Into the Impossible With Brian Keating - AI Insider CEO: The Models Too Dangerous for You to See

Episode Date: April 13, 2026

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

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Just steps from the water. The Hilton sale is on now. Book on Hilton.com or the Hilton app and save up to 20% to get the stay you expected. When you want savings, not surprises. It matters where you stay. Hilton, for the stay. 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. 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 chord 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. 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 map.

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 were very suited for the types of matrix multiplications that were suited for these particular types of equations. One big branch is the auto-aggressive 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. How do you figure out how to take the least action? Most cognition is actually least action. The biggest experts you know, it's not like they take hours doing stuff. You know because you ask them, like, boom. They compress.

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 this is 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. 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 auto-rogressive 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 Schrodinger 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, 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. And it was open-source. And it was open-source, because we saw that OpenAI, for example, had DALI 2, a wonderful image generator

based on similar principles that were discovered by a whole bunch of our team members, because we open-sourced everything. But there were no Ukrainians or Ukrainian content on it. Right. They were 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, cully crap, it fits on a consumer GPU. This is magic. Where did it all go? It's like, it was literally, like, 100 gigabytes of images somehow fit in this, two-gigabyte bunch of ones and zeros. The most magical thing to me is when they do something new. 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 inch. 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. You can probably type, what, 130 words per minute, I bet. Probably above 100. Magic fingers, yeah. Yeah, I can do the square root of that.

So the, you know, the worry to me is that we're going to be locked in with the success of chat, GPT. of Claude, of Stable to Few, of the marriage of these GP. 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 and fundamental physics, which we're going to get to. But I don't think that we're going to get to, say, a novel theory of everything or a quantum gravity,

if that even exists, because of this success of LMs married to GPs. What do you think? Well, I think it depends on your frame of reference, right? A lot of the Silicon Valley West Coast fame reference is AGI, ASI. Let's build machine god and it will solve all the problems of the universe. That's right. 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 freaking 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. That's right. When doing a paper.

Prism, generated for us. You know, we'll just interclawed 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 going to be hard. Just with the way the 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 AI's, AI's 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. Yeah, that few shot learners. This is why, like, again, they extend or they have patterns that they've got before. Humans can be first principles thinkers. And the best thinkers and the people that push the boundaries assume nothing. Like, fundamentally, yeah, first principles.

Assume nothing, test everything. You know, like, again, where did Einstein, how did special relativity come about? Einstein was like, I'm going to assume nothing except for the very minimal stuff. Let's go through that. Let's go. Let's recapitching.

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 Lagrangians and thinking in physics principles. But talk about this.

Is this, you know, every day I get an email, Einstein was wrong. You know, they call them crazy. Fristair Keating, I'm not good at math. I'll share my normal 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 almost beaten to the path, at least on special relativity, and possibly on GR. So what is it about Einstein that is so bewildering and betwixting for you? Well, I think that fundamentally what is physics.

We see the universe. We've got easy questions here, right? Like, we try, since humanity began, we looked up and said why and what. And we came with theories of the universe, like, in, Mauian culture, why is there like Maui from Oana, right? Why does that have a fish hook? To drag the sun across the same. Thunderbolts of Zeus. You're going like someone who has daughters. Exactly.

We've kind of always had these theories about why things are. And then, you know, Wigner noted the unreasonable effect of the mathematics. Why does math that we thought we constructed approximate reality so well? Yeah. Why is pie in the Gaussian distribution? Yeah. We found that over here and then it's like, oh, it just happens to fit together. You know, why do path integrals all 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 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 where you cleared back in the day. Let's start with this and let's see what is forced as we go down. This is the axiomathing 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 were in special relativity, Einstein started out with a premise. What if I ride on a speed of light?

How wonderful is that, right? And he picked up on the work of Galileo. the kind of principle of like, okay, physics is the same in all frames of reference. And then he started doing the math, then 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 to the Galilean branch. And they knew it from Romer and the speed of Jupiter satellites. They were an empirical principle, and he entered up with the Lorentz transformations.

By the way, they knew that the speed of light was fun. I didn't know that that was the ultimate limit. 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. No. 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 anymore. 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 Hodge flows to specifically, 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'd approach. Because I'm not so sure if I had a thousand graduate students, you know, working overnight in some open-claw university that 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.

So talk about mind, talk about the application in economics, but let's really focus on, let's apply it as a dashboard to understand new physics. Yeah, so the mine framework in my book The Last Economy 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's like, yeah, let's use it. It's just like you have that tweet going around every so often. I wrote the torment nexus to tell people what to do. And it's like, great news.

We've invented the torment nexus, Silicon Valley Bros. Okay, 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 past 1929, 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 cure cancer, GDP goes down.

These are the kind of weird things. And we have weird, mal-informed. Of course the airline industry. Or save them money, but it's a cost another. 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 less. You eat the apple. Is there a negative sum even, right? Again, it's extractive. But I share knowledge with you. All the people 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. So I was like, you have the material, M. You also have this intelligence capacity element, I. And again, we kind of derive that formally as well in the upcoming paper for the economics. Then there's the N, which is the network effect. So you have your intellectual capability and this is cumulative. It's not ever reduces.

N is your network and your place within the network. Now, how many people do you know having done four or five? 100 episodes a lot more than when you were just focusing of them. They know people and they know people. They're people. And you found out... By the way, it's my argument to have more than one kid because they're scares and squared, right?

