martinis:
Well, I haven’t studied this and it sounds like you’ve had, but I’ve been said, talked to, I’ve talked to theorists about this and they say it’s very rare that a theory kind of precedes an experimental observation. And the one example they give is the Josephson effect where Brian Josephson understood this. And basically you have to do the calculation to second order in order to understand what the superconductivity does. But the way this all came about, it was very murky at the time. And You know, if you look at it, John Bardeen gave Brian Josephson a very hard time with this, which is actually kind of amazing because superconductivity in BCS is a second-order calculation. Okay. People hadn’t put that all together at the time. So that was one of the few times, I’m sure it’s not the only one, but the few times where it preceded it and the theorist was given a hard time.

John Martinis:
But of course, the Nobel Prize. That meant that it was very strange. And I would say quantum computing, I hadn’t thought about that, but I’m— that’s right. This came from very theoretical concepts. And then, you know, people work through it experimentally once, you know, they understood it would be interesting to do that. Let me tell you what the problem with quantum computing is, is if you abstract it away to qubits, Okay. You abstract away to idealize qubits and the Schrödinger equation, and then it looks very simple and very nice. Okay.

John Martinis:
But the problem is real experimental systems are much more complicated. There’s all these dirt effects. And it’s kind of easy to think that, okay, you can just build that without having to go through and all the, you know, understand what’s going on. So So I like to say the best qubit out there is what I call the paper qubit, a theory qubit. And it’s only by doing the experiments do you know that everything is wrong. Everything’s wrong with it. And it usually takes decades to figure this out. Okay.

John Martinis:
And it’s not a magical thing. The other thing is a lot of the efforts are actually headed by theorists. If you, if you look at it, not all of them, but a lot of them are.

Brian Keating:
Yeah.

John Martinis:
And that’s because it’s very easy to abstract this away. I actually think, again, history will borne this out. I actually think that this is a little bit of a problem because in actually to build a thing requires you to, you know, really understand all the problems. Okay? So by abstracting all the problems away, you can be very optimistic and, you know, do things. But it’s only, you know, going into lab and realizing what all the problems are and then fixing them that you can actually build it because physical qubits are not perfect by any means. Some people claim that their technology is great. There are always problems. Okay.

John Martinis:
That’s just the way that nature wants to fight back. But I think in the end we can, we can fight back harder.

Brian Keating:
Yeah. Now I want to talk about quantum computing. And again, I tend to be a little more cheeky and provocative, so don’t be afraid to, you know, put me in my place. But in 1981, you know, Feynman didn’t say quantum computers are gonna replace, you know, your desktop, your MacBook, your laptop, your Chromebook, whatever. He said nature is quantum, so we should probably be using, you know, quantum systems to do computation. I always joke, and, and I’ve done work with a firm called Quantum Rings, which does a lot of software and simulations of quantum mechanical computers. But I, I kind of joke sometimes that, you know, quantum computers are the best system to model how quantum computers work, A, and then, you know, they’re good at—

John Martinis:
Quantum systems in general, yes. Quantum systems.

Brian Keating:
clear, you know, sort of like an answer, you know, to a question maybe. And again, I’m saying this with, with probably lack of humility, but what are quantum computers really gonna be good for? And you can’t use the words, you know, cryptography, and you can’t use Lagrangians or material science or quantum computers. So outside of that incredibly impressive domain of portfolio, I mean, it’s like if you said my computer can only be used for doing, you know, spreadsheets, word processing, and, and internet browsing, right? I mean, it could do a lot more, a general computer. So what can a quantum computer do? Besides those 3 things that are very important and very hyped up?

John Martinis:
Yeah, I’m really interested in, partly because I’m a physicist, okay, is quantum computers modeling, simulating other quantum systems. And, you know, there’s a huge amount that it can help with there because right now a lot of classical computers or supercomputers are used to do so. And, you know, you can only model something so big. Before you run out of memory because quantum computing is hard and run out of speed. Okay. And then you have to do approximation methods, which are fantastic, but, you know, they only work. And in fact, a lot of it is that, you know, certain approximations work for this problem and that problem, and you have to compare with physical systems to kind of choose that. It’s a little bit cheating, but, you know, okay, it’s very practical and that’s good.

John Martinis:
That’s what I’m, I’m really interested in. And, you know, just the example, I don’t know if this is a good example, but we all are interested in rare earths now, let’s say for electric motors and electrification of transportation system, et cetera, et cetera. But they’re rare and there’s a supply chain issue there. And I’m sure ecologically there could be issues with that. If you could use not so rare earths, let’s say by inventing a new chemical or process or maybe make it more ecological to mine, that’s a huge benefit to society. And you could say the same things with drug discovery and other things. I think this is actually a big application if you like academic industrial applications, but that’s more how regular computers got started. And then over time, I can imagine there could be other things, let’s say for optimization, it’s not so clear, There’s a killer application for that.

John Martinis:
Okay. A lot of people are looking at, a lot of people are claiming things. It’s not clear whether a clever classical optimization would be good. So it kind of can be a little bit like AI where people try various things for decades before coming on some, you know, the right way to do it. I also look at that as very important. So, you know, it’s a powerful computing engine. And it’s gonna take a while to figure it out. And, you know, the quantum computers we can build right now are too small.

John Martinis:
If we can make them bigger and then help with the theorists to inventing the clever algorithms, I feel, you know, very confident we can do something with this. But the big problem is we’re trying to compute— compete against these huge data centers, okay? Which are getting huger and huger every day. Eventually the exponential power of a quantum computer computer can overcome that. We just have to make it big enough and be clever enough for the algorithms.

Brian Keating:
Yeah, that’s right. And that’s what you and our mutual friend, Alan Ho, who introduced me to you and is co-founder of your company, Colab. We’ll get to that in a second. But now I want to take kind of the pushback on myself. You know, I’m kind of, you know, maybe bipolar this morning, but now I’m going to make the argument that these things are incredibly powerful and perhaps with great power. I just talked to the foremost AI safety researcher in the world, Roman Yampolsky, who coined the term AI safety. And he basically says super intelligence is either almost here or about to be here, and it’s uncontrollable. It is unaccountable.

Brian Keating:
It is unverifiable. We have no control over what we just created. So I want to make that argument for quantum computers, and then I want to take us back to, you know, like 1947. You know, the government didn’t let, you know, Oppenheimer set up Oppie’s Atomic Bomb Company, you know, just selling his own little portable nuclear device, right? He kept it classified. And, you know, should they be classifying, you know, is it okay that Google, IBM, and even Colab, you know, hopefully you’re going to be just as big as them, right? So tell me, make the argument. Why shouldn’t you be regulated right now before the genie escapes the bottle as it has for superintelligent AGI?

