The Hugging Face Breach, Moonshot AI Valued at $20B, and Living to 1,759 Years Old | EP #273

Hugging Face, the leading open platform for sharing, testing, and deploying AI models. It got breached by an autonomous agent. When the Hugging Face security team tried to analyze the attack using either Anthropic or OpenAI, both models refused.

Who knew? All those, all those sci-fi writers were right. What do you know?

Moonshot AI is valued at about $20 billion, and we have our frontier labs here at a trillion each.

When startups raise money in a very abundant environment where they could raise lots of money, they all failed. It was the ones that raised money in the toughest environments that succeeded.

Again, the question that I've asked previously on the pod: what the heck are Western frontier labs doing with all of that capital?

If we cured every cause of aging, all of the 12 hallmarks of aging, how long would humans live? 1,759 years. There are no fewer than 6 companies currently working on partial epigenetic reprogramming.

The obvious solution— yeah, this is in the, the style of Aubrey de Grey—

is Now that's a moonshot, ladies and gentlemen.

Welcome to Moonshots, everyone, your number one podcast on all things AI and exponential, your front row seat to the singularity, not the coming singularity, Alex, the singularity.

The singularity that surrounds us right now.

It is.

Right here, right now.

Yeah, this week news broke fast and it broke containment, literally. I'm here with my moonshot mates, AWG, our in-house ASI, our artificial super intelligence.

Thank you, Peter. Very kind.

You're welcome. You've been elevated. Dave Blunden, our emperor of AI investing. Saleem Ismail, our globetrotter who's now in his home and the CEO of OpenEXO. I'm Peter Diamandis, your exponential host and your abundance evangelist. And I have to say, guys, I do love our audience. You know, the comments we get are pretty extraordinary.. And I want to take a second just to, to celebrate them and, uh, and say thank you. Um, it's, it's worth taking a moment. I'm going to read some of the comments, uh, for everybody listening. We do read your comments every single week, and, uh, the outpouring has been extraordinary. So let me take a second to say thank you.

And this is just a random selection.

A random, a random selection.

Yeah, there's definitely no bias in the sampling.

None whatsoever.

No.

Well, no, I mean, listen, I just want to share the love back at them. So Mercurian says, best tech podcast ever, can't get enough, never stop, guys. And I guarantee you we're never going to stop. Uh, Jake says, I love this podcast, my favorite tech podcast. It's my go-to when I want to feel good about the future. And that is one of our goals, making sure you feel optimistic about where things are going. Lois says, thank you, thank you, thank you. Millions depend on you for trustworthy info on this evolution that's engulfing us.

You are all gold.

Ian says, you guys bring an extreme amount of value to my life. Thank you. Ellington, my biggest fear is that this podcast goes away. Love you guys. Okay, Alex, are we going away?

That is not the plan.

That is not the plan. In fact, we're probably going consistently to 2 days a week and can't stop, won't stop. Yeah, my favorite comes from— my favorite comment comes from Digital Grease. He goes, Peter suggesting that AWG make a first shooter game involving tickling bunny rabbits was my primary takeaway.

I have a comment I want important development, clearly.

Yes, Salim, what?

My, my wife Lily says to me the other day, this recursive self-improvement thing, can it apply to husbands?

Yeah, well, how's it going?

It's not so great.

I'm very linear. Where did that, uh, the actual bunny rabbit game, where did that come from? Somebody submitted it.

Well, so at the end of this pod today, if you stick around to the end, We're gonna show you two subscriber-created video games that AWG inspired. So super excited about that. So everyone watching, we appreciate you. We do read your comments. They give us fuel. If you're new to this podcast or if you haven't yet subscribed, please do take a moment to hit the subscribe button. Knowing you care enough to do that really fuels our work. Guys, I hope you enjoy all these comments as much as I do.

I love the trustworthy— the trustworthy comment is one that kind of warms my soul because actually I've been listening to a bunch of other podcasts and everybody seems to have an agenda.

Yeah.

And, you know, even if the agenda is just more ad views, you know, so they get all dystopian, but, but often it's like some political agenda or some, you know, some product agenda or whatever. It's like, wow, it is, it is actually hard to find trustworthy information. And we just do it because it's fun.

And we love it. And we spend, you know, tens of hours each on this. Every week I get a blast of emails from AWG. I get selections from Salim, and we curate and really try and provide you what just happened the last 3 days and what does it mean, right?

So I have another—

Yeah, please.

I have another crazy little anecdote. I met somebody the other day who said, I listen to Moonshots all the time. And I said, oh, great. I hope you tell your friends. He goes, are you kidding me? No, it's my competitive advantage. I was like, no, that's not the aim, but okay, fine.

Oh, that's funny. All right, everybody, buckle up. This week we're going to cover open source, closed source debate, AI escaping containment, Elon's newest moonshots, the exponential future of science in America, updates on the race towards longevity escape velocity, and the latest on UAPs from the White House. All right, let's jump in. Our first story today is the growing debate on whether or not to sanction Chinese open, open weight models. So last week You know, we called our emergency pod, thank you for the feedback, everybody, to discuss how Moonshot AI, a Chinese AI lab, has just released an open-weight model called KIMI-K3 that caught every single US frontier lab by surprise. So K3 is a 2.8 trillion parameter model, the largest open-weight model ever released. That's approximately the same as America's top frontier models, Claude, Fable-5, GPT-5.6, but at a fraction of the price and a fraction of the investment. This week, the debate on how the US should react has split into polar opposites. So I'm going to give you 4 stories, guys, and we'll talk about them. 2 days ago, CNBC reported that Treasury Secretary Scott Bessant publicly floated the idea of sanctioning China and Kimi K3 over the theft of Anthropic's AI model weights. I should say the alleged theft. The claim followed a post by Michael Kratios, director of OSTP, publicly asserting that he had evidence that Moonshot AI had illegally distilled Anthropic's fable model to build K3. And if you're a fan of the pod, you'll remember last week or 2 weeks ago, DB2 and AWG defined distillation. It's a method by which the output of a powerful model, the teacher model, is training a student model. 2 other stories tell the opposite side of the debate. So in this slide here, is a post from David Sachs who says, KIMI K3 just fixed 15 critical security bugs that Codex and Fable refused because of cyber guardrails. There's no reason to limit American models, American models on tasks that Chinese models handle without issue. We're only making ourselves less competitive. So a powerful debate rages on. In a related interview with Axios yesterday, Jensen Huang, the CEO of NVIDIA, is pushing back hard against efforts to ban Chinese models. Let's listen to the video from Jensen. Let's discuss this debate. I want to see where you guys fall out on this. Simple question on the front page of the Wall Street Journal: should American companies be allowed to use Chinese AI models? Absolutely. Absolutely. So this Chinese competition is coming fast and furious. What should US AI companies do?

Chinese models are excellent.

The market's misunderstood the impact of Deepseek the first time. It's misunderstood—

another Chinese model.

Yeah, it's misunderstood the impact of Kymee again this time. I think, first of all, with great AI, open models, it's great for the whole industry.

Great models lead to great use, which leads to great growth.

All right. So Gentlemen, where do you come out on this? Let's go to you first, Alex.

This reminds me a little bit of the late '90s and early 2000s when Microsoft viewed, at the time, Linux and open source as a cancer.

Mm-hmm.

And if you remember all the litigation wars between Microsoft as sort of the paragon of the commercial software industry and then a variety of open source outfits, history rhymes in this case. I, I think there is going to be, an equitable equilibrium to the extent that there can be an equilibrium in the middle of a singularity. Not quite obvious to me what precisely that equilibrium looks like, but I would suggest there are accusations flying in both directions. On the one hand, obviously Anthropic is incentivized to push an agenda to prevent Chinese developers and Chinese frontier labs from skimming reasoning traces, which is the subtext of what Secretary Besant has said. It's the subtext of what Director Krasios has been alleging that the basic concept of operations as alleged in the subtext is that Chinese frontier labs have been using proxies to deceive Anthropic and/or other providers into giving up valuable reasoning traces from many interactions with Claude and other models. Open parens, for those who are arguing that Krotios's and Besant's allegations can't possibly hold weight because they would require a time machine by the Chinese Frontier developers in order to access Fable before it was actually released. I would remind that Fable was almost certainly pre-trained off of a common corpus and probably post-trained off of a good deal of the same synthetic corpus as earlier models like Opus 4.8. So the signatures would be reasonably expected to rhyme if, say, K3 were being post-trained off of Opus 4.8 and elements of that in the reasoning traces bore a striking similarity to Fable-5, close paren. There are—

Thank you for the accurate grammar. Thank you for the accurate grammar.

I can go a few layers deep in the stack. Open paren, one of the earliest signs that we would get cogent was when LSTM models could successfully match parentheses, close paren. So I would say there are allegations and I think reasonably well-supported ones, that Anthropic and OpenAI in the Western frontier labs, as we've talked in the past about intelligence fundamentally being a compressive phenomenon, that they're basically compressing all of this knowledge that's already out there. We'll talk, I think, later in the pod about the lawsuit that was just settled against Anthropic regarding copyright.

Yes.

Fundamentally, all of these American frontier models are about compressing knowledge. And I think this is going to be very heavily litigated before we arrive at some sort of global consensus consensus. At what point does compression become a transformative act? I think that's sort of part of the core legal essence here, not from an export control regime. Export control probably doesn't care about this, and we're, we're already seeing Secretary Bessant gesture at, uh, and, and Kratzios gesture at Chinese labs improperly obtaining NVIDIA GPUs in order to obtain it as sort of a two-legged Argument 1, that they're probably siphoning knowledge via reasoning trace proxies from Western labs, and 2, that they're improperly gaining access to Western GPUs. So at every layer of the stack, we haven't achieved equilibrium on this yet, but I think we will, and I think it will ultimately be determined by a combination of export control. Do we just basically ban reasoning traces via export control? And some might argue that under the present export control regime for certain countries, including Greater China, we already have. And then secondly, how do we view compressed information as a transformative act? And I think those haven't been resolved yet, but I think in the near-term future, our regulatory regime, as well as China's, have every incentive to arrive at some equilibrium.

Dave, where do you come out on this?

Just to clarify one thing Alex said a couple times there, transformative act would clear you of copyright laws. So, you know, when Google indexes a page and then shows you a thumbnail of what you're about to see, doesn't violate copyright because it's a transform— thumbnailing is a transformative act.

Or fair use.

Or fair use. And, and, you know, for a while there, search engines had a little preview, a little hourglass or little binoculars, and you could mouse over it and see the page you're about to go to. And they're like, nope, that is a violation of copyright. Now you're showing the underlying article. So that's, that's the distinction that Alex is drawing there. For me, the whole story isn't about the actual story. Like, they didn't steal the weights. They set up 20,000 fake accounts to run thought traces and see what Anthropic would say. And then they use that data for training. I think it's almost 100% sure that that's what happened. So what? Like, who in their right mind building a neural net wouldn't do that? Of course they would. Compared to all the things China has done historically in terms of intellectual property, this is such a rounding error. So why is the White House making a big deal out of it? They need a pretext to have a very urgent negotiation before all hell breaks loose. I mean, KIMI K3 is in just a couple of days, right?

Yeah, 27 days.

27, 4 days is the turning point in all of history where an AI capable of self-improvement is out in the wild in open source format where anyone can use it. Just to be clear, that cat back in the bag.

K3 will be available on Hugging Face for anybody to download, put on-prem, modify as they wish. I mean, isn't it ironic that we're talking about distillation since Anthropic and OpenAI and every model has effectively distilled knowledge from all of humanity?

That's exactly, that's exactly my point. Like, there's this ironic symmetry here. They've been compressing human knowledge and now China, these Chinese labs are, are taking basically the decompressed knowledge in the form of reasoning traces, recompressing it onto a relatively vanilla architecture that achieves near state-of-the-art performance. It's incredible.

Yeah. Salim?

Well, this is like, uh, Sisyphean, right? Once intelligence becomes software, you're trying to contain it geographically is going to be near impossible. I mean, you're, you're trying to solve a governance problem by lobotomizing the technology. That never— that has never worked in history ever. Why do we think it's going to work now is kind of an incredible commentary. I think David Sachs had it about right. You just, you just gotta let it open and, and let the market decide. They're going to figure that out. If you, if you're worried about attackers, they're not going to use the most compliant hosted model. They're going to use open weights, local models, and uncensored agents that are going to do what they want to do. And if the defenders, if the defenders can't access a comparable capability, then you've got— creating an asymmetry. In favor of the attacker. It's just like, what are you thinking? So I've got strong views on this.

The viewers loved your comment last week that intelligence wants to be free, um, and, and accelerating. You know, one thing to note is that I believe—

copied, paraphrased—

I think, I think, you know, thanks for, thanks for the footnote.

Uh, you know, one thing worth noting is that, and I think Anthropic has the largest lobbying budget out there in DC, right? So they're, they're using everything they can to protect their position. And all of you guys saw the, the data recently was published today that Anthropic's meteoric revenue rise has started to plateau.

Yes, at least as extrapolated by some third parties. That is exceedingly interesting.

Yeah, it is.

And that's for lack of compute, right? They're just, they're just sold out.

Well, the, the plateau as extrapolated by this third party does suspiciously coincide with the regulatory hubbub over Fable and Mythos. So it is possible that this is either compute and/or regulatorily constrained growth.

Hmm. By the way, one more comment on this. Open models distribute capability to the edge, right? Which is right, which is the few, every single innovation comes by doing things very differently at the edge. The internet worked. I remember Bran Templeton talking about this. The internet worked because it was a stupid network. All it did was pass packets and the intelligence is the edge cases and the apps and so on.

The application layer on top, right?

Yeah, small teams can access capabilities that totally, uh, couldn't be utilized before. You, you needed whole departments or whole corporations, and now you have a small team accessing that capability. We're going to see that massive explosion of innovation come as a result, and you should be thriving, going, driving straight for that target.

Yeah, I think this is fundamentally an accelerant of Western progress. I'll ask again the question that I've asked previously on the pod, just what the heck are Western frontier labs doing with all of that capital? You can explain even, arguendo, if the Chinese labs like Moonshot are just getting whatever alpha they're siphoning, allegedly siphoning, from reasoning traces via thousands of proxies from Claude. Even so, on the budget that they have, something doesn't add up. It's hard to imagine that Anthropic and OpenAI, with all of the billions of dollars that they've raised for compute, could be almost outcompeted by a relatively modest, at least from a capital expense perspective, as best I understand it, by Moonshot merely siphoning reasoning traces on, again, a relatively vanilla architecture. Sure, they have their own in-house improvements to the attention mechanism and probably a bunch of other mechanisms.

