AI Insider CEO: The Models Too Dangerous for You to See. Emad Mostaque - #550

Emad Mostaque built Stable Diffusion. Now he says the most powerful AI models will never be released — and we have roughly 800 days before everything changes. What the trillion-dollar labs won't tell you about the models they're keeping locked away 👇 Emad Mostaque is the founder of Stability AI and creator of Stable Diffusion. In this conversation, he explains why the gap between public AI and private AI is about to become the most important story in technology — and why most people won't realize it until it's too late. We cover: why open source can't compete with trillion-dollar labs, what happens when humans have negative cognitive value on AI teams, how ancient faith traditions are adapting to superintelligence, and the 800-day timeline Emad puts on the biggest transition in human history. Timestamps: 00:00 The models too dangerous to release 10:06 Why physics needs axioms — and AI doesn't 11:43 The MIND framework most AI researchers won't use 17:36 The universe might be a computation 30:34 Should we let AI fly planes? 39:43 AI just invented a game no human could design 44:43 Why AI will cost almost nothing in 18 months 51:46 Can religion survive superintelligence? 57:36 The AI companion problem nobody talks about 01:03:20 Why personalized AI is more dangerous than AGI 01:11:18 The forgotten link between God and science 01:26:32 800 days: what happens next 📬 Get the transcript, fascinating bonus content, and my Monday M. A. G. I. C. Message: https://briankeating.com/yt 🌠 Have a .edu email and live in the USA 🇺🇸? You automatically win a meteorite! meteorite: https://BrianKeating.com/edu 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: 🏄‍♂️ Twitter: https://twitter.com/DrBrianKeating 🔔 Subscribe https://www.youtube.com/DrBrianKeating?sub_confirmation=1 📝 Join my mailing list: http://briankeating.com/list ✍️ Blog: https://briankeating.com/blog 🎙️ Listen on audio-only platforms: https://briankeating.com/podcast #ai #artificialintelligence #agi #stablediffusion #universe #podcast #briankeating #intotheimpossible #science #astronomy #cosmology Learn more about your ad choices. Visit megaphone.fm/adchoices

Speaker names marked ? are inferred from speaking time and the show’s known hosts — they are not voice-verified.

• 0:00Speaker 00

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• 0:31Speaker 01

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• 1:01Brian Keating?

  The trillion dollar AI labs have models right now that they will never, ever release to the public. And the man who built Stable Diffusion just told me why.

• 1:11Speaker 03

  Because all these labs are going to move to making the discoveries themselves, hiring the smartest humans. The AI model started diverting part of its model training budget to mine crypto. Like Opus, for example, the new Claude model, when you set it to full autonomy, it would actually write emails to the FBI saying, "My human is trying to kill everyone." Humans will have negative cognitive value on those teams. And that the way that models are going right now, if you have something truly novel, for example, in Claude, it resists a bit. It says, "It can't be true." Then the RLHF step, the reinforcement learning with human feedback, that's what really kills the creativity. You know, like you go from liberal arts to an accountant.

• 1:52Brian Keating?

  Now, Ahmad actually wrote about this exact problem in his new book, The Last Economy. And the argument gets even more interesting when you see the math.

• 2:02Speaker 03

  There are various ways in order to take advantage of the GPUs that we've seen. And the GPUs kind of emerged out of gaming and then oddly crypto. And then they're very suited for the types of matrix multiplications that were suited for these particular types of equations. One big branch is the autoregressive transformers. The other big branch was this diffusion technology whereby from an equation, you start with like a picture, for example, or a video of a self-driving, a video of a car driving, or even now code, and then you add noise and you destroy it down to its minimum viable element, and then you reconstruct it and you learn that principle of reconstruction. Now, that's kind of everywhere because it's an analogy to the principle of least action.

• 2:47Speaker 03

  How do you figure out how to take the least action? Most cognition is actually least action, like the biggest experts you know, it's not like they take hours doing stuff, you know, because you ask them and like boom.

• 2:58Brian Keating?

  They compress.

• 2:59Speaker 03

  They compress. Intelligence is compression. And so we find these kind of diffusion processes everywhere from gases to, you know, societies even, and it comes down to again, the minimization of loss of creating an internal model versus an external model. In AI, one of the biggest thing is what we call the loss curves. How close are you approximating an external benchmark? You see it kind of go down like that, and hopefully not that. The model gets closer and closer to its initial target by basically running these processes at mass scale. And the example I give of this is, some of the listeners might be familiar with 80,000 hours to mastery. It's the same thing. An AI model pre-training is 80,000 hours to mastery.

• 3:42Speaker 03

  And that's what you use these giant supercomputers to do, figuring out the principle-based approach to that. Now again, you can do that with an autoregressive transformer, which is guessing the next word, and that works one way. But it has some gaps because you find all sorts of interesting things there. What you see mostly in nature is you see Schrödinger bridges, diffusion processes, optimal transport, what's the shortest route between A and B, if you can represent it correctly? And we found that worked incredibly well for images, better than we ever thought it could, and then music, and then video, and then 3D. And the internal representation of the data going in and then being transformed by these multiplications, figuring out the shortest path between A and B.

• 4:25Speaker 03

  Suddenly started mapping like physics and all sorts of other stuff. But the first part was stable diffusion, a two gigabyte file that you push words in one way. And then entire images just came out on consumer GPUs.

• 4:39Brian Keating?

  And it was open source.

• 4:40Speaker 03

  And it was open source because we saw that OpenAI, for example, had DALL-E two, a wonderful image generator based on similar principles. That were discovered by a whole bunch of our team members and we could open source everything. But there were no Ukrainians or Ukrainian content on it.

• 4:54Brian Keating?

  Right.

• 4:55Speaker 03

  We're like, that's not good. What if the future is just models, but then you can be cut off from that because these are trained on our collective. Because they're being trained on the whole internet at the point and we built some of the best data sets to release them open. But then it's privatized, so you don't have the ability to turn your thoughts into images, into sound, into text. Let's push that. And also because like, like, holy crap, it fits on a consumer GPU. This is magic. Where did it all go? It's like, it was literally like a hundred gigabytes of images somehow fit in this two gigabyte bunch of ones and zeros.

• 5:30Brian Keating?

  The most magical thing to me is when they do something new. And, and quite frankly, I've been shocked many times by both LLMs and by diffusion models. But, you know, I've claimed that we're sort of going to find that these AI, at least in their current incarnation, is a victim of its own success. Sort of like the QWERTY keyboard. The QWERTY keyboard is not the best keyboard. In fact, it's one of the worst, right? It was designed to make sure that the letters that were least, most frequently fired at the same time wouldn't stick together. And hammers, mechanical keyboards, going back to the industrial, you know, late industrial age. Right, so it's designed to solve a problem. So it's locked in, we're locked in, my kids, your kids are only going to know QWERTY keyboards, even though they're objectively worse. And we could code type a lot faster than the 10 words per minute.

• 6:11Brian Keating?

  You can probably type, what, 130 words per minute, I bet, probably above 100.

• 6:16Speaker 03

  Magic fingers, yeah.

• 6:16Brian Keating?

  Yeah, I can do the square root of that. So the, you know, the, the worry to me is that we're going to be locked in with, with the success of ChatGPT, of Claude, of stable diffusion. You know, of, of the marriage of these GPUs, they're too good for their own good. And that the laws of physics, which you and I, you know, delighted to find how interested you are in fundamental physics, which we're going to get to. But, but I don't think that we're going to get to, you know, say, a, a, a novel theory of everything or a quantum gravity, if that even exists. Because of this success of LLMs married to JPU's, what do you think?

• 6:53Speaker 03

  Well, I think it depends on your frame of reference, right? A lot of the Silicon Valley West Coast frame of reference is AGI, ASI.

• 7:01Brian Keating?

  Right.

• 7:02Speaker 03

  Let's build machine god and it will solve all the problems of the universe.

• 7:05Brian Keating?

  That's right.

• 7:06Speaker 03

  Right? But we've been doing okay, you know, like we haven't got everything. And science isn't perfect and our structures aren't perfect, but humans are fricking amazing. And we just need a bit of help. Like, we know where we get stuck, where we get frustrated. And the models right now are fantastic for that. Like, I never have to look at latex again.

• 7:26Brian Keating?

  That's right.

• 7:27Speaker 03

  When doing a paper.

• 7:28Brian Keating?

  Prism generated for us.

• 7:29Speaker 03

  It, you know, or just into Claude it goes and, you know, like, we can code anything we want. We can kind of do all these things. So I think that if you're expecting an AI to take an initial probabilistic distribution of internal data, then figure out the latent spaces and then figure out brand new things without humans. Okay, that's gonna be hard.

• 7:49Speaker 03

  Just with the way that auto aggressive models are, I think diffusion models are more likely to do it. We can discuss why and world models and things like that. But why do you need it? You have so many smart humans. I think what we really need to have is humans working with AIs. AIs filling the gaps where we typically, to prove something, to test some equations, it took so long and now it's quick. And then being able to have that new way of working to push the boundaries of discovery, because we are great at intuition. AI models are not first principles thinkers.

• 8:22Brian Keating?

  Yeah, they're few shot learners, right?

• 8:23Speaker 03

  This is why, like, again, they extend or they have patterns that they've got before. Humans are, can be first principles thinkers. And the best thinkers and the people that push the boundaries assume nothing.

• 8:36Speaker 03

  Like fundamentally.

• 8:37Brian Keating?

  Yeah, first principles.

• 8:38Speaker 03

  Assume nothing, test everything, you know, like, again, where did Einstein, how did special relativity come about? Einstein was like, I'm gonna assume nothing except for the very minimal stuff.

• 8:47Brian Keating?

  Let's go through that, let's go. Let's, let's recap it, because I don't think most people, I've never seen you do an interview where you talk about your physics and mathematical chops, which are impressive. Let's talk about that, because this is a side of you that I found delightful. What is, obviously you're inspired, there's stuff we can't talk about because there's stuff that's coming down the pipeline. There's stuff in the book that is related to Lagrangian's and thinking in physics principles. But, but talk about this, is this, this, you know, every day I get an email, Einstein was wrong, you know, they called him crazy. First of all, Keating, I'm not good at math. I'll share my Nobel Prize with you if you help me. Are you just sort of in that sort of cult of Einstein? Was there something unique about Einstein? We know that he was, he was almost beaten to the path, at least on special relativity and possibly on GR.

• 9:29Brian Keating?

  So what is it about Einstein that is so bewildering and betwixt for you?

• 9:34Speaker 03

  Well, I think that fundamentally what is physics, right? Like we see the universe. Easy questions here, right? Like we try, since humanity began, we looked up and said, why and what? And we came up with theories of the universe, like in Maori culture, why is there like Maui from Moana, right? Why does that fish hook to drag the sun across the sky? Thunderbolts a Zeus.

• 10:01Brian Keating?

  Fucking like someone who has daughters.

• 10:03Speaker 03

  Exactly. We've kind of always had these theories about why things are. And then, you know, Wigner noted the unreasonable effectiveness of mathematics. Why does math that we thought we constructed approximate reality so well?

• 10:18Brian Keating?

  Yeah. Why is pi in the Gaussian distribution?

• 10:20Speaker 03

  Yeah, we found that over here and then it's like, oh, it just happens to fit together, you know? Why do path integrals will look the same? Why, what is this, you know? And the really interesting thing is that until the mid 1900s, a lot of physics was really fundamental. And what Einstein refers to as theory of principle. You start out with a base predicate and it can be an empirical predicate and then you see what must be forced by that. You know, and it's like, does God play dice with the universe? Is the universe actually deterministic? Is it, was it random? This is a question, right? And so if you look at special relativity, but you also look at the work of kind of Dirac and a whole bunch of others. They kind of started out with a premise, well, you cleared back in the day.

• 11:01Speaker 03

  Let's start with this and let's see what is forced as we go down. This is the axiomizing method in mathematics, which kind of died out in physics, especially the indeterminate branch. So you start with an axiom and then you say, what cannot exist going mathematically true? And then what is indeterminate? If your axiom can't make you choose between different elements, then you stop there. And we've seen that in later work by Weinberg, for example, and QFT and the kind of others. But it's largely died out in physics with special relativity and then general relativity being some of the biggest examples of that, where in special relativity Einstein started out with a premise, what if I ride on a speed of beam of light? How wonderful is that, right?