John Martinis:
First of all, we’re going to learn a lot from superintelligent AI. And that’s the immediate issue to deal with. And that’s here and it’s coming. And, you know, I agree people should be thinking about this. I think we’re going to learn from that. Okay. And we should take the lessons from that and then figure out what we’re going to do. The problem with quantum computing is it’s just not here yet.

John Martinis:
And yet there’s this big race. And to be honest, the race is the US versus China. You look at the papers from China, they know what they’re doing. It’s a serious race. developing kind of in the wild is actually an efficient way to get things done, just like with AI happened. Okay. Now, there was a secret program within the government for quantum computing, but that’s not where the biggest developments happened. I don’t think I have to explain that to readers.

Brian Keating:
Yeah.

John Martinis:
Okay. And it’s just that this competitive landscape I’ll just call it savage capitalism. Okay. Actually, it’s pretty efficient if you want to do that. In fact, I argue that the way that the projects in China is operating is maybe more savage than the capitalism in the US. I don’t know all the details, but it could be. These are very good questions and I’m concerned, but on the other hand, we’re trying to develop it in our own particular way. But we’re being very careful about who we do.

John Martinis:
We know that, for example, the US government is going to want us to build our quantum chip in the US. And that’s how we’re organizing the way that we do that. Some of the other more classical control that can be done worldwide with our good diplomatic partners. And we’re being a little bit careful about that. But I think it’s the Google and IBMs and where they’re really on the forefront, and I’m sure there’s a lot of discussion that goes on there.

Brian Keating:
Now, I can’t resist asking you this question. I mean, you’re the ideal person to ask. You’ve probably collapsed more wave functions than any human in history. What do you think is happening, John? Is it a collapse? Is it Copenhagen? Is it some non-unitary evolution? Is it a many-worlds branching? Tell me about your epistemology.

John Martinis:
What are you thinking when you do these I explicitly dislike the many-worlds interpretation because it sounds very Trumpian in the sense that you’re generating real estate. That’s, you know, that’s doing that.

Brian Keating:
So I’ve never thought of that. Now he’s gonna, now he’s gonna make a good point.

John Martinis:
In a humorous manner. But I’m very much thinking that, you know, the measurement and the dissipation and the decoherence is what’s giving you the state collapse. And again, if you look at Exploring the Quantum, they have a very nice, elegant way to talk about how these things called pointer states are exponentially sensitive to decoherence, and a small amount of decoherence can collapse you into these measurement states. And for me, that’s the clearest explanation around. I know some people don’t like that, and that’s fine, but that’s the way that I view it.

Brian Keating:
Jim Peebles once told me to shut up and measure when I asked him about some aspect of—

John Martinis:
If you shut up and measure, we wouldn’t have done that experiment, or people would have. So these are good questions. You need to do the experiments. And like in Exploring the Quantum, they did very nice experiments to flesh out what the theory was and to argue that this is what’s going on. So I think it’s important to study this and understand that. But for me, this is a question that has been answered via decoherence phenomenon. It’s just like not understanding entropy and thermodynamics. So to me, it’s the same kind of understanding.

Brian Keating:
Take us back to the, you know, the quantum supremacy and you had achieved this incredible result for the first time, but you soon after left Google. I’m curious, was that a blessing in disguise? I mean, it led you to co-found a company with, as I said, Alan Ho and others. It’s such a brilliant idea, this company. The point is the divorce from Google. Would you be willing to talk about that? We don’t have to, but—

John Martinis:
You know, after that experiment, Google decided to reorganize. And instead of being congratulated for leading this project, I was essentially demoted. Okay. And there were reasons for that that we don’t have to get into. And I tried that for about 9 months. And, you know, basically I went from the head of the hardware To, let’s say, 1 over N authority. It was very much a socialist thing, but I actually found I had negative authority after that. And if you want to understand negative authority, just think about when you had teenagers.

John Martinis:
Okay? That’s your negative authority. And frankly, I don’t think the people in Google thought that I was that technically competent. I was okay. But you know, you can tell when people feel that way and it was just time to leave. And what happened is that was definitely lemons. Okay. I still regret everything that happened, but it’s what happened. But what I would say is working with Alan and then Robert, we figured out, well, what is it we really wanted to do? Not, you know, next year or to meet the next milestone, but if we wanted to build a million-qubit quantum computer, what would we have to do? And we really focused on the qubit manufacturing and the wiring and scaling up and we came up with a series of ideas and we published a paper on that.

John Martinis:
We started a company and we’re feeling really good about this and we’re doing something that’s really different than everyone else. That’s exciting. And our view is that when we get this to work, it’s very foundational shift to the field, which is great. You know, that’s what you want to do is do something important. On the other hand, it’s risky because Because the general consensus out there is that you need to fabricate the qubits with this liftoff process because it’s much cleaner and the like. Whereas you do a complicated deposition and etch, you have problems. That’s the thing. And what we’ve figured out is that’s kind of right, but you have to fabricate it in the proper way and then you can get it to work.

John Martinis:
And we’ve kind of figured out what that proper way is and we’re working very hard to do the steps that you need to make it very clean. And, you know, in the end, semiconductors are— no one uses liftoff. I mean, this just doesn’t work. You use deposition and etch. But of course, I don’t know, there’s billions, trillions of dollars figuring out how to get that to work. We think we understand enough now to be able to do that on a modest startup economy.

Brian Keating:
I think your approach is so fascinating. It’s a sort of a 3D printing, but, you know, massive scale. My teenager This for me. That’s one of the few things that he does for me with my negative authority.

John Martinis:
Look, you know, young people want to do their own things. I get it, you know, and they want to break free from their family, which is what Google— what happened at Google. Normally the kids leave the house to break through and that they don’t kick the parents out of the house. But okay, you know, that was the easier thing for Google to do. And I understand that I had done things that the Google people— I’m too much like Elon Musk to work at Google. Okay, put it that way.

Brian Keating:
Well, I just note that it was exactly at that time that they went peak woke. And within a few months they had things like you ask it to create a picture of the founding fathers of America, and it was like Violet Davis, you know, Violet Davis, black and white hair and all sorts of interesting features.

John Martinis:
Well, you know, for example, all the co-writers of the Attention paper, which was the big breakthrough, they all left Google. I think They’re different reasons, but they’re similar reasons. And, you know, it’s not a surprise that certain people don’t fit into a corporate environment. They’re more entrepreneurs, and I’m very much an entrepreneur. And what I’ve been able to do is I’ve been able to kind of unleash my creativity in a private company. Now, we don’t have the money. I think I could be way more productive at Google, but if that’s not the way they want to run it, then, you know, it’s great. to be doing this in your own company.