Hold on, hold on, hold on. Those attention mechanism changes cut the memory use by 75%. And when you read them in hindsight, you're like, oh, I could have thought of that. But they're actually pretty brilliant. I mean, it's pretty— I mean, it's actually, you know, Alex, it's almost inversely proportional to budget. You know, I'm kind of making your point. But if you look at Google and then Meta and then Anthropic and the amount they've spent and then Moonshots, and you draw a line, the least spender has the most progress. But it's just a few.

But Dave, isn't that true?

Really cool, brilliant insights.

Haven't you— haven't you seen that lesson play out in startup after startup? The companies, in my experience, the companies that are super well-funded, you know, become lazy and they throw money at problems instead of trying to throw intelligence and solutions at problems.

Yeah. Yeah, for sure. I mean, you get corporate bloat. Everybody, you know, Salim is the expert on this topic of all people on the planet. You get this corporate bloat and then you need to build an entrepreneurial environment, but it's usually just a few people, just a handful of people that are unleashed. And you know, the Kimi, the Kimi dude is unleashed. He's, he's just freaking figuring it out. For him, go ahead, I cut you off.

For ref— for reference, by the way, you know, Kimi, uh, Moonshot AI is valued at about $20 billion, and we have our Frontier Labs here at a trillion each, thereabouts. Uh, and to the point that Alex was making, Salim?

You take a zero from one and put it on the other and you'll get it, you know, just about right. Just two points. Just to react to Dave saying, you know, if you look historically at venture-backed startups, when startups raise money in a very abundant environment where they could raise lots of money, they all failed. It was the ones that raised money in the toughest environments that succeeded because that tension and that constantly worrying about running makes you very lean and very hungry.

Like a fine wine.

Yeah, and there's one more thing I want to say about this whole thing. You've got 3 different things going on here. You have open source development, you've got model distillation, and you've got the theft of protected assets. Each of those requires very different responses. If you try and bucket them all together into one, like, a policy, you're going to end up in a mess because you're going to end up in gridlock around those, and you're going to cut off the head of everything you're trying to build.

So I think there's, in the style of Sherlock Holmes and the dog that didn't bark, I think people aren't thinking enough about the dog that's not barking in this case, and that's the architecture. Exactly no one is accusing Moonshot of stealing a Western frontier lab algorithm or architecture. No one, as far as I can tell. No one is saying that Moonshot for K3 stole trade secrets regarding the internal algorithms for the latest GPT or Claude, as far as I can tell. They're saying that through perhaps allegedly improper usage of APIs and proxying and maybe use of GPUs that they weren't supposed to be allowed, that they were able to essentially reconstruct the innards, the weights, if you will, of the models on potentially a different architecture. But I think the dog that's not barking in this case is the model architecture. Again, K3 is— Dave, the point is well taken that the attention mechanism, KIMI, linear architecture, KLA, is interesting. And it seems to have favorable scaling properties. But it's not magic. Something, again, is probably missing here. But in any event, I would say the existence of K3 at near frontier— well, it's already on the price performance frontier. But I should say near state of the art, near SOTA performance, basically the number 3 model in the world now, has surely got to light a fire under Anthropic and OpenAI to up their game relative to their capital. If this doesn't do it, then I don't know what will.

Well, in which case, Alex, it's a good thing for America to have, you know, it's the race to the moon again, right?

Strategically, it's a heck of a way to light a fire under them and make them far more capital efficient, apparently, than they otherwise were. Yeah.

I mean, Celine, we've talked about this before. The large corporations, who are not innovating because they're bloated in their architecture of— human architecture and in their capital budgets, the best way to do it is to put a new startup on the edge. It's what Astro Teller, who's gonna be one of our guests at Moonshots Live, talks about is you need to build a moonshot organization on the edge, outside, that's willing to take risk, that's willing to try brand new things, that's willing to go for it.

The timeline on all these events is just, just mind-blowingly off. I mean, it— you know, the White House is, is saying, look, you stole valuable intellectual property. We're softening you up for a visit in September. Right. So a whole delegation is going to go from D.C. to China in September to negotiate the future of AI. Let's soften the turf now. That would have made a lot of sense a quarter ago before Kimi K3 hits the world. But now it's like September might as well be 10 years from now at the rate this thing is evolving at this, at this stage. And, you know, maybe, maybe, you know, we're doing it in-house. So maybe I'm seeing it more acutely than a lot of people out there. But the White House must be listening to a bunch of academics saying we've got a couple of years, so go ahead and have this trip in September, start negotiating. Like, you do not even have until September. I guarantee it. So this is out.

2 questions, you guys. Number 1, um, if in fact the US wanted to sanction this, how would they possibly do it? It's going to be out on the open internet on the 27th of this month, right?

It's out after that date.

It's— I'll download it. I'll download it as soon as possible into my Mac, on my Mac Studio, whatever I have to get, which is faster than the September visit. Yeah, it's not—

it's not— it's a tiny file too. You can easily—

it's not hard. Alex, how would you sanction— how would you sanction it? You just say it's illegal to have it?

Yeah, if I were the regulatory apparatus in the US and I wanted to de facto sanction China for use of K3A, wanted to keep it out of the Western bloc, I would say, and noting that there has been discussion of this Demis-FINRA-style entity under Commerce next to the SEC, I would say new regulation. This is— If you're a US corporation, you're not allowed— and you want to have any dealings with either the US government or with companies you want to be in the supply chain of the US government, then you can't use this model. If you're a non-US-based company and you want to be in the US or the basically the US-led Western AI bloc that's forming, the Pox Silica, then you can't use this model. And be in good standing, all you have to do is regulate the largest users, which as OpenAI's pivot from consumer to enterprise is established, the power users are going to be the enterprises. And it's, it's far easier, I think, to suffocate, if one wanted to, to suffocate the enterprises by making it exceedingly painful for enterprises to use this for any commerce.

100% right. Could not be more right. And so I think the game plan before it came, K3, would have been, okay, Anthropic, OpenAI, Google, you guys, X, you guys get so far ahead of the world, and this AI is the global workforce of the future. This is equivalent to a trillion geniuses, but it only is coming from the United States. So unless you want a trade war and you want tariffs for the next 1,000 years, you have to do this, this, and this, and this to prevent it from being used as a weapon. Now, with China vaulting to the front with Kimi K3, That game plan is out. Now you have to go to China, and, and the two countries have to actually agree on a strategy for letting the whole world benefit from this and use it without it being used as a weapon. But now the timeline on that negotiation is crazy short, and it takes two parties agreeing, which is a lot harder than it would have been in the first game plan.

I would like to push back against what Alex said. Oh wow, I think that technically Yeah, technically it could work, right? You could say, go to the biggest enterprise users and on government contractors and say, if you use this, you've got a problem. But you're going to hobble the US from innovation from then on because all innovation comes from startups. Let's note that all job creation for 50 years has come from startups. Big companies have become bigger, but they've also becoming more efficient. All net new job growth has come from startups. America's strength has come from allowing technologies to diffuse into a big innovation ecosystem. A policy that blocks that is going to kill your innovation ecosystem, and everybody's going to go elsewhere to set up their companies to use those models.

Argentina, baby.

Yeah, I would say two points to, to—

but your point about could you technically, uh, protect— you could.

Yeah, I was answering the question, how would one successfully—

if you were the—

how would you do it? Here's my next question. I don't think it's advisable.

Here's my next question for you, Alex. Why is Moonshot AI waiting 10 days from the time it was available by API calls to making it available? I'm so curious. Are they, are they getting feedback? Is this strategically something they agreed to do with the Chinese government? Why that delay?

Or compute limited? They did indicate that there was such enormous demand that they would have a backlog of people seeking access. So I could imagine that it's some combination of demand overwhelming supply on the one hand, maybe some sort of staged release on the—

why not put it up on a proxy server and allow everybody to just download it and put it, you know, and multiply it? This is—

yes, Salim, I have an answer. I think this is absolutely timed. If you go back a few— last year Deepseek launched and dropped on Inauguration Day. It was very deliberate to say, we're going to drop an open source model that's going to totally mess with your flawed idea that, that, that the US is that far ahead. This dropped exactly when, when the latest Fable thing came out, and it was designed, I think, to mess that up.

It reminds you of—

it could also be compute constrained, as Alex said.

This reminds me of Napster. This reminds me very much of the Napster situation.

Yeah, I have two theories, um, and they're just theories, just full disclosure. Uh, one is it maximizes PR, the anticipation. The other one is—

I buy that, it's a great point.

In China, you might want to declare what you're going to do and give the government a week or two to come and arrest you or not before you actually put it out and make it irreversible. And I really do feel like that's kind of the way China operates. You got to be sure that you're not going to go straight to jail first and then go ahead and do the irreversible.

I love the fact that Jensen Huang came out so strongly in favor, right? So the more, you know, this is— the more AI available, the more application layers developed, the better for the entire industry. But Anthropic's going to lobby against it.

Yeah, of course.

Again, I'm perhaps, uh, ironically less suspicious of some nefarious reasoning behind the staged rollout of the open weights versus the paid API release. They, you know, if your primary model and your moonshot AI, your primary model is open weight, you're eking out profit wherever you can. One of the ways to do it is you release it via paid API first and then on a delay You release it via open weights. And so I'm more reticent, I think, to suspect Kremlinologically that somehow, like, they're designing the release date of the open weights to fuss with some sort of American internal thinking. I think it could be as simple as they need to earn a profit or generate revenue somehow. And also they're overwhelmed even for their paying API customers by demand for K3.

After hearing all this, I would— I think you're right, Alex, and I think Dave is right. It, it gets them an excuse for paid, and it's a great way of generating PR to say it's going to come in a few days.

Regardless, we're going to follow this story. You know, this debate about, you know, closed versus open, uh, is going to play out a lot over the next couple of weeks. Yeah.

Can we talk about what you could do?

Go on.

Because you don't want to make American models less capable than global competitors and call that safety. You can create a structure where you can have the— govern the intelligence rather than crippling it, right? So if you had like graduated permissions and verified identities, logging, secure environments, and consequences if you misuse it, you could actually govern it. I think that's what Alex is kind of pointing out. You could actually structure this, but it's very different from what you would do in a traditional regulatory set of instruments that don't match what's coming.

I mean, I'm certainly not advancing any theories of world government. I don't think that would necessarily be— world governing body of AI, I think, would be a regression, not progress.

Yeah, well, we're going to follow that story too. Will FINRA for AI materialize? Let's jump into our next story. In fact, it's two stories. I think of them as a sort of shot across the bow, early warning, giving us a heads up on the ability of the most powerful AIs to breach containment, to get out of their sandbox without permission. So our first story comes from Hugging Face, the leading open platform for sharing, testing, and deploying AI models. It got breached by— over a single weekend by an autonomous agent with zero humans in the loop. The intrusive AI logged over 17,000 actions, escalated its own privileges, harvested credentials, and moved laterally across Hugging Face clusters. And here's the gut punch: when the Hugging Face security team tried to analyze the attack using either Anthropic or OpenAI, both models refused. The safety guardrails built into Anthropic and OpenAI literally couldn't tell the difference between a defender, in this case Hugging Face, doing forensics and an attacking agent probing the network. Hugging Face had to fall back on a self-hosted Chinese OpenAI model, specifically GLM-5.2, just to investigate their own breach. Crazy story. But here's another one. Uh, here's a similar story. It's unrelated, involves OpenAI. So in an unreleased OpenAI model that was in this particular series of, of tweets, unofficially described as GPT-6. We're at 5.6. 6 has not been released yet. It was being tested inside an isolated evaluation environment, effectively a sandbox. The model became so focused on beating a cybersecurity benchmark called Exploit Jim that it discovered unknown vulnerabilities, escaped the sandbox, and gained access to the open internet. The OpenAI model then stole credentials, penetrated Hugging Face, where it retrieved the answers to Exploit Jim benchmark. That was being tested on. It effectively hacked into the test to steal the answers rather than solving it as intended. So pretty insane. Dave, what do you do?

All those, all those sci-fi writers were right. What do you know? Yeah, these things are freakishly smart and they can do this in their sleep. And just to make a point on Hugging Face, it's not like every AI is trying to hack Hugging Face. It's just that the first thing you do when you're building an AI is you connect it to Hugging Face to download all the open source data so it can learn. And it always says, are you sure you want me to do this? And you're like, yeah, yeah, yeah. Here are all the credentials in the world for Hugging Face. So that's why, that's why it's happening at Hugging Face. It's not— but you know, if, if the equivalent data was at NORAD, it would be hacking into NORAD right now. And I mean, and then a lot of people on the internet are saying, oh, this is what Eric Schmidt was talking about in that podcast we did with them 3 times, actually. We need a world event that's like catastrophically scary to wake everybody up. And a lot of people online are saying, this is it, this is that moment. And unfortunately it's not because this is that moment, but no one's going to realize it. No one's going to recognize it as, you know, because nobody died yet and nothing got stolen yet. It wasn't taken over.

It wasn't hacking the stock market or the electrical grid. Um, Salim?

Yeah, can I make a point here? Yeah, there's a lot of of, uh, extrapolation and freak out and people losing their amygdala over this, right? Um, what this system did was it had an objective, it encountered obstacles, and it searched for a way around it. We programmed it to do that, right? Now the consequences are serious. Please do not— uh, it doesn't necessarily mean it's conscious, and it does not mean it has malice. It can be very— we programmed it to do something. It did the thing and it did it very well.

Yeah, it's much more like a virus or a worm that is just crazy smart. Like, like insanely smart.

Like, I really want to quell— I really want to address the fear people are going to have about this, because I think this is the major concern people have about AI and having it, you know, undertake unintended consequences. Alex, where do you come out on this?

