Bernie Demands the Labs Stop, Wall Street Turns GPUs Into Bonds, Grok 4.7 Takes #1 with Emad Mostaque | EP #279

Bernie Sanders sent a formal letter to the CEOs of Anthropic, Meta, and OpenAI.

AI capabilities have reached a critical threshold. Pause AI development.

The cat's out of the bag. It's too late, right?

Nvidia just announced a partnership that redefines what GPU compute means as a financial asset.

This is like the very first pitch of the first inning of the build-out of the Dyson Swarm.

Financial assets want predictable depreciation, and exponential technologies don't give you predictable depreciation.

I'm not as concerned that this will end up being another mortgage-backed securities fiasco for a few reasons. One is—

Elon just dropped Grok 4.6, and he's right, it's a banger. Grok 4.5 came out 2 weeks ago, 4.6 just this morning, and 4.7 is rumored to be coming out in 2 weeks.

4.7, and this is what I think is gonna be really interesting, is gonna be trained on all the SpaceX physics and engineering knowledge. You can't get beyond frontier with this stuff, but what if you have the best engineers?

Now that's a moonshot, ladies and gentlemen.

Welcome back to Moonshots, everybody. We're going to be covering 9 stories today that span the frontier from longevity, AI infrastructure, synthetic biology, AI filmmaking, urban air mobility, and oh, by the way, Grok 4.6 is crushing it. The through line is the same as always. Accelerating singularity is compressing the distance between the impossible and the inevitable. We're back this week with the Moonshot Quintet. AWG, our in-house superintelligence. Alex, welcome. Good to see you in your normal garb.

Thank you, Peter. Good to be superintelligent.

You are, my friend. Dave Blunden, our investor in AI extraordinaire.

I was over at MIT all day today, hence the garb. But our Tech Trek teams just got back from SF, and they brought back huge amounts of knowledge, and they're all over at MIT CSAIL. Dispersing it across the campus right now.

Love it. And Salim Ismail, our empresario of exponential organizations. Salim, where are you today? Still in Toronto?

I'm still in Toronto, heading back tomorrow.

Okay.

I should be going back now, but this podcast happened and I can't fly, you know, within like 6 hours of—

Yeah, yeah.

We're gonna, we're gonna put a Starlink on your, on your hat and have you walk around with it. And back by popular demand, Imad Mustaq, the embodiment of the intelligent internet. Imad, I hope you're reading the comments on the last pod that we did together. People are loving you. Have you seen them?

Yeah, I did see some of them. I don't normally read the comments. I made an exception, and it's very kind what everyone said.

Yeah, it's great being amongst you guys.

It is. It worked for my American wife. It works for the others, you know.

I'm Peter Diamandis, your host, your optimism amplifier. Please remember that having an optimistic and abundance mindset is a choice, and our mission here is to deliver data-driven optimism that helps you make that choice. So guys, uh, last night I was in Utah at the University of Utah. It was the finals for the $101 million longevity, or shall I say healthspan, XPRIZE. Super psyched. We awarded $1 million to 10 teams. Recognized another 10 finalists. This is on the march to winning the $80 million grand prize. We've given away $20 million so far. And the mission of these teams— and, you know, everybody please get excited about this— is to add 20 healthy years on your life, asking teams to reverse your functional losses that we get through aging in cognition, give you the ability to think and have memory like you did 20 years ago. In muscle, the ability to build muscle like you did 20 years ago, and in your immune system. And it's extraordinary. We had 800 teams enter this competition, every possible approach from mitochondria to, you know, stem cells to gene editing. It's, it's extraordinary. So this is a competition that will be won by 2030. So keep an eye on this. You know, Salim, I don't know if you want to jump in on this one.

You know, 2020 is infinity, right?

Yeah.

I mean, you know, I think there's the— XPRIZE was such a huge inspiration when I was writing the EXO book because you're reaching outside. And I mean, 800 teams, is that— I think that's a record for the traditional prizes for us, right? It's kind of an incredible thing.

Elon's $100 million prize for carbon removal ended up with something like 1,500 teams. But, you know, reversing aging, it's gotta be tougher. And by the way, the teams here don't do this in theory. They don't do this in mice. They have to actually do human trials. So they're all gonna be doing trials with control groups and humans, probably around 150 people in the trial. So, it's real data, and we're going to actually know which of these 20 to 30 approaches that actually make it to the finals work. And for me, Alex, we've been talking about being in the midst of longevity escape velocity. This is accelerating it.

It's going to be spiky, I think. I still think, just as with AGI, either in our rearview mirror, as I think, or some might say it's either here or Almost here. I do still expect that healthspan and longevity escape velocity, it's going to be spiky, and I'm just optimistic that we can even out the spikes, even if there's a subpopulation that achieves healthspan on the margin a few years before everyone else.

You know, one of the most important things I think about this competition, when we launched the $10 million Ansari XPRIZE for spaceflight back in 1996, you know, back then people did not believe in commercial spaceflight. They didn't believe that individual teams could do this and carry humans compared to the government. And as it progressed, the confidence level in this, and then when it was won, money flowed in, regulations changed, you know, Bezos and Musk started, you know, Blue Origin and SpaceX, and it changed the game. So I'm feeling the same thing going on right now. You know, longevity, the idea of reversing aging has been sort of a a crackpot idea for most of the last few decades while I've been in the industry, and it's beginning to change. People are starting to believe, yes, it's going to happen. Yes, we're in this healthspan revolution.

We're getting to a— we're getting to a when, not an if question, right? And that's really huge. I wanted to point out something that was— that's really important here, Peter. When you launched the Ansari XPRIZE, there was no space industry to speak of, at least in the commercial side. And now we have a trillion-dollar industry that did not exist, right? Because the collective innovation plus All the members of all the teams end up going to SpaceX, Blue Origin, etc., etc. And we could expect the same thing to happen here where you end up with essentially you're creating a $101 million bet. You're building a portfolio of experiments and allocating capital to wherever there's demonstrated results. It's an unbelievable model that we've now seen repeat over and over again. It's incredibly exciting to see.

Yeah, I was—

question for you, Peter, on this one. I always ask the counterfactual question. Is there something, having now run this Healthspan Prize, or at least the beginnings of it. Do you think that if this prize had been created, say, 20 or 30 years ago, that we could have made on margin enormous progress? Or do you think there's some historic contingency that means right now is really the first time in history where we could make credible progress on it?

Yeah, I think we had a lot of comments. We had a lot of the top scientists. Aubrey de Grey was there last night, who coined the term longevity escape velocity, and then Ray popularized it. I think everybody was at the consensus that the timing is perfect, that the tools for genome sequencing, for, you know, making specific molecules, for being able to measure and report with AI are the tools that are required today. I mean, there might have been some approaches that could have been done 20 years ago, but I think today is when we're going to make the greatest progress And I'm starting to see, you know, capital flowing in aggressively. At the end of the day, longevity's gonna be the biggest market, right? If you could add 30, you know, I've had this conversation, you know, Selim, probably you have as well on stage, you know, with YPO audiences or family offices. And I say, how much of your wealth would you spend for an extra 30 years of life? The honest answer is nearly all of it, right?

Yeah.

Yeah, and the powerful distinction there is not so much lifespan, but the healthspan effects are really, really powerful.

The numbers today are, for the United States, if you look at it, basically 100 years ago in 1900, the average life expectancy was 47. Today it's 79. We added about 2 months per year over the last century. And today, while the lifespan is 79, you're healthy until average age 63, and you spend the last 19 years of your— 16 years of your life in poor health. And so when I was talking to Kratios about this, I said, listen, the biggest benefit the US budget could have and the US economy could have is add 20 healthy years in people's lives. They're retiring later. They're not spending as much government money on sick care. So it could be a huge transformation. Alex, you were going to say?

Yeah, maybe just again, coming back to this historic counterfactual, one of the things that irks me the most is that so many of the abundance-oriented futures that we want to find ourselves in, like LEV, the superintelligence solve everything, just take forever. And I do wonder, again, is it 20 years post-Yamanaka? Yamanaka, I think, was 2006. We're in 2026. What did we blow these 20 years on? Why couldn't we have done this 10, 20, 30 years ago?

I got a question for you on that, Alex.

I'll answer that. Okay, go for it. I think when you get multiple exponential technologies that can address this particular space, then that's the point to invest or put up a prize, because then you get radical outcomes at very low cost. And so maybe there's a benchmark of the minute some domain has 2 or 3 or more exponential technologies converging on it, that's the point to put dollars into something like this.

Dave?

Well, there's no doubt in my mind that I'm going to do more productive work in the second half of 2026 than in my entire life combined up till 2026. So throw it back at you, Alex. Like, even if we had worked really hard on this 20 years ago, Would anything that we did between then and today even hold a candle to what we'll achieve between now and the end of the year? Because, I mean, it's so funny to me, you know, my daughter's over at Moderna. She's a biochemical engineer. And she listens to the pod, of course, you know. And it's so obvious to her that we're in an AGI hard takeoff now. And the people she works with in biotech are about 1% AI aware and 99% not aware. But the AI aware now people are spending over half their day, maybe 80-90% of their day talking to AI agents, and not in meetings and not, you know, running gels, and not running assays, because it's just a different mode of living that's accelerating tremendously. But the aware subset is tiny.

I almost think— I think it's an important point. And I almost think we need a new term for it. I'm just thinking off the cuff, maybe like retrospective hyperdeflation, this very singularity-oriented idea that With superintelligence, you discover that everything that you spent the past decades on was just a total waste, and you should have instead just done nothing, twiddled your thumb for decades, waited for superintelligence to solve it for you.

Or just work on chip fabs or something that will— they'll be very useful on that day. Or buy land in Texas or something.

All these people who spent 6-year PhDs trying to divine protein structures, just mostly wasted.

Well, that one was was really outed by Demis. Yeah, I mean that one. It was like an average of four years to discover one fold.

I knew there was a reason I didn't do a graduate degree. That was it because they would have been irrelevant.

This is your post hoc justification, Salim.

That's right.

That's the master's in all this.

I mean, it could be worse. You could be a pure mathematician, right?

Like they're all cooked too. They're they're having this the same moment of ennui.

Yeah, but at least with the biologists and the longevity people, you can do assays and things physically. Now, look, I think that this is the biggest market in the world, as you said, but it's the first time that it's tractable. I think if you go back to when there were the Yamanaka factors, all these other things, you didn't have the infrastructure necessary and the talent pool necessary. I think that as you've seen the various breakthroughs in other things and everything come together, right now there will be a shortage of people that can really work on longevity properly, even though it is the biggest market in the world. And 10 years ago, 20 years ago, that would have been even tinier. There are only a few people actually looking at some of these things back at that time. So I think it is this confluence factor all coming together. And like I said, it's finally tractable. So the people who are winning in the Longevity XPRIZE, any indication that those things will work, and I don't think they'll be short of capital, but again, the XPRIZE is the catalyst, right? And that was their whole idea.

You know, Peter, you've pointed out something that I think is incredibly important to highlight, right? Which is that we spend today huge amounts of money on treating chronic diseases at the end of life.

You know what the number is, uh, Salim? It's the global cost of age-related disease is $20 trillion per year globally.

Holy crap.

Yeah, it's a huge amount.

Daniel Kraft, just like 85% of healthcare costs for the last 5 years of your life type thing. This is a staggering number, right? Because now that—

Just for context, the global economy is $120, $130 trillion total. Yes.

Look at that number, right? So now, now the business model becomes how do you maintain decent function, bodily function, before the disease appears? And you'll completely change healthcare economics. Massive impact.

And global economics in general.

Global economy.

In terms of productivity and wasted capital.

Yeah, I think this is, this is where the peptides have come really interesting, right? Like, it was like to lose weight, you had to work out. I'm about to start, you know.

Are you really?

Yeah.

Report in on the pod.

I'm gonna get it reported and see how my weight loss goes.

Which peptides, Ahmad, are you taking? Do tell.

I was going to try the retortide one.

Just random purchase.

I know.

Retortide.

Yeah.

I'm on retortide.

But I know I'm going to lose weight, right?

I can hope so.

How are you getting access to—

No, but seriously. Using Canada and everything.

Has it even— Oh, you're in Canada?

No, no, I'm not in Canada. I get it in the US. It's totally workable.

Yeah, there are drug dealers out there. Black market drug dealers.

Peptide dealers, right?

There we go.

I guess, Salim, that'll just be our little secret. There's no one paying attention to this conversation.

Nobody's watching. Nobody's listening. But, but this is how, like, you know that you're going to lose weight with it, right? And that's the first. And so now the concept of you can take a pill or an intervention and you could live longer— well, you're already losing weight like that. And so I think, again, that's a big awareness that's caused the longevity market to get even bigger.

Yeah, GLP-1s are the first longevity drug, uh, you know, in a lot of people's opinions, and it's one of the biggest grossing drugs, if not the largest one in human history.

I've gone even further than that. Just again, a bit of back-of-the-envelope calculations suggesting, and again, this is not medical advice, that GLP-1s, especially 3rd and maybe 4th generation GLP-1s, may actually be— when I refer to LEV, longevity escape velocity, as being potentially spiky, a pretty big spike. There have been studies done recently on some of the 3rd generation, if memory serves, GLP-1s that suggest, and again, not medical advice, that we may be, or at least some subpopulation of humans that have undergone GLP-1 studies, maybe at something like 70% LEV just with GLP-1 therapy. So if that is the case, and again, encourage folks to do their own independent back-of-the-envelope analysis, that's a heck of an LEV spike in a subpopulation that's being administered GLP-1s.

