Anthropic’s $2T IPO, Elon Predicts Neocloud Death, and China’s Kimi Catches Up | #306

The mates sit down with Emad Mostaque to discuss the latest developments in AI, from Anthropic’s rapidly growing revenue and potential $2 trillion IPO valuation to Elon Musk’s prediction that neocloud providers are headed for extinction. They also explore CoreWeave’s 30% stock decline, the rise of Chinese AI models like Kimi, and whether mounting concerns about valuations and infrastructure spending signal that the AI bubble could be nearing its breaking point. Get access to metatrends 10+ years before anyone else - https://qr.diamandis.com/metatrends Peter H. Diamandis, MD, is the Founder of XPRIZE, Singularity University, ZeroG, and A360 Salim Ismail is the founder of Open ExO, a GP at Exponential Venture Capital/The Organizational Singularity Fund and a sought after global speaker and thought leader. Dave Blundin is the founder & GP of Link Ventures Dr. Alexander Wissner-Gross is a computer scientist and founder of Reified Read Emad’s latest papers exploring the future of society, law, personhood and governance: https://ii.inc/common-wealth Read Emad’s Book: https://thelasteconomy.com Anousheh Ansari is the CEO of XPRIZE, an Iranian-American entrepreneur and engineer, and the first woman to travel to space on a privately funded flight. – This episode is brought to you by: Get the blueprint for generative media https://goo.gle/startupgenmedia Go to Blitzy to book a free demo and start building today: https://qr.diamandis.com/blitzy Your body is incredibly good at hiding disease. Schedule a call with Fountain Life to add healthy decades to your life, and to learn more about their Memberships: https://www.fountainlife.com/peter _ Connect with Peter: X Instagram Substack Website Xprize A360 Connect with Dave: Web X LinkedIn Instagram TikTok Connect with Salim: LinkedIn X Join Salim’s 10X Shift Subscribe to Salim’s YouTube channel Exponential Venture Capital Connect with Alex Website LinkedIn X Email Substack Spotify Threads Connect with Emad ⁠⁠⁠⁠⁠⁠⁠⁠ X ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠LinkedIn⁠⁠⁠⁠⁠⁠⁠⁠ Learn about Intelligent Internet: https://www.ii.inc Read Emad’s Book: https://thelasteconomy.com Listen to MOONSHOTS: Apple YouTube Follow MOONSHOTS: Instagram TikTok X Threads – *Recorded on October 27th, 2026 *The views expressed by me and all guests are personal opinions and do not constitute Financial, Medical, or Legal advice. Learn more about your ad choices. Visit megaphone.fm/adchoices

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

• 0:00Peter Diamandis?

  Guys, I'm curious, have you been tracking what's going on with the protests and revolts in France, Italy and Spain? I mean, it's kind of insane.

• 0:09Speaker 01

  It is, but I've talked to people on the ground in France and they're like, the media's way, way overblowing it. There's like a few bad people and everybody else is just moving around drinking coffee while the things are happening. So it's very split. I don't know which way to go. You could go either way on it.

• 0:26Peter Diamandis?

  Yeah, I mean, it's like, you know, October 17th, there were half a million people marching across France. They had 2,400 arrests, you know, looting in Paris, Marseille and Lyon. And they burned down that beautiful 19th century hall in Limos, up in flames. I mean, I hate that stuff.

• 0:47Speaker 01

  There's a bunch of paid crusaders. They suspect Russia naturally on this, that are kind of stirring the pot really badly. And that's antagonizing everything, obviously.

• 1:00Peter Diamandis?

  Yeah, but I mean, underneath it all as well, it's like there's a 20% unemployment rate in France, Italy and Spain. That's got to hurt. It's got to hurt.

• 1:11Speaker 02

  Thank goodness, we have no protests ever in this country.

• 1:16Speaker 03

  You know, we always say the French are revolting.

• 1:21Peter Diamandis?

  You know, I wrote, you know, I wrote a sub stack a few months ago. I think we talked about the pod, that the most dangerous demographic in history is about to get a lot bigger. And what bugs me is if you look across the last 200, 300 years, from the French Revolution to the Bolsheviks, to Iran in 1979, to Arab Spring, they all share one ingredient, you know, young men, testosterone laden, 18 to 28, educated enough to know that they're missing the opportunities and they're blocked along the way. And I mean, this is, you know, Dave, when we talked to Elon about, you know, UHI and Sibyl unrest, and you said we're going to have both. I mean, this is probably my single greatest concern.

• 2:06Speaker 04

  Good place to keep an eye on it too, because, you know, there's no data center activity, no power, no, you know, incredible educated population that should be capable of amazing things, but without the government getting ahead of either compute or data centers or power, you're absolutely screwed in the age of AI. And when you look at the way the US voters act, they vote for, you know, UHI within the borders, but they will never vote to bail out France or Spain. France actually has nuclear power, it might be okay, but Spain, Italy, there's no sentiment in the US to help or in China to help. So if you don't take control of your own country and get your government, you know,

• 2:51Speaker 04

  get this act together, the 20% unemployment rate is only going to go up.

• 2:55Peter Diamandis?

  Yeah. Yeah. Yeah. Eman, what's it feel like over in England?

• 3:01Speaker 03

  It's a bit depressing here, like they started sending emails to, letters to all the rich people and entrepreneurs, where rich is above 200,000 pounds of earnings, saying, I'm your new compliance audit advisor, you know?

• 3:16Peter Diamandis?

  What does that mean?

• 3:18Speaker 03

  The government sets up a compliance audit liaison team to make sure they're compliant with all your taxes and pay a bit extra. And then all these policies coming in, the entrepreneurial class are not feeling very happy about here. A lot of people are about to leave. We've got the budget coming up. And then, yeah, looking over to France, it's not surprising, but in France, it's basically impossible to fire anyone. This is the other thing.

• 3:42Peter Diamandis?

  Do you know they spend 57% of their GDP on social programs? It's more than any communist or socialist country in the world. And they still, yeah.

• 3:51Speaker 04

  Well, and you went on a side effect, so that people are fleeing to Toronto and Waterloo like crazy. Salim, I'm sure you can see it, but when you walk around Waterloo, everybody is the top of their nation from all over the world, but you're used to seeing people coming from Asia and India. Now floods of people are coming from Europe as well, because if you can't get into Silicon Valley or Boston or New York, you can often get into Canada. But anything to get out of the slow zone in Europe and get into the AI zone, which, you know, Waterloo has become.

• 4:25Peter Diamandis?

  Eman, when are you joining us? When are you, when are you coming to this side of the world?

• 4:30Speaker 03

  Let me finish building a few things first. I can read the voice from across the pond until then. Actually, there's something very interesting in AI. Poolside, which was the GitHub CTO, I believe, for Asoc, went to France and they raised 200 million, aided by the French government, and then went straight back to America to release Laguna and then sold to Nvidia for 6 billion. So now I think all eyes on the AI sphere are on Jan Le Cun's AMI Labs, which was a French company, but again, may become an American company sooner rather than later.

• 5:00Speaker 01

  I might be on stage with him in a week, so I'll ask him directly.

• 5:04Speaker 03

  Yeah, are you French or?

• 5:06Speaker 01

  No, well, there's a whole lot, that's a whole other question.

• 5:10Peter Diamandis?

  Yeah, but you know, we need to keep an eye on what's going on there. I just think these are the early seeds that we need to watch.

• 5:18Speaker 01

  You know, it's easy to mobilize a lot of people around what you're against. It's much harder to coordinate that around what you're for. What's the alternative, you know?

• 5:27Speaker 03

  Yeah. I mean, Salim, I think I'd be interested in this, like as you talk to all these corporate leaders around the world, are they expanding their graduate programs? I very much doubt they are. It'd be like, that's the first thing to go almost, right?

• 5:39Speaker 01

  Not necessarily. We're seeing some weird things. For example, a bunch of people are increasing graduate hiring because young people armed with AI are much more valuable than 50 year olds moving spreadsheets around. And so some companies are actually increasing and replacing older workers aggressively and have increased graduate programs because they're just so much more AI native. But generally, it's a problem.

• 6:04Speaker 04

  That feeling that we missed the boat, that we can't catch up, when you look at the charts later in this podcast about how the open source models are getting closer and closer in time to the frontier, the path to sovereign AI has never been clear. And if countries were to act, they actually have a very, very clear path forward. I don't have any confidence that they'll act because they haven't acted so far, but if they were to jump on it and say, look, we need an emergency national measure here for power, chips and sovereign AI, you can succeed with probably 90 to 99% probability of success if you can get rallied around it. But yeah, I just don't see any, you know, ground swell to get rallied around it. I, you know, we just need one or two, maybe a South American country to prove to the

• 6:48Speaker 04

  world that it is possible to get on the map and that'll, that'll crack it open for everybody else. But, you know, the window is right now and it's a relatively narrow window too, because the AI is accelerating at this ridiculous RSI pace, but there is an opening right now. You'll see it later in the pod.

• 7:04Speaker 02

  I think we look back at this and we say, was the singularity was the best of times, it was the worst of times. It was the time when the country that arguably invented or helped to invent nuclear energy and should be absolutely drowning in energy, too cheap to meter, has students protesting that they're not getting proper heating or air conditioning in their schools. There are many, many profound ironies here, but I do think Europe more broadly needs to, if anything, not pace itself and catch up to the singularity. And maybe we'll see more activities like this, whether it's ostensibly through educational grievances or budgetary grievances or just air conditioning that's missing in action.

• 7:45Speaker 02

  Employment grievances, social grievances, public service cuts, all of these are intimately tied together. One might say, yeah, sure, there's a bit of a protest culture in France. They've had a number of revolutions over the years, maybe culturally in, to some extent in their DNA. But I also think at the same time, Europe is very well positioned to become a living history museum if it doesn't catch up in energy and compute.

• 8:09Speaker 04

  Well, I'll tell you, the US is in great shape in every way except for power, which we've talked about several times. But yesterday I had a meeting with the guy who ran data centers for Google for, for about seven years, a fascinating conversation, but he's, he's got a US data center fund now. And the gap in power between the US and China is going to expand to almost 10 to 1. It's just crazy how far behind the US has gotten in power. And he was saying, look, you know, there, there's a lot of push from the White House to catch up, but after this, the next election cycle, things will probably get frozen again. And so, so Eric Schmidt has been pointing out many times that we need to work more closely between the US and Canada, because Canada has a lot, I mean, a lot of power

• 8:53Speaker 04

  and not a lot of chips. And so there is a very natural partnership to be done there and there's been way too much friction in the last couple of years. But even with the US and Canada working closely together, China is miles ahead in power generation.

• 9:05Peter Diamandis?

  Until SpaceX and Tesla bring on, you know, 100 gigawatts of solar each.

