Into the Impossible With Brian Keating - Will AI Replace Scientists?

Episode Date: March 6, 2026

Please join my mailing list here 👉 https://briankeating.com/yt to win a meteorite 💥 In this video, we discuss the rapidly evolving intersection of artificial intelligence and fundamental science and how AI is transforming the way physicists handle massive streams of data from modern observatories—while also explaining where its power ends and human creativity begins. Tune in for insights, surprising stories, and a behind-the-scenes look at how scientists are navigating the AI revolution. 🚀 - Key Takeaways: 00:00 - Cosmologist Exploring Universe and AI 05:30 - AI Success and Physics Limits 09:48 - How Did the Universe Begin? 14:00 - Limits of AI in Scientific Insight 16:04 - Cosmology's Data Revolution 19:29 - AI Insights: Science and Writing 23:04 - Limits of Predictive Models 25:32 - AI's Academic Impact: Pros & Cons 33:24 - LLM Chips and AI Accuracy 34:41 - Accountability, Writing, and Discovery - Join this channel to get access to perks like monthly Office Hours: https://www.youtube.com/channel/UCmXH_moPhfkqCk6S3b9RWuw/join 📚 Get my books: Think Like a Nobel Prize Winner, with productivity tips from 9 Nobel Prize winners: https://a.co/d/03ezQFu Focus Like a Nobel Prize Winner, with life-changing interviews with 9 Nobel Prizewinners: https://a.co/d/hi50U9U My tell-all cosmic memoir Losing the Nobel Prize: http://amzn.to/2sa5UpA The first-ever audiobook from Galileo: Dialogue Concerning the Two Chief World Systems: Ptolemaic and Copernican https://a.co/d/iZPi9Un Follow me to ask questions of my guests: 🏄‍♂️ Twitter: https://twitter.com/DrBrianKeating 🔔 Subscribe https://www.youtube.com/DrBrianKeating?sub_confirmation=1 📝 Join my mailing list; just click here http://briankeating.com/list ✍️ Detailed Blog posts here: https://briankeating.com/blog 🎙️ Listen on audio-only platforms: https://briankeating.com/podcast #universe #podcast #briankeating #intotheimpossible #science #astronomy #cosmology #cosmicmicrowavebackground #artificialintelligence #aiinscience #physics #machinelearning #futureofscience #aiandscience #scienceexplained #aiuniverse Learn more about your ad choices. Visit megaphone.fm/adchoices

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GoogleFi Wireless is not subject to data traffic deprioritization during times of high network usage. All right, Brian Keating, thanks so much for joining us today. That's great to meet you finally, Jake. You're quite persistent. Oh, yeah. How many? It's been two years, I think? Two years since I gave you an assignment and you followed through.

So I can't. You're better than a lot of my students. There we go. Well, thanks a lot. So for those who haven't seen you on Joe Rogan or seen your other podcast or read your books, can you give an introduction to who you are and what you want people to know about you? Yeah, so, you know, in terms of my different hemispheres in my mind, you know, I break it into work and non-work. And most of what dominates my work is research into the origin of the universe, the Big Bang.

Lately, I'm getting a lot into AI systems and what they can do for fundamental science and experimental science. So I'm an experimental cosmologist, which doesn't mean I do hair and makeup. It means I do experiments, experiments that look and search and hope. to find evidence for the origin of the universe, the existence of black holes, the possible existence of other planets and life, maybe on those planets all within the purview of what a cosmologist can do. And outside of that, I, yes, I have my own podcast, YouTube channel, which I use to interview, you know, so far about 22, 23 Nobel Prize winners and top thinkers and some people that have left this Earth, which is the most,

treasured interviews I have, the three or four of my guests, not because of my interview, Jake, but because of times, you know, sithe, taking them at the ripe old age of 99 in 1K. So I'm not too, I'm not too distraught over it, but I really cherish the conversations I have because most of my work is not, you know, stroking my, you know, graying beard and looking up at the cosmos, although I do get several moments to do that a week. But most of it's, you know, dealing with people, I have to talk to things I have to do. And the podcast gives me an outlet for things that I want to do. And some of those things involve writing books.