John Martinis:
And in our company, we can set our culture and set what we do.

Brian Keating:
What’s the limiting factor just on a technical side? I mean, we have a dilution fridge, we don’t use it that often. We have deposition facilities here. What’s your limiting pacing item that is an obstacle, but you’re going to overcome it?

John Martinis:
If I gave you 10,000 dilution fridges, if I gave you unlimited time with 300-millimeter wafers, what do John, you need refrigerators to do a lot of testing, but you also need professional fabrication facility where you can do rapid turnaround. And then the third thing you need is a principled understanding of what’s going wrong. Right now it’s a little bit, you know, just people try things. However, I think we have a principled understanding now, so we have to work on the other two and, you know, obviously take more data and the like. It’s all of the above. In the end, I, I’m just gonna say in the end for us, it’s funding because with more funding we’d buy more dilution refrigerators and we could work with the companies and pay for having a bigger effort.

Brian Keating:
One final question is just related to the title of the podcast. The only way to know the limits of the possible, Arthur C. Clarke said, is to go beyond them into the impossible. John, what one piece of advice, you had 20 seconds with your 20-year-old self, What would you give the advice to him to go into the impossible with the courage that you’ve had over your career?

John Martinis:
Well, what happens is you’re a scientist, you’re always working on projects. There are projects that are kind of incremental and you know what to do and you’re going to advance your field and whatever, but always be on the lookout for the impossible, something new, something other people don’t think will work that if It does work. It’s very foundational and changed the field. Now, you’ll have to curate those ideas really well because most of your ideas aren’t going to work out. And I have ideas all the time and I curate them, and then you choose the best ones and try it. Our company, Collab, is what everyone thinks is not the right way to go. I’ve thought about it carefully. We understand why it could work and it’s looking good.

John Martinis:
But you have to think very carefully about it. But yeah, always be on the lookout for the impossible, right?

Brian Keating:
I love it. I’m going to make that the motto of the show. John Martinez, winner of the 2025 Nobel Prize in Physics, thank you so much for being an inspiration. You’re just a physicist’s physicist. John, thank you so much. Have a great weekend. We’ll talk again soon.

Fermilab’s Scott Dodelson on Cosmology’s Crisis

sabartigas — Sat, 15 Aug 2026 21:17:28 +0000

Fermilab’s Scott Dodelson on Cosmology’s Crisis

Transcript

Scott Dodelson:
What we’ve done in order to make the story of the Universe work is introduce dark matter, introduce inflation, introduce dark energy, and none of this has been found in the lab. How many free passes do we get? Exploring the tension, killing the model is my dream.

Brian Keating:
That’s Scott Dodelson. He runs the Cosmic Physics Division at Fermilab. He teaches at the University of Chicago, and if you’ve ever taken a graduate cosmology class, you probably took it from Scott. He also spent 10 years running the sharpest test anyone has ever made of the Standard Model of cosmology, the model he helped build. The answer came back 2.5 sigma off. Close enough to call it a triumph, not close enough to stop staring at it in disbelief. I’m Brian Keating. This is Into the Impossible.

Brian Keating:
What is new in the field of dark energy?

Scott Dodelson:
Yeah.

Brian Keating:
Before we go any further.

Scott Dodelson:
Well, since you’ve been in the field, we’ve had this fiducial model of cosmology. I guess you— well, you helped establish it really. So it’s called Lambda CDM. I’ve heard you talk about it. And now there you’ve had Kyle and other people on saying that it’s under stress. So the fundamental question that I’m interested in is not— or one of the questions I’m interested in is how will we change our mind, if we will? That is, we have this pretty simple model, and then there are these data points which are saying, oh, this doesn’t work. It doesn’t work. Oh, maybe it doesn’t work.

Scott Dodelson:
And so we have to digest that, and everyone digests it in their own way. And then how are we going to collectively— land on another model. And so I know people have written books about this, Thomas Kuhn’s Structure of Scientific Revolutions, but we’re living through that time now. So I’m kind of—

Brian Keating:
And maybe in many ways with AI, we’ll get to that later.

Scott Dodelson:
Yeah, right. So it’s not, and of course it’s not just in cosmology. So cosmology is the lens through which I can understand stuff. But as you say, in society in general, we’re losing faith in institutions. So we don’t know which institutions we’re gonna land on and which to believe, which to trust. So I think it’s kind of an important question. And so I’ve been trying to explore it in this little corner of our world, cosmology, which is in some ways the simplest thing we do. It’s very hard to be a parent, to be a spouse, to be a friend, but cosmology’s really easy ’cause there’s no people and it’s an easier thing.

Scott Dodelson:
So that’s why I’ve been trying to explore it in that context. For about 10 years, I was heavily involved in this project called DES, the Dark Energy Survey, and that started taking data in 2012, and as you know, It takes an enormous amount of time to process and analyze this data. So we only put out our final results a few months ago. So that’s what’s been occupying me for the last 10 years or so.

Brian Keating:
Talk about the connection between, you know, the type of science that I do, which is the first light in the universe, the cosmic microwave background. I’ve talked a lot about that. I’ve talked less about the kind of science that DES does, though, with the exception of, you know, conversations with people like Kyle and others. But talk about what was DES? What, you know, again, it’s somewhat strange. Not only are you not using, you know, particle detectors and whatnot, but you’re using optical telescopes, right? So what does DES do? What is it comprised of? You mentioned how long it took, but what really went into that? How much— what’s the portfolio diversification, you know, between theory, which is what you do, experimental hardware, observations, big data, machine learning? What are the different ingredients in DES? And first of all, what does it stand for?

Scott Dodelson:
DES stands for the Dark Energy Survey. The detectors on the telescope, which was in Chile, are made of silicon. And so they leveraged a lot of the silicon technology that Fermilab was always already an expert in. So that’s one piece of it. The camera itself was built by Fermilab. So that’s a tremendous contribution. Then there’s the whole data processing thing. And again, in that realm, high-energy physics has a huge advantage because they’re used to processing tremendous amounts of data.

Scott Dodelson:
So there’s those 2 pieces. And then there’s the analysis, which takes us an enormous amount of time. And that piece is ongoing. It’s really thinking back to when it was first started. We thought of it in one way, and all our analysis tools have evolved partially due to AI. So that’s been kind of interesting to be a part of. But getting back to the science part of it, there’s a strong connection between what you do and what I do in the sense that one of the things you do is you measure in the cosmic microwave background the fluctuations. So I don’t know if people can see this, but there are small hot and cold spots in this microwave background.