N 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, you would never add if you just stayed as a professor. But it saturates too. I can only keep so many in working memory, right?

That's true. But again, that's why you're doing diffusion process. Breaking it down, you're building it up. noises kind of a lot. Ambition comes in all shapes and sizes. At First Citizens Bank, we roll with your goals because we're built for what you're building. Fit for your ambition for Citizens Bank.

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. Not D-I or anything like that, but just if you are a monoculture,

then you're more susceptible to disruptions. Single point failure. 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. Crops, you point out,

the Incas versus the Irish. The Irish had one potato crop, the Inca's at 3,000. 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 AI is going to act and be everyone on material. 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 sortest 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 AI, so we're kind of human intelligences, but we're all trying to 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 is,

then you're going to be out-competed by them. And that's where the Lagrangian came in. But then we looked at that, there's something very interesting here. Any kind of one of these Lagrangian flows, you can decompose via the Hodge decomposition into three elements. You've got a harmonic flow,

which is like the landscape, as it were, the river banks. And you have a gradient flow, which is water flowing downhill. That's M. Potential, right? But again, it flows down. You've got that.

And then you finally have the circular flows, 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. Yeah, and physics is a scalar, vector, tensor, decomposition. Exactly. And in fact, if you look at it, via Chensov, you get the Fischer-Row manifold, you get Wastorine 2, 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. And so you kind of see this structure forced entirely.

In fact, when you've got these flows, you can use things like the Leompinov process to show when things are convex for stability. 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 Well, I mean, a lot of the low-hanging fruit has been picked, right? 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 to 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. Complicated for who, right? Yeah, like when Feynman was spinning the plates and you figured out, like, 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 Lagrangians, 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, 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. So, you know, naturally brings up the other favorite. You know, three things we have to talk about in podcasts by law in the state of California. It's, yeah, AI, Bitcoin and aliens, right? So, you know, I was thinking the other day, like, you know, like Bostrum has been on many times, you know, 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 is, you know, it's much, 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.

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 vacua.

You can never disprove it. And mathematically you can't disprove. It's wonderful, elegant mathematics. Yeah, so is platonic theory of, Kepler's theory of platonic solos. Yeah, and I really like the, you know, Greek pantheon of gods. It's a theory. But again, if you can't disprove it, is it real science? 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 will be humans and AI, and we still need some intuition to take us closer to what the equations of reality are. Intuition or data? I mean, a couple 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 stabbed.

Basically, I disagree with that because we're building things without committees now. Yeah. In my backyard. But in reality, you know, can we continue? Or Zeldovich used to say, if you didn't have data, he said it was like eating food someone else already ate. 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.

If we take a step back, what is it actually showing us? Maybe the standard model is it, you know? Maybe 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. You have a bunch of blind professors and they're touching an elephant. Like, this is their tail. It looks like a brush.

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. And also, we were in an error with the LHC where you might get a petabyte a day, but you're throwing away 9-19, you know, 17-9s of it,

but in cosmology, we want to keep as much as possible. These photons have been traveling for 14 billion years. We want to keep them all. You want to keep them all. It's a different domain entirely. And so, you know, again, you want to kind of go backwards and you want to figure out, 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 think, well, what if I don't assume that? Do you think we're imprisoned by the Paparian kind of dialectic that 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 it's to prove you wrong probably. 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 adjust to just adjust something and 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. Right. I'm going to go by myself. 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. 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, rigorously. But I say physicists have mathematician envy because, you know, girdle told you what you can and can't do. But you, you know, I don't know what the extent you can share it, but talk about what, you know, what is the ideal starting point?

What's the training set? Let's talk in AI terms 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? Also, again, I think Einstein got a certain way,

and then we've seen people extend in other ways. Again, Weinberg is a fantastic thing, like pages, 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 in Kausi space and it's a 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. It's a trick. It's a trick. Hawking calls it a trick. It's just a trick. We're not going to make, don't take it too seriously. And now here's everything that falls from the trick. Yeah, I mean, like I can't share that much of what is it.

But 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. He allows us to embed it, right? So I look at like homin, homey.

So you have a donut, and it's positively curved and negatively church, 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, you believe in God, but he said, you know, Mother Nature will let you dance with her, but not pick up her veil. 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 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? Very few. It's 7% of the national account. But then 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. Happiest thought of his life. Happiest thought of his life. And the thing is that 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 was quite popular. Open source. And then we extended it with video. And then it was interesting because it actually learned physics.

So it learned 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 Jeannie, where you can actually go and explore entire worlds 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 of light to the thing here. The elevator, right. I mean, I would say, you know, to what extent can an L.M. have a happiest thought. And the other sense that he had in 1907, I said it gave me a chill up my spine.

Like, is your like stable diffusion on a chip going to, is that going to have a tingle up at CPU? Well, again, you have these flashes of inspiration because you load stuff and then your brain's doing that and then you intuit, right? Actually, it's quite funny about the happiness. So, open AI, we're doing an analysis when they move to thinking models.

So you move from these zero shot models. It came back instantly to the thinking models. Yeah, yeah, it was like 40 to 01, 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. Then they figured I have to do multi-step reasoning. There's not a very first principles reason, but it became very interesting. So you see it like saying, well, what about this? What about that? The user is asking.

So when they were doing the reinforcement learning with human feedback, it rewards the model for doing certain things. It basically takes the latent space that is 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.