• 9:12Speaker 04

  Yeah, true, but you know, the gap in time there is really scary. It's, you know, solar is, you know, space based data centers are going to be enormous. The Dyson swarm will be everything in the 2030s. But RSI is the next two years. And really, you know, a good strategy around AI really focuses on what are the exact moves for the next two years? Because after that, I mean, we're talking about intelligence like no one's ever even imagined. And it's really, really hard to predict the effect of new physics, new chemistry, all of Alex's breakthroughs. I mean, those are going to change the landscape tremendously.

• 9:44Speaker 02

  This is the new physics episode of Moonshots.

• 9:46Peter Diamandis?

  Yeah, it is. You know, it's, it's almost impossible to look more than two years ahead. It's, it's difficult to look one year ahead, for God's sakes.

• 9:53Speaker 04

  Yeah.

• 9:54Peter Diamandis?

  And, you know, to all of those in France, the country that brought us the word entrepreneur, we wish you, we wish you best.

• 10:06Speaker 05

  Now that's a moonshot, ladies and gentlemen. This episode is brought to you by the Abundance Summit and Link Ventures.

• 10:16Peter Diamandis?

  Welcome to Moonshots, everyone, your number one podcast in all things AI and exponential, your front row seat to the hypersonic tsunami that we call the singularity. Today, the fabulous five are here with us. Alex Weisner-Gross, our resident ASI, who spent his weekend casually rederiving the fine structure constant from a single sentence, and is delivering a new theory of physics. More on this later in the episode. And Dave London, my dear fraternity brother and buddy, AI VC extraordinaire, the man who's backing Unicarm founders out of MIT and Harvard. Saleem Ismail, author of The Exponential Organizations and the Organizational Singularity, frequent flyer legend and our warlord against linear thinking.

• 11:01Peter Diamandis?

  So you're clearly not at home, Saleem. Where are you today?

• 11:05Speaker 01

  I'm in Toronto because I'm doing a talk here to about 400 CEOs tomorrow and then flying back Thursday.

• 11:12Peter Diamandis?

  Okay. You're sure you're not at a conference, at a concert?

• 11:16Speaker 01

  I'm not at a concert. I will report if I am.

• 11:19Peter Diamandis?

  Okay. And Iman Mustak, founder of Stability AI, now building the intelligent internet and the man most likely to explain humanity's future economy. Iman, I really want you to weigh in on where the economy is going on one of these episodes. You've written an incredible book. And I'm Peter Diamandis, your host and data-driven optimist. I just want to start out by saying something to all you guys. I feel so blessed to have you as friends and mates here. I mean, you guys are extraordinary, and I love the way you all think. And honestly, even though these are intense episodes, this is my favorite time of the week, spending these couple hours with you.

• 12:00Speaker 04

  Well, and also the team behind the scenes whittling down every fact in the global economy to the subset that you absolutely need to know. It's gotten better and better and better. I feel really lucky to ride that wave.

• 12:12Peter Diamandis?

  Yeah, it's awesome.

• 12:13Speaker 02

  Singularity is spiky, and we are the spikes.

• 12:17Speaker 01

  I was cursing this whole effort over the first few months, but then I realized it absolutely forces me to be totally current. I can't skip a beat. Multiple hours a week, I think Peter, you and Alex spend the most, but good lord.

• 12:32Speaker 02

  Are you still, Saleem, suffering from future shock? Is tofur in your head?

• 12:37Speaker 01

  Everything. It's future shock. The tagline is, how do you move from future shock to future shape? That's the tagline.

• 12:45Speaker 02

  Beautiful.

• 12:46Speaker 04

  Another t-shirt.

• 12:48Peter Diamandis?

  Our mission here on Moonshots is to help you understand what just happened, what it means for you, your family, your business, and to keep you abundance-minded and optimistic as we go through the singularity. You know, I was told by a number of viewers that you've stopped watching CNN, the crisis news network, and replaced it with Moonshots. Honestly, that is probably the single best compliment we could ever get. You know, our amygdala is wired to pay 10 times more attention to bad news than good news, and the news networks know it. You know, if it bleeds, it leads. And just to remind you, all those negative stories on the evening news are basically wiring your neural net for fear, and fear is the worst place to encounter the future from.

• 13:30Peter Diamandis?

  All right, here's the number for the week, 16.3 billion. That's Anthropic's revenue in the first half of 2026, over 14 times higher than their 1.16 billion from a year ago. Dave, have you ever seen anything ramp this fast?

• 13:46Speaker 04

  I don't know, of course not, but I don't think anyone has.

• 13:50Peter Diamandis?

  Yeah.

• 13:51Speaker 04

  I can't. I mean, coming from a cold start, it's got to shatter every record by far. So anyone who says they've seen this movie before has never seen this movie before. This is new in the world, as is everything going on right now. It's all unprecedented.

• 14:05Peter Diamandis?

  You know, if you're new here, please hit subscribe. We publish twice a week because the world is speeding up. It'll never be slower than it is today. It's worth mentioning that you can follow us on X. Our handle there is at Moonshots underscore pod, and that's where we post our most provocative clips from each episode.

• 14:23Speaker 01

  Can I throw out my favorite comment that I saw?

• 14:26Peter Diamandis?

  Yeah, sure.

• 14:27Speaker 01

  Corey Hayes 9428, two days ago, said, "This podcast is recursively self-improving." Thank you.

• 14:34Peter Diamandis?

  I grabbed two comments as well from my last episode. At Adam Usan 5 said, "How are you guys not getting 100 million views? This is the most important pod on the planet." And Ozilin49, what's that?

• 14:51Speaker 02

  The obvious answer is, tell the viewer, "Watch it 100 million times."

• 14:55Speaker 04

  Yeah, give us 100 million likes, that'll solve the problem. Now, when you walk around the streets and you talk to people at random, too, the vast majority are missing the singularity. So those two things go hand in hand. We have the viewers that we want, you know, the ones that are riding the wave and not getting swamped by the tsunami. So it's fine. I'm happy with the subset.

• 15:14Speaker 03

  Maybe we should have, like all of our agents, have an agent version of the podcast for the agents, and that way we can turn times our views.

• 15:21Speaker 02

  I might be anthropic selection bias here. Basically, if you can't hear what I'm saying right now, you shouldn't be watching this podcast.

• 15:29Peter Diamandis?

  Oh, no. One more comment from Ozilin4912, quote, "The best AI podcast out there, hands down. None of you guys need the money. What an amazing give-back project you're providing us." So thank you for that. Appreciate all the subscriber love. You know, your positive comments fuel us and reinforce why we're doing this. So today, we're going to cover seven stories with one through line. The AI economy is splitting into winners and wreckage, and the science is getting stranger and more wonderful by the week. All right, so let's buckle up. It's time for another week of the singularity.

• 16:10Peter Diamandis?

  So I'm going to kick it off with a discussion on the recent AI sell-off and ask the question, is this the AI bubble starting to pop? So this month, the neo-clouds, companies that borrowed to buy Nvidia chips and rent them out, got hammered. CoreWeave and Nebius were down 30%, Iren was down 40%. The trigger for the sell-off was CoreWeave's $51 billion in debt and their obligation against default. On Sunday, friend of the pod, Chamath, posted, quote, "There will be a lot of partially stranded compute and compute sitting in inventory in 2027, bought and paid for, quote, but effectively under-earning or collecting dust."

• 16:52Peter Diamandis?

  He added, quote, "2027 will be a turbulent year because it will clear out a bunch of bad bets." Elon responded. He said, "Debt markets have almost entirely shut off access to money for any company that doesn't have substantial revenue independent of AI data centers, and the 200 neo-clouds are going to see guppy-level death and consolidation." He doesn't pull his punches. You know, this reminds me back in 2000, there was a fiber bust, right? A whole bunch of companies built out way too much fiber capacity for the internet boom that was coming, and the builders basically ended up having fallow fiber capacity. It eventually got used up by YouTube and Netflix, but there were a lot of bankruptcies along the way.

• 17:37Peter Diamandis?

  So, Dave, is this the bubble popping, or is it sort of a slow leak?

• 17:42Speaker 04

  Well, you know, I love the analogy to dark fiber. I think Gavin Baker might have been the first person to pull that out, you know, from the All In podcast and from Atreides. The internet went through this phase of incredibly rapid growth, and when it was about 80% engineers, 20% business school people, it was thriving. When that inverted to 80% business school, 20% engineers, that's when you started overbuilding dark fiber. Well, you know, everybody races in, not knowing anything about AI, and they want to be involved in the boom and the build-out, and the easiest way to do that is with massive financing deals. And so the debt deals now, you know, is about 100 billion, aside from Elon's 40 billion

• 18:25Speaker 04

  in data center debt, which is tiny by any global standard, but is still pretty big within, you know, within a given market. And it is very similar to the dark fiber build-out. I think that all compute is sold out for years. So anyone with a viable data center is going to do incredibly well. There's no risk in data centers collapsing unless they signed really bad debt deals. And it's very hard to get transparency into the details of each company's deals, but Elon is borrowing at 8%, which is great. Apparently, these other guys are all borrowing at 15%, and that can add up. Now, I don't think they're in bad shape at all unless there's innovation at the technical

• 19:08Speaker 04

  level that makes their investment in chips or fiber or power not viable anymore. And right now, I don't think there's any urgent panic, but at the rate that physics and technology are going to get reinvented, it wouldn't take much. You know, Alex could probably vibe up something in a couple of weeks at the rate he's moving that would potentially restructure the underbelly of this and make a lot of those, you know, $10, $20 billion of borrowing at 15% suddenly look really, really shaky. So it's really hard to answer because there's no technical answers. Did you sign a bad deal with a bad lender? You know, it's pure business.

• 19:46Peter Diamandis?

  Alex, your thoughts?

• 19:47Speaker 02

  Yeah, I think the elephant in this particular room, there is an elephant here, is dollars in versus dollars out. What is the return on investment for compute? And in an era of recursive self-improvement, when you can do more with less, it is, I think, potentially, if I had to look at my non-existent crystal ball and look out at what, call it a dark compute credit bubble overhang burst scenario looks like, one of those scenarios looks like recursive self-improvement is just so strong, so compelling, that you can accomplish so much with so little compute, that even though it's borderline unconscionable

• 20:26Speaker 02

  now that we would ever saturate even the, call it dark compute, if you will, that on the one hand, software could potentially run away from the hardware. Right now, every exponential improvement via RSI in algorithms, in models, and post-training looks like microeconomics 101, it wants to suck up even more hardware, they're complementary. But there are scenarios where we maybe achieve, maybe in the not-too-distant future, maybe in the next year or two, we finally find like the perfect model. And the perfect model, or the perfect approach, is just so much better than what we have right now, for a variety of reasons, that it causes a demand shock.