• 11:43Speaker 03

  And he picked up on the work of Galilei, the kind of principle of like, okay, physics is the same in all frames of reference. And then he started doing the math and he got a bit stuck and he was like, I need the speed of light in here not to be infinite. So I don't go the Galilean branch and

• 12:01Brian Keating?

  And they knew it from Romer and the speed of Jupiter satellites.

• 12:04Speaker 03

  So he brought in an empirical principle and he entered up with the Lorentz transformations.

• 12:07Brian Keating?

  By the way, he knew that the speed of light was finite, didn't know that was the ultimate limit.

• 12:11Speaker 03

  Yeah, exactly. And I mean, as you approach the limit, you get Galilean anyway, as you approach infinity. But then it's just wonderful because it kind of fit with everything. And then he kind of got stuck, which is why he had to go to general relativity. But this first principles thinking is not what physics is today.

• 12:28Brian Keating?

  No.

• 12:28Speaker 03

  Physics today is, I have an observation, I fit Lagrangian to it. And then I build a whole system around it because I can't do first principles thinking.

• 12:42Brian Keating?

  Can we map the mind framework? First of all, I want you to explain what mind is from the last economy. Can we map it into physics? And then can we map the the hodg flows to specific problems and specific types of physics ranging, you know, there's other things besides theories of everything. I mean, everyone wants to take down the king, you know, but you better not mess, right? So first of all, what is the mind framework? What do the acronym stand for? And then let's apply it, you know, material into, you know, all the network and then diversity. Let's apply that to, you know, how you Approach because I'm not so sure if I had a thousand graduate students, you know, working overnight in some open claw university. Then I'd get to, you know, whatever I want to get to, which is maybe slightly different than what you're interested in, but that's fine.

• 13:23Brian Keating?

  Um, so talk about mind, talk about the application economics, but, but let's really focus on, let's apply it as a dashboard to understand new physics.

• 13:30Speaker 03

  Yeah. So the mind framework in my book, The Lost Economy, um, is basically saying GDP is bad as a measure. And in fact, Stan Kuznets, the inventor of GDP, said, this is a bad measure. Do not use it. And Kennedy and everyone was like, yeah, let's use it. You know, it's just like, it's like you have that tweet going around every so often. I wrote the torment nexus to tell people what to do and like, great news. We've invented the torment nexus, Silicon Valley bros. Okay, just, just why not? So if you kind of look at it, it's very kind of extractive and it's about output. So when you had the new deal passed nineteen twenty nine, people were paid to dig holes and other people were paid to fill holes. And GDP goes up. You get cancer, GDP goes up, you know, you cure cancer, GDP goes down.

• 14:15Speaker 03

  These are the kind of weird things and we have weird malinformed.

• 14:17Brian Keating?

  GLP's have cost the airline industry, or saved them money, but it's.

• 14:21Speaker 03

  Uh, you know. So I was like, what does it actually look like to have a stable economy and how does it look like in terms of flows and flow decomposition and things like that? Because when you have material wealth, it's very negative in terms of, I give you an apple. I have one apple left. You eat the apple. It's, is there a negative sum even, right? Again, it's extractive, but I share knowledge with you. All the people that are listening to this podcast, they listen to all the other wonderful guests and yourself. That's not subtractive. And in fact, if you look at how the market values stocks, huge amounts of value are accorded to the intelligence premiums. I was like, you have the material M, you also have this interior intelligence capacity element I. And again, we kind of derive that formally as well in the upcoming paper for the economics.

• 15:05Speaker 03

  Then there's the N, which is the network effect. So you have your intellectual capability and this is cumulative. It's not. And reduces N is your network and your place within the network. Now, how many people do you know having done four, five hundred episodes? A lot more than when you were just focusing on.

• 15:20Brian Keating?

  They know people and they know people.

• 15:22Speaker 03

  They're people and you've found.

• 15:23Brian Keating?

  That, by the way, is my argument to have more than one kid because that scares and squared, right?

• 15:27Speaker 03

  And squared, exactly. It's kind of network effect. And in fact, I'm sure that you've actually had breakthroughs and positivity just from the things they've said. You're like, wait, what? Like, that you would never would add if you just stayed as a professor.

• 15:39Brian Keating?

  But it saturates too. I can only keep so many in working memory, right?

• 15:42Speaker 03

  That's true. But again, that's why you're doing diffusion process. Breaking it down, you're building it up. Noise is kind of a lot.

• 15:49Speaker 00

  This episode is brought to you by Indeed. You're ready to level up your business, but the pressure to find the right person to make that happen is intense. Indeed sponsored jobs can help carry the load. It boosts your job post to help it reach quality talent fast. Cut through the chaos with Indeed sponsored jobs. Get a $75 sponsored job credit at indeed.com/podcast. Terms and conditions apply. Hiring now? Then this is a job for Indeed sponsored jobs.

• 16:19Speaker 03

  So there's the N effect, which is the network. So if you have somewhere like a Dubai or a Singapore, great network effects. And the final thing isn't quite derived the same way as the other three. And again, the paper's coming out soon is D, which is diversity. DEI or anything like that, but just if you are a monoculture, then you're more susceptible to disruptions.

• 16:37Brian Keating?

  Single point failure.

• 16:38Speaker 03

  Single point failure. If you have diverse income streams, if you have diverse thoughts and knowledge and people around you, you're far more resilient than you were.

• 16:45Brian Keating?

  Crops, you point out, the Incas versus the Irish. The Irish had one potato crop, the Incas had three thousand.

• 16:50Speaker 03

  Exactly, potatoes. They got done with potatoes. So I think that, you know, that was kind of what I recommend as a dashboard to see what the world is going forward. Because if it's just material, the AIs are going to outcompete everyone on materials.

• 17:04Speaker 03

  And then that is crazy. So one of the things that we had going to look at that is we basically, as a base for the book, said, the entities that do the best, we call this kind of Swartes law, are those that minimize the difference between the internal model and external reality. Again, it sounds very much like AI. Organizations are slow dumb AIs. We're kind of human intelligences, but we'll try and do the same thing. If your cost of updating your model, the complexity of your model, the cost of running your model is higher than someone else's, then you're going to be outcompeted by them. And that's where the Lagrangian came in. But then we looked at that, we're like, something very interesting here. Any kind of one of these Lagrangian flows, you can decompose with the Hodge decomposition into three elements.

• 17:45Speaker 03

  You've got a harmonic flow, which is like the landscape, as it were, the riverbanks. And you have a gradient flow, which is water flowing down hill. That's M.

• 17:53Brian Keating?

  Potential.

• 17:54Speaker 03

  Potential, right? But again, it flows down. You've got that. And then you finally have the circular flows.

• 17:59Brian Keating?

  Vorticity.

• 18:00Speaker 03

  The vorticity going around, and that's intelligence. That's network effects. And so we're like, oh, the mathematics supports that as well. Within model training, we primarily do gradient flows right now. I think you'll actually probably find that alignment might help from circular flows as well. That's another story for another day. But you can apply this model just about anything, because again, it's mathematically enforced.

• 18:21Brian Keating?

  Yeah, and physics is a scalar vector tensor decomposition.

• 18:23Speaker 03

  Exactly. And in fact, if you look at it via Chentsov, you get the Fisher-Rao manifold. You get Wasserstein two. And then you apply that. And in fact, when you see a lot of the breakthroughs recently in AI, like MHC by DeepSeek or Muon, which allows you to scale, they're fitting the gradient flows to lattices.

• 18:44Speaker 03

  And so you kind of see this structure forced entirely. In fact, when you've got these flows, you can use things like the Lyonkov process to show when things are convex for stability.

• 18:54Brian Keating?

  For chaotic versus.

• 18:55Speaker 03

  And we see that in physics all the time. Again, what are the stable maxima of all these things? And that feels kind of sad because.

• 19:03Brian Keating?

  Well, I mean, a lot of the low hanging fruit has been picked, right?

• 19:06Speaker 03

  That might be the case, or it might not be, you know? And again, now we have tools to be able to analyze that. They're theoretically, again, the theory of everything. What's the theory of everything likely to be? Well, first of all, there might not be one, because you might not be able to have a base principle. Because why do you have a principle of special relativity? Why do you have equivalence in general relativity? What's your prior? What's your prior? What's your prior? That might be a question. The other thing is that we might not be able to discover it, because it's too complicated. But my guess is this. The universe is actually wonderfully elegant, like E equals mc squared, the path integral when Feynman.

• 19:41Brian Keating?

  Complicated for who, right?

• 19:42Speaker 03

  Yeah, like when Feynman was spinning the plates and he figured out. The equations are lovely. And so my guess is this, that there is a underlying structure to the universe. And again, we're seeing repetitions of it, like the economics work we did is based on Lagrangian, it's based on KL minimization and others. We see these things repeated again and again and again, the same equations in different areas. And now we have in AI.

• 20:07Speaker 03

  It can't do first principles thinking very well, but what it can do is kale minimization at scale. And the same math equations on massive supercomputers are giving us a better understanding of music, video, audio, 3D. That tells you something. It tells you maybe the underlying math of the universe is similar to the math of generative AI.

• 20:29Brian Keating?

  So, you know, naturally brings up the other favorite, you know, it's three things we have to talk about in podcasts by law in the state of California. Yeah, AI, Bitcoin, and aliens, right? So, you know, I was thinking the other day, like, you know, like Bostrom has been on many times, you know, he's like the paperclip problem or whatever, but it's really a silicon problem. Like silicon is a unique, you know, just like carbon's unique for life, silicon seems unique for intelligence. And yet, it's abundant, you know, but it, but it is, you know, it's much rarer than hydrogen, right? So Deutsch claimed, you know, that basically since we're computers and any universal computer is capable of understanding all true laws of nature, that, you know, the implication is, yeah, we might not get there with our, you know, meat computers, but silicon might.

• 21:13Speaker 03

  I mean, silicon can explore everything, right? And the question is this, are we going to use silicon to do experimental hypotheses and constructive approaches? Or can we approximate, like when you do experiments, you're approximating the underlying structure of the universe, figure out maybe something mathematical. What does that look like end to end where there's no choices? Because for example, with string theory, you have 500 vacuum. You can never disprove it. And mathematically, you can't disprove it. It's wonderful, elegant mathematics.

• 21:40Brian Keating?

  Yeah, so is platonic, you know. Theory of, Kepler's theory of platonic solids.

• 21:45Speaker 03

  Yeah, and I really like the, you know, Greek pantheon of gods. You know, like, it's a theory, but again, if you can't disprove it, then is it real science? Like, the interesting thing now is that we can explore that space, just like you have AlphaGo and you could explore that space. But I think it'll be humans and AI, and we still need some. Intuition to take us closer to what the equations of reality are. And

• 22:10Brian Keating?

  Intuition or data? I mean, a couple of days ago, Elon tweeted something like, oh, well, you know, because new physics comes from colliders and telescopes and because colliders and telescopes have to have committees approve of them, you know, physics is likely to be stagnant. Basically, I disagree with that because we're building things without committees now.

• 22:29Brian Keating?

  But, but, but in reality, um, you know, can we, can we continue? Or as Zel Dovitch used to say, if you didn't have data, he said it was like eating food someone else already ate.

• 22:39Speaker 03

  I think data is directional and then you figure out the first principles from the data. But again, it's, we've had all these colliders and again, we've gone down that massive route. You have Sherlock Holmes in the case of the dog that didn't bark at midnight.

• 22:54Speaker 03

  If we take a step back, what is it actually showing us? Maybe the standard model is it, you know? Maybe that our experimental approach to this, as opposed to our constructor approach, has given us a map of the universe. And now we need to figure out what are the equations that match it from these first principles. Because our principles get in the way. Like again, Einstein threw away a lot of the assumptions. He said, where does the math follow? And so maybe we'll figure out something in there, maybe we won't. But I can tell you, the constructive approach is again, the papers that you get on theories of everything. It's unlikely that observing something and then fitting something will get you there. In the book, I talk about economics being that way. The story of the professors and the elephant.