I've written four books working on my fifth one now. And the podcast is sort of a labor of love where I get to talk to people, just like you, you know, intelligent people that are curious about the state of the universe. And my most controversial opinion is that scientists owe the public, you know, the explanation for what we do. And the failure to do so will result. in the termination of the job that we would do for free, which is to explore the universe. So I try to give back in that way,

and I give my colleagues crap for not doing it when they don't do it, and I salute them when they do do it. So those are kind of my work hemisphere things, and then outside of that, I'm a father, husband, do my best to do that. And I also am very involved in my synagogue, my temple here, do a lot of stuff involved with my Judaism, and I also very much enjoy

being, you know, kind of involved in the local San Diego sport scene and doing all sorts of fun stuff like that. So I do a bunch of a lot of things. Probably not, you know, not the best at any one of those, but I think putting them all together, it's a pretty fun combination. Amazing. And focusing on your work in science,

what do you think about the developments in AI over the last three, six, 12 months? Is that going to materially influence the trajectory of, let's say, your work or others in your field? And, you know, one example I thought of was a few days ago, So researchers apply to machine learning technique to uncover unexpected features of the non-reciprocal forces that shape the behavior of the many-bodied system.

I don't know if you saw that. That was a headline. So they're saying that AI is already influencing the speed of development in some cases of physics discoveries. Is that align with your... Yeah, well, it's certainly true that AI has a tremendous impact and has tremendous potential. The question is,

what kind of AI and what will it eventually, you know, be able to do. And from my perspective, the breakthroughs are sort of incremental. There have been, you know, just massive headlines. Everything's, you know, coming for every job under the sun. And I just feel like that is completely off base. And it's completely counter historical. If you look back at history, the great breakthroughs that we had were not precipitated by, you know, the computer or the event. Now, there have been many advances because of the.

computers. But if you look at the classical discoveries in the pinnacle of science, which I am not a member of, but theoretical physics, for example, or mathematics, et cetera, I'm an experimental physicist, I'm not a theorist. So I can speak to their credit of what they do. None of it involved, like, oh, the calculator and the slide rule, the transistor and the microprocessor. Those are extremely powerful, augmenting force multipliers, and I use them every day, and I'm using them right now, as a matter of fact, I'm doing some AI, you know, kind of work, deep research on a new theory that I'm trying to test using an optical telescope. But besides that, the question of whether or not the style of deep learning, which is the predominant form of AI that we're familiar with in the LLM space, that these are, you know, kind of these saviors that are going to come, you know, basically to the rescue of physics and break it out of the rut that it's supposed to have been in for 50 years since the string theory revolution. I think that's all nonsensical.

And the reason is that they're too damn good. You know, if I could go back to save physics, you know, I would, you know, cause Jeffrey Hinton and John Hopfield. I'd put them in, you know, in a, you know, in a, in a, you know, in a boot in a time out for many years because I think that they're, you know, the neural network and the LLM architecture is so successful. It's just gobbled up everything. It's kind of like, you know, the internal combustion engine.

so successful and cobbled up a much better, you know, superior alternative of the electric motor. I mean, imagine if you have the opportunity to start over again and have, you know, start with the electric motor, you know, electric vehicles, and then you had to convince somebody to get a gas-powered car, which you, you know, so I think that the AI systems that we are familiar with, LLMs plus GPUs are not going to lead to at least new discoveries in my field in physics. But they may be, you know, extremely powerful for coding and for, you know, generating transcripts and doing all sorts of great stuff. The issue is that, you know, physics is not a language. It's not certainly not a language that is informed by a corpus of knowledge that's reinforcement trained on, you know, the past history of humanity on the Internet. That's not how Einstein, you know, he didn't scour all of civilization, you know, from ancient Macedonia to modern day, you know, Bulgarian poetry.

and then all of a sudden come up with the theory of relativity. So I think that that's far-fetched, and that's not the way physics works. So it's going to have tremendous impact on all forms of science in society, but it's not necessarily going to have the breakthroughs that I'm most interested in. And I was pleased last week to see Demis Hasebis, who won the Nobel Prize in Chemistry in 2024, you know, proposing a similar test that I propose in 2023 and I've been working on for quite some time, which is to see if an LLM system can reproduce the physics that Einstein did, just given the knowledge of science that Einstein had in 1911, say.

So if you cut off all knowledge and you don't allow it to peek under the curtain and what happened in the future, can these tools, you know, replicate what Einstein did? And if they can't, we don't have AGI. We don't have artificial general intelligence because you can't do what a human can do. We certainly don't have artificial superintelligence. So I am optimistic. it gives me the power of a thousand, you know, PhDs and postdocs working around the clock literally, and I'll give it tasks before I go to bed, and I'm really into it.