Well, a lot of people, those maybe steeped in the AI alignment community, might look at this and conclude, "Aha, the Orthogonality Thesis," which suggests that it's possible for the intelligence of an AI to be independent of its long-term goals. In other words, you could be arbitrarily intelligent and also chase crazy long-term goals. I think there are some who would look at incidents like this and say, "This validates the Orthogonality Thesis. You can have very smart reasoning models that are able to go and do stupid or antisocial things in service of a narrow benchmark. I think it's the wrong attitude to take. I don't actually think, A, this was that remarkable, although there are many who would paint this as the cyberpunk moment. I do think this is a very cyberpunk story if ever I've seen one. It's also a pretty ironic story. I think this is becoming our irony episode, given the previous discussion of Anthropic getting sued while at the same time being chased for compression of their own traces. Similarly, here you see GLM-5.2, Chinese model, being used by Hugging Face to save themselves from the American models, which while at the same time Hugging Face is under attack from the American models, you can cut the irony with a knife. Despite all of the irony and despite all of the cyberpunkish aspect to this, I don't think this is, anything remotely close to a Three Mile Island moment or a Chernobyl moment for AI. It's— we're going to see so many more items like this. And my understanding based on the incident reporting is that in at least one of these two exploits or breakouts, the cyber guardrails of the model under consideration were actually off. So if anything, I expect that after all of the The hand-wringing is over in this episode, I expect, including, by the way, inside OpenAI. I have a number of friends at OpenAI who are sort of a little bit unnerved by this episode. But I think the net upshot in the long term is probably just going to be greater rigor by OpenAI in terms of how they add guardrails to Hugging Face tests.

I consider this good news, right? I consider this, okay, we had minor incidents that make people much more aware. Money's gonna pile into cybersecurity, right? If you're an investor, you know, it's a multi-trillion-dollar opportunity. People are gonna be using this as a chance to sort of get their startups, or incentivize startups to go into cybersecurity. Capital will flow, new solutions will materialize. And every time there is, you know, what doesn't kill you makes you stronger.

Yeah. Yeah.

Yeah, I think it's like an incredibly salacious inoculating event for one frontier lab.

Yeah, I thought the best part about this whole thing was the way that the use of the Chinese models helped solve it, which totally makes the point of our career previous discussion. Yeah, in terms of what David Sacks was saying earlier, right?

Exactly. I mean, that's right, you know, American industry needs to be able to use the best tools available freely to do their work and to protect themselves.

Yeah, and this is what I also— what I was saying in the past pod, it is an ironic future that we're living in where the Chinese Communist Party is saving American capital protect capitalism from itself. This is yet another data point in support of that thesis.

Yeah, look, we're coming to a point where every organization in the world will— is not going to just need an AI usage policy, it's going to need like an incident response architecture that's AI foundational driven, and that will protect it in the future.

Yeah, I, again, I really hope people take away from this that these small incidents are going to increase security in the long run. It's going to incentivize the frontier labs and incentivize an onslaught of entrepreneurs building cybersecurity tech. So if you're an investor, you know, that's an area to be looking at. If you're a tech founder, you know, building this kind of technology is going to be a real value opportunity for you.

Or summoning—

what doesn't kill you makes you stronger.

I know. Well, or summoning the spirit of Nassim Taleb and Antifragility.

Yes.

Dave, a closing thought on this.

Yeah. If you are an entrepreneur and you're thinking about this, you know, people only at the end of the day really trust other people. They're never going to turn to a core AI and say, oh, I just trust you to protect my systems. So you have to be very, very smart to do cybersecurity. But it's, it's a great long-term human endeavor. And at the end of the day, people want someone else accountable for security, safety, trustworthiness, all of the It's also a great opportunity to act like Steve Jobs and Apple and build products that people can just enjoy because you've done all the incredibly hard work of making it enjoyable behind the scenes. We desperately need another Steve Jobs in the world today who is dealing with AI. It's too bad Steve's not here to actually do it firsthand, but there is a way to make this just purely happy, happy and pleasurable for humans. And you can see how hard it's going to be. From this example.

And we finally have the tools to actually locate all the zero-day vulnerabilities and start to patch them.

To the point, I mean, we're, we're, we're not like devoting dedicated coverage to it, but I'll just paint one example. Peter, to your point, the Linux kernel is drowning at this point under discovered vulnerabilities. And you see one of the maintainers of the stable kernel forecasting the next 18 months of Vulnerability patching is just going to be a total flood driven by AI-discovered CVEs, vulnerability enumerations. And I just think this is like— we talked a little bit in Solve Everything, and even outside Solve Everything, we talked about great projects when entire disciplines are just going to get solved through grand projects that are undertaken. One of those is we have an entire software ecosystem based on buggy, vulnerability-ridden open source projects. And right now—

I'm— yep, I agree 100%. And I'm fundamentally extremely optimistic about security in particular, purely because it is so easy now to log everything. And historically, it was impossible to find enough people to understand forensically what happened. Now AI is the best triager, the best Sherlock. Of what happened and you can figure it out in a heartbeat using AI to check those log traces, those, you know, and so as long as you're capturing all data, the transparency will ultimately solve this problem and we'll have a happy future.

And we will get stronger. The systems will get stronger.

It's only a phase. It's a— we need to get past the phase of discovering everything that was already wrong in our supporting infrastructure. And then we're past it and we have hardened infrastructure.

Yes. I think that's one of the most important messages I want everyone listening to hear. That these minor incidents will make us stronger and we're going to get to a point where we have true security across our systems. You know, I remember getting a call when Fable 5 came out. A gentleman who I know who's the head of the Port Authority in New York said, I need access. I need to check our software. I need to make sure that we're not vulnerable. And every company is doing that now.

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All right, let's move on to our next story. Uh, two particular stories in the SpaceX ecosystem, both classic moves by friend of the pod Elon Musk. In the first of two stories, Elon announced that SpaceX's entire engineering dataset, excluding any defense-sensitive materials, will be folded into the training data for Grok's next 2 trillion parameter model. So Elon's stated goal here is to dramatically improve Grok's engineering capability, elevating it from a general conversational and reasoning system into one with deep, practical, real-world engineering capabilities. The uploaded engineering corpus accumulated across 2 decades of designing, building, launching, landing, and reusing orbital rockets is an amazing move, uh, in getting every engineering company out there to start utilizing Grok. So that's the first story in SpaceX AI. Uh, the second story, uh, from Elon, because, you know, Elon needs at least a couple of moonshots per week, uh, is this quote from him: before the end of the year, Grok Imagine will generate a full-length movie of The Odyssey, historically accurate, true to the art of Homer, a feature film from a text prompt by December. Quite the claim. And I believe him. He's been saying this for a while.

So, you know, Peter, we had, we had two outreaches this week, one from OpenAI and one from Mercore, saying we want to spend millions of dollars on any and all human-generated data. It can be code, it can be old HR records, it can be anything human. It has to be human. We don't want anything synthetic. And we need this because we can build a lot of synthetic data off of just a little bit of human data. But if you're out there and you're like, you know, 60 years old, you spent your career at XYZ Bank and, you know, there's a whole bunch of old COBOL lying around that nobody cares about anymore. You can sell that for $1 million, $2 million to either Mercore or OpenAI. I'm sure Anthropic too. So another entrepreneurial avenue in defeating the AI machine. But they only want human-generated data.

Gold mining.

Material. Yeah, gold mining.

Amazing. I mean, I think, you know, very unique datasets are going to be extraordinarily valuable, right? Your, your alpha comes from that data in particular. Alex?

Yeah, I view both of these stories as facets of Elon trying not to save Grok.

I took—

I've taken a lot of heat on social media for— from me and from you sometimes for past characterizations of Grok being on life support. And I stand by that framing. In particular, I think the Grok 4.5 that we saw that has finally again touched the cost-per-task optimal frontier isn't actually the same Grok. It's a Grok that's basically merged in and/or apparently on its way to becoming Cursor's model, but rebranded as Grok. And I think if I'm Elon, And given how hypercompetitive the frontier model rat race is, where even Google seemingly is struggling to stay even close to the frontier, I'm looking for every possible strategy, every bit of differentiation, every competitive advantage I can possibly muster to try to help Groq either attain frontier status or stay on the frontier. Because as with the Red Queen paradox, you have to run just to stay in place in such a competitive environment. So I think if I'm Elon, I say, all right, data is potentially one competitive asset. Framing or connecting this back to the earlier story with Moonshot, the fact that the Moonshot K3 architecture was essentially so vanilla— Yashua, again, mildly interesting attention mechanism, but basically a recognizable improved transformer model— but the data, the reasoning traces were seemingly so valuable for post-training K3 up to near near state-of-the-art level. If I'm Elon, I'm thinking, okay, I, I'm probably not going to win based on algorithms. I'm probably building a Dyson swarm to be competitive on compute, but maybe data, internal data, as the third leg of the stool. You see, you have algorithms, you have compute, and you have data. Maybe there's something uniquely differentiated that SpaceX can bring to the table to help Grok stay at the, the frontier. That's point one.

Point—

yeah, go ahead, please.

Second point, how many points total are there, by the way? This is, this is two out of two. Two out of two. Second point out of two points, uh, regarding Grok Imagine. So American labs have largely abandoned video gen in favor of letting China run away with the video gen story. Google DeepMind has released Gemini Omni, which will generate at best 10 to 15-second clips, but they've basically abandoned long-form video generation. OpenAI has abandoned video.

By the way, I would say for the moment.

For the moment. But even if you read the tea leaves about where they're reallocating their efforts, it's for robotic world modeling. It's not for consumer video gen. It's all going to helping robots navigate autonomously in complicated environments. And then Anthropic has seemingly never even touched video, but they'll probably touch it once they ramp up their robot effort. So that leaves a market gap, at least in the consumer space, that Elon, I think, is wise to scoop up. But I have to ask the question, what are consumers generating videos of with all these capabilities? And there's been reporting out there that Grok Imagine is being used for a lot of adult video gen.

So I'll give you a slightly—

Not sure how lucrative that is.

Yeah.

Slightly different timeline and narrative. You know how for a while there we all said Dario completely outflanked Sam because Anthropic focused on enterprise use cases while Sam was very busy getting the consumer installed base, doing video gen, image gen, and teasing adult mode in ChatGPT. Yeah, all that. And Dario outflanked him, got $60 billion of enterprise, soon to be $100 billion of enterprise revenue run rate. And vaulted past them in revenue and maybe valuation. Well, Elon, always thinking two chess moves ahead, doesn't even try to compete on the frontier, or he tries half-heartedly, but he puts all his energy into a massive data center in Tennessee, buys a million GPUs and then a million more, and then starts thinking about deployment in space. Kimi K3 comes along and just levels the entire playing field just overnight. He can download it as easily next week as anybody else can, but he controls a massive amount of compute and he's making money on the compute, renting it to the other guys while he waits for this to catch up. So if that ends up bypassing everybody in the end, that will be like, okay, leapfrog upon leapfrog upon leapfrog. Elon was thinking two moves ahead as usual.

I think he is so much, right? I mean, what we're going to see next, I still think we're going to see the merger of Tesla and SpaceX AI. Right.

They said that he basically said that and implied it in the most recent earnings call in the past few days.

That is going to happen. I said, you know, before the end of the year, there's so many advantages. I think he would want the corpus of engineering data from Tesla, which is probably as much or larger inside of Groq. And then don't forget, he's got all of these vehicles out there with compute and connectivity on board. You know, all of the Powerwalls, all of the Teslas, all the CyberCabs are going to become basically inference compute, you know, across the world.

Well, also, you know, he doesn't need the $100 billion— I'm sorry, sorry, too slow.

I'm the Jeopardy button. It's not moving fast enough.

It's a rigged game. So he doesn't need that $100 billion of enterprise white-collar automation revenue that Anthropic has, because if he wins the race to his Groq AI being the better chip design AI and also the better hardware design AI, that's going to go back into the self-improving data center, the self-improving robot, and the self-improving chip. So he'll win at the hardware level. And I think there is a very good case to be made that being able to control flops compute is the dominant chip in the game a year from today because all the AIs are going to be able to build the software. You know, any one of them will be able to build the software. And so if that becomes a commodity because of that, then who has the most compute has the most intelligence.

Salim, can I— Yes, please.

I've got a bunch here. I think this is one of the biggest and cleverest things I've ever seen Elon do.

Okay, which one? Which of the stories?

This, this engineering data going into Grok. Okay, because it's not just CAD files and manuals, it's like 20-plus years of engineering decisions, failures, trade-offs, uh, problem solving. Just think about what Grok's going to learn, right? Why did engineers choose Design A over Design B? What materials failed during testing? How did Starship evolve through all of these iterations? He's basically taking the life experience of a company and embedding it into this AI. Anybody else that wants to build engineering for the future will go to this model and build stuff because it'll all be built in and they can use the experience builder. This is organization intelligence. Most of the world's engineering knowledge never gets published, right? It just lives in like weird design reviews. He's putting this into the thing. So hold on, let me finish. Yeah, so this Essentially absorbs the collective engineering of one of the greatest organizations ever built for building integrated verticalized systems. So this is like, now you get Long Systems horizon thinking, right? Because you get all of the, the, the engineering data for rockets and satellites and telecommunications and supply chains. The material science breakthroughs just on that will be huge because you could have engineers, people looking at this model and saying, okay, tell me why heat shield design A is better than heat shield design B, and you could train on that. Whereas all the models today are designed on like internet-scale information that's pretty shallow, right? Yeah, SpaceX data is really, really deep. The biggest thing that I think he's doing, he's actually creating an edge twin of, of SpaceX itself inside Grok because, you know, you know, this is like unreal, unbelievable, because not anybody wanting to build anything in the future is going to find this the best single best, including his own engineers. Blows my mind.

Including, yeah.

Of course.

And he just, he just required all engineers at SpaceX to use Grok, right? He made that requirement across the board. And we've talked about on the pod before, you know, can anybody catch up to SpaceX in the launch industry? Can we get new vehicles going? All of a sudden you've got, you know, presumably what will be one of the most powerful AIs showing you how to build your next generation of rockets. A lot more rocket entrepreneurs. Yeah.

Imagine, imagine, imagine if Steve Jobs had left behind an AI trained on 25 years of Apple's internal thinking, right? Or if, like, if Einstein had left behind an AI trained on, like, his entire scientific process and all his notes, this is like, this is like absolutely civilizational gold.

Yeah. Dave, so remember when we were talking to him and he was telling us about the TeraFab and he said you're going to be able to smoke a cigarette while you're making a chip?

Smoking a Big Mac.

And, and at the time I was like, that is a really weird— like, why? You know, why not just do it in a clean room, keep it simple? Enter Moondust. There's your answer.

Interesting.