Scott Dodelson:
Basically what that means is the temperature, say on this spot over here, was a little bit about 1 part in 10,000 higher than the temperature over here. All that means is there were more photons there, and because there were more photons there, because the photons were also talking to the electrons and protons, there were more electrons and protons there. So if you go back to the early universe, what you have provided us with is a snapshot of the early universe, and we know what it looks like. It was incredibly homogeneous, so the universe was basically the same everywhere, and with very, very small fluctuations of 1 part in 10 or 100,000. So that’s what you’ve provided us. What this theory that we’ve landed on, Lambda CDM, predicts is how those small fluctuations will accrete more and more matter over the course of 13.7 billion years. And we can test that with DES and see whether those fluctuations have grown to the size they were supposed to according to the theory. So that’s, to me, the most fundamental test we’re doing.

Scott Dodelson:
If it hasn’t, that means this theory is wrong. So to me, that’s the stress test that’s occupied me for 10 years.

Brian Keating:
Why do we tend to kind of just, as I describe it, you know, have the series of descriptions to our students, you know, from Nobel Prize to Nobel Prize without loss of enthusiasm? How do you view the way that we should teach cosmology?

Scott Dodelson:
I’m not sure there’s a direct answer, but this is my experience of it, which is in DES, to me, the most important thing has not been the papers we’ve written or that discovery we made that basically the fluctuations are what they should be in this theory. But the most important thing has been the people. So, and I think we as mentors get an enormous amount of satisfaction by working with these young people. So communicating not just facts to them, but also the very little that we know has been, I think it’s eye-opening to them. I’ll give you an example. I was at a collaboration meeting a couple of years ago, and this first-year grad student from Spain was there, and she was talking about her work on this very complex piece of analysis. And I went up to her afterwards and I asked her a question. I said, well, do you understand this and this? She goes, you don’t understand.

Scott Dodelson:
I’ve only been working on this for 3 months. I said, no, you don’t understand. You’re in charge. So it’s like these people, the young people, they’re the ones who basically— it’s not like there’s some threshold above which you become and Einstein, we’re all kind of swimming around in the sea of ignorance trying to figure out things. We have a little bit more experience than younger people, but they have advantages that we don’t have, for example. So it’s, to me, that’s been the best part about DS is working with these young people. It’s been great.

Brian Keating:
You talk in your Substack, which we’ll link below, and that you kind of had this really phase change, I think is the only way, revolution, April 1992. Talk about the day, you know, the music died. Talk about Kobe. I remember that I was in college at the time and I was in a summer program or starting a summer program. And I kind of even had a glimpse that this was something important, but I didn’t know I would do it for the rest of my career. Talk about 1982, 1992, why that was so transformative for you in your career. It really seems to have pivoted you in a completely different direction, which is wonderful to say about your flexibility intellectually. But why was it so important to you?

Scott Dodelson:
Yeah, I think what happened before 1992 is there were a lot of people who had really fascinating ideas about cosmology. And one of those ideas, what I just described, that there were small fluctuations in this cosmic microwave background, and they grew to be the structure we see today. But there was absolutely no evidence. There was no prediction that had been made that had been verified. There was only postdictions. People observed stuff and they said, oh yeah, I can explain it this way, that way. So any reasonable person looking at that would say, I don’t trust these guys. And in fact, quite a few astronomers and other people didn’t trust the few cosmologists working in the field for for very good reason.

Brian Keating:
Yeah.

Scott Dodelson:
So what happened on April 24th, 1992 is the first detection of anisotropies in the cosmic microwave background. So the fact that there are slightly spots that are slightly hotter than other spots. So that discovery was a prediction. Stephen Hawking called it the discovery of the century, if not ever. So, I mean, he was one of the driving forces behind the whole field of cosmology. So he recognized how important it was. And another thing that I highlighted there is that in the old days, they used to print— you’re probably too young for this— the New York Times on paper. And so the first column of the New York Times on the right-hand side was the lead article.

Scott Dodelson:
So that was the first, I think the only time in the history of the New York Times that a science article was on the first column. And I think that they got that right, because that was the discovery that underpins modern cosmology. In 1,000 years, that’s the story people will be pointing to, I think. For me, actually, at the time was kind of depressing because I was working on all these fanciful things. that were fun to work on. And to understand as a theorist what the physics that goes into that required me to learn a whole new set of tools, which I didn’t, you know, it wasn’t up my alley. So I knew I had colleagues who ended up just staying and not moving forward, but I kind of had a family to support, so I figured I got to learn this stuff. So I tried really hard to learn this stuff and end up writing this book to help me learn it.

Scott Dodelson:
And so I’m able to stay in the game to some extent. Yeah.

Brian Keating:
And that pivot from, you know, kind of particle theory to part of, you know, theoretical astroparticle cosmology, which you’re one of the major instigators of, I think, you know, to be, to be fair to you, there were also, you know, separate, you know, physics and cosmology, physics and astronomy, astronomy and cosmology. They were all kind of separate things.

Scott Dodelson:
Yeah.

Brian Keating:
As I told you, the previous occupants of this office was, you know, Jeff and Margaret Burbidge. This plate is one of the Palomar plates that she took with the galaxy.

Scott Dodelson:
That’s cool.

Brian Keating:
I’ve got some redshifts. So they— and they didn’t believe in cosmology basically until the day they died. You know, Big Bang cosmology. Jeff used to go into paroxysms of rage when a speaker would have the misfortune of mentioning it. He was a steady-state proponent until he died, long after COBE. What do you make of that? Are there modern-day versions of people like Jeff, you know, that are just kind of eminent, brilliant scientists that just do not accept either inflation or maybe it’s string theory or the standard model? Do you see any parallels between the Big Bang deniers of that age, which you lived through partially, Yeah. In today’s age?

Scott Dodelson:
I think so. I think we tend to weight things according to our experiences. So I just wrote a thing about something that is obvious to you, but maybe not obvious to most people, which is the sky. The light from the stars or galaxies comes in different colors. You can view a given galaxy with one filter and see it as one color, or view it with a different filter and see it in a different way. And that’s a metaphor to me of the way we perceive the world, right? We’re all perceiving the world via our own filters. So as one example, there’s kind of a raging controversy now about neutrinos, which are these very small, very light particles that actually there’s about a billion of them that just passed through my hand that were produced in the early universe. We don’t know their masses.