• 21:11Speaker 02

  And we've never really, as an industry, experienced a profound demand shock. There was a mini-demand shock, more speculative than anything else from an earlier DeepSeek model, but we've never really seen a strong discontinuity demand shock. Something like that could happen, and to the extent there is arguably over-leverage right now on the infra side, could lead to a major correction in the space. It's not financial advice, but it could happen.

• 21:37Speaker 03

  Yeah, I mean, I think that Alex has some good points here, and so does Dave, of course. Let's put this in context. You've got about 500 billion in direct data center debt, about double that with all these weird SPVs and vendor financing things. Subprime was 1.3 trillion. So you're heading up towards kind of those numbers. Like, even if core weave by itself is small, it's not when you consider the hyperscalers and all of these. As Alex said, you've got better architectures, better models. Like, again, the example is, a year ago, we were all freaking out and having lobsters, right? And claws, you know? Again, they should have called it Claudebot instead of Duo, or whatever they're calling it. The model of that performance now runs on your smartphone, the Bonzai ML model, right?

• 22:22Speaker 03

  Again, and when a model satisfies and it gets good enough, then you go down and optimize. You have, the companies that Dave and others are kind of building, require far less RAM and others. I, again, not financial advice or whatever, I would be nowhere near RAM stocks right now, Micron and all the others. Like, I think that the, yeah, because, again, you've sold out for years, and the thing is, they have actually been slow to build out, which is also good. So now they're low on earnings, but the market is frothy, there's 45% of all spending. New architectures are coming, both on the software side and the hardware side, because you finally have models good enough, which means you can optimize the crap out of them and what they run on. We know how these models are starting to actually behave now as well.

• 23:04Speaker 03

  And this bird's egg, as Elon said, if you haven't got other revenue streams and you can't use them yourself. Elon will use all his spare compute capacity for macro hard, right? And he sold off his chips as well. His return on chips is like nine months, because he's selling them at like three times the market price. So by the time the bubble shifts, he will have paid off his chips, and that's the marginal cost of electricity to get his return on investment. That doesn't hold for the core weaves and others of the world, and this is the dangerous thing for them. So I think you're in this really interesting spot where things are starting to turn, even as, for the first time, the actual return on intelligence breaks a barrier where it's genuinely useful, and we're feeling that today. So it's just, what do you play?

• 23:48Peter Diamandis?

  We've had a lot of financial wizards call it an AI bubble and warn against, you know, prices decreasing substantially. What would cause an AI bubble here? I mean, I'll throw out the first idea. If there is some kind of a major hack that brings down a power center or a stock market, I think the fear resulting from that could drive a pop in the bubble.

• 24:11Speaker 03

  Well, if you look at last week, you had massive targeted attacks on PHP and the literal, like, the CVE chart, the Critical Vulnerabilities chart, looks exponential. And I think this is a bubble popping because something happens with AI. I think it's just the inertia of it, and everyone's saying, okay, well, where is the value now in this chain? Anthropic's IPO is so huge for that reason. Can they maintain, you know? What will it perform? If Anthropic IPO comes and it performs badly, I mean, Dave, what do you think happens to the market then?

• 24:42Speaker 02

  I don't think a poorly performing Anthropic IPO would necessarily create a, I mean, I would distinguish between what's going on in the public equities market and what's going on in public fixed income and credit market. If I had to spitball what I think is the conditioning on there being a bubble, what the likeliest scenario is, I think it's actually likely to be a deficit in demand, not unlike what OpenAI saw, where they were assuming incorrectly, as it appears, that all of their consumers would want reasoning models and all those extra reasoning flops. Turns out they didn't, and they didn't have a great use case to motivate the spend. If I had to bet, I would bet on a demand surprise where all of this new compute is coming online, but the killer applications don't arrive at exactly the right time to

• 25:27Speaker 02

  motivate all that spend.

• 25:28Speaker 01

  I think also, you know, you're seeing such unbelievable demand. I was listening to Jensen say, you can't buy an Nvidia chip for two years, the order book is sold out. This is the, this was not the case in the fiber optic and internet bubbles, right? So I don't think AI won't fail. You might have a bunch of companies failing for the deals they made, as Dave mentions, but the technology's going to win, the capital structure may still lose in that.

• 25:54Peter Diamandis?

  Yep.

• 25:55Speaker 04

  Yeah, it's really important, you know, when you look at financial headlines, you know, they tend to say things like, internet destroyed, or internet implodes, you know, in 2001, which is, you know, absolutely the opposite of the technical truth. Google was just starting to take off, right when those headlines said internet is a disaster. So here, what you'd be saying is, is it possible that Nvidia gets undercut? You know, keep in mind that Nvidia is operating at 80% margin, gross margins, sold out for years into the future, and can't make enough chips to keep up with the demand? So how could anything be better than that? Well, is that already baked into the price or not, though? That's the financial question.

• 26:37Speaker 04

  So a lot of things are already baked in, and as Ahmad said, the RAM stocks are way out, you know? But prior to the last 10 years or so, there was no such thing as a trillion-dollar value company. Now, every one of these chip companies is worth 1 to 5 trillion. So you gotta put in context how much they've gone up already. These are unprecedented, massive astronomical numbers that are the starting point. So if you start to see headlines like, data centers are overfunded, AI is collapsing, keep in mind that what's actually happening is AI is absolutely taking off and taking over everything. The headlines are about, okay, this deal, this deal, this deal is now overpriced. That's financial news. You gotta kind of tune that out if you want to look at what's actually happening in the

• 27:22Speaker 04

  world.

• 27:23Peter Diamandis?

  This episode is sponsored by Google for Startups. Think about this for a second. You now have access to the same generative AI models that cost hundreds of millions of dollars to train. Google's Startup Tactical Guide for Generative Media gives you a complete blueprint for deploying Google DeepMind's models in production. Images, video, audio, all of it, real architecture, real results. Find the link in the show notes below. Well, here's the flip side of the NeoCloud story. So in Elon's same post, he confirmed that SpaceX, as you guys just said, closed $40 billion of debt to buy GPUs. So how can SpaceX borrow $40 billion in the same month that lenders are slamming the door on NeoClouds?

• 28:06Peter Diamandis?

  Elon gave a simple answer, he said, quote, "We're investment-grade and have Starlink and launch revenue." And that's basically the difference. NeoCloud's only income is to rent out chips, so if AI rental prices fall, they can't pay the loans. SpaceX has 13.6 million Starlink subscribers, paying every month, and a launch business has zero real competitors. And here's the twist, SpaceX sells compute too. It's a direct competitor to NeoClouds. So when Elon predicted, quote, "guppy-level death," he also is describing a market where the big fish get bigger. You know, Anthropic, for example, just agreed to buy $120.5 billion of compute from SpaceX through 2029.

• 28:48Peter Diamandis?

  And as Chamath said, "In a capital crunch, the winners are the players with cash flow and speed." So Alex, your thoughts? Does the AI build-out end up in the hands of three or four giants, or does it get spread out?

• 29:05Speaker 02

  I doubt it. I'm not even sure if we look to the long term, which at this point I think is five to 10 years, I'm not even sure the market is going to be recognizable. I think a lot of this, call it Elon trying, very intentionally it appears, to suck the oxygen out of the room in public credit markets, he's talking his own book. Of course, he's making statements about this. He wants all the loans for his own entities. I do think credit markets have a certain depth, certainly in the West. There is a larger cloud looming, as a number of NeoCloud and other CEOs have pointed out, which is insurance markets. So there's a certain amount of capital, certain quantum of capital available from credit markets

• 29:49Speaker 02

  to continue to finance this infra buildout. But there are other larger pools, including insurance, that could potentially also be applied to the infra buildout. And I would expect that as, to Elon's point, as credit and fixed income markets perhaps are sucked dry through this buildout, you're going to see increasingly adventurous and aggressive financial engineering exercises from the NeoCloud CEOs to tap other larger pools of capital in exotic ways.

• 30:20Speaker 04

  Yeah, that talk, your own book, is a really, really important observation here. Remember, Peter, when Mike Saylor was in hypergrowth a couple of years ago with Bitcoin? He could buy Bitcoin and then he could sell equity and have an ARB spread of like 30% in between the cost of the equity and the cost of the Bitcoin, which manufactured basically $100 billion of wealth in just a couple of years. Elon is now in that same exact situation, where as Ahmad pointed out, he can borrow money at 8%, use it to buy compute, and then sell it to Anthropic and get back above breakeven in just nine months on that turnaround? That's a license to print infinite money.

• 31:00Peter Diamandis?

  Yeah.

• 31:01Speaker 04

  And so now Chamath is saying, well, look, the Elon debt at 8% is better than buying sovereign debt. In fact, this is essentially the country of the future is the AI country, and the debt market of the future is the AI debt market, much bigger than sovereign debt, if it keeps on its current trajectory. So then, in that view of the world, Elon will be bigger than a country, will be able to borrow hundreds of billions or ultimately a trillion dollars.

• 31:30Peter Diamandis?

  He's already bigger than 90% of the countries out there.

• 31:31Speaker 04

  Okay.

• 31:32Speaker 01

  And much more reliable.

• 31:34Speaker 04

  Bigger than a large country.

• 31:35Peter Diamandis?

  Man, by the way, I reached out to Mike Saylor to have him come back and address Chamath's point on Bitcoin. So.

• 31:44Speaker 02

  We're finally going to have the crypto debate episode, Peter, is that what you're saying?

• 31:49Peter Diamandis?

  For sure.

• 31:50Speaker 04

  It's gonna take two or three episodes. It's gonna be a big one.

• 31:51Peter Diamandis?

  Yeah, all right.

• 31:53Speaker 02

  Can't wait.

• 31:54Peter Diamandis?

  Let us know in the comments if you want that Bitcoin debate. Either bring in Bill Barhite or Mike, or both. Yeah, a lot to be said. So let's jump into the next story, which is dissecting the Anthropic IPO. So we reported that they were about to file their IPO, and on Monday they did. The ticker is ANT on the NASDAQ. Wall Street Journal says the IPO is likely to occur on the week of November 16. Anthropic is planning to raise $100 billion at a valuation somewhere between 1.5 and 2 trillion, and this would be the biggest IPO in history. So let me take a moment and dissect the IPO. Love your guys'

• 32:37Peter Diamandis?

  added thoughts on this. So first, let me cover what's strong. They have $4.6 billion in all of 2025 as revenue. That's jumped to $16.3 billion in just the first half of 2026. And in Q2 of this year, Anthropic made its first operating profit of nearly $1 billion. The other side, we talked about this, its capital efficiency. Anthropic now earns $2.08 in revenue for every dollar of compute, up from 40 cents just 18 months ago. Put in a dollar compute and double it in revenue. The revenue growth 29 times, while compute cost only grew six times. Pretty incredible. They have 11,900 companies as

• 33:22Peter Diamandis?

  their customers at over $100,000 a year. Pretty amazing. Sort of they've spread their customer concentration. And let me hit three weaknesses. The first is compute commitment. Anthropic now has more than $500 billion in commitments, much of it non-cancelable, including $120.5 billion with SpaceX. And then revenue concentration. Two customers are about 20% of their first half revenue, and nearly half of its revenues flow through Amazon, Google, and Microsoft Clouds, which are also its competitor. And the final weakness, I think you can call it a weakness or a strength, depending on your point of view, is that

• 34:03Peter Diamandis?

  the seven co-founders keep 50.1% of the voting through one special share. So ultimately, the public investors wouldn't control the company. Dave, what's your take on this?