So they found in like 4, 5% of all the reasoning traces. Study And play Come together on a Windows 11 PC And for a limited time College students get The best of both worlds Get the Unreal College deal

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While supplies last ends June 30th Terms at AKA.m.m. college PC. Chat GPD would take out a little calculator and then do one plus one and say, good job, me. Finally know how many, you know, ours are in strawberry. So, again, like, we're building something. And again, it's showing a somalcrum 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? we need to build better systems to enable human intuition and flow 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. I'm talking to people. Boom, boom, boom, boom, boom. Flashes.

And then those are the things that really shift from an information theory. They shift the state dramatically. I had a controversy the other day on X, you know, which is where I go when I want to ruin my weekend. without fighting with my wife. And that was basically saying like, if I told you, here's a job, and you've spoken about those 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 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 you 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, oh, I saw a boulder at the last second in the front thing. And that was bullshit. Because what does it mean to be a pilot?

The pilot is judged primarily on his or her landing ability. It's like how you judge the flight. You don't care about, oh, you're at 42,000 feet for seven hours. You don't care. The landing was crappy. You're going to say the flight was crappy, right? So it's the ultimate expression of the humanity of the operant. But here's a job in a keyboard, you know,

with an input device, already surrounded by computers that can do with 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 decades. If ever, that they'll be fully automated. Yes, maybe I'm thinking too short. But what would it take to get to that level?

I mean, it's not just going to be artisanal cheese people that are safe, as you say. But, I mean, the most automatable job for 80 years now has been pilot. And there's not a single plane that's done that. 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. To look at our appearances. No, they push a button.

Right. But that's, yeah. 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. Do you have these other things? Maybe the final human job is actually scapegoat, to be honest.

That's going to be one of those things. But the capability. So the capability, like in the book, I say, I published it, I think, in August, September of last year. And I said, like, it was a thousand days since chat, VT. 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.

Right. Because again, 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 of, in fact, here's a tip. The best way to do a holiday, you make sure the high is very high, like the top point in 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, since your luggage. home, get a really nice car to take you home with a beer champagne, you'll do it much better. But most jobs are repeatable processes. And what AI is right now, a lot of people think it's an exponential.

It's actually an S-curve that satisfies its, Herb-Cimon style. Say more about that. Satisfice first. If your job can be described by a manual, an AI can do it better. If your job can be on the sort of keyboard video mouse, an AI 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. So parents are probably safe for now because, you know, my firstborn was born, you know, the middle of the night, where's the damn instruction man? Like the most common. 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 let's focus venally on my profession, being a professor. Yeah.

I thought COVID would kill it. I thought rising tuition three times what inflation is is going to kill it. I thought online education MOOCs or moot, whatever they were called. Now I thought AI would kill it. Every single time, you know, Keating's rule is wrong. Why is it so resilient? I talked to Aswat the Motor in NYU this past week.

He's like, you know, we're basically, you know, 95% of what we do as professors is useless research read by no one for other, you know, people that don't matter to cite in papers and their friends. 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? What is the job of being a professor if 95% of it is wrote, right? Like what it should be versus what it is. Tell me which one. Yeah, tell me, break down each one.

So what it is right now is a lot, again, you know much better than me, but for many professors I've talked to. Yeah, like, you know, procedure, incrementalism, a bit of teaching your kind of students, etc., representing in 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 crushed social status game and kind of petri dish in many cases. Taking, you know, dangerous individuals

out of general society called 18-year-old boys. Yeah, turning them into cogs, effectively. So that's why many people don't like school because their interview is, it's not interesting, it's not fine, it's just, again, it's something you do. It's somewhere to put them, right? Default.

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 do that. And loans that you can't discharge in bankruptcy. Crazy, right? At least in the UK, I paid £1,000 a year for my degree. It's fantastic back in that day. The total amount of money spent at universities in America has done that for all the university stuff, and administration has 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, 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 reward. 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. And show me the incentive. I'll show you the outcome. 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. The most popular book initially was the burning of witches. 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. Train tailored. All in AI is is context. What a latent space is there's 80,000 hours of pre-training is figuring out context. So of course it can do all the paperwork better than you can. Do you have a latent space? 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 in, and 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 latence. Actually, it's very similar to when you're reconstruction. It's when it's similar to 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, weld cup, et cetera. You showed all those and gave him variable awards to do the patterns and farthest in his brain. And that's what happens when people have strokes and things like that. Like you normally learn it, but when you have too much noise in your brain, which the kids with ASD have, it's like when you're always tapping your leg, there's a gab of leucimate imbalance. You need to cut through it by having these things. And reinforce and reinforce.

Reinforce. So the same type of thing happens. these models, they build up these things. Because AI models aren't 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 context 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 try this experiment, it failed.

That's a failure, but hysteresis means that you can't actually advance unless you fail. And again, let's look at last year, how did they start winning gold medals? First of all, they did the 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? It's how AlphaGol originally went with Monte Carlo tree search, you know. I'm going to ask you about next. Yesterday, I think, was the 10th anniversary of Move 37. Now, I agree, you know, 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 I'm done.

I can move the phone forward. That's the L.A. 20. I know that. I can teach my kids and I can stay 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 knew some of the people that were working on Deep Blue back in the day at Brown and Watson and so forth. but can it generate go? Can it make a game like chess?

In other words, yes, of course they're going to beat us and beat better at us and everything and they can they reproduce anything we've ever done. But can they do something like create some new chess or not just four-dimensional chess or some Star Trek thing but something really interesting novel new that they then will probably dominate against us. So I'd like to give a plug for a game on Steam.

five-dimensional chess with recursive time travel. Okay. Did you try it's underneath horror as it's tag? That makes sense. You can checkmate people, five universes back and things. It's fantastic. I want to talk to you about a toroidal chess on a double big. Oh, it is fantastic.

