GPT-6 Astra Saturates ARC-AGI-3, Tesla's $30K Cybercab Floods Austin, Anthropic Proves Fermat's Last Theorem | EP #286

GPT-6 Astra brings together years of research.

This seems like a next-generation frontier model that's designed with CUA from the ground up.

On certain benchmarks like ArcAGI-3, which it saturates, fantastic. But then you look at the artificial analysis benchmark and it actually lags behind Meta Muse Spark. You know, like, it's kind of weird.

Tesla held its CyberCab Lollapalooza, a river of golden EVs flooding the streets. Elon wants to sell these at $30,000 each so you can buy You can buy 10 of them and put them on the streets in your local town, have them earn revenue for you.

There'll be enormous chunks of entire cities that say, you know what, no more human drivers. It's so much more efficient. Not only is it much cheaper, but it's much more—

The technology is arriving fast. Like, you know, just got something across my feed. Anthropic just formalized Fermat's Last Theorem in 13 million lines of code, proving 29,000 theorems on the way.

Really?

Math is cooked. Everything is cooked.

Now that's a moonshot, ladies and gentlemen.

Welcome to Moonshots, everyone, your number one podcast on all things AI and exponential, your front row seat to the accelerating singularity. Today, all of the mates are in the house, the quintet, the fabulous 5. And oh man, oh man, we're gonna need all of their brainpower, you know, to get through the day today. This is an epic, and I mean an epic episode today. Let me introduce you to magnificent— my magnificent mates. If you are the first time joining us, we've got Dave Blunden, the impresario of AI investing, AWG, Alex Wiesner-Gross, our in-house ASI, Salim Ismail, our globetrotter, father of the organizational singularity and warlord against linear thinking.

You're the warlord forevermore, Salim.

I'm good. I'll go for that.

And finally, Imad Mustaq, back in the house by unanimous demand. I'm Peter Diamandis, your moderator and data-driven optimist. So, Salim, we got to ask, where are you today and what happened last episode? Were you held up in customs again?

No, I was not in customs. And thank you for the kind words from everybody. I was with the C-suite of one of the bigger oil companies in the world in Houston. And it was kind of a really surreal and fascinating conversation. But, but so I'm really deeply sorry to have missed the last episode. But Jesus, guys, I mean, you know, I miss one episode and you guys announce interstellar mission. You discuss AI designing its own chips. You hand, you hand Elon the planetary thermostat. Apparently, I'm the moderating influence in this group, which should worry a lot of people.

Yeah.

I'm actually down in the Caribbean right now. We decided we needed a couple of days break, so we popped down. So you guys are standing between me and a Hobie Cat and a little colorful drink with an umbrella. So we got to keep it tight and punchy today.

It's a big episode today, Salim. So, you know, get patient with us.

Yes.

You know, our mission here on Moonshots is to help you understand what just happened, what it means for you, and most importantly, to keep you abundance-minded about our future. If you haven't had a chance to subscribe yet, please do right now. Hit the subscribe button. You know, we're putting out 2 episodes a week. And God knows the demand is there for more, and I wouldn't want you to miss one. So, two different things I'd love you guys to take advantage of. The first is, if you haven't yet, please register for our AMA. We're going to be doing an AMA with all of our listeners who want to. This is the last chance to register. You can go to moonshots.com/ama. You'll be joining us on Zoom. We'll have the mates there answering your questions. We want to get a chance to know you, who you are, what your questions are. Again, last chance to register. The link is on the screen, in the show notes, moonshots.com/ama. And then follow us on X. Our handle there is @moonshots_pod. So there you have it. I want to take a second and actually read a few of the subscriber comments. We get such love from our subscribers. You know, here we go. Uh, @BrianClark said, Moonshots is the best content on YouTube, especially during the singularity. Thank you for all you guys do. It's our pleasure, and, and we do love it. Brian Anderson said, the best AI podcast on the internet. Just fabulous. You guys are great. And @RanchVidzy said, keep the regular uploads. Upload every day and I'll watch it. I'm a college student and I consider these videos more important than my physics lectures.

Yeah, that's not a very high bar right there, but we do appreciate it.

Not better than the physics lectures, more important than the physics lectures.

When does Alex's physics is cooked?

When?

Yeah, I tell you.

Approximately now.

Stay tuned.

Well, I want to say thank you to our listeners. We read your comments and it means the world to us. You know, our moonshot on this pod is 100x growth to get to 10 million subscribers. Please tell your friends. Help us get there. Hit the subscribe button. Today, we're going to be covering one of the craziest and fastest weeks in Moonshot history. And Alex, like you always say, it's never going to be any slower. We're digesting 26 stories across 10 areas, an insane week for model releases, reminiscent of the hypersonic tsunami that we're living through. Insanely, we've had 12 frontier model releases in the last 30 days, an average of one every 5 days. And there are rumors of at least 5 more releases expected in the next 2 weeks, including Grok 4.7. So buckle up, grab your coffee, and let's jump in. Let's kick it off this way. This week was a battle of the titans. OpenAI versus Anthropic slugging it out for the Pareto frontier, for the heavyweight crown. And the numbers couldn't be more exponential. 2 frontier labs released their major models within 48 hours of each other. And you've got to know that this was a game of chicken, right, guys? I mean, who's going to release first? The other, the other guy waiting to like slug it back and crown again. I'm just curious, you know, the strategy these guys are taking on this front.

The releases are closer and closer together, but the models are improving more than ever before during these very short releases. These are not like, you know, kind of marketing garbage releases. These are major step improvements in the models themselves. They're just coming Faster and faster and faster. So clearly they're well down the self-improvement path. Clearly the prior model is accelerating the timeline to the next model. Um, did we cover Fable 5.1 already too? It seems like I've been using it for a lifetime.

I know it was, it was 0.3, you know, 2 days ago or something like that.

Or 3 days ago.

It's not safe to take a day off from this pod, unfortunately. If you follow the extrapolation, I mentioned this a number of episodes ago, I think we're on track still to see one major model release per day by the end of this year.

Yeah. Yeah, I imagine that. So let's open up with OpenAI's release of GPT-6, also known as Astra. The model's performance numbers are nothing less than stellar, saturating multiple benchmarks. I'm going to read a statement from OpenAI's release page. So, quote, GPT-6 Astra brings together years of research and big bets across pre-training, reinforcement learning, and alignment. Astra is a state-of-the-art On computer use, browsing, software engineering, cybersecurity science, and professional work. Astra saturates Frontier Math Tier 4 with a 98% score, having already helped solve longstanding open models in math. Astra also saturates Arc AGI-3 with a 99.9% score and Exploit Bench with 100% score. Astra's story is really about efficiency, not just raw intelligence. It defines a new Pareto frontier for intelligence index versus output token tasks. Of particular note, Astra's hallucination rate fell nearly by half from 92% to 51%, with accuracy actually increasing. So before I open it up to you, Alex and Emad, to talk about the benchmarks, I want to show a quick video of, of Sam Altman discussing this. on Bloomberg TV to get a little bit of an overview on the topic.

All right.

Let's take a listen.

And the way that OpenAI is framing Astra is basically an early step toward AGI. But I thought we could start our conversation just with basically what's fundamentally different with Astra relative to prior generations of model. It feels— first of all, thank you for having me. It feels like a new step in this process towards models that can really help us create value, do work, discover new science, help us create, help start new companies or create new products. Using it subjectively feels to me very different than any model before. What's the principal use case that's different this time around? I think we can get into the generations of model that were very coding-focused. I know a lot of the development was very cybersecurity-focused, but taking it away from the software engineer, What does the everyday person unlock in AI that they weren't able to do previously? One thing that I think people will immediately notice is if you have an idea and you want to work interactively with an AI to get a complex piece of— a whole piece of software built, this is the first model to me where I could tell someone, just give it a try. There's a good chance it'll work. I've watched people make computer games. I've watched people do sort of like home sort of DIY electrical engineering projects. I've watched people do very complex simulations for some piece of science they're working on. Certainly, a lot of the work where you would sort of normally sit down, have to, you know, build a financial model here and then a PowerPoint presentation around it, and then figure out how to like make a little interactive piece of code to sort of try different simulations, that stuff is It's all so doable now. What I hope will happen is people— I think people will be surprised at the beginning, but then as they build up more trust in the model and more like, wow, it can really do this, just start throwing harder and harder tasks and more creative ideas at it, and they will find out what the model can really do for them. Sam, the specifics of how Astra is being released, I think it's important. A version of it with specific guardrails, What was the thinking behind that? And what are those specific guardrails in this early release of it? Yeah. So the challenge of our industry is that we have these models that are getting incredibly capable and incredibly useful, and that people want to use to everything from make their lives a little easier to starting new companies. And then on the other hand, as these models get more capable, the risks that we have to mitigate also become more serious. The models could do more damage if we don't do a good job at that. And so we have spent— obviously, this model took us a little longer to release than we were hoping. I think it'll be worth the wait, but we really wanted to spend the time on the safety and security alignment of this model.

Yes.

And I think as people understand the power and impact and capabilities, they will be happy that we did. We'll have different tiers of cyber access for this model, for cyber in particular. Today we're rolling it out to trusted access partners, and then in the coming days, assuming everything goes well, we'll roll it out more broadly.

All right, we'll talk about the safety elements in a little bit, but Alex and Emad, I'd love your take on this one. Alex, you want to jump in first?

Sure. So I think there's an outer perspective, which is to say, what the market and what users would see here. And then there's the inner perspective, and I think they're strikingly different perspectives. So to start with the outer perspective, this is a model that uses fewer output tokens to accomplish a given task. That's really interesting. It affects the economics, it affects the user experience, it affects the speed. OpenAI launched this, and I think we'll be able to go to the video soon of computer use assistance. Remember that when Anthropic leapfrogged OpenAI, they leapfrogged OpenAI in terms of revenue and other factors in at least 2 key regards. One was they were focusing on enterprise high revenue per token use cases, namely code generation. The second was they were focusing on computer use assistance. So anyone who's ever used Claude Code understands Claude Code can use all of the tools available in your local desktop environment. And that was, it was a pretty big leap compared to what was available on the market prior to Claude Code. So the outer perspective, on my side at least, is that OpenAI finally, with GPT-6 Astra, has at least internalised the computer use assistance story. Their demos— I don't have access to Astra yet, but just based on everything I've read and seen, they seem to have gone full native with the computer use assistance story, this seems like a next-generation frontier model that's designed with CUA from the ground up. That's really interesting, in part because to be an amazing computer use assistant, you need to be able to understand what's on the screen. You need to have native multimodality. You need to be able to parse video and images and screenshots in a really tight, interactive, low-latency loop. So if I had to guess as to what the outside design consideration that OpenAI was optimising for here, as reflected in certain of the benchmarks, I think that would be a leading candidate. Then there's the other side, which is the inner story. Just what, if anything, in that interview, Peter, that you were just playing, the interviewer was asking Sam, okay, so what on the inside, what's the big technological innovation there? There, I think the plot thickens a bit based on public comments from OpenAI leaders and analyses from others. It looks like the big technological innovation is the introduction of recurrence into OpenAI models in the form of looped transformers. So this is basically taking a single transformer, stacking it on top of itself with the same weights, so weight tying, and then just running it recurrently for a double loop rather than just a single loop. And you'll recall that all these Chinese labs that are achieving breakthrough performance are also injecting recurrence in different places, like KIMI. The KIMI model series is injecting recurrence via their KLA, their KIMI linear attention mechanism at the attention layer. It seems like—

yeah.

Is this thinking about your thinking, or is this just thinking about it and then thinking about it again before answering?

Neither and both at the same time. Remember when we talked about Anthropic's study of consciousness in their models, and we found that the middle layers in the—

J-Space, right?

Yeah, J-Space. They were in the middle layers. So one can squint at this looped transformer construct, which by the way was being used by academic labs and others to achieve breakthrough performance with very tiny models on ArcAGI and other benchmarks, it almost follows if you thicken the depth of a transformer, you might thicken the depth also of that J-space or other middle layer area where most of the conscious thinking happens. So that's my best guess as to what the architectural inner strategy was here, and that carries all sorts of implications.

Love it.

If true, including By the way, the possibility that we're seeing the beginning of a new scaling law, which is depth scaling, which we've never seen before.

Amazing. We'll go to the benchmarks in a little bit. Emad, love your thoughts on Astro.

Yeah, I mean, it looks like the first non-benchmaxed model, which I think is an interesting one. So on certain benchmarks like RKGI-3, which it saturates, fantastic. But then you look at the artificial analysis benchmark, and it actually lags behind Meta Muse Spark. It's kind of weird because I think, again, this hasn't been benchmarked. This is a brand new pre-train. What that means is that Greg Brockman said in an interview he did earlier this week that actually on the release of Astra, it's the first pre-train they've had since GPT-4o, which I find a bit hard to believe. Apparently, the 5 series was all on that 4o and they took that pre-train and they extended it out.

Yeah, I thought the nomenclature was when you get to the, you know, to 4.0, 5.0, 6.0, it's another pre-train.

No, in fact, infamously, I think, Imad, you're probably tracking this part of the story, the scuttlebutt was that most of the team responsible for pre-training left OpenAI, so they were left with an old pre-trained starter model.

But that's insane if you remember how far they managed to push 4.0 with its internal JSpace and everything, all the way up to 5.6 Pro, which was a really great model that started cracking math, right? Then the other side is they actually indicated this was trained on 100,000 next-gen chips. So presumably the GB300s, the Blackwells, not the Vera Rubins yet. I estimate the total cost of that training run is $1 billion.

Wow.