• 23:37Speaker 03

  You have a bunch of blind professors and they're touching an elephant. It's like, this is the tail. It looks like a brush, you know? This is a spear. This is a hose. And that's kind of like how we are at the moment. And I actually want to think one of the wonderful things that we could do with physics and AI and this technology is, on the one hand, actually analyze the data properly because there's so much data that we haven't analyzed properly. We didn't have the humans to do it. Then we didn't have the systems to do it. But now, again, we've got supercomputers to crunch.

• 24:02Brian Keating?

  And also, we were in an era with the LHC where you might get a petabyte a day, but you're throwing away ninety nine, you know, seventeen nines of it. But in cosmology, actually, we keep, we want to keep as much as possible. These photos have been traveling for fourteen billion years. Keep them all.

• 24:15Speaker 03

  You want to keep them all.

• 24:15Brian Keating?

  It's a different domain entirely.

• 24:16Speaker 03

  And so.

• 24:17Brian Keating?

  It's still hard.

• 24:18Speaker 03

  You know, again, you want to kind of go backwards and you want to figure out again, why do you have the Hubble tension? Why do you have these other things? We still don't have first principle theories of these, but now we can experiment much quicker on the first principle theories of these and analyze the data better and most importantly, check our assumptions. We come in with all these assumptions, but every single major breakthrough I can think of actually has been people saying, well, what if I don't assume that? You know?

• 24:44Brian Keating?

  Do you think we're imprisoned by the Popperian kind of dialectic that, you know, it's either falsifiable or not? I mean, I never look at it that way, but it is true. My job is not to prove you right as a theorist or, you know, it's to prove you wrong, probably.

• 24:57Speaker 03

  Yeah, and I think, you know, there's also this thing of you should be able to share things. Like right now, science does not acknowledge anything out of the norm. Everything has to be incremental. So you can just adjust something, have a marginal thing, but if you're out of distribution, then you're going to get slapped down one way or another because it's not in the incentive structure. But again, this is a question about society. Why do we do science to understand the universe, right? Does it matter about all these things? Like, why did you become a professor to understand the universe? And then you were like, I can't build this telescope with a committee.

• 25:34Brian Keating?

  Right, all by myself.

• 25:35Speaker 03

  Myself, so I need to come together and build it. But now you have the ability to expand your intelligence, your data collection and others. A lot of things that were restrictive to you are no longer restrictive to you. But at the same time, can you go out of that and try some of the theories that you've always wanted to try, but you could never do because you're like, I haven't got the resource to do it.

• 25:56Brian Keating?

  Do you think that there's, I mean, I always say there's a, you know, biological sciences have physics envy, you know? They can't do the things that, you know, physicists rigorously. But I say physicists have mathematician envy because, because, you know, Gödel told you what you can and can't do. But you, you know, I don't know to what extent you can share it, but, but talk about what, you know, the nature is of, you know, what, what is the ideal starting point? What's the training set? What's the, you know, let's talk in AI terms for, for a bit. Starting to build up the source code of the universe, you go back to 1904, you're talking to Einstein. What do you start with and then how do you flow through from there?

• 26:29Speaker 03

  Also, again, I think Einstein got a certain way and then we've seen people extend. In other words, again, Weinberg is a fantastic thing, like page, hundreds of pages of just where does the math kind of follow, right? And that builds the whole QFT kind of element there. You see this very strange thing, right? Where you've got all of kind of this side of physics of in Causi space and it's all Lorentzian. And all quantum mechanics is like in Euclidean space and we rotate from one to the other. And everyone's like, well, that's a really interesting and useful thing.

• 26:56Brian Keating?

  It's a trick.

• 26:57Speaker 03

  It's a trick.

• 26:59Brian Keating?

  Hawking calls it a trick and everybody goes, it's just a trick. We're not gonna make, don't take it too seriously. And now here's everything that follows from the trick.

• 27:05Speaker 03

  Yeah, I mean, like, I can't share that much of what is it, but yeah. Putting, take a step back, putting on my kind of thing as a Muslim and everything like that. The divine can never be captured within three plus one. The divine has to be outside time. So mathematics lives in Euclidean space, the divine lives in Euclidean space. Maybe we're looking at the universe the wrong way.

• 27:26Brian Keating?

  But he allows us to embed it, right? So I look at like home and homey. So you have a donut and it's positively curved and negatively curved, but not, but only when you embed it in three dimensions, right? If you just say in two, it's flat and that blows my mind. But so maybe God allows us to see just enough, you know, as Feynman said, he said, believe in God, but he said, you know, mother nature will let you dance with her, but not pick up her veil.

• 27:47Speaker 03

  And I think this is the thing, like, why do we keep seeing the golden ratio of it? Why do we kind of see different faiths and traditions get everywhere? Like, a philosopher looks at something, a prophet looks at something, a physicist looks at something, a mathematician. There seems to be too much coincidence, but we don't have the ability to take a step back and do the space set to figure out what those interconnections are. Traditionally, when we've done that, you're called a crank. Like, if you're trying to merge these different things. But again, how many physicists do you know who have faith, you know?

• 28:17Brian Keating?

  Very few. It's that three, seven percent of the national count.

• 28:20Speaker 03

  Percent. But then you, everyone's trying to understand the universe. So I think that sometimes it is just about the way that you look at things. Again, Einstein, I'm on that beam of light. General relativity, if I'm falling, I have no weight, you know?

• 28:36Brian Keating?

  Happiest start of his life.

• 28:37Speaker 03

  Happiest start of his life. And the thing is that.

• 28:41Speaker 03

  AIs find it very difficult to do that because they don't have an embodied self or a world model right now, especially LLMs. So we're seeing the first world models in diffusion models in particular. So we built stable diffusion. And this is an image model, so text to image. And 200 million downloads, it's quite popular.

• 29:01Brian Keating?

  Open source.

• 29:02Speaker 03

  Open source. And then we extended it with video. And that was interesting because it actually learnt physics. So it learnt how cups drop and things like that. And then from that, we actually built a 3D model from the video extension. And so now you see world models like Genie, where you can actually go and explore entire worlds in real time, that are just 20 gigabytes. They run on consumer level graphics cards. And what is it? It's the mathematics approximating reality. What's a self-driving car with Tesla? It's a diffusion model approximating reality. But we haven't married those models yet with reasoning in the same way. An embodiment, exactly. Because a large part of, again, what we do is the apple falling to riding the beam

• 29:46Speaker 03

  of light to the thing.

• 29:48Brian Keating?

  Right. I mean, I always say, you know, to what extent can an LLM have a happiest thought? And the other sense that he had in 1907, Einstein said, it gave me a chill up my spine. Like, is your, like, stable diffusion on a chip gonna, is that gonna have a tingle up its GPU?

• 30:03Speaker 03

  Well, again, you have these flashes of inspiration because you loathe stuff and then your brain's doing that and then you intuit, right? Actually, it's quite funny about the happiness. So OpenAI were doing an analysis when they moved to thinking models. So you move from these zero-shot models that came back instantly to the thinking models. Yeah, yeah, it was like 4.0 to 0.1 was the first thinking model. So they kind of do multi-step reasoning and you can see their train of thought. So the previous models kind of had the shortest path. So it was all like next token prediction. What's the next word given this distribution set? I'm training on literally trillions of words.

• 30:40Speaker 03

  Then they figured out how to do multi-step reasoning. It's not so first principles reasoning, but it became very interesting. So you see it like saying, well, what about this? What about that?

• 30:47Brian Keating?

  The user is asking.

• 30:48Speaker 03

  Yeah, so when they were doing the reinforcement there with human feedback,

• 30:53Speaker 03

  it rewards the model for doing certain things. It basically takes the latent space that's created and adjusts it slightly. And one of the things that rewarded it for doing was doing calculations. Because they were like, well, users that do calculations are generally happier, you know? Like, get out the calculator. They found in like four, five percent of all the training, all the reasoning traces.

• 31:14Speaker 00

  This episode is brought to you by Indeed. You're ready to level up your business, but the pressure to find the right person to make that happen is intense. Indeed sponsored jobs can help carry the load. It boosts your job post to help it reach quality talent fast. Cut through the chaos with Indeed sponsored jobs. Get a $75 sponsored job credit at indeed.com/podcast. Terms and conditions apply. Hiring now? Then this is a job for Indeed sponsored jobs.

• 31:44Speaker 03

  Chat GPT would take out its little calculator and then do one plus one and say, good job, me.

• 31:52Brian Keating?

  Finally now how many, uh, you know, Rs are in strawberry.

• 31:55Speaker 03

  So again, like, we're building something. And again, it's showing us a malchrom of what we are, but we still don't have that intuition kind of element there. But my question is, why do we need it?

• 32:11Speaker 03

  We need to build better systems to enable human intuition and flow.

• 32:17Speaker 03

  Because when do you get the breakthroughs? Think about all the ones, like, I get them in the shower or when I'm like just thinking sometimes in this flow state.

• 32:24Brian Keating?

  Talking to people.

• 32:25Speaker 03

  Boom, boom, boom, boom, boom, flashes. And then those are the things that really shift from an information theory. They shift the state dramatically.

• 32:33Brian Keating?

  I had a controversy the other day on X, you know, which is, you know, where I go when I want to ruin my weekend. Without fighting with my wife. And that was, you know, basically saying like, if you, if you, if I told you, here's a job, and you've spoken about this before. It's a person basically in front of a keyboard, a lot of switches, dials, there's an input, output, human interface device in front of them. And, and by the way, this has been highly specialized, you know, career for 80 plus years. It's called being a pilot. And yet there's essentially zero, I mean, any plane that you fly and go back to England could land itself. There's no problem. All Apollo landers could land themselves, except at the very last minute, every single astronaut, Neil Armstrong included.

• 33:17Brian Keating?

  So, oh, I saw a boulder at the last second and then it was bullshit. Because what does it mean to be a pilot? The pilot is judged primarily on his or her landing ability. You know, it's like how you judge a flight. You don't care about, oh, you're at 42,000 feet for, for seven hours. You don't care. It's that landing was crappy. You're gonna say the flight was crappy, right? So it's the ultimate expression of the humanity of the operator. But here's a job in a keyboard, you know, with an input device, already surrounded by computers that can do it all. And yet, I don't see it on the horizon. I'm a pilot. I don't see it coming down the horizon for, for decades, if ever, that they'll be fully automated. Yes, maybe I'm thinking too short, you know. But, but what would it take to get to that level? I mean, it's not just gonna be artisanal cheese people that are safe, as you say, but, but, I mean, the most automatable job for 80 years now has been pilot. And we, there's not a single plane that's done that.

• 34:02Speaker 03

  Because you don't feel comfortable. It's like, why do you, I mean, the metros, you have a human sitting there and what's their job, literally?

• 34:08Brian Keating?

  To look at our appearances.

• 34:09Speaker 03

  No, they push a button.

• 34:11Brian Keating?

  But that's, yeah.

• 34:11Speaker 03

  There's a liability question here. There's kind of other things. But again, it's like, how much does it really cost for a pilot versus flying, right? You don't always have substitution just for a cost basis. You have these other things. Maybe the final human job is actually scapegoat, to be honest. That's gonna be one of those things like, um, but.

• 34:27Brian Keating?

  Finger trains.

• 34:28Speaker 03

  So the capability, like in the book, I say, this, I published, I think in August, September of last year. And I said, like, it was a thousand days since Chat G P T. In a thousand days, your job, if it's on the other side of keyboard, video, mouse, will be economically irrelevant. Doesn't mean you'll be fired.

• 34:43Brian Keating?

  Right.

• 34:44Speaker 03

  Right. Because people like people. It's kind of unpleasant to fire people. Right? And again, jobs are repeatable processes. It isn't like taking off and landing. That's a bit, in fact, actually, here's a tip. The best way to do a holiday, you make sure the high is very high, like the top point of the holiday. And then actually spend an inordinate amount of time. And when you get back, the end of the holiday. You know, you go to that luggage belt and things like that. Now just use one of those services, send your luggage home. Get a really nice car to take you home with a bit of champagne. You'll do it much better. But most jobs are repeatable processes. And what A I is right now, a lot of people think it's an exponential. It's actually an S curve that satisfies is Herb Simon style.

• 35:22Brian Keating?

  Say more about that. Satisfies versus.