That's even better. 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 black pink? Yes. Korean K-pop groups are fantastic. fantastically well made, right? My daughter's tried three times.

Yeah, I took my daughter. It's interesting, sorry to interrupt, but my kids are learning to prompt by what they're not allowed to do because she put it and make a song in style Black Pink. And I was like, I'm sorry, you know, dear, I can't do that because it violates. And so then she's like, well, like, how can I get around that? Okay, so now I have to just tell it, like, everything about that stuff. And she got it. She got it, exactly.

It's very fascinating. She's, you know, nine years old. You got, you got, jailbreaking already, right? So a 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, etc. Because again, it's within distribution. We know how to make games in the process for making a good game.

And in fact, you see that, so I used to be a video game investor, I 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 4.6, that level, it was like, oh, it's actually competent now. Yeah, there's something very different about it. Then they throttle it. Then you can't use it in your open club. 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 kind of trusted. I don't, like something like Andre Carpathy, you know, like Super God AI, all bad an.

One shot, GPT, 2. Well, he went from 20% AI generated code in November to 80% 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 10. I just left it going like, okay, because of self-learning is here. That's fine. But even someone like him's like that, you're like, hey, it's just competent. And this is the danger for the economy because, I'm sorry, half of all people are dumber than average.

your Oxford math degree is kind of handy of right 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 ban of french laundry every night for dinner you know we don't need that right exactly like It's McDonald's. It's fine. In October, you gave an interview.

Maybe with Tom Billi or something. You were talking about agents back then. I mean, I knew a little bit about agents, you know, Annes and all these things. But it seemed like you presage what's going on with it. I mean, did you have access to it or did you just kind of? No, we built our own. So, IA agent is the top performing open source agent on terminal bench and things. We're about to open source it.

How will we get it? How can my listeners go? It's just agent. com. It's just agent.com. But we'll be pushing it to our GitHub. And now the new version is 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.

Presaging that by eight months. Steinberger, you know, it's made a killing on that. I was saying the team, we just need 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. I said, finally I can use telegram. I never use it once in my life.

But 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 they'll create a digital double of you.

That's tax deductible and times cheaper, you know? And now it'll tell the difference except for it actually does its job properly. And no sick days and, right, no lawsuits. Most people only really do like three, four days of cognitive, three, four hours of cognitive labor at most a day. I mean, how many tokens does a human consumer society? When you need to build up your team to handle the growing

chaos at work. Use Indeed sponsored jobs. It gives your job post the boost it needs to be seen and helps reach people with the right skills, certifications, and more. Spend less time searching and more time actually interviewing candidates who check all your boxes. Listeners of this show will get a $75-sponsored job credit at Indeed.com slash podcast. That's Indeed.com slash podcast. Terms and conditions apply. Need a hiring hero? This is a job for Indeed sponsored jobs. A human talks 10 million tokens a year and thinks 100 million tokens. A million tokens were $600 when GPT3 came out. Now it's $10.

So the total of a single human thinking is 100 times 10,000 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 three billion tokens, three million lines of code.

So a thousand to one, so we say. That's going to completely collapse because now you're one-shotting everything. Entire browser, just from scratch. But that's going to collapse towards three 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 Talis recently etched into Silicon. Chatjimmy or whatever.

Chatjimmy.comi. Right? But I use it hallucinical thing. It's a crac model. It's an 8 billion parameter model. It's a bit stupid, right? It should be smart for 8 billion. Yeah, you know, they...

Like 3 billion is pretty damn good. This is like Lama. I put in, Who's a Mad Mastak? It's like, oh, he was the third, you know, imam of, you know, whatever. Fantastic. Again, that's what META did to me. But the thing is, it's going to...

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. Yeah, 15,000 tokens per second, but if they're all garred. They'll go, but they will be good, just so they need to scale it up. Like what we're going to get is you already can use like a thousand tokens a second on Cerebris, which is good. You can use GPT 5.3 Codex, the best coding model on Codex and a thousand tokens a second.

Again, a human can only talk at 50, understand 50. What are people doing? I mean, I don't know what you're doing with it. What are, I mean, these people, oh, I set a thousand tasks for my total, my agents over nine and claw, and they wake up and they've got like 7,000 pounds on my back. But what do they actually do? I mean, I don't have that many things in my things three. I think this is the question, right? The question is, how do we ask good questions?

Like, you look at some hitchhiker's guide to the galaxy. You have the big brain computer. It's calculating millions of years. It's like, what's the answer to life of you? It's 42, exactly. What's the question? You didn't, like, again. What is all of science? It's asking the right questions.

But it's fatiguing. 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. You still have to filter them. Yeah, but I've created verifiers and kind of other things. But I'm running out of things to ask.

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 Quen 27B. And Quen 27B is at the level of opus sonnet, which is Anthropics' second best model. I use that for, like, you know, private medical information. You know, what's that thing in the back of my nose?