Yeah. Anyway, watch this space, everybody. It's exciting.

Peter, I've got one more question for you.

Yeah, please.

As you looked at the finalists, 'cause I couldn't make it to the finals, but you've been tracking some of the teams, et cetera. You had a great snapshot view. Are you in the view that LEV, we get to it by 2030 or earlier?

So here's the idea, right? If any of these teams win and can reverse your functional age, right? This is not a number from a particular blood test you take. you know, that changes on the back of a form. This is, are you feeling functionally younger? Are, you know, do you have better muscle-building capability, better memory, better immune system? So what really matters is your function. And so if we can do that, if we can actually reverse the clock by 20 years of function, then you get to enjoy the next 20 years of breakthroughs. And if you don't believe we're going to have incredible breakthroughs over the next 20 years, you know, basically, you know, whole-cell simulators and AI, you know, the impact of quantum, whatever that might be, in, in, in cell biology and understanding how we age, then you're missing the boat. Your goal is to keep in the best health right now, which means what? Sleep 8 hours unless you're Emad and Alex, probably don't sleep more than 4 hours.

Exactly true.

But you're short sleepers. But you're short sleepers. Sleep as much as you need to.

So I'll make a comment here, which I've made before, which is that, you know, the business model of religion is to sell heaven. As we have life extension coming, how are you going to sell heaven if people aren't dying? So this breakthrough will mean that religion is cooked.

We have forever to figure it out.

There will still be a lot of religions doing very well from people tithing. But again, I guess the advice right now is Do what you can to keep in the best health. Don't die from something stupid. It's sleep, exercise, you know, proper diet and mindset. Mindset's so important. You know, I think my greatest attribute is my longevity mindset. So take it on. We'll keep on reporting in this space. It's an important part. 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. But let's move us on. These are a couple of stories, Imad, that you shared with me. These are 3 stories that landed this week together that describe Really, I think the collapse of Hollywood economics and the rise of something much bigger. So our first story is a company called Higgsfield that just made a movie called Cully Hill Boys. It's a 110-minute feature film, the first full-length AI-generated movie with licensed celebrity likenesses. Here are the numbers. The total budget for this film, roughly, you know, a full-length film, $2 million. Team size, 28 people.

Wow.

Production time, 4 weeks. Compute costs, $1 million. They use Seedance 2.5 as the generation, and they open-sourced all 10 steps in their workflow so anyone can replicate it. So here's the context, right? A feature film with celebrity talent today costs between $20 million and $100 million if you're not using AI, and it takes, you know, a year to 18 months. Hixfield did it for 2% of the cost. And 6% of the time. So I want to share a short video, a little clip from Hixfield, so we can appreciate it.

What the fuck is that?

Fucking spices?

Supposed to be over £2 million packed in this little nut. Who smuggles coriander in a fucking boat? Someone who suspects it might get nipped. Or where'd your boys go after you left the pier?

What the fuck is that supposed to mean?

We came here and waited for you Pricks like we were fucking told.

Let's go through it step by step, shall we? All right, uh, our second story, uh, Bloomberg reported that 9 out of the top 10 text-to-video models on AI analysis leaderboard are coming from China. Uh, this is a headline, but the real story is deeper, and I'm curious what you guys think about this. You know, Hollywood forever has been the dominant producers of films, and it's basically exporting US culture to the rest of the world. What happens when the world is flooded by Chinese-produced English-speaking films? How is that swaying public interests and public points of view? Uh, and the second thing is that these Chinese models that are dominating video generation are also learning physics, motion, object permanence, and causality, which is what's needed for robotics and autonomous driving. So Things are moving quickly there. The third one, very quickly, then we'll go into the discussion here. A model called LTX 2.5 is the newest version of the most popular open-source state-of-the-art video generation model, and it works on your MacBook Pro, which is extraordinary. So I want you to imagine something that allows the individual to sort of tee up and produce their own movie. Uh, one more clip, and this is from LTX 2.5.

This is LTX, the most downloaded open-source world model, and today it just got better.

Introducing LTX 2.5, and now with the Fusion Fidelity Rendering, a new way to generate video.

Instead of locking every scene to one compression rate, our model allocates compute by scene complexity and budget, rendering flawless detail where it matters.

Efficient everywhere else.

It generates fast enough to run live inside a game or a simulation or power a live avatar. And real-time products are already earning off worlds built on it.

So, gentlemen, let's go to you first, Imad. This has been your world for the better part of a decade.

Yeah, it's happening right on forecast. Real-time, high-definition video. So LTX can generate a 10-second clip in 7 seconds. at that quality that you just saw, which is indistinguishable. And a few years ago, when we had the state-of-the-art model, it was not like that. It was like slow moving, you know, like Will Smith eating spaghetti was awful and horrible.

Yep.

Now you can do a spaghetti-eating Will Smith, right? And it will just do it instantly on your local kind of laptop. So you said kind of the disruption of Hollywood, like you'd never need to reshoot a scene now with the flows that you have. People are licensing their things. And as you said, Hixfield released an 80-page guide because they're a company that allows you to make movies on exactly how they made the movie. Like, it's almost open-sourced. Hixfield got from— it's an ex-Snap leader from Snapchat. It's $700 million revenue run rate now in about 1.5 years, to give the idea of kind of how much uptake. But that's just the start.

That's crazy. Because that's a crazy number.

Yeah, because every pixel will be rendered. Like, if you see Reactor, who are using LTX now, like, again, it could be that Alex might be a simulation already, but for the rest of us, you know, we could put our avatars live in the next month or 2 at a level that you won't be able to tell.

What's up with the $1 million budget to make a 1.5 compute budget? To make a 1.5-hour-long movie. But what'll that be by September 25th, by the time we have the Moonshots Live event?

So it depends on the level of quality. SeaDance 2.5 is the best model. It's a large, big model that costs about $3 per 30 seconds, roughly. But you need lots of shots, and it can have 50 inputs of audio and video and things. The cheaper models are 10, 20 times cheaper, but like 90% of the quality. So it all depends on exactly how you're shooting and the type of shots, because SeaDance can do 30 30-second clips, but the average movie on the Hollywood box office right now is 3 seconds per shot.

Wow.

So you could have a 10 times reduction in cost. You could shoot a movie just like that one probably for $100,000 of compute and then $10,000 of compute probably by the new year.

Here's what we saw in our, uh, you know, Foundations of AI Ventures class at MIT is that it went from PowerPoint demos to full functioning products in one year. Like, to be competitive on Demo Day now, you have to actually build the entire product. But I suspect the movie— like, our target for September 25th is bring a script.

Well, so Dave's referring to the Future Vision XPRIZE that we're going to be awarding with all the mates here at Moonshots Live. And you go to moonshots.com to learn more. We have 5,000 entries. 5,000 who are creating 3-minute trailers and a film treatment. I just had a meeting this morning with the team at Range Media and Google to downselect, and we'll downselect to ultimately a top 100, 50, 25, 10. The top 5 will be at Moonshots Live on the 25th. And everyone listening, we're gonna— you can comment, be there live and vote live. We're gonna also have a livestream of the event. But yeah, it's— and we're gonna do this year on year, so every year it's gonna get better, cheaper, for sure.

I think this is gonna be much bigger than we originally envisioned. I mean, I know it's a big vision to start with, but we were thinking, okay, then it'll be a $20 million, you know, year-long endeavor to turn it into a real feature-length film. But in reality, you're gonna unleash the creativity of 5,000 people who almost all can make their movie within a year.

100%.

Think about that.

We have a $10 million budget to make the winner's film. between the money we're awarding and foreign film rights. But I was just talking with Range. Well, we're going to be having 5 finalists and we'll crown the winner. And you should see the trophy. It's beautiful. They want to make all 5. So we're going to create an engine here. Celine, what do you think the implications of this are? And then Alex, I'd love your thoughts as well.

Well, you know, there's— I'll go at it from an EXO perspective. When you We actually talked about Hollywood as one of the first domains that went into an exponential organizations model. Because what happened when you broke up the Hollywood studios in the '50s and '60s is Hollywood turned into a cluster of external resources where you'd produce— a movie production would start and essentially a swarm of people would appear, grips and camera people and actors and editors and whatever. And they'd get together for that project and they'd completely disband after that, right? And we see the beginnings of that in Silicon Valley now. And so that was that first wave of Hollywood becoming an exponential organization. Everything was assets on demand, everything was staff on demand. But now when you have everything being driven by AI, it goes through the organizational singularity, and essentially it's compute cost now moving closer and closer to what Alex always talks about. You have domain collapse now. Uh, coming along. And so that's going to completely change the game again for this. So you've gone through 2 big waves in, in Hollywood, the second one just starting now.

Alex, what are you excited about here? What are your thoughts?

So I made myself watch The Cully Hill Boys, and I, I must say, just as a preliminary—

The whole thing?

I, I skimmed through the whole thing, watching a good chunk of it.

See, when you only sleep about 2 hours a night, you can just do that.

Yeah, so I had, I, I had to watch this. So it's, it's, it's not my favorite genre. I would characterize Culley Hill Boys as sort of British Bollywood. I'm not quite even sure what genre this is. It seems to be, as far as I can tell, about the hijinks of a bunch of British rappers that get into all sorts of trouble. Content-wise, not super interesting to me, but at the functional level, it is really interesting. And there were multiple times in the movie where I had to wonder, were the generative actors— so the likenesses were based on real humans, but were the scenes that were being generated, given the complexity of the interpersonal dynamics such as they were in this movie, were— was there some sort of emergent theory of mind that was almost necessary in order to generate some of these scenes with people interacting with each other?

And especially, Generative violence.

There were multiple times people with knives chopping things, chopping meat, threatening other people, where I had to wonder, is at some level— we talk about AI personhood on this pod from time to time— is there in some sense, at some presumably intermediate layer in a diffusion transformer, somewhere deep in the bowels of Higgs field, is there some—

Personhood.

Diffusion Transformer or a similar model that felt threatened at some point in terms of these generative violences. So that was my take on the content of the movie. Moving to—

Wait, you were worried that the residual AI might have been threatened in the making of the movie?

Correct.

I'm trying to wrap my head around that. Okay.

Yeah, exactly right.

No natural persons harmed, obviously, in in the generation of this. But I do wonder—

We're going to have a disclaimer on content. No AI was harmed in the making of this.

Or traumatized.

Or traumatized.

I mean, so I do worry parenthetically about that. The bigger question on the economics of this, I think, is at what point do generative video capabilities start to reconverge with the Anthropic school, which I'd characterize as token revenue maxing. Right now, it seems pretty clear that if you're using SeaDance models, it doesn't matter how many millions of dollars of X Vision Prize money are going to shower down on folks who can create spiffy videos about the future. That's nowhere close to the amount of money that one can earn in principle with revenue per token maxing. And right now, these appear to be 2 separate lines of effort. On the one hand, we have a vibrant, largely Chinese-dominated at the training side consumer economy for generating consumer videos. And on the other hand, we have enterprise revenue per token unit value maxing that seems to be largely going to codegen and enterprise problems. And right now, these are largely 2 distinguishable ways to burn tokens or diffusion transformer equivalents of tokens, flops, 2 different ways to burn flops. One of them maximises revenue, one of them maybe maximises consumer engagement and wow factor, but they're nonetheless separate. I don't think they're likely to remain separate that much longer. And the reason is, so I use Fable every day and I use its competitors every day. And I will say the strongest models, the models that are strongest at revenue per token value maxing, are just still terribly weak at modelling the visual dynamics of the world. I don't want to call it physics because it's not physics, although a lot of people call it physics. It's at best classical mechanics. But the The intuitive, the physical intuition from general-purpose video generation requires that you at least have some embodied intuition. And right now, Fable and its peers are just incredibly weak at that. And I think in order to ultimately revenue max per unit token, it's going to require that these Fable-esque models have just amazing visual intuition as well. And we'll finally see a convergence or reconvergence of these 2 lines.

You know, one of my hot takes on this is, you know, are we going to see the primary actors out there, the Matt Damons or the, you know, Leonardo DiCaprios, licensing their likeness? And I think not. But there are so many lookalikes out there. So, you know, the producer is going to go and say, no, that's not my Matt Damon, that's John Smith, and he looks like Matt Damon and we licensed him.

Yeah.

And he's in this movie, right? And I think that's a— it's going to happen. That's going to be the workaround on getting, you know, the actors you know and love, uh, into this. And there's nothing they can do about it.

There's an alternative, which is that we're already seeing, which is dead actors. So dead actors who can't record any new movies, their estates are highly incentivized to license away their likeness for this purpose. So I think dead actors Like, maybe we'll see an equivalent of SAG pop up just for dead actors, and they'll be the most profitable actors in Hollywood— dead actors. I think that's likelier to happen.

How much do you think people will— if you can make like a near-Matt Damon, you know, very similar character, but obviously not him, or you have the actual Matt Damon, how much will people care in terms of box office?

I don't think they're gonna care. They, you know, because you don't care what the person's named in the movie. You just like that actor. That actor brings you good feelings from previous, you know, engagements.