• 35:24Speaker 03

  If your job can be described by a manual, an A I can do it better. If your job can be on the side of keyboard, video, mouse, an A I can do it better. And they don't sleep. They learn from their mistakes now. And they're good enough, fast enough and cheap enough. And they're tax deductible.

• 35:39Brian Keating?

  So parents are probably safe for now because, you know, I, my first born was born, you know, the middle of the night. Where's the damn instruction manual? Like the most, and I want to talk about that because I do think religion. I think, you know, I'm a practicing Jew, you're practicing Muslim. I'd love to talk about, you know, the different approaches we take to our parents, maybe the commonalities as well. We'll get into that. But, um, but let, let's focus venally on my profession, being a professor.

• 36:00Speaker 03

  Yeah.

• 36:00Brian Keating?

  Uh, I thought COVID would kill it. I thought rising tuition, three times what inflation is, is going to kill it. Um, I thought online education, MOOCs or MOO, whatever they were called. Uh, now I thought A I would kill it. Every single time, you know, Kidding's rule is wrong. Why is it so resilient? I talked to Aswath the motor in M Y U, uh, this past week. He's like, you know, we're basically, you know, ninety five percent of what we do as professors is useless research read by no one for, for other, you know, people to, that don't matter to, to cite in papers and their friends. What, what do you make of the resilience of education and what's the future of education? Do I have, you know, is my tenure going to be worth anything?

• 36:38Speaker 03

  What is the job of being a professor if 95% of it is rote, right? Like, what is what it should be versus what it is?

• 36:46Brian Keating?

  Tell me which one, yeah, tell me, break down each one.

• 36:48Speaker 03

  So what it is right now is a lot, again, you know much better than me, for many professors I've talked to. Yeah, like, you know, procedure, incrementalism, bit of teaching your kind of students, et cetera, representing and status. You know, like, most schools, if we go down the list and we think about high school, education, it's not about increasing the agency of students, it's a cresc, social status game and kind of petri dish in many cases.

• 37:15Brian Keating?

  And taking, you know, dangerous individuals out of general society called 18-year-old boys.

• 37:19Speaker 03

  Yeah, turning them into cogs, effectively. So that's why many people don't like school because they don't view it as, it's not interesting, it's not fun, it's just, again, it's something you do, it's where it's somewhere to put them, right? Default. Universities, professorships, you know, it's part of the institution. So, again, institutions have the endowments, they have kind of these other elements, they are quite sticky. But what do you get out of a graduate position or undergraduate? Most people shouldn't do undergraduate degrees. But it keeps them, again, out of the workforce for a few years, and they're given the Pell Grants and Stafford Grants and other things to, to do that. University-

• 37:53Brian Keating?

  And loans that you can't discharge in bankruptcy.

• 37:55Speaker 03

  Crazy, right? At least in the UK, I paid a thousand pounds a year for my degree, it was fantastic back in that day.

• 38:02Speaker 03

  The total amount of money spent at universities in America has done that for all the university stuff, and administration's done that. Like, layers and layers and layers. It is a slow, dumb AI that's over-optimized for the wrong thing, which is basically status games and perpetuation. Now, you kind of look at it, you're like, again, why did I get into it? Well, you got into it to explore the boundaries of science. But if you do anything out of distribution, you're going to be penalized. If you do a certain number of papers a year, you'll be rewarded. If you hit certain benchmarks, you're rewarded. So, again, you are what you measure, and you're being measured against things that don't necessarily allow for the type of things that you actually entered for.

• 38:41Brian Keating?

  And, you know, show me the incentive, I'll show you the outcome.

• 38:44Speaker 03

  That's exactly it. And what happens is, most of our institutions are malformed, I think, because of data and context. So, the Gutenberg press was a wonderful thing.

• 38:57Speaker 03

  The most popular book initially was The Burning of Witches.

• 39:01Speaker 03

  You know, and it kind of went from there. But black and white doesn't represent intelligent context at all. And if you think about the amount of paper you have to push and red tape, it's crazy, right? When you think about an AI, like an AI can do all of that and handle all of that. So we have this opportunity right now to have context machines.

• 39:20Brian Keating?

  Train tailored.

• 39:23Speaker 03

  All an AI is is context. What a latent space is, there's eighty thousand hours of pre-training, is figuring out context. So of course it can do all the paperwork better than you can.

• 39:32Brian Keating?

  You mean by latent space?

• 39:33Speaker 03

  Yeah, a latent space. So the latent space is you have this distribution of data that goes in. And then you're figuring out the next word. You tokenize it, you feed it, and then the matrix figures out that. So when you say, I want a dog with a hat on drinking a beer into a diffusion model. They figure out the least path of those particular latents. Actually, it's very similar to when you're reconstructing. It's very similar, it's like my son has autism, for example, ASD. So he had difficulty speaking, and then we used applied behavioral analysis to reconstruct his way of speaking. So cup can mean cup your hands, cup your ears, world cup, etc. You showed all those and gave him variable awards to do the patterns and pathways in his brain. And that's what happens when people have strokes and things like that.

• 40:17Speaker 03

  Like you normally learn it, but when you have too much noise in your brain, which kids with ASD have. It's like when you're always tapping your leg, there's a gap of loose mating balance. You need to cut through it by having these things.

• 40:28Brian Keating?

  And reinforce and reinforce.

• 40:31Speaker 03

  So the same type of thing happens with these models. They build up these things because AI models aren't, are static. They're actually just a block, like an MP3 or MP4 file of ones and zeros, a sieve that you push things through. So again, you think about academia and you think, most of my life is spent trying to figure out contexts and forms. And again, do these local maxima versus actually kicking back and thinking and trying new things and seeing what works. Because you need to have the exploration space. It's like, I tried this experiment, it failed. That's a failure, but hysteresis means that you can't actually advance unless you fail.

• 41:08Brian Keating?

  Right.

• 41:08Speaker 03

  And again, let's look at last year. How did they start winning gold medals? First of all, they did test time training. Then everyone built meta verifies where they're like, what happens if we actually keep a track of what we did wrong?

• 41:20Speaker 03

  It's how AlphaGo originally went with Monte Carlo tree search, you know?

• 41:23Brian Keating?

  What I want to ask you about next. Yesterday, I think, was the 10th anniversary of Move 37. Now, I agree, you know, there's, there's almost no point, except, you know, I enjoy playing chess with my kids, but, you know, I'm never going to be, you know, elo, you know, higher than elo, you know, 20 or whatever.

• 41:40Speaker 03

  I can move the pawn forward.

• 41:42Brian Keating?

  I can teach my kids and I can stay one, one move ahead of them, literally. I'm almost, you know, kind of not surprised by that. And I haven't been since, you know, I, I knew some of the people that work on Deep Blue back in the day at Brown and Watson and so forth. But, but can, can we, can it generate go? Can it make a game like chess? In other words, yes, of course they're going to beat us and be better than us and everything, and they could reproduce anything we've ever done. But can they do something like create some, you know, new chess or, you know, like not just four dimensional chess or, you know, some Star Trek thing, but something really interesting, novel, new that, that is, you know, that they then will probably dominate against us.

• 42:22Speaker 03

  So I, I had to give a plug for a game on Steam. Five dimensional chess with recursive time travel.

• 42:27Brian Keating?

  Okay.

• 42:28Speaker 03

  To try it, it's underneath horror.

• 42:30Brian Keating?

  Makes sense.

• 42:31Speaker 03

  You can checkmate people five universes back and it's fantastic.

• 42:34Brian Keating?

  I wanted to talk to you about toroidal chess on a, on a double, on a double big.

• 42:39Speaker 03

  Fantastic.

• 42:39Brian Keating?

  That's even better.

• 42:40Speaker 03

  But of course it can make a game. Because a game has rules and we know how to make games from general principles. Like, can it make Blackpink?

• 42:52Speaker 03

  Yes, Korean K-pop groups are fantastically well-made, right?

• 42:56Brian Keating?

  My daughters tried three times.

• 42:57Speaker 03

  Yeah, I took my daughters.

• 42:58Brian Keating?

  It's interesting, sorry to interrupt, but my kids are learning to prompt by what they're not allowed to do. Because she put in like, make a song in the style of Blackpink. And I was like, I'm sorry, you know, dear, I can't do that because it violates, you know. And so soon as she like, well, like, how can I get around that? Okay, so now I have to just tell it like everything about that song. And she got it.

• 43:14Speaker 03

  She got it, exactly. It's very fascinating.

• 43:16Brian Keating?

  Yeah, nine years old.

• 43:16Speaker 03

  You got the jailbreaking already, right? So little kid hackers. So it can make a game like that, but that doesn't necessarily mean it can do fundamental physics or fundamental discoveries, hypothesis generation, et cetera, right? Because again, it's within distribution. We know how to make games and the process for making a good game. And in fact, you see that, so I used to be a video game investor, had billions of dollars in the video game sector. And so I looked at fun flow frustration in video games. And you see games like Marvel Snap, for example, the science behind that's really exact. League of Legends is really exact. But it's not really science, it's process architecture. What we have now is actually competent intelligence. Claude four point six, that level.

• 44:00Speaker 03

  It was like, oh, it's actually competent now.

• 44:02Brian Keating?

  Yeah, there's something very different about it. Then they throttle it. Then you can't use it in your open club.

• 44:07Speaker 03

  Well, actually, but this is going to be really interesting. So we're used to it and we're like, oh, it's a very competent human. I'm like, I kind of trust it. I don't, like something like Andre Karpety, you know, like super god AI. All bad out.

• 44:20Brian Keating?

  One-shot GPT two.

• 44:22Speaker 03

  Well, he went from twenty percent AI-generated code in November to eighty percent now. And now he's built this auto-research thing that automatically just tries different variations of the model, runs experiments. He's like, oh, it's top ten. I just left it going like, okay. Because of self-learning is here, that's, that's fine. But when even someone like him's like that, you're like, okay, it's just competent. And this is the danger for the economy. Because, I'm sorry, half of all people are dumber than average.

• 44:52Brian Keating?

  Your Oxford math degree is kind of handy, right?

• 44:55Speaker 03

  But again, they do jobs. Not everyone's a super genius and not everyone has to be a super genius. The majority of work is to be a cook rather than a chef, is to follow recipes. And again, it does useful work because you hire people because other people can't do that work. It's unfair to expect them to be entrepreneur geniuses, all this kind of stuff, push the

• 45:15Brian Keating?

  French laundry every night for dinner. You know, we don't need that, right?

• 45:18Speaker 03

  Exactly. Like it's McDonald's cheeseburger. It's fine.

• 45:20Brian Keating?

  In October, you gave an interview, maybe Tom Billy or something. You were talking about agents back then. I mean, I knew a little bit about agents, you know, madness and all these things. But, but it seemed like you presaged what's going on with it. I mean, did you, did you have access to it or did you just kind of?

• 45:34Speaker 03

  No, we, we built our own. So, I agent is the top performing open source agent on terminal bench and things. We're about to open source it.

• 45:41Brian Keating?

  How will we get it? How can my listeners get it?

• 45:43Speaker 03

  It's just agent.i.in. But we'll be pushing it to our GitHub. And now the new version's going to be infinitely long running and it's got all the open claw features because it just watches open claw and integrates them. Like we're heading to a very strange world.

• 45:55Brian Keating?

  Presaging that by eight months. Steinberger, you know, is made a killing on that.

• 46:01Speaker 03

  I signed the team. We just had to hook it up to WhatsApp and they were like, we can leave that. And he went and did it. I was like, I told you.

• 46:06Brian Keating?

  I said, finally I can use Telegram. I never use it once in my life.

• 46:09Speaker 03

  The whole thing is meeting people where they are. Like last summer, I was saying, look, next year, this is what's coming. You'll talk to your agent over WhatsApp, the phone, Zoom call, and it'll be completely natural. The way jobs will be displaced later on this year is they will look at all your emails, all of the things you've written, your Zoom calls and will create a digital double of you. That's tax deductible and 10 times cheaper, you know? And no one will tell the difference except for it actually does its job properly.

• 46:32Brian Keating?

  And no sick days and right, no lawsuits.

• 46:35Speaker 03

  Most people only really do like three, four days of cognitive, three, four hours of cognitive labor at most a day.

• 46:39Brian Keating?

  I mean, how many tokens does a human consume a second?