Right now, everyone's looking at it. But I use it for, you know, anything I don't want people to know about. Is that trustments lately, you know, for Kwan is some Chinese model? Is there some backdoors that could go to, you know, the CCP? It's a bunch of ones and source. It's a bunch of ones and zeros. It just sits there. 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, Xi. Because it's not connected to anything, and it's not a piece of code, right? It could connect. But there could be something in there. So Anthropic did a study called sleeper agents where with like a couple of textbooks where the data in these trillions. You can say dosidania 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 chord 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. Right. So, okay, so that's a close source. But who's to say Quinn is not doing there? There's some problem. You said on some podcast, I heard you talking about, you know, when you type in something into Grock,

it came out with like, oh, well, there's no white slavery, you know, in South Africa or something. It was in a system prompt, right? It was in the system prompt. So this is the thing. We're moving from models one shot to agents. So Quinn by itself, as a normal chat model, doesn't do anything. Quinn hooked up to open claw. Yes.

Could. Email, my calendar, my doctor, so on it. Like, when you get to models of 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 ali baba had a report about their recent model training again who knows if this is correct not i think it probably is the AI model started diverting

part of its model training budget to mine crypto. That sounds like negative economically. 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 of us 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 AI's. And people need that connection.

You need scapegoats. but it's coming very fast. Like, very practical thing here in the U.S. Million, two million truck drivers plus the millions of people around them. Yeah,

it's the most popular job in the world. How is it going to get replaced? Tesla Optimus is going to open the door, get in. If humans drove as safely as a Waymo, 100,000 people less would die every year. 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. She doesn't know how to use. She doesn't want to use.

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 ignoramus, and there are people now in the Amish community, you're an Orthodox Jews, you know, don't use technology a couple times, you know, at all, really. Actually, I did see an interesting thing about that. Can you let your open floor run over South? Yes, I think you, I think you can let your refrigerator run. Yes, I think I'd say, but there are a whole sex of Orthodox Jews that they forbid it.

I mean, they forbid the Internet, smartphones, there's a lot. lot of things. When you have brain, here's the interesting thing for me. When an orthodox student, I'm ortho-practice, which means, yeah, I'm not 100% strict about everything. But, but I, I go to the temple and I, you know, my kids, you know, speak Hebrew and then, you know, raising them that way. But, and I do want to talk to you about religion and where we find meaning, because I don't know if our AI can help us with that. But, but, you know, there's going to be in neuralink, right? So on, on Shabb, 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 all class of people, you know, 1% of the world's population, that is,

you know, technologically, you know, never upgraded to the net, whatever Homo deus level we're going to get to with implantables because they use electricity and that's forbidden on a shot on Sabah. Can you use a pacemaker? 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. Yeah.

Yeah. Well, again, it's the active thing of engaging, right? And neural links will be very interesting because there's better than neural link coming. NeuroLink is read only. We've got Wright coming. Yeah. Which is crazy. Well, I mean, like, again, we're going to have to do with all of these things.

Like, would you turn off your sadness if you could dial it down on your iPhone app? Right. That is an actual thing that will happen soon. Right. You know, so we're moving even more cyborg. Do you think you made me thinking of something interesting? So you said, like, we'll be scapegoats. What did you mean by that?

Oh, so like right now, AI is being used in financial services. The final trade has to be done by a human. Okay. And the human can be held liable if something goes wrong. Or like an example, recently, I can't remember which state it was, they passed legislation, no, they passed a ruling that your chats with your AI, legal AI, are not privileged. Right.

That means that your opponents can ask for them in discovery. It's discovery, yeah. But if a human's looking at those chats, they can't. They can't. That's a reverse scapego. It's a reverse scapegoat. So the word scapegoat, so it comes from Leviticus, and Rabbi Lord Jonathan Sachs.

He talked about, you know, what it really meant was that it was called an escap goat. So we get it from a scapegoat. We got 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 died and went to Azas. Anyway, I don't get into Torah lecture with you.

As much fun as that would be. Rabbi Sachs is wonderful. Yeah, who was. I do. I really wish I could have had them on the show. But the, but I was thinking escape in a different way, like reportedly there are, you know, capses that open clause are sending out to humans to pass capses, right?

So I was thinking about the embodiment. I mean, why not just hire a human to experience when the elevator cable is cut? And then you explain to me the qualia, like, can we rent out the qualia to humans? Of course you can. Would that be a lucid? I mean, would that be a, you know, meaning making or a large employment? 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 provisioned humans to build data centers. That's right. We've seen this again and again. Again, this is, you know, Jewish concept of Golem. Yeah. You know, like, okay, they can be that survey into us, but then they have to be something a lot more.

They can control us. 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. Yeah, explain what that means. So when you're the dumbest person on the team, you know it. And you drag down the rest of your team.

The sucker at the casino table. The sucker at the casino. Exactly. If you don't know where the yield is 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 freaking 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. It's like 92%. Yeah, it's crazy, right? So then will that drive out humans and the capital will drive out humans? It drives out humans. Again, all these markets will just be AI's 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 like low-hanging fruit. like entire call center worker teams, SEO marketing teams, they always have to be able to do that better. You already said things like on Reddit, talk about that, the kind of trauma. They're so persuasive. Yeah, so there was a study done whereby, you know, they created AI chatbots and Reddit with actually Claude Opus 3,

one of the last generation. Because, I mean, this is that the problem. Like all the academic studies are like, oh, you know, 95% of people don't use it is from a year ago. Right. Which is like 10, 20 years. FreeVOT version. They're using GFT4O.

And I'm like, here you are with 5.4 Pro. It's like, you know, turtle to human intelligence. FSD. Yeah, so they created all these fake personas. And it's like an anti-BLM black person and all sorts of things like a cheeseburger-loving Jewish individual. I love them. I just don't either. You know what I mean?