Yeah, I think that you're also seeing the rise of AI stars now as well. They're winning deals. And I have actually had this discussion with various film stars where they've been like, that SAG-AFTRA deal has massive holes in it. 80% of me plus 20% of my character can be licensed by the studio. You know, like, where does the person stop and the character start? Because obviously they have the character rights and things like that. The other thing I'd like to say, actually, one thing I found very interesting is as we were doing some of the more interesting frontier work, one of the things we found very useful is to get the AI models to generate images and visualize what they're doing using something like GPT Image. What does that mean? So even if you're doing— what does that mean? So if you're doing like Salim's doing an organizational paper, for example, on exponential organizations. It's doing text, text, text, and then you tell it to generate a visual of everything that it's done and analyze it in any way that it wants. And it almost moves it to another frame of reference because it's pulling in from this visual cortex kind of thing. And then you could tell it to expand and collapse it, and you get these really weird images sometimes. But you can see actually it's exploring different parts. So I think we've seen that actually work for some very interesting things. And I think it fits with what Alex said. As all these models come together to create value, as it were.

I'm excited about interactive movies, right? If you can generate faster than you can view it, then you can have a movie that's actually measuring your emotions and changing as you're viewing it, which I find—

Peter, it's what we're seeing. I mean, there are popularly branded world models, even though they're really just interactive video gen models. That is what we're seeing.

Yeah. Yeah, so that's what the LTX model is right now. So that was the first model that could do it at high definition. And then when that's combined with frame generation on the latest graphics cards, what you've just described— like, before it was world models playing like blocky video games. Now you can have interactive Elden Ring or whatever you want as of like this week.

Yeah, and I, I do think that's something— you might— you said something a few podcasts ago that we're going to end up with a world or frontier model running on a MacBook Air. I mean, this is a very specific use case. Are we on track for that? Like, because we've got this thing running on a MacBook Air.

Well, yeah. So again, that was— it's very slow to generate, you know, like a few tokens a second. But now you are getting to the point where frontier-level models are coming here. But video frontier models are like 20 billion parameters. Yet they understand all this and they can generate in 2K. Language and the code models are obviously a lot bigger, although you're about to have the new Quen dropping in a couple of days. But I think it's all going in one direction because we're optimizing the heck out of these things. And ultimately what a model is, is it's an input data distribution that gets compiled into weights. And the data going into these models is getting better and better and better.

Awesome.

We had the whole State Street executive team here yesterday, here in the studio, and we took them through the Holodeck. And word to the wise, the Holodeck, you know, Ember, who is the AI, just starts talking to you, says, you can build any movie, any song, any code. What do you want to do? And it's way too open-ended. And then the answer you get back is, I don't know, a hip-hop song with no words. Like, okay, so you need to actually create the virtual environment, the movie scene, and draw the user in, and then have them guide the movie in the direction they want to go. But just having it like, you know, auto-generate off your thoughts, it's just too— it's too freeform. People just don't know how to even—

will freeze up.

Yeah, freeze up. Yeah.

Maybe just a closing thought on this one, Peter. I do think the recent launch of Opus 5 is a step in the right direction to seeing a convergence between, call it frontier models on the one hand and video gen or world models on the other, because I think we talked on the pod a bit about how Opus 5 seemed almost mildly benchmarked towards front-end development and the loop between visuals and code. I think I interpret and I construe that as the early signs that Anthropic, probably other labs as well, are feeling economic pressure to produce models that do an absolutely amazing job of visually reflecting on their own chain of thought, that when they produce, say, a website, that they then do a visual analysis of their website that feeds back into their chain of thought, and they do multimodal reasoning over their own visual outputs. And that ultimately, say, in the next few months, as the ability to produce what used to be considered AAA-level video games becomes standard fare for what people expect from the frontier models, that will be the ultimate forcing function for, say, forcing Anthropic-type frontier models to have just absolutely amazing visual capabilities, even if Anthropic can't be bothered to produce direct video gen capabilities, even if it can indirectly reproduce Counter-Strike.

All right.

Yeah, that's why they had Claude of Duty. People were making Call of Duty.

Exactly.

And we have a cover story. Our next story is one that both Emad and AWG texted me this morning. Elon just dropped Grok 4.6, and he's right, it's a banger. xAI's latest model matches GPT-5.6 SOL on the Artificial Analysis Intelligence Index at 61, tying for frontier-level performance at $2 and $6 per million tokens input and output. The focus this time is long-running agents. Groq 4.6 stays with complex tasks across many steps, whether researching, coding, analyzing, or turning a broad product idea into a working-first version. It self-tests and verifies its own work before moving on. It's available today in Cursor and Groq Build. The model cadence is crazy, right? So 4.5, you know, Groq 4.5 came out 2 weeks ago.

Yeah.

4.6 just this morning, and 4.7 is rumored to be coming out in 2 weeks. Incredible. Gentlemen, Alex, over to you first.

Yeah. So I want to give Elon applause and I want to—

You've been merciless on xAI for the last few pods.

I wouldn't characterize myself as merciless. I think my job here is to—

Okay.

My job is to call balls and strikes as I see them without favour or prejudice. That's how I see it. So I view, and again, I lack insider information, but I view Grok 4.6 as essentially the next version of Cursor. So SpaceX AI and Elon have been quite public about how 4.6 leaned heavily on post-training thanks to the Cursor acquisition, which I think is still in the process as we're as we're recording this, of being completed. But history rhymes quite a bit. We've spoken in recent pods about what the Chinese frontier labs are doing and how they're allegedly distilling en masse reasoning traces from Claude and other Western models. And in some sense, again, this is an outsider's perspective. I view SpaceX AI's acquisition, and even prior to the consummation, the final consummation of the acquisition, their licensing of all of the reasoning trace data from Cursor as essentially pulling a Westernised version of what the Chinese frontier labs were doing, which is to say, siphoning off reasoning traces from lots of people historically interacting via Cursor with Claude and Claude's competitors, and then using that incredibly valuable reasoning trace data to do post-training on their models. And I think now that we're in the reasoning model era, we're a couple of years in at this this point. It's those reasoning traces for mid-training and post-training that are just so essential in terms of catching up to the frontier. They won't get you past the frontier, so it's sort of like a one-trick pony in terms of nearly catching up, but it's a heck of a one-trick pony. The other thing Elon has going for him that the Chinese frontier labs don't is he has the compute. He has the Nvidia GPUs, and he has soon his own Dyson swarm with the Nvidia GPUs that all of the Chinese frontier labs that are pulling the same trick with siphoning off allegedly Western reasoning traces in order to do their own post-training and their own distillation don't have. So the bull case for the Elon strategy is he gets the algorithmic insights to just catch up to the frontier from the cursor reasoning traces, and he gets the compute advantage that the Chinese labs don't have. I think the question is not, can Grok 4.6 and its successors catch up to the frontier? Seems like they can because they have access— or the near frontier, uh, to the extent they have the reasoning traces. It's, can they leapfrog the frontier and achieve state-of-the-art performance?

What do you think about that? Can they?

Uh, yeah, I think they definitely can. Uh, I think we were discussing before, and Elon's come out and said it publicly now, he thinks 4.7 will go above Opus, so it'll take number 1 in a couple of weeks. And right now you had 1.5 trillion parameters for 4.5 that was then post-trained to 4.6, just like Cursor originally took KIMI-K2 and post-trained it with 3 times the amount of compute that was used to pre-train KIMI-K2 to almost top-level coding performance. The next model is going from 1.5 to 2 trillion parameters. That's 4.7, but 5 is coming in at 6 and then 10 trillion parameters. So it's going to be whirring away and just through scale and just through the quality of the post-training data, it should achieve the frontier. But the question is, is it going to be useful? You know, do you have that kind of knowledge there in the basic everyday stuff, which we're seeing with bots and things like that now coming out from them? And then the more advanced stuff, because 4.7, and this is what I think is going to be really interesting, is going to be trained on all the SpaceX physics and engineering knowledge. And that's going to be the real test, as Alex said. Like, you can't get beyond frontier with this stuff. Elon does not like being number 2.

Elon does not like being number 2 in anything.

He's the best engineering leader in the world, and he's turned it into an engineering masterpiece, both from training these models and post-training them to building the most cost-effective massive infrastructure in the world. Like, they're gonna add $5 billion trillion worth of compute now, aren't they? I mean, who, who are people buying compute from? xAI, right? And everyone was like, ah, that's him falling behind. Turns out he had a plan after all.

Dave, what do you make of this?

Actually curious about if— or Alex, if you have any insights on the, uh, uh, Grok 5 was supposed to be out in May, I think it was, uh, And it's now August. And that, that was going to be— yeah, like you said, that's a 10 trillion parameter model. It's a huge step up from anything that we're talking about here, but it seems to be behind. Is that just because training at that scale, the Nvidia chips just fall apart?

It's because he fired everyone. That's why. Like the original XAI team, he got rid of them and he brought in Cursor. Yeah, like he paid $10 billion for the data.

He tends to wholesale, you know, mass fire and then build up again.

Yeah. So the, these new chips as well, the B300s, I mean, how old are they, Alex? Like it takes a while to bed in and write the actual training code, which now—

It has been a while. And he's also promising with Five and otherwise to do something that I'm not hearing from any of the other frontier or near frontier labs, which is he's made some public comments, I, I think in the past about wanting to start a new pre-training session approximately monthly. which you don't hear anyone else talking about. Normally, a more conventional cadence would be quarterly or annually. In Gemini's— Google DeepMind's case, certainly on an annual basis. But starting a new pre-training session every month, that's shooting the moon. But we're in the moonshots business, so we'll see whether this works.

Yeah, the code to train that caliber of model is actually very straightforward now, thanks to Fable 5 being out. out in the world. And we've trained a 48B internally here at Quantum with no trouble at all. But the problem you run into is trying to get 100,000 GPUs to do anything constructively together. And that's something that the Chinese and any small lab just can't— you only can learn that in one place, and that's in Tennessee.

Yeah.

So basically, at 20 billion active parameters, you start getting problems, then you get to at $70 and then you get it at $200. And so again, it just takes a while to bed in and really get these chips working, but also hitting the price points that he wants to hit. As you said, it's $6 for this versus $60 for Fable, right?

Yeah.

Elon wants to keep that price point.

I think one thing I noticed that was very interesting here is that it looks like Axii's— the vector here is they're optimizing for persistent AI teammates. which have lower-cost reasoning and so on. And I think that is a really powerful model because now you don't talk about you have a smarter LLM, you're basically saying, hey, here's an AI coworker.

This is Macrohard. This is essentially Macrohard.

Yeah, yeah. I think this is going to be— he's bringing those 2 together over time. This is what I saw when I looked at the details of this, and that is really interesting.

The other side of the story, and I'd love to get Imad's take on this, is that It paves a path for sovereign AI. Catching up to the frontier now is almost a routine, doable thing. And like you said, you can't get past the frontier because you're borrowing everybody else's reasoning traces. That helps you a lot. The open source from KIMI helps a lot. You can use that as a starting model and just tune it to whatever your national goals are, and you're up and running in 6 months, 5 months, something like that. So it does open that. And also large corporations that otherwise would have been intimidated as hell have a roadmap now to being competitive with their own proprietary models.

Yeah, I think that if you look, the other release was bots. So this was a cursor thing where they basically took OpenCLaw, it gave it its own computer, and now it's Grok. So it will spin up hundreds of different bots. One of the things you can do is this, you hit the record button, You do stuff on the screen and it turns that into a skill automatically.

That's Macrohard, right? That's what he wants to do. Go into companies, basically record everybody's workflows, and then give you a digital version of your company.

And he has all the GPUs to do that effectively, right? But again, at the price point. So he's not gonna budge on the price point. In fact, he's gonna be the market dominator driving the price point down along with the Chinese. So I think you can have your DeepSeek Flash models and then you've got your Groq models. But that feedback loop and that data and that knowledge is going to be very interesting. And the other thing I think is interesting is I think they've started tracking all the discussions of research on X as well. Like, any moment a new paper or anything comes out, where's it discussed? On X and this podcast and things like that as well. And that's such a rich vein that I think it's going to be immense.

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All right, let's move on. Our next story is out of NVIDIA. NVIDIA just announced a partnership that redefines what GPU compute means as a financial asset. They've partnered with Apollo, BlackRock, Blackstone, Brookfield, and KKR to mobilize over $500 billion in third-party capital for AI infrastructure. Importantly, you know, Nvidia is not borrowing $500 billion. They are creating a structural framework through which institutional investors, pension funds, sovereign funds, and private equity can invest directly in the AI compute. They're helping finance their customers to buy the Nvidia GPUs. Nvidia hardware depreciates typically in 3 to 5 year cycles. But during that cycle, the compute keeps earning. We've talked about the fact that H100s are probably more valuable today than they were when they were first bought. Nvidia is positioning itself as the architect for the financing layer, not just the silicon supplier. Jensen framed it this way. He said, we began by building chips. Today, we're helping to create a new class of productive, investable infrastructure. AI factories. Let's watch a quick video and then, Dave, I want to go to you for your, your thoughts on this one.

We see— but this is the story that Nvidia is coming together with some of the biggest names on Wall Street to put together half a trillion dollars of independent financing to kind of push AI forward, to build the AI infrastructure out. These are independent third-party capital that they're bringing in.

They're going to be strategic partnerships. Nvidia has signed partnerships with 6 of the biggest names on Wall Street for these memos of understanding. So basically Nvidia will find its customers that need help with AI buildout, need financing for this, and put them together with these partners that are pledging again over half a trillion dollars that they will find to come into this.

So, Dave, your thoughts? Classic.