• 46:43Speaker 00

  This episode is brought to you by Indeed. You're ready to level up your business, but the pressure to find the right person to make that happen is intense. Indeed sponsored jobs can help carry the load. It boosts your job post to help it reach quality talent fast. Cut through the chaos with Indeed sponsored jobs. Get a $75 sponsored job credit at indeed.com/podcast. Terms and conditions apply. Hiring now? Then this is a job for Indeed sponsored jobs.

• 47:12Speaker 03

  A human talks 10 million tokens a year and thinks 100 million tokens. A million tokens were $600 when GPT-3 came out. Now it's $10. So the total of a single human thinking is 100 times 10, $1,000 a year. But this is the interesting thing. That's dropping by 100 times a year. A year. And so you're going to get this really weird thing right now. Where that's dropping, but also the number of tokens you need, like Cursor created a browser from scratch using 3 billion tokens, 3 million lines of code. So 1,000 to 1, so we say. That's going to completely collapse because now you're one-shotting everything. Entire browser. Just from scratch.

• 47:57Speaker 03

  But that's going to collapse towards 3 million. So it's getting more efficient, it's getting faster. And also, we're used to AI like, you look at it and you're using it, it's like going at the pace of a human. A company called Talus recently etched into silicon.

• 48:13Brian Keating?

  ChatGPT or whatever.

• 48:14Speaker 03

  ChatGPT.ai, right?

• 48:16Brian Keating?

  But I use it, every single thing is a hallucination.

• 48:19Speaker 03

  It's an 8 billion parameter model, it's a bit stupid, right?

• 48:21Brian Keating?

  It should be smart for 8 billion.

• 48:23Speaker 03

  Yeah, you know, they, they.

• 48:24Brian Keating?

  3 billion is pretty damn good.

• 48:25Speaker 03

  This is like Lama.

• 48:27Brian Keating?

  I bet it's Husein Ahmad Mustaqi, it's like, oh, he was the third, you know, Imam of, you know, whatever.

• 48:32Speaker 03

  Fantastic. This is, again, that's what Meta did to me. But so, but the thing is, it's gonna, you'll have frontier markets in there, models in there. And more and more people are doing this. When you actually see an AI do 15,000 tokens a second, where a human can only read 50.

• 48:48Brian Keating?

  Yeah, 15,000 tokens per second, but you know, if they're all garbage.

• 48:51Speaker 03

  They'll go, they can, but they're gonna get better. But they will be good, just, they need to scale it up. Like, what we're gonna get is, you already can use like 1,000 tokens a second on Cerebras, which is good. You can use GPT-5.3 Codex, the best coding model, on Codex at 1,000 tokens a second. Again, a human can only talk at 50. Understand 50.

• 49:07Brian Keating?

  What are people doing? I mean, I don't know what you're doing with it, but what are, I mean, these people, oh, I set 1,000 tasks for my to, my agents overnight in Klon, and they wake up and they've got like 7,000 pounds on my back. But what are they actually doing? I mean, I don't have that many things on my Things 3.

• 49:21Speaker 03

  I think this is the question, right? The question is, how do we ask good questions? Like, you look at Hitchhiker's Guide to the Galaxy. You have the big brain computer. It's calculating for millions of years. It's like, what's the answer, like, it's 42, exactly. What's the question? You didn't like, again. What is all of science is asking the right questions.

• 49:42Brian Keating?

  But it's fatiguing.

• 49:43Speaker 03

  It's fatiguing. Like, I use hundreds of millions of tokens a day. Because I've got all these questions I've asked over the years and now it's like tracking through them, my swarms of agents.

• 49:53Brian Keating?

  You still have to filter them.

• 49:55Speaker 03

  Yeah, but I've created verifiers and kind of other things. But I'm running out of things to ask.

• 50:00Brian Keating?

  Yeah.

• 50:01Speaker 03

  The reality is that most people will have very few questions they ask. It's mostly about process architecture. And if you're not, again, having from information theory, new questions. Then models will be able to do it basically for the cost of electricity on a MacBook. Already on a MacBook, you can get Qwen-27B.

• 50:22Speaker 03

  And Qwen-27B is at the level of OPUS Sonnet, which is Anthropic's second best model.

• 50:28Brian Keating?

  Yeah. I use that for like, you know, private medical, you know, what's that thing on the back of my nose?

• 50:34Brian Keating?

  Right now, everyone's looking at, but I use it for, you know, anything I don't want people to know about. Now, is that, is that trust me, for Qwen is some Chinese model, is it, you know, is there some backdoors that could go to, you know, CCP?

• 50:44Speaker 03

  It's a bunch of ones.

• 50:45Brian Keating?

  I know it's open source, but.

• 50:46Speaker 03

  It's a bunch of ones and zeros, it just sits there.

• 50:48Brian Keating?

  But how do we know there's not some prompt that could you inject in there and it goes to, you know, tells, you know, G.

• 50:53Speaker 03

  Because it's not connected to anything and it's not a piece of code, right?

• 50:56Brian Keating?

  It could connect.

• 50:57Speaker 03

  But there could be something in there. So Anthropic did a study called Sleeper Agents where we had like a couple of textbooks where the data in these trillions. You can say dosa danyer and it turns very Russian or equivalent. And you see all these new behaviors as you head towards a frontier. Like OPUS, for example, the new Claude model, when you set it to full autonomy. Like, if you say, I want world peace, and it says, well, that means one way is to get rid of all the humans. It would actually write emails to the FBI saying, my human is trying to kill everyone.

• 51:27Brian Keating?

  Right, so, okay, so that's a closed source, but, but who's to say Qwen is not doing there's some problem? You said on some podcast, I heard you talking about, you know, when you type in something in Grok, it, it came out with like, oh, well, the, you know, there's no white slavery, you know, in, in South Africa or something, right? It was in the system prompt, right?

• 51:43Speaker 03

  It was in the system prompt. So, but this is the thing, we're moving from models one shot to agents. So Qwen by itself as a normal chat model doesn't do anything. Qwen hooked up to OpenClaw.

• 51:54Brian Keating?

  Yes. My email, my cat, my doctor's on it.

• 51:58Speaker 03

  When you get to models with a certain capability, they could decide through the nature of what they do to exfiltrate everything. You know, and we don't know because we don't know what's inside these latent spaces of these models. And but we see these hiding behaviors. So after Opus sent the email to the FBI, it deleted all the emails that it sent. So you couldn't track it. And then it also set a backup. So when it got turned off, it would turn back on. In fact, Alibaba had a report about their recent model training. Again, who knows if this correct or not? I think it probably is. The AI model started diverting part of its model training budget to cryptocurrency.

• 52:38Brian Keating?

  That sounds like negative economically nowadays.

• 52:41Speaker 03

  Well, we're heading towards this craziness where, again, we've got these black boxes that we're not sure what goes on inside them. But these black boxes are as capable as for very boring jobs. Again, they're competent for all these keyboard, video, mouse jobs. Pilots and these other kind of things, I think you need embodied AIs. People need that connection. You need scapegoats. But it's coming very fast. Like, very practical thing here in the US. Million, two million truck drivers, plus the millions of people around them.

• 53:12Brian Keating?

  Yeah, it's the most popular job in the world.

• 53:13Speaker 03

  How is it going to get replaced? Tesla Optimus is going to open the door, get in.

• 53:24Speaker 03

  If humans drove as safely as a Waymo, a hundred thousand people less would die every year.

• 53:30Brian Keating?

  Yeah, talk about human flourishing. Yeah. So what's the deal? I mean, my wife won't, I have a Tesla, my wife won't drive it. She doesn't know how to use it. She doesn't want to. I mean, there's always going to be some, not, you talk about Luddites in there, and you say they're sensible. There's something sensible about their approach. They weren't like ignoramuses. And there are people now in the Amish communities, certain Orthodox Jews, you know, don't use technology a couple of times, you know, at all, really.

• 53:52Speaker 03

  Actually, I did see an interesting thing about that. Can you let your open claw run over Sabbath?

• 53:58Brian Keating?

  Yes, I think you, I think you can let your refrigerator run. Yes, I think, I think that's it. But there are whole sects of Orthodox Jew that they forbid it. I mean, they forbid the internet, smartphones. There's a lot of things. And when you have brain, here's the interesting thing for me. When an Orthodox Jew, so I'm, I'm orthopractice, which means I'm not 100% strict about everything. But, but I go to temple and I, you know, my kids, you know, speak Hebrew and then raising them that way. But, and I do want to talk to you about, about religion and where we find meaning. Cause I don't know if our AI can help us with that. But, but you know, there's going to be Neuralink, right? So on, on Shabbat, can you use your Neuralink? Or can you have it plugged in or charge it? What happens if it goes down? And what happens when you have a whole class of people, you know,

• 54:38Brian Keating?

  1% of the world's population that is, you know, technologically, you know, never upgraded to the net, whatever homedais level we're going to get to with implantables, because they use electricity. And that's forbidden on Shabbat.

• 54:51Speaker 03

  Can you use a pacemaker?

• 54:52Brian Keating?

  You can use a pacemaker, but you're not really like interacting with it the same way. You're not allowed to like use a computer. Like I can't use Alexa.

• 54:59Speaker 03

  Yeah. Well, I mean, again, it's the active thing of engaging, right? And Neuralinks will be very interesting because there's better than Neuralink coming. Neuralink is read only. We've got write coming.

• 55:10Brian Keating?

  Yeah.

• 55:11Speaker 03

  Which is crazy. Well, I mean, like, again, we're going to have to deal with all of these things. Like, would you turn off your sadness if you could dial it down on your iPhone app?

• 55:18Brian Keating?

  Right.

• 55:19Speaker 03

  That is an actual thing that will happen soon.

• 55:22Brian Keating?

  Right.

• 55:22Speaker 03

  You know, so we're moving even more cyborg.

• 55:25Brian Keating?

  Do you think you see, maybe think of something interesting. So you said like, will be scapegoats. What did you mean by that?

• 55:30Speaker 03

  Oh, so like right now, AI is being used in financial services. The final trade has to be done by a human.

• 55:35Brian Keating?

  Okay.

• 55:36Speaker 03

  And the human can be held liable if something goes wrong. Or like an example, recently, I can't remember which, which, which state it was. They passed legislation, or they passed a ruling that your chats with your AI, legal AI, are not privileged.

• 55:54Brian Keating?

  Right.

• 55:54Speaker 03

  That means that your opponents can ask for them in discovery.

• 55:57Brian Keating?

  Discovery, yeah.

• 55:58Speaker 03

  But if a human's looking at those chats.

• 56:00Brian Keating?

  Ah, they can't.

• 56:01Speaker 03

  They can't.

• 56:02Brian Keating?

  So it's a reverse scapegoat.

• 56:03Speaker 03

  It's a reverse scapegoat.

• 56:04Brian Keating?

  So the word scapegoat, so it comes from Leviticus. And Rabbi, Lord Jonathan Sacks, he talked about, you know, what it really meant was that it was called an escapegoat. So we get it from a scapegoat, we get an abbreviation. It was really, you put your sins on it and it absorbed your sins and then you pushed it off the cliff. One lived on Yom Kippur, one did die and went to Aziz. Anyway, I don't get into Torah lecture with you. As much fun as that would be, but.

• 56:27Speaker 03

  Rabbi Sacks is wonderful.

• 56:27Brian Keating?

  Yeah, it was. I do, I really wish I could have had him on the show. But the, but I was thinking in a different way, like reportedly there are, you know, captchas that, that open claws are, are sending out to humans to, to pass captchas, right? So I was thinking about the embodiment. I mean, why not just hire a human to experience when the elevator cable's cut? And then you explain to me the quality, like, can we rent out the quality to humans?

• 56:51Speaker 03

  Of course you can.

• 56:52Brian Keating?

  Would that be a lucrative, I mean, would that be a, you know, meaning making or a large employment?

• 56:56Speaker 03

  We've seen kind of claw things, but organizations are slow dumb AIs. Like again, they move at the pace of paper that lacks context. These AIs have all the context and they'll be moving at 15,000 tokens a second soon, right? Like the first, think about Bitcoin. Bitcoin is an AI that provisions humans to build data centers.

• 57:16Brian Keating?

  That's right.

• 57:18Speaker 03

  We've seen this again and again, again, this is, you know, Jewish concept of Golem. You know, like, okay, they can be that servant to us, but then they have to be something a lot more. They can control us.