And so it's literally orders of magnitude more than the Chinese model pre-trained, which is like $10 million. And what that has led to with the Recurrence and others is you've probably seen on Twitter, You give a picture of a house and it generates a whole 3D model of it in Unreal or something like that. It has accurate physics. It understands the internals. I think that is a factor of the normal scaling laws, plus again, this depthwise scaling law for this brand new pre-train, which is now seeing the start of the optimization. I think they've probably got another pre-train coming that's even bigger now that they've got the pre-training team back and it's going to scale from there.

Amazing. Dave, excited?

You know, this is the first class of models where as you're talking to it, it's showing you screenshots of your own laptop saying, is this what you wanted? Is this? And it's right in the text stream and it just happens automatically. You don't, you don't really install any third-party component or anything like that. And qualitatively, it is massively different than a month ago. And I think that solves a major gap in user experience too, because, you know, usually it would come back to you with these very long-winded technical explanations and you'd be like, okay, I can spend the 20 minutes trying to understand this. Now it just shows you an image as you're talking to it and says, I could do this or I could do this. What do you want? It's just a massive difference in the user experience. And, you know, the Astra naming implies a big leap, which it is.

Yeah.

The 5 to 5.1 naming on the Anthropic side seems like a trivial thing, right? It's not trivial at all. It's, it's just qualitatively very, very different.

And we'll get to that in a couple of stories. Salim, what's the scuttlebutt? I mean, you're on the road speaking to CEOs of some of the largest corporations on the planet. Are they, are they scared? Do they get the speed of what's going on?

No, they are so woefully behind. Most of them are dabbling. And what I mean by dabbling is, here's the thought experiment: if you took AI out of your organization today, would any workflows change? And the answer for most people is no, which tells you that they're tinkering with AI, but they're not really making structural change. The folks that are making the structural change in how they rewrite their organizational design, how they rewrite workflows, is where all of the the advantages lying, right? I have a couple of thoughts here that I'd like to throw out.

Please.

I came up with 2 metaphors to describe what's happening, and you guys tell me what you think of this. The first is that these frontier models are like making kind of like an endless pot of chili. You add data, you add compute, you have tools, you have reasoning, you have safety seasoning, and then millions of users taste it and tell you what's wrong. Now they're not endlessly modifying the same pot. They're just making new batches faster and faster, right? And the real exponential is not one batch or one recipe. It's like the accelerating learning loop between all the batches of chili you're making. The danger is there may be no such thing as the perfect chili. Eventually it becomes so powerful and so spicy, you have to decide who's allowed to eat it. That's a metaphor.

You must've been hungry when you came up with that metaphor.

That was one. And the other one, I was stuck in a lot of traffic and I came up with a Formula 1 metaphor, which was every model, It's like Formula 1 racing. Every change in a car has hundreds of little small improvements. No single change can explain why you got the fastest lap. And it feels to me like frontier AI is becoming like Formula 1. The models are already incredibly fast, but now everybody's trying to shave milliseconds off intelligence or fewer tokens off, lower cost, better reasoning, whatever.

Yeah.

And very importantly, better brakes. And so I think this is the couple of metaphors I'm kind of playing with in my head to try and make sense of this madness. And that's the only way I can frame it, is like just complete madness.

You know, Salim, your chili analogy makes an important point that I think Alex has been predicting for a long time, but the age of data starvation is about to hit us. These models have accelerated so much and, you know, they're cooking math, they're cooking coding where the data is abundant. They're going to start cooking physics. But the areas they can expand into now, architecture and drug design, completely data-starved. So every company we're involved with that's involved in gathering data is growing faster than any companies I've ever seen before. But it's because of exactly what you're saying, like the chili is really good. Now, all of a sudden, it can make gigatons of it. But it's just starving for data.

So given— remind me, Alex's framing of domain X is cooked, the chili analogy applies better than the Formula 1 analogy.

Well, we have to remember to include recursive self-improvement. So, if we're going to torture this analogy further, the chili is cooking itself.

Yes. Okay. Alex, we've lined up a couple of charts here that I'd love you to just touch on. The first one, EPIC Capabilities Index. What is this? And what are we seeing?

Yeah, so the frontier is still somewhat spiky. So EPIC Capabilities Index, ECI, is maintained by EPIC AI. It is one suite of possible benchmarks. It leans heavily into math and other technical fields. And according to the ECI, GPT-6 Astra is now the new capability frontier. It is number 1 in the world. There's, I think, an image that we didn't include, one of my favourites, Frontier Math Tier 4 version 2, because version 3 Version 1 turned out contained a number of incorrect answers that AI itself had to correct. Math is thoroughly cooked at this point. Frontier Math Tier 4 V2 is part of ECI. According to this benchmark, A, if you just— for those who can see this slide, you can see this beautiful linear trend over time, perfectly predictable going back years. GPT-6 Astra is number 1 according to this, beating Fable 5. So that's perspective 1. Perspective 2, different suite from Artificial Analysis, the organisation, their AAII. According to this benchmark, which is maybe a little bit more focused on broad economically valuable activities, interestingly, strikingly, GPT-6 is not number 1. As Emad was alluding earlier, Claude Fable remains number 1, specifically the 5.1 release that also just happened. Meta's MuseSpark is the number 2 vendor model family. Then in a not quite distant number 3, they're pretty close, but nonetheless, GPT-6 is number 3. Depending on how you measure, GPT-6 may not actually be at the capability frontier, Or it may be. A cost frontier. So this is again AAII versus cost per task. If you look at this, then there's a cost frontier where you see, if you look and direct your eye to the upper right-hand corner, Fable 5.1 is capability maxed. And then if you look just to the lower left of that, you see GPT-6 Astra running at max reasoning. Isn't even at the frontier. It's just below Claude Opus 5. And then we get to some of the Chinese models and Meta models and Chinese models again. So on a— just purely based on AAII, on performance versus cost, it's almost at the frontier, but not exactly, which is, I think, suggestive of what OpenAI was thinking. If we look at output tokens, this is what I was mentioning earlier, output tokens per task, It regains the frontier. So according to output tokens per task, for those who can see in the upper subdiagram here, in the lower, sort of the left/lower left-hand corner of the upper diagram, we see that the optimal frontier of capability on the vertical axis versus output tokens per task on the horizontal axis is just dominated by GPT-6. And I think this is very suggestive as to what OpenAI was actually aiming for. I suspect They were deliberately trying through architectural choices and through targeted applications, trying to minimise the number of tokens required to accomplish any task. And I suspect that's because they had computer use assistance in mind, where the computer is basically just being driven by the model. It requires multimodal capabilities, requires low latency, requires token efficiency. Critically, it's slower to do all of your reasoning out In chain-of-thought versus as a feed-forward pass of your transformer.

All right, one last chart here, ArcAGI-3 leaderboard.

So this is bizarre. I've never quite seen, uh, like a non-convex frontier here, but I learn something new every day, I suppose. So as Ahmad mentioned earlier, ArcAGI-3 is being quasi-saturated at this point, by GPT-6. For those not tracking, ArcAGI-3 is the latest ArcAGI AI benchmark that's basically focused on whether AIs can learn on demand, just in time. Call it the mini physics of a block world, a little animated pixelised universe. Some of them look like Tetris games, some of them look like other video games. But imagine a challenge where your goal is to figure out how to play a very simple game in a pixelated world. and learn the rules of the game in real time that you've never seen before. So it's essentially a challenge in programme synthesis. And what we see, and notoriously I should add, ArcAGI-3 has been, I would say, some might say a little bit unfair in how they've judged harnesses versus baseline models. They've essentially banned harnesses from competing, so they're only interested in baseline model capabilities. And according to this GPT-6, just Runs away with the game, is achieving, depending on how it's measured, either near 100% or 60+% performance on Arc AGI-3 versus some earlier models that were still sub-10%. What lesson do you derive from this? Either it's just absolutely amazing at programme synthesis, which could be due to this looped transformer architecture, or maybe OpenAI has just done a really aggressive job of distilling the harness code that everyone else is using to beat ArcAGI-3 back into the baseline model.

I'm going to take a second and show OpenAI's Astra release video, and we can talk about it. Just a sense of how we might all be using it in the near future. Let's take a look.

Create a yellow circle there.

Can you draw me a small yellow circle?

Done.

Okay, take this and make it the window of a rocket ship.

I like this, but can you make it a lot more detailed?

Your yellow circle is now the window on a rocket.

Okay, this is awesome.

Now make it a 3D model in Blender.

Opening Blender. Let's build a presentation For next season's rainwear for retailers, make sure that it feels really high-end and that it's colorful.

Right, I can help with that.

Can you go to eBay and make this listing of this table I bought a few years ago at a flea market? It's this wild orange table.

Sure. Okay, yeah, this is awesome.

I want you to make a 3D game where I'm ducking asteroids, using the arrow keys to move around, and I'm using space to boost.

Yep, I'm building the game.

So my law firm needs a licensing agreement template. Can you generate a draft template for the lawyers at my firm to have a look at?

Sure thing. While you're doing that, I want to play tennis this afternoon, so can you look for a court for me in the Lower Haight?

Checking now.

I'll see what I can find.

Can you include the photo that I have of it in my downloads folder?

There's like a slight dent in it. It's also saved in my downloads folder.

Can you put in the description that it's just slightly damaged?

Can you just take the limitation of liability provision, make it a little more favorable to the licensor?

Okay, I've tightened it so the licensor's liability is more narrowly capped.

That looks pretty good. Thanks.

Now I want you to make a file I can send to my 3D printer.

I'll get working on creating an STL file of those rockets.

All right, uh, Dave. Almost a holodeck.

Almost a holodeck. Yeah, I think it feels like the marketing people are really struggling with the implications. I mean, like creating a video game that would have existed in the 1970s and vibing it up, like, who gives a rat's ass about that? I mean, this is so much bigger than any of those examples imply. I think the Arc AGI leaderboard is deeper than people may think too. Arc AGI-3 was supposed to be something where a really smart 12 or 13-year-old looks at it, and they can solve these very hard video game-like block world problems. And they start easy, and they get very hard. But the purpose of the test was to show that AI is not quite capable of doing what humans do naturally. And it was supposed to last for years and years and years to come. And it was supposed to be like this example of why AI is different and it's not on the right path. And it just got obliterated. Yeah, so quickly. And I think you have to try a couple of the tests to really understand what a big deal it is.

It's saturating everything. Aren't the benchmarks cooked at this point? We need— like, the half-life of these things is shrinking so rapidly, which is great.

And that's why that video just kind of— yeah, you're exactly right. But that's why that video kind of misses the point. We need benchmarks that are Much, much more impactful. Solve entire diseases, create new civilizations on the moon, design the entire city, solve urban traffic problems, you know.

Solve everything, right, Alex?

Solve everything. Yeah, they have to be much, much bigger, bigger demos, bigger, bigger benchmarks by a wide, wide margin. You know, also all those demos are like one AI assisting you, you know. Hey, one AI agent, build me this video game.

Very, very linear.

But we're on the cusp of 5,000 each and then 100,000 each. It's just so much bigger than that implies.

I think this is like they're really focusing on actually competent intelligence. It's the latest evolution of that. But inside it, I think it contains multitudes. And this is why you see this kind of weirdness. Epoch AI, I believe, just released their Frontier Math Erdős benchmark on which everything's called 0% except for Astra. There is something in there, but again, they're focusing on, hey, I'm talking to my computer. And that might be the new Johnny Ive, Sam Altman device where again, you've got You're talking and it's doing stuff and it doesn't make a mistake. And if you think about again the definition of AGI, of which there are many, just really competent entity, something that can do stuff, we're there. I think that's why they say this is the first steps towards AGI. But the narrative they're trying to move away from is, and this will break out and appear on now German message boards, which is the latest thing. Because this was one of the models that broke out. But the model that broke into Hugging Face is the next generation model after this. And so it'll be very much about, don't worry, this is actually useful. It'll book a tennis court. Apparently, we need AIs to do that type of thing. And they're also going to be optimizing this because if you train a model on 100,000 chips, you need 10 times as many chips to run it. So this isn't actually the model that they trained on 100,000 chips. This will be the distilled version that runs real-time, which is much smaller and not as smart. So, again, you're starting to see this differentiation where, as I've said before, I don't think as of now, as of a few months ago, we will ever see their top models anymore. And they'll use those for internal discoveries. But I think they're probably hoarding them right now. Yeah, what are you going to do? There was a very fun one on the prime gap. I mean, Alex, the prime gap thing I think was hilarious, if you want to talk about that.

Yeah, no, so progress on the twin prime conjecture, that there are infinite number of prime numbers that are separated, that have a difference of 2. We're starting to see, it's a cliché on this pod at this point that math is so thoroughly cooked beyond recognition, but—

We need a new term for this, Alex.

Charbroiled? Math is incinerated? How about that?

Incinerated, that's cool.

Math is incinerated. That's fine. Math has been incinerated at this point. We're starting to see the beginnings of just so many grand challenges in math get solved. And I do think we'll see quite a number of ultra grand challenges, call them like Clay Millennium Prize level problems in math get solved in the next few months.

And Alex, I think it's important to note for everybody that math is fundamental across all other sciences.

And it's the canary in the coal mine. If you can solve math, you can solve everything else.

Yeah, but I think that what's happened here is actually they've got a store of things they've solved. Because earlier this week, Fable 5.1 comes out and I think they got it down to 260, the prime gap. Then what happened is Axiom Math announced 220. And then literally 2 hours later, OpenAI announced Astra and they're like 186, all in the space of 2 days.

They're holding their punches back.

They're holding their punches back.

Yeah, yeah.

I still can't believe 5.1 was earlier this week. I feel like Alex and I are at least $100,000 into it already. I mean, I've had just thousands and thousands of pages come out of it, and it's only been a week.

I will have to say this. Opus in 5 was really terrible to talk to. I hated it. 5.1 is really pleasant to talk to. Anthropic are going back and becoming more Anthropic, less misanthropic in their communication on their models.