Scott Dodelson:
You and your colleagues have done experiments which lead to the conclusion that their masses are smaller than they should be according to experiments that have been done by particle physicists here. And when you raise that to the particle physicists, they just don’t believe the cosmology, basically. So I think that’s an example of a whole class of smart people who don’t necessarily buy into this field, really, or all of the field. Yeah.

Brian Keating:
And it’s partially natural historically. You think, you know, we’ve never detected a new particle, weighed the mass of a particle except in an accelerator or something like that. So to use the cosmos as your accelerator, which is natural to people like me, But yeah, it’s very— it’s sociological in science that it’s different.

Scott Dodelson:
Have you seen examples of that?

Brian Keating:
Yeah. I mean, I’ve asked people about that, like my particle physics friends. Don Lincoln, I’ve talked to, is in your neighborhood, right? Will you believe it when a particle physicist sees a cosmologist say, here’s the mass of the neutrino? Now we’re working on a paper with Shasha Arani that you met over lunch that really seems to suggest that we’re going to need all 3 different types of things. Neutrinoless double beta decay, we’re going to need laboratory experiments, and we’re going to need long baseline, we’re going to need cosmology. And that spectrum actually will make the case much stronger than only the cosmologists see it and give up all hope. Let’s talk about the Dodgson-Widrow mechanism. That’s sort of where I first got exposed to you. Very intimidated.

Brian Keating:
It was my second year of grad school, 1994. Where did this come from?

Scott Dodelson:
This is at Brown.

Brian Keating:
I was at Brown, and I was trying to understand, well, dark matter. I’m still trying to understand that. We’re going to talk a lot about dark matter. And these are called sterile neutrinos. So first, what the hell is going on here? What are neutrinos? Just let’s do a recap. Neutrinos, flavor, oscillation. What does it mean? Where are they oscillating? Are they jiggling around in here? What’s a sterile neutrino? What’s a Majorana particle? What’s a Dirac particle? Let’s go through it. I want to get my money’s worth.

Brian Keating:
Flew you all the way out from Chicago.

Scott Dodelson:
Okay, 10 questions.

Brian Keating:
That’s my forte.

Scott Dodelson:
A neutrino. People are familiar with electrons because that’s what they’re made of. And so particle physicists tend to think of neutrinos as being partnered with electrons. So the fundamental theory of nature says that every thing like an electron has to have a neutrino associated with it. So for example, you mentioned muons earlier. Muons are kind of cousins of electrons. They also have their own neutrino with them. There’s another thing like that.

Scott Dodelson:
They’re called— all these things are called leptons. There’s a tau lepton. It has its own neutrino. So neutrinos are associated with electrons in this case, or electrons or muons or taus, whereas the electrons are charged. They have a negative charge. Neutrinos do not have electric charge. That’s why they’re so hard to detect. Whereas neutrinos have a mass that we know, neutrinos have masses which are at least a million orders of magnitude— a million, sorry, a factor of a million smaller, probably a billion smaller.

Brian Keating:
Yeah.

Scott Dodelson:
Than the electron. So that’s what they are. They’re very, very hard to detect. However, just like the electron, they do participate in what’s called the weak force. And that’s important because there are various decay processes that are important to life and everything that produce neutrinos. So for sure neutrinos exist. We’ve seen them, but they only interact very weakly, so they’re very hard to detect. So that answers your first question.

Scott Dodelson:
What is a sterile neutrino?

Brian Keating:
Those are called flavor states. The partnering, pairing between the subatomic elementary particles, mu, tau, and electron, those are the flavor states, but they are not the mass states.

Scott Dodelson:
Right, exactly. So what I mentioned is that these neutrinos, the electron neutrino is paired with the electron. So that is, um, so that’s one type of neutrino. But if you imagine the possibility of a given quantum state that is a superposition, probably you’ve had a lot of quantum computing people on, so people are familiar with superposition, a superposition of an electron neutrino and a tau neutrino. So that’s possible. And it turns out that a superposition of those neutrinos are actually eigenstates of mass. That is, they’re eigenstates of mass. That means they— the mass eigenstates are the things that propagate through space.

Brian Keating:
And respond to spacetime curvature, for example.

Scott Dodelson:
Right, exactly. If there was no mass, then there would be— there would only be this one basis, the flavor basis. But because there’s mass, things get mixed up. It’s kind of a quantum mechanical effect. And because they get mixed up, it’s possible— and this was first detected from neutrinos from the Sun— that neutrino could be produced in the Sun in one type, one flavor, and be detected as another flavor. So that’s the oscillations you mentioned. So I hit 2 of your questions.

Brian Keating:
Yeah, yeah, sterile neutrinos.

Scott Dodelson:
Sterile neutrinos. Okay, so each of those things are a type of neutral particle. It turns out that it’s quite possible that there is another neutral particle associated with them that does not experience the weak force. So neutrinos are not charged, so they interact very weakly, but they do interact. A sterile neutrino is even less weakly interacting than that. It’s completely divorced from the weak force. It’s not produced in decays. et cetera.

Scott Dodelson:
Why do we think they exist? There’s kind of a complicated technical reason for it. That is, if you think about electrons, they’re made up of left-handed spinning states and right-handed spinning states. The neutrino that we know are all left-handed spinning, so we kind of expect there to be right-handed spinning states also. Those are sterile neutrinos. That’s sterile neutrino.

Brian Keating:
And the sterile neutrino was hypothesized to be, via the Dodgson-Widrow mechanism, a potential but no— by no means confirmed dark matter candidate. Now let me say one thing. I tell people we’ve detected dark matter. We have unequivocally detected— I told Neil deGrasse Tyson this to his face, and he was astonished. And I said, they’re neutrinos. They satisfy every possible property except for the fact that they don’t make up all of the mass that is seemingly required to explain the dark matter gap between the amount of luminous matter and the amount of total matter that we see, right? So that’s like saying, you know, and then they’re like, well, it doesn’t solve dark matter. That’s like saying, well, hydrogen doesn’t explain all baryonic matter. It’s irrelevant.

Brian Keating:
There might be a whole— as George and as past guests on the podcast, Mike Turner and others have said, there might be a whole periodic dark matter table, the dark periodic table, right? So am I wrong? Should I shut up about this dark matter detection already occurring?

Scott Dodelson:
Well, let me challenge you with one thing. Have we detected cosmic neutrinos?

Brian Keating:
We’ve detected— I believe we’ve detected—

Scott Dodelson:
Oh, we have. Yeah, we have. You have detected.

Brian Keating:
Not direct detection.