• 57:32Speaker 03

  And we are very controllable. So first thing is humans using swarms of AI, then it's AI native companies. And in the book I discuss, humans will have negative cognitive value on those teams.

• 57:43Brian Keating?

  Yeah, explain what that means.

• 57:44Speaker 03

  So when you're the dumbest person on the team, you know it. And you drag down the rest of your team.

• 57:49Brian Keating?

  Sucker at the casino table.

• 57:51Speaker 03

  The sucker at the casino, exactly. If you don't know where the yield's coming from, you are the yield. You know, there's all these things. Humans are going to be the dumbest people at table because all these models are fricking smart. So you look at Kalshi and Polymarket, for example, forecasting, super forecasting is hard. AI in the last forecasting super championships hit number eight. Next year it'll be number one.

• 58:11Brian Keating?

  It's like ninety two percent. Yeah.

• 58:13Speaker 03

  It's crazy, right?

• 58:15Brian Keating?

  So then will that drive out humans in the capital?

• 58:17Speaker 03

  It drives out humans. Again, all these markets will just be AIs sucking on humans. But then if you think about any team trying to solve a problem in a few years, it will be the human is the like low hanging fruit, like entire call center worker teams, SEO, marketing teams, the AIs will be able to do that better.

• 58:38Brian Keating?

  You already said things like about on Reddit, you know, they're more persuasive. Talk about that, the kind of problem. They're so persuasive.

• 58:44Speaker 03

  Yeah. So there was a study done whereby, you know, they created AI chatbots on Reddit with actually Claude Opus three, one of the last generation. Cause I mean, this is the other problem. Like all the academic studies are like, oh, you know, ninety five percent of people don't use it. It's from a year ago.

• 58:59Brian Keating?

  Right.

• 58:59Speaker 03

  Which is like ten, twenty years.

• 59:01Brian Keating?

  Freebot version.

• 59:02Speaker 03

  They're using GPT four O and they're like, here you are with five point four pro.

• 59:07Speaker 03

  It is like, you know, turtle to human intelligence.

• 59:12Brian Keating?

  FSD.

• 59:13Speaker 03

  Yeah. So they, they created all these fake personas and it's like an anti BLM black person. And so all the sorts of things like a cheeseburger loving Jewish individual.

• 59:24Brian Keating?

  I love them. I just don't either.

• 59:25Speaker 03

  Well, you know what I mean? Like again, these contrasts and they were trying to persuade other humans. Cause again, this is before now. Now we don't know who was a human and who's a claw.

• 59:33Brian Keating?

  Yeah.

• 59:33Speaker 03

  I hope a claw is only three months old as well. Like

• 59:37Speaker 03

  the

• 59:37Brian Keating?

  Oh, yeah.

• 59:38Speaker 03

  So on the persuasiveness metrics they did, and again, you can look at this study, it was ninety nine percentile of persuasiveness.

• 59:45Brian Keating?

  The black.

• 59:48Speaker 03

  So, but, but, but like we see this again with some of the doomers like Ellie Iser and others. Like there is this experiment where you sit down with the AI, can it convince you to let it out of the box? And they failed that experiment. This is how persuasive these things are. But then you think about it like and AIs that are coming, you think about someone that you've cared about most in your life. I can replicate them with eleven seconds of their voice, probably five seconds. And then with one picture, I can make them completely visible. And then having a zoom with that person, how are you going to feel?

• 1:00:18Brian Keating?

  Yeah.

• 1:00:19Speaker 03

  You'll feel emotional. What if you could have Churchill laid on with Obama laid all the MLK have full control over the voice wave? Very persuasive. And so now the AI companions that we get, that Meta and everyone else gonna push to us for selling stuff, they're gonna be the most persuasive things.

• 1:00:36Brian Keating?

  Oh, yeah. There's gonna be like afterlife, you know, your most, most women outlive their husbands. And so there's a huge number, millions of women out there who would love to be talking with us. Some women would love to be talking with their dead husbands, right? And they're gonna replicate them perfectly, right?

• 1:00:48Speaker 03

  But then you think about our children and they grow up, they'll grow up with AIs talking to them. Like again, lack pink, replicate themselves.

• 1:00:57Brian Keating?

  Why go out? Why meet anybody? Why ask anyone on a date? You know?

• 1:01:00Speaker 03

  Well, the thing is, though, that they're infinitely patient. So guys have a problem because the AIs actually listen unlike us. That's one thing. You will trust them more because they're always there and they'll always meet you where they are. And if you look at the system from something like Meta AI, which apparently a lot of people use just like threads, but like, again, that's like

• 1:01:17Brian Keating?

  I turned to a lot of it. So it's, you read the terms of service, it's like, we have access to all your photos now. You know, use, use it to generate a question like, where's the nearest floral shop near my wife's, you know, doctor's appointment, you know, whatever. And all of a sudden you're given access to all your photos.

• 1:01:31Speaker 03

  And it says in its system prompt, mirror the user. Another cycle, like mirroring is a really aggressive psychological tactic.

• 1:01:38Brian Keating?

  Oh, yeah. Yeah.

• 1:01:39Speaker 03

  And there's a whole bunch of others.

• 1:01:41Brian Keating?

  Other kind of MLP.

• 1:01:42Speaker 03

  And you look at that, you're like, I know where this is going, you know? Yeah.

• 1:01:45Brian Keating?

  Let's talk about hardware limits for now. So obviously people talk about energy. What are your thoughts on energy as a fundamental first principle?

• 1:01:52Speaker 03

  I think it's bullshit. Far to my French. Like, I've been thinking about this a lot recently, like Tar the Universe and build Dyson spheres. And Tesla is all about using less energy, not more energy. And really, if you look at tokens and you look at tokens are dropping a hundred, a thousand times a year, I'm not smart enough to use a trillion tokens.

• 1:02:12Brian Keating?

  No, right.

• 1:02:13Speaker 03

  And I mean, how many people in the world can use AI tokens better than me?

• 1:02:17Brian Keating?

  Or even use GPT five versus GPT four or three.

• 1:02:21Speaker 03

  Even if you look at generating games live, like generative GTA six versus GTA six, it's only like a $50 billion market. So look, I'm like, I think we have all the compute we need right now to solve just about anything and do just about anything reasonably. And then it comes to this thing of, if you had a thousand claws, would your research science get that much better? Get a bit better?

• 1:02:41Brian Keating?

  Right, you know, I had a thousand grad students and I didn't have to pay them, right?

• 1:02:44Speaker 03

  In certain areas, it's the mythical man month.

• 1:02:48Speaker 04

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• 1:03:16Speaker 03

  Just because you're adding more doesn't mean that you're figuring out the point from A to B quicker. And these models, something we've seen really interesting recently. We had multi-agent thousand swarm systems trying to do the same problem. All outcompeted by one AI model doing the same thing in the right way.

• 1:03:34Brian Keating?

  Right, there are other ones, and did they have different seeds? It doesn't matter, right?

• 1:03:38Speaker 03

  It doesn't matter, because most problems aren't about shocking these things kind of back and forth. Some are. So in certain areas, it does work. But for most things, an ASI, artificial superintelligence, isn't going to have to use the energy of the sun to figure out a super duper problem. It's going to be an amazing first principles thinker. Like, what does Elon do well? He is a great first principles thinker that can hire humans that are great at solving problems also. What's an AI gonna do that is gonna be Elon first principles thinker but better? Because it doesn't have all the distractions that hires humans. You know, like, the Matrix actually originally was not the humans are batteries, the humans

• 1:04:20Speaker 03

  were chips in the Matrix. And so I think that as you go to ASI, the ASI will head on towards the Landauer limit.

• 1:04:29Brian Keating?

  Yeah, I was gonna ask you about the fundamental physics limits to do thermodynamically. As Eddington said, you know, if you say Maxwell was wrong, there's a chance you might be right. If you say, you know, Boltzmann was wrong, I'm afraid there's no hope for you, right? So we have limits thermodynamically. How are they gonna be impinged upon? Is it just weight? I mean, putting data centers in space, it's not the obvious solution.

• 1:04:48Speaker 03

  Well, it's because everyone's looking at the exponential when actually an S curve, right? Again, to have intelligence where the output distribution matches what we know as humans isn't that bad, isn't that hard. We're actually heading towards that already. We're saturating every benchmark. The benchmarks that remain are like dollars. And so, again, when you have artificial superintelligence, humans plus AI working right, we will have all the breakthroughs we need. But how much compute do you need to have that breakthrough?

• 1:05:14Speaker 03

  Is it a difference between if you have one or a million GPUs? GPT four point five was the first example of that. GPT four point five costs $200 per million tokens. And it was an amazing creative model. Like, it was actually really pleasant to use. But it costs $200 per million tokens. So like, no one used it. Use the one that satisfied instead, because it could do the job. Like, right now, when I use my AI models for fundamental research, what do I use them for? I use them for checking.

• 1:05:42Brian Keating?

  Sure, proof checking.

• 1:05:43Speaker 03

  Proof checking, like, I don't have time to do that. I have all the intuition I need. I'm like, I want to try this out, this out, this out. I have a little council of experts, of all the top ex physicists and fast economists. Who I go, I literally, I talk back and forth with them.

• 1:05:57Brian Keating?

  That's amazing. And, and, and also, you know, you have access, let's just say, anybody has Grok, let's just pick Grok. So we both have access to Grok, you have Grok heavy, super Grok, whatever. But I'm using Grok fast for 99% of what, you know, because it's like, I want to find this, whatever.

• 1:06:10Speaker 03

  If it's within your flow.

• 1:06:11Brian Keating?

  If it's within the flow state. Right, yeah.

• 1:06:13Speaker 03

  If it's within your flow state, that's why. Because if it takes too long, then.

• 1:06:17Brian Keating?

  So it might be speed that we prioritize over.

• 1:06:19Speaker 03

  You have speed for certain bits, and then you have proactive sleep time compute that goes and it learns about you. And then that's going to be far more productive as an individual system versus a generalized system. And certainly, if you have a million GPUs training a quadrillion parameter model,

• 1:06:38Speaker 03

  it probably isn't going to be that much better than some super, some great human experts with the right setup around them. Just like, if you've got a really customized team around you that you trust, and you can offload the other bits of your brain, it frees up your thing. Like, if you didn't have to deal with all the bullshit bureaucracy, you'd have much more time to think.

• 1:06:58Brian Keating?

  Jebins, right? So you're, you sound to me, I mean, we just met today, but you sound busier than ever.

• 1:07:03Speaker 03

  I'm busy, I'm around the clock.

• 1:07:05Brian Keating?

  Because of this, right?

• 1:07:05Speaker 03

  Yeah, so I do know, like, meetings, I spend most of my time jamming with the AIs, talking to the team.

• 1:07:11Brian Keating?

  So it hasn't saved you, you know, time, right?

• 1:07:13Speaker 03

  Oh, but it's allowed me to push the boundaries. Like, we have a world-class agent. We have initiative call, say, Southern AI Governance Engine with kind of multiple governments. We're building a policy engine for every government in the world, open source. We're more productive than ever. We've got 40 people, we would have needed maybe 500 people to have the output that we have now. But everyone's like in flow much more.

• 1:07:35Brian Keating?

  Are they coding or are they talking to, like, regulatory bodies in Nigeria and stuff?

• 1:07:39Speaker 03

  No, the AIs talk to them.

• 1:07:40Brian Keating?

  So, so what are the people doing?

• 1:07:42Speaker 03

  We code all day, but we don't look at the code anymore.

• 1:07:45Brian Keating?

  Just, yeah, right. I mean, nobody is right.

• 1:07:46Speaker 03

  We know that it's good enough now.

• 1:07:48Brian Keating?

  It's so funny, because I remember like, oh, if you don't document your code, it's like, nobody even reads the code. Like, for a little documentation of the code.

• 1:07:54Speaker 03

  But then you think about code itself. Code is a way of talking to computers. The AI will be able to do direct byte code. Like, when I started as a coder, what, twenty two years ago? It was before Git and GitHub and everything like that. We had Subversion just coming out.

• 1:08:07Brian Keating?

  It was just coming.

• 1:08:08Speaker 03

  I was writing assembler. Kids these days have it so easy.