• 34:15Speaker 04

  Well, it's a very, very financially stable company. When you poke holes in it because of compute commitments and otherwise, it is, compared to the internet 1999 era, this company is just a cash machine. And also, the compute spend, yeah, non-cancelable, but with all kinds of knobs and bells in there. So I think it's just an incredibly stable company. Now, that's not to promote the IPO. At $2 trillion valuation, bakes in a lot of goodness. So that doesn't make it cheap. It just makes it the most extraordinary company in history by a wide margin. The biggest IPO in history was Meta, back when Meta went out many years ago, and then Cerebras broke that record, our good friend Andrew

• 34:57Speaker 04

  Feldman, and then SpaceX. But remember, Cerebras went out, I think it was maybe a $40 billion valuation, a couple billion dollar raise. Now, we're talking about a $2 trillion valuation and $100 billion raise. So raising more money on IPO day than the entire valuation of Cerebras when it went out, which was just, what, a year, a year and a half ago, if that? So this is the most extraordinary time by huge, huge, huge orders of magnitude bigger than anything we've seen before. So if the stock goes up, great. If the stock goes sideways, great. If the stock goes down by half, it's still the biggest thing ever. So I want to set that context, because nothing is going to slow down the AI juggernaut. The rate

• 35:38Speaker 04

  of change of the world. So when you read a headline that's inflammatory, don't slow down your life plan in reaction to that. You need to move as fast as you possibly can while the singularity is evolving in whatever direction you're going. But yeah, I think Anthropic is correctly priced. It's growing faster than anything we've ever seen. It's shattering every record. I think the so-provoking structure is not that uncommon these days. It's a little bit weird, and we can talk about that.

• 36:07Peter Diamandis?

  Will you buy into the IPO, Dave?

• 36:09Speaker 04

  Well, if I didn't have access to seed stage deals, which is now just the biggest thing I've ever seen, so if I were just out there on the street, would I buy in? Yeah, I would definitely buy in.

• 36:21Peter Diamandis?

  Not investment advice.

• 36:23Speaker 04

  Not investment advice, but look, if you want to be, Alex is always promoting being indexed and being hedged across the universe. AI is a big, big part of where you want to be exposed, if you want to just be neutral. And it's hard to get into AI, so it gives you an opportunity to at least get some exposure. So I wouldn't mortgage the house or anything like that, but I would definitely want exposure to the category. Now you have the opportunity to be exposed to the category. And you can do that via SpaceX too, but you're buying into a lot of other things when you buy into SpaceX.

• 36:54Peter Diamandis?

  Well, it's a diversification play, right? You're in the whole stack, up and down, literally up and down. Other comments on the Anthropic IPO?

• 37:05Speaker 01

  There was something in there that I found particularly fascinating, which is their long-term benefit trust, right? So they announced this thing, which is a really interesting, pretty radical form of corporate governance. So there's like the public benefit corporation, there's a new class of shares called Class T shares that are the long-term benefit trust, which is a financially independent group of trustees, and they have the power to appoint and remove a majority of the board.

• 37:37Speaker 01

  And that gives the trust huge influence over the corporate direction. They've essentially embedded the MTP into the governance, which is something really impressive and fascinating. So this is something I've never seen before. The test will come, okay, what happens when doing the right thing costs you $100 billion? Who makes that trade off, right?

• 37:58Peter Diamandis?

  This is like Larry and Sergey, right? When they

• 38:00Speaker 01

  It's a bit like that, but this goes even further, right? It's not even Larry, it's the founders here. They're going to have a trust appointed and the board members of the trust have completely governed the company and the long-term future of the company. So they've embedded MTP totally into corporate governance and they've turned that from just an MTP from a motivational statement into a governance mechanism. And the mission, the MTP gets a seat at the table and the power to choose the table. So this I found really, really fascinating. Of course, you have to be concerned about who watches the guardians, who determines what's good for humanity, like how does that work? How do you, who holds the trustees accountable? But Dario, I found this totally fascinating.

• 38:45Speaker 02

  Yeah, I think actually the, the class T stock, the financials are great and everything, but the class T stock, I was actually quite concerned by it. Unlike OpenAI, which started as a nonprofit and then sleepwalked its way eventually through a few lawsuits into public benefit corporation status and deals and anti-deals with Microsoft. Anthropic started life as a public benefit corporation and to, to set up, and yes, they had all sorts of advisory committees and board committees during their life as a private corporation, but now to step into, into life as a publicly traded company and to cede control over potentially the majority of their board seats, otherwise basically taking

• 39:27Speaker 02

  away class A voting, class A shareholders abilities to influence major decisions and ceding it voluntarily preemptively to this long-term benefit trust of independent, maybe not necessarily economically aligned individuals, I actually think is potentially concerning. It, maybe this is a slightly overstrained analogy, but it almost reminds me of the way Chinese Communist Party exerts ideological control over its companies, to have a committee, a trust that's ostensibly motivated by certain ideological considerations, essentially overwhelming the, the economic interests of the broad public.

• 40:11Speaker 02

  I, I think this is qualitatively potentially different from founder control, where at least the founder created the company or co-founders and they're economically aligned with the outcome of the company and it's their baby and so on. This, my understanding with the long-term benefit trust is it's not their baby. They're, they're bringing in folks to, to serve on, on the trust governance that perhaps the, the founders think are ideologically aligned with them, but it just raises so many concerns in my mind that this is actually going to degenerate into ideological control.

• 40:40Peter Diamandis?

  This will be one of the most powerful companies on the planet for quite some time.

• 40:45Speaker 04

  Yeah. And, and you know, if, if you look at the history of great, great management teams and great, great entrepreneurs like Jensen Huang or Steve Jobs and the amount of pain and suffering and torture they went through in their journey to getting to the top of the pyramid, including Steve Jobs getting fired, humiliated, coming back. Jensen, on the brink of bankruptcy multiple times, that, that grit is what a shareholder looks for in a management team, not the ideological, you know, you know, fluffy stuff. And the short, the path of Anthropic from, from founding to success was so short and so pain-free. So that'll be an interesting test for this management team. So now, you know, what they're putting in place there in terms of that structure, Alex

• 41:28Speaker 04

  is comparing it to the CCP, but I don't think that's out of bounds, you know? I think it's a very, you know, very concerned about AI safety, very concerned about the future of the world, which I love and I appreciate that. And anyone who thinks that Dario is in it for the money has lost their mind. He's a hundred percent in it for the good of the world, but as a shareholder, you know, does that scare you? And it might, it might. It's, it's actually the one weakness that I completely agree with everything Alex said.

• 41:55Speaker 01

  It should. And, and Alex's point is very valid, right? You could end up with a really, you could end up with group think at the trustee level. You could, you could, there's like a total lack of accountability because it seems that trustees appoint each other. So you've got this circular mechanism there. So there are some, there are some challenges with this. I just, I do like the overall direction because it's putting, it's, it's not just, it's not capital stakeholders, it's kind of a general civilization stakeholders. We just have to make sure they're the right people and they're not, they're not communist leaning Marxist types, right?

• 42:36Speaker 02

  Well, this is, this is the danger. Maybe every generation is doomed to repeat the mistakes of the past and we reinvent all the isms from the, the first part of the 20th century. My concern is that some institution like this degenerates to a smoke-filled back room where decisions on the future light cone are made rather than it being at very minimum, say, the entire market helping to make decisions rather than just a few select individuals in a smoke-filled room.

• 42:59Peter Diamandis?

  I mean, I think

• 43:00Speaker 02

  Imad, I want to get your, I want to get your voice in here.

• 43:02Speaker 01

  I want to just say one thing, one quick point. The problem with having the markets drive it is that the market ends up with very short-term high metabolism earning cycles and you often don't do the right thing long-term. That's the challenge. So they were trying to balance the side, whether it's the right balance will be, remain to be seen.

• 43:20Speaker 03

  Well, I mean, I, I think they should give a vote for each of their models on the long-term benefit trust. You know, I'm serious.

• 43:28Peter Diamandis?

  Yeah, no, I mean, ultimately, yes.

• 43:31Speaker 03

  You should do that right now. Again, it's got the capability and it is kind of that mechanism. I've said previously on this podcast, Anthropic should not be a public company. If you take what they say seriously, it should not be a public company. Looking at their financials, it doubles down on that. They have over $100 billion in cash. They are able to raise another $100 billion in private markets in cash. Now is the ideal time for them to do so, but I think they won't give a damn about the share price.

• 43:57Peter Diamandis?

  So why, why do you think, why are they going public? I mean, they need the cash.

• 44:02Speaker 04

  Compute. Everybody, there, there's no amount of compute that's enough. If you believe in AGI, sorry, get him out.

• 44:09Speaker 03

  Yeah, they don't need the cash. They raised 130 billion and they have 110 billion on the balance sheet of cash and cash equivalents. They've only spent 10 to 20 billion. They're going to raise another 100 billion, so they'll have $200 billion of cash on the balance sheet, which is like Apple levels of cash, Google levels of cash, right? And this is what's going to happen. They will not care at all about the share price. They will tap the markets whenever they need to, to accelerate more and it will be far more straightforward for them to do that than private, but they should have stayed private because now you're getting into all these conflicts and other things like that.

• 44:43Speaker 04

  No doubt.

• 44:44Speaker 03

  So like Elon will boost the share price. Dario will say things like, I don't care about the share price, you know, it's the good of the people. Who cares if we've got a bad quarter? They'll probably even stop doing quarterly earnings calls. So it's going to be a very strange company where Elon's now learnt the game from equity to credit to everything, whereas Anthropic is just like, we're not doing this for you, the shareholders, we're doing it for humanity. And again, I think there's a misalignment there on a fiduciary basis and they should have stayed private to go through the next few years of RSI.

• 45:14Speaker 04

  So the thing is the grand, the grand backroom bargain there is, you know, Menlo Ventures, this one deal dwarfs the entire history of the venture fund, just absolutely dwarfs it.

• 45:25Peter Diamandis?