Scott Dodelson:
Not direct. You have indirectly detected them. So yes. So you have indirectly detected neutrino dark matter. There’s a difference between direct detection where you actually build a detector that tries to see these cosmic neutrinos, a billion of which just passed through my hand. That, do you know Joe Formaggio from MIT? So he has told me that every experimentalist in their career goes through a period of 2 years where they try to detect cosmic neutrinos and then they realize it’s impossible.

Brian Keating:
And unfortunately their startup runs out.

Scott Dodelson:
Yeah, exactly. Anyway, but you have detected that, you and your CMB colleagues have detected them indirectly. If they weren’t there, then the pattern of anisotropies that we see would look much different. So kudos, 100% agree. Yeah.

Brian Keating:
So what is the Dodgson-Lidro mechanism?

Scott Dodelson:
In the late ’80s, early ’90s, there was a guy named John Simpson, and there was evidence— he provided evidence for detecting in the lab a neutrino with a mass of 17 kiloelectron volts. Did you ever hear about this?

Brian Keating:
No.

Scott Dodelson:
People were like astonished. It turned out to be an experimental artifact due to magnetic fields, but for about 3 to 5 years, everyone was talking about it. As you probably know, such a heavy neutrino would— makes a lot of problems for cosmology. So I started thinking about what would happen. Would they be produced and stuff? And so then Larry and I came up with this idea that putting aside the 17-kV neutrino, which turned out to go away, maybe it’s possible that the ordinary neutrinos in the very early universe oscillate into these sterile neutrinos, And maybe they have a mass, so produce enough of them so that they constitute the dark matter today. I still think it’s a good idea because we know neutrinos exist, right? As opposed to every other dark matter candidate around where we don’t know exists. So to some extent, my prior on that is higher than other things, but of course I’m pretty biased. So yeah.

Brian Keating:
Talk about the mechanism. There’s something called a mixing angle, which is quite beautiful when you think about it, that the these states, these quantum mechanical eigenstates, which are superpositions, that they have this ability to effectively rotate just like an ordinary rotation of a ball or any object in 2-dimensional. It’s the simplest thing you could imagine. I guess 1-dimensional would be simpler, but they can basically be thought about as rotating in this abstract space. How do you think about that? How do you visualize this? Is it purely a mathematical thing in a theorist brain?

Scott Dodelson:
Yeah, that’s a great question. And you’re absolutely right. that you don’t need flavors or anything like that. It’s actually just a 2-dimensional space, the regular neutrino and the sterile neutrino. Actually, this comes back to the way different people perceive and think about different things and how everyone’s opinion, everyone’s brain sees things differently. So my mind works best very linearly and mathematically. I’m not good spatially. So I just think about a 2 by 2 matrix, which is a pretty simple mathematical thing.

Scott Dodelson:
So I just think of these things as If you take 2 by 2 matrix with off-diagonal elements and diagonalize it, that’s what I think of as an oscillation. But people like, I’m sure you think of it in a more spatially, in more of a physically intuitive way. I don’t think I have that physical intuition.

Brian Keating:
So what’s physically happening, the way I think about it, I do think about it mathematically also, but in quantum mechanics, phase is an important quantity. Even though we can’t directly measure phase, you can measure phase differences, right? And so stop me if I get this wrong, but energy differences lead to phase differences, which then can be imprinted And then you can get physical oscillation. I mean, we get oscillations of— and that was the solution to the solar neutrino problem, right? It was effectively this oscillation, both abstractly in the phase space of quantum mechanical rotation space, but physically these things are oscillating as they travel. If the distance between the Earth and the sun were different, we would’ve gotten a different answer, right? We could have been in this weird position where it exactly always came out to be an electron or something like that. We just didn’t happen to live there, right?

Scott Dodelson:
Yeah.

Brian Keating:
So how do we explain this, that the neutrinos interact with matter? But sterile neutrinos wouldn’t interact with matter, say, in the same way?

Scott Dodelson:
It turns out matter plays a quantitative role in it, but it’s not— I don’t think it’s a qualitative thing. So I think the qualitative thing is exactly what you said, that in the solar neutrino issue, the electron neutrinos that the sun produced convert as they travel into muon neutrinos that Ray Davis, et cetera— did he win a Nobel Prize?

Brian Keating:
Yeah.

Scott Dodelson:
So that is an oscillation in a 2-dimensional space. It’s the exact same thing without the flavor thing. The 2 dimensions are that the regular neutrino and the sterile neutrino. It’s the same exact process that we think might have happened in the early universe to produce— we had a lot of regular neutrinos around in the early universe. They oscillated and produced these sterile neutrinos that could be the dark matter today. So it’s the exact same process.

Brian Keating:
Now, a year ago, Kyle Dawson sat in that very chair with Dan Green, and we had the spirited conversation about DESY, right? And a lot of the conversation was kind of a little bit, you know, he’s very statesmanlike and wonderful scientist and just exceptional person. But I detected a little bit of hedging. You know, he’s saying, yes, there’s a 4.2 sigma tension. And I often say, you know, we’ve got the Hubble tension, we have the sigma-8 tension, now we’ve got the W tension. So what do you make of these different tensions? First of all, I’ve had eminent scientists, including a partner of one of your, you know, former partners, Mike Turner, Lawrence Krauss, alleges— he sometimes says he came up with dark energy, and on his weaker moments, or maybe he’s being more accurate than I’m giving him credit for, but with Mike Turner, that they sort of independently came up with some ideas that suggested dark It was real. And he doesn’t believe it. Lawrence Krauss said he thinks they’re wrong. It’s not a— it’s a cosmological constant.

Brian Keating:
Now, Einstein was wrong once before. It’s too bad. He could have had a good career, right? But tell me, Scott, where do you come down on this? Where— what is it, a legitimate tension? Where does it rank in the tensions that I mentioned? Sigma-8, which is clumping of matter, uh, Hubble tension, which is disagreement at early times and late times, and, uh, and now this new tension between dark energy and the cosmological constant.

Scott Dodelson:
Just to focus on one thing, this SA tension you mentioned, that’s what I’ve been spending the last 10 years on. This idea that think about Manhattan in 1790, there were 50 people every square mile in Manhattan, very overdense compared to the rest of the country. Today there are 50,000. So why is it that the inhomogeneities grew like that? That’s a fascinating question, right? Could someone in 1790 have predicted that there would be exactly 50,000 people? No way. But we’ve done that. Lambda CDM predicts that what you measured in the microwave background evolves to be precisely the inhomogeneities we see today, that precisely modulo the fact that it’s off by about 2 sigma or something. So that’s what you call the S8 tension. So I guess my question back to you is, is it tension or is it, wow, that is unbelievable we’re able to get that close? I’m kind of depressed that it’s not a 5 sigma tension because then it would be whatever, like just like kind of Kyle is saying.