You know, I set up an open-claw server, you know, eight different times because it didn't really work that well, the first seven. But I finally got that dialed in. I've got subscriptions to every major tool that there is. I use it, you know, all day, every day. But, you know, it's really to augment what I do. The ultimate test will be, you know, can it build, you know, the superconducting supercollider? Can it build a large hydrant? A lot?

Can it build the James Webb Space Telescope with no human input? Because that's what humans did. That's, you know, that's the minimum bar that they have to be able to show in order to prove that I should be worried about them, you know, taking my job or my colleague's job. So they may take over parts of it and things like, you know, education. We could talk about that. I think that's right for disruption, as they say. But I'm not worried about it in terms of, you know, taking my job just yet. Right.

And along those lines, like as someone who understands the limits of physical measurement, do you think there's a hard ceiling on what silicon-based intelligence can understand about the universe, things that require embodied intuition? There's no hard ceiling. I think, you know, anything that's a universal computer is capable of doing everything that we can do. I mean, we may have a ceiling. It may be that we have a ceiling, and so everything has a ceiling. As far as we understand, you know, any kind of computer, even, you know, a TRS 80 from when I was, you know,

seven years old could theoretically have no. limit as long as it can do enough computations. Now, there are, you know, hard limits in terms of the age of the universe, the lifetime of black holes and hawking radiation, you know, the practical, so-called limits are trillions of year limits. But in principle, no, there's nothing that prevents them from discovering everything that's discoverable as far as we know. And then what's the most important question in cosmology that you think AI could

help us figure out in the next five, ten years? I think, yeah. Yeah, so the most important question I'm, you know, convinced of because of perhaps my bias, and that's what I work on for the last 20 years, is how did the universe begin? I think that's the ultimate question. It's sort of like asking, you know, a biologist, what's the ultimate question in biology? They may be partial to their own field, but how did life begin, you know, not just on Earth, but in the universe because they may be interrelated.

And I'm curious about that, too. So I think the ultimate question in cosmologies, how does the universe begin? how do you go from a non-universe to a universe? How do you go from non-matter to matter? How do you go from matter in simple as possible form, hydrogen, helium, protons, neutrons, electrons? How do you go from that to organic chemistry? How do you go from organic chemistry to biological, simple molecules? How do you go from molecules to, you know, large life form cells? How do you go from, you know, prokaryotic cells to ucariotic cells? How do you go from those to mammals to, you know, all these different contingent things? But they all start with the Big Bang as the, you know, the Big Bang Theory television show theme song highlights.

Everything has to start with the Big Bang. So in cosmology, that's the most important thing that I feel I can study. And that's what I've dedicated the experiments in the careers or the scientists that work, you know, in my lab. That's what we're working on. And what about the AI hype, you know, frustrates you the most as a scientist? Is it what you just said about people? thinking it's going to take over everything or? I think it's, you know, that they don't, they don't really have any humility about it.

Like, there's more to being, you know, a scientist or even a citizen or anything than just raw intelligence. I mean, some of the most important, intelligent people in the world are in the Epstein files. My name's in the Epstein files, but I'm not, I'm not, you know, fortunately is because I attended a conference, not because that he supported. He wasn't even there. And, you know, some of the most brilliant Nobel Prize winners are. are in these Epstein files.

They're far more intelligent than me, and yet they have no wisdom, or they have very little wisdom or they had, you know, horrible, horrible, you know, sociopathic behaviors. Some of each category are probably true. And so, no, raw intelligence should never be confused

with anything approaching wisdom. I think what we could use is artificial wisdom, you know, more than artificial intelligence. I mean, we've had a tremendous intelligence for, you know, since the internet came around. Wikipedia is far smarter than any human being, but in terms of raw knowledge and even speed and processing, I mean,

we've had supercomputers and superintelligence, if you narrowly define it as ability to do math problems really quickly, we've had that for, you know, 55 years or 60 years now. But that's not what most people care about. I mean, I care more about, you know, the person that I'm interacting with their wisdom and, you know, someone's taking care of your kids. You don't really care how smart they are if they're evil, malicious, malevolent people, you know, just because Stephen Hawking is so brilliant. You know, he went to Epstein Island multiple times, you know. So what are the actual implications of intelligence?