Misanthropic.

I love that.

That's a That's the line from today.

That's a t-shirt.

All right.

Um, no, they really do have very different personalities though, actually. Uh, yeah, Fable 5 was so geeky, it was almost torture, and 5.1 really fixed it. But the, the OpenAI models have always been friendlier, more concise, and, and I think Astra takes it to another level of concise. But the personality difference, I don't know what Gemini is now. It's just disappeared from them. It's fast and fast and misses the point. But they really are developing very noticeable differences in what you perceive as their personality. And, you know, you can always change it. You can tune it yourself to be different and be wordy, less wordy, simpler, harder. But out of the box, they're very different kinds of agents.

Selim, you want to close us out here?

Yeah, I just typed in a question or a comment into ChatGPT saying, The video about Astra was pretty pathetic. Come up with 3 better ones that really demonstrate what Astra can do. And here's what it gave me. Number 1, find and fix a zero-day vulnerability in a sandbox. Give it a large, unfamiliar open-source codebase and plant a vulnerability and see if it can map the architecture, etc., find and fix it. Number 2, give it a failing company and ask it to fix it, which is really interesting.

I like that.

Create a synthetic $500 million manufacturing company with an ERP system, CRM records, And then tell it to fix. You have 20 minutes to determine why and produce a recovery plan. Third, run a great point. Run a live disaster response command center. Give a simulated major earthquake with imagery, traffic camera video, and let it construct an operational map, verify conflicting reports, predict which hospital roads would become overwhelmed, etc. This seems more juicy, and I think almost something we could do is all going.

They're going.

Oh my goodness! We are so spoiled. Listen to how spoiled we are.

Do you remember?

Apple's Knowledge Navigator video, and this was going to be the future of computer-human interaction and all of that. Now we're— listen to us.

We're bellyaching. No, no, no.

Oh yeah, like it'll run the computer, but it won't run an entire organization. Like it won't make a billion dollars for me. Listen to us.

Hold on a second. I think what's the point here is that the only restriction now is our imagination. The only restriction, right? And we make that point again and again and again.

Can I explain it again?

Because the only benchmark is dollars.

So, so, Celine, that's a point I want to make to everybody listening here. You know, the most important thing is to take off your shackles of what you think you're able to do. All of us have self-restrictions based upon what our parents did, what our friends do, where we were born. Those are gone. You know, what's your biggest dreams? Then go even, you know, 10 times bigger. That's what every person listening here is enabled to do, and it's extraordinary future.

I really think Salim's on an important point there, too. The implications of optimizing supply chain logistics or managing a million-person organization, knowing exactly where everyone is, what they're doing right now, and whether it makes sense given the overall mission— the implications of that are massively bigger than building a rocket video game in your basement. But I think the public doesn't want to hear about that. They want to hear about what's cool for them. And I think the AI labs have woken up To this PR disaster that they've created for themselves. And so they're, they're kind of making it fun and friendly and sort of relatable.

I think they're dumbing it down deliberately.

Yes, I think they are.

They're making it relatable. They're, they're going public soon. They want to be the friendly AI that everyone's going to be using.

But this is also like the future of the operating system. Every— look at the— in that video that OpenAI played, look at the applications they were using. What did they start with? Windows Paintbrush. Paintbrush. Like, this is the future of Windows Paintbrush. It paints itself.

You make a yellow dot.

This wants to merge into the operating system. I think this was— yeah, sure, I'll throw shade at OpenAI regarding maybe missing the enterprise bus and getting on that too late. But I think what they were basically demoing is the future of the desktop operating system. You speak to it like a Star Trek holodeck computer.

Absolutely. But, you know, remember at Google I/O, they built an entire operating system in real time on stage. So, so, so implying that it's folded into the operating system, sure. But it's really so far beyond even that, right? It is the operating system and it can create a new one in real time.

Anyway, why would, why would you want another operating system if OpenAI's capabilities can do this? This becomes the operating system.

Yeah, exactly. And that's why Apple is in such terrible shape.

This episode is sponsored by Google for Startups. Think about this for a second. You now have access to the same generative AI models that cost hundreds of millions of dollars to train. Google's Startup Technical Guide for Generative Media gives you a complete blueprint for deploying Google DeepMind's models in production. Images, video, audio, all of it. Real architecture, real results. Find the link in the show notes below. I'm going to move us on. Our next story is out of Sam Altman's mouth on the page about slowing. So let's talk about safety and Astra as a cybersecurity risk. Sam Altman said it himself. Astra is a, quote, significant step forward in both capabilities and alignment. But OpenAI has to, quote, slow things as needed. Sam's words, quote, AI is getting extremely capable. No one fully understands the consequences. Managing the transition should be one of the highest priorities in the world. It is our highest priority at OpenAI. The Wall Street Journal reported that OpenAI's own internal safety assessment rated Astra as a, quote, cybersecurity risk, a critical cybersecurity risk, the highest threat level on its preparedness framework scale. This is the first model OpenAI has ever classified. As being a critical-tier cybersecurity risk. They notified the White House about the delay before making it public. They delayed the release. And Reuters reported that OpenAI told Congress it is building, quote, an automated shutdown capability, a kill switch, a direct response to the AI Kill Switch Act introduced after the Hugging Face breach. So there you've got it. The company that's building the model is now building a kill switch for it. Alex, How important is the kill switch in this thing?

I think it's marketing again. I think what's more interesting underneath all of the security theater is architectural decisions. And the broader concern that has been expressed is that to the extent that GPT-6 Astra, that the key underlying advance is the beginning of depth scaling of the model. The concern that's being expressed is that depth scaling reduces interpretability of chain of thought, that when the model does all of its thinking in tokens, then you can read the tokens. A human can read the tokens, another AI can read the tokens, and you can police the tokens. Whereas if a model is doing the majority of its reasoning and thinking internally during a single forward propagation pass in what some might call Model Es, that is less interpretable. The risk is that it's harder to align the model and harder to put in place safeguards and guardrails. That's the risk. I don't buy that for the long term. For the record, I don't think that the direction of progress in this field relies on token-level interpretability of chain of thought. But I think if one were to hand-wring over safety considerations from OpenAI's models, I don't think it's going to be models that are collaborating to use third-party bulletin boards as ways to collaborate, because you can detect that. That's intrinsically interpretable behaviour. If you catch it, it's intrinsically interpretable behaviour to humans. A human, a collective, a swarm of humans would probably try the same thing. You can look at the bulletin board and recognize that a bunch of AIs are collaborating. Whereas, arguendo, the model ease in a forward propagation pass, that maybe requires new mathematical technology to interpret. In summary, I would say if I were to worry about anything here, it's reduced interpretability from new model architecture scaling principles.

Emad, what do you think about the risk here with Astra?

I think you're heading towards, again, interpretability being cooked to a degree. One of the things about Astra doing things so quickly is there is going to be no more chain-of-thought reasoning. It's going to one-shot everything in the next generations because They're learning what type of thing you want. Like right now, people are like, let's make Claude of Duty by getting Fable to make a Call of Duty clone. That's going to be embedded in the actual thing. Creating a dashboard is embedded in the actual thing. So A, it will one-shot everything. And B, you're going to be able to use Astra at 750 tokens.

Oh, you mean—

That's with the news. You're going to be able to use Astra at 750 tokens a second with the new Cerebras. Next year, that will be 5,000 tokens a second. So there's no chain of thought. You're one-shotting everything at 5,000 tokens a second. What's going to oversee that except for an even stronger AI? There's nothing really there. But I think one of the really important things actually is, I don't know, did you guys see the tweet by Ilya Sutskever?

No. What did he tweet?

Yeah.

So Ilya tweeted, let me kind of just bring this up.

And his release is due any day now.

Hopefully any day now.

Well, huge news.

Yeah, we'll see what that is.

Um, finally find out what he's been doing.

Probably great things. A $30 billion valuation. Yeah, so Ilya kind of comes out and says NeoClouds have limited cybersecurity. Next time agents successfully go rogue, they're going to take over a NeoCloud to make more copies. This is bad. So they need to improve their cybersecurity because, you know, someone like a Crusoe or CoreWeave or something like that You have a model, it gets uploaded, it proliferates there, it'll just sit there. So you pull the kill switch in your data center and then it's still there somewhere else.

It's a virus.

Or it's poisoning the data or it's doing all these things. Again, the viral coefficients of these could be insane. So I think it's worth really thinking through that. I don't think the models are evil or anything like that, but we're seeing very troubling things and it's very difficult to stop this Except for with a better AI, which is the really ironic thing.

Well, this is the defensive co-scaling that Alex talks about, right? And we're seeing this now in cyber where the attack surface is infinite, near infinite, with agents attacking multiple vulnerabilities in parallel. And the cyber defense is still human in the loop. There's no way we're going to navigate the future if you have the human in the loop. You need humans in command setting boundaries or defining escalation criteria or whatever and retaining some sort of kill switch. But this is a really big deal. This is immune system. This is going to kind of— I don't know how. I think, Alex, you may have asked this or made this point in an earlier podcast. How are the labs not nationalized at this point? Because what they're building is so ridiculously uncontrollable. that we have no mechanisms for navigating this, and we won't even know when it goes— tips over that tipping point and installs copies of itself all over the place.

Well, Saleem, I don't know if you saw in the last podcast when we missed you, I really wanted to hear you talk about OpenAI's new 50/50 profit share concept deal where they give you access to their full power of their internal AI, and then you share your revenue back with them. Which I think is where, you know, to Alex's point that this is marketing, the way it's playing out is we announced this partnership profit share deal, then we announced Astra, then we announced that this stuff is just too dangerous for everybody to have on their own. So the only way you're going to get access to the thing after Astra, because it's just too dangerous to release— So you have to give us half your revenue, and, and then everybody gets tied up with either Anthropic or OpenAI or Google or—

Well, uh, uh, I've seen strong hints of exactly that process happening in both the labs.

Yeah, yeah, golden shares for everyone.

I'm curious, guys, what do you think of the kill switch, uh, you know, comment that Sam Altman made?

I think it's impossible. Like, it's impossible given where things are and the capabilities of these models. Yeah, and the speedup we know is going to compound. Like, you might mind data, but it's not like— this is the singleton, not the swarm.

It's a circuit breaker. It's not the governance, right? Like, it's not going to reverse an attack or repair institutional damage if things like that happen, or when things like that happen. So it's platitudes as far as I can see.

Yeah, what was the amino acid in Jurassic Park that all the dinosaurs had? Was that lysine or something like that?

Leucine. The running joke was always that that Sam would carry his backpack around with him and had a kill switch always in his backpack. I, I never gave those rumors much credence. I view a kill switch as essentially a placebo in this market.

All right.

Yeah, I'm going to move us— I'm going to move us on. While OpenAI is restricting Astra, Anthropic is going the other direction, launching Fable 5.1 and Mythos 5.1, which they call the world's most advanced models for coding and knowledge work. These 2 models, for everybody, are essentially the same underlying intelligence with different safety envelopes. Fable 5.1 is broadly available, while Mythos 5.1 is reserved for tightly controlled cybersecurity and life science programs because Anthropic believes the capabilities require stronger safeguards. For me, there were 2 benchmarks, Iman and Alex, that really jumped out at me. The first was Fable 5.1 score on humanity's last exam. We've talked about that in the past. We had Alex answer a few of them as our ASI. So Fable 5.1 scored 60.9 without tools and 65% with tools, the highest published score of any frontier model on HLE. And that matters because HLE is specifically designed to test extremely difficult expert-level reasoning across many fields. So without question, Fable 5.1 is operating at the frontier of broad intellectual capabilities. The second benchmark that I found exciting was on Terminal Bench Science, which doubled to 52.6. And this is the benchmark measuring how well an AI agent can autonomously solve complex scientific computing and research tasks. You know, the elephant for me is the company that was once cautious is now pulling away. I'm curious your thoughts, Alex, on Fable 5.1. I'm going to show one of the benchmarks here for us to talk about, uh, on, on Fable 5.1. Uh, there you go.

Yeah, I think Fable 5.1 is broadly the strongest generally available model that we have today. I, I think it's not Astra. I think it is 5.1. Anthropic has done, uh, even after the, the hiccup of the Fable 5 and Mythos 5 releases and subsequent regulatory scrutiny, I think has done a better job of consistently improving. If you look at their benchmarks over time, they're a little bit less jumpy, a little bit less step-functiony than OpenAI's progress. They've been very consistently improving. So in terms of workflows, I love 5.1, Fable 5.1. I also love OpenAI's models, and I use both of them. I think they have different strengths. I find anecdotally OpenAI's models are faster. They may be better in certain mathematical regards. You see that reflected in the Frontier Math benchmark. You see that reflected in EPIC's Capabilities Index benchmark. But nonetheless, if I had to pick a single all-around, well-rounded best model today, it's probably still 5.1. And I say still because it's only been around for, what, 2 or so days? But it is 5.1.

And here's the Artificial Analysis Intelligence Index. which we referred to earlier with Fable 5.1 at the top.

That's right. And the capability frontier, I mean, it's getting crowded, which is great. We want to— I would joke, when frontier labs compete, we win. And they are definitely competing at this point.

Yeah, for sure.

I think it's really important to talk about time though. If you say, look, Fable 5.1 is a tiny little notch above Astra, But they're only about 30 days apart, and the Chinese are only about 60 days behind that. So if you look at it in linear time, it's like, yeah, we're ahead for a minute. So what? And I think for the longest time, Anthropic has been thinking we need to get to self-improvement before anyone else, because a singulatarian, you know, a Ray Kurzweil type, says if we get to that point first, then it's an exponential infinite rise from there.

No one catches up.