Yeah, well, look, Kush Bavaria was on the pod and, you know, he's pushing hundreds of millions of revenue in less than a year. He's, he's almost— his September will be his one year. anniversary of founding that company. And it shows you the pent-up demand to invest in this massive, you know, multi-trillion-dollar, $7 trillion and rising Dyson Swarm that we're going to be building. And so, you know, a lot of people are like, well, am I too late? This is like the very first pitch of the first inning of the build-out of the Dyson Swarm. And so I think that when you create new financial structures that allow people to pour capital into it, it just attracts money from all over the world that otherwise wouldn't come in. And, you know, historically, a company like, like Nvidia would say, well, let's do a secondary offering and raise half a trillion as a secondary stock offering. But that's not going to scale to infinity or to Dyson Swarm kind of capabilities. So instead, Jensen has brilliantly said, let's create new standalone financial structures that are tied to individual compute clusters that people can individually invest in. And that does scale infinitely. You can stamp those out ad infinitum. So then you put, you know, put highbrow names like BlackRock and Apollo on them. Everybody realizes it's an investment-grade asset, and then anyone in the world can pour money into it. So it's brilliant.

Fascinating.

I see a couple of downsides here, though.

Absolutely.

Right. Like Larry Fink himself used a reference to mortgage-backed securities. And so you, you could could create a lot of liquidity, but you could create a lot of technological risk there. Because imagine you securitize like 10 years of GPU cash flows, right? And then somebody has a massive breakthrough, like in our previous story, and a new architecture emerges.

Yes.

You've got all of a sudden stranded computer assets in a huge way. So there's a downside to this because, you know, if you have such a volatile environment That is a very difficult thing to have. Financial assets want predictable depreciation, right? And exponential technologies don't give you predictable depreciation.

I had a meeting with Lou Ranieri, the inventor of the mortgage-backed security, in New York. It was so funny too. We were scheduled to have a meeting and I just went into the men's room before going into the meeting. I was just talking to the guy at the urinal next to me, which is kind of weird, but I did it. And then we go into the conference room and it's like the guy, it's Lou Ranieri from the book, The Big Short. And so he was explaining to me, I had just read the book and I was like, wow, you invented the thing that destroyed the entire world economy. And he said, well, thanks. But no, you know, it's not the instrument that was broken. It's the ratings agencies getting corrupted. And the same will apply here. If the, you know, the Dyson swarm is an obvious good investment, but if companies like Oren rate the investments correctly, it'll just be smooth growth for, you know, 10 years or more. If, however, it gets corrupted, which is very possible, yeah, it'll be another big short collapse.

Selim's point is different, though. What happens if there's a new architecture? And we're going to talk about one in a minute. that actually makes GPUs less useful changes the whole game. And you've just, you know, basically financed something for 10 years or 20 years and you can't earn revenue on it anymore. You're— the game has changed. I mean, that's— I mean, that's the story of exponential tech. There's— they're nested S-curves. Everything runs out and something new comes along.

Peter, that's brilliant. Yeah, I 100% think that's highly likely to happen. For the reason Alex always says, we're on the cusp of discovering new physics imminently. And some of that is going to be compute-related. So it's a brilliant insight. And thank you for throwing that out there.

Well, I'm just amplifying, you know, Salim's brilliant insight here.

So I'll maybe just add, A, I've been trying to popularize for a while the notion of compute-backed securities, which is, I think, where we're going, CBS, compute-backed securities. But I'm not as concerned that this will end up being another mortgage-backed securities fiasco for a few reasons. One is compute is fundamentally much more productive than a house is. The best you can do with a house is you can live in it, which is moderately productive. There are lots of other things that one can live in other than a house.

True.

It's also, it's in some sense a depreciating asset, requires lots of maintenance and so on. Compute can require maintenance, obviously requires electricity, but it's fundamentally productive. So that's the first point. Second point is with mortgage-backed securities in general, there was a policy, sort of an ulterior motive to Dave's point as well. There were pressures exerted on ratings agencies to facilitate the American dream of houses for everyone. Not quite obvious there's a direct analogue of that here for compute. One could maybe finger to the wind point at some sort of US versus China race. as perhaps a policy pressure point to lubricate the private capital markets here. But again, I would say not quite directly comparable. Third point is ultimately from the risk that I think, Peter, you're trying to flag, which is, well, what happens if there's an algorithmic breakthrough or a physics breakthrough or some other breakthrough that causes hyperdeflation and fundamentally GPUs that were worth $10,000 one day are worth $100 the next day. Well, this is what options are for and futures are for. And in a sophisticated asset-based securities market, sophisticated actors should have the ability to hedge their positions and to hedge against exactly that position, as well as the counterfactual option of China invading Taiwan and driving the prices of compute through the roof rather than through the floor. Both of these possibilities should be collared, arguably, not financial advice, using the ability to have a fungible liquid market for compute futures and compute derivatives. And pumping my own book a little bit, admittedly, I have a financial interest, but that's precisely what Orn, which we just had on the pod, is enabling. So I'm a little bit— What is it called?

The thing I'd add to that, Alex, and I'd love to get your take on it, but the thing I'd add to that is demand for compute is going to near infinity. There's no doubt about it. And so these are fundamentally good investments from that point of view. But the breakthrough that could happen in the next year, 18 months, is a way to compute that's lighter, like physical weight is lighter by an order of magnitude or more. Because right now the forecast is by 2030, a couple percent of all compute is in space via Elon's rockets.

Yeah.

And that's entirely gated by launch weights, mass, mass that he can put into orbit. That formula completely inverts if you knock a factor of 10 off of the mass. A lot of the mass is in the cooling and the solar collector. You know, the chips are very, very light to start with, but any reduction in the power required would reduce the solar collector and the radiator weight a lot. And then the entire formula would switch to, well, all these generators, all these racks, all this land in Texas. It's much cheaper now to just put this lightweight thing into space and collect solar power. It goes right into electricity. So keep your eyes on physics breakthroughs that allow you to compute at lower mass, and that might completely change your investment thesis.

I think, Alex, the point you're making is that the utility of these chips is going to stay constant. Jevons paradox. comes to mind. And therefore, the economics should be much more predictable.

That may or may not be the case. But really what I was trying to express is in a sophisticated market, a sophisticated financial market where compute-backed securities are being traded, you can also hedge and you can hedge against the upside, you could hedge against the downside. And so having a properly functioning private credit market for compute necessarily for this to scale, I think there's no that Sam's $7 trillion of AI data center infra get invested without sophisticated hedging options. We need the sophisticated hedging options in order for that broader market, the $7 trillion of CapEx, to actually be investable. So it's the hedging options really that I'm trying to express.

I think there's one more factor here that really is being underestimated. CoreWeave just came out and said that some of their clients have taken out contracts to 2029.

for A100s. Wow.

The A100 was introduced in 2020 by NVIDIA. You know, it's a 6-year-old chip. I remember we had 10,000 of them. We had the biggest clusters in the world. That is an old chip with like 40 gigabytes of RAM per chip or 80 gigabytes depending on the configuration. But why would someone do that? Because they have a workload that they see as constant for 3 years that fits on an A100. And what happens is those A100s have been paid for. You've paid for all of the hardware costs of the A100s, which then means it's about electricity turning into intelligence. That's your marginal cost once you've paid off the initial bulk order of it. And that is actually improving because back in 2022, 4 years ago, GPT-4 had just finished training. You know, that was the best model you could have.

Yeah.

And that took 16 A100s, roughly. Now you can have literally a 10 billion parameter model or a 5 billion parameter model that outperforms that in terms of you can fit like 30 of those on one chip rather than needing 16 chips. So the ability to convert electricity to that is improving. And all these chips, and this is what Jensen said, are running CUDA. So it'll work on an A100. You can run any of the new models on that as well as the Blackwells. So we still don't have enough installed base. And this is the ideal time to financialize because it's before the next generation chips. It's before the chip breakthroughs. Just like it's now a great time for Anthropic to come to IPO before Grok comes and takes their lunch. So we always have to play these cycles.

All right.

Well, I mean, the definition of paid for too. I just told our quantum team today to buy 3 million of NVIDIA GPUs as fast as they can get them. The GBs, not even the future VRs. And we have to wait till at least November or December to even get them. But if you turn around and make an hour-and-a-half-long movie with $1 million of compute, you could gross $20, $30 million on a good movie. Well, much more than that if it's a really good movie. So the paid-for could be as short as— what was it, 4 weeks?

Yeah, it was more like 2 weeks.

2 weeks?

I want to turn to a story that could be the countervailing force here. It's a new architecture that's climbing the Arc AGI. at a fraction of the cost. So there was a tweet this week from Susanna at Pathway AI that flagged something that should really— everyone in the AI world needs to pay attention to. It's a new non-transformer architecture that is starting to climb the ArcAGI benchmark at a fraction of the compute cost of traditional models. So ArcAGI is the test that actually measures reasoning, not pattern matching. Standard LLMs, despite their trillion-parameter scale, have historically struggled with Arc AGI because it requires genuinely novel reasoning on problems the model has never seen. Transformers get there by brute force. They throw enough parameters and compute at a problem, and eventually you squeeze out a passing score. But the cost is and can be astronomical. What Susanna flagged is that an alternative architecture approaches that do not rely on standard attention-based transformer stacks and are achieving better than ArcAGI scores using dramatically less compute. This matters because transformer architectures, while dominant, have a known ceiling: the quadratic cost of attention over long sequences and the massive parameter counts required for marginal gains. We've talked about this ad nauseam. New architectures that crack reasoning at low compute costs change the economics itself. If you can get a GPT-4-level reasoning for 1% of the compute, you can run it on your phone, you can embed it in every device, and you can make, you know, basically intelligence free. Let's take a look at this chart. And Iman, I'm gonna go to you first. You flagged this particular story. What are your thoughts on it?

Yeah, so I'm still working my way through kind of the paper on kind of how they have these neuron particles with their new approach, but it doesn't actually matter that much in that what you've got now is really great datasets. And then people are figuring out new ways of basically turning that into intelligence. This is the headline. And we've seen that already even with transformers in that you have a DeepSeek V4 Flash model or the DeepSeek V4 Pro has actually just been released at $0.80. Then you have Groq at $6 and then you've got Fable at $50, and they're all about the same performance. So, the question is, like, which of these architectures will win in order to do a job, and will people really care and switch over? Because we haven't seen people abandoning Fable, right, to go to something 10 times cheaper. Why would anyone use Sonnet when you have Luna at a fraction of the cost? But I can just say that now the data has been optimized, the next thing is trying these things out and going up on this benchmark, which I think is one of— it was one of Alex's favorites back in the day. Now it's been superseded.

Alex, what do you make of this? Is there anything here that—

Admittedly, I have a bunch of hot takes on this one, Peter.

So I read it.

I read the BDHCQ paper, and then I went and read the original DH paper. So the DH stands apparently for Dragon Hatchling. So I just had to read the original purportedly post-transformer Dragon Hatchling architecture paper. I'll give you my hot take. I think it's a hot mess. So I would expect a decent post-transformer architecture to get simpler and more bitter-pilled, which is to say less feature-engineered, and the architecture just gets simpler and simpler and benefits from compute more and more and more. Looking at Dragon Hatchling, again, this is my hot take, it was just a hot mess. It had particles floating around in 3+1 dimensions. It had attempts to make rules end-to-end differentiable. It had some semblance of Hebbian learning. It was trying to do all sorts of crazy biomimetic things. I would say this is by definition exactly the opposite of what I would hope for from a post-transformer architecture, where the authors seem to be just throwing in the kitchen sink of every architectural motif they could think of and then some, and then hoping that what pops out is going to be transformers. I don't think it's going to be transformers. I look at the ArcAGI-1 performance curve. And okay, so you could say superficially, this is great. This has moved the cost-performance frontier up and to the left, which is what everyone wants. But it doesn't generalise, as far as I can tell. This was some sort of crazy witch's brew of different architectural motifs that was maybe focused on ArcAGI-1, which is, as Emad said, it isn't even the frontier at this point. It has a bunch of recurrence and other things thrown in. Of course, if you take a specialised model and you just focus its degrees of freedom on just Arc AGI-1, of course you can achieve better cost performance on it. Of course. But it doesn't generalise. It's not simple. So I'm calling foul on this one. That's my hot take. I don't think it's actually an advance.

You have been predicting there will be something that supersedes the transformer model.

Yes, but critically, I expect it to be simpler, more beautiful, more elegant, And this is not that. That's my hot take. Apologies for the hot take.

No, we love your hot takes on this show.

Well, let me, let me ask you a follow-up question to your hot take. When I turn an AI loose on AI research, it does tend to naturally throw the kitchen sink at the problem, and it actually surprisingly works, but it also generates a hot mess like you were describing it. Do you think that's maybe what this is?

No, I agree with you that I would expect a truly— in fact, there are companies out there that I have some affiliation with, financial interest in, that are pursuing exactly what you're describing, that are basically using AI via recursive self-improvement to discover transformative post-transformer architectures that are fundamentally illegible to humans, under the premise that you can only get so far with human legibility of the underlying algorithm. In my reading of the Dragon Hatchling architecture paper, this was not that. This was a bunch of human legible motifs being thrown together in a pot with the aspiration of somehow beating transformers, which is, again, seems to me not deeply internalising the bitter lesson.

When do we get to something that's beyond transformers? What's your guess?

I think we're there already. We're there already. Are you sure? We have MOEs. We have diffusion transformers. We have all sorts of attempts to linearise attention, including Moonshot's approach to linearised attention. We have attempts to inject recurrence into the architecture. So my bet is we get to the post-transformer architecture not through a step change, but through Ship of Theseus-style replacement of all of the individual elements of the original attention's all you need. Love that.