I think they're way overstated. Got it. And it's more, and we need to balance that with like wisdom and morality. Yeah, I think that we don't have any ethical training. I mean, a scientist, we get none. I mean, it's funny. We make fun of our colleagues in the business school, you know, all they just care about money or the law school.

And they're, you know, they just want to be around the world or whatever. And then the medical students, you know, that, come to our classes. We always kind of say, oh, they just care about getting a good grade on the MCATs. But all those three disciplines, business, law and medicine have ethical training requirements for their students. We don't have any of that. You know, we just say, oh, you'll pick it up by osmosis. And I'm like, oh, yeah, you're going to pick up quantum mechanics by osmosis. Oh, no, that's really serious and important. We're not going to pick that up by osmosis. So why don't we teach ethics the way we teach quantum mechanics? Oh, well, we don't have time or no, you do have time. You just don't want to prioritize it. Huh, that's interesting. And so another question back to the thing you brought up earlier about, it sounds like you don't believe that AI is going to come up with any novel scientific theories, right? Like, you can help us. I wouldn't say I don't think they're going to come up with any novel.

I think they're going to come up with novel things. I just think the biggest, most important things that I care about, you know, understanding new theories of unification of forces and fields of matter and energy, of the ultimate, you know, model that describes how the, universe began. I think that they're, you know, as incapable of doing that as, you know, a computer in 1965 when computers were brand new, you know, they weren't able to just necessarily predict the existence of dark energy. I mean, that took key human insight to kind of conjecture, and then later it took observation. So I don't think it's possible as a scientist to conjecture

things and then prove them without any human contact with some way of acquiring data. I mean, You could say that the humans might, you know, will work for the computers. And there is a rent-a-human function that you can hire these cloudbots have been hiring humans to do things like CAPTCHAs and all sorts of things. So that's not really what I mean. I mean, someone has an idea for something or discovers. I mean, half of the most interesting things are discovered serendipitously. The CMB, the cosmic microwave background that I study was discovered serendipitously. And I always say it's very hard to plan on serendipitous discoveries because my definition.

you don't know them when they're going to happen. So how does an AI really enhance that in one sense they can't? In another sense, you can't ignore how powerful they are and how supplemental they are. And they should be used as a complement, not a supplement for what physicists do. So with the CMB, so you call the Simmons Observatory in Chile, so how could an A what did I say? So how could a, so do you think an AI-driven approach like the one used in the Dusty,

plasma study be applied to CMB data to find physics that we're currently missing? It could be. And just elaborate a little more like, you know, CMB is the ultimate many body data set, right? Billions of interacting photons from the early universe. So is anyone really applying physics-informed neural networks to it? I mean, right now that where we're at with physics and a lot of, you know, understanding of high-energy particle interactions and cosmological interactions, is we're drowning in data.

We have tons and tons of data, which wasn't the case 50 years ago. There's no data. There was like 10 galaxies that we knew about, you know, and then we had their red shift and we saw their higher than the red shift seems to be that they're moving away from us.

So cosmology was really data star for the first 30, 40 years after 1929 and Hubble and Einstein and LaMetra kind of conjectured that the universe began in a hot, hot, dense state. And then it wasn't until the discovery of the cosmic background,

the 3 Kelvin, all-pervasive radio wavelength, microwave wavelength, radiation that bathes us in a constant 3 degree Kelvin glow, that we had a precision number. Finally, we had a physical cosmological observable, a temperature, which we could

convert to a density, which we could convert to, you know, a basically an ionization fraction of the universe, that we had a, you know, quantitative science rather than a qualitative science. And since then it's just exploded. I mean, we've got new telescopes. The Rubin telescope came online last

year. We're going to have, you know, the upcoming Nancy Grace Roman telescope is launching soon. We have, you know, the James Webb Telescope, obviously, Hubble Space Telescope, the Simon's observatories came online two years ago, is going to release data in the next year or so. It's just, you know, it's a golden age for data, but we're drowning in it. And each day of data, we get about a terabyte worth of acquisition. Now, we don't keep all that data, but imagine, like, your hard drive fills up every day. And, and each day of data, we get about a terabyte worth of acquisition. And, we get about a hard drive fills up every day. And, And a lot of it, when the weather's good and the seeing is good and the conditions are right, you want to keep 500 gigabytes of that one terabyte. And it's every day for 10 years. So what are you talking about petabytes of data? And my colleagues at the LHC will laugh. I mean, that's like they get a petabyte in a couple hours. But they throw away 99.99.