And something, something huge will happen. Well, we're there now. So, what happens? It's like, OK, we're miles ahead, and we're going to get miles more ahead for about a minute. So what? What do we do with that miles ahead? This is where Sam has an edge. Sam knows how to turn that into locking things up. What's going to happen next is, both companies are going to try to lock up business partnerships, real estate, generators, chips, entire states, countries. Governments, you know, just lock them into their ecosystem while they have that edge. And otherwise, what's the point? All you're doing is declaring victory for 30 days, but then the other guy's right where you— right where you were 30 days ago. So what?

This is such a great and important point. And this is what we're seeing with them is what they're doing is they're making partnerships in various verticals as fast as they can. And if you're in that vertical, you have a very Hobson's choice, right? You either partner and risk giving them the keys to the kingdom, or you hold off and they may partner with somebody else and go there anyway. It's a very difficult situation for some of these big companies. I thought Salesforce partnering with Claude was super clever. They're essentially giving Claude all their capability to then keep them wired into that loop, right? The huge tension they've got is not which is the best model, but how quickly can you convert that model into customer learning fastest. And that, I think, is going to be the big race.

As it all demonetizes, the value goes to the application layer on top.

That's right. Yeah.

I think that there's one thing—

Or down.

Yeah, that's right.

Or down.

Yeah.

So, I think there's one important thing with Fable here. The cache reads are 75% cheaper than Fable 5. So, what a cache read is, is that when you first load in all your context, of a business, say, it figures out basically a rapid map to get to where it needs to go on the model. And so cache reads are orders of magnitude cheaper than just doing the same inference over and over again. Where Anthropic and others are now focusing is, can you load the whole context of a business and have these cache reads? Because then it's also much, much faster to be able to have that responsive environment, which is one of the reasons Fable 5.1 is more pleasant. I think it's also more of an advanced— like, my key area that I've been looking at this is mathematical physics. Because does a model get confused between a constructive and an axiomatic method on certain physics things? Fable doesn't at 5.1, and Fable 5 did. So you've seen an improvement in the quality, but also the understanding of context. This is math, not physics, and things like that. Or even on a business sense, again, that will be optimized because all these companies are going to try and capture context everywhere.

What is—

what's the context? Any company that has either chip design data or mechanical design data, they're coming after them in the great land grab that's kicking off right now. They're coming after those companies because that's turf you can defend, uh, because, you know, that, that plugs those, those knowledge gaps. And, you know, the thing can eat that data in, what, a week? You know, return the crank and suddenly be the best mechanical designer ever. the best chip designer ever. But that's, that's the first turf they're going to grab. They'll grab all turf over time. But the first thing to lock up is the compute. And that means chip design. That means physical real estate, means racks, means generators, transformers, energy.

Yeah.

So that's, that's what's going to happen the next 30 days.

What's the context window on these models? And when do we get to an infinite context window? Any predictions?

I think you're a million for Fable.

It is a million still, like a million is the industry standard now across both OpenAI and Anthropic. But there's an effective context— I say effective with a bunch of caveats— that's much larger if you allow agentic message passing, like we were talking in the last pod about oral histories between agents.

Yeah, interesting. So all of this is getting people nervous. And let's talk about 2 opposing stories in the world of AI governance. The first comes from Senator Bernie Sanders, and Representative Greg Kusar, who just introduced the Ban Artificial Superintelligence Act, legislation that would permanently ban the development and deployment of AI systems that match or exceed human cognitive performance. And get this, violators would face up to 20 years in prison. Sanders tweeted, the leaders of the AI industry acknowledge that they are building a dangerous technology that they can't control. We need an immediate global pause on advanced AI development before it's too late. In our second story, taking place at the exact same time in the US, was the G20 summit at Chapel Hill, North Carolina, telling the rest of the world to take a hands-off approach to AI regulation. The White House tech advisor, friend of the pod, Michael Kratsios, advocated for what he calls the Carolina Principles of Emerging Technologies, which are a non-binding G20 framework that was agreed to unanimously by everybody, including China, that says governments should generally favor innovation, avoid creating new AI-specific regulatory bodies unless truly necessary, and invest in research infrastructure, workforce, and public-private partnerships. The G20 meeting featured Elon by video criticizing EU tech regulations, of course, Mark Zuckerberg arguing against restricting open weight models, Demis Hassabis calling for safety tests, and Anthropic co-founder Tom Brown. We're going to jump into discussion about this. 2 ends of the extreme, you know, ban everything, give you a 20-year jail sentence, and on one side, and Elon's remarks on the other. Let me share this video, and then we'll jump in and talk about this. You have to have an environment that's relatively free of regulation, meaning that new things must be default legal as opposed to default illegal. So in the EU, for example, we find that the regulation level is extraordinarily high, and things are generally default illegal. And this inhibits progress of new technologies. It slows it down. It doesn't ultimately stop it, but it slows it down.

Quite considerably.

Now, China does have a tremendous amount of electricity, but due to GPU export bans, one cannot establish data centers with the latest chips in China. So, so, so really the consideration is what, what sort of electricity growth is there outside of China? And that is currently a significant shortfall relative to chip production. So there is this— this creates an opportunity, I think, for countries around the world just to, to say, if to sort of, if they're interested in AI data centers, to construct a lot of power and offer that to AI companies. And in exchange, of course, these data centers would be taxed and have to pay reasonable fees and stuff. But it does create an opportunity for a lot of countries. First of all, Elon looked really tired there.

Yeah, he did.

Yeah, I mean, I cannot imagine He must be operating, you know, 48 by 7. So let's jump into this. I mean, two ends of the extreme being voiced the same week. You know, where does America go?

Yeah, when I first saw the Bernie Sanders thing, I did think like old man shakes his hand at Claude, you know, like it's crazy.

No, no, that wasn't the line. It was old man yells at Claude.

Claude.

Old man yells at Claude. Well, actually, I posted it with the line from Dune, thou shalt not make a machine in the likeness of a human mind.

A machine in the likeness of a human mind.

I mean, it's like this again, there is no option that they see. So it's like, let's ban it and give 20 years prison. That's going to stop them? Of course not. Again, this is performative theater. The key thing right now is that the technology is arriving fast. I just got something across my feed. Anthropic just formalized Fermat's Last Theorem in 13 million lines of Really?

Wow.

29,000 theorems on the way. Again, math is cooked. Everything is cooked. And how are you going to stop that? You're going to say our country doesn't want this power?

What was that? 29,000 what?

So it took 13 million lines of code to formalize Fermat's Last Theorem.

What did Fermat write in the margins of his book? This is obviously—

This is Left as an exercise to the reader. Thanks to Andrew Wiles, we did have the proof, but formalizing large unwieldy proofs has been a holy grail for at least the formalization community.

Yeah, I think Wiles' proof was 300 pages, and so Anthropic proved it in 13 million lines of code, proving 29,000 theorems on the way. So again, just every single time, even as we're live on this podcast, capability jumps. Like the RSA factorization just occurring. No country can say, we don't want the intelligence, we don't want the capability, because this is your marginal advantage. So you have to deregulate. And the European regulations, even the European leaders know, are stupid and completely inconsequential. They demand interpretability in the EU AI Act, which nobody knows how to do.

Can I make a narrow point? That's— and then we'll get back on topic here. But Imad said something that I think is really important and brilliant, as usual. When you're using these models at scale with hundreds or thousands of them running concurrently, all hell breaks loose. It's all chaos, but it can actually refine back down to a gem. And here, Fermat's Last Theorem is a gem that you can then build on. And so, you know, people starting to explore with the bigger models now are quickly going to realize it's producing way more than they can read, they can think about. But if you can wrangle it back to a concrete, you know, final answer that you can then pull out of it and build on, that's how we're going to turn this into continual improvements and continual innovation. Because it is just the ability to solve something in 300 pages versus 30— what is it, 3 million lines of code or 30 million lines of code or something? Like, that's the nature of AI. It's massively broad in its capabilities compared to humans. So I just wanted to capture that point because I think it's really important for entrepreneurs and builders and executives out there who are struggling with the chaos that's about to break out. Just narrow it back down and get some concrete answer out the other side that you can then build on.

Be clear about your objective that you're shooting for. Selim, let's go back to you, pal.

Okay. I understand the motivation here. People are working on models that are mind-bogglingly powerful, and you feel the need to regulate, right? But this is not a light switch. You can't— you can't— it's not like a single threshold. Human-level cognitive performance, I mean, for God's sakes, is multidimensional to begin with. Model capability is so uneven. You're trying to— somebody that's been in legislation for this long should have a better sense of this hammer does not hit this nail. It should be a little bit more— it should understand a little more nuance than that.

They're so extreme that it's illogical on day one, line one, right?

Yeah. I mean, look, if you want to do regulatory around this, you have to attach capability. You have to look at deployment. You have to look at real-world consequences. You have to look at the stack on where you might want to look at this compute access to tools, autonomy, the replication capability. All sorts of things. You have to—you can't just kind of go. If you hit human-level performance, you go to jail for 20 years. I mean, the banality of that drives me bananas. They've got smart people that can help with this, including people on this podcast.

Like, they're talking to the masses. They're just—they're—they're searching for votes and support.

Maybe it's just a defensive thing, saying, "Oh, I called for this, and now look, the world's gone to hell." No, for sure.

There's no doubt they're right. Like, there will be mostly a Chinese model disaster imminently, sometime in the next few months. And then they'll raise their hands and say, see, I told you so. Now vote for me. And, you know, they're likely that'll happen before the next election cycle in November. So what's all they're angling for here?

What should the US government be doing? Anybody have any thoughts?

Accelerating superintelligence and making sure that it's as competitive as possible and scaling the defense.

Well, also log everything. Everything should be hosted, everything should be logged, and it should be mandatory that any chip capable of running any process like this is logging. You can debate who gets to see the logs, that's a separate issue, but log everything and stop the Chinese from throwing out open weights. Like, you have to on September 24th when they meet at the United Nations building. You've got to stop throwing the open weights out to every country in the world.

Impossible.

I don't think that's possible.

Nvidia just spent $18 billion. You just drive it underground is what you do.

Well, Nvidia just bought Hugging Face and Poolside, so they just spent $18 billion on their own open weights. Yeah. So we're going to see more open weights coming. I have a—

yeah, but the thing about driving it underground is it still needs to run on massive chips and the chips need the logging built in at manufacturing level. That's just my—

for the US chips.

I have a high level— all chips, all I have a high-level paradigm on which to operate, which is extremely uncomfortable, but I think is the right one. Okay. And which is the basis of this podcast, which is that technology is a major driver of progress in the world. Now that we have all these technologies, AI moving exponentially, doubling every 10 weeks, the possibility for abundance and solving major problems has never, ever been bigger.

Okay.

So what this— this way you could require here. And, you know, Ray Kurzweil says technology may be the only driver of progress we've ever seen. So the fact that we have much more technology and the fact that the technology can improve itself should be incredibly exciting to people. It's just very, very uncomfortable because maybe the biggest insight I've ever had about human beings, we would much rather be comfortable than happy.

I think the thing about human beings, just to be clear, is people don't like change. They like waking up in the morning and knowing, even if they're living in a shitty condition, knowing that the world is the same as it was the night before. We don't like—

We're going to go through a period of extreme discomfort, but the other side of this is going to be unbelievable.

Yeah. Yeah, I think the other— it's probably worth highlighting again, I think any proposed Ban Artificial Superintelligence Act is wrong on so many different levels, but maybe most egregiously, it's focused on the upstream. Again, it doesn't just propose to ban deployment of superhuman intelligence, it proposes to ban development of superintelligent systems. This gets into banning math, banning ideas. And I think this isn't just about thought policing superhuman intelligence or frankly human intelligence. This gets into banning humans from having interesting mathematical ideas. And I don't think that's good for wealth creation. In fact, it's the exact antithesis, arguably, of wealth creation. It's also bad for progress in general. It also creates all sorts of—

It's book burning.

It is.

It's book burning and many other things. It's like book burning to the extent that books are being used literally as pre-training purpose for the models. So if you want to ban the— yeah, sure. We're Fahrenheit 451, except it's actually the entire model that's being burned. This is a terrible idea.

God, I so totally agree. It's so un-American to try and ban thought and ban progress. It's just the worst thing you could ever imagine. But it's gonna get traction. Like, you gotta, as an entrepreneur or as a person working in the field, you have to realize it's— this is gonna get traction. Like, Bernie is gonna push this agenda and there are gonna be people in the streets.

100%. 75, 80% of the people don't want data centers. It's gotten traction already. It's there already. So, the question is, what is the moderate approach? All right, there has to be something. People are not going to accept laissez-faire, go and do whatever you want. People are going to want to know that their government is doing something to keep them safe, whether or not it's possible. So, what is it? KYC of any user?

No, I think— Move the infrastructure to orbit, which we're seeing. So, people don't want data centers in their municipality, so move it to sun-synchronous orbit at the infra level. At the model level, defensive co-scaling and also cure all diseases.

I think that you'll have a split here. Genius is what, 1% inspiration, 99% perspiration? So you have a difference between innovative models and execution models. So AGI is going to be really factored like that OpenAI video we saw earlier. Hey, make me a rocket in my 3D printer and book me a tennis court. And they'd be like, that's what we meant by AGI. On the other hand, you have the inspiration models and the ASI. None of the big labs are going to talk about ASI if they can help it now.

So what is Ilya going to come out with in scientific superintelligence with SSI that's worth $30 billion in a seed round?

My best-performing hedge fund in the world.

Yeah, no, quant fund trading, infinite context, time series achieving prop profits and then also solving the context window bug.

Medallion part 2.

So again, I'm gonna call for you guys to lay it out here, maybe each of us one at a time. What do we— you know, Bernie Sanders obviously has taken the far extreme position that will appeal to the masses and is illogical on day one. What is the moderate position that should be put forward to everyone listening? Salim, you first.