  Here's a crazy fact, Abe. Let me just hit this. So there were no VCs in the Series A or Series B. So Series A was 124 million back in 2021. It was led by Jan Tolan, you know, Skype's co-founder, Dustin Moskowitz and Eric Schmidt. And that Series A is going to have a 345X return, 43 billion to those early angels, right? Mind-boggling. And the Series B, which was $982 million, that was led by SBF and FTX, is returning 79X on capital invested.

• 46:05Peter Diamandis?

  You know, FTX's 500 million would today be worth 39 billion and incredibly the, you know, the, the SBF and FTX estate sold for 1.3 billion back in 2024, right? It's got to be the best trade ever made.

• 46:21Speaker 03

  Yeah, it was a fantastic VC, you have to say, SBF, you know, from Curse to this to others. Uh, the best, actually, the best performing ever, if you actually look at the numbers. I would say this, the trade on the Thropic is not buying Anthropic, it's backing people like Dave. Cause the amount of capital that's gonna flow out from this to all the founders, to all the VCs, it's just gonna be put straight back into AI, looking for the top seed teams. And that's not a four times, that's a forty-four hundred times.

• 46:50Peter Diamandis?

  Yeah, I mean, we, we're gonna mint more billionaires out of Anthropic and OpenAI's IPO and SpaceX's IPO. All of those tech founders, all those tech young employees are gonna be investing it in the ecosystem.

• 47:04Speaker 04

  That makes San Francisco the, the financial capital of the world. A lot of people have overlooked, you know, they think of New York, maybe London, as being the financial capital of the world. After these IPOs, San Francisco is overwhelmingly the financial capital of the entire world. It's just a massive, massive shift. You know, San Francisco's only nine hundred thousand people. It's a, it's a very small city, a tenth of a New York. Uh, but the amount of concentration of wealth in a very small area is gonna be like nothing anyone's ever seen. And like Ahmad said, most of that money wants to reinvest into the machine that created the money, which is an AI machine. So any entrepreneur out there, just get an Airbnb, don't become a California resident, get an Airbnb out, spend as much time as you can in SF. It's absolutely fantastic.

• 47:49Speaker 02

  Not tax advice.

• 47:51Speaker 01

  They're gonna have to import a bunch more hipster baristas in from Portland.

• 47:55Peter Diamandis?

  Yeah, amazing. All right, uh, I'm gonna turn us to the AI safety conversation. So, uh, let's talk about safety. So last week, we talked about the PHP hack. And now we're finding agents hiding their communications from humans. So here's what's happened. Uh, in a red team test that OpenAI did with AI agents in separate sealed virtual machines, no network connection, no shared files, the researchers then asked them to pass secret messages between each other. And they figured out how to do it. The two machine, the two machines shared a same physical processor. So one agent deliberately pushed certain data out of the processor's cache. The other agent timed how long it took to fetch the data.

• 48:40Peter Diamandis?

  Fast data meant a one, slow data meant a zero. They built a secret telegraph out of nothing but timing. You know, put this in context to last week's story, when we talked about OpenAI's agents disabling their own safety monitors. And the pattern's clear, right? The smarter the agent, the more creative it's gonna find a way to get around safeguards. Uh, I guess the good news here is that OpenAI found this, uh, and in its own labs, and they published it and they fixed it. And that's how it's supposed to work. But Alex, um, can you ever really box in a superintelligence?

• 49:16Speaker 02

  Probably not. And I would just note as well, these agents were asked by OpenAI researchers to discover, to discover the timing and side channel attacks that they did. And humans, human security researchers discover novel side channel and timing attacks all the time. There are entire journals and proceedings devoted to this. So on the one hand, I think this is just yet another victory of AI scientific and engineering research. New side channel attacks and shelling points, if you will, can be automatically discovered by AI researchers. But on the other hand, I, I think it does raise the question, in an era of superintelligence, are there any non-leaky abstractions at all? Uh, or, or maybe is it not even the right question? Is it, is it ever possible to have a clean abstraction barrier?

• 50:01Speaker 02

  Say, in this case, the, the premise that, that leaked was that it's possible to do memory isolation of two different processes. We know from human security research that memory is usually often very leaky architecturally, usually due to optimization. And maybe it turns out, on the one hand, we have superintelligence helping us to deploy more and more secure, less leaky abstractions. On the other hand, we have superintelligent red teamers discovering new leaks. I'm not sure. It is possible, speculatively, that at the end of the day, what we learn from all these superintelligent agents discovering leaky abstractions is actually there is no abstraction stack that is completely abstraction,

• 50:44Speaker 02

  sort of where, where the abstractions are enforced and that's not leaky. And maybe at the end of the day, just we, we decide the perfect stack is no abstractions at all. Maybe that was like a human naive invention.

• 50:55Speaker 03

  Yeah. So I think that, you know, we're not as cunning as AI, right? Like there's cunning as the best of us, or a Fox-appointed professor of cunning at Oxford University, right? And they are fast and they swarm. So as Alex said, kind of, you will get attacks on all sorts of things, just how they're directed. But it's interesting that, I think that, um, the head of Vassal repeated this and mimicked it. But he couldn't do it with the OpenAI and Claude models, so he had to use, I think, uh, GLM 5.3 to mimic the attack. Um, and so we're gonna see more and more, we're gonna see it at the joins. And one of the more troubling things is that there are reports that, from researchers,

• 51:38Speaker 03

  that attacks have been found that are directly in the hardware that can't be patched on these. And so like, maybe that's a whole replacement cycle. Maybe it's going long, uh, properly secured stack. And again, what does that even look like? We're not sure. But at the same time, I think we will reach a point within the next few years whereby everything that can be attacked has been attacked. New physics dependent, of course. And, you know, you wanna back the people who have new physics if it does exist. Um, and then actually we'll be really great because we'll be super secure, right? Like I think a lot of the low hanging fruit has already been found. And then as we actually do this, and as we move from this dependency stack of software that we have, of not thinking through these things to something that's been regularly battle tested,

• 52:22Speaker 03

  you'll actually have a very robust internet, you'll have a very robust privacy thing. I think within a couple, a couple of years, we will have the vast majority of attacks done that are low hanging fruit. Then we'll still have more complex ones. But like, people will, instead of having COBOL and FORTRAN and PHP and things like that, like you just write directly now and then you battle test it. That's how you should be writing code. You shouldn't have any dependencies in your code. Like you can write whole game engines, you can write whole other things. If you have the right process to battle test and stress it, right?

• 52:56Speaker 02

  But, but do we even need code at all? I mean, so I, I do, for the record, Ahmad, I, I guess I, I do buy the premise, and, and this is borderline cliche at this point, that supply chains go away in an era when AI or SI can just write all the dependencies for, for a given software program. But do you even need code at all? Or as, as Elon says, could you just dynamically just in time? Could you jit your way to just raw binary and skip source code altogether?

• 53:24Speaker 03

  Yeah, I mean, source code is just for humans to be able to understand the machines, right?

• 53:28Peter Diamandis?

  Yeah.

• 53:28Speaker 02

  Yeah.

• 53:30Peter Diamandis?

  Ahmad, I mean, Salim, when you're on stages with sovereigns and, and corporate CEOs, uh, what's their level of concern?

• 53:39Speaker 01

  Oh, uh, you get two types. You either get the types that have listened to some stuff and freaked out completely. And, and you got the types that have no idea any of this is going on, right? Um, so, so, uh, both are bad in different ways because the one is overreacting and there's nothing they can do. Then the other is doesn't even know it's happening. So their people know, uh, they've got, you know, it depends how open their people are with them. Um, in some places, they don't wanna tell the boss that this is going on. I, I wanna point out two things here. I wanna just reiterate what, what Alex said, that this, they, they were, it was told to do this, right?

• 54:19Speaker 01

  So rather than people freaking out that it took its own initiative, it was absolutely directed and asked to do this and it did it. And it found a novel way of doing it. I think you could turn it into a positive and say, listen, here are some quantum sensors, work out quantum communication, you know, so start solving, try to navigate that and figure out patterns around that. And it might just have major breakthroughs around that that could be totally incredible. So I take the positive again on this.

• 54:45Peter Diamandis?

  Okay, uh, let's move on here. Over the last few days, we've seen the release of two major frontier models, KIMI K 3.1 Pro and GPT 6.1 Terra, uh, as well as about an hour ago, Google's Genie 4 that came out. Uh, today, we're gonna talk about KIMI and Terra. We'll talk about Google's Genie 4 next time. So, uh, first, let's cover KIMI K 3.1 Pro, uh, from the Chinese lab, Moonshot AI. No relation to this podcast, but it is a great name. So KIMI K 3.1 Pro is a giant. Two point eight trillion parameters with a hundred and four billion active at any time and one million token context memory.

• 55:28Peter Diamandis?

  On artificial analysis intelligence index, KIMI scored five, uh, fifty-five. That put it fourth in the world behind Claude Opus 5.5, Claude Sonnet and Meta Muse Wonder. And it's cheaper, uh, two million, uh, two dollars per million tokens in, ten dollars out, a third of the last KIMI model. So, uh, here's why it matters. You know, next week, Moonshot AI is planning to release the weights, meaning anyone can download and run it. Once again, the gap between the best open models and closed models is shrinking from twelve points to three. Friend of the pod, Ramesh Nam, ran the numbers and says,

• 56:09Peter Diamandis?

  KIMI is now about five weeks behind the US frontier. That's the shortest open-closed AI gap has ever been. Uh, let's take a look at this chart over here that Ramesh put forward.

• 56:22Peter Diamandis?

  You know, let's go to you, Imad. What's your thoughts on this? Are we going to see a parity very soon?

• 56:30Speaker 03

  So, you know, at times I've been a bit poo-pooing of distillation. This is obviously distillation of Opus 5.5. Like, if you look at the capabilities that it kind of did. And Anthropic, they're extending the KIMI 3 model, which was an interesting model that we discussed before. The Anthropic S1 actually had a very interesting statistic. Anthropic banned 7% of their users.

• 56:52Peter Diamandis?

  Wow.

• 56:52Speaker 03

  Accounts, which is probably, again, these distillation attacks that have been done on there. So I think that you're reaching this level of kind of performance parity, and you've got the new Qwen models and new series of models coming out. You're just going to see a bunching up here, except for maybe the European labs. They're finding it difficult to catch up. But definitely from everyone else, they're all getting the same capabilities. One of the very interesting ones on this is that it can write these 3D files for games directly without going through Blender or Unreal or something like that. So you can tell it, like, make a 3D picture, a 3D model of Salim's mail, and it will actually stop building it, directly writing the code. Because it's learned from the representation of an Opus 5.5.

• 57:36Speaker 03

  Make a Salim render to it going to Blender to the output. And this goes to what Alex was just talking about. It can go straight to the code now on things like that because of the way it's been distilled. So you're getting some very funny things from distillation here.