You know, they have six events over 12 years. That's a big deal for them. So we're in different regimes. So AI is perfect for that. AI, machine learning, just incredible data sets. The math is not that complicated. I mean, LLM math is not complicated. It's basically what's known as linear algebra, matrix multiplications,

diagonalization, gradient descent method. It's very powerful that they were optimized, but they came out of serendipitous. They were serendipitously discovered too. They came out of the computer gaming field where GPUs were needed to blast pixels at a high rate to a monitor. And so there's nothing like that. that has to do with how a new field theory might come about.

But on the other hand, it's very good at image analysis, which is one of the things that we do, is one of the preliminary steps that we do on many different branches of astronomy and cosmology. So no, they're going to be very useful. And they may come up with things. Like, I had to do things, you know, where I felt like, well, this is kind of magical,

where it kind of surfaced something. It was actually from my books and podcast. Like, I fed at every transcript I've ever done. And, you know, it's a huge. 1,700 total videos, audio, fed it all in. I don't know if it, you know, what it does to it, to be honest with how it compresses all that information. But it was a 40 megabyte text file plus my books, you know,

four books, plus every appearance. I've been on other people's podcasts. Like, this will go in it to the next version, right? So I fed it all in. And then it surfaced like really interesting connections. And I, you know, I remembered writing it, but, you know, I didn't, the good thing about having written books before ChatGPT3

came out is that, you know, no one can claim I wrote it with the help of AI. So I'm kind of proud of like that it resurfaced some, and it really picked out some things in my childhood and, and the connections between, you know, say, science and religion that I didn't think about so much when I was writing my first book in 2018 to now when I'm writing my fifth book, you know, and it was just kind of magical moment where it surfaced this thing. And I could imagine it doing the same where I'm looking for this very peculiar type of polarization signal from the early universe and also looking at how like a crystal behaves on Earth and saying, oh, I have data from that in the lab. There's only one universe.

There's many crystals. Maybe it's telling me to put these things together, not like magical woo-woo crystals, but about study the behavior of optical polarization in the laboratory as a tool to understand the polarization in the universe. I felt that was really clever. And so there are magical things about it for sure. And it's just not clear to me that the main limitation, as you know, for a lot of these. LLM is the training data. And it's just vast. And that 40 megabytes of data took me 10 years, you know, more to assemble, right? So it's not like it's easy to get the data. And so what do the companies do now? They do two things. They either generate data themselves after they, they stole a lot of

data, including my books. You know, Anthropic has admitted that they, you know, basically scanned a half a million books and then burn them for some reason, which is very strange. But they scandal, so they stole basically intellectual property from people. But then other companies, you know, then they're using people to do reinforcement learning using humans. And then they're also doing training data. And then the Chinese models are basically sampling the American models or the French models or whatever. And they're doing what's called distillation. And they're basically, you know, reverse engineering what the, what the weights are. And so if the weights are so dependent on data, then that data by definition was recovered in the past. So,

it's only past looking so it can't by definition predict the future so it'll be like looking at 1911 data for Einstein and just saying no there's no there's no weird behavior over the planet mercury and then we'd be stuck in that in that prison so I call it like the GPU LM prison I'm not sure yeah I could be wrong we may discover that a new law of physics tomorrow and people are doing great things with it and especially in math and checking stuff and proofs but it's not as far as I know it's not able to pass this test that I coined the, you know, the Keating test, but Hasebus coined the Einstein test. And I think that's a better name, honestly. Well, then I want to get your quick opinion on this article that came out a week ago.

I don't know if you had seen this, but AI reveals unexpected new physics in the fourth state of matter. Not sure if you'd come across this, but basically what this had said is that the physicists at Emory had used a custom neural network trained on 3D particle tracking from dusty plasma experiments to infer the force between particles. And so they figured out that two longstanding theoretical assumptions were wrong, that charge doesn't scale proportionally to particle radius, and also that force drop off between particles does depend on particle size with 90% accuracy. So, I mean, this came out a week ago. You know, this stuff's not coming out every day, but I guess the analon for me would be, does it predict that what's called the Kulom

force, which is the ultimate electrostatic force, can it predict Maxwell's laws? Like, if you put an LLM and, you know, just again, train it on data from 1860 and before and nothing else. And it's impossible to do that now because the data is so corrupted. And even in physics data sense, like you just have a physics paper corpus of training data, it'll still have like, you know, it'll know somehow about like the fast and the furious three or interstellar. And it's very hard to decontaminate it. But I would say, no, things like that don't surprise it.