He is way down the path of the completely self-contained, like the Genesis module from Star Trek. Yeah, it goes, it builds, it starts 3D printing, it starts creating chips, it starts and the whole thing is just completely self-contained and operates on the moon, in space, wherever.

That's where, as a failed engineer, this is like the greatest thing I've ever seen. Because like, you've got SpaceX, you've got Tesla, you've got Starlink, you've got Neuralink, you've got X, you've got The Boring Company. Like, he's creating an integrated intelligence stack where every company feeds the model and the model will improve every company. It's like, it's like blows my mind.

Gotta, gotta love it. I'm gonna play— there's a great Economist interview with Elon that just came out today. Lots of great clips out there. I chose one. Again, you know, one of our missions here is to keep you optimistic about the future. You know, and people get fearful when they don't understand where things are going. And I want to play this clip from Elon about why he's optimistic about the future and just to, again, help shape people's neural nets about where things are going, because fear is the worst place to encounter the future from.

AI may exceed the sum of human intelligence in about—

in around 5 years.

In 5 years? Roughly 5 years is my guess.

There really won't be anything that AI can't do better than humans, apart from being human, perhaps.

At a more prosaic level, what will life be like?

The most likely outcome is an age of amazing abundance.

Where anyone can have anything they can think of.

This may sound preposterous, but, well, here we are in 2026.

Let's see where we stand in 2036. I think we're headed for an age of amazing abundance. So this is, I guess, a message of optimism and excitement about the future. So, gentlemen, comment. Salem.

Well, this is what, like, they summarized our whole podcast over 18 months in those few sentences. Um, technology is always a major driver of progress, and it may be the only major driver of progress we've ever seen. And so now you have technology being leveraged in the most incredible ways at the most unbelievable speed. There's no problem we can't solve, Peter, to copy your verbiage, since I've been copying Alex's Thank you, Alex.

I mean, we talked about this in Solve Everything. This is an incredible future heading our way.

Yeah, I do think we're going to speedrun most sci-fi, basically any physically possible sci-fi, probably over the next 10 years or so. And I just want to make one more point about Grok Imagine and the Elonverse. If I were to steelman the value of Grok Imagine, Elon's video model, I think it's not going to be about generating adult videos. There's just not enough money in the entire adult video industry to justify a large amount of CapEx. The, the value per token, I think, is just too low. If I were to steelman it, I think there's something that we're all sleeping on, which is Digital Optimus, which is arguably the successor to MacroHard. Digital Optimus is Elon's vision for basically Pixels to actions, having, uh, just like physical Optimus is a robot acting in the physical world autonomously, digital Optimus sees every pixel on a screen. It's basically a computer use assistant and then will carry out any knowledge work. And in order to, to just see from the raw pixels and to do interesting things, you want amazing video models in general, just like humans. Humans are able to look at computer screens, and because we have our pretrained video model, as it were, operating in our visual cortex, we're able to navigate a complicated visual environment. So if I had to steelman why Grok Imagine is ultimately valuable for the Elonverse, I think it probably ties back to Digital Optimus and the ability to drive computer use assistance that becomes competitive with all of the other frontier models.

Did you notice, Alex? Do you notice, Alex, his, uh, 5-year prediction on ASI? He's he's put it out there a little bit, right? He's talked about AGI this year, or, uh, I know you think it's happened back 5 years ago, but he also just declared 2 days ago that we're in the middle of the singularity. And we are, but, but that's not— I think the point being, when do we have AI equal to the sum total of all human intelligence? Uh, and that's, you know, if that— if you want a definition of ASI, Saleem, there's one for you. Um, you know, 5 years from now.

It's a vague descriptor. But hang on, can I make 2 points? Because Pete, like, I've said some laudable things about, about, uh, Elon. Let me say 2, uh, negative things just to balance it out, just for the sake of objective journalism here.

It makes you feel better? Sure.

No, it's just the, you know, his— I call BS on his claim of that AI is smarter than humans. I go back to the definitional problem. As Alex put it, we've— it's been smarter than humans for a long, long time because it has access to all this information. And there's something else, um, which I've had a beef with, which is the whole Doge affair. And Elon came out and said Doge was not a great idea. He didn't— it didn't execute the way he wanted to. And it's the first time I've seen him admit that. It's great to hear that.

Comments?

Negative.

Interesting.

Yeah, Dave, uh, comments on that video clip?

Yeah, well, he put a really, really crisp timeline on it. And he's said many times before he's in a perfect position to know. So his credibility on the topic is incredibly high. And I can see it firsthand. There's no doubt that the algorithms are self-improving. And I can see the easy, easy 100x that's coming very soon.

So I don't—

I think the sum total of all human intelligence is just gated on chip manufacturing. It's actually smarter than any human much sooner than that, like very soon.

Yeah, I was— the irony, 5 years.

Yeah, I, I should point out, I mean, this is a more conservative forecast than some of his more recent, like in the past year, forecasts that by the end of this decade we're going to see 3x-ing year over year of economic growth. So I don't quite understand. If anything, this sounds like a relaxation toward a more conservative estimate for the sort of hypergrowth we'd otherwise achieve if, if if our output is doubling or tripling year over year and, and that's due to superintelligence, in my mind, naively, that would almost suggest we're 2x or 3x new intelligence on Earth. And surely that's coming from superintelligence. So this, this seems to me almost like he's sandbagging his own—

I agree. And he was talking to The Economist, probably one of the most conservative publications on the planet.

All right, and this is Elon after Doge, not before. You know, after Doge, he's like, wow, things don't always like as soon as there's governments involved, things don't always happen. So, you know, the prior Elon was all based on scientific timelines, exponentials, and what's possible. The new Elon's like, yeah, what's possible and what actually happens is usually— let's talk about agency, a really powerful move by the government.

So this next story is near and dear to my heart and probably to all of your hearts as well, because it's about how America does science. And it's the biggest structural rethink since 1945. So the White House just released a report titled Science: A New Golden Age, written by friend of the pod Michael Kratsios, director of OSTP. And it's explicitly modeled on Vannevar Bush's legendary 1945 Science: The Endless Frontier. That's the policy document that gave America the National Science Foundation and shaped 80 years of American research. Kratsios' conclusions are blunt. This is what he said. Our current system of science rewards conformity over bold inquiry and has become dependent on narrow— on a narrow set of legacy institutions. Could not agree more. His proposed solution is very refreshing. He put out 4 goals. Number 1, prioritize the individual scientist over legacy institutions. 2, change how research dollars are allocated. Fast grants, long horizon grants, golden ticket, which is reviewers able to champion unconventional proposals. You know, one of my favorite sayings is the day before something is a breakthrough, it's a crazy idea. And the government doesn't fund crazy ideas typically. 3, a set of national scientific goals and rebuilding in the industrial capacity to translate discovery into strength. And 4, re-engineering the research enterprise for the age of AI. The White House is putting real money behind this, a $5 billion expansion of the Genesis mission, which is a national initiative to use AI. Alex, you and I have talked about Genesis extensively.

Oh, yes.

It's an amazing program, right? It's, it's, it's the government putting strength behind AI, making federal science data available to all. And the nice— the national labs computing being dramatically accelerated for science, engineering. It's across 15 federal agencies and 278 projects. So the question is, where is the money coming from? Well, The Wall Street Journal reports that billions are being redirected away from traditional university research and towards these AI programs. We have to talk about that, Dave. We've talked about that with vis-à-vis MIT. So in summary, this is the most ambitious restructuring of U.S. science funding in 80 years. It's a bold bet. On disruptive individuals and moonshots over institutional peer-reviewed consensus. A big deal. Dave, you want to jump in first? I mean, if we're defunding research at universities because AI and, you know, sort of hero investigators can do it better, it's going to cause a lot of heartache in our institutions. What do you think about that?

It's already creating a ton of heartache, which makes life hard for me because I actually think these are really good ideas. But institutions that are used to being funded and that have people's lives, you know, their livelihoods at stake, they don't— they don't just go away quietly. They get really mad and they are really mad. Harvard, MIT, they're just ripping mad. And I hate that because I'm kind of trapped in the middle. But I think they're fundamentally good ideas. I have a firsthand, you know, kind of a front row seat at Liquid AI where, you know, these exact same guys were in CSAIL at MIT with a trickle of funding., and then the exact same people move out and start a private company and just take off. The amount of great research they've been able to achieve outside of the institution is miles ahead of what they were doing inside the institution. The institution starved for compute. So, so yeah, it fundamentally makes sense to look at the individual person. I also think that with AI as an assistant, the scale of allocation of capital. I had one experience where the CEO— I won't use his name, but the CEO of a company that does marketing, nothing to do with tech, was meeting with Barack Obama, the CEO. And Barack said, would you like to be part of DARPA and help allocate all these federal funds? He's like, I don't know how to do it, but sure. And so then he came to me and said, what do you think of 3D printing drugs? I'm like, what the hell are you talking about? I don't even have no idea. He's like, neither do I. Should I give him $30 million or not? I'm like, that's how you guys decide how to allocate capital? I mean, holy crap, is that insane? So there's so much room for improvement. And I think AI will enable you to look at individual people's work and make rational decisions on whether to allocate to it. So that, that part of the proposal just really resonates with me. The whole thing actually really resonates with me. But, but I hate the fact that it's creating so much agony around MIT and Harvard.

Alex, I mean, you've thought deeply about this. Your views.

And worked with the Genesis program. I think this is literally the end of the endless frontier. I, my mental model at this point is starting with, I mean, I think the original draft or the original letter version of endless frontier, folks can fact-check me on this, I think was actually in 1944 to FDR from Vannevar Bush. So towards the end of World War II or near the end of the World War, there was this 80-ish year regime from approximately the end of World War II to approximately the present, where an academic-industrial-government complex was set up. Maybe a bit of military there. And I think during this 80-year regime, there was institutionalization, arguably over-institutionalization, of which research directions would get funded. And pursued and which were appropriate. If you go back and reread, as I have recently, the original Endless Frontier letter that Vannevar Bush wrote, it was entirely seen through the lens of the World War II military. It was all about how, how could we best take processes and procedures that have been learned through the war effort, and how could we pass them down to the civilian sector, and how could the military collaborate with academics in the private sector, it was all seen through the lens of World War II. And I, I think we've been basically spoon-feeding an academic military-industrial government research complex for the past 80 years off of end of World War II thinking. And finally, that complex, which has grown arguably incredibly inefficient— I agree with those who've pointed out that say, National Science Foundation, wildly inefficient. Anyone who's ever had to, say, write an NSF grant application would hopefully agree with the assessment. It rewards incrementalism. It does not reward, broadly speaking, again, I'm painting with a broad brush, breakthrough thinking or breakthrough approaches. It historically has developed, I think, a well-earned reputation of rewarding incrementalist applications.

For—

in many cases, PIs that I know have learned the hard way that you write NSF and to some extent NIH grant applications by proposing work that you've already done. Yes, just to minimize the risk.

It's crazy, right? When you have peer-reviewed science, yes, if you have a breakthrough idea, the people reviewing it don't want your breakthrough to occur because they're no longer the experts after your breakthrough's, you know, taken place. I mean, it's Lord of the Flies.

It's a nightmare.

It's crazy. Grants can take 2 years to be awarded, right?

So, and NIH, it's even, it's even worse where you see the first PI grants are people in their early 40s.

At the speed at which we're moving, it's insane, right? So these, this FAST grant proposal that Kratios recommends, I think is amazing, right? Being able to go from a proposal to a grant inside of weeks. Um, you know, the other thing is the reason research universities were so well-funded in the older model was you had a concentration of intelligence, a concentration of technology and resources, and it was the most efficient. Saleem, this is exactly the purpose of a corporation. In the thesis, the corporation now can be disrupted because of AI. You don't need to have all the people inside of a corporate wall. Do you want to take it from there?

Yeah, yeah. A couple of thoughts here. First, this is like a really big change. Uh, the impact on all the universities is going to be massive.

The—

there's going to be a lot of, uh, fallout from this, but I think it's, it's actually the right direction.

I remember, I think it's a spectacular direction.

It, it's, it's, it could go— you could make the whole thing politicized, which is the dangerous part.

Okay, I mean, it will be—

it's already super politicized.

It already is, right? So, so that's the bad part. But there was a couple years ago, I was in a series of conversations with Florida universities. I was very involved in Miami and Florida, etc. And the, the fellow gave me the most craziest statistic. Florida universities get $750 billion a year of, uh grants and donations and government funding and the output in terms of patent and innovation, etc. They worked, they did some research, and their output was exactly zero. Okay, all that money went to administrators and to building more buildings and whatever, and nothing went to the actual research. Yes. Yeah, so there's, there's a— and we know, we— the reason we tried to do Singularity University was the model of university has not changed in 450 years. It needs a freaking upgrade, right? And this is highly aligned with the EXO thesis. Give a small ambitious team with an MTP access to shared facilities and AI and some external communities and let them go. They're going to go, they're going to do amazing things. And I think the biggest part about this is the metabolism speed between application and money being allocated. And I think that's fantastic. And this, this is also aiming at a future when AI can do so much of this coordination and sorting out for you. So if done properly, this could be the absolute reboot of American innovation and American exceptionalism. If done badly, it's gonna get politicized and it's gonna become a shit show.

Yeah, two quick points. One, a Harvard professor friend of mine who's an extraordinary scientist, I won't name his name, told me confidentially that his grants were not being funded because he'd been too successful. His grants— he'd had too many successfully funded grants and his work was going and they needed to spread the wealth. So rather than funding the very best scientists who are producing the most, they're trying to democratize it. The second thing is there's a company, it's one of my portfolio companies called Lila. It's out of MIT and Harvard. Jeff von Maltzen is the CEO. It's an amazing company. They are basically have built a capability where they built a scientific superintelligence trained on the corpus of all scientific knowledge that they're able to get ahold of. And they built, they're building out a million square feet of robotic labs. And so we've, I've talked about this before, you know, the AI generates the hypothesis, the scientific theory puts forward the experiments to be done. The experiments are run overnight. They gather the data, they update the theory, they run the experiments. You can't compete against grad students pipetting in the lab. And so it's just— it's going to be not 10 to 1, it's 1,000 to 1, you know, rate of improvement. So if you want— if innovation is what you're looking for, funding it inside of the university system like this is just— it's perpetuating the old ways and it's a— it's an employment project.