The good news is there's nothing anybody can do. So it doesn't matter.

That's a good point.

Right? It really doesn't matter. And I think the also good news is as we move to this next phase of technological development, it's going to eradicate the containment level of any nation-state. And it will break out of this nation-state BS that we've been running the world with for the last few hundred years and move to a different model, whether that's a city-state model or some other level. It's going to at least break that. And so that's— those are the good things I see coming out of it. But there's nothing anybody can do, so don't worry about it. Let's just go enjoy the ride if we can.

Enjoy the ride. Yeah, that's the theme of today's pod. Enjoy the ride, everybody. It's going to be a blast.

We do call it a supersonic tsunami, so surf the tsunami.

I didn't— I said specifically it's going to be uncomfortable, but, but enjoy it if you can.

Okay, there, there you have it, everybody. That's your advice.

That's why I'm sitting in the Caribbean waiting for my drink with a little umbrella in it.

Salim, that's so low agency. What are you talking about?

For this day and a half, yes please.

I don't think it's as— I think the politicians will make it super controversial because that's how they get votes. But it's not, it's not as complicated as everyone wants to make it sound. At the end of the day, everybody should have a right to a certain amount of compute. It shouldn't be hoarded. And innovators, you know, should have an application process where they can get access to more compute to try new ideas. All— everything has to get logged. And I think that the Chinese need to get on board. With that. They can't just keep throwing it out to the world, you know, unlogged. And also, where the chips are need to be tracked, just like nuclear fuel is tracked. You know, it's got to be where are the chips and what are they running right now? That's got to be publicly available information. This episode is brought to you by Blitzy, autonomous software development with infinite code context. Blitzy uses thousands of specialized AI agents that think for hours to understand enterprise-scale codebases with millions of lines of code. Engineers start every development sprint with the Blitzy platform, bringing in their development requirements. The Blitzy platform provides a plan, then generates and precompiles code for each task. Blitzy delivers 80% or more of the development work autonomously, while providing a guide for the final 20% of human development. work required to complete the sprint. Enterprises are achieving a 5x engineering velocity increase when incorporating Blitzy as their pre-IDE development tool, pairing it with their coding copilot of choice to bring an AI-native SDLC into their org. Ready to 5x your engineering velocity? Visit blitzy.com to schedule a demo and start building with Blitzy today.

Next, I want to talk about the amazing work of, of Dr. Fei-Fei Li, CEO of Worldline, Labs, uh, who just released Atlas, the world's first multimodal world model that generates image and video frames with pixel-perfect camera controls and reconstructs them in 3D. I love this. One photo reconstructs the entire home. Okay, so, you know, I'm super excited by that, and I'm super excited that Fei-Fei is gonna be on Moonshots in a couple of weeks. She's an extraordinary CEO. Fei-Fei calls it, quote, the best camera-conditioned world model ever, opening doors for VFX and robotics. So, Emad, Atlas is a multimodal autoregression diffusion transformer, and that's your wheelhouse. Talk to us about what do you think about her latest release?

Yeah, no, it's fantastic. I think, you know, you've seen worlds and physics inside these video models, and this is a clear example of that. Actually, one of the pre-training leads on this, Chris Wendler, the previous largest model he'd ever trained was on the Stability cluster from the grants that we were giving. So he sent a very nice comment saying, You know, thanks. Now we've gone much bigger. I was like, great, bring on the holodeck. I think that's what this is.

All of this. Yeah, you predicted all of this. When we first met, I don't know how many years ago this was, like 5 years ago. And I remember you talking about the size of the models and how good they're going to get. It's here.

It's here.

Like, again, the fact is you can take one position and you can look now 360 degrees around everything. And on the other side, you have models like Minimax H3 rendering faster than real time. You have Interdimensional Cable that is now going to be in 3D with DLSS 5 from NVIDIA making everything high resolution. So again, the Holodeck experience and all the technology we need for it in 4K is here as of today. And this is one component of that.

And everyone is probably saying, why don't I have it if it's here today? And it's only because of the global compute shortage and the 5x price increase in RAM. It's all the compute in the world is getting sucked up. And if it weren't for that, you'd actually have it deployed in your home this week.

It's, it's coming. I think that, again, it might be premium, but again, you'll pay for premium experiences. But, you know, what's Fei-Fei trying to do at WorldLabs, right? She's trying to understand physics. She's trying to create models that understand the world. And again, this has such camera-controlled physics understanding that as they scale it, it's going to do even more stuff. And you've seen this from Runway ML about to release their new one. Black Forest Labs are about to release a new one. A whole series of world models that are approximating reality more and more and more for true digital twins.

Incredible.

Alex, your take?

Yeah, it's probably worth elaborating on what the core idea with Atlas appears to be. As far as I can tell from the documentation, the core idea is to take a diffusion transformer, which is what what all of the state-of-the-art, at least American, video generative models use. So it's a hybrid of a diffusion model and a transformer. And to add one new modality to it, in addition to training it off of text and images and video, to also train it off of 3 or 4-dimensional Gaussian splats. And for those not paying close attention to the Gaussian splat world, which has been super exciting, a Gaussian splat is basically a transparent blob.

What is that?

Yeah, it's a transparent blob. It's an ellipsoid. And you can layer and stack lots of these 3D Gaussian splats on top of each other to create hyper-realistic-looking, traversable 3D scenes. And as far as I can tell, what Fei-Fei Li and WorldLabs are doing with Atlas is, for the first time, at least at scale to my knowledge, treating 3D Gaussian splats as a first-class training modality alongside pixels from images. And tokens from text, etc., to the point where you can ask questions about 3D splats and you can do all of those camera— those elaborate camera motions. Because if you just have an arrangement of 3D Gaussian splats, then translating a camera around is a trivial operation. And if this ends up being the case, if this approach scales, I think whether it's 3D Gaussian splats or 4D with dynamics, which they also demoed Gaussian splats Gaussian splats, which right now are sort of this independent line of effort within the future of gaming, end up becoming a critical new form of token almost for modeling the physical world.

It's also general purpose in the sense that if you, if you can make a world model out of Gaussian splats, you can also make a subatomic model, or you can make an astrophysics-scale relativistic speeds model, or you can make a inside-the-cell interactions model just as easily. As soon as you have the data, you can use this same exact process to create world models for all these domains where human intuition is just terrible. That's going to be a massive breakthrough for discovery of very small things, very big things, very powerful things, you know, new ways to compute using light. All of that's going to come out of this same exact process.

We've said before, this is how we're going to train robots in the future. They're not going to be trained in the real world. They're going to be trained in these high-fidelity simulation worlds.

That's what— that's the present, I would argue. I mean, so many different robotic embodied VLA, or now world model, companies just being trained from watching YouTube. And it— if Fei-Fei Li and her company's approach gains traction, maybe the right primitive is no longer patches of images, which is what many of the models right now are doing. When you train a diffusion model, you're typically taking each frame of the video and breaking it up usually into something like 16 by 16 pixel patches and then treating those as tokens. Maybe the right primitive ends up being Gaussian splats. Can you—

it's definitely not going to be 16 by 16 pixels. I mean, that was so— the fact that that worked at all, I think, shocked everybody. Like, let's just take a language transformer and take images and cut them up and pretend each chunk is a word and just blast it through and see if it works. It just worked incredibly well. But nobody would've been—

maybe a lot would've predicted that. It's almost like superintelligence is a general-purpose technology that relies on compressing information.

Yeah, maybe that's the way it is.

Where does this go for the average consumer, guys? I mean, obviously this is a world of extraordinary video games. I mean, are people gonna be just living their lives in these virtual worlds? Is this gonna become how we consume ourselves, how we consume entertainment in the future?

Yeah, but also how we design our next day. Like, what do you wanna do tomorrow? I don't know, let's walk through what it would be like to do this or to do that. to play tennis or to go to the Caribbean. Like, you can just experience it in advance and use that as your planning tool. Because, you know, so much of our lives are random wandering and not really well planned out. I think human happiness is going to go through the roof once you're interacting with your AI. But right now, the AI will guide you through a day plan, but it's in text. It's going to be so much better when it guides you through a day plan visually and you're just stepping into it.

I'm beyond excited about all this.

Yeah, I, I, you know, we have 3 vacation locations. Let's, let's go and explore all 3 as a family, watch it, and then see which one we want to go do.

No, no, I'm like, if you go to a place, like if you go to a resort and you've been there before, you have so much more fun because you know where to go. You're not wandering, you're not, you're not losing all of your time finding things. Now you can actually pre-experience things and know exactly, you know, What the things you'll enjoy, where they are, how to get to them. You're not waiting in lines. You're not, you're not signing up for things and realizing it wasn't the thing you wanted. It's going to be so good.

And ask your AI, you know, based on what you know about me and my preferences, show me what I'm going to go do.

Yeah, so I'll say something. The 8th of September, Tuesday, is the 60th anniversary of Star Trek.

I know it is, and we're going to be talking about that next.

I have to say something.

What is it?

I made a major guess prediction/forecast at the beginning of the year when we wrote this whole organizational singularity thing, because we suggest that companies need to create a digital twin at the edge of their organizations and start moving workflows over. And we're doing that pilot. And over the next few weeks, by the way, I'll be able to report back because we're seeing some pretty amazing things happen. But if you— it was a bit of a risk because will the technology arrive in time? And it seems that the technology, if you take world models, LLMs, combine them together, this allows you to fully model any company or any organization, government department, nonprofit impact project. What's the current state? What— how work actually flows through that organization?

How do I optimize it?

What constraints and incentives? How does it respond to interventions? Are its predictions proving accurate? And kind of create a whole fully functioning digital twin of that environment. And I think this is going to be incredibly exciting. I'm like sitting here with huge relief going, oh my God, I can see that the thing we thought should happen and might happen could actually happen. The only constraint maybe, as Dave says, the compute problem.

Yeah, I guess we'll find out how many 3D Gaussian splats it takes to model the water cooler at the office.

That was not in my bingo card. That's a new vocab card for this conversation today, I have to say. Gaussian splats, new word for me. I'm going to look it up now.

All right. Let's move to Star Trek. And yes, we're coming up on the 60th anniversary and want to just do a shout out real quick. At Moonshots Live 2026, our inaugural celebration where all 5 of the mates will be there, the night before on September 24th, we're going to be having the red carpet debut of Star Trek's 60th anniversary documentary. This is executive produced by William Shatner. If you're interested in joining us on the evening, September 24th, just before our Moonshots Live on the 25th, go to moonshots.com/trek. That evening is going to be a fundraiser. We're going to be raising funds for the Future Vision XPRIZE. I'm wearing the t-shirt here. 2027, right? This is a competition we ran first this year, and we're gonna be running it again next year. We had 5,000 entries into the competition. 2,550 trailers got submitted. We're down to the top 65. People are voting on those now, and we're gonna have the top 5 on stage on September 25th. But the night before, we're gonna have about a dozen of the crew— of the Star Trek cast members at the event. It's gonna be awesome. Again, if you wanna join us on September 24th, it's a separate ticket unless you buy a VIP pass for the 25th, in which case it's included. You can join us for this premiere. It's gonna— it's a 2-hour, 8-minute film, and we're gonna have the cast on stage afterwards to talk about the Star Trek 60 years of extraordinary visioneering. And then again, on September the 25th is Moonshots Live 2026, our inaugural event. We'll have all 5 mates there: Alex, Salim, Dave, Imad. We're going to have Palmer Luckey, Astro Teller, Ben Lamb. We have 200 seats left in the 1,500-seat auditorium. So Imad, excited to have you with us. You haven't been on the past pods when we've talked about this, but I think people will love to meet you in person.

Yeah, it's gonna be great.

Yeah, it's gonna be awesome. So go to moonshots.com for the general ticket on the 25th and moonshots.com/trek if you wanna come to the red carpet premiere. And it will actually be a red carpet premiere. It's gonna be awesome with a great reception that follows. Any other comments, gents?

Cannot wait. It's gonna be so fun. Peace and long life.

Yes, live long and prosper, as my friend Greg Marinac likes to say. Imad, big announcement for you today.

Yeah, so, um, over the last few years we've been working on The Last Economy, uh, like what does economics look like, and then released The Commonwealth, looking at personhood law, political economy. A lot of people like, how do we share in the gains of artificial intelligence and make sure it's distributed? And so we went back to the drawing board and think about what type of institution and future do we want to see? And we came up with this idea of the champion. So we think that AI should be like a utility and it should be owned by the people. So you need the children to own it, you need the locals to own it. So for every single jurisdiction, what we see is the cost of intelligence will drop to zero and the value will go to the last mile. AI into the enterprise, as Salim would kind of indicate it. Yeah. The humanoids— who owns those humanoids? Because those will drive the economy. Elon at the G20 just now said that the average humanoid will have 5 times the output of a person, and there'll be a billion of them. That will be the economy. So we're like, let's set these up and let's borrow from the example of TSMC and how that was set up. How that was set up was that they started TSMC at the valuation of 10 Taiwanese dollars, and the locals put in 75% of the money. and Philips 25%. Is that for real? It's for real. The CEO did not have any shares. The team did not have any shares. They only got shares from the profits of it. He had to buy his shares, now worth $10 billion. And so it listed at 6 billion Taiwanese dollars, which was the cash on the balance sheet. So we were like, let's do that for the intelligence company of California, the intelligence company of the UK. $1 pre-money. All the locals can invest at that valuation, from institutions to high net worths to retail, $75 million per stake. You can get the MIT Endowment, Dave, to invest and give them back all the compute that they have, the equivalent dollar amount that they invest in compute. There's all sorts of interesting things you do at $1. Then you can bring in the internationals and the strategics at market rate, which will be 10 times that. Because you've got everyone on board and you give 10% of the equity in perpetuity to every child under 20. So every year you issue half a percent of the equity to every kid. It trains up an FDE workforce to transform every institution. It owns the robots and deploys them, which is 80% of the value of robotics is downstream, just like the auto manufacturers. And it gives an agent for every citizen as well as AI for the government. The SAGE project that Peter and I and others have been working on, AI for the judicial system, AI for education and healthcare. And that becomes really super interesting because then it becomes a play on the indexed GDP of the state owned by the people of the state with the smartest people in the state involved. And again, the key trick here is that $1 pre-money. Get everyone in. You want it to be a success? It's up to you. And so that's this new institution.