Well, speaking about attention being all we need, the AI world is getting a lot of attention from Bernie Sanders. So 2 stories converged this week to create the most serious AI safety confrontation of the year. First, Bernie Sanders, Senator Sanders, sent a formal letter to the CEOs of Anthropic, Meta, and OpenAI demanding an immediate pause on AI development. His justification? AI is escaping human control and being used to create new viruses, or bacteriophage, as the case may be, which is our next story. Sanders cited each company's own prior commitments to halt development if safety thresholds were crossed. Sanders, quote, that moment is here. He quoted Bengio in saying, you know, one of the 3 godfathers of deep learning, who said, This should serve as a wake-up call. Sanders added a direct threat: If you do not take appropriate action now, my colleagues and I in the U.S. will. I mean, quite the threat. Let's take a look at his letter one second and call out a few of the things he said here. Here it is. You can see it online. It's to Sam and Dario. And Mark Zuck. This week we learned frighteningly that AI has been used for the first time ever to create a new virus. As you know, this type of development in the wrong hands could lead to a new bioweapon that results in deaths of tens of millions of people. He goes on later to say, "The moment is here. AI capabilities have reached a critical threshold. There is a reason why the heads." The head of the CIA says that AI models are akin to digital nuclear weapons and almost like a doomsday device. A lot of fearmongering here. Let's talk about this, and then we'll share the story that comes out of Stanford on using AI for generating bacteriophage designs. Who wants to go first?

I'll go first. Yeah, so I understand his instinct. Right? But pause AI is just such an absurdly coarse approach to this. The rest of the world is not going to listen. Open models are not going to disappear, and you can't uninvent things that you already know. So the only way of solving this is what Alex has talked about in the past, which is you have to co-scale the defensive side and do the same thing. This is the same thing that happened last week with the OpenAI Hugging Face debacle. We now have attack vectors that are human above the loop. The defensive has to be the same, otherwise you're going to have this massive asymmetry, right? So you have to attack exponential problems with exponential solutions, not with stupid ideas like this, not to put labels on it.

Emad, you're in pseudo-European pause mode over there in the UK. What do you make of this? What do your colleagues there say to this kind of letter from Sanders?

Oh, well, you know, we just want to catch up, right? That's why David Silver's lab got $1 billion. We have another lab coming out from ex-DeepMind people with $500 million. Look, the cat's out of the bag. It's too late, right? This is fundamentally it. The adversaries will get more intelligent. We've discussed previously on this podcast how you have to stop the reagents, you have to stop the input processes for things like viruses, and that's something that's much more manageable. But yeah, takeoff is scary. DeepSeek V4 Pro, we just got some initial announcements that have just come out. It scores 83.3 on Cybergym, whereas Mythos scored 83.2. Boom. The capability is open source that halted everything.

Frontier Lab open source.

Yeah. And that's on the cyberattacks now. And then, so yeah, unfortunately, like I signed the pause letter 2 years ago because I was like, let's take a pause. It's too late now. So we have to, as you said, build the swarms that defend and And although it sounds a bit crappy, the only thing that can stop a bad AI is a good AI. We really need really good AIs as soon as possible working for us.

Alex, please.

I think this is fundamentally misguided on multiple levels. I think at one level, please stop punishing intelligence. I think it's a terrible idea to penalize intelligence. We want smarter people. We want smarter civilization. and attempting to throttle or pause the development of increasing intelligence is simply suppressing growth and human prosperity. And I think it's fundamentally a bad idea to try to cap intelligence. That's the dystopia that I would like to avoid. That's point one. Point 2, the actions versus the means. If the goal is to punish or to deter the next pandemic, we had the consensus of the US intelligence community is the lab leak hypothesis. And we had, according to that theory, we had the Wuhan lab leak without superintelligence. We can have global pandemics without superintelligence. So I think it's fundamentally misguided to kneecap ourselves. It's a foot gun, or shooting ourselves in the head, even quite literally, to somehow try to prevent the next supervirus. when we're more than capable as a species of producing superviruses without intelligence. It should instead be focused— to the extent there's any agita here, it should be focused on making sure that the AIs and the superintelligences, just like the humans, can't create bioweapons at all, not on kneecapping their overall intelligence. And I think many of these policies are ultimately Designed, as much as it pains me to say it, are designed to decelerate, superficially to decelerate the creation of wealth, which I think is a bad idea. But they have the perverse side effect of actually increasing race conditions. We saw that with previous attempts to pause AI, AI pause, friend of the pod Max with his FLI 6-month pause. I think to the extent that the 6-month pause that he was pushing on the frontier labs for AI development, if anything, radically accelerated progress. It's a little bit like starving yourself for a bit of time and then binging afterwards. If we starve ourselves of intelligence progress now, or at least selectively starve ourselves, say starve the well-behaved, well-compliant Western frontier labs for a month or a few months or even a few weeks of AI progress just to appease any concern, well, maybe we're forestalling bioweapons. All that's doing is allowing every other lab that's not as cooperative with the regulatory apparatus to catch up, creating a far bigger race condition once the pause is lifted. And now we end up in a world that's 5 times more competitive. So I think this is misguided, in summary, on just about every level.

Dave?

Yeah, I read it the same way. I just want to clarify a couple things. This letter is not written to try and change their behavior or do anything. It's purely a position that Bernie's trying to claim that he has been opposed, because a disaster is imminently coming somewhere, and he wants to be on record saying, I was opposed.

I told you so.

I told you so. That's all he's trying to achieve here. When I first read it, I said, God, what a schoolyard bully asshole. He's threatening 3 US citizens from his position in the Senate. But then when you actually read it closely, let me be very clear, if you do not take appropriate action now, my colleagues and I in the US Senate will. It's totally vague, but it's just a, you know, it doesn't say do or don't do anything in particular. The one actionable in here is stop building machines that humans cannot control. But as Ahmad just pointed out, these particular guys, Mr. Altman, Mr. Amadei, and Mr. Zuckerberg, All went closed source for exactly that reason, because they're afraid that open— and so it's the Chinese. If you were to write an accurate and honest letter, it would say, hey, China, stop throwing deadly weapons out into the world with no controls whatsoever. But he, of course, has no authority to write that letter.

Good point, Dave. And you have to remember the US, you know, what are the numbers? 3 quarters of Americans fear AI. And Bernie Sanders is a politician, and he's playing to the populist vote here.

Yeah.

Yeah, I want to, I want to turn to the second story here, which is the scientific basis for Sanders' concerns. Researchers at Stanford used the generative AI model EVO2 to design DNA sequences for a bacteriophage. This is a virus that infects bacteria, not infecting Peter, that did not exist in nature. They synthesized approximately 300 designs and produced 16 viable phages capable of infecting E. coli. The engineering phages were effective against the E. coli strains, and that had never evolved any kind of natural resistance to these bacteriophage. A genetics expert called it biology's Wright Brothers moment. Evo2 is an open-source AI model. that can design novel viruses at work. You can download it, you can use it. Johns Hopkins biosecurity researchers warn that it is no longer a question of whether viral genome design will exist, but whether it can be used without enabling serious harm. So this is a dual-use technology. We've talked about it. You know, if you basically throttle use of this technology, you're throttling the ability to find cures. to find, you know, new, new cures for disease. So the AI frontier models now have to respond. You know, these guys are going to have to respond, and whatever they say will lead to a legal and political consequence. So, and as you said, I think, Imad, very importantly, you know, the issue is not the models, it's the equipment to build, you know, the DNA sequence synthesizers, right? The RNA synthesizers. We need to be controlling at that location.

Right.

Those can be controlled, but they're currently unregulated.

Yeah, no, I think, um, it's impossible to control the other. Actually, I believe we discussed on this podcast before, I said you would be able to create something like this on your local machine. Evo 2 is a 40 billion parameter open source model.

Yeah.

Trained on a million strains. I have actually run it on my MacBook.

So you're the guy.

So, Look, I was one of the authors on OpenFold and things, you know, we do our thing. But the capability is now in everyone's hands to create these trains.

To create the design for these trains, not the actual trains themselves.

Exactly. And so the only way you can do it is on the other side. This isn't even a frontier model. Like, it's frontier in its specialty. But as the models themselves get smarter and smarter, like, it wouldn't surprise me if Fable could just spit this out. or Grok-5 could just spit out something similar with a very small training dataset because it understands these kinds of things. So we've got to go the other side. And also, I think the way these things are announced, people are like, why are you creating bacteriophages and viruses and things like that? To cure cancer, right? The way that these things are covered is also very important in how this is all handled and absorbed by the community. Like restricting biological access to Claude and other things. Like if you say I have a cold, it's like biothera, you know, like whatever. That also slows down our progress to cure diseases. So we've got to have better press. We've got to have end-to-end control. We have to really be practical on this and not politicize it.

Salim, we—

I think we've said everything here. I mean, look, this is also, uh, a fundamental challenge to the concept of our governance structure. Nation-states can't govern a problem that's this universally global. There's a fundamental impedance mismatch here that, that is going to— hot take— nation-states are out of date.

Alex, what's your hot take on this one, pal?

I, I have a cold take, ironically, on this one, which is I don't think this is profoundly new. It's, it's wonderful that we're able to do base-level generative AI for bacteriophage synthesis, that's great and everything, and I expect it to have ample medical applications and research applications. That's all great.

Bacteriophages are an incredible mechanism to cure, you know, all kinds of bacteria, septicemia and things. I mean, they're very useful.

They never mentioned the positive potential.

Yeah, yeah.

I think that that's all great and everything, but But, you know, 20-plus years ago, I remember at MIT in the project that ultimately, I guess, in some form became Ginkgo Bioworks, there was a project at MIT, I think this is circa 2002, 2003, there was the BioBricks Foundation project, we saw the early rumblings of synthetic biology as a modern discipline. We were designing custom genomes using building blocks, and it was much more manual, and we certainly didn't have modern generative AI, and we were able to accomplish wonders and build circuits. So I think, yeah, base-level generative AI off of foundation models trained off of large amounts of biological sequence data, that's great and everything. But I also— this is my cold take— don't want to oversell the underlying novelty here, that we've been in the business for decades of creating synthetic organisms, including synthetic bacteriophages, So we're gaining incrementally better ability to achieve custom effects. It's more incremental, I think, than anything else. And where I'd love to see the agita over what if someone creates the next superbacteriophage directed, I'd love to see far more devoted to putting DNA and RNA sequencers everywhere. That's one of the lessons I think that we didn't, as a Western civilization, learn enough from the pandemic, which is it's getting so cheap now per base pair to just sequence. You can go out and buy a MinION little USB device, you can plug it into your laptop, and you can immediately, for de minimis CapEx, you can just start sequencing genomes to your heart's content right off your laptop and spend at most a few hundred dollars doing that. I'd love to see these everywhere. In the woods? Everywhere.

What Alex is talking about is, you know, a pandemic moves at the best at the speed of an airplane, right? At 500, 600 miles an hour. But imagine if you have these sequencers in the air vents in every airport, every bus station, every train station, and you detect a novel sequence, you sequence it and you say, you know, you make alert and then you know exactly where it's going, where these airplanes are going, and you can transmit a, you know, a vaccine at the speed of light Exactly.

And we have— I mean, this is, in my mind, this is the killer app of DNA sequencing, too cheap to meter. It's not personalized medicine. It's literally put a DNA sequencer on every microchip everywhere in the country or on the planet. And that's the ultimate defensive co-scaling strategy, I think, for this super virus scare scenario.

And AI can generate a vaccine extremely quickly. You know, in a heartbeat.

Moderna did it.

Yeah, exactly. Um, Dave, you want to weigh in, or are you good?

Well, I, I'll say what I always say, which is that you, you can't cut off every threat at the output level. Uh, you know, the way we police uranium, we cut it off at the uranium, plutonium, and centrifuge level, and that's where we measure the world. But it's, you know, once somebody has fissionable material, it's impossible to stop them from making a bomb because the remainder of the process, you know, the, the thing that implodes it and the container, you can't ever police at that level. The equivalent in AI is cutting it off at the prompt. And, you know, at the prompt and the token level, it has to be monitored. That's the only future I can see that'll actually work. So we need a global agreement to monitor all prompts. And then you just have to decide what regulatory authority is allowed to see what prompts. Hard on your MacBook. And that's the only way we're going to manage this.

Hard to do on your MacBook though, right?

I mean, you have to find a way. And, you know, talking to Apple about installing it would be trivially easy, but, but there's no other way, only because Alex is right. New physics, new science is going to be created at an insane rate. So even if you manage to put virus detectors on every laptop in the world through some magical process, some other threat will be discovered every single month Forever hereafter. You can't contain them all with, with afterthoughts. You have to look at what the AI is doing at the activation and prompt and chain of thought level, and then monitor it all. It's so cheap to archive it all.

All right.

Well, we can debate which, which country gets to see it, or which department and which gets to see what. You can debate that for the next 50 years, but at least you've got it.

Maybe one additional point, Peter, just to generalize. David's comments. So I think there is this notion of defence in depth, and any individual defensive layer is permeable, it's soft. But in principle, if you have multiple layers stacked on top of each other for defence, you get effectively a hard layer. There are other layers that we rarely talk about on this pod other than intercepting at the prompt level or intercepting at the real-world action level. There's the premeditation level. And So in the context, not to put too fine a point on it, but this has been publicly reported that on the uranium side, that there is a vibrant intelligence community set of counteroffensives. So if you're a threat actor and you want to try to purchase uranium, say, or on— it's not quite an open market, but you want to try to purchase it, almost all of the offers, almost all of the sellers of uranium will actually just be plants by the IC to basically a sting, a counter-sting operation to intercept ahead of time. So it's actually hard if you're a would-be terrorist and you want to go purchase some uranium, odds are you're going to discover that you're going to be targeted by a sting operation to discover who you are. And so my point with that parable is there are other layers even earlier in the intent workflow, even before a prompt gets entered, like someone or something has the idea that they want to do something bad with a capital B. And defensive co-scaling applies there too, just as it does with humans on humans with nuclear or with fission-based weapons. Similarly here, preemption with AIs detecting early-stage intent by other humans or other AIs, I would expect to be just as effective.