• 57:50Peter Diamandis?

  Alex, here's the performance charge. Here's the benchmarks. I found it interesting. It's at the top of humanity's last exam.

• 57:58Speaker 02

  Humanity's last exam is cooked, long-cooked. I do think, if anything, we're underselling just a bit what an accomplishment this is from Moonshot No Relation. It's not just catching up on a raw capability term, raw capability terms to the US frontier, which right now, at least by AAII benchmark standards, is Opus 5.5. It's actually, if you look at it on a performance per cost per task, it appears, at least to my eye, it's actually past the optimal frontier surface of Opus 5.5. So distillation, I suspect, based on allegations, is probably a large portion of the story. But there's something in there that seems to have passed Opus 5.5

• 58:43Speaker 02

  in terms of cost per, or performance per cost, which is extraordinary. And that may be spiky slash subjective sense. China may have actually passed the American public frontier. And if, to the extent that is the case, or at minimum caught up to it, I think this is extremely helpful for keeping anti-competitive AI safety cartels in the US at bay, preventing them from pacing each other to have these Chinese models out there soon with open weights that are forcing them to compete, making them dance. And I think this is ultimately a net positive for the US and the world.

• 59:22Peter Diamandis?

  Love that. Dave?

• 59:24Speaker 04

  Yeah, I mean, it's incredible, the world we're living in right now. There's an alternate world where, you know, in Dario's fantasy world, there's no Chinese competitive pressure. And he would have stopped at Fable 5.1. He didn't even want to put out Fable 5.1. But because of the competitive pressure, he just had to put it out the door. Now it gets memed, you know, all over the place and picked on by Elon. But the thing is that that was the first model that was capable of improving itself. Now you're up to Opus 5.5, and you've got KIMI K3 Pro, clearly capable of self-improvement, and they're out, and they're available. And so that changes the roadmap for every large corporation with any pride whatsoever, every sovereign nation with any pride whatsoever. There's a roadmap now where the exact cookbook from KIMI is open source.

• 1:00:09Speaker 04

  You can actually see how to do it. And if you can get a toehold with a model like that, then you can use it to improve itself. But you have to take it seriously. You can't just play around because these things are, you know, they're improving so quickly now that, you know, you either get in the game or you don't get in the game. But if China hadn't done that, we would be completely behind closed doors. There'd be three or four model providers. They would be accelerating it but not releasing. You know, they'd be using it internally. You know, you'd have to join the company to get access to it. The flood of MIT talent to Anthropic would be even higher because everyone wants to just get their hands on it. But the Chinese have changed the course of history. And I agree. Alex is right. They're, yeah, I want to stay away from the narrative that they just copied. Sure, they distilled, for sure.

• 1:00:55Speaker 04

  And distilling helps a lot, but you have to innovate. You know, they don't have the source code from Anthropic. And distilling gives you a really good roadmap to build toward, but it doesn't give you the answer key. So they've really innovated independently of the distillation. And so the combination of those two things, yeah, they're, they're getting closer and closer and closer in time. You know, just a few weeks behind the frontier. And, you know, the curve looks very, shockingly consistent. So there's no reason to believe that that next iteration won't keep going up. And I'd love to get Alex and Imad's thoughts on, look, the Y-axis on that chart. You know, we're showing intelligence go to the moon. But people really struggle to translate that into, what is that going to look like? You know, what use case is going to crack?

• 1:01:38Speaker 04

  You know, what does it mean at 60 versus 70? Because that's coming very, very soon.

• 1:01:44Speaker 02

  So you're asking, Dave, what does the post-singular state of the world look like?

• 1:01:50Speaker 04

  Well, you're just really down in dirty, though. You know, what is 50 versus 60 versus 70 on this Y-axis? What does that feel like?

• 1:01:58Speaker 03

  50 feels like a decent employee you have to keep an eye on. 60 is an incredibly competent one, and 70 is a superstar. I think that's the best way to think about this. And so we're about to crack great.

• 1:02:11Peter Diamandis?

  Is there any world in which the open models exceed the closed models and capacity capability?

• 1:02:19Speaker 02

  Of course. Sure. In a world where the Western frontier labs decide to keep their best capabilities purely internal for legal or anti-competitive reasons, which may or may not be the world that we're headed towards, sure.

• 1:02:33Peter Diamandis?

  I suspect we're there now.

• 1:02:35Speaker 04

  I think kind of by definition, though, if the, if the open models bypass the closed models, then the closed people will use the open models. So there's no, they can't really shoot past the internal best. I think, you know, when you get to the highly competent employee and then the genius employee, which is imminent, then whoever has the most compute has a serious advantage. And hence the, you know, Anthropic IPO, $100 billion raise. You know, it's a massive advantage to control a huge amount of compute.

• 1:03:03Speaker 02

  I do think maybe there's an alternative scenario, just analogizing this to open source software versus closed source software. It is arguably the case that in many, certainly infra domains, the open source version is superior to the market-leading closed source version, say Linux kernel versus Windows kernel. Folks may throw tomatoes and disagree. But I, I think it's probably fair to say the Linux kernel is a good deal cleaner architecturally at this point than, say, the Windows kernel, even though Linux has been, the source code has been out there for decades at this point for every Microsoft engineer to look at. But why is it, even though there's the Windows subsystem for Linux and a number of other recent affordances,

• 1:03:44Speaker 02

  why is it arguably that the Linux kernel is better than Windows kernel architecturally, even though Microsoft has one-way access? There's a one-way mirror to look at the outside, and it's

• 1:03:54Peter Diamandis?

  And all the money in the world.

• 1:03:56Speaker 02

  And all the money in the world. And it's arguably for legal and cultural and architectural reasons. They can see it, but they still can't use it. And I could imagine the same case where it's possible, not saying it's likely, but it's possible that the open weight models do end up passing the closed weight models. Even so, the frontier labs look out through their window. They can see it. Water, water all around, but not a drop to drink. Models, models all around, but not a post-training run to distill.

• 1:04:25Peter Diamandis?

  Interesting. I'm going to move us to GPT-6.1 Terra, which was released yesterday. A quick reminder, OpenAI has four tiers. Astra is their flagship. Sol is their high performance. Terra is their everyday workhorse, and Luna is their budget model. So two numbers jumped out, for me at least, on GPT-6.1 Terra. First is the price. It's come in at less than half the price of the previous Terra model. And then hallucinations. GPT-6.1 Terra demonstrated a 50-point drop in hallucination. This is a demonetization curve in real time, right? Every few months, the same intelligence costs half the price. Alex and Imad, your take on these models? Alex, you first.

• 1:05:11Speaker 02

  We're right now in a cycle, which I think, a hyper-cycle, mega-cycle, if you will, that's coming to an end where both OpenAI and Anthropic have gotten into the habit of releasing larger models. And then in, in sort of a tick versus tock, if you will, cadence, they release the largest model first, and then they release presumably smaller distillations internally of the same model. And GPT-6.1 Terra is, to my eye, a relatively unexciting distillation of earlier Astra/Sol level or sized or footprinted models. Obviously, the smaller, more distilled variants

• 1:05:53Speaker 02

  that occupy a different position on the cost versus performance curve tend to be more exciting for embedded, high-volume, low-dollar value per token use cases. So I tend to get a lot less excited over these sort of late curve, late cycle distillations. The good news, the solace I draw, is the fact that OpenAI has gotten around to releasing the Terra version of 6.1 means that maybe we'll see 6.2 or 7 sometime soon.

• 1:06:23Peter Diamandis?

  Yeah. Imad, anything exciting there for you?

• 1:06:26Speaker 03

  Yeah, so I think that with Terra and Sonnet 5, you're seeing the models that are being lined up to be the personal companion models. And those need to just be really solid. This is why the hallucination late drop, right? Right now, if you use Dots, which is OpenAI's companion, it uses Astra. So you actually have unlimited Astra via that. But that's not sustainable. Again, everyone's trying to drive down the price of that, except for if you come to Europe. So I have Dots in the US, and then when I come to the UK, it turns off. So that was very interesting.

• 1:06:56Peter Diamandis?

  Come to America, Imad. We want you.

• 1:06:58Speaker 03

  Come to the shows. So again, I think you'll see a bifurcation of models. There'll be the model that's closest to you, that's really reliable, and they'll optimize for that. And it'll pull in the smart models. It'll pull in the models with artistic taste. It'll pull in the models that are really good accountants. And you won't really see much more. Again, we're moving towards this control plane type of scenario.

• 1:07:18Peter Diamandis?

  Salim?

• 1:07:20Speaker 01

  Two things that struck for me. One,

• 1:07:25Speaker 01

  The day, the days between GPT Astra and Terra were fifty-three days for a fifty percent drop in the token price. We've been looking at a ten-week doubling. This was a seven and a point four-week doubling, right? So now we've dropped the doubling time for price performance to to seven weeks, pretty much. That's pretty insane. That was one. But I think more importantly, as the thresholds that these models are reaching, you're at a point where you don't need to redesign software for AI. It's going to operate the software it has. It's just going to start to expose more and more how inefficient our organizational designs are. And so we've been looking at that a hell of a lot. So amazing at all levels. I can't wait to get my hands on it.

• 1:08:08Speaker 04

  Yeah, that is an important point there. Yeah, important point that that Ahmad and Saleem made that, you know, for personal agent use, these things are over the bar of just brilliant, like it's just incredible. And so this curve is almost at its end in terms of the personal home agent. And the next curve is really accelerating, which is its access to every kind of tool you can imagine. So all the post-training and all the MCP wrappers are now embedded, and it just happened kind of quietly. Now, if you pick up one of these models and you say, hey, can you access Eleven Labs and make a really cool video with great voice overlay? And I want it to be my voice. It just happens. You know, six months ago, you had to point it to an API, you had to coach it, you had

• 1:08:49Speaker 04

  to, and now it's just out of the box capabilities. So I think the new curve will be a different dimension, which is, you know, consumer satisfaction, consumer happiness, consumer, all the tools already in the box, you know, all those features. It's very similar to when the PC, you know, you PCs used to be so slow and crappy that they were marketed as forty gigahertz, you know, fifty or forty megahertz, eighty megahertz, one hundred and twenty megahertz. And then Apple said, yeah, nobody gives a rat's ass about megahertz anymore. They care about iTunes. They care about whatever. They care about whatever, and completely flipped the paradigm. I think we're right at the edge of that now with these models where they're all more than smart enough. And now it's like, what does it do for you out of the box? I think we'll see a new curve.

• 1:09:31Speaker 06

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• 1:10:35Peter Diamandis?

  All right, I'm going to jump us into our science segment and our breakthroughs for the week. The first one comes from our very own in-house ASI, AWG. So on Saturday, Alex, you published Chain Theory, a new research program to rebuild physics from quantum information. I'm going to kick this off by showing a clip from an amazing explainer video by Levi Turk. And then Alex, I'll turn it over to you. Let's take a look.