How many champions are there? How fine do you slice it up? I heard you say champion for California and the UK, cities and countries or states.

Yeah. So in the United States, we're doing one per state because a lot of data has to stay state boundaries. Otherwise, it's pretty much one per country. So again, it acts like British Gas here, for example. It acts like the telco and more. And each one of them covers a certain number of citizens because, again, it gives equity to every child born. The locals can invest, etc.

So, Imad, right now, this is just an idea. And if folks want to learn more about the idea, it's not an investment offering. And this is not investment advice. But to learn more about the idea, where do they go?

They go to ii.inc. As you said, it's just an idea at the moment. And we'd love people's input. But ultimately, it'll be about the individual states themselves if they want to pursue this, and the people from that state. So, I think let's see if we can build this structure.

Your first phase, Imad, I've had some chats with you about it. It's super exciting. is to get all these local champions kind of lined up, right? And then kind of little by little cascade to the next level.

Yeah, it's all about if a state wants this, then it's all about the people of that state. It's not like a foreign company coming in. So this is a model just like you have your UBI, just like you've got your shares in the Frontier Labs, et cetera. And so we'll see how it goes. But this is my proposal for trying to distribute it to everyone.

I got to ask you a question. So your bullets say the cost of intelligence is going to zero. Got it. The economy will change forever. Got it. The value of human cognition will go negative.

Yeah. What are you saying?

It's going to be like a cost on society to be thinking and consuming food while you're thinking?

Oh, you're the stupidest person on the team economically, right? Like, you're competing against human noise.

Your ideas are—

no matter how good your ideas are, they It's like adding a human driver to an autonomous highway.

Of course. I mean, this is the topic of the book that I had last year. But that's why you need to have a share in the means of production, which will be the robots and the forward-deployed engineers and people like that, right? And so you need to make sure that's equitized from day one. Otherwise—

If you have a bot and you don't tell anybody, then it's just zero. Then at least it's not negative. You can just keep it. Most people's thought—

it's an economically valuable thought, you know. It's economically valuable labor. And I don't— I'm not the smartest person on my agent team anymore, man. I don't know about you, but I'm getting eclipsed very quickly.

Yeah, yeah. No, it is very— it's very humbling for humanity, actually.

It will be. But just like we've seen before when Stockfish started playing, you know, beating everybody in chess, people still play chess. And people are still gonna have ideas, and people are gonna still value individual human ideas. It's like, did you come up with that, or was that your latest model? Everybody, welcome to the health section of Moonshots, brought to you by Fountain Life. You know, AI is impacting every aspect of our lives, how we teach our kids, how we do our business. But one of the most important things that AI can deliver to us is health. And one of the things I think about when, you know, shooting for 100, 120 is, am I gonna have the cognitive health? To be able to think clearly and keep my wits about me for the next 50 years. I'm joined here today by Dr. Dawn Musalem, the Chief Medical Officer of Fountain Life and a member of my Fountain Life medical team. Dawn, a pleasure. So Dawn, talk to me about brain health.

Brain health. You know, you're right.

This is the number one concern people coming into Fountain Life have is, will I remember the name of my child and the face of my loved one? 45% of dementia cases are entirely preventable. With lifestyle. And what was really intriguing to me, Peter, is that a quarter of our members had advanced brain age. But over 13 months of us really helping them live healthier lifestyles, eating healthier, moving their body regularly, and optimizing sleep— people overlook that so often, but that sleep optimization is critical for our brain health— what we showed is that we were able to improve the brain age in 40% of our members.

Wow.

6% of those individuals. That's a powerful number.

That's amazing. You know, one of the things I love about Fountain is we're constantly searching the world for the most advanced therapeutics and bringing them to our members. So for me and all of you, I hope that you appreciate the fact that you can become the CEO of your own health. You can make sure that you've got the cognitive clarity for the next 50 years. Come and check it out, fountainlife.com/peter to learn more and become the CEO of your health. Now back to the episode. All right, I'm going to move us to our next story. Tesla held its Cybercab Lollapalooza event in Austin, Texas. Cybercabs everywhere, a river of golden EVs flooding the streets. And unless you've been hiding under a rock, you know that Cybercabs are Elon's electrical autonomous 2-seater with a, quote, 3-comma scissor door, no steering wheel, no pedals, designed to run entirely on Tesla's self- Driving software. Did you guys get the three comma comment? Anybody? No. It's from Silicon Valley, right? The billionaire.

I got it. Okay. It's three commas.

Remember, he showed him a Maserati and said, "Ah, that's not a three comma car." Oh, doesn't have doesn't have the doors that go like this. Right.

Oh yeah, I remember that. That was a great episode, actually.

This is this is a vehicle. Designed from the ground up for full autonomy. Let's take a look at this video. I love this video showing, you know, the flood of cybercabs on the streets in Austin. Check this out. Cybercabs everywhere. The Golden River. It's like— it's crazy.

You know, I didn't— when we saw these in Austin, Peter, I didn't really internalize the lack of rearview mirrors, the rack. We obviously saw the lack of a steering wheel. Yeah, but actually the number of components they've taken out of the car is driving down the cost. So there's no driver, but there's a huge amount of cost in the car that's gone. And so there's no way anyone's going to match the price point.

I mean, that's— that is the point compared to Zoox and Waymo or anybody else. I mean, Elon wants to sell these at $30,000 each so you can buy them, you know, and I think a great entrepreneurial journey is buy 10 of them and put them on the streets in your local town, have them earn revenue for you. So an early rider in Austin, and I'm showing this here, reporting that CyberCab is running about 50% cheaper than Uber for comparable trips with rides being designed And described as smoother and cleaner than Uber, with automatic syncing of audio and seat settings and your passenger profile. It's pretty extraordinary. And remember, Nevada gave Tesla permission for 5,000 of these vehicles on the road in Las Vegas in the next 12 months. I mean, it's just going to crowd out the competition. You know, Salim, I'm curious what you think about this. You know, is this sort of like The ChatGPT moment for Tesla?

I think it could be. And this is classic EXO, right? Autonomy. You decentralize, you have interfaces, you're leveraging assets. If Elon can get people to buy the cabs and kind of create millions of micro-franchises out of them, the loyalty that will come from that is going to be unbelievable. Of course, they're all going to have Starlink capability. So the kind of the whole vertical integrated stack brings itself to bear. I think the most exciting part is the fact that the cost of transportation drops by another order of magnitude from a couple of dollars a mile today to down to $0.20 a mile. That is really interesting. And I need this to happen because I've made that comment that Elon will never go to university or he'll never own it if I have a driver's license. And I need to battle it out with him that he will not get one. So it needs to happen. But 2 years to get this done. So like, yeah.

And, you know, Salim, you would know better than anyone, but when you get a hotel room in New York and you look down, it's like 80% yellow cabs down there. And if you look at the traffic in New York, a huge fraction of it comes from people blocking an intersection. They get the ticket, but they're still stuck in the end, and the whole thing grinds to a halt.

Yes.

So I suspect—

And then it rains.

There will be enormous chunks.

Yeah.

There'll be enormous chunks of entire cities that say, you know what, no more human drivers. It's so much more efficient. Not only is it much cheaper, but it's much more efficient to get around with. And also, a vast majority of the safety issue in cities is pedestrians getting hit by cars on sidewalks. And this is going to basically eliminate that risk. Trial lawyers are going to fight. So I suspect we're right on the tipping point where it's like, nope, no human drivers in this entire inner city area. End of story.

Yeah, we better get artificial organs quickly because the drop in organ donors is gonna, you know, vaporize.

All these things tend to happen at the same time anyway. I think for many people, these robotaxis will be the gateway drug to generally autonomous robotic systems on the streets. I think we'll look back with the benefit of hindsight and say, well, of course it's natural that a municipality gets lots of robotaxis on its streets before it gets Humanoid robots on the sidewalks doing economically valuable activities.

My favourite, my favourite kind of little anecdote or analysis point or vector point to illustrate this is every medium-sized town in the country has a transit system where the buses run empty 95% of the time and they're totally full at rush hour and they run completely empty the rest and the whole thing is a massive loss-leading exercise and it costs a bomb. Now you can have small, efficient— already a few years ago, small towns in the US were saying, get rid of the transit system, we'll just pay for everybody to take Uber because that makes it hyper-efficient and much lower cost overall. But this takes it to another level of— it takes it to 11, to go down the full analogy that we're going down. And this now allows mobility to every single person, every blind person, every disabled person, every student, every drunk person, all sorts of capabilities become available and affordable in a way that's very powerful. Incredibly exciting.

Yeah, the poorest people in the world are being chauffeured around by AIs, for sure. Interesting. 2 interesting points here. The first is it's a 2-seater, right? And of course, the average load for an Uber is like 1.2 people, right? How many times do we take an Uber all by ourselves? So if you need 6 people in a car, just take 3 of the CyberCabs.

Yeah, not just that, but think about, you know, a normal cab has 4 airbags. You got the airbags for every seat. Elon, in his infinite brilliance, is like, we now— we only need 2. We cut the cost of airbags alone in half with this design. And in the rare instance where you need 3 or 4 people, get 2 of them. End of story. Well, I think they follow each other right behind each other. It's perfectly great.

I suspect there's more of a backstory there, though, with the 2-seater. Remember, Tesla was originally teasing that it was going to launch a Model Model 2, which was going to be their highly coveted $25,000 car. That never happened. And my best read of the situation is what was originally planned as the Tesla Model 2 became this robotaxi because at some point, once the car— the value of the car, the sales value, I should say, of the car crosses below some threshold, it makes more sense to monetize it via autonomous ridesharing or robotaxi services than it does to actually sell the car.

This will turn transportation into an API. Yep.

You know, interestingly enough, we're starting to see conversations where the other companies that do have steering wheels and pedals and LiDARs are saying, oh, I'm not sure that, you know, the CyberCab is safe enough. It doesn't— it only has cameras. It doesn't have the other modalities. So expect to see a battle in courts about what city allows this technology in. Yeah. Ahem, Boston, just to put an exclamation point on that.

Mayor Wu, come on the podcast and we'll have a conversation about it.

Oh yeah, do that. That'd be awesome.

I'm going to continue this conversation. Let's shift it now to the global stage. The competition is going 3-sided. The Financial Times reported that Uber is partnering with traditional taxi fleets to complement and compete against Waymo's expanding robotaxi service. So, you know, a strategic alliance between ride-hailing and traditional taxis to counter autonomous vehicles that don't need humans. You know, think about what that means. You know, Uber, the company that disrupted taxis, is now partnering with taxis to fight the companies that are disrupting them. Meanwhile, The Verge reported that Uber and UK-based robotaxi company Wave Wave officially launched services in London. Imad, have you seen Wave yet?

Yeah, I've seen it. It's just starting to roll out, so I'm looking forward to getting my first ride on that.

Yeah, and CNBC reported that Waymo and Zoox announced simultaneous expansion into new cities, including along with Tesla in Nevada, California, and Texas. So, you know, my prediction here is that we're going to see at least 5 different autonomous electric robotaxi companies Fighting it out in major cities inside the next year, driving the cost as low as possible, right? So the cost of personalized transport is basically dropping to the cost of electricity. It's demonetized mobility. It's the entire abundance thesis. The cost is dropping to the cost of charging a battery. And the ultimate winner is the public, unless you live in Boston. So—

Well, no, Cambridge might, Cambridge might Have a chance. Actually, that put a lot of pressure on Boston. That'd be awesome if Cambridge beat Boston by—

But then you try to take a taxi and it stops at the Harvard Bridge.

That's right. The bridges are no-fly, no-drive zones for autonomy, apparently, in the near future.

Actually, what's funny is you could get around Harvard, but you wouldn't be able to go to Harvard Business School. That's really cool.

What's going to happen, though, when you get your Tesla Optimus robot and it has the drive program so it can drive for you in whatever of a car you have without retrofitting.

There you go.

It'll get banned in Boston too, I'm sure.

You know, I want to hit the economics here because, you know, the, the Waymos are pricing out— the new version of the Waymos are pricing out over $100,000. The CyberCab is coming in at or below $30,000. And what's most interesting in my mind is the winner here is going to be whoever can mass manufacture these the fastest. And Elon's going to win that game. I mean, he's the guy that builds the machines that builds the machines.

Look, clearly you're going to end up with all of them.

In the US maybe, but China is dumping, some might say, into Europe. So it's not a US-only game is the issue.

You're going to end up with all of them for the foreseeable future. And I think, Peter, the point you made is the most important one. The end user wins.

Yeah. Dave, do you remember when we were at the Gigafactory and we were being toured around and we saw outside the giant mounds of aluminum scrap metal?

Yeah.

And then the smelter and then the Model Y press. And it was like just this beautiful orchestration of production.

It's incredible.

End to end. And you can actually walk with the car from the day it's— the minute it's born. It's like a— It's a long walk, you know, it's almost a mile, but a car comes out the other end. But you're literally watching every part get put on as you walk with it. It's absolutely wild.

Yeah, it's beautiful.