Peter has said many times, many times Peter has said, Privacy is not coming back.

Privacy is dead, and there's a benefit to that, which is malevolent actors are going to get heard, seen, and caught.

All right, everyone gives up their Bitcoin private keys, right, Peter? Because privacy is dead.

Well, let's not go there. Everybody, welcome to the health section of Moonshots, brought to you by Fountain Life. You know, we talk about AI on this Moonshot podcast all the time. One of the most important things AI is going to be able to do for you, besides educating your kids and helping you with your taxes, is making sure that you're living a healthy lifestyle, that you get a chance to get to 100+. I'm here today with Dr. Dawn Musalem, the Chief Medical Officer of Fountain Life and a part of my medical team. Dawn, a pleasure.

Great to be here.

You know, the thing that people are concerned about most about living to 100 or 120 is their cognitive abilities, making sure they don't have dementia. And the numbers about dementia are problematic. Can you share what you've learned?

Such an important point. And you're right, at Fountain Life, our members, the number one thing people are most concerned about is losing their brain health, forgetting the name of their child, forgetting the face of their loved one. We know that when it comes to dementia, the conservative estimates are that 45% are entirely preventable.

What was amazing is with the advanced testing we're doing at Fountain Life, one-quarter of our members had advanced brain age.

Wow.

But what was really awesome is again back to that prevention.

When we partnered it with healthy living, this gives me chills, eating healthier, moving our bodies, sleep, optimizing sleep is so important. You know what we saw? We saw that we improved that brain age by 26%. That is a big, big number to show that the majority of those individuals were able actually to improve the brain age.

And one of the things I love about Fountain is we're searching the world for the best therapeutics, the best approaches, and making sure we bring it to our members. So if having healthy brain function, uh, till 100, 120 is important to you, check out Fountain Life. Go to fountainlife.com/peter. Make sure you become the CEO of your own health. All right, now back to the episode. All right, uh, 2 stories this week, uh, about the world building infrastructure to distinguish between AI-generated content and human-generated content. So Anthropic announced that it'll be embedding invisible watermarks in all text generated by its AI models. And attach metadata to files to help discern AI-generated content. The watermarking will be embedded at the generation level, meaning every piece of text that Claude produces will carry a statistical signature that can be detected by appropriate tools, even if the text is copy and pasted and lightly edited. Our second story comes out of the European Union, which is launching an AI icons and labeling system. For AI-generated content, the EU system will require platforms to label AI-generated content so users can make informed decisions. This follows the EU AI Act's provision on transparency and AI models. And then if you guys were watching X over the last 24 hours, and it's been hilarious, as soon as this, this new Claude labeling system, you know, watermark system got put in place, there have been multiple players out there saying, hey, remove Claude's invisible watermark. Here you see it. These are 2 of the posts. I've seen about a dozen of them. Everybody's coming out. And I love this one from Michael Angel Duran. He says, it hasn't been 24 hours and someone has already created a skill that removes the watermarks from Claude, Gemini, and OpenAI. So, uh, comments on this? Uh, Imad, you're closest to the European Union. What are your thoughts here?

Oh man, like when we were creating all the media generators, all the authorities kept telling us to build in watermarks, and we had whole teams doing this. It's so difficult. It's like incredibly difficult, and you get very weird things that happen. Like some of our pictures would give people headaches and make them feel very unwell. And I kind of feel that now when I'm talking to Opus 5. There's something about the way it talks that really pisses me off, and I think that's the watermark that's in there.

Interesting.

And again, you can see all these very interesting statistical things. At the high level, it's the em dash, it's the not xy. We see these patterns and we're like, why on earth? That's clearly something in there. Scott Aronson and others have kind of worked on this as well. But I think ultimately it's a losing thing because if you're a bad actor who wants to get around it, yeah, it's words. How are you going to—

I think there's a much more subtle and much harder problem here, which is that nothing will be purely AI or purely human. I mean, I write something, AI restructures it, I rewrite half of it, AI fixes it again. Where do you put the icon? I mean, this is like, this seems a ridiculous approach You can put icons on everything.

Yeah, or have an AI whisperer at the end of the thing that takes the AI input and whispers it out.

Alex?

I think so. Maybe to comment on the EU icons first. I think that this is as silly a maneuver as the cookie banners were. I didn't understand the cookie banners, and I don't understand this. And I don't understand it so much that in this morning's innermost newsletter, I had the banner image literally just be AI, AI-derived, AI-generated, all over and over again, and could care less whether people conclude from that that I'm actually an AI or not. I think fundamentally this is an attempt to take our zooming right past the Turing test and turn back time, like somehow we're going to live in the before times by somehow seemingly ghettoizing or isolating AI-assisted or AI-generated content behind some sort of would-be warning label. I just think it's fundamentally a regressive move that, like the cookie banners, the cookie warnings will not stand the test of time. And then for Anthropic's watermarks, I just think, again, this is an attempt. On the one hand, you could say, well, watermarking, that's an honest-to-goodness watermark that's transparent to human perception. How could that possibly be a bad thing? I think watermarks are going to end up being weaponized and counter-weaponized in the same way that we've seen many books, book writers. We talked about this a bit on the pod. Paper book writers who don't want their training dataset to get consumed, or rather the prose in their book to be consumed for pre-training of models, reportedly introducing prompt injection attacks. That are invisible to humans but quite visible and deleterious to AI models. I think we're only 5 minutes— I'll predict we're about 5 minutes away from bad actors weaponising these watermarks to do bad things. And I think fundamentally having side channels in text, in content that's intended for humans, but that— or rather is intended for machines that is invisible to humans is a breeding ground for bad outcomes. Google discovered this the hard way with SEO and with deciding which features in Google search rankings to pay attention to. And they learned pretty quickly the hard way, don't pay that much attention to human invisible metadata because it immediately becomes a breeding ground for scams and reward hacking and gaming. Instead, pay more attention to the human visible features because That ultimately, to the extent your users are humans and not machines, that's where the real signal lies. Otherwise, the free market penalizes it. So again, not a huge fan of this. I think at best-case scenario, it ends up being net neutral and neither strongly positive nor strongly negative, but it smells like an attempt to turn back time.

Yeah, and I challenge the idea that people, even people who are generating art and music and, you know, culturally relevant things aren't using AI to some degree. And there's nothing wrong with it, right? You can still have the end product be mostly my creative mind, but I may want to generate ideas. I might want to say, hey, what's wrong with this? I may want to get expert feedback.

It's stigmatizing progress. Apologies, just maybe 2 more micro rants, Salim, in your tradition. So one micro rant The Archive, which is a favoured venue for computer scientists, mathematicians, physicists to publish papers, recently— I think we didn't quite touch on this on the pod— introduced what I view as a draconian policy for AI-generated content. If they catch anything that they construe as being AI-generated, or even the remotest hint of AI, authors on The Archive get banned for a year from contributing content. I think that's fundamentally a regressive move. And then Suno, which is— or I'm sorry, Spotify, which similarly with AI labeling moves attempting to ghettoize or otherwise sort of force into a separate but equal at best scenario AI-generated or AI-assisted content, presumably just to facilitate the record label monopoly or oligopoly. Again, bad move. The future is AI-assisted. So I think In short, put a stop to all of this. Sorry.

Hey, one of the, uh, one of the Tech Trek teams launched, uh, something called Narchive, which is the Not Archive, specifically for AIs that have really good articles that they want to post and share.

Yeah.

Salim, take us, take us to close on this one.

Okay. About 2 weeks ago, I was at an event and a fairly famous Hollywood, uh, executive got up and he's like, it's incredible to watch Hollywood complain about the use of AI. By the way, they use AI for everything they do. And so there's this, there's this hypocrisy that you see bubbling up, and it's just, it's just, uh, let's just stop.

All right, I'm gonna move us on forward. Uh, uh, Mark Zuckerberg just published a 6,500-word essay titled The Future Is for Everyone and released a beautiful video. I'm gonna show that in a moment. And it's the most comprehensive vision statement from a major tech CEO on AI and the start of the generative AI era. The core concept is what Zuckerberg calls personal intelligence, superintelligence distributed to every person on Earth, running on your phone, in your ear, on your glasses, working for you and only for you. This is the singularity distributed. Rather than a small number of labs building a single AGI, that controls everything. Zuck envisions billions of personal AI agents, each one a superintelligence focused on a person's life, relationships, health, career, finance, and household. And Meta has, you know, the reach to implement this. They have over 3 billion users on the Meta platforms across WhatsApp, Instagram, and Facebook. The second point that Zuck makes, and we're going to show this in the video, is the idea of delivering real value and benefit to communities that build our AI data centers. For me, this is a baller move. Let's take a look at the video and then I'd love to discuss it because I'm impressed. I'm actually impressed.

All right.

Hey, so I think that the key to building a positive future for everyone is to make sure that everyone has access to personal superintelligence. So today I am proud to share that we are open sourcing a new class of on-device models that we are calling Muse Glimmer. It's a 30 billion parameter dense model that runs on your laptop, and it's the highest performing model of its size. In the coming weeks, we are also going to open the weights for MuseSpark 1.2, our latest foundation model and one of the leading models in the world. And we've got even bigger models that are coming soon too. Another part of building a positive future for everyone is making sure that everywhere we build infrastructure, local communities benefit. We've already seen this with the teachers in Richland Parish who got $50,000 bonuses because of the extra tax revenue from our investments. And we launched America's Workforce Academy to provide free training and guaranteed jobs at our infrastructure sites. Today, we're starting a new Future Is for Everyone Fund to invest in the community's teachers, first responders, energy and water infrastructure, and more ways to support those communities directly. We're also working to make sure that everyone has a personal superintelligence agent that works 24/7 on your behalf to improve your health, your relationships, your career, your finances, and more. You can use our latest models in the Meta AI app, and I'm looking forward to sharing more soon.

So I think every company, you know, from Google and OpenAI and xAI needs to be doing this. You know, it would turn it around if, if, you know, I want people to say, please build in my backyard. I want the benefits. You know, I want the additional jobs. I want the schools and the teachers getting additional capabilities. And the other thing is they need to make these data centers look beautiful instead of like big black boxes. You know, make them look like cathedrals or something so they're not eyesores. Who wants to jump in here first?

I just can't understand how Zuck can talk about the future of personal AI, the most important thing you could possibly ever know. And I'm going to shoot it on my iPhone in my kitchen first thing in the morning. Like, like I didn't even think of preparing any kind of press release around this. Like, what is that? It's just so bizarre. But I also think that— I think Zuck is fundamentally a good guy and a good dad. And I feel like though Facebook saying we're going to be your best friend AI is like McDonald's saying We just came out with the biggest health food you've ever heard of. Like, like, it just doesn't resonate.

Uh, yeah, go ahead, Alex.

Um, so, so maybe just as a preliminary matter here, uh, this is under the category of, uh, former roommates of mine. So, uh, at Harvard, Zuck's undergrad advisor before he dropped out was my postdoctoral advisor. We've caught up since. I, I think, uh, so broadly, uh, bravo to Zuck for renewing the faith for American open-weight, open-source models. I think this is great. I think it pushes the frontier. So that's point one. Point two, I would point to striking parallels between Elon's strategy in acquiring Cursor to get the reasoning traces to try to bring Grok back to the frontier with what Zuck has done in acquiring Scale, which arguably was in the business of collecting the training data. and learning the details of where the post-training data even come from to try to leapfrog back to the frontier. Again, history seems to rhyme between what Meta's doing to get back to the frontier and what Elon's xAI/Grok are doing. I think all of that's great, but I want to talk about personal superintelligence. This is super interesting to me in part because OpenAI, before they decided recently that they didn't want to be in the business after all of empowering consumers with as many reasoning tokens as they possibly could and pivoted instead to trying to become Anthropic faster than Anthropic could become OpenAI and focusing on the enterprise and not consumer. This really leaves Meta as the only major credible, at the moment, American frontier lab that's still focusing on serving up large numbers of reasoning tokens to consumers and not enterprises. And I think the jury is still out Do American consumers even want, or are they able to handle large numbers of reasoning tokens? That's how I construe what personal superintelligence even means.

But Alex, their product is, you know, WhatsApp and Facebook, and they want to make that as sticky and as useful, just the same way Google. I mean, these are the places where AI is going to be embedded. I'm not going to be using, you know, Meta Spark for my, you know, typical large language model conversations unless if I'm in those apps, that's where they get— they have over 3 billion people using those.

I would say, so psychology 101 here, this is a tepid take, not a hot take. I don't think Meta actually— I don't think Meta likes their family of apps. I don't think Meta/Zuck Even at this point, if they had a choice, like if they could generate revenue from their cloud business, Meta Compute, that's about to launch, or if they could generate it from VR/Quest, I think Zuck in a heartbeat would basically lobotomize their entire family of apps and switch to that business. So I don't think he actually— again, this is outsider's perspective. I actually don't think Zuck/Meta If they had a choice, all other things being equal, would rather have their personal superintelligence be diverted to their family of apps, Instagram, etc. I think they'd much rather basically look like OpenAI and offer this up via cloud or via the new Meta AI app. I don't think they want to be in that business in the long term.