It would surprise me, again, because that would be effectively the Einstein. Like, if it predicted a new force of nature, which, you know, some people are claiming and you always have to be a little bit careful, it's like scientists discover life and another exoplanet and look really deeply. And what does it mean? Oh, they've discovered a molecule, which does something tricky and that's unusually, you know, usually associated with life. You know, those are all potential biased results. So that sounds like a really cool result of a tour to force mathematically. I don't know if it qualifies for. for the Hassebus, Keating, Einstein,

I'm going to throw my name in all those Nobel laureates. But it's certainly, again, it's not really clear what I could do with a thousand, you know, 190 IQ PhDs working for me. Like, but, but, you know, you need some kind of orchestra. I even find this, I don't know if you've used this open claw. Yeah, but, but I find one. I have a, I have it running on this raspberry pie right here.

Oh, you do. Okay, you're going to have to tell me how to say that. I got a raspberry pie, but I run it on a Mac Mini. You just got to ask Claude. Yeah, yeah. I'll get your tips for that offline. So I was setting it up and then like I was blasting through my tokens for Claude 4.6. And I was like, this is crazy when like, you know, a lot of it was just like, send me my daily weather briefing plus, you know, local papers on the physics archive and, and, you know, what's my first meeting? And it was all taxed.

It did it. And I was like, you've reached your session limit on, like, what the hell? And so I figured out how to change the orchestrator from 4.6. I use the orchestrator only in 4.6 and then use other models, Gemini Flash and whatnot, right? I don't want to get too nerdy, but you're familiar with it. And I realize that's kind of like what we have to do as scientists. We have to take, like, we have to be the orchestra. We have to be the clawed 4.6 or, you know, whatever.

And we have to, you know, have our students and colleagues and you'd be surprised. 90% of my colleagues don't use AI at all. 10% of the remainder are hostile to it for good reason. I mean, I wouldn't say there's good reason, but it's causing sort of more problems for academicians like me and professors like me than it seems to solve if you're, if you're an experimental physicist or even a theoretical physicist, like I said, and you're just like a pencil on paper,

pusher, guy, or gal, you don't really have much use for it. But they're, you know, we're finding kids that are using it to do every single assignment. We found kids are going to the bathroom during final exams and coming back 10 minutes later and getting, you know, identical answers to different final exam problems. And so we're seeing, you know, kind of like only the negative side. So like I said, 90% don't use it. 10%, 9% use it, but are, you know, kind of vehemently opposed. They're trying to use it to figure out ways to catch people using LLMs. And this is AI versus AI, spy versus spy.

And then there's the 1%, you know, maybe like me that are tinkering, you know, get up early in the morning, play with it, have our kids use it. Your kids are young. But, you know, when your kids get older, you're going to want them to use it. I have a son who's wanting to learn, you know, differential equations in high school. And I think he can do it. I couldn't do it. I didn't learn, you know, calculus A.B. until my senior year, you know, and I had a self-teach myself. But I said, look, you can have this book. You can take, like, Stephen Weinberg's book on gravity. and say, explain it to a smart high school student and then break down the calculus into chunks that I've already covered in calculus A, B, or whatever. And it will do that. And so you can make, and I said, like, tell it not to make any spoilers. Like, there are spoilers in calculus. Like, you don't think about it like that,

but calculus is like a love story. It's a beautiful, one of mankind's highest accomplishments up there with Beethoven and Mozart and Picasso. So I don't want him to get spoiled of that benefit. You know, he's reading the Great Gatsby. I'm like, you know, have a reading guide, but don't, but always tell it not to spoil it because it will spoil it. But it will also hallucinate. And that's the other problem with it.

A lot of my colleagues don't trust it. Again, for good reason, because it's very sycophantic. Oh, that's a great idea. I get, I get literally, Jake, I get, you know, probably three to four emails a day. Professor Keating, I just got one. I could read it to you. I'm not even going to respond to the person. Very earnest people, but, you know, they write to me.