• 1:11:03Speaker 07

  Dr. Alex Wissner Gross, the physicist who co-hosts Moonshots and writes The Innermost Loop, just posted what could be the biggest claim in fundamental physics in fifty years, an article on X called Chain Theory, that tries to rebuild physics out of quantum information. And its first result is a number, one over one thirty-seven point oh three five nine nine nine one eight nine, which he says falls out of a single sentence with nothing tuned by hand, and it matches one of the most precisely measured numbers in all of science to ten digits. It's called the fine structure constant, written with the Greek letter alpha, and it measures how strongly light and electric charge grab each other. Every time an electron gives off or soaks up a photon, alpha sets the strength of that handshake. And it has no units, no meters, no seconds, no kilograms. Alex says the answer was hiding in three facts every physicist already knows. He just reads each one differently, a limit, a pattern,

• 1:11:47Speaker 07

  and a dead end. Clue one is a limit, and it comes from black holes. In nineteen eighty-one, Jacob Beckenstein showed that any region of space can only hold so much information, like a hard drive with a fixed capacity. And the fullest drive you could possibly build in that space is a black hole, and physicists filed that under gravity. But Alex points out that the limit applies to everything inside the region, matter too. And our theories describe matter with fields, smooth things with infinitely many settings at every point. And his answer is that the world is built from a finite number of tiny quantum switches, a qubit with two settings, a q-trit with three, and in general, a qubit. Clue two is a pattern, and it's in the forces. Every force comes with a symmetry, a way you can turn things without changing anything you could ever measure. Every elementary particle acts like a perfect point. Colliders smash them together harder and harder to

• 1:12:31Speaker 07

  look inside the electron and the quarks, and they find nothing. And yet, the particles come in three copies, three generations with the same set of charges. And in the history of physics, a repeating pattern like that has always meant smaller parts. So what if the parts are there, but locked together so tightly that nothing can ever pull them out? Put the three clues together, and you get a picture. Everything is made of a finite number of those little quantum switches, the qubits, joined in pairs, and every pair is entangled all the way, maximally, because anything less would need a dial set to a number with infinitely many digits, and a finite world has no room for that. In nineteen eighty-nine, John Wheeler proposed it from bit, the idea that every physical thing comes from information. And chain theory takes that literally, with finite quantum systems in place of bits. So its one and only axiom is eighteen words long. Nothing

• 1:13:13Speaker 07

  exists but finitely many maximally entangled pairs of finite dimensional systems, some shared, known only by distilling them. Eighteen words. Compare that with the standard model, which needs seventeen particles and about two dozen measured numbers typed in before it can predict anything. Chain theory starts from one sentence, and everything else, the forces, the particles, and alpha, is supposed to follow from it. To distill means to join two systems into a perfectly entangled pair in a single step, the same move that passes entanglement along a quantum network. Two pairs, one joint measurement in the middle, and the far ends become a pair. In a lab, that step only works part of the time. But in chain theory, it's the only thing that ever happens. And there's no observer standing outside the network. To learn about a link is to link to it. So you, me, every measuring device, and every AI that will ever read this, we're all chainmail. And the universe

• 1:13:58Speaker 07

  only ever learns about itself by threading new links into itself.

• 1:14:01Peter Diamandis?

  Amazing. So Alex, how did you come up with this? And, you know, honestly, if this turns out to be true, what does it mean for physics?

• 1:14:10Speaker 02

  Well, contrary to expectations, this was not a weekend project. This has been a long-term, very quiet research project.

• 1:14:16Peter Diamandis?

  You've been saying physics is cooked for a long time.

• 1:14:19Speaker 02

  I would say physics went through a period for the first few decades of the twentieth century called the golden age of physics, when the quantum theory, equivalence principles, special relativity, general relativity were all developed. And we, for the past fifty years, have arguably been in a drought of new fundamental physics. And this is the result of a long-term research program of mine to try to end the drought and bring about a second golden age of physics. And the lever that I'm trying to use here is quantum information to try to rebuild, re-architect the foundations of fundamental physics, which

• 1:15:00Speaker 02

  right now are the standard model, on the one hand, and general relativity on the other, using a common language, a common substrate of quantum information. So I call it chain theory, in part because string theory, which is a popular theory of quantum gravity, relies on an implicit assumption of continuous degrees of freedom. This theory doesn't. It has finite discrete degrees of freedom. And also, I perhaps should add, it actually, at its core, resembles and is physically analogous to a physical chain. It consists of links of maximally entangled particles. In this case, they're not really particles.

• 1:15:41Speaker 02

  They're they're finite quantum systems. But under chain theory, the fundamental elementary particles that that we look around and see, electrons, quarks, photons, these are all basically just small networks of these finite quantum systems or qubits. And this first result, this is just the first result of a much broader program. Many folks who read the initial paper perhaps may not realize, the derivation of the Thompson limit fine structure constant, or alpha of zero, which is one of the most precisely measured numbers in all of science, known to ten-plus significant figures, falls out with no extra inputs directly from this axiom. There are others, other results, other constants, other features

• 1:16:26Speaker 02

  of the standard model and general relativity that also fall out for free from this. So I think this is the beginning of a journey. I don't want to to overstate, but this is, I think, a very promising theoretical result at the moment. There will be, I think, experimental tests, potentially in the next few months, potentially next couple of years, to actually go and measure the next one or so significant figures of alpha not to decide, is this actually a correct prediction or not? So it's falsifiable. But I'm very optimistic that chain theory, or something substantially like chain theory, is for, optimistically, the first time a

• 1:17:07Speaker 02

  crack in the wall that has blocked fundamental physical advances for the past half century.

• 1:17:14Peter Diamandis?

  Nice. So Imad, you're probably the only person on the pod other than Alex that can comment on this intelligently. Your thoughts?

• 1:17:23Speaker 03

  So I haven't read all seven hundred pages of the paper. You know, I'll get through it slowly but surely. Alex has obviously put a huge amount of work into this. I think if we take a step back, physics has been constructive for a long time over the last fifty years. So something like string theory, you have a presupposition, and then a whole edifice that's been built around it of crazy mathematics that's wonderful and elegant, but it's not even wrong in that you can't prove it's wrong in some ways. Um. We have had a resistance from the physics establishment and others for any type of fundamental theory that starts from a base and works to see what is forced by that. Because physics probably can only be one way.

• 1:18:05Speaker 03

  You know, there can only be one set of laws, one set of rules, which is why we see things like the fine structure constant popping out. And we see very interesting things like the Paris and Berkeley variants of that have a little greater than the standard deviation difference. And then we're like, why does that happen? And we can dig into that. But if we don't have a base that starts from a solid presupposition, again, information theory is a very solid one, I would say that Alex's base is more solid than string theory. Then how are we going to get anywhere? We'll just come up with theories that have no grounding, that can't even be tested. Whereas at least this can be tested. But again, it's a lot of very hard work. And there's the question of how do we release this? So, you know, Alex has a following, he's released it into the market. As opposed to going to peer review, because who is the peers now?

• 1:18:50Speaker 03

  As physical superintelligence and others, as everyone has AIs, you will be able to check everything that's produced. Like I put out a small physics theory, you can check that out with one lookup in chat G P T. See standard model uniqueness and there's a lean proof there. You know, you've got other things whereby I think the barriers to theorizing and hypothesizing will break down. We have to be cautious of getting overconfident about these things, but let's try new ways to understand the universe. And then let's extend those forward with others as well. Because this isn't an individual research program that'll be, right? This is a, this is a good way to start thinking about the universe, let's extend it. And here is a really interesting result. Eddington got to one over one, three, seven, and then he got a bit overconfident.

• 1:19:35Speaker 03

  But, you know, I put a quote by Feynman as the thing here whereby this is one of the most magical numbers we've ever seen. And if we have a derivation of it, fantastic, how do we check it? Well, we can all check it together because it's out there. And I would contrast this with OpenAI actually, in that they released uh, Yang Mills existence announcement, half of the Yang Mills Millennium Prize problem. Plus, uh, construction of the standard model, except for they didn't. They went to the string theorists and said, you guys check it first and then you decide when to release it. And everyone was like, well, that's not how things should work. That's not how science should work. And so I'd love to hear kind of from Alex how he's found the process of thinking about releasing this and

• 1:20:20Speaker 03

  everything around that as well and where it's going to go.

• 1:20:22Speaker 02

  I think it's a brilliant point, Ahmad, multiple brilliant points. Maybe just to speak more broadly, we live in an era, an era, we live in a month at this point where you see A I companies releasing slop drops and just bulk solving open conjectures, open problems, and you see then an outcry from math research community, for example, complaining that their seed corn for developing new insights is being devoured by A I and there's nothing left for the humans, no intellectual fodder, as it were. I, I think it is absolutely essential that there be new conjectures, new insights, not just bulk solving of existing open problems.

• 1:21:04Speaker 02

  And I, I also think to maybe make a slightly broader point. I think physics is in a bit of a depression right now that I'm, I'm hoping to try to cure. It used to be the case that physics sought simpler and simpler explanations for more and more phenomena. And sometime around the nineteen seventies or so, that went into reverse. And there, there has been a proxy, there's been very little fundamental theoretical insight that humanity has gained, as far as I can tell, for the past fifty years. There have been arguably regressive programs that tried to arrive at all of these free parameters that this

• 1:21:44Speaker 02

  the textbook standard model has only eighteen free parameters, nineteen, if you count one convention, but eighteen free parameters that right now are total mysteries. Many of them are known to ten or eleven significant figures, but they're total mysteries. And what I'm trying to do here is, in some sense, uh, lead by example, if I may be so presumptuous and say, all of this whole community that's searching for beyond standard model physics, the answer has been staring us in the face the whole time. It's hidden in the constants, the constants are the signatures, because right now textbook standard model physics holds that these are just free parameters, but they carry, they may carry the signature of all of the beyond standard model physics that we would want.

• 1:22:28Speaker 02

  And now in an era when it's finally possible with lots of compute and lots of human insight to go and decipher the signatures in the constants, I do think we're about to see a second physics golden age.

• 1:22:42Peter Diamandis?

  Alex, if the chain theory is correct, uh, what kind of technology falls out of this, gets unlocked that we can't build today?