Scott Dodelson:
But on the other hand, we’re so close on this incredibly amazing story that we’ve created. We’ve created a story of how we got here.

Brian Keating:
I guess the pushback, I would say, you know, sometimes we, I feel like we gotta grow an extra arm to pat ourselves on the back. And you’re doing a great job of that with the experimental, you know, kind of kudos to us. But by the same token, I have kids, you have kids, right? And you remember when your kids were young, you’d take ’em to the doctor at age 2 and they’d measure their height and they’d say, basically they’re gonna double in size. Right? And how do they know that? Well, they’ve had about 100 billion humans have lived since, you know, time equals zero when Og and Magog, you know, came out of the cave and somewhere in, uh, 250,000 years ago. They estimate about 100 billion people. Let’s say pretty good statistics on this, right? Now we only have one universe, and we always complain about how hard it is to be a cosmologist, you know, world’s smallest violin. There’s only one universe. I always say at least astronomers have, you know, 100 trillion stars, you know, perhaps in the observable universe.

Brian Keating:
So there’s a lot of different, you know, statistics that they can do.

Scott Dodelson:
You’re making my point that because there’s so many regions of that size, you and your colleagues are able to measure it at one time in the universe, and you’ve measured it incredibly precisely to a few percent, right? That’s what, that’s what your point is, that there’s so many different regions that you can measure it so precisely. And we, we’re not quite— we haven’t measured quite the area you have, but we’ve measured a lot. And we can measure it at a time 13.7 billion years later, and we get an answer that’s 10,000 times bigger, but precisely on what the prediction gives. So it’s the precision that to me is astonishing and is a great success.

Brian Keating:
So why do you still work on it? Like, what’s left to unravel? I mean, I’m, I’m not an expert in some way. Your time is very valuable, right? So why are you still so deeply invested in it?

Scott Dodelson:
So you’re looking for something that if you found them, these B-modes in the cosmic ray background would prove beyond a shadow of a doubt, inflation, something that happened in unimaginably small times, it would be incredible. The rest of us are focused on unkinging Lambda CDM, like basically just dethroning it. So there are people like Kyle do it in one way by trying to figure out whether the distance that they measure to distant places agrees with what you guys in Lambda CDM predicts. And I’ve been doing it in this other way by seeing whether that precise prediction, which is so precise, Is correct. And so the answer is it’s still called an SA tension. That is, we’re not exactly right. So I overstated it a little bit, but we’re— so we’re 2.5 sigma off, which means there’s technically only a 1% chance that theory’s right. But we kind of know that that’s probably— yeah, yeah, yeah.

Brian Keating:
Millions of one things happen.

Scott Dodelson:
Yeah.

Brian Keating:
So today here.

Scott Dodelson:
So I mean, exploring the tension, killing the model is my dream.

Brian Keating:
Is that because there are certain dependencies on sigma 8 that go as like, what, sigma 8 to the 7th power? There’s some interesting— either it’s in the power spectrum or I seem to recall there— aren’t there some very crisp tests that are available because of the deep sensitivity on sigma 8? Like, in other words, if you nail sigma 8, you can get other things to extremely 8 times higher precision.

Scott Dodelson:
I mean, that’s probably true for galaxy formation, the halo mass function and stuff, but that’s That’s not what I’m most interested in measuring. This very simple statistic, it actually comes back to something you mentioned earlier. We’re doing it with the simplest of statistics, what’s called the 2-point function or the power spectrum, which is some of the same thing that you use. And I wonder if we’re going to be supplanted with AI and ML techniques that use more of the information. So essentially what we’re doing is we’re compressing the data into 100 numbers, right? And from those 100 numbers, we’re extracting out this one number. and comparing to the one number that you guys measure. So that may not be the best way to test the theory. There might be better ways to do it.

Scott Dodelson:
So people are thinking about that.

Brian Keating:
Higher-order functions. Yeah.

Scott Dodelson:
Yeah.

Brian Keating:
Last time you gave a colloquium here, which is, you know, decades ago now, but, but it was extremely well received. You made this beautiful kind of point about the discovery of dark matter, you know, in our solar system, which was— goes by the name of Neptune. And Neptune was discovered by Le Verrier famously. I went back and I looked at his paper from 1843 or whatever. And they basically, you know, it’s just this lionizing hero worship. It’s like he discovered a planet with a pencil, you know?

Scott Dodelson:
Wow.

Brian Keating:
It’s basically just this lone genius that set this thing. And then you made the case that, like, okay, so that was discovery that there was this weird sort of effect that was happening to the planet George, Uranus, which I think still should have been called George. But anyway, you took us back to that time, you know, when there could have been some problem with Newton, Isaac Newton’s gravity, or it could have been dark matter that was just unseen. And it turned out to be dark matter. Then you made the analogy, let’s fast forward to 1911, 1913, ’14, Einstein’s coming up with GR, and Le Verrier and the acolytes of Le Verrier were still— actually, Le Verrier was proposing that Mercury was being perturbed by an unseen companion, just like Neptune.

Scott Dodelson:
Called Vulcan.

Brian Keating:
Right. And then you said, well, that time he was wrong, right? So the hammer theory is everything looks like a nail when all you have is a hammer. Where do you think we are with dark matter? What is your take? I’ve had Mordecai Milgrom on the podcast. What do you make of the, you know, kind of alternative, you know, history rhyming again and again, looking for dark matter, looking for changes to gravity? Where do you think we’re going to end up? What do you— what— I mean, you worked on this for a long time. Where do you come— what is dark matter?

Scott Dodelson:
Yeah, I have no idea. I actually had this debate with Stacy that this is an organization that promotes civil debates. So we had this debate and I don’t So just to give context, Stacy McGaugh is one of the leading astronomers who doubts the existence of dark matter. And he and Mordechai Milgrom and other people, most notably from a theoretical perspective, an Israel— a Mexican-Israeli physicist, Jacob Bekenstein, put forth alternative models of gravity that would not require dark matter. The problem with those is they don’t explain what you guys have seen. And to some extent, it’s another example of what we’ve been talking about, It all depends on your filter. What they look at is, oh, I can see that galaxy over there. Let me look at that.