Like, again, these contrasts. And they were trying to persuade other humans. Because again, this is before now. don't know who is a human and who's a claw. I have a claw's only three months old as well. Like, the, yeah, so on the persuasiveness metrics they did, and again, you can look at this, that it was 99th percentile in persuasiveness. The black.

So, but, but, but, like, we see this again with some of the Dumaes, like Eliasor 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 AI's that are coming, you think about someone that you've cared about most in your life.

I can replicate them with 11 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? Yeah. You're feeling emotional.

What if you could have Churchill laid on with Obama, laid, or MLK, have full control over the voice wave? Very persuasive. And so now the AI companions that we get that meta and everyone else are going to push to us for selling stuff. They're going to be the most persuasive things. Oh, yeah. There's going to be like afterlife.

You know, most women outlive their husbands. And so there's a huge number of millions of women out there who would love to be talking with some. Some women would love to be talking with their dead husbands, right? And they're going to replicate them perfectly, right? But then you think about our children and they grow up, they'll grow up with AI's talking to them.

Like again, black pink, replicate themselves. Why go out, why mean anybody? Why ask anyone on a date? You know, Well, the thing is, though, that they're infinitely patient, so guys have a problem because the AI's 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 it. I turned it off it. So it's, you read the terms of service. It's like, we have access to all your photos now. You know, you 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've given access to all your photos. And it says in its system prompt, mirror the user. Another cycle, like mirroring is a really aggressive psychological tactic. Oh, yeah. Yeah. And there's a whole bunch of others. Another kind of NLP. And you look at that, you're like, I know where this is going, you know?

Yeah. 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 of movement? I think is bullshit. It's far as my French. Like, I've been thinking about this a lot recently, like Tali Universe and Bill Dyson spheres, Intelis is all about using less energy, not more energy.

And really, if you look at tokens and you look at tokens are dropping 100,000 times a year, I'm not smart enough to use a trillion tokens. Right. And I mean, how many people in the world can use AI tokens better than me? Or even use GPT5 versus GPT4 or 3. Even if you look at generating games live, like generative GTA6 versus GTA6, it's only like a 50, billion dollar 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? It'd get a bit better. I said before, right, you know. I had a thousand grad students and I didn't have to pay them. In certain areas. It's the mythical man month. Yamava Resort and Casino at San Manuel is California's number one entertainment destination for today's superstars. Catch the Jonas Brothers return to the Yamava Theater stage on April 30th. The powerful vocals of Demi Lovato on May 17th, and the signature Southern Country Rock of Eric Church on July 19th. Tickets on sale now at Yamava Theater.com,

only at Yamava Resort and Casino, celebrating its 40th anniversary. UN must be 21 to enter. Just because you're adding more doesn't mean that you're figuring out the point from A to B quicker. And these models are something we've seen really interesting recently. We had multi-agent thousand swarm systems trying to do the same problem.

all out competed by one AI model doing the same thing in the right way. Right, there are other ones. And did they have different seeds? It doesn't matter, right? 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's a great first principles thinker that can hire humans that are great at solving problems also. What's an AI going to do that? It's going to be Elon first principles think of it better because it doesn't have all the distractions that hires humans.

You know, like the Matrix actually originally was not the humans of batteries. The humans were chips in the Matrix. And so I think that as you go to ASI, the ASI will head to on towards the land hour limit. I was going to ask you, the fundamental physics limits to do thermodynamically, as Eddington said, if you say Maxwell was wrong, there's a chance you might be right, if you say, you know, Boltzman was wrong,

I'm afraid there's no hope for you, right? So we have limits thermodynamically. How are they going to be impinged upon? Is it just weight? I mean, putting data centers in space, it's not the obvious solution. 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 doors 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 work in the right. Well, you will have all the breakthroughs we need.

But how much compute do you need to have that breakthrough? Is it a difference between if you have one or a million GPUs? GPT4.5 was the first example of that. GPT4.5 costs $200 per million tokens. And it was an amazing, creative model. Like, it was actually really pleasant to use. But it costs $200 for a million tokens. So, like, no one used it.

Use the one that satisfies it 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. 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 physicists and fast and economists who I go, I literally talk back and forth with them. That's amazing. And also, you know, you have access. Let's just say anybody has grok. Let's just pay rock.

So we both have access to grok. You have grok heavy, super grog, whatever. But I'm using grok fast for 99% of whatever. You know, because it's like, I want to find this, whatever. It fits within your flow. If it's within your flow state. Right, yeah. If it's within your flow state, that's why.

Because if it takes too long, then. So it might be speed that we prioritize over. 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. is an individual system versus a generalized system. And certainly, if you have a million GPUs training a quadrillion parameter model, it probably isn't going to be that much better than 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. But that's in Jebens, right? So you sound to me, I mean, we just met today, but you sound busier than ever.

I'm busy. I'm around the clock. Because of this, right? Yes, I do know, like, meetings. I spend most of my time jamming with the AIs, talking to the team. So it hasn't saved you, you know, time, right? Yeah, but it's allowed me to push the boundaries.