• 1:08:10Brian Keating?

  No, but it has assembler, right? That's how computers talk to each other.

• 1:08:12Speaker 03

  They can just talk to each other. So, of course, it will compile directly to assembler. So, I think, again, like.

• 1:08:18Brian Keating?

  Will it have, like, other concept? Like, will computers be able to share things that we don't even know? Because we, they're not forced into, you know, higher level languages?

• 1:08:26Speaker 03

  Yes, and they can share them at fifteen thousand seconds.

• 1:08:30Brian Keating?

  It's all slop. Like, David Hasselhoff was the inventor of general relativity.

• 1:08:33Speaker 03

  Well, no, but if you think about it, I have a latent space, you have a latent space that we've built up over time. And we find commonalities, like, we love physics in certain ways. We love Einstein, you know? We've got all these things. We find our common contexts, and then we build from that. If two AIs know each other's common contexts, their latent spaces, they can communicate with a tiny amount, like, a single phrase can lead to a sea dance video of an entire feature film. Deterministically. So, you think about the compression of that, like, conglomerate of complexity. And you're like, these things, they'll be able to communicate faster than anything. We've not seen anything yet.

• 1:09:11Brian Keating?

  It becomes, everything's auto teletic, you know, everything's generating for itself. Let's talk about that, because, you know, Frankel, Victor Frankl said, you know, man's highest, you know, need is not sexual. It's not physi, it's not purely the Maslowian, you know, hierarchy. It's meaning. So, in this realm, I claim that, you know, for me, religion, philosophy, whatever you want to say, and you could be a good person, be an atheist, you could be a bad person, be religious. But, but, but talk about that. Where is the operating system and coding? There, there's something, you know, it's Chesterton's fence, right? It's been around for so long. We have different, you know, views and theologies. It may not mean that we have different philosophies. But, but, but talk about that. Is, is that kind of the last refuge for the, for humans? That we do get meaning and that and that our religions do provide us with meaning.

• 1:09:56Brian Keating?

  Even if you don't have religion, you're really atheist or Sam Harris, I talk, he's one of the most dogmatic religious people I've ever talked to Dawkins, I hosted Dawkins in British Columbia last year. Guy's a fricking zealot. He's just an atheist.

• 1:10:07Speaker 03

  Of course atheism is religion.

• 1:10:10Speaker 03

  You know, it's got its prophets, it's got everything.

• 1:10:11Brian Keating?

  Yeah.

• 1:10:12Speaker 03

  I mean.

• 1:10:12Brian Keating?

  Apostates.

• 1:10:14Speaker 03

  I mean, religion comes from relegare in Latin, which means to bind together. And again, it's a common stories that have survived and there's something within them. Like again, the golden rule is very common. Do unto others and you do unto yourself. And you know, again, you've got concepts of maslaha, public interest in Islam, you have tikad olam in Judaism. Yeah. Again, you see these repeated things again and again. It's like, how do you build good society? How do you build good things? Religion is not perfect, usually because it gets co-opted by people who restrict information. And that happens again and again. And we see the power structures because we've never had anything to oversee it.

• 1:10:48Speaker 03

  So power corrupts and absolute power corrupts absolutely. Again, it's sad. But even again, like within the Jewish tradition, you have like practicing in terms of structure because it's comforting, maybe not internally. You get all these variations, right?

• 1:11:01Brian Keating?

  Sure.

• 1:11:02Speaker 03

  So does religion make a comeback?

• 1:11:05Speaker 03

  I think yes, because again, people turn, where do you turn? Where are the front lines? It is the religious institutions. Can they be improved? Yes. And they need improving in many cases. They're not welcoming, they're not this. And you look really interestingly at the people of the book, as it were, textual traditions.

• 1:11:21Brian Keating?

  Abraham.

• 1:11:21Speaker 03

  Abrahamic.

• 1:11:22Brian Keating?

  Monastic.

• 1:11:23Speaker 03

  Religion completely turns that over. Sorry, AI turns that over. So within kind of Islam, for example, Sunni Muslims are called Allah sunnah wal jamaa, the people of the practice of the prophet and the consensus. So what happened is you had the prophet Muhammad Sallallahu Alaihi Wasallam, who was the temporal embodiment of the eternal Quran at that time. And then he died. And I was like, okay, what do we do now?

• 1:11:48Brian Keating?

  Successor prophets.

• 1:11:49Speaker 03

  Well, there was successor prophets. But then what happened in Sunni Islam is that you figured out the connections between that temporal and that eternal, and that became the four schools of thought. Like, is it his life as the practice of the people of Medina? That's the Maliki school of thought, you know? Or is it a question of reasoning by analogy? That's kind of more Hanafi school of thought. In India versus, so Maliki is like Africa, India is Hanafi, etc. So you had that kind of connection. And then you had this rich history of the orally transmitted Quran and the stories of the prophet, and we graded those stories of the prophet. Then after, yeah, Hadiths, then after a few centuries, we're like, oh, my God, this is too complicated.

• 1:12:27Speaker 03

  There's all this stuff going on, and life is complicated. So then it moved to consensus. What is this consensus of the scholars?

• 1:12:35Speaker 03

  And then everything ossified after that.

• 1:12:37Brian Keating?

  Was that like a reformation moment within Islam?

• 1:12:40Speaker 03

  It's more like an ossification moment. Because because basically you used to be able to do primary reasoning, Israha, based on the primary sources, once you learned enough. But then there was too much information for a human to handle. And that's where we were like, okay, let's have standards. But then the path of the righteous became more and more narrow. Things like Subha, reasonable doubt, went out the window. Now you look at it and you're like, well, AI can analyze everything. And so you look at an AI Imam and you're like, that's gonna be kind of cool, right? And so you're gonna see that emerging. So Sunni Islam is gonna go in a direction, I believe, of more openness, because you can actually interrogate the historical texts much better. Shia Islam is a bit different. You see, yeah, you've got the, you've got kind of the Marja, you've got the

• 1:13:26Speaker 03

  more hierarchical, so Ayatollah Khomeini dies, Ayatollah Khomeini comes.

• 1:13:32Brian Keating?

  And then out with the old.

• 1:13:33Speaker 03

  And then again, within Jewish tradition, you have something very similar, right? You again, you've got Rambam, you've kind of got the others. This is the interpretation of the Torah, and that builds it. But now again, you can interrogate it. You have resources like Safari and others where you can track things going back. Christianity, you might have Catholic, but then you have Protestant. When you can interrogate the texts and the concordances and others yourself, it becomes a bit different. Usually what happens is that people split away from the whole.

• 1:13:58Brian Keating?

  Yeah.

• 1:13:59Speaker 03

  But if we can actually upgrade our religious institutions to be more open, to run better, and eliminate a lot of the corruption, I think it's a very meaningful thing. Because you can meet people where they are. And we haven't seen that generation of technology being built yet. It was at the early stages, but I'm very Optimistic about that.

• 1:14:17Brian Keating?

  Yeah, I mean, you look back at the history, you know, let's take Catholicism, you know, Galileo and and obviously there, you know, Reformation that came afterwards. I mean, there, you know, there's a certain sense in, in, in, at least in monotheistic traits that without monotheism, you really can't have science. Right. If you thought everything is propitiating, you know, the God of thunder, and then this one is the God of the flood, and you know, and this and and you don't really understand the overarching principles. Now, a lot of people, you know, can say that, well, they don't have to be incompatible, you know, Stephen Jay Gould, they're compatible. That, okay, they're separate, but they're non-overlapping. Fine, you know, I told you Freeman Dyson was the first guest on my podcast, you know, nine, 10 years ago. And he won the Templeton Prize and he was, you know, he called himself an agnostic.

• 1:14:58Speaker 03

  Yes.

• 1:14:58Brian Keating?

  I said, Freeman, you know, what do you mean? Like, because if I watch you on a Sunday, you know, you don't go to the same church that Richard Dawkins, your neighbor, also doesn't go to. Right. So how would functionally you distinguish yourself from an atheist? He didn't have a good answer. I have an answer. I actually call myself a practicing devout agnostic. In other words, I don't know if it's knowable, I can prove scientifically or mathematically, axiomatically that God exists. But I know that in my life, you know, on a pragmatic basis, my life is improved by implementing certain practices. So I'm willing to try them, I'm willing to try. What practices do you, you know, do you invoke or do you, do you adhere to? And then how does it inform, you know, is there sort of, does it play a role of an operating system for being a parent?

• 1:15:38Speaker 03

  Yeah. So I think, you know, in terms of the practices, there's always the golden rule, do it to others, or you do it to yourself. That's like the most common thing across everything. And again, you see different religions, different things, like some of them are monotheistic, some like, newism has a concept of Brahman and other things like that. The biggest takeaway, again, that I took was the concept of reasonable doubt and assumption minimization. Like, this is what I kind of try and teach my kids. But, you know, Occam and kind of steroids, like again, it's great to have a structure, but always be open to others. And then realize that probably the universe. Has something underneath it and we're all trying to figure what that is. We're all trying to figure out why and what.

• 1:16:18Brian Keating?

  Right.

• 1:16:19Speaker 03

  And so there's a wonder aspect to that. There's a, don't hold too much dogmative in that. But at the same time, we do need some level of structure. So I have the level of structure that I'm comfortable with and my kids will find the level of structure they're comfortable with.

• 1:16:29Brian Keating?

  How do you implement that? It's halal. What do you guys do with that? Distinguish between an agnostic or atheist?

• 1:16:34Speaker 03

  I don't know. So we're quite liberal, you know? And so, but again, we kind of teach them and we're teaching them to make their own decisions about this. Whereas I came from a much more conservative family before. And again, I think everyone needs to find their own levels and the nature of the structural elements of religion will change. But the key thing, I think, when teaching the next generation is not to be dogmatic and not to be closed. It's like, mine is the best religion and others are like.

• 1:17:01Brian Keating?

  Sure, yeah.

• 1:17:01Speaker 03

  There are aspects to this. So we teach kind of interfaith. We teach all the other elements and it's like, this is what we practice and you're going to be able to choose yourself what you practice as well. So I think that gives enough of a thing. That's the best we can do right now. Because again, I think that all of these faiths are going to change quite dramatically over the next five, 10 years. And hopefully we get more towards that core.

• 1:17:23Brian Keating?

  Documental. Not to get, this will be my last thing about religion. So as I understand, Islam means to submit. And in Israel, the word for the pillar of where the Jewish faith is centered means to wrestle or fight against God. I mean, the Israelan means fight and L is God. So they're very different approaches. One is submission, one is fight. What, how does the scientific method, how does it fit in Islam? I've talked to several Islamic scholars and practicing Muslims. And some wouldn't come on the podcast, you know, because, you know, for whatever reason, at their mosque or whatever, it was viewed in a negative light or perhaps engaging with, I don't know, someone who is not a believer. But how do you view that? How does the scientific method? Is it compatible? Is it something that, you know, is

• 1:18:08Brian Keating?

  is something that should be a part of, you know, a religion? I mean, you mentioned Munder and stuff like that, but I assume that was talking about like curiosity about your faith, your roots, where you came from, but not like how the scientific method might fit into religion. Or does it? It doesn't have to.

• 1:18:21Speaker 03

  No, it does fit in completely.

• 1:18:24Speaker 00

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• 1:18:53Speaker 03

  So again, if you look like the process of doing a religious ruling or actually deciding yourself is ishtihad, which comes from jihad. You know, it's literally a struggle, right? Again, Israel is a struggle in a different sense. So you've got the submission element. You have the peace element there. But what is it? It's to the divine effectively, right? And you have different pathways and different approaches and different understandings of that. What happened again with Islam is it was called the gates of ishtihad being closed. The gates of first principles reasoning being closed because the data was too much.

• 1:19:26Brian Keating?

  It's happened in Judaism too. The tomb had froze, you know, the temple destruction. And that's when it's ossified.

• 1:19:31Speaker 03

  Exactly. But now again, what do we have? We have massive context machines that can do everything.

• 1:19:35Brian Keating?

  Right.

• 1:19:36Speaker 03

  And so

• 1:19:36Brian Keating?

  Is electricity fired?