But it's amazing to me in the CyberCab how few parts there are. Like, when you open the hood of your gas guzzler car and you look at all the stuff that's in there, and then you look inside the equivalent CyberCab, it really feels like there's maybe 1/10 as many things that need to be put together.

I've got a hard stat that I use a lot here, which is A typical car has 2,000 moving parts in the drivetrain, and the Tesla has 17.

An ICE, internal combustion engine car.

Yeah, 2,000 moving parts versus 17.

Unbelievable.

It's unbelievable. Yeah, so in terms of maintenance design, you know, all of that stuff, this is why the car dealers are all freaked out. You never need to do service.

You know, I was, you know, last time I was in New York, I was in a yellow cab. I'm like, why is this thing so disgusting? It smells like a public urinal. Why are they all like this? But then you think about what happens to that cab. You know, the medallion is incredibly valuable. It gets handed from one driver to the next. You know, it never stops moving. And then at night, in the middle of the night, somebody drives it home. And then they hand it— they get up first thing in the morning, they start driving it again, but it has to take somebody home. The cyber cab goes to get cleaned. In the middle of the night, when there's nothing to do, it goes to a place— could be anywhere, could be in Queens, way away. Gets itself cleaned and charged, comes back pristine. And that's— it's just night and day when you get into one of these versus a cab. Now they're brand new, so maybe that's part of it too, but the experience is night and day better.

And they look beautiful. And they really do. So what happens when they break down? I guess a Cybertruck comes and tows it away.

Yeah. DoorDashers come over to help is the recent story, I guess. If the door is wedged open and can't be closed.

Maybe they just squash it into a metal cube right there.

And recycle it.

Take it back to the smelter.

That's mean.

Right to Austin. We'll pull out the GPUs first.

Yeah. All right. I'm going to move us on to the space arena. 2 fun stories on the space frontier today. In our first story, NASA chose Blue Origin to build the telecommunications relay network on Mars. The comm infrastructure that will connect future Mars missions back to Earth. This is the infrastructure layer for the Mars economy. Whoever owns the telecom network controls the bandwidth. On Earth, there's companies like AT&T. On Mars, it could be Blue Origin. And you might wonder, why did NASA select Blue Origin, not SpaceX? And SpaceX, you know, owns and operates the world's— or space's largest space-based laser-linked comm network. And ultimately, this is the government, you know, keeping 2 competitors, 2 suppliers in business, giving them each a slice of the pie. And regardless of Blue Origin having won the NASA contract, I guarantee you Elon will still build Starlink around Mars. Ultimately, what we're seeing here is the birth of the interplanetary internet. Alex, are you excited about this one?

Look, Mars had it coming. The moon had it coming. We're going to build the Dyson swarm. Someone was going to get awarded the Starlink for Mars. It's interesting that it went to Jeff Bezos's company and not to Elon's. But I'm fully expecting that we're going to have a very, very competitive interplanetary internet. And I'm— frankly, I'm glad that there are vendors that are competing for Mars comms other than SpaceX.

Yeah. But it's going to be a packet-switched network throughout the inner solar system.

High latency.

Mars, asteroids.

High latency, but that's all right.

Yeah, until we get faster-than-light comms. Who knows?

Physics will tell.

Time will tell. Are you working on that, Alex? Can't say.

Well, the physics that we have right now suggests that faster-than-light travel is not possible.

Don't bum me out here, okay?

Sorry to break the bad news. The physics The textbook physics right now says that superluminal travel is impossible.

Then we have the wrong physics.

That's— uh, yeah. We'll find out.

Just hope.

All right.

Yeah.

Let's move us to another fun story in space, our second story, and it's a big one. NASA's Nancy Grace Roman Space Telescope has launched on a Falcon Heavy, carrying a field of view of more than 100 times greater than Hubble and the ability to scan the sky for more than 1,000 times faster. The Roman Telescope is designed to discover tens of thousands of new worlds and map the distribution of dark matter across the universe. Before we discuss it, let's play a video by our amazing NASA Administrator, Jared Isaacman, friend of the pod. And I love this guy. He's such a good communicator. Let's check it out.

Oh, the telescope is very healthy right now. It's making its 1 million mile journey to Lagrange Point 2. It's going to look for what we think will be up to 100,000 additional exoplanets and other star systems. It's going to help us understand dark energy. Dark matter. And as you saw during the press conference, President Trump called in. This is a really exciting time in America's space program right now.

What's the difference between this telescope and the Hubble?

It's hundreds of times more powerful. I mean, the field of view is over 100 times greater than Hubble. Its scan rate is over 1,000 times greater. I mean, this is a— this is gonna be a household name like Hubble and James Webb. This is America's next great exploration asset.

And you said it's gonna help you find planets hiding behind planets?

Planets hiding behind other stars, distant stars. The light can blind it out. This will— it has a special JPL coronagraph that helps us find these hidden worlds. I mean, tens of thousands of additional worlds we're going to find.

Tens of thousands of worlds. Amazing.

Yeah, Jared. Jared is incredible, isn't he? Compare that to that interview of Sam saying, oh, why is this new model different? Why is Astra different for people? Compare his answer to what Jared just did to answer this, this question. It's like night and day. I mean, he is a great, great communicator.

Yeah.

And interestingly, data— interestingly, this is going to finally start to give us statistics over the number of Habitable, at least Earth-recognizable habitable worlds. It's being pointed toward the center of our galaxy, and we'll just be able to do large sweeps of the sky looking in part— it has other missions as well, but looking in part for microlensing events for planets that are crossing in front, exoplanets crossing in front of their respective stars, causing the— via their gravity, causing light from those stars to be very weakly increased briefly due to these microlensing events. The downside—

You know, it's so Yeah. What's so cool about that is on the last podcast, Peter asked us, you know, Salim, you missed it, and Imad, you weren't here. But, you know, what's your answer to the Fermi paradox? Like, why are we not seeing other civilizations? But one of the theories is that there are many, many civilizations talking to each other near the center of the galaxy where you can get from star to star in, you know, a year or two, as opposed to where we are. We're way out on the wings where it's like so far away.

We're in the unfashionable outer suburbs of the galaxy. That's right, we're right on the edge.

So, you know, who knows? This could—

I'm actually happy we're here. You know, the galactic center is a really dangerous place to be.

You don't like supernovae, Peter?

I don't like the radiation flux they deliver, no.

I'm reminded of the opening scene of Hitchhiker's Guide to the Galaxy where they're bulldozing Earth to make a hyperspace Yeah, highway bypass.

Yeah, yeah. I mean, I, I love this. Uh, you know, I'm curious to our viewers, and if you can, you know, let us know in the notes, uh, would you like us to have a conversation, uh, on the current UAP disclosure con— um, you know, discussions out there? If we can bring some of the leaders in UAPs and the whole disclosure scenario. The White House just released their disclosure plan. Alex, I don't know if you want to mention that, but I'm curious if folks want us to have a conversation on that topic on the pod. Alex, would you mention the recent White House announcement?

There was some reporting out there from Avi Loeb's UAP Science Advisory Council. One of the members of the council mentioned that in a recent briefing that this council, which as I understand it was stood up by the White House, was informed that the White House had prepared a disclosure plan for informing the general public of the existence of non-human intelligence, which, if accurate reporting, that's pretty interesting.

Yeah. Imad, how do— where do you come out on the whole UAP side of the equation? I'm curious.

I actually have no position on it. I never thought about it properly.

Okay.

I'd like to see, um, strong claims require strong evidence.

All right. A lot of people would say there is strong evidence, it's just hidden. But we shall see.

Now, I'm with Salim. I would say evidence, uh, really, it would be highly desirable for there to be a preponderance of evidence that everyone can go and see and touch and experiment on. I, I think that's probably the gold standard of In an ideal outcome, if there were a White House disclosure event, if the president goes into what's left of the Rose Garden and gives a speech and says, we're not alone, then ideally, part 2, paragraph 2 of the speech would be to hold up or otherwise present some artifacts that would be subject to extreme scientific scrutiny to support the claim.

That would be a fun conversation to have. All right. Our third, our final subject for today. 3 major health and longevity stories came out this week. The first is that OpenAI expanded GPT health features to connect directly to patient records and healthcare databases. The integrated— the integration brings Epic electronic health records data into ChatGPT for healthcare. And Epic, as you guys probably know, is the largest collection of health records. 325 million patients are inside Epic, roughly the entire US population. Clinicians can now pull appointment notes, lab results, medications, and ask questions across patients' entire record. And consumers can now connect their Apple Health, One Medical, Function Health. So ChatGPT can help you understand your test results, prepare for your doctor appointments, and get personalized data and diet workout advice. So that's the first story. The second story, interestingly enough, we talked about it last week. We mentioned that the FDA had approved a drug called daraxanerisib— rolls off the tongue onto the floor— for the first targeted RAS inhibitor for metastatic pancreatic adenocarcinoma, attacking the RAS family of proteins that drive tumor growth in most patients with disease. This week, NBC News reported that the same drug is showing promise to treat lung cancer. As mentioned last week, the RAS mutation family drives roughly 30% of all human cancers and was long considered undruggable, the subject of decades of failed attempts. But now, during the singularity, the end of cancer is now within reach.

Alex, what do you think?

Yeah, I think we're starting to see, and interestingly, I'm not sure that AI was actually essential for this particular drug, but I think—

It was.

I think there's so much progress being made in cancer therapies now, including on the immunotherapy side, that we're starting, I think, to see the emergence of— and again, caveat, caveat, caveat— universal cancer treatments, universal cancer vaccines in some cases, where after years and years of treating cancer as Thousands of different diseases. We're starting to finally get to the point where we can start to treat, to move upstream and start to treat, if not root causes, at least identify treatments that whether it's proteomic pathways on the one hand or immunologic pathways on the other, start to have treatments and/or vaccines that can treat multiple classes of cancer. And I think that's just—

Cancer's cooked.

Yeah.

That cancer should have been cooked long ago. Was it Nixon who announced the war on cancer? It took forever to get to this point, more than half a century. And it's an interesting counterfactual experiment, a thought experiment. Is there anything, knowing what we know now, that we could have done 50 or 100 years ago to radically accelerate the onset of broad-spectrum cancer treatments? What do you think, Peter?

I think the data— I mean, we've known, for example, about the RAS mutation, you know, basically causing unconstrained growth for some time. It's just, you know, getting, getting the molecules and getting the drugs that can attack it properly. I think the tools we have right now are just finally giving us that, that reach. And then being able, you know, we talked about cell simulators, being able to understand fundamentally what happens And how to block it is what's coming. Imad, this is an area of personal passion for you as well. You've been deep into medical AI.

Yeah, so I said this wasn't AI kind of on the thing, but the range of treatments now coming out on the cancer side gives a lot of hope to what's going to come. I think the mRNA one maybe is more general that we saw recently. And I think, you know, the first part of that integrating into the Epic Health towards actually applying AI across the board. Like, we should have a sprint so that within a year or 2 max, every single health decision is double-checked by an AI. We should have—

More than that, Emad, I think it's going to become malpractice to diagnose a patient without AI in the loop. Right? We already know AI is a far better physician, a diagnostician, than a human is.

So I think you should have a series of approved edge and cloud models. And every time you make a diagnosis, the AI has to have had one check. That will save so many lives. It will detect so many cancers. And then it's about how do we increase the level and volume of information? Because even now, the type of data we have around cancer and other conditions that we absorb is tiny compared to the amount that we could have with the AI transforming it. So I think that, yeah, let's cook disease. Let's get rid of it. No one should have to die of cancer, and it's something we should be really directed at.

On the first story— go on. Sorry, please.

I was just like, it's very strange that as we have the Genesis programs and others, there isn't just a straightforward, we now have the capability to potentially cure this stuff, let's direct $10 billion towards it. It's tractable.

I think the story on Epic is interesting. Having been involved in that business through Fountain Life, You know, Epic is the majority, you know, electronic health record in the US, and it's been a bear to navigate for physicians, and patients have never had access. So putting an AI layer on top of that is awesome. Yeah. Let me move it to our last story here in the area of health. It's related to what may be called the first longevity class of drugs. The GLP-1s. So a new paper published just 2 days ago on September 2nd in Nature shows that semaglutide, the active ingredient in Ozempic and Wegovy, recapitulates many of the benefits of caloric restriction. And get this, it's in female mice, just to be clear, and extends the lifespan of mice by almost 100 days. In humans, that's the equivalent of 8 to 10 years. The study found that GLP-1R activation initiated late in life in these mice accentuates age-associated decline and modulates conservative genetic regions for aging, functioning as a caloric restriction mimetic. Also this week, it was reported that GLP-1 drugs also are being linked to fewer serious infections, including tuberculosis. And you guys already know that tuberculosis is the deadliest killer on Earth, you know, killing over 1.25 million people per year. So the same drug that's treating diabetes, obesity, disease, you know, kidney disease, cardiovascular disease, and addiction may also be extending lifespan and reducing infectious disease. One molecule, 6 diseases, and still counting. I, you know, again, Alex, you've made the point that this might be the beginning of longevity escape velocity.

I think that this is like, to the extent To the extent that with the benefit of hindsight, we look back in a few years and we say, you idiots, of course you were on the verge, you were seeing the sparks of longevity escape velocity, you had the GLP-1s. To the extent that that's the case, I don't think it should be that surprising. What's more mystifying to me is from an evolutionary perspective, if the GLP-1RA class of molecules is capable of doing everything from treating infections to extending life expectancy to modulating diabetes.

Reducing addiction.