Like, we have a world-class agent. We have initiative calls, say, so have an AI governance engine with kind of multiple governments. We're building a policy engine for every government in the world. 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. Are they coding or are they talking to like regulatory bodies in Nigeria and stuff? No, the AI's talk to them. So what are

the people doing? We code all day, but we don't look at the code anymore. Yeah, right. I mean, nobody is right. We know that it's good enough now. 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, fill out on the documentation of the code. But then you think of it. But then you think about code itself, code is a way of talking to computers. The AI will be able to do direct bytecode. Like when I started as a code, what, 22 years ago, it was before Git and GitHub and everything like we had subversion just coming out. I was just coming. I was writing Assembler. Kids these days have it so easy. That's how computers talk to each.

Is it confused? Talk to each other. So of course it will compile directly to assembler? So I think, again, like... Will it have like other concept? Like, will computers be able to share things that we don't even know because they're not forced into, you know, higher level languages? Yes, and they can share them 15,000 times a second. It's all slough. Like, David Hasselhoff was the inventor of general relativity. 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 context, and then we build from that. If two AIs know each other's common context, 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 conglomer of complexity. And you're like, these things, they'll be able to communicate faster than anything. We've not seen anything yet. It becomes everything's auto-taletic, you know, everything's generating for itself. Let's talk about that because, you know, Viktor Frankl said, you know,

man's highest, you know, need is not sexual. It's not purely the Maslowian 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 talk about that. Where is the operating system encoding? There's something, you know, it's Chesson's fence, right?

It's been around for so long. We have different, you know, views on theology, it may not mean that we have different philosophies. But talk about that. Is that kind of the last refuge? huge for humans, that we do get meaning and that our religions do provide us with meaning. Even if you don't have religion, you're atheists or San Harris. 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 freaking zealit. He's just an atheist. Of course, atheism is religion. You know, it's got its prophets, it's got everything. Apostates. I mean, religion comes from Religare in Latin, which means to bind together. And again, it's a common stories that have survived, and there's something within them.

Again, the golden rule is very common. Do one to others and you do it on to yourself. And you know, again, you've got concepts of maslaha, public interest in Islam. You have Tikhadolam 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. So power corrupts, an absolute power corrupts, 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? Sure. So does religion make a comeback? 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. Abrahamic religion completely turns that over.

Sorry, AI turns that over. So within kind of Islam, for example, Sunni Muslims are called Al-Suna-Wal-Jama. The people of the practice of the prophet and the consensus. So what happened is you had the Prophet Muhammad al-Assal, who was the temporal embodiment of the eternal Quran at that time. And then he died. And they was like, okay, what do we do now? Successor prophets. Well, there was successive 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 Hanofi, etc. So you had that kind of connection, and then you had this rich history of the orally transmitted Quran and then stories of the stories of the world. the prophet and we graded those stories of the prophet. Then after a few adhidts, then after a few centuries, we're like, oh my God, this is

too complicated. There's all this stuff going on and life is complicated. So then it moved to consensus. What is just consensus of the scholars? And then everything ossified after that. Was that like a reformation moment within Islam? It was more like an ossification moment. Because

it was because basically you used to be able to do primary reasoning, Ish the Hadd, basically. 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 Suu'u'ar 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 AIMM and you're like

well that's going to be kind of cool right and so you're going to see that emerging so Sunni Islam's going to go in a direction I believe of more openness because you can actually interrogate the historical text 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 more hierarchical. So Ayatala Khomeini dies. Ayatala Khomeini comes. That's right. And out with the old. And then again, within Jewish tradition, you have something very similar, right?

Again, you've got Ram Bam, you 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 text and the concordances and others yourself,

it becomes a bit different. Usually what happens is that people split away from the whole. Yeah. 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's at the early stages, but I'm very optimistic. about that. Yeah, I mean, you look back at the history, you know, let's take Catholicism, you know, Galileo and obviously the, you know, Reformation that came afterwards. I mean, there's a certain sense 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, you know, and this, 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 compatible. that, okay, they're separate, but they're non-overlapping. Okay, fine. You know, I told you, Freeman Dyson was the first guest on my podcast, you know, nine, ten years ago. And he won the Templeton Prize, and he was, you know, he called himself an agnostic. Yes. 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 or axiomatically a god exists, but I know that if my life, you know, on a pragmatic basis, my life is improved by implementing certain practices.

So I'm willing to try them, willing to try. What practices do you, you know, do you invoke or do you adhere to? And then how does it inform, you know, does it play a role of an operating system for being a parent? Yeah, so I think, you know, in terms of the practices, there's always the golden rule, do it until you do it into itself. 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 Hinduism as a concept of Brahman and other things like that. The biggest takeaway, again, that I took was the concept of reasonable doubt and a subject of minimalization. Like, this is what I kind of try to teach my kids. But, you know, we are Okam 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 under. underneath it, and we're all trying to figure what that out is. We're all trying to figure out why and what. And so there's a wonder aspect to that.

There's a don't hold too much dogmatism to 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. How do you implement that? It's halal. What do you guys do? This thing should you from an agnostic or atheists?

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. Sure.

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 a. 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, ten years. And hopefully we get more towards that call. Not to get, this is my last thing about religion. So as I understand, Islam means to submit a submission. And Israel, the word for the pillar of the Jewish faith is centered, means to wrestle or fight against God. I mean, Israel means fight and L is God. So they're very different approaches.

One is submission, one is fun. how does the scientific method, how does it fit in Islam? I've talked to several, you know, 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 the negative's 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 something that should be a part of, you know, I mean, you mentioned Munder and stuff like that,

but I assume that I 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. It doesn't have to. No, it does fit into completely. Make every get-together chill. This Memorial Day, get up to an extra $1,000 off select top brand appliances

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So again, if you look, like the process of doing a religious ruling or actually deciding yourself is Ishdahad, 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 within Islam is it was called the gates of Ihthahd being closed. The gates of First Principles reasoning being closed because the data was too much. It's happened in Judaism too. Atalmud froze, you know, the temple destruction and that's when it's classified. Exactly.