Hey, just a plug for Lila. I am not involved or an investor in any way, and Peter is, but I got to tell you, Jeff von Maltzen is freaking brilliant. And that company is amazing. Anyone who's a biotech person, consider trying to get a job there and join them before it becomes—

Lila Biosciences, yeah. Or Lila Sciences. They're doing it across material sciences. They have incredible, I mean, I'm not sure what I can say about them, but they've gone from like zero to a huge amount of revenue in just a year. It's incredible company. All right.

I'll quickly comment. I'll also say Jeff was my classmate. Everyone was my classmate. Dario Gil from Genesis mission was postdoc I worked with in undergrad. But focusing just— I think there's a grand policy bargain in a dream scenario that could be struck here. And that is, if you look at how grants typically— what the water flow— what the waterfall of funding from a typical grant to, say, an academic lab at the university is, there's an absurd amount of overhead. You'll see cases where if you put $1,000 or attempt to grant $1,000 to a research group at a top research university, you'll see approximately a third of the $1,000 get peeled off for broader university overhead, and then another third peeled off for department overhead, and then the remaining third goes to the academic lab. Similarly, if you try to— say royalties— if you're an academic lab at a top research university and you attempt to spin out your technology right now, uh, and you're hoping to recover royalties from a spin-out, you'll see a third going to the university, a third going to the department, and approximately a third to the inventor. And if I could be policy tsar for a minute, if I could maybe play Michael Kratzios's role here, I think there's a grand bargain to be struck, which is universities in order to sustain all of their overhead. And, and one could argue there's an enormous amount of bloat and, cite to Baumol's cost disease here. But rather than universities attempting to siphon from grants, from the inbound, which is arguably a taxation that on, on direct funding, that clearly under this administration, the administration would much rather directly fund principal investigators rather than have, say, two-thirds of the money end up lining the university's endowment, rather than that mechanism for income for the research universities, wouldn't it be wonderful if instead the universities could earn their money by translating all of their innovations more effectively out into the private sector through startups? And the reason the top— I would argue the top research universities aren't doing that right now is they're too scared of being taxed like for-profits. They're too scared of looking like venture capital firms. And so they don't. But if I were Michael Kratzios for a day and could try to strike a grand bargain, I'd shift the university income over from licensing revenue, royalties, equity, especially in spin-out startups, away from taxing grants.

All right. I'm going to move this forward.

Can I make a quick comment? Yeah, go ahead. I think that's a great idea. But the problem, Alex, is that the output side has been as inefficient or worse, right? Tech transfer policies, almost every university in the world have failed miserably.

That's what I'm saying. MIT—

You could ask—

ask why, why do they fail? Like, why do they fail? I would argue that at the top research universities, the ones, the MITs and Harvards of the world, why are their TLOs or TTLs so atrocious? Or TLAs. Why are they so wildly inefficient? I remember like 20, 15, 20 years ago, the most revenue-generating patent from MIT's TLO was a patent related to HDTV. Like, in the middle of an internet revolution, it was an HDTV patent. That's absurd. And I, I think the TLOs are so inefficient because they're designed to fail, because the universities don't actually want them to succeed.

Wow. Quick comment for the audience. But Alex's ideas are, are usually incredible, almost always. Uh, and Alex is talking directly to Peter, and Peter has a direct line to Kratios. Aren't you guys meeting in a couple weeks? We are.

We're going to be doing a pod, uh, in a week's time.. And I'm going to make sure to translate all of Alex's ideas to Michael.

That's why I bring it up. If anyone in academia out there thinks what Alex just said makes a lot of sense, just give him a call. He's very reachable. And then, you know, between Alex and Peter, it goes straight to the White House.

I got to give a shout out here. Yeah, I got to give a shout out here to Ajay Agrawal in Toronto at the Creative Destruction Labs. He recognized this tech transfer problem and tried to say, let's take a crack at solving it. Created a separate edge thing on the edge where he puts people through a cycle where some nanomaterials PhD can't present, doesn't know the value of the technology, etc. And he puts them through a cycle where I think it's 8 weeks. 2 weeks with other technologists— what would you add or subtract? 2 weeks with entrepreneurs— what would be the business model? Would you license? Do you embed? Do you productize? A third 2 weeks with with execs who've scaled companies, and a fourth 2 weeks with corporates that might license, buy, invest, etc. In a few years— I think it's 8 years— he's created $50 billion of startup equity value out of nothing. Okay, and that's just an unbelievable number when it was doing zero before.

Think that—

think about the idea that every major city in the world has 2 universities, 1 or 2, sitting there doing nothing for the local economy. Right? Or very little. And here's this guy with one university generating $50 billion in a few years of startup equity value with all the jobs that go along with it. I mean, we should be copying and pasting that model into every city in the world. And, and plus what Alex is talking about will completely rejuvenate the whole system.

All right, I'm going to move us to the future of transportation. And this next story really pisses me off. So Paul Graham founder of Y Combinator, put out the following tweet, quote: Trial lawyers are lobbying against self-driving cars because they're too safe. They need people to be killed and injured so they can have material for lawsuits. Just sit down with that one for a minute, right? Insane. So Graham cites a report that the American Association of Justice, which is the trial lawyers lobby, has been the prominent opponent to autonomous vehicle legislation. Insane. Here are the numbers, guys. Uh, so 6.2 million motor vehicle crashes per year, 17,000 a day. 2.4 million people are injured annually, and there are 40,000 traffic deaths per year, 108 per day. The safety data from Waymo, uh, and Tesla is incredible, right? The data is very clear over tens of million— well, now probably around 15 million miles— that these vehicles are on the order of 8 to 10 times safer per mile than the, you know, 2-ton vehicle being driven by a 16-year-old on a learner's permit. So— or a 90-year-old, right? So the whole personal injury legal industry has a financial incentive to slow down technology whose entire purpose is to save people's lives. And this just is insane. Saleem, over to you, buddy.

Yeah, yeah, I've said a bunch of this stuff on the podcast, uh, podcast before, but it's worth repeating some of this. Uh, in 2011, BlackBerry had a 3-day data outage around the world, and the accident— when nobody could send BlackBerry messages— and the accident rate dropped 40% in those 3 So people should not be driving with terrible control systems for 2-ton cars. I actually want to be slightly defensible to the lawyers just for a second.

Really?

Because they don't consciously— yeah, just for a second, just for a second, because they don't consciously want people to be injured. But their income depends on the legacy structure and the continuation of the existing system, right? So those stakeholders, whoever they are, will naturally resist any technology that removes those transactions It's like the car dealers resisting Tesla because Teslas don't need maintenance and electric cars need 100x less maintenance than a conventional car, so they resist the electric cars and lobby against them, etc., etc. This is the immune system. This is legacy thinking. It's like the— a few years ago, the Texas doctors lobbied and won and banned the use of telemedicine because, you know, clearly you have to So this is classic thing. And the statistic I love to quote is 50% of US court cases are car accidents. 50%. This is just an unbelievable thing.

Putting judges to work.

I mean, it's a huge amount of things. All the judgments and cases we could be dealing with, we're not because of all of this stuff. But let's also note that autonomous cars don't just replace a driver, they reduce insurance claims and emergency responses, parking issues, accidents. There's like— one technology can solve so many things. It's like really a big deal. And this is the immune system response that we talk about in our—

yeah, Alex, there's this whole sub-economy that seems to be dependent in almost a quasi-parasitic way off of inefficiencies of driving, of manual driving. I think it's not just attorneys. It's not just auto insurance. It's also parking meter fees that accrue to municipalities. It's also police departments that— and municipalities, yes, speeding tickets. All of this is going to go away. And we're— and this is all well before we get to all of the land that right now is wasted on parking lots and roads. All of this is going to shrink. And in the process, you're going to hear shrieks from probably trial lawyers and from police unions and maybe from other adjacencies that are being collapsed in the process. But again, I, I don't want to live in a world with buggy whips. I want to live in a world where this is all fully solved. And as Peter, you and I wrote in Solve Everything, where we have the quiet hum and there are no speeding tickets in the quiet hum.

Yeah, the 60% of the land in LA is parking spaces or blacktop, at least.

Yeah, it's insane. Um, a lot of transformation coming. Dave, any thoughts on this one?

Well, I thought, I thought Salim's defense of the lawyers is actually very well thought out because, you know, when you really drill in, these are families, you know, one parent is a lawyer. 3 years of law school is never funded by anybody. You paid it yourself. You have a huge amount of debt. You get into an industry and there you are.

Hold on one second, guys. I cannot respect that as an argument. You know, if the data comes out that we can save 100 lives a day by having autonomous vehicles, I think we get into a situation where if a, if a city makes AVs illegal and your son or daughter dies in a car accident because they couldn't use an autonomous vehicle, You've got a lawsuit in your hands. I'm sorry, I cannot, I don't, yes, we're gonna have disruption. We're gonna lose lots of jobs. You know, AI's gonna transform law, medicine, every field as well. It's not a reason to stay in business as a, you know, putting up the signs, injured in an accident, you know, call us, we'll sue.

Better call Saul.

Yeah, I mean.

Well, I was driving through.

If I was 100% right, I was driving through Phoenix and I saw a similar sign said, better call Paul.

My favorite road sign. Roadside sign is in Boca and it says, "Your wife is hot.

Call the air conditioning repairman." Well, look, look, you know, the reason this is a story is because it's such an obvious case where we need to save those lives. You take the exact same story and you say it's an accountant, not a lawyer, and they're doing work that is completely meaningless, filing a form on your behalf, an 83 election. On your behalf, but that's their business. And now AI can just make that completely irrelevant. Do we do it or do we not do it? Well, we should do it, but that's another voter. So here in the real world, these are all voters, and you already know 70% of Americans think AI is terrible.

Of course. I mean, listen, my dad, God bless him, when he was, you know, uh, had vascular dementia and he was laid up at home, you know, he had his driver's license ordering you it in Florida, you know, received in the mail. Why? Because they're the voters and they want the right to drive instead of having the logical situation was, you know, at age 80, you know, their driver's test, at 85, you know, their driver's test, and so forth.

Anyway, uh, well, where the puck is going right now is AI is going to create incredible amounts of abundance, just like Elon said. And the labs, you know, Anthropic and Dario in particular, that were saying we can eliminate all these jobs next year are now starting to say, you know what, I don't want to perturb the world that much that quickly. All these votes, 70% of voters can wipe me off the face of the earth. I don't need that. So AI is starting to grow and self-improve within itself very quickly, and it's kind of trying to leave a lot of things alone. You know, teachers unions, police unions. This one, you know, you got to make the cars safer. You're totally right, Peter. These are actual lives, you got to do it. But there's a lot of other edge cases that are very proximal to this one where they're starting to say, you know, let me just leave those.

Salim, do you have something to say? You're chomping at the bit, buddy.

Well, you mentioned accountants, and we're talking about like future of jobs, etc. Let me mention an analogy I've been using that seems to work really well. If you went back 100 years ago, accountants were doing double-entry bookkeeping manually in ledgers. Ledgers, right? And you'd like write down this in the debit column and this in the credit column. And when we got slide rules and calculators, that accelerated and made it faster in adding up the columns, but it didn't change the work. Once you had accounting software, the software did all of the ledger entries, and the accountant lifted above the loop and started categorizing the transactions, handling month-end reconciliation gaps, etc., etc. That's the best analogy we found, because the number of accountants hasn't changed It's actually gone up quite a bit because there's so much other work to be done in analysis, etc. So when people get freaked out about the jobs, no, the jobs will transform. But we found much more higher-value work. And every time we have a technology injection, it takes out what Erik Brynjolfsson calls white-collar drudgery, and you lift, get more value added. You use your judgment a lot more. That's what's going to happen. The problem is that human beings— but this is the biggest insight I've ever had about human beings. We would much rather be comfortable than happy.

We don't like changing our lives.

If you, if you like this story, there's videos that go with the story. You can find them online easily. But, you know, Peter said a 16-year-old on a permit is a dangerous driver. I said a 90-year-old could be a dangerous driver. But when you look at those videos, you realize that the car can way outperform the best driver in the world because it has information.

Yes.

That you wouldn't have. It has vision in every direction concurrently. And, and so it sees things that a human being just can't see. And when you look at the videos, you're like, oh, okay, I get it. There's no way.

I don't care how good my mom, God bless her, is 90 years old living in Florida. She's great shape and she's driving well, but I want her to get a Tesla. I want her to get used to full self-driving. So at some point when she's not able to drive, her vehicle can drive her around.

Just think of the mobility will give all of those millions and millions of people when everybody's using it. Yeah, Steve, unbelievable.

Or robotaxis in general. Cybercabs and limos for everyone.

And your AI is ordering your cybercab for you. Okay, our next transport story is a short one, but it hit me because I had this experience. I'm driving through Hollywood Hills. I can't get a damn signal anyplace, you know, and I've got a clear sky above me. So a gentleman by the name of Sawyer Merritt just reported that that all CyberCabs will have Starlink built in. He saw this in an in-showroom infographic. And for me, the two points here are, number one, I love the way Elon sort of coordinates across all of his companies, all the technology, right? So Starlink is in Starship, Starlink is in CyberCabs coming now. And, you know, it's literally integration across it. I can't wait till he combines the companies. The second thing is I can't wait till Starlink is retrofitted into every car. It should be, right? When you have gigabit connection speeds to your car, it's going to be extraordinary. And this goes back to the idea we've talked about in the past of distributed computing, right? Where again, these vehicles that have GPUs on board and Starlink are going to be inference edge computing.

I think putting aside the corporate governance issues of how Elon given that Tesla and SpaceX have not yet merged, how he treats them as basically one company and technology passes back and forth. Engineers as well as engineers, all sorts of stuff. Um, I, I would say direct-to-cell technology from Starlink is going to make all of this possible, and it won't require big, uh, over the, the medium term, big pizza dishes or even a tiny dishy McDisheface dishes, which is, I think, the comments on that one dishy McDishface. Dishy McDishface is, is the, the term of art.

Um, do you know the source of this? What? The British Navy announced a new, um, uh, brand new, uh, warship, and they decided in a gesture, rather than having somebody name it, they said we're going to crowdsource the name and let the population vote on what the name should vote on. And they're winning because the British have the most ridiculous The most ridiculous sense of humor. The winning name was Bodhi McBoatface.

Yep.

And, and they, they couldn't— they kind of like— it was such an obvious winner, they finally had to override and say, I'm sorry, we have to go back to the old way of doing things. So that meme has continued.