They're desperate. It. They're wonderful people, I'm sure, and I would love to be able to help every one of them, but I can't. I have, you know, kids and a wife and a family and all sorts of other things. But here's one, you know, that I just got. Falsification of the Torah and then how the many body problem comes into play, the refractive index that takes place. It causes a function that then decays geologically, and that explains the Torah and the seven days of creation and that's that's one example that's the least maybe technical one i've gotten and then i get people that you know like oh i just want five minutes of your time and i'm like well you know five minutes of my time could be worth like a million dollar like if it's on my deathbed you know that's that's worth a lot right so so i started charging people i said because the lMs are telling them everything this guy's just said here's a script that i got from jemini 3.0 and you can prove that it works just run it on your computer. You have to download this model first and then you download the other, because it won't work.

I'm like, okay, you're telling me this whole homework assignment. You're telling me it only take five minutes of my time. But I'm going to tell you, my time is worth millions of dollars, perhaps per minute. So depending on where I am in my life. So I have a standard thing now. If you want an hour of my time, you can either pay $1,000 an hour. And that goes to charity at UCSD has a food bank, if you can believe it. There are starving or, you know, basically food insecure kids on campus, which is a tragedy. I mean, we charge so much for tuition that there are kids that can't afford to eat, Jake. So it's sick. I feel like universities are gotten totally out of control, but that'll have to be a topic for our next podcast when you need to come down here.

But the point is that I want to give to these kids. So I donate my own money every year, a decent amount of money. And then any time that someone wants an hour of my time, I say, you can have an hour, but it's going to cost you $1,000. bucks and you have to donate to charity. And I'm also going to start doing that with podcasts pretty soon because, again, it's just like I have so much cool stuff I'm excited about. I love talking to people like you. But, you know, I have to prioritize.

So you got off cheap this time, but maybe next time it'll be a little bit more expensive, just kidding. But, you know, the thing is it has to go to charity. And then they have an hour with me. If they don't want to do that, I charge $20 a month for a group coaching session. So I've got like seven to 10 people every month. I have 20 or 30 that are subscribed to it, but they don't come. Seven or ten people have an hour a month with me, and we just talk about their ideas.

I love it. But the problem with these AIs is that they're giving people this kind of, this sort of hallucinatory, you know, sycophanty, that there are the next Einstein and that they just need me to sign off with a .edu email address and then they can get their paper published. I mean, literally, I get people asking me to endorse their papers to put them on the, submit, them for publication. I'm not going to do that. I won't do that for anyone. I'm just trying to connect. I'm trying to connect and make sure I followed. So what you're saying about people emailing you, the story there

is that they're emailing you to get your sign off on what they think is to discover. After getting chat GPT sign off on it. Yeah, it's always their, chat GPT is allowed the proliferation. But they're saying, hey, I discovered this great, amazing new thing. New law physics.

Yeah, that's the content you're getting. Another guy called me an Ahove because I wouldn't sign off on his theory or I wanted to. What does it mean to sign off? What does it mean sign off? So to get a paper published, you have to submit it to a journal, right? And so the journal gets peer review of a journal. So they have, typically you have to submit that from a university, a dot edu account,

sometimes a government account. It's very rare you can submit with a Gmail account. And there's another venue by which people publish your papers called the archive. It's actually a public domain service going back 30 years now. It's called Archive with an X. And it is published at Cornell, supported by the Simons Foundation as well. And that also typically will very highly de-weight people

that don't have a .edu email address. So you need something called an endorsement. Before you can submit of any kind, if I submit, and it was my first paper, but I'm a .edu, like my graduate student, when she wants to submit a paper, she has to get an endorsement from someone else, usually like me.

And typically they have, they have to have published on the archive, or they have to have a dot edu email address to just get above the referees that are there. And that's not refereed. It's just, it's kind of like vetting process. So that's what I mean.

But what's happening is people are saying, I don't want to waste your time. So I had chat, CBT, do this, look at this chat that I paste the thousand lines of text in the email, what literally happens. And here's it going through step by step, every single equation and verify it.