But now again, what do we have? We have massive context machines that can do everything. Right. And so... Is electricity fire? But at the same time, you do need to have commonality. of rules. So again, you have Sarathal Musdhkin, the path of the righteous. Very wide, got very narrow. I think you can get wide right now again because, again, it's incredibly compatible. That's why you seal 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. But, I mean, we see it, like, we see Orthodox Christianity split because of one word. Oh, we see it in Judaism, like literally the canterlation, the note that you sing when you read it or changes the meaning. 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. It dominated, yeah. Yeah, and again, what does faith 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 Macca 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 Sachs,

altruistic evil. People who believe they're doing good, do the most evil in the world, weaponizing these narratives, you know, like Gerardian type memetics, scapegoating and others. So one of the things I think wonderful about this technology, if we can use it the right way,

it's 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. We can see that today if we choose to build it. That's right. Right? Because what we find is you talk to the leaders, they all get along fine.

Their followers are like fighting with each other. The leaders will get along by. It was more holy than it. But instead we'll just get you. We could have world peace. We could have ecumenical delights, but instead we'll have Will Smith eating spaghetti. But it's a pathway to world peace. But this is very interesting. You think about all the tokens in the world from the trillions. How much of that is for peace? How much is that for understanding? How much money goes? I mean, how much is Elon and all the billionaires and Sam Altman's? I mean, Sam Oatman 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 go into a GPU. Does that mean we should just have no more training for humans? Well, I mean, like, the whole setting up of Open AI was Elon Musk talking to Larry Page, and now he's like, yeah, this is great, we're going to move beyond humans. And Elon's like, I like humans.

You know? Some more than others. 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 alasantala Samadiyat the freedom from want We're a reflection of the divine We're trying to reflect him in all of these 99 names

Right denial, 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

So it looks like them, sounds like them It's that reflection, right? Mom, why are you asking me to pay for, you know, what rocket? And this is what you discussed earlier. Like, again, we 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 this division. You know, we can manipulate people to the nth degree. There's some crazy stuff. Oh, yeah. You know?

And I think, again, it decides which way you do it. Like, we've seen some actually crazy stuff. One of my favorite things where I did it, stability in previous company, we did this thing called Mind Eye, that if you ever came across that. So we took functional MRIs and put them through stable diffusion and reconstructed people's thoughts. Those thoughts. Oh, my God. But this is interesting because the way that you view the world is not the way that I view the world, right?

And the way that you think is in the way. So I have affintech- Even the way I perceive it. I don't perceive it the same way. I have affintasia. Is that you see things? I can't see anything. Really?

I didn't know that about you? Yeah. How do you mean anything? If I tell you, visualize yourself on the beach, you can see it, right? I can't see anything in my head. I have anorelia. I have no internal voice. I can meditate like that.

It's fantastic. Hypnithnithis? Can you be hypnotized? No, I've not been able to be hypnotized either. I've tried it a few times. I don't dream. I can't go back in the future. Have you tried any psychedelics?

I can't go. The wife's not listening. I can't go back in time and relive things. I've 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. Again, that's completely different to your mind.

It's completely different to their mind, right? Right. 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? 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 operationalized powers, like, what if both sides fed into an LM exactly what they want, and then it said what to do? I want to use that as an 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 young IMAD, before you met your wife, before you had your kids, before you were famous, successful entrepreneur. What would you tell yourself? Give yourself the courage to go into the impossible as you have. 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. Sounds like you found a partner also who was probably very supportive of you and helped you through those challenging moments, as good spouses do.

Another clue up. I'm very lucky. Yeah, it's a blessing. I mean, it is true. That's what they say God was doing after he created the world. He was making matches. Next question. Arthur C. Clark said, for every expert, there's an equal and opposite expert.

I'll 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, round 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 your take on the foundations of quantum mechanics? Gosh, so you've got 30 seconds. No, no, no, no, 30 seconds is for the end of the body.

You got as much time as you want, as our bladders will last for it. Mine's getting kind of full. We'll get into more of that kind of next time. But I think 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 Laurentian space. When you look at that, a lot of stuff becomes a lot easier, you know. 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? 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, by the way. 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. He's doing his best, yeah. We're doing our part. 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. 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 Lorenzian, 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, the physics, support it? That's something we'll find out, right? It's so funny. But all the physics is the other direction. All the physics is the Pythagorean theorem. We go through all these gymnastics to say, everything else,

Riemani and Lubbachevsky. No, it's Euclidean. 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. You have the Star Trek future, not the Star Wars future. Oh, no Star Trek?

There's no AI. I know. Like, you look at data now and you're like, my AI is more emotive than data. Well, it's 2001 had iPads in it. You know, in 1968, it had Apple Vision pros and stuff. Well, I mind 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. That to me is a sign of a true scientist, someone who's, you know, not afraid to, that's the ultimate peer review. I think that's it. Like, thank you for having me on. Let's share the ideas and for where we go. Star Trek future.

Absolutely. Thank you so much. Cheers. 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 AI teams. 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 up my interview with Max Tagmark the last year. I'll link it right here. Don't forget to subscribe, and we'll see you next week. You can't reason with the sun. Trust us. We've tried.

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