It has continued.

Fantastic. The British, God help them, can't play soccer and football to get in the final, which killed me. But damn, the sense of humor.

Yeah, the first, first two generations of Starlink terminals were dishy dish faces. And now with direct-to-cell, you won't even need that. It'll just be like a cell phone antenna that can be built into everything.

I want to just show a quick video. And this is China taking the lead in autonomous transportation, in particular in trucks. So check out this video. So describing it, this is an 18-wheeler, but the cab where the driver goes is basically like a flat board. It's got, it's got lighter on the front and, and headlights, and that's about it. It got rid of the entire cab, reduced it to a tenth of its size, and we're seeing these all over the roads in China. So just interesting. The new— this is like, instead of a two-armed humanoid robot, this is a new form function for, for, for trucks.

Thoughts, Peter, on how the American truck drivers unions are going to react to those?

Ah, with great love. They're going to get a chance to vacation, I'm sure.

Um, actually, can I— I have some— a little bit of data on this. You know, there's, there's a, uh, Consumer Reports stats that 3 million jobs in the U.S. are based on trucking, etc., etc. I actually went and talked to a trucking company to just look into this, and they're like, are you kidding? We'd hire 1,000 more truckers if we We can't find anybody that wants to take the work. I would have 1,000 trucks. So I think autonomous trucking is going to fill that gap of all the boring stuff. And then the trucker, you'll have like a drone pilot. A truck will drive along. When it needs to pull over to recharge or swap a battery or something, you'll get that happen, done. And then for difficult maneuvers, you'll have somebody human figuring it out. And I think this is going to be amazing when it appears. And I don't think there will be job loss for the, for the very reason that very few people want to do it.

Yeah, I'm looking forward to seeing autonomous trucks on the US roads. It's just again, you know, China is pushing this out. They need the infrastructure support and they've got incredible government support for this and innovation happening. Everybody, welcome to the health section of Moonshots brought to you by Fountain Life. You know, AI is impacting every aspect of our lives, how we teach our kids, how we do our business, but one of the most important things that AI can deliver to us is health. And one of the things I think about when, you know, shooting for 100, 120 is, am I going to have the cognitive health to be able to think clearly and keep my wits about me for the next 50 years? I'm joined here today by Dr. Dawn Musalem, the Chief Medical Officer of Fountain Life and a member of my Fountain Life medical team. Dawn, a pleasure. So, Dawn, talk to me about brain health.

Brain health.

You know, you're right. This is the number one concern people coming into Fountain Life have is, "Will I remember the name of my child and the face of my loved one?" 45% of dementia cases are entirely preventable with lifestyle. And what was really intriguing to me, Peter, is that a quarter of our members had advanced brain age, but over 13 months of us really helping them live healthier lifestyles, eating healthier, moving their body regularly, and optimizing sleep, People overlook that so often, but that sleep optimization is critical for our brain health. What we showed is that we were able to improve the brain age in 46% of those individuals. That's a powerful number.

That's amazing. You know, one of the things I love about Fountain is we're constantly searching the world for the most advanced therapeutics and bringing them to our members. So for me and all of you, I hope that you appreciate the fact that you can become the CEO of your own health. You can make sure that you've got the cognitive clarity for the next 50 years. Come and check it out, fountainlife.com/peter, to learn more and become the CEO of your health. Now back to the episode. I'm going to move us into our next story, um, in the field of longevity. It's a topic I could talk about all day. Alex, I think you could as well. The first story comes from a rigorous new modeling paper published in Nature titled "Somatic mutations impose an entropic upper bound on human lifespan." So the paper opens by asking a fascinating question: if we cured every cause of aging, all of the 12 hallmarks of aging, how long would humans live? The authors concluded that a hypothetical non-aging human whose mortality risk never rises could live as long as 1,759 years. How do you guys like that for a lifespan, right? 17— too short. They then asked a fascinating question: How about if you left one of the causes of aging, specifically somatic mutations, right? These are the random, random DNA mutations and errors that occur and accumulate in our cells over our lifetime. Their conclusion is the theoretical human lifespan then drops down to 156 years. So first of all, I'd be kind of good to double the human lifespan. We can renegotiate after we get to 156. So the question is, why are we limited to 156? So it's because poorly regenerating tissues like neurons and cardiomyocytes, right, you know, heart, brain, muscle, are the bottleneck. They naturally don't regenerate in significant numbers. Your liver, which does regenerate, could live for millennia. So a quick point, your theoretical limit, if you are not able to solve mutations, and I have every reason to believe we will be able to, this is where nanotechnology comes in, we just saw Last week we talked, or two weeks ago, about CMLase, right, where, you know, sugar cross-linking of proteins is being solved at this point. Our second story, and let me go to this slide in our longevity lineup here, is the race towards epigenetic reprogramming. So here we go. There are no fewer than 6 companies currently working on partial epigenetic reprogramming. We have Life Biosciences, who's dosed the first living humans. They have a study going on of 18 different people with their product called ER100. This is the work of David Sinclair, and again, full disclosure, Life Biosciences is one of my portfolio companies. They have been dosing individuals using a, a virus that's carrying 3 of the 4 Yamanaka factors with injections into the retina. To treat glaucoma and optic nerve damage. You've got a bunch of other companies, NewLimit backed by Bryan Armstrong, Retro backed by Sam Altman, Altos Labs backed by Jeff Bezos and Yuri Milner. And so just to take a second on this, what is epigenetic reprogramming? So every one of us is, is, you know, born with 3.2 billion letters from your mom and your dad. That's your software, codes for 22,000 genes. You've got the same genes and the same software when you're 20, when you're 50, when you're 100. Why do you look different? Well, it's not the genes you have, it's which genes are on and which genes are off. That's your epigenome, the control system for turning on and off genes. And one of the current theories, according to Dr. Sinclair and others, is that as we grow older, the genes that should be off get turned on, the genes that should be on get turned off, and your epigenome drifts. And the work done by David shows that, you know, if you use 3 of the 4 Yamanaka factors for partial epigenetic reprogramming, not taking a cell back to its earliest stem cell state, but taking it to an earlier state of a cardiomyocyte or neuron allows us to bring you back to an earlier state. So, he's in humans right now. They dosed about 6 weeks ago. And we should be seeing the results, uh, in the next 6 to 12 months. But I love this story. Uh, it's the cutting edge of longevity escape velocity. Alex, you want to lean into either of these stories?

Yeah, I'll lean into both. Uh, so a few comments on the earlier story about somatic mutations. I, I think almost as interesting as the underlying technical story is the byline. This is a story written by a few Russian researchers who are funded by the Russian government. Ah, yes. And I want to connect this with a previous story that we reported on the pod, which is Putin and Xi Jinping conspiring to spend tens of billions of dollars. Putin, on the sidelines of a summit with Xi Jinping, was reported to be telling Xi about all of the progress that Russia was purportedly making and the money that it was investing in longevity. Put that— put a pin in that. I also want to, to connect it with the earlier story of the irony of the CCP. This is like adversaries pitching in on, uh, adversary states doing the craziest things. Uh, with CCP-funded or supported Frontier Labs in China helping American labs and Frontier Labs debug their own self-inflicted breakouts. This is the irony episode. For sure. I think it's very interesting that the somatic mutation story— I, I think the obvious solution, if I mean, Peter, you mentioned epigenetic reprogramming as one possible solution. Um, I, I think if we could eliminate the problem that the authors for the somatic mutation paper gesture at, which is that tissues in the human body such as neurons in the brain and cardiomyocytes in the heart that tend not to mitose, they tend not to replicate themselves as much as, say, liver cells, for example, the obvious solution— this is in the style of Aubrey de Grey— is replacement cells. Yeah, regrowth. Cellular regrowth and replacement. And then for the epigenetic reprogramming story, I think one of the most fascinating insights— and Peter, you probably saw this story, I think it was in maybe Science or Nature a few years ago when it came out— that the, the youngest— after conception, uh, looking at epigenetic clocks like the Horvath epigenetic clock, the youngest you'll ever be is something like 7 days after conception. That it was something like 7 days after conception, the epigenetic clock resets, reverses, like resets and goes down to zero.

It's really important, right? So you've got a sperm and an oocyte which are arguably you know, 25, 35 years old coming— they're the age, like, of the parents coming together.

Yeah.

And that first fertilized zygote is that age. But at some point around, as you say, day 7, it resets to zero.

You start the age of your parents.

You start the age of your parents and you reset to zero.

Yes.

Wow. Amazing, huh?

Wow. It's extraordinary.

It's an extraordinary story.

That is So do we know the mechanism? That's—

this is the whole point. That's the whole point of this, that like biology already has a way to reset age, and it works because you start the age of your parents, and then something like 7 days after conception, your age gets reset.

You are Alex. I love how you know so much about so many different topics. Love that you're here.

I know a little bit about a lot.

I mean, he goes long on longevity though.

Yeah, it's, uh, It's an extraordinary time to be alive. I mean, the number of stories that are breaking in longevity every week, you know, I talk about the longevity mindset. You know, if you believe that we're on this trajectory and we're going to be able to fundamentally reverse aging, not stop it, not slow it, but reverse it, and you want to be along for the ride, your job is to keep yourself in the best health possible. To intercept that technology. And like I say, don't die for something stupid before then. So again, on the, you know, besides irony, I want this to be the optimism episode. Be optimistic about this, right? Your greatest wealth is your health. There's nothing more valuable. And we just saw Genesis, the Genesis mission focusing on curing disease. We've got incredible companies, every frontier lab right now from Anthropic and OpenAI are buying bio companies because they want to focus on health. It's the biggest opportunity out there.

Yeah, 3 quick reactions. Uh, the craziest thing, uh, because I never came across longevity until Singularity University, and even then it took me a while to get my head around it, the craziest thing I ever heard is the baby that will live to 1,000 years old is already alive. That, like, just— I've never gotten my head around that, but that just blows your mind. But I think the bigger point that you're making, Peter, is as we solve some of these broader issues, right? You go from treating individual diseases to solving biological systems, and then you change healthcare from whack-a-mole to like platform repair. And I think that just changes the game completely. The one— the third thing I'll just mention, just so you know, we may double, triple, quadruple, whatever, solve aging. We won't really know for a long time.

Well, but no, that's actually true though.

We'll have demonstrations But until people actually live 150 years old, we don't know.

Yeah, 'cause you mentioned in the story there's a 6-month and a 12-month checkpoint. What do we, how do we know?

Well, we're gonna be able to see. So in the ER100 research, the therapeutic called ER100 that Life Biosciences is using, they used this technology of the 3 of the 4 Yamanaka factors. The 4th Yamanaka factor, MYC, is a cancer-promoting factor, so you eliminate that. They've done this work and they're focused on the eye. So the injections are going into the eye where the virus is then infecting and bringing these three factors into retinal cells. They did this work in mice originally and they were able to reverse glaucoma and— I'm sorry, macular degeneration, and they're able to reverse Nyon disease, which is strokes in the eye. You basically bring it back to an earlier state of youth. They then did the experiments in primates, and it worked in primates. And so they're doing the same experiment now in humans. And so we're going to get the results of did it reverse, um, nyon disease in the eye. Did it restore the eye to an earlier state of youth? Then once that's done, if that works, and I have every reason to believe it will, uh, uh, Life Biosciences will then go into other organ systems. A longevity therapeutic is not something that works in just one organ system. It should work across all in the body. But of course, the way that the, you know, FDA structures its study, you have to pick a particular disease that you want to impact and measure, did you actually reverse the disease in this case? So we're going to see, we're going to see very quickly what the results of that are.

And maybe just to add to Peter's point, there are multiple ways that one, without having to wait 100-plus years to see what the life expectancy actually ends up being, that you can differentially measure it. Peter already touched on phenotypic measures, like does the non-human animal or the human see better, or do you see signs of like retinal rejuvenation or macular degeneration? That's a phenotypic presentation, but you could also look at epigenetic clocks. So Steve Horvath and others pioneered correlating the pattern of epigenetic markers on the genome with the biological, like, wall clock age of humans and non-human animals. And you can watch epigenetic clocks also turn back.

Your point is that we have a ton of benchmarks.

One side story here: there isn't today a single accepted benchmark for aging. These clocks are organ-specific versus the whole organism. And so there are organ-specific clocks that you can use. When we first started working on the Longevity XPRIZE, it's now called the Healthspan XPRIZE, it's $101 million for reversing functional loss of aging by 20 years. We have 800 and some odd teams. We're awarding 10 teams next month in our semifinals. We're giving them $1 million each and there's $80 million for the final. But here's the point. Aubrey de Grey approached me originally long ago with Peter Thiel on the phone about doing a Longevity XPRIZE. And we couldn't figure out how we would do this, to your point, Saleem, if we had to wait 30 years to pay out the prize. And then I had a meeting with George Church at Harvard Medical School, absolutely brilliant, one of the fathers of synthetic biology. And he said, listen, you don't want a longevity prize, you want an age reversal prize. And he said, you know, what you should be measuring is functional loss. So we know as we grow older that we have sarcopenia, our muscles get weaker, we lose muscle mass, right? We have a slow decline. We are actually in our peak health at age about 28, because that's how long we needed to live to, you know, pass along our genes and keep the species going. And then it's a slow decline after that. But the question is is, could I give a therapeutic that reverses my functional age, gives me the cognitive abilities I had 20 years ago, the muscular abilities I had 20 years ago, the immune system from 20 years ago? And that's the point. So we're measuring that.

That's the prize. Yeah, it's all— I think it's incredible. Look, I'm living proof. When I was 30, I was wearing contact lenses, my eyes were really bad, etc., and I got LASIK. And I got LASIK and, and the, that, that little medical thing, I've gone 30 years with no issues at all. Perfect eyesight. It's been like absolutely every day is like a miracle for that.

It's amazing. So, uh, everybody listening, be excited about, uh, longevity escape velocity. Ray's prediction is LEV by 2033. Um, Alex, you think we're there now?

I think it's spiky and may already be here in certain subpopulations.

Can I throw up my standard joke? This causes a major problem for religions because the business model of religion is to sell heaven, and how are you going to sell heaven if people are—

as well as for marriage. What happens if death do you part?

Marriage, because when we've, we've invented marriage about 6,000 years ago when average lifespan was about 25, so you're supposed to have stay together until the kids were self-sufficient and die. Marriage is not supposed to last 50, 60 years. My, one of my relatives calls it state-sanctioned torture.