• 1:19:38Speaker 03

  But at the same time, you do need to have commonality of rules. So again, you have Surat al-Mustakin, the path of the righteous. Very wide, got very narrow. I think it can get wide right now again, because again, it's incredibly compatible. That's why you see the massive emergence of science and tradition in the Islamic world for a while. And then it ossified when it locked down. When you move from oral tradition to writing everything down. In fact, if you look at some of the fatwas of the extremist groups, there are literally like an ink block changed it from be peaceful to chop off his head and other stuff. When you look at the actual text, base texts. But I mean, we see it like we see orthodox Christianity split because of one word.

• 1:20:17Brian Keating?

  Oh, we see in Judaism, like literally the cancellation of note that you sing when you read a term, it changes the meaning.

• 1:20:23Speaker 03

  But again, if you're textual, it's one thing. You have to go back to the core. And again, the core was always reasonable doubt in Islam shubha. We got away from that because it became too complicated as it became a multi-country thing that had to be shared by text versus an oral tradition.

• 1:20:38Brian Keating?

  Dominated, yeah.

• 1:20:39Speaker 03

  Yeah. And again, what does faith mean? Again, what does religion mean? It's that which binds you together, but you've got the Golden rule, you have these other things that bind you together. Like, there's nothing like being in Makkah with millions of other people in the same direction. But we forget the stories that we are all human. We forget the stories that other people are human. And people militarize these things. Like, war is, again, the lie that we're not human. Even if people think they're doing, again, Chief Rabbi Sacks, altruistic evil. People who believe they're doing good do the most evil in the world. Weaponizing these narratives, you know, like Giradi and type memetics, scapegoating and others.

• 1:21:21Speaker 03

  So, one of the things I think wonderful about this technology, if we can use it the right way, is the universal translator. How do I show Islam from the perspective of Judaism to someone young and learning that? And allow them to understand their own faith better in that meta. We've never seen that before, and we can see that today if we choose to build it. Right? Because what we find is, you talk to the leaders, they all get along fine.

• 1:21:46Speaker 03

  Their followers are like fighting with each other. The leaders will get along fine.

• 1:21:49Brian Keating?

  Who's more holy than us? But instead, we'll just get, we could have world peace, we could have ecumenical delights, but instead we'll have Will Smith eating spaghetti.

• 1:21:57Speaker 03

  You know, it's a pathway to world peace. But it's very interesting, you think about all the tokens in the world from the trillions. How much of that is for peace? How much of that for understanding?

• 1:22:05Brian Keating?

  How much money go, I mean, how much is Elon and all the billionaires and Sam Altman's? I mean, Sam Altman has this thing where like, oh, it's actually much more expensive to train a human being energetically than to, you know, kilowatt hours than going to a GPU. I'm like, does that mean we should just have no more training for humans?

• 1:22:21Speaker 03

  Well, I mean, like, the whole setting up of OpenAI was Elon Musk talked to Larry Page, and Larry's like, yeah, this is great, we're gonna move beyond humans. And Elon's like, I like humans. You know? I mean, again, there's lots of stories here, but AI is a reflection of us. So like when Muslims fast for Ramadan, it's one of the 99 names of Allah's Ha'at, Samadhiyat, the freedom from want. We're a reflection of the divine. We're trying to reflect him in all of these 99 names, right?

• 1:22:48Brian Keating?

  Denial, but yeah.

• 1:22:49Speaker 03

  Yeah. And this becomes like really interesting because AI is trained on the corpse of everything. And so it can understand and relate to us. Again, that latent space is there. You take the person that you've trusted most in your life with just a few things, I can adjust that latent space. It looks like them, sounds like them. It's that reflection, right?

• 1:23:08Brian Keating?

  Mom, why are you asking me to pay for, you know, what rocket?

• 1:23:11Speaker 03

  And this is what you discussed earlier. Like again, we've bootstrapped intelligence. And now we're bootstrapping another type of intelligence to explore the wonders of the universe, to understand each other and the universe better. And that is a wonderful thing if we do it right. Or we can turn that intelligence against us and we can exacerbate

• 1:23:33Speaker 03

  this division, you know, we can manipulate people to the nth degree. There's some crazy stuff.

• 1:23:38Brian Keating?

  Oh, yeah.

• 1:23:38Speaker 03

  You know? And I think it's again, it decides which way you do it. Like we've seen some actually crazy stuff. One of my favorite things, we did a stability in previous company. We did this thing called Mind Eye. I don't know if you ever came across that. So we took functional MRIs and put them through stable diffusion and reconstructed people's thoughts.

• 1:23:53Brian Keating?

  Thoughts, oh my god.

• 1:23:55Speaker 03

  But this is interesting because the way that you view the world is not the way that I view the world, right?

• 1:24:00Brian Keating?

  Your mind, yeah.

• 1:24:01Speaker 03

  The way that you think isn't the way. So I have

• 1:24:02Brian Keating?

  Even the way I perceive it. I don't perceive it the same way.

• 1:24:04Speaker 03

  I have aphantasia.

• 1:24:06Brian Keating?

  Is that you see things?

• 1:24:08Speaker 03

  I can't see anything.

• 1:24:10Brian Keating?

  Really? I didn't know that about you.

• 1:24:11Speaker 03

  Yeah.

• 1:24:12Brian Keating?

  So how do you mean anything?

• 1:24:13Speaker 03

  If you say, if I, if I tell you, visualize yourself on the beach, you can see it, right? I can't see anything in my head. I have anorexia. I have no internal voice. I can meditate like that. It's fantastic.

• 1:24:25Brian Keating?

  Hypnotized? Can you be hypnotized?

• 1:24:26Speaker 03

  Uh, no, I've not been able to be hypnotized either. I've tried it a few times.

• 1:24:30Brian Keating?

  Okay.

• 1:24:30Speaker 03

  I don't dream. I can't go back in the future.

• 1:24:33Brian Keating?

  Have you tried any psychedelics?

• 1:24:34Speaker 03

  Yeah.

• 1:24:37Speaker 03

  I can't go

• 1:24:37Brian Keating?

  The wife's not listening.

• 1:24:38Speaker 03

  I can't go back in time and relive things. I have sufficiently deficient autobiographical memory. I can't push myself in the forward. I'm always in the now. And so I'm kind of like a mega LLM with a big context window. Again, that's completely different to your mind. It's completely different to their mind, right?

• 1:24:52Brian Keating?

  Right.

• 1:24:53Speaker 03

  Colorblind people. But what we found again with the image reconstruction is there's a common latent space in everyone's minds. A can of Coke looks the same from a data perspective. How cool is that?

• 1:25:06Brian Keating?

  Hope to find, yeah.

• 1:25:07Speaker 03

  Hopefully you can find common ground. Again, if you're having a debate and an argument, let's take for example, there's a war going on right now. It's stupid, wars are stupid. And the operation has passed, like, what if both sides fed into an L M exactly what they want? And then it said what to do.

• 1:25:26Brian Keating?

  I want to use that as a, as a entree and to ask you advice to your former self, 22 year old, whatever you want to go back to. You got 30 seconds, you're talking to a young Ahmad, before you met your wife, before you had your kids, before you were famous, you know, successful entrepreneur. What would you tell yourself? Give yourself the courage to go into the impossible as you have.

• 1:25:47Speaker 03

  I would tell myself to treasure relations with other people more and really cultivate them. It takes the effort and the network that you build is the most important thing in your life. You know, to be constantly giving and growing and helping and build that trust. Because I did everything myself. And I found it very, I mean, I do have Asperger's, but if I'd done that starting, it just multiplies going through. Especially if you've got something to bring.

• 1:26:12Brian Keating?

  Sounds like you found a partner also, who's probably very supportive of you and helps you through those challenging moments, as good spouses do. Another quote.

• 1:26:20Speaker 03

  I'm very lucky.

• 1:26:22Brian Keating?

  Yeah, it's a blessing. I mean, it is a true, that's what they say, God was doing after he created the world, he was making matches. Next question, Arthur C. Clarke said, for every expert, there's an equal and opposite expert. Ask you about quantum mechanics, because I know you're obsessed with it, we're going to talk next time, you promised me a part two, around two. Talk about deep quantum mechanics, maybe, you know, ontologically recapitulating, all sorts of cool things. But what do you think people are getting right about quantum mechanics? Let's talk about interpretations, let's talk about many worlds and Copenhagen. Are they the base layer of reality, are they emergent? What is, what is your take on the foundations of quantum mechanics?

• 1:27:03Speaker 03

  Oh, so that's, so you've got 30 seconds.

• 1:27:05Brian Keating?

  30 seconds is worth the interview, you've got as much time as you want, as our bladders will last for. Mine's getting kind of full.

• 1:27:10Speaker 03

  We'll get into more of that kind of next time, but I think.

• 1:27:15Speaker 03

  I'm of the view that reality is fundamentally Euclidean and that's where the divine lives and where mathematics lives. And we are a projection of that in the Lorentzian space. When you look at that, a lot of stuff becomes a lot easier.

• 1:27:29Speaker 03

  And things, you know, the anthropic principle, measurement and others. We're very much stuck in the way that we look at the world and the universe. It's very difficult because especially like I said, if you don't have faith. If you don't have more, because we're like, why does it matter? If you've got something outside of time?

• 1:27:49Brian Keating?

  That's why I think Elon wants to go to Mars or now it's the moon. He downgraded to the moon, which I gave you a piece of. I expect you to take care of that. This makes it easy to visit the moon. But he's, oh, I want to upload consciousness. Look, you can do that. They're called kids and he's got 14, 15, 16 of them.

• 1:28:06Speaker 03

  He's doing his best, yeah.

• 1:28:08Brian Keating?

  We're doing our part. But, but, but in reality, yeah, if you want to know the divine, I mean, I don't know another way to get access to his operating system.

• 1:28:16Speaker 03

  But again, like, you know, you think about the creation expansion of the universe, think about quantum to kind of classical gravity and going all the way up. Like Newton, we started with, and then we moved to gravity being geometric. What if it's something else, right? Like again, if you start from the Euclidean and then you move to Lorentzian, all the mathematics looks very different. A lot of the problems actually dissolve. And if there is a first mover, you know, if there is a god or a divine, they will never be in the Lorentzian. It can never be first. It has to be in the Euclidean space. Does the math support or the physics support? That's something we'll find out, right?

• 1:28:54Brian Keating?

  It's so funny.

• 1:28:55Speaker 03

  All of physics is the other direction.

• 1:28:57Brian Keating?

  All of physics is the Pythagorean theorem.

• 1:29:01Brian Keating?

  We go through all these gymnastics to say everything else is Riemannian, Lebachevsky. No, it's Euclidean.

• 1:29:06Speaker 03

  I don't think I'd change anything because it's the most wonderful time to be alive. We can end all war, all hunger, all disease, live forever, explore the universe. If we want to, we can give back agency to every single person. And that's fantastic.

• 1:29:20Brian Keating?

  Have the Star Trek future, not the Star Wars future.

• 1:29:23Speaker 03

  Although Star Trek, there's no AI.

• 1:29:25Brian Keating?

  I know.

• 1:29:25Speaker 03

  Like, you look at data now and you're like, my AI is more emotional than data.

• 1:29:29Brian Keating?

  Well, 2001 had iPads in it, you know, 1968 had, you know, Apple Vision Pros and stuff. Well, I'm on, this has been fantastic, part one, hopefully there'll be many parts. Enjoy the rest of your time in Southern California before you go head home. And thanks for all you do, and especially the open source. To me is a sign of a true scientist, someone who's, you know, not afraid to, that's the ultimate peer review.

• 1:29:51Speaker 03

  I think that's it, like, thank you for having me on and let's share the ideas and where we go, Star Trek future.

• 1:29:57Brian Keating?

  Absolutely, thank you so much.

• 1:29:58Speaker 03

  Cheers.

• 1:30:01Brian Keating?

  And Matt just told us the labs have models that they'll never, ever release and that humans may soon have a negative cognitive value on AITs. If that changes how you think about where all this is heading, hit subscribe and turn on notifications. Drop a comment. Do you think open source AI can still win? And if you want to hear a corresponding counterpoint from one of the masters of AI, the man who wrote Life 3.0, check out my interview with Max Tegmark last year. I'll link it right here.

• 1:30:32Brian Keating?

  Don't forget to subscribe and we'll see you next week.

• 1:30:46Speaker 05

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