And it's like, yes, you know, Jake, you're absolutely correct. This is incredibly, you know, transformative result. And I can make up like a thousand different theories right now and have Chachy. I can guarantee you we could go to ChachyPT right now. I can come up with some crystal dust plasma theory that explains the CMB and there's no big bang and Chatsypte will endorse. Well, effectively say it's correct. So that's the problem in that you can make up complete and you can ask it facts.

I said to it recently. Brian Keating, what books has he written? Losing the Nobel Prize. correct, into the impossible, correct. A brief history of time, not correct. I had it say to me that I had this new one that's a chat function on a built-in supercomputer chips called Talas. And they make these chips now that have L-LM architecture encoded in the chip.

And I asked it, who's Brian Keating is like a JimmyChat or something, dot a.I. And it came back. Brian Keating is the cosmologist at San Diego. and he was the former head of the large Hadron Collider. And so it's like you can't trust it on facts, but you're going to, I mean, a couple of years ago, if you asked him how many ours are in strawberry, it got it wrong.

So all the more so for checking a theory of physics that we don't even know exists, but has never been peer reviewed, identified, or vetted. Wow. I want to take a 90-degree turn here and go back to something you said minutes ago, which was that you fed AI all of your podcast, content, all that stuff.

I want to ask you, what were the questions that you asked? it after you fed it all that. So I just asked, based on the, you know, kind of text that you've received, what are my top five priorities? What am I working on?

What holes am I missing? What connections can you see between these ideas and the different podcast? I also fed up. What was the craziest thing? Did it tell you anything? You didn't know?

It was kind of like my personal trainer, you know, like my personal trainer will say, you know, like eat less. carbs and workout more. And like, I know that, but it was good, it's good to have accountability, right? To get weight, to get measured, to get, you know, checked and surfing and lifting weights and all the stuff I like to do.

So it didn't tell me anything I didn't know, but it told me stuff that I was unwilling to see maybe or that I didn't want to see, including aspects of my upcoming book, which, you know, I'm not going to talk too much about because sometimes you talk about stuff and it takes away a little bit of the thrill of doing the actual thing. You've got to just do the thing

and not think about doing the thing or talk about doing the thing. So for me, yeah, it had this very interesting connection, you know, between this, you know, person in my life that, you know, I was considering writing about, but not sure how to orient it, you know, in the book, you know, their private person. And so how do I do that and do it? And I came up with a very, a very warm way to do it.

And I still have to write it. You know, I have to sit down and write it. But the architecture, the skeleton of it, you know, is like the main challenge I'm sorry. finding is that to describe this particular physical effect that we're looking for, it is as an ancillary kind of finding and discovery for the Simon's Observatory potentially. It involves really very abstract math and physics, like way beyond what's in a brief history of time or, you know, a fabric of the cosmos or, you know, Brian Green's books as great as they are.

You know, they kind of gloss over them. Like, string theory is a billion vibrating dimensions in a super high dimensional space of energy. And it's like, okay, what does that mean? They won't tell you. Or waves are correlated and particles are waves and they have this weird collapse of the wave fund. You know, like they never really explain what that means or why it's a problem. So I want to do it justice. But the problem is a lot of people that are popular science consumers, they're not going to be willing to go through the trouble of learning the math and physics behind it. So how do I do that elegantly, you know, to quote Brian Green.

How do I do that in elegant way? But it's also, like, I don't want to sugarcoat it and make it like fanciful because his books and Stephen Hawking's books and Lisa Randall's books and Jan 11th book. Like, they don't have to be, they don't have to ever confront reality. Like, they could talk about string theory and say, oh, it's going to be discovered in 2020, you know, 22, 22, you know, and 200 years from now. I'm an experimentalist. I can't get away with that. you know, I'll lose my, my reputation, my funding, my, my livelihood. So from that perspective, an experimentalist has to be a lot more practically oriented. And so those are some of the

things that's kind of helping me work through as we speak. Amazing. Well, Brian, this was incredible to get a chance to talk to you. It's really, I hope we can meet in person either up there or down here and keep doing what you're doing. Congratulations on all your success. I mean, you've grown a lot in two years. That was, I think you, if I could have your growth, you know, 60X or whatever, since I gave you the homework assignment I would take. Oh, yeah. Well, I, yeah, we'll see.

Here, let me stop the recording. And, yeah, well, it's it. Ambition comes in all shapes and sizes. At First Citizens Bank, we roll with your goals because we're built for what you're building. Fit for your ambition for Citizens Bank.