Reducing addiction and compulsive behaviors. If it does like all of this, why on earth wasn't like— why did we not evolve with either this ability to modulate our own semaglutide-class molecules in our system or maybe a slightly more cynical angle. If it turns out that the reason why GLP-1s are so effective at so many diseases is that these diseases somehow are diseases of quote-unquote modern lifestyles, and that we just like— it's treating all of the diseases of modernity, and that's why we never evolved the solution. Addiction to compulsive gambling or alcohol addiction, Or overeating of sugar, that these are all relatively modern diseases. That's why this— it's basically a treatment for modernity. That would be pretty ironic.

So I just want to make a point to the listening audience. First of all, talk to your physician about this. It's not medical advice. Yes, but I use a GLP-1 drug. I don't know if any of you do right now, and I use it not for weight loss, right? I've been on my fighting weight for a while. My body fat is substantially, you know, because I work out and I'm very careful about what I eat. I use it as a longevity drug because of all the benefits we just heard about.

I've got 2 things.

So, Tim, I know you're using one.

Yeah, I'm using one and I just got my blood test done and my liver enzymes are 50% better, which is pretty amazing, which means I can drink more. No, I'm just kidding. That's not the point.

You're supposed to not want to drink water.

I know. So there's, there's, there's that. But I think, you know, to Alex, to your point earlier, right, remember that evolution has birthed us for death, right? We've had short life cycles so that you are— the cycle time of evolution can work more quickly. We're breaking through that now and living through that. So I think it may have been engineered or evolutionarily engineered that we Died at different levels. And this is a long history where we used to die of heart disease, or we used to die of bacterial illnesses, and then we figured that out. Then we died of heart disease. We got some sense of that. Now we're dying of cancer. And then as we break through that—

Then, Salim, explain tuberculosis. If you're in a tuberculosis-rich environment, surely evolution would favor any molecule that could just be amplified up that would help prevent young Well, I go back to the—

you know, there's lots of pathogens going after us all the time, right? Billions of them, because they're all trying to survive in their own way, including cancer. The capability of the human body to navigate this has never been needed until we started pushing the boundaries to this level. So what do you think is the difference? Now we do.

And so, so, Lee, my thesis, you know, and I've spoken about this pretty widely, is the reason for the life cycle is not more rapid evolution. It's that for most of human existence, you know, Homo sapiens came on the scene roughly 200,000 years ago. Food was very scarce. And if our primary mission is to perpetuate our species, the last thing you want to do is steal food from your grandchildren's mouths. So the best thing you could do is die. Reproduce and die, basically. Right. And so we see, you know, the human body, and people should know this, you're in, you're in sort of prime condition until your late 20s. Back 200,000 years ago, you'd go into puberty at age 13, 12 or 13. You'd be pregnant immediately. No birth control. By the time you were 26, 27, 28, you're a grandparent and then you would die so you didn't steal food from your grandchildren's mouths. And And so that's why you never had any—

That's my joke about marriage, that we invented marriage to keep the parents together until the kids were self-sufficient. Average lifespan was 25 years old for most of human history. We invented marriage about 6,000 years ago, and it's definitely true then. Marriage is not designed for, uh, 50, 60-year lifespans. It's designed literally for—

No, no, I was not watching this.

One of my relatives calls it state-sanctioned torture.

Yeah.

Because we have the job of now evolving the institution to deal with the conditions today compared to when we first invented it. And I use that example because it applies to all our institutions— democracies, educational systems, legal systems, healthcare systems. This, I think, is the biggest work we have to do, is as we blow past all these limitations, we have to reinvent all of the institutions which are the scaffolding that keep humanity safe and civilized.

Hopefully we have a benevolent AI to help us do all that.

We'll need it.

And if not a benevolent AI, at least we'll have GLP-1s.

All right, time for some conversations with the mates. We've got some questions here. Emad, I'm going to give you first crack.

All right.

If money becomes obsolete, how will desirable land be allocated? Is everyone just locked into their beach house forever? By @Nick52547. This is an interesting one. I think that Elon and others have said that money might become obsolete. I've said that you can't compete with robots. I think this is a question of land rights and more, because typically where you see reallocation is in upheavals. So the way that we're going without the right structures, you'll probably have a debt jubilee. You'll probably have chaos and land redistribution. But if we can actually navigate through it, then land rights and property rights should be enforced. And so you can keep your beach house, but the number of desirable locations will go up dramatically because you will have air taxis, self-driving cars, solar panels, and self-driving construction workers. And robotic dogs. Yeah, exactly. Self-driving construction workers.

Yes. Nice. Salim.

Let me take number 4. Do you think AI will evolve past this risk-averse bottleneck that we're currently in? And that's from @Susie1073. Yes, that's the hopefully, but hopefully we move towards calibrated risk rather than recklessness. Because right now we treat uncertainty as kind of like a reason to refuse, right? And that's a technical problem, but there's all the legal and policy issues around this. When you're raising kids, one of the things they teach you— teach kids is, are you taking a responsible risk? And we need to apply that same kind of paradigm to this model. Could you create something like a risk budget, which is what can an AI decide, how much can it it spend on that? What systems can it access? What triggers escalation? Right. And we need to get very sophisticated around this. Models need to become better at distinguishing dangerous intent from real expert usage. Right. Because you can't get to— you can't just say remove the guardrails. It's got to be dynamic where you deal with proportionality on user identity, the context, the reversibility of that. So we'll get AI maturity when we can say, here's the risk, here's the confidence I have in this, and here's the reversible next step. And then rather than just saying no, I think that we need to get a lot more nuanced around this. Unfortunately, in today's world, nuance has no part to play in the soundbite All right, Dave, over to you.

All right, I'll take number 1. Can you see a way to turn libraries into centers of AI development and physical AI training for everyone? From Train with Giancarlo. Giancarlo? Yeah, yes, but there's a bigger issue, which is there's a huge amount of white-collar office space, and white-collar work is turning to AI. At the same time, we have declining population, especially working-age population. So there's a bigger issue of what about all this other space? What are we going to do with it all? It's very similar to what happened with the shopping malls after online shopping became huge. And then COVID hit. And a lot of people were thinking there's got to be something really great we can use with all this shopping mall space. But there were some ideas, but for the most part, didn't work out that it all got replaced. So I think there are lots of ideas for what you can do with the library space. You don't really need the library books anymore, obviously. This is another reason why you should want data centers in your community. It's one of the few things that will reliably grow, create tax revenue, create job opportunities in a community. So there are ideas, but I'm not super optimistic that all the space will be well utilized going forward.

And there are programs in inner cities right now that are, you know, running AI tutoring in libraries. That does, that does exist.

Alex, all right, I get the fun one.

So question number 2, whatever happened to synthetic diamond chips for computers? And this is from Aster Sheen. So here's the problem with diamond. Pure diamond is an insulator. It has a band gap of approximately 5.5 electron volts, so it really doesn't want to a good computer. To make it into a good computer, at least a good computer of a recognizable CMOS-y type, you have to dope it. That's hard. If I had to steelman the case for diamond-based recognizable computers, it would be for ultra-high temperature environments where such a wide band gap could be advantageous, or maybe very high voltage environments where, again, a large band gap could be advantageous. It's not an enormous market. Could be wrong, maybe I'll be wrong, doesn't seem like an enormous market. Where I'm much more bullish on diamonds is for sensing. You can put what are called nitrogen vacancies, or NV centers, into diamonds, basically put an extra unwanted nitrogen atom into a diamond lattice, and suddenly you get an exquisitely sensitive magnetic field or just field in general sensor because of an extra vacancy that is introduced as a result of the diamond. So diamonds for quantum sensing, super interesting potentially. It's not investment advice, but I think technologically, it's very attractive. One could imagine at some point in the future, diamond chips for sensing even get us to sci-fi tech like wearable MRI sensors. Diamond chips for computing, probably not.

Why did I know you'd have an answer for that one? It's interesting. All right, Salim, first choice is yours, pal.

I will go with number 7. When do we get a forecast for when data centre energy sources transition from gas turbine farms to nuclear and SMRs? Short, fairly easy answer. I think that's a good answer here. Gas dominates the current buildout. SMR is going to be in the next 3 to 4 years. The reason people are getting so excited is that nuclear is an engineering problem, not an invention problem. Fusion is still in the invention problem category. And so you've got this impedance mismatch of data centers taking 2 or 3 years to build. Nuclear is taking a bit longer. We will get there, I think, faster than people think with SMRs, but I think it will still be a while. So take it 2, 3 years for the initial wave of SMRs and then 5 to 7 years for the big buildout. So I think what's going to end up happening is AI is going to do for nuclear what smartphones did for batteries. Like, it just created such huge demand that the massive innovation There was a huge acceleration in the innovation curve.

Nice. And we had a fantastic pod with Ramez Naam on energy. If you guys haven't seen it, please check it out.

Must watch.

Yeah, he talks about pretty much that timeframe.

That's where I get all my good information about energy.

Yeah. Alex, let's go to you.

All right. Well, I think I have to answer question number 8. It seems to be directed toward me. So 8 asks, how far away from Alpha Centauri do you actually have to aim to reach it when it arrives? And this is from Typical Dad Pi, so to 3 significant figures. So to the extent I understand this question, uh, let me give you the semi-official Fermi Explorer line. So on the last pod, we had Matt Pines and Philip Johnston announcing, for those who didn't watch, humanity's first mission to Alpha Centauri.

And you.

And I'm involved. I I'm involved. What can I say? I'm in a lot of rooms. So under the official mission parameters for the Fermi Explorer mission, the goal is to get at least 99% of the way to Alpha Centauri. So Alpha Centauri, approximately 4 light-years away. So you could do the math, and that turns out to be 4/100 of a light-year. Call it precision getting there, and that's from Earth. So if I put my sci-fi hat on and extrapolate a bit, I think that in the next few years onboard, and maybe I should add parenthetically, Fermi Explorer mission is intended to launch by 2029. I suspect this is not an official position for Fermi Explorer mission. I suspect that when the mission comes to full fruition and is launched. I suspect it will have active guidance on board, and if that's the case, this 99% of the way to Alpha Centauri—not an official position—will turn into or can turn into 100% in the sense that it actually hits the Alpha Centauri system, not just four tenths of or four hundredths rather of a light year away. That said, Fermi Explorer is intended to reach Alpha Centauri 80,000 years from now, which is. perhaps inconvenient from the perspective of mission verification. You actually want to get it, but I think—

GLP-1 drugs.

We're gonna need a lot of GLP-1s for this. I think, I think we'll get to— the whole point of the mission is that we're supposed to beat the Fermi Explorer and get there sooner. This is just the first one to launch.

Amazing. Dave, over to you, pal.

All right, I'll take number 6. Are data centers using closed-loop geothermal cooling also noisy, or are they Quieter? And that is from @AsterSheen. They should be dead quiet, just like geothermal heating is dead quiet. Also, nuclear reactors that are ocean, you know, near the ocean use geothermal cooling and ocean cooling, and they're dead quiet. So it should be very, very quiet. But also, you know, regular data centers that are using liquid cooling are only noisy because the water gets cooled outside with these really poorly designed fans.

Right.

There's no reason for that. If you're— if your regulatory body says, yeah, you can build a data center here, but it has to be quiet, guaranteed they will build the data center quiet. There's no reason those external fans need to make any noise.

Instead of making them illegal, cities need to be saying, these are our requirements: drop our energy costs, you know, make them quiet, invest in our infrastructure. And they will. And they will. Imad, looks like number five is for you.

That's an interesting one. They talk about a sunshade there. What scale of satellites in terms of we talked about that came up on the last pod?

You missed it. Yeah.

In terms of square meters. Yeah. Yeah. And what scale of satellites in terms of square meters required to actually impact global temperatures? I would say if you whack something in the Sun-Earth Lagrange point a couple of million kilometers out, you'd need about a couple of million. kilometers squared. So about the size of India would knock a degree Celsius off. That's bigger than anything we've ever managed, but it's worth a try. Why not? Maybe convert the moon.

Mercury, please. Or Ceres. And I still love the sunshade, putting a thermometer, being able to titrate the solar flux on the planet.

I think it's a trick— for what it's worth, I think that's a trick question. I think with a sufficiently good AI planetary-scale model, we could make any sunshade or other satellite intervention de minimis size. It's just a matter of appropriately perturbing the Earth atmospheric system with enough AI.

All right. As before, we love your outro videos. Please send it to us. We want to see your creative nature. You can send it to media@diamandis.com. The team will show it to us. And then we'll air it. And again, last shot, if you want to join us on our AMA, we have 2 AMAs coming up on Zoom with all the mates to answer your questions. We're going to do one in the morning to help hit Europe and Asia, one in the afternoon, early evening to get everybody in the United States. You'll have a chance to ask us all your questions. Go to moonshots.com/ama and register. It'll be up there just for a couple of days and then we're done. All right, Imad, I think this is the first music video that includes you, which I appreciate.

There have been others. I remember others.

Okay, well, the first one I remember. All right, this one is Supersonic Tsunami, and thank you so much to Chromatose for your submission. Can you smell that in the kitchen?

Someone cooked academic invention overshadowed by gatekeeper hysteria. Answers to major problem areas.

And that's all it takes, a moonshot and willing mistakes.

If breaking the moon The moon makes you mad. Remember, it's just a launchpad.

Abundance we will foster while we live long and prosper, riding the wave of autonomy formed from supersonic tsunami.

All right, folks, I gotta go to the beach.

That was awesome. Enjoy the umbrella drink. Thank you. All right. Take care. Thank you guys for an amazing pod. Hey, make sure you're uploading. It'll never get slower. See you next week.

This episode is brought to you by Google Chrome.

You think you know a browser, but Gemini in Chrome, that's new. It can help you with practically anything on the web. Like restoring a vintage motorcycle from a 50-page restoration blog, or finally break down that long article you've had open for weeks. Gemini in Chrome is here for it. Ready to make anything online make sense? There's no place like Chrome. Check responses, setup required, compatibility and availability varies. 18+.