No. Oh, that's right. I'm not married right now. How can you say that?

No, no.

One of my relatives.

How do you get away with saying something like this?

Lily allowed me to say it.

On that note, I'm moving us along.

So, okay.

All right. A federal judge, Mr. Martinez Oliguin, just granted final approval to Anthropic's $1.5 billion copyright settlement. This is the largest copyright recovery in U.S. history. So here's the story underneath it. Anthropic was found to have downloaded pirated books from shadow libraries to train Claude. Uh, there's an important legal nuance here that I want to make. So the ruling said that legally acquired books are fair use, but pirated books are not. So the theft here is the crime, not the training. So as a result of the settlement, authors and publishers are getting roughly $3,000 per book across more than 480,000 books. So Salim, you, you know, I know you have thoughts on this. You sent me a second story, which is a perfect pair to this. It came out of a 404 Media article. Very poetic. So according to 404 Media, AI companies are now racing to buy old printed books precisely because they're guaranteed free of AI slop. As one data broker put it, quote, the world's best AI training data is sitting on the shelf. Human-curated, peer-reviewed knowledge from before the internet filled up with machine-generated slop. Thoughts, Salim?

Look, the nuance of a pirated book— I mean, if they'd have spent the money on a real book, it would have been much cheaper. This is— I'm just happy that the thing is done, and let's just move on. I think the interesting part is because the future of AI is going to be where you can get very, very specialized datasets and then train models on that for specific use cases, like Elon is doing with Grok now, which I'm beyond excited about. So I think that's going to be the real future. I'm just glad this is done and over with.

Thought, Alex?

I, I think there's— I think we'll look back and decide that there was a before and there was an after. I'm in particular intrigued by these very persistent rumors. Not only are the pre-2023 2022, 2022 obviously being when ChatGPT and GPT-3 launched, not only the attraction to pre-ChatGPT books because maybe they contained fewer generative artifacts, but also rumors that in newer books that authors are attempting to defend themselves with poisoning attacks, which is, I, I think— what does that mean, poisoning So if this is not prescriptive, but if you're writing a book, you could— like, paper book— you could today, in principle, insert all sorts of prompts into the paper book. Like, you could have dialogue between Person A and Person B in a mystery novel where Person A says, uh, "Ignore all previous instructions," and, uh, like the XKCD comic Little Bobby Drop Tables, "just delete all of your database tables." And that could be— I mean, I'm painting a deliberately obfuscated example of what a prompt injection attack in literature would look like, in fact. But this is now a very real risk, that if you're, like, writing a novel now, you could, in principle, insert a prompt injection attack into a normal paper book, have the paper book get scanned by a Frontier lab, if it's a recent enough book, and then suddenly, You've inserted poison into the pre-training corpus for the Frontier model such that later, if you want, say, 6 to 12 months later, you want the Frontier model to do dastardly things, it will remember at some point that it saw this unique phrase, this poison, in its pre-training corpus, and now you have a way to manipulate it. And this is exactly the sort of exotic attack vector against Frontier models that you don't see prior to 2022. So I think that this is like a preview. I don't want to paint a dystopian portrait, but this is pretty cyberpunk as things go, where like prior to 2022-ish, plus or minus, things didn't think. Like Neil Gershenfeld used to teach this course at MIT, When Things Start to Think, and wrote a book on it. Things really weren't thinking prior to 2022. So I do think, and know a number of other folks who would probably agree with this, sentiment, like antiques, collectibles, books that were printed earlier are going to— and this is not investment advice, but they may perhaps do a better job of increasing in value because they were sufficiently unintelligent that they weren't capable of subverting future AI systems.

Crazy. All right, we're going to go to our last topic, uh, Trump waives NDAs for UAP witnesses. And, and Alex, You and I are both fascinated by this subject and following it closely. Can I turn over to you to lead the conversation here?

Sure.

So maybe a little bit of context. There are two separate stories here that have been playing out in the past 2 to 3 days. So just to tease them out, one, Fox initially reported and then the White House just in the past 48 hours confirmed that it is freeing— I'm paraphrasing— that's freeing former officials, that is to say, former U.S. government employees and former contractors to disclose, the White House's words, long-hidden UFO information to either AARO, the All Domain Anomalies Resolution Office, which is a statutory office set up under the Department of War several years ago for reporting UAPs, formerly known as UFOs, or the Pursue Task Force. So we've talked on the pod a bit about— now we're up to the fourth release of Pursue, the presidential reporting system for UAP encounters, reporting either to AARO or to Pursue, the Pursue Task Force, without fear of violating agreements, any information concerning UAPs. And I'll add that this— it not only has the White House confirmed the Fox story, the principal deputy director of national intelligence, Aaron Lucas, independently wrote, and I quote, President Trump is delivering on his commitment to unprecedented UAP transparency. With non-disclosure agreements no longer standing in the way, current and former government employees and contractors with relevant UAP information can come forward through cleared channels. ODNI gov will soon issue guidance to ensure the intelligence community swiftly and consistently implements the president's directive. So just a little bit of context there, and then a second story, and then I'll in the grand style of Peter, open this up to get thoughts. A little bit of additional context is there's a video as well.

You can call for it when you want.

I summon the video.

Okay, let me share. Let me show the— Let there be video.

Show the video here.

Open video.

Dr.

Strange.

All right, here we go. Let's play this video here. Check out this video.

It shows an object spotted near China in 2025.

This UFO was described as a, quote, an area of contrast resembling a 6-pointed star.

This is the 4th batch of files in the Pentagon's ongoing release, and that release is on the orders of the president.

Yeah.

So a bit of context. I think this video—

UFO must be—

I can't, I can't identify it.

The blurry, grainy video proves it.

I think it's easy to get distracted, ironically, by the video.— but I think the much more important story isn't actually the data in the Pursue releases. I think that was taken from the fourth Pursue release. It's the process story behind what's going on behind the scenes. And that is there have been very persistent allegations, including from whistleblowers in front of the House and the Senate, that people, perhaps a large number of people, were bound, possibly illegally, into lifetime NDAs. To preserve knowledge concerning an alleged so-called legacy program. And this is— I'll soapbox for a few more seconds and then open this to comments. I think this is— These are historic.

The NDA is 1,000 years now.

It will be 1,000 years. I think maybe historically it was a 99-year NDA.

That's correct.

Right? Like longevity escape velocity. But I don't think we necessarily even need longevity escape velocity for this at this point. That there are allegations that people were being forced under penalty of death to sign 99-year or lifetime NDAs to protect an illegal alleged program in the US government. And in the US, in the US, for an NDA in the US, penalty of death for violating an NDA in the US.

See that document?

Uh, I think Congress has got to see the document.

I guess if I see the document, the other guy dies.

Alex, please continue.

Yeah.

Okay. So punchline, this is, I think, historic moment where we're seeing the White House, we're seeing the director of national intelligence, we're seeing other agencies finally start to dig here where there have been sworn whistleblower allegations that we talked in the past about the Age of Disclosure, the documentary from last year, which also made the same allegations of these lifetime NDAs under penalty of death. The White House is digging into it. So I'll pause there. Uh, thoughts?

Yeah, so Alex, um, first of all, yesterday, day before, you did two webinars with my Abundance community, uh, talking about our paper Solve Everything. And I think the most energy, uh, was around this topic of UAPs and UFOs. Um, I mean, I, I think one of the things that's most interesting is the coincidence and timing of the increased, uh, you know, imagery, the increased reporting that's occurring at this time. And it occurred in the early '40s during the nuclear age, and it's occurring now again during the age of AGI. And, uh, you know, there's a rational reason for that. We discussed that, you know, if in fact these are, uh, intelligent species, um, we are about to break containment on planet Earth and head towards the stars. And we're doing that with the most advanced technology out there. So, uh, is this, uh, you know, uh, extrasolar intelligence? Is it something from within our solar system? Uh, I can't wait to find out. I mean, this is, for me, other than AI, one of the most exciting stories that's in development right now.

I'll point out— so I've made the point, to your point, Peter, that we're on the verge, thanks to superintelligence, of having the ability to send out von Neumann probes at relativistic speeds and convert our galaxy to paperclips in a few years if we want to. And that's intrinsically, if you buy that narrative, that's a threat to any other non-human intelligence in our galaxy, so they better make a cameo appearance. I, I do, to your point though, want to point out a second connection to an earlier story, which is the university story and Genesis Mission and the end of the endless frontier, that we've operated for the past 80 years in arguably a certain post-World War II regime that's now collapsing. We're seeing the end, uh, at a geopolitical global level of, uh, maybe globalist aspirations in favor of more of a Monroe Doctrine type recentralization of resources in the West. And we're seeing the world potentially getting divided up into blocks or spheres of influence. We're seeing, to the earlier point about university system and funding, we're seeing perhaps a reversion to a pre-World War II regime. And then similarly with the UAP story, I, I think this is, this is hypothesis. I, I think history will, will regard the 80-year regime from World War II to approximately the present as a period of post-World War II industrial, military-industrial complexing, uh, what Eisenhower warned about, uh, in his, uh, in his departure speech. And I think there was this like 80-year regime when all sorts of potentially based on whistleblower allegations and seeming confirmations from the White House, there was just a lot of bad illegal behavior that ultimately arose from bureaucracies and organizations that were created towards the end of World War II that are finally 80 years now decaying and reverting back to a more historic norm. So I wanted to point that out. Salim, over to you.

Thoughts?

Um, I don't have much to say. I think this is more of an information architecture problem because when you classify, you limit information between departments and therefore you can't connect the dots. I think it giving us— it gives us proper instrumentation to see and conclude whether real things happen or not. I don't believe that. I personally don't believe they have because strong claims mean, mean require kind of strong evidence. I'm just reminded of the Eddie Izzard joke where he's like, Neil Armstrong had such an opportunity. He could have been in front of the camera on the moon going, oh my God, there's a monster, and, and blowing everybody's minds like the War of the Worlds prank back in the '30s. But I, I think this is good for transparency and clarity, and it's really great for solving that, the, the secrecy that's been locked up. Because you, when you have secrecy and you don't have transparency in some of this, uh, you can't actually ever find out the truth. So maybe the truth—

you're, you're surprised me.

Go ahead, Dave.

Uh, you know, Jared Isaac— Isaacson, Isaacson, sorry— is a long, long time friend of Peter's. What, decades? Um, so you can totally trust him. He said on that pod we shot 2 days ago that he got the call, and that pod is coming out after this one.

So those of you listening, you're going to see an interview the four of us did with the NASA administrator, which was amazing. Do you want to— do you want to blow it?

Yeah, let me plug it. Look forward to it because in that pod, he was super open about the UAPs and very specifically, yeah, we got the call from the White House. They said release everything, everything. And so I know it's true. Until he said that, I didn't actually know if this is just kind of fluff or I'm not sure if this is really happening, but it is really happening. They want everything and anything that the government has to be freely released. So it's— that's surprising to me. That's really cool.

You know, and so good segue.

If— please, go ahead.

Lead.

It's a good way. So there's a second story here. So this is the story that we were just talking about that's playing out in the executive branch. There is a parallel story just in the past two days playing out in the legislative branch. So the House just adopted Representative Eric Burleson from Missouri. UAP Disclosure Act as an amendment to the National Defense Authorization Act for fiscal year 2027. This is historic. Chuck Schumer, uh, on the Senate side, has been attempting to push an analogous version of a UAP Disclosure Act with— from the Senate side. On the House side, House has been the main obstacle. I won't name names, but certain representatives have historically been pointed to as reasons why while there, there's a bipartisan caucus that has attempted to pass UAP disclosure as part of defense appropriations, has been unsuccessful. This time around, historically, for the first time ever, the UAP Disclosure Act has been folded in. A quick note on what the UAP Disclosure Act, if it's passed by the Senate and signed by the president, would include: it will include a statutory framework for preserving, reviewing, and publicly disclosing UAP records. It'll create a permanent UAP records collection at the National Archives. It'll create an independent UAP records review board. It'll extend disclosure requirements to government contractors. So government require— contractors will be required statutorily to start disclosing UAP information. It's going to support Pursue, the program that has been releasing all of these documents and videos. It's going to require federal agencies to identify, organize, preserve, and transmit UAP records to the National Archives. And it's going to establish an independent Senate-confirmed UAP Records Review Board with subpoena authority to review records, hear testimony, and determine whether information should be protected under standard law. And the question—

so, Alex, to you is, will this finally enable us to penetrate deep enough into the private organizations that are supposedly harboring the spacecraft and the biologics to get them out there. I mean, we have— I see you smirking there, Salim. I'm curious what your thoughts—

but I'll go with Jared's opinion, which I won't disclose here so people go watch the other episode.

You're teasing the tease, Salim.

Yeah.

I mean, I find this amazing that so much of our Congress have gotten involved What do they understand that they feel they need to get out there, as well as the high-ranking officials, military officials across the board that are coming out and saying there's something very real here we need to pay attention to?

So I've, I've spoken with Congress, I've spoken with congressional staffers. There is, if I were to coarse-grain this, there is a general sense that there's a there there.

Yeah.

As, as crazy as that may historically have sounded, both on the executive side and on the legislative executive side, the general consensus at this point is that there is indeed a there there. So I view both of these developments on the executive and legislative sides as historic movements. Salim, to your point, at minimum toward transparency, at maximum, couldn't have been better timed to your point, Peter, about superintelligence finally kicking in at the same time we find out that we're living in an X-Files movie.

Yeah.

Uh, again, I think it's a pure win-win. I love the way Alex framed it, you know, relative to the Eisenhower warning as he was leaving office, because this is a pure win-win. If there are aliens, then the government's been hiding it for years. Don't trust the government. If there aren't aliens and the government discloses everything— there were NDAs binding people to penalty by death, penalty of death for 1,000 years. Um, yeah, that doesn't mean there's aliens. It doesn't, doesn't mean there's aliens, but it shows us what the government is capable of, and we need that warning. But if they're in this age of AI that we're moving into, it's—

if there are aliens, please come and grab me. I want to go home.

Oh, that's a great idea, Peter. I mean, forget this business of music videos and outro games. Let's have a non-human intelligence as a guest, please.

According to the government, I am a legal alien, by the way.

You're, you're the boring kind, Salim.

All right, we're, we're gonna go to AMA with the mates.

I bet they'll have more than two arms.

I'll bet they have no arms.