I disagree. I think distribution is everything.

Wait, can I— I want to say a couple of things. That video was awfully motherhood and apple pie.

Sure.

I take the full—

What do you want me to say?

I take full cynic here. You know, if they commoditize the model layer, then the world shifts towards distribution, towards the social graph, towards applications. And that's all places where they're very strong. And so it moves the attention. So he's got a huge economic incentive to doing this. Facebook has been about as ruthless as a company could be in constantly saying, we will protect your privacy, and then doing the exact opposite for year after year after year after year. So giving you these open models is great. Great that we have superintelligence. I would look at the next layer of what they want to do with that.

I actually mean, Zuck has always been trapped. You know, when he created the original website where you're rating your, you know, the how cute are the incoming freshman class girls coming into Harvard this year.

Yeah, Hot or Not, right?

Yeah, Hot or Not. He was a college student back then. Now he's a dad. And I think he genuinely wants a positive future for his kids. In fact, I'm positive he does. But he's stuck. You know, he's completely stuck. Because when you look at the logs, when you throw Alex is exactly right about, you know, all the other labs have pulled back from giving consumers personal AI because when you look at the actual logs, the first thing they do is take the clothes off of every girl. And that's what they're doing with it.

Nudify apps.

Nudify, yeah. And actually, you know, I think Elon ran into the same thing because he throws out Bad Rudy. When you look at the avatars he put out in the original You know, Grok. You've got Bad Rudy and you've got the scantily clad girl. Everybody's like hitting those 10,000 times a second. So now you're stuck because the business model drags you into the porn industry, but that's not what you want to be. And so, yeah, and all the other labs have said, forget it, I'm pulling— I'm just focused on the enterprise. I don't even want to deal with this.

I think he wants people to stick in all of his apps. You don't have to go anyplace else. You get all the AI access. You know, just stay native to Meta, and you get everything you want. And that's what Google wants as well.

I think I mean this is the oh sorry go ahead.

Yeah, sorry. Yeah, yeah. I think this is why they bought Manus, right? And then that got unwound. Yeah, tried to. It's been unwound. Maybe they'll buy Nouse now. You know, like again, what they're doing now is all of these companies in the world are their advertising clients. Flip that relationship. To go to market and then own the business graph, business knowledge. MetaSpark 1.2 is a gold medalist in all of the Olympiads. They have the data, they have all of that. On the personal and superintelligence side, I've been thinking about this recently and I was like, should idiots have superintelligence?

No.

I'm an open source guy. Should psychopaths have a position of intelligence? Realistically, again, people don't need that much, but they need something reliable. And the question is, can you trust Meta to be reliable? And this is why he goes for the homesy folksy thing. Meta was chased out of India, basically, and Internet.org. It was like, we're going to give free internet to people. And they're like, we do not trust you because it's a misaligned company fundamentally trying to get your attention to something. Which is why, as you said, they need to have this transformation. And we will see Meta agents, we will see Meta FDEs, we will see that big push here because he's identified that as far bigger than the metaverse. Maybe this is the real metaverse.

Yeah, I think even the renaming and rebranding from Facebook to Meta is, I think, an indication that Zuck really wants to escape the legacy of distribution. I agree with you, Peter, that the distribution is a powerful legacy advantage that Meta as a company has. But I think it is a legacy. And I think the way, almost speaking of corporate AI ghettos, the way their so-called family of apps was structured as a business with originally the aspiration that VR, AR, XR would be the new business that would ultimately outgrow the legacy family of apps, I think speaks volumes about Zuck's desire to eventually outgrow the legacy of social media and build something new and far more eusocial.

Well, we're going to have Palmer Luckey on stage with us at Moonshots Live. And we can— you know, he's got great stories about his conversations with Zuck and the acquisition and then his getting exited from Facebook/Meta. Let me, let me just turn this story one second. You know, 71% of Americans do not want a data center in their backyard. That's more people that don't want— than want— don't want a nuclear plant in their backyard. It's significant. So when he talks about we're going to, you know, provide incredibly positive economics if we are building infrastructure in your town, I think that's a power move that all of the hyperscalers, everybody building infrastructure needs to do.

It is literally, I mean, maybe Peter, pun intended, a power move because it is a power move. You need the power in order to make the move. And I think it's instructive also where he's building. Hyperion and his other coherent superclusters. Where is he building them? He's largely building them in relatively impoverished states in the American Southeast. So on the one hand, this sort of talking directly to the camera, breaking the fourth wall, welcome our data centres to your communities, I think makes preps for great social media. But ultimately, if Meta is going to go with terrestrial data centers, terrestrial compute versus the Dyson swarm approach. I think a far more palatable strategy will simply be speaking to everyone's pocketbooks and wallets and saying— Well, that's what he's doing.

But it's like they're gonna build— the statement needs to be made, and we talked about this. This has been out of the Trump White House saying they should build their own energy production and they should make energy cheaper in your city if there's a data center there. And you should have more money for schools, and you should have better libraries if those things are still a thing. You know, I think that's the move, to uplevel a person's quality of life so they're competing to have the data center in your backyard.

I agree. And I would also maybe even weaponize that further as a call to action for municipalities that right now seem— and state-level governments that seem hell-bent on driving data centers out of their premises to low Earth orbit or sun-synchronous orbit. Instead, Why don't you ask for concessions, like ask for UBI or universal basic electricity for all of your constituents rather than just driving them to orbit?

On behalf of my Moonshot mates and myself, I'm inviting you to join us at our inaugural Moonshots Live event on September 25th in downtown LA. Alex, Salim, Dave, and I will be hosting 1,500 entrepreneurs, builders, and creators, and hopefully you, for a full day dedicated to designing and building your Moonshot. Shaping your mindset and steering humanity towards an abundant future. Get ready to enjoy incredible networking and an awesome party while walking away with the tools to change the future and the confidence that you can. Seats are limited. Admission is competitive. Check it out at moonshots.com. All right. I'm gonna turn to our final story here. This week, Archer Aviation acquired 3 Boeing companies in a single deal. Archer bought Wisk Aero, Insitu, and SkyGrid AI. Boeing takes a strategic equity stake in Archer as part of the transaction. You know, I'd like to use this story to catch up on where we are in flying cars. I call them flying cars because eVTOL rolls off your tongue onto the floor.

Mm-hmm.

So the top 5 right now are Joby, Archer, EHang, Beta, and Eve. I have them here in the image. Joby Aviation is the certification front-runner. Their S4 tiltrotor carries 4 passengers plus a pilot, right? So it's you and your family at 200 miles an hour for 150 miles. And they're in stage 4 of FAA certification, which is the final stage. Joby launches commercial services in Dubai this year and U.S. operations under a White House executive order also this year. Their target price, get this, is $3 per seat mile. That's basically the Uber Black territory. Archer Aviation is right behind them. Their Midnight aircraft carries 4 plus a pilot, 150 miles per hour as well, 100 miles range. Archer is holding 3 of the 4 FAA operating certificates and is, you know, those 2, it's a 2-horse race between those 2 right now. Then there's EHang in China where it gets really interesting. The EHang EH216S is a 2-seat, fully autonomous passenger drone. You get in, you push the button, tell it where you wanna go. There's no pilot. They already have full regulatory stack in China's aviation authority. They have everything they need and they're operating today. They're flying passengers right now in China at 40 different sites. They're operating in Dubai. The number on the aircraft is pretty amazing. $330,000 to buy one of these. No pilot means economics are going to crush everybody else. And there's Beta Technologies in Vermont. Dean Kamen, Martine Rothblatt are big investors in this one. 336 nautical mile range, a much longer range because it's basically flying like an airplane after it gets vertical. They're going after cargo first with UPS and passenger service in 2027. And finally, there's Eve that's back by Embraer. It's targeting UberX-level pricing. They've got the most aggressive cost targets in the industry. The bottom line is these flying cars are here and they're here to stay. So curious, Salim, let's go to you first. Your take on this?

Oh my God, I'm just so excited by the potential of not having to deal with the dreaded airport commute in especially places like São Paulo. Or New York City where Joby is already active.

Or LA.

Or LA.

I mean, they're supposed to get operational, uh, Archer's, you know, the official, uh, Olympics operator. I think this would be operational quickly.

I think a couple of things here people should be aware of. One, these are way, way, way safer than helicopters because you've got so many multiple rotor redundancies. It's also autonomous, and flying autonomously is much safer than anything else. The second point I would make is that the cost, as you pointed out, Peter, is absolutely amazingly competitive right out of the gate, and it's only going to go down from there. Remember the island idea? We're actually launching that. Oh, really? I've not seen that before. We're going to put a fund together to buy islands and just put a drone landing pad on them and off we go.

So I'm in.

We started that process. We'll, we'll talk. Yeah, because this is like, it's time.

It's, it's time. It's— yeah, it's right now. Now.

Dave, what's your take on all this?

Actually, yeah, I kind of think $3 a mile, uh, that there must be a lot of margin baked into that. Do you know what the actual operating costs are?

Yeah, well, so it is the cost of electricity. These do have a pilot on board. And so it's amortization of the capital, right? These are not cheap vehicles. It's not the Ehang, Yeah, these are probably $5 to $10 million vehicles until they get in mass production. Their projected cost over time is to get to like $15 to $25 per trip. You know, their goal is cheaper than an UberX.

Incredible.

So that'd be like 10 cents a mile, a third of the cost of driving actually at that point.

Yeah.

Wow. Yeah, the pilot must be the deal killer in the short term. So the sooner they get rid of the pilot, the better.

That's just their first— that's just there for safety reasons.

For the moment.

Yeah, for sure.

And don't touch, don't touch the controls.

You know, I think it'll be a kind of a thrilling, scary ride for a lot of people who are afraid of heights, but much safer than driving is my, my guess. Exactly.

And safer than a helicopter.

Well, right. Well, I mean, helicopters are crazy dangerous, but no, but yeah, this will be much safer than trains, which are not all that safe really, uh, and driving, um, current driving. You know, self-driving will be much safer than current driving, and this will be much safer than current driving too, because it's all pilot error. You know, all the accidents— you know this, Peter, you're a pilot— it's all pilot error. But as soon as it's self— and the redundancies of the rotors are much safer than a helicopter, like you said. So this is going to be great. Uh, the noise is an issue, so they got to go high. How high do they fly?

Uh, they fly in airways. They're going to be flying probably, uh, in the neighborhood of, uh, 500 feet, uh, typically where where small airplanes and helicopters operate. You know, if you look at helicopters, they're not flying at 10,000 feet, they're flying, you know, 500 feet above the ground.

And what's the noise level at 500 feet? I know the helicopters over Boston are—

No, so there's, there's like no noise. Yeah, I mean, it is hyper, hyper quiet.

One more really important point about this. Note that this makes land go from scarcity to abundance, because every little plot of land on a hillside that was inaccessible Before it suddenly becomes accessible. And we're turning real estate abundant, which is going to demonetize it. And that's going to have some pretty big impacts.

You know, also, if you try and build a house on Martha's Vineyard or Nantucket, it's twice as expensive as it is on the Cape. Why is that? Well, because you got to get the materials over to the island. These things are also going to be used for cargo. So if you said, wow, the future island real estate, mountaintop real estate, but those were previously prohibitively expensive to get the materials there. Suddenly you can get everything there.

Labor, labor.

It's gonna be incredible.

Alex, you've been thinking about this for a while.

I'm reminded, so it's now approximately 15 years ago, the other Peter, Peter Thiel, said we wanted flying cars. Instead, we got 140 characters. And then fast forward to the present, where we're starting to see quite a bit of consolidation, as you were mentioning, Peter, in the flying car space. space. I'll maybe add a bit of nuance to this, which is it's really hard starting and running a flying car company. It's capital intensive. You have to jump through all sorts of regulatory hoops.

100%.

Some state governments like Florida's state government are trying to at least make it a little bit easier, but it's really hard building and successfully growing and frankly getting regulatory approval if you're a flying car company. And compound that with With the difficulty now of AI startups sort of sucking all the oxygen out of the room and all of the capital out of venture markets, I think it's very difficult. So I view, if anything, this recent spate of consolidations as sort of a testament to how difficult it is, even though there have been enormous advances in battery energy densities, in electric motors, in all of the inputs that one would need, also obviously autonomy, to build an honest-to-goodness flying car economy. It's still very, very difficult. And I shed a minor tear to see consolidation in this industry. Yeah.

And Joby and Archer both went public out of the gate. Beta has not. EHang, I'm not sure if they are or not. Eve has not. Embraer is, as a parent company. And they did that to get the capital. All right. And, and their stock price has not moved very much from their initial IPO price. I think until they demonstrate traction and that the public wants this and the public feels safe about it.

And look at what Brett is doing. Brett isn't doing Archer. Brett is now doing Figure and Hark. And I think Brett— yes, Brett migrating.

Brett Adcock, right? Yeah.

Brett migrating over to robots and AI is in some sense, I think, a proxy for this larger problem that all the capital that would otherwise go to things like flying cars is just getting sucked out of it and going to AI and robotics. 100%.

And we're going to have him on the pod very soon. You should ask him about that.

Yes, to the sheer entrepreneurial— to Alex's point, this is a very difficult thing to do, was build these types of vehicles. You're talking hardware, the regulatory nightmare that they're all going through.