Scott Dodelson:
And I can fit this better with my theory of modified gravity. What we tend to think— what I think is simpler is the stuff that you measure, because there’s no people there. It’s just electrons, protons, and photons. It’s really simple to understand. But from their perspective, that’s like a zillion miles away. How can we possibly understand it? So it’s 2 different lenses on the same universe. And from their perspective, we’re never going to convince them that this distant stuff has any information about what is guiding stars in a galaxy near us. So I don’t think there’s much hope of bridging that gap.

Scott Dodelson:
However, Stacy, who’s a very reasonable person, does not believe MOND is correct. And if you look at the generalizations of MOND, such as the ones that Bekenstein set up, they’re actually so close to theories of dark matter. They introduce new fields, that it’s the same thing, basically. They’re just introducing new stuff. And so I actually think another possibility is that we’re just looking at everything wrong, that there’s someone’s going to come around and say, we have to just rethink everything. Because we— what we’ve done in order to make the story of the universe work is introduce dark matter, introduce inflation, introduce dark energy, and none of this has been found in the lab. So How many free passes do we get? I don’t know.

Brian Keating:
No, it’s extraordinary that you’re, again, candid and you’re honest and you’re willing to admit where these lacunae, these gaps, these flaws. But I often say the most exciting thing, and you hinted at this earlier, is a flaw, right? Because I tell my students, a flaw leads to a law, and we just don’t know it yet. The Big Bang was kind of a solution to a lot of the problems in the steady state model. The inflationary universe is sort of this patchwork of fixes and kind of home improvements to the Big Bang model, fiducial model, and Lambda CDM. And there’s alternatives to it. But one thing I thought about, 61 years ago this month or last month, the CMB was discovered and the announcement was discovered. And many people, including Jeff Burbidge, who used to occupy this office, and his longtime colleague Fred Hoyle, and the recently deceased, and also I’m very proud to say past guest, Jayant Narlikar, they went to their graves believing in the steady state, quasi-steady state. They added some stuff to it.

Brian Keating:
They were eminent scientists. I mean, these are people that were Nobel-worthy adjacent to Willy Fowler, did win the Nobel Prize for the, you know, BBFH, and incredibly eminent scientists. Hoyle, of course, coined the term Big Bang as a pejorative. And my question to you is, when the CMB was announced, Hoyle said, well, they found it to be, you know, 2.7 Kelvin. But if it had turned out to be 27 Kelvin or, you know, 270 Kelvin, they would have explained that too. And it reminded me of this quote that I have in my book from you, from eminent cosmologist Scott Dodelson. During BICEP2, you said inflation can produce a B-mode signal as small as you like. And I kind of used that in the rhyming sense with our friend Fred Hoyle, who said, you know, they would’ve found any value.

Brian Keating:
So it was almost like anything that we found would be consistent. And you’re not the only one who said this, by the way. And many people said things much, much more hyperbolic, like, hello multiverse, Max Tegmark said. And Lawrence Krauss said, this now means there’s proof that God does not exist. It’s an incredible For me personally, obviously. I talk about a lot of these in Losing the Nobel Prize, my first book. But what do you make of that? If a theory accommodates any result, is it a theory? I mean, what is it? What are we to make of it?

Scott Dodelson:
Yeah, I don’t want to go too far towards ignorance, and the fact that smart people don’t believe in the microwave background doesn’t mean they’re correct.

Brian Keating:
Yes.

Scott Dodelson:
Right?

Brian Keating:
Sure. I think here, there’s a sidebar. I think the human need for meaning is the most paramount need that humans have. Viktor Frankl, Yeah. Man’s search for meaning, right? I want to just kind of take this counterfactual, hypothetical, 10 years from now, 2036, DESI’s done, Simon’s Observatory’s done, CMBS4 never happened, but unfortunately, rest in peace, it should have happened. Lightbird, there’s many different projects hopefully that will augment and complement and compete with us. And so I think the most healthy thing is to have a competitive scientific environment. And so I welcome our kind of competition from China.

Brian Keating:
We have a Chinese team that’s trying to do this. And there’s many other great and brilliant scientists, but we’re there. What does cosmology being done look like? What does a solved model of cosmology look like? And is it on the horizon in our grand grad student careers?

Scott Dodelson:
Okay, I have no idea, but—

Brian Keating:
What would satisfy you?

Scott Dodelson:
Let’s agree that cosmology is a millennial-old pursuit, maybe more. Maybe those people from 75,000 years ago, maybe they also— And we’ve been just incredibly fortunate to live in a time where the, you know, the amount of information we’ve gotten has been, you know, extraordinary about the universe. We have very good reason for believing that the universe is not on the back of a turtle, right? You know, we’re not any smarter than the people who made up that theory, right? Because we have data. So we’ve just been incredibly fortunate. But it’s still a 1,000-year-old science, and it’s probably going to be evolving. So the chance that we’re going to identify dark matter In my career is, I would say it’s pretty small. The chance, well, you tell me. I mean, the chance that we find B-modes, so you’re gonna be extending the reach by a factor of 5 to 10, is that right?

Brian Keating:
Yeah.

Scott Dodelson:
So that, I mean, you have to have a prior on what inflationary models produce that. I mean, it would be amazing. As you probably know, I went all goo goo gaga the first time you guys reported something. So it would be, you know, it would be amazing. We have to be clear about what we, what we know and what we don’t know. If you guys discovered B-modes, that would not be the end of our studies of inflation. Then we’d put up a gravitational wave thing in space to detect gravitational waves at higher frequencies, right? For sure. What they used to call the Big Bang Observatory, right? So we would definitely hone in on those B-modes because they would teach us about physics 12 orders of magnitude larger than can be probed at the Large Hadron Collider.

Scott Dodelson:
So that would, that would open things up. In dark matter, if we detected something in the, in the 100 GeV range that pointed to supersymmetry, that would open up the, the pathway, I think, to more colliders being built to understand things. So I think the possibility of, especially this marriage of quarks in the cosmos, the possibility of discovery would be unlikely to shut things down. But as I said, I really, I really don’t know.

Brian Keating:
I tell my students a flaw leads to a law. And we just don’t know which law yet. Scott spent a decade in the trenches working on one flaw: whether the lumps in the universe grow the way the theory says they should. There’s a second way to stress test the same model. A year ago, Kyle Dawson sat in the same chair and told me what DESI found: 4.2 sigma. If Scott’s crack and Kyle’s crack are the same crack, Lambda-CDM is finished. That conversation is on screen now. Click it, watch it, subscribe and share it, and then tell me which tension you’d bet the most on.

Brian Keating:
I’m Brian Keating. This is Into the Impossible. Tune in next time.

Scott Dodelson:
Tune in next time.

BRIANKEATING

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