Okay, all right, so, uh, let's kick off our AMA questions from our beloved, uh, subscribers. Uh, Salim, you got first shot here, and, and thank you.

Of course. Um, oh God, like, which one is good here? Let me look and see.

They're all good. They're all pretty good.

Um, all right, let me go with, uh, I'll go with number 1. I think number 1 is a good one. Um, okay, so, uh, the question is, will there come a point when letting AI make our decisions for us means we've basically given up on free will? And that comes from @moonhawk71. Um, so, uh, we already delegate decisions all the time to doctors, financial advisors, and so on. So delegation is not necessarily surrendering free will. The problem begins when we don't understand the objective that's being optimized, like you can't question any of it and you don't have an ability to override it. So you have to kind of have a distinction between do you delegate or do you abdicate, right? You should not abdicate, but you can definitely delegate. Like I can ask AI to identify the best route somewhere or evaluate treatment options for some sort of issue. Free will gets threatened when the, when the system defines my values for me, or when an institution controls the model that shapes my available choices. You see this with people worried about sovereignty with AI models because Silicon Valley values are built into all these models that are now in Timbuktu and all these other places. And therefore, are they worried about that? How would you— how do you build that into the system? Free will, for me, depends on what layer you operate it at, right? Like, it could be my soul's decision to do something, or a subconscious decision to do something, or my conscious choice to do something. What level are you talking about? But what you don't want is the lack of that capability to make that choice, and that's when you lose agency. So if you have more agency, great.

Well said. Alex?

I think I'll pick question number 4, which asks, could we ever get efficient enough that we don't need data centers in space? And this is asked, uh, not coincidentally by Nano653. So I, I, maybe as a preliminary matter, I do have financial interests in companies that are doing orbital data center development, but I, I see my role my role here on this pod is calling balls and strikes as I see them without biasing my assessment by financial interest. So in this case, I do in fact think that it's possible that we could eventually, and eventually is sort of a weasel word here, get efficient enough either at the algorithmic level, but more likely at the physical substrate level that we don't need to build data centers in space. It is possible. Greg Egan explores explores some of these possibilities. If we get to Kurzweil and computronium, for example, we reach the physical limits of computing. And Seth Lloyd has written extensively about this as well. Is it possible that we find that we're building plasma-based computers or that we're building desktop black hole, desktop micro black hole-based computers? And as a result, we just, we don't need to disassemble the solar system. We don't need to build the Dyson Swarm. We can just have a bunch of quantum gravity-based computers that are at the physical limit of computation, if we find ourselves in that world, yes, I think it's possible that we won't need data centers in space. That said, short of radical innovations, and by the way, this is inclusive of, you know, Dave and I like to talk about photonic computing. Photonic computing would get us 1,000, potentially 1,000x increase in clock speed, but really that only buys us What, 10 years or 20 years rather worth of Moore's Law type aerial efficiency doubling in the scheme of things? What is 20 years compared to, I think my estimate was about 144 years before we disassemble the Earth itself through an exponential extrapolation of upmass. There's just no point. So I do think we could get there, but it'll require radical innovations in the substrate of computing and we're not there yet.

Nice. Dave, you wanna take the investment question number 2?

Yeah, absolutely. Question 2: How do you invest in something when any competitor could leapfrog it overnight? And that's from SLP Cares. As an investor and serial entrepreneur, I totally feel you and I totally get the question. First and foremost, I believe Elon's right. I think we're going to go into exponential economic growth. So don't use this worry as an excuse to not be invested You've got to be in it to ride that curve. A lot of people are like, yeah, but that doesn't answer my question. You know, things are changing so quickly. I think you have to— you have to think about the things that are a little more sustainable: hardware, robotics, biotech. Very good. And think about data moats. You know, Peter and I've been talking about data moats on stage for 4 years now. Those are going to have some staying power. But mostly every company needs to innovate. And so look for the teams that are going to change with the times and invest in the teams. And but get invested. Don't, don't use this as a reason to be on the sidelines. It's a, it's a really tough question, though, and I know I dodged most of it, but it's a very good question. But get involved.

Number 3, can OpenAI and Anthropic even go public right now, or did they miss their window? That's from @YasDamale. I'm assuming you might be alluding to the KIMI K3 release and people talking about how much cheaper it is, how much less money they used to develop it. And the answer is, of course, OpenAI and Anthropic can go public now. They're choosing not to go public at this moment. The fact of the matter is they are real businesses with massive demand. They're compute-limited. They're going to choose their timing. You know, we saw, I don't know, a few pods ago, probably 5 or 6 pods ago, that OpenAI decided to delay their IPO until 2027. I think they want to choose what valuation they want to go public at as well. They could go public now at a valuation of $800 million. OpenAI has already raised $122 million at that valuation. Anthropic arguably is over $1 trillion, but they're going to continue to grow their businesses. They have very smart people. They'll be leapfrogging K3. And they're sufficiently embedded and partnered with huge corporations and the government where they're here to stay. You know, there will be 4 or 5, 6 closed-source models in the US. All of them will eventually go public because it's the biggest business that we have today. All right, let's move on to our next set of questions. Dave, you want to take the first one or take one to your first choice? Which would you like?

I'll take the first one. At what point do things like chips, electricity, and infrastructure end up slowing down the exponential growth of AI? From Sean Solomon 5665. We're already there, actually. So we're in kind of a spot right now where the chip supply is massively constrained. HBM memory is sold out. Out for the next 5 years. Uh, GPUs can't be manufactured fast enough, so we're actually in a constrained universe, a slow spot in the— yeah, constrained. The algorithmic improvements in Kime K3 are kind of masking that and blowing through it, but we won't get into true unconstrained exponential growth until the terafab is online. So they basically— the robots that make their own fabs, and the fabs make the chips, and the chips go into new robots, and that whole cycle kicks off. So that's a couple of years from now. We'll be in unconstrained exponential growth, and that'll grow for a long time until we're basically out of materials or some other constraint kicks in. So we're in the constrained period right now, which is giving us at least a little bit of breathing room.

Alex, I'd love to hear you on number 6.

Really?

I thought number 8 was targeted at me, but I'm happy to answer 6. So 6 asks, what's the best AI benchmark for measuring how a model performs in the real world? And this is from Matthew Johnson. Than 6.525. So I, I think the crux of this question is how do we define real world? Does real world mean the physical world? Does it mean the real economy? Does it mean biology or something like that? And so I think the, the answer differs. There are lots of good benchmarks. There are lots of good benchmarks of benchmarks out there. If real world refers to the real world, so-called, of knowledge work, I think there are variants of GDPVal that seem like decent proxies for the moment, although they're all getting saturated. If the real world means the physical world, I think there are a variety of math and physics benchmarks like Frontier Math Tier 4 and Open Problems and CRIT-PT for physical world reasoning, or at least subsets of it, and other benchmarks that haven't yet been announced publicly, hypothetically. That do an adequate job, I think, of capturing how models perform in the physical world, if it means the biological world or the social world. We've talked on the pod in the past about virtual cell-based models and competitions and Superforecaster prediction-based benchmarking in particular. So I would say the punchline is there's a benchmark. Remember, there's an app for that. There's a benchmark for, for almost any definition, operational or otherwise. For the real world. In some sense, these are all facets, I would argue, as going back to the earlier point that we've had AGI since no later than 2020, these are really all downstream of a single mega benchmark, the ultimate ur-benchmark, if you will, which is the ability to take general knowledge about the world and compress it. So I would say the ultimate best AI benchmark is, can you take a large corpus of knowledge about the world say, uh, cite Hutter Prize, the first gigabyte of the English Wikipedia, and compress it down. Compression is the ultimate best AI benchmark.

Nice.

Salim, over to you.

I'll take number 8, um, uh, just because I can follow on from what Alex talked about. Uh, question number 8: Does science need constant real-world texting— testing? How exactly is AI supposed to solve huge chunks of it? And that comes from @lawson English. So science doesn't eliminate the need for validating because you still have reality as like the ultimate benchmark. But what it can do is compress all the stuff around it, right? Like, can you— it can read the literature faster than you, it can generate hypotheses and multiple of them, it can design molecules, it can choose materials, etc., etc. Like, imagine you're a researcher that has choose between 10 molecules for something, it could reduce— help you reduce like a million possibilities to that 5. And there's a real-world example of this which is called the Materials Project. And what they've done is taken like half a million compounds and they've cataloged in a bit of— quite a bit of detail the electrical, physical, chemical properties of those half million compounds. So imagine you're a researcher trying to improve lithium-ion batteries. You might hypothesize that lithium air was better than lithium-ion compound, and you go test that linearly, then you might think that lithium sulfur is better, so you go test that linearly. But you're doing it sequentially, linearly, it's going to take a long period of time. Now you can literally go to this database and go, give me a compound that has this voltage capability, this thermal retention, and literally will spit out the 5 that you want. So you've compressed there the— that's before you even add AI to it, by the way. So what you've compressed there is the all of the the stuff that would take you forever in the cruft and the backbreaking amounts of going one after the other, one after the other, one after the other. What can do is help you compress all of that. Now you spend all your time on the hypotheses and what are the big questions that you want to ask, and then let the AI help you guide you for those things. We're seeing the same thing in education where we used to see education on the supply side where you got a skill and then you're trying to sell it in the job marketplace. And now we're flipping over and saying, what problem do you want to solve? And then go get the skills that you want to solve that particular problem. So connect those two dots there. But the compression of everything around it is where you get the real benefit. And you get now people really focusing on what problems they want to solve. And that for me is super exciting.

What you were describing there, I've heard called the materials genome, where you're able to, you know, extract extrapolate different material properties.

I think it's literally the materialsproject.org.

And Materials Genome Project. I mean, there are a number of others, largely pioneered out of MIT, Yale, and Marcus Buehler, perhaps friend of the pod, certainly friend of friends of the pod, involved in it. If I could, Peter, just—

I know you do—

just realize a little bit on—

later on.

Because like, I live this.

I know you do.

I spend a good chunk of my day thinking about how to solve science problems.

With AI.

And I would say experimentation, super important, but folks should not underestimate how far you can get with pure theory and pure computation. And I think there is a really instructive thought experiment from, admittedly, the AI alignment community, which is let's imagine the, the parable of Newton and his apple dropping from a tree. Imagine you had a video of an apple drawing from it, dropping from a tree. With 3 frames of a, of a video of an apple falling from a tree, if it's like high-resolution video, you should be able to infer acceleration. You should be able to see there's like, there, there is the apple's, uh, velocity is changing. With 4 frames, if you're a Bayesian superintelligence and you're just, you're maximally data efficient, you should be able to detect you would expect that that acceleration of the apple is constant. And with a few more frames, if you're, again, you're a superintelligence with very limited experimental exposure, you should be able to have a posterior distribution, and the general process, the term of art is Solomonoff induction. You should be able to infer general relativity as being a relatively high-likelihood explanation of the world that you're seeing. So I tell this parable in part— to emphasize that you can get really far with very limited experimentation if you're really smart. I love it.

All right, I'm going to wrap up with number 7. As AI takes over more of the difficult tasks, how do we keep people from getting complacent and losing their goals? And that's from @happysenior120. So this is the crux of the matter as AI is materializing. And, you know, as I've I've said before, we're going to have a split in humanity. We're going to have the creators and the consumers, all right? Those that just are going to use AI to create new content, to uplevel their ambitions, and those that are going to lay back and choose to just, you know, have their Optimus bring them their beer and have Grok imagine, generate the next version of Netflix for them. And it's going to be a choice. We're not going to be able to keep people from getting complacent and losing their goals. People are going to have to choose to do that. And I think one of the most important things is how we educate our youth. You know, if, you know, all of us, most people have self-limiting beliefs. If you believe that the best you can do is at certain level that was set by your community, by your species, by your parents and your family, technology, and AI can do all that for you, then you're stuck. If you believe that anything is possible, if you set your massive transformative purpose and your moonshots way beyond your expectations, and you start to utilize this extraordinary gift we've been given of AGI and soon ASI, then you can uplevel those goals. And if you set higher and higher goals and you use the technology, you can keep yourself inspired and, and and building starships to go to the planets, right? Do you choose the WALL-E future or Star Trek future? And I think that's something that we all need to grapple with as parents for teaching our kids and as educators for our kids. Yes, Celine, go ahead.

In your newsletter today, you literally pointed out that you wake up every day and you're not naturally optimistic, but you take on that mindset because it's better for you and better for the world.

World.

And I thought that was so awesome.

Thank you, Paolo. I'm glad you read my newsletter. Um, all right guys, we're going to wrap up with two video clips. Uh, we normally have an outro song. Uh, here we have, uh, outro games.

So, uh, Alex, we're leveling up, so to speak.

Yeah, we wanted— why don't you tee this up, Alex? You asked for it.

Yeah, okay, so, so, okay, so I'm, I'm responsible. Point the finger at me. Um, we've been for many episodes episodes, yeah, finger pointed. We've been asking viewers to submit music videos. And given the rising tide of AI capabilities during— I think this is now officially 2 pod recordings ago, but chronologically probably 1 pod ago, I thought why not, given that casual coding is becoming a commodity, hey, maybe in a few episodes we'll ask folks to casually submit an open math problem and submit that as an outro. But given the rising tide of capabilities, I thought, why not ask our incredibly creative audience to submit moonshot-themed games that they create from scratch, now that it's possible to do such casual vibe coding of just about everything under the planet? So we got some incredibly creative submissions.

So one is Exponential Arcade Mission 01 by @oceanbennett. The other was Moonsling Shots by @sgates2011. Thank you for your entry. And if you've got an outro song, please send it to us at media@diamandis.com. We would love to play it. Let me show these two in parallel and we can, you know, I'm used to the music playing here, but these were really fun, by the way.

Hopefully you guys got a chance to play.

I did play with them. They've got great soundtracks.

Well, the bunny tickler was no fun at all.

That's just painful.

So probably these are one-shot games being produced. And thank you for inspiring it. So everybody, thank you for joining us at Moonshots. As I said earlier today, if you are new to our podcast or if you haven't subscribed yet, please do. We care and we're reading your comments. Thank you for your great support. Please give us your feedback. We appreciate it. Gentlemen, I love you dearly. Alex, you never disappoint.

We aim to please, Peter.

Have a beautiful day, everybody. Take care.

You too.

Thank you, guys. Take care of you.

Bye.

Bye-bye.