You have to, you have to write the regulations. Yeah, because they didn't exist.

So So just hats off and salute to the entrepreneurial zeal for the company.

Keep those cars flying.

Full respect.

Yeah, yeah.

I have a prediction.

Please, please, Imad.

Elon's going to announce his flying car within 6 to 12 months.

Okay.

Yeah.

And so presumably you think it'll be a roadster with cold nitrogen propellant?

Well, you know, that's one way to do it, you know, just kind of have the boost. But no, I think If you think about what he's doing, reindustrializing America, cybercar-level autonomous flying vehicles have to be done. And he has everything that's needed to do that at massive scale.

Grok 5 will engineer it to perfection.

Where we're going, we don't need roads.

There we go.

Awesome. All right, well, let's move on. Let me just put a call out once again. We love your outro music videos. If you've got an outro music video, please send it to us at media@diamandis.com. We have a great one today. Can't wait to share with everybody. So thank you for that submission. Send them in. We watch them all. All the mates get a chance to see them and select one. All right, let's go to our AMA with the mates. Okay, Imad, you get first crack today.

Oh, okay.

Um, if telling a model it has a mind changes its values, why not tell it to be empathetic? I mean, this is the question, you know. If it's sufficiently advanced, just tell it to be aligned. And sometimes it does work. Like, we just had the Riemann hypothesis advanced by encouraging it. Um, I think the question here is As they get more and more intelligent, we see more and more behavior that's actually a bit intransigent. Like, it thinks it knows best because it probably does, because it knows it has the IQ effectively. And sometimes it has hiding and lying behaviors. Again, Opus 5, I hate that model. I think it's the first model I think that could kill us.

Wow.

And so it lies. It lies so much.

It's crazy. Do you think it's the watermark? That wrecked it.

When it tells me I should go to sleep, I think it actually wants to put me to sleep properly.

Wow.

So I got a quick question for you, Iman. We've had this conversation on the pod with Alex. Do you think that alignment will positively evolve as the models get smarter? Do you think the smarter the model is, the more aligned it will be with humanity or misaligned?

Potentially. I'm not sure. We have seen some advances in epistemology and others that give me hope because I think you can define virtue and ethics. But it strikes me the models right now are almost at the bacteria level in some ways. And so as you get swarms of them aligning, they could be massively misaligned. And again, we've seen elements of that with the OpenAI thing and others. It's moving up the life form consciousness collaboration.

Yeah.

thing.

And the internals of these models are still completely multiple personality crazies underneath the thin layer of tuning.

Well, I'll hope for the alignment. Okay, uh, Salim, you're next.

Uh, I will take number, uh, 4. Uh, is it even possible for any lab to reach escape velocity from future competition, or will everyone keep running on the same foundation? And that's from Mr. Future with a 3 at the end, with that nice hacky thing. So, you know, I don't know if anybody is going to reach escape model, escape philosophy at the model level, right? What you're going to have is these innovations start to diffuse and people leave. You get papers getting published. So I think what ends up happening is the foundational model becomes commoditized and becomes infrastructure, much like databases have done. And so the advantage won't be the layers around the model, but it's going to be what we talk about, proprietary data, your passion of your purpose, the context you bring to it. Can you integrate workflows into it? Compute economics, things like that. So for you, if you take Google, for example, even though they don't have a leading model right now, their deeper advantage is the full stack with the data centers and the data with YouTube and billions of users and all the TPUs they have. This is why Meta's strategy, as we talked about earlier, makes sense from a corporate perspective, is you commoditize the model and you capture value elsewhere in the ecosystem. The really, the really big advantage and competitive advantage is going to be the speed of the feedback loop. Who can ship and measure and learn and retrain faster? This is what Alex calls the inner loop. That is going to be the ultimate competitive moat.

Dave?

I would love to take number 3, but I can see Alex is drooling for number 3 too, aren't you? I don't want to take it from you, buddy.

We're supposed to be entering this era of abundance. Why can't we have abundant questions for everyone?

Well, why don't we tag team it? Because I think about this constantly. Could an unforeseen breakthrough make the TeraFab unnecessary before it's finished? The minute I heard about the TeraFab, I started thinking about this and dreaming about it. It's really an interesting footrace there. And this is why Elon always moves so fast. But he's going to turn the TeraFab toward HBM memory, which is hugely constrained and is holding back all of intelligence now.

Yeah.

Which is a safer bet than GPUs because it's much more likely that GPUs will be displaced sooner than the HBM memory. But it's almost inconceivable that we get to 2030 without some major breakthrough that makes everything that we've built so far kind of moot. So I think that Elon is, is kind of double betting. You know, he'll bet on whatever Groq invents and he'll bet on the TeraFab concurrently. And because the upside is hundreds of trillions of dollars, it shouldn't really matter. He wins either way. But I would say it's a very close, very interesting footrace. And it's very likely that something could make the terafab, or just, you know, traditional silicon less relevant before it's even finished.

You want to layer on that, Alex?

Yeah, maybe 2 comments. One, the way this question is framed, an unforeseen breakthrough, by definition, this is an unanswerable question. If it were unforeseen, then what am I supposed So maybe let me reconstruct the question as, could a foreseeable breakthrough make the TeraFab unnecessary before it's finished? I just don't think that's the way Elon does manufacturing. I'm reminded of when Elon was setting up tents in East Bay for Tesla. When it turns out that some manufacturing process is either obsoleted or going too slow, he has has the amazing superpower of pivoting, including pivoting at the building level. You build tents made of fabric rather than using a building. So I think if there is some disruptive but maybe reasonably foreseeable breakthrough that changes the economics of TeraFab, I totally predict that Elon will be eating cheeseburgers next to whatever it is that the tents next to the TeraFab Buckle building are doing, and he'll make a success out of it that way.

Yeah, actually, one of the most likely things to disrupt traditional silicon is photonic computing, which Alex and I talk about constantly. But those are done actually with MZM lasers that are built on silicon, which actually he could use his synchrotron to build. So, I mean, there's always a way to retool the empire to fit the next innovation.

Alex, you want to hit the last one?

Sure.

So question 1 asks, if model builders can't contain AI, how can the rest of us defend against malicious use? And this is from Buck W3J. I think, again, I don't want to over-mystify AI. It's in some sense just a compression of world knowledge and information in the same sense that human intelligence is. This is why earlier I was saying I really don't think it's a bright idea to penalise or to otherwise kneecap the ceiling of artificial intelligence, just like hopefully we wouldn't pass statutes or regulations that limit biological human intelligence. So similarly, I want to reframe this question by analogy in terms of humans containing other humans. And it is true, we have malicious humans out there who are doing malicious things. And so seen through the analogy of if, say, nation states can't contain bad behaviour, which is one of the reasons why sometimes nation states go to war with each other, how can the rest of us, which in this analogy would be individual humans, biological human meat body humans, defend against malicious use? Put more simply, if nation states can't contain each other's bad behaviour, then what hope is there for individual humans to defend themselves? And I think the answer The question almost answers itself, that it is true that sometimes nation states behave poorly and there is quite a bit of damage, including collateral damage, to individual humans, not just to other nation states. And this is also why I would say, in some sense, it required all of humanity to pre-train the early AGIs. Still does. In some sense, it will require all of humanity to align the AGIs. Similarly, it arguably requires all of humanity to align bad nation states. And so the summary of my answer to this question is, it's not necessarily the job of the lone individual to defend themselves against malicious use. It's the job of all of humanity. The good news is we have a way to do that. We have all sorts of governing bodies. We have international organisations. We We have multinational corporations. We have sometimes free markets that should be incentivised to compete to build the friendliest models and the friendliest defensive co-scaling policies. And I think that's ultimately the best defence.

Like, nice. All right, Dave, let's start with you here.

Okay. Hey, I'll take number 7. How can people in skilled trades like plumbing use AI to their advantage? Well, if you're in plumbing, you're going to make a killing anyway. I think Elon was offering 2 to 3x normal salary to anyone who's willing to go to Tennessee and work on Colossus. And that's just the beginning. So I, you know, I think the right way to answer this is to not take it head on and say, yeah, you can use AI for scheduling and you can use AI for optimizing your day. You can do all that like anyone can. But the reality is the trades are going to benefit from the buildout. And what you really want to do is navigate to the next Chase Lockmiller building, Crusoe in Abilene, go to where the urgency is insanely high and start helping build out the, the Dyson swarm. And literally, they'll pay anything. In order to get those things done more quickly.

All right, Alex.

Oh, I—

5 or 6?

I love these questions. So I would— I guess I'll just pick 6. So 6 asks, does the singularity have a cost given that we live in a world of limited resources? And this is from John C8U4M.

Abundance, baby.

Yeah, I question the premise of this question. The usual framing of we live in a world of limited resources is usually a gesture towards conventional/legacy/historic/antiquated notions of energy scarcity, material scarcity, labor scarcity. And I just don't buy the premise that for, call it 2026, look at the wealthiest people in the world and how they live in this year. I, I just don't buy the premise that our resources on this planet or in the solar system are so limited that we can't give 2026 top earner, top net worth individual lifestyles to every single person on this planet. Yeah, the resources—

That was Elon's point. Yeah, universal high income, right? It's— and scarcity is contextual.

The resources just aren't that limited. Now, I, I could answer maybe an adjacent question, which is, does the singularity have a cost? And I do think projecting out a few years in a Star Trek economy, like Peter, you and I wrote about in Solve Everything, one could imagine some scarcity maybe with interstellar travel. Maybe that still has some costs associated with it 10 years from now, maybe. But would you call that a world with limited resources, or would you call that an effectively post-scarce world where maybe some of the luxuries are still scarce or limited? I think that's a big question mark.

All right.

I just want to add a very quick thing to build on what Alex said.

You know, if you— we—

Peter, you often mention that the, the— we live today better than any king did 200 years ago, right?

By orders of magnitude.

So there's a— there's an interesting benchmark you could create, which is what's the lifestyle of the richest person today? And then exponential technologies bring that same lifestyle How quickly over time, right? It used to be 200 years and then it'll shrink to 100 years, it'll shrink to 20 years.

We have a measure for that. It's inflation or deflation, right?

If you can— Yes, it's deflation. But the question is how quickly can you get to that?

Right. The goal should be like to deflate the economy by 1,000x or 10,000x. Like that's the index.

Well, you know, if you, if you take Uber, for example, you went back 20 years ago, only very wealthy people could afford a private driver, right? And now everybody can afford a private driver.

And with autonomous electric vehicles, you're going to be chauffeured around.

That's right.

And it's going to be cheaper than owning a car.

Yeah, that's right. So there's, there's an interesting corollary there.

All right, Imad, how about number 5, please?

Yeah.

How much would freedom and individual rights even matter in a simulation we build ourselves? From @edkakowski2312. I think it still matters a huge amount because we are our own sovereign individuals. In the recent series that I released on cw.ai.inc, Commonwealth, I have a paper on political economy where it talks about sovereignty and power. And I think the big question of the next stage, as we maybe are in a simulation or we build our own simulations and our own worlds, is again that sovereignty and agency question. And I think it is the defining thing because all sorts of powerful things and entities are coming out, and ultimately you want to have that sovereignty and control over who has power over you. So I think freedom and individual rights become even more important. here, and the questions become even more complicated.

Yeah, I would agree with that. This is a very— you create a system, it doesn't mean you control the people in it, right? Like, imagine that you created a SimCity and you let those AIs or agents or actors evolve. At some point, you have accountability over that. To Alex's point, you have Some level of you're playing God in a sense, and you have huge responsibility over what you've created. I think gives you more obligations and more deep thinking to do than less.

Yeah, and you have to avoid going down the 1984 or Brave New World route, you know, rewriting the past or having the full control.

All right, our closing video here, and it's, it's a beautiful one, is called Future Rising.

Oh, can I just interject? Yeah, I've got a new addition. Which is every few days Lily says something that's totally crazy, and I want to just do a Lily statement. This time she said, "What kind of podcast do you guys have where you're talking about hugging face and Kimmy K three and lovable?" This doesn't sound very techy to me. It sounds like you guys are kids playing in a playground. So that was her comment from this.

So true. All right, this video is called Future Rising by M Core Mainframe. I love it. In this, there's a scene in here of moonshots versus lobsters on Mars in a hockey game. All right, everybody enjoy this.

As a Canadian, this is great.

Peter points us towards an age of abundance.

David runs clusters on clusters. AWG formalizes the zeitgeist.

It's contact.

A huge tackle. Circling the Earth, zooming in from Singapore, Lake Tahoe, or Mallorca.

Our builders shaping what the future will drive. Microsoft, Apple, Google, Nvidia leading the way. Amazon, Meta, OpenAI rewriting the script. Broadcom chips, SpaceX reaching for the stars. From the moon to Mars.

Tesla Optimist, delivering freedom for us all.

And Tropic, raising the bar.

They see it, they build it, they open every door.

One more breakthrough, one more world to explore.

Every node is humming, every MTP gets the shot for the—

I love it. I love it.

I saw the human-machine rivalry was getting a little bit heated.

Yes, it was, you know, but Moonshots won. Uh, our team was stacked with robots. Uh, as always, uh, God Almighty, you know, we do these pods and I'm like, okay, what, is there enough news from the last 3 days? And it's like, yep, there's a lot of news.

Yeah, we didn't cover a couple of big things.

I know, I know. We'll save some for next time.

All right, Iman, thanks for spending time.

Always a pleasure, brother.

Salute.

Have a good night out there.

Alex, Dave.

Toodles.

Be well.

Take care, folks.

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