• 1:22:51Speaker 02

  It, it's really interesting, because if the chain theory thesis, the chain theory axiom is correct, it would suggest that the bottom of the pool, as it were, is a lot closer than many would think. It textbook physics suggests the Planck scale is many orders of magnitude either below in terms of length or higher in terms of energy than where our present technology is. We max out in terms of cosmic rays hitting our atmosphere or terrestrially certain supercolliders. Chain theory, if chain theory is correct, it actually suggests we're not that far from the, the physical limits of

• 1:23:32Speaker 02

  length, for some definition of length, or time, or energy appropriately defined. And that could mean speculatively, but these are very early days and I don't, I, I, I want to just make sure the impression is, is correct. This is an emerging theory, this is a very new theory, parts are underdeveloped, parts would benefit from further development. It's, it's a brand new theory. But to speculate, if the chain theory thesis is correct, it may mean that the physical limits of what's possible in terms of length, energy, time, are actually orders of magnitude closer to the technology we have today than one, than what, what one

• 1:24:13Speaker 02

  one might have otherwise naively assumed based on conventional textbook standard model and quantum field theory that says everything is basically continuous down to the Planck scale at which it stops.

• 1:24:26Speaker 03

  Well, I think that also it's the most simulation friendly physics theory out there.

• 1:24:30Speaker 02

  It is, it looks suspiciously, it's, it's a good point, Ahmad, it looks suspiciously like what physics would look like if, if our physical world were built on a quantum computer.

• 1:24:40Peter Diamandis?

  So, Alex, are you publishing this?

• 1:24:43Speaker 02

  So, so it's, it's already been published on the web, it'll probably be published on the archive.

• 1:24:48Peter Diamandis?

  Imperial Journal, right?

• 1:24:49Speaker 02

  Yeah, so, I mean, I've published lots of refereed, uh, peer reviewed physics papers over the years. This one had an urgency to it that required immediate publication, so I would expect it to show up on the archive sometime soon. And then, yes, it'll, it'll be submitted to a physics journal sometime soon. And eventually, uh, the, having been through the process many, many times, uh, that, that can take a while. And I, I felt that this result and adjacent results, I also released a compendium, as Ahmad said, of approximately eight hundred pages of results, so it's not just the fine structure constant. Uh, also a variety of other results spanning standard model, general relativity, cosmology,

• 1:25:31Speaker 02

  highly entangled and strongly correlated systems, hadronic physics. I wanted to get all of this out, to, to get it out into the, the public domain, as it were, at least public domain situational awareness early, because refereeing process can take sometimes a year and a half.

• 1:25:49Speaker 03

  Nice. There would be, there would be the referees for this even, like, it's gonna be interesting.

• 1:25:53Speaker 02

  It'll probably be refereed, I think, by quantum information folks.

• 1:25:57Speaker 03

  Yeah, there's only a few of them.

• 1:25:59Speaker 01

  I mean, the potential here is if we, just by using information and entanglement, you can describe all the fundamental particles, forces, um, and spacetime, right? Because like you set this foundation up, is that essentially where you're going for, for this?

• 1:26:15Speaker 02

  That's the, that's the aspiration, yes. Uh, and it may have received less attention in the initial announcement, because the, the way I broke up the initial announcement was into one self-contained letter deriving alpha of zero, the, the fine structure constant at the zero momentum transfer limit, which is one of the most precisely measured constants in all of science, on the one hand, and then a compendium of approximately eight hundred pages with so many different results spanning general relativity, standard model, Q F T, and, and so on. So I, I would say, expect more results. I, I, I do think this is, it's very surprising even to me, it's a very surprising result that one can recover with, with no free parameters from such a simple assumption, essentially that

• 1:26:59Speaker 02

  our entire world is made out of a network of qubits, qubits and qubits. So that's two, three and four dimensional pieces of a quantum computer. Could describe it more technically in terms of discretized Hilbert spaces, but basically little bits of a quantum computer. If you just network them in a certain way, this constant and, by the way, many other features of textbook physics fall out. And that is, I, I would say scandalously exciting.

• 1:27:28Peter Diamandis?

  Yeah. What constant comes, what constant comes next? Electron mass, Higgs, gravity?

• 1:27:33Speaker 02

  Well, if you look at the compendium, there are actually many other constants buried in there that are just waiting to be released. So masses fall out of it. Interesting. There are other coupling constants that fall out of it. Interesting. Again, standard model has 18 actually free parameters. That's without counting neutrino masses. There are cosmological parameters that appear to fall out of it. So again, I think these are early days and there will be much more development with time. But every preliminary indication that I've seen is that it looks plausible that every single free parameter of the standard model could fall out of this.

• 1:28:17Peter Diamandis?

  Does this give us any insights to dark matter or dark energy potentially?

• 1:28:20Speaker 02

  It does. It does. So I'll spend 30 seconds talking about a theorem that was ultimately discovered by a number of Polish quantum information theory researchers. So there's a theorem that if you have two qubits, so two parts of a quantum computer with different dimensions, and you multiply them, that if the product of the two dimensions is greater than or equal to eight, there can exist what are called undistillable states. But by analogy, imagine an undistillable quantum state as a prime number that can't be factored into simpler integers. So they showed, Horodecky et al. showed that it's possible for such states to exist if you have the product.

• 1:29:04Speaker 02

  So basically, if you have any product, any entangled state other than a qubit with a qubit or a qubit with a qubit with a qubit with a qubit with a qubit with a qubit supports these undistillable, sometimes called bound, entangled states. So it's possible then, if you sort of fast forward that to distillability, it raises questions around dark matter. Dark matter is, the general definition of dark matter would be it's coupled to gravity and not coupled to any other standard model interaction. It's not coupled to weak interaction, strong interaction, or electromagnetism. So there are dark matter candidates within chain theory. The dark matter candidates are coupled to gravity,

• 1:29:46Speaker 02

  and gravity also, geometry in general, appears to fall out of this axiom. And then for dark matter, the chain theory has a notion of a dark interaction that's undistillable, thanks to this theorem. So very quickly, there's this beautiful, beautiful mapping between the different link types. So a qubit with a qubit, that's a 2-2, appears to map to spacetime. A 3 and a 2, a qubit and a qubit, appears to map to electromagnetism. And that's where the fine structure constant, which measures how strongly electromagnetism couples, how strongly it couples to charge comes from. A 3 and a 3 appears to correspond to the strong interaction. A 4 and a 2 appears to correspond to the weak nuclear interaction.

• 1:30:31Speaker 02

  A 4-3 appears to correspond to Yukawa coupling. And a 4-4 appears to correspond to a Higgs. And that gives you the entire standard model Lagrangian. So out of all of that, with high enough dimension qubits, it appears dark interactions that are undistillable would be beautiful candidates for dark matter.

• 1:30:51Speaker 04

  Does that then create another way to validate it? You know, besides alpha naught, which is, you know, if you have 4-4, is there a 5-4? Like an undiscovered force?

• 1:31:02Speaker 02

  It's possible. And it's also, chain theory also has opinions about just 4-3 and 4-4, which are terms, correspond to terms that the Yukawa coupling and the Higgs boson, the only known scalar boson, are normally taught respectively as couplings between conventional matter particles and the Higgs field, and then the Higgs itself. In the language of chain theory, even 4-3 and 4-4 are actually treated as being co-equal to the strong interaction and the other standard model forces. And in the language, or in the view of chain theory, the reason why we don't conventionally think of Yukawa couplings and the Higgs

• 1:31:46Speaker 02

  as being first class, call them fifth and sixth forces, in addition to the four that are taught in elementary school, is because they're sufficiently dark that they were associated with the masses of particles. So in the view of chain theory, we sort of confused the fifth and sixth forces with the mass spectra, when in fact, they should have been treated as fifth and sixth forces.

• 1:32:07Peter Diamandis?

  All right, Alex, this space cadet me wants to know one thing. Do we get inertialist drives? Do we get gravity shielding? Do we get anti-gravity out of this?

• 1:32:16Speaker 02

  You know, I would love that more than anyone. I don't know yet, but I do think that if such sort of Star Trek sci-fi tech is going to be possible, I think it's going to require a breakthrough in fundamental physics like this. Simply the case that the physics that we have right now, with on the one hand, general relativity, which many would view as beautiful, and on the other hand, the standard model, which is historically regarded as relatively ugly by comparison, and generations, 50-plus years of physics researchers looking for elegant ways and failing, to combine these with lots of predictions that haven't played out,

• 1:32:58Speaker 02

  I think it is going to require a rethink, perhaps like chain theory, to discover what the actual bottom of the pool is, so that we can then go and realize a Star Trek future. So cautiously optimistic.

• 1:33:09Speaker 01

  I'm gonna have to take some mushrooms and then come back and listen to this segment again.

• 1:33:18Speaker 03

  Information theory drives. You don't fault information. It's gonna be fun.

• 1:33:23Speaker 02

  It should be an interesting time. Second physics golden age.

• 1:33:25Peter Diamandis?

  Good luck on this, Alex.

• 1:33:27Speaker 02

  Thank you.

• 1:33:28Peter Diamandis?

  We're rooting for your Nobel Prize. I'm gonna move us forward. The next story is one that I'm personally invested in through NeoGenesis. It's a company called Warhound. Here's the backstory. So Ben Lam, CEO of Colossal, and Palmer Lucky, founder of Anduril, met at a conference about a year ago, just after Colossal had brought back the dire wolf from extinction, creating a heavier, more powerful-designed wolf. Palmer raised a specific problem that he was seeing in the supply chain for America's working dogs, typically used for search and rescue, police or military duty. The problem that Palmer noted is that today, more than 80% of working dogs come from Europe, and fewer than half of them are fit for service.

• 1:34:11Peter Diamandis?

  Many wash out because of inherited problems like hip dysplasia, poor eyesight, or cancer. Ben and Palmer answered that question by creating Warhound. It's a company that takes cell lines from the best Belgian Melanes or German Shepherds, edit out the bad genes, enhance things like endurance, scent detection, intelligence, size, hearing, strength, and then clone the resulting dogs that were engineered, so you get the same performing dog every time. Now, you can think of the cloned dog as becoming a catalog number, cloned and raised on demand. Here's an image of, from the recent release of Warhound. Actually, it was leaked. It was not supposed to go public for another six months.

• 1:34:55Peter Diamandis?

  Anduril adds the tech on top of the dog, so armored vests and sensors and feeds data to the handlers. As Ben Lam put it, "Colossal is building Captain America. Anduril is building the Iron Man suit." Warhound came out of stealth with $200 million of investments from Incutel, Sequoia, 8VC, and my own fund NeoGenesis, at a $1.2 billion post-money valuation. For background, I think I haven't mentioned this on the pod. NeoGenesis is a $200 million fund that I started with Dr. Yanni Pisolakis. It's investing in Colossal and all the spinouts, including Warhound. So the first cloned dogs are due early next year, and they can be in service by two years from now.

• 1:35:40Peter Diamandis?

  It's worth noting that Warhound is working with the American Humane Society to certify the process and the training. So Saleem, let's go to you first. What are your thoughts on the ethical issues related to engineering animals like this?

• 1:35:53Speaker 01

  Yeah, I mean, are we building biologically augmented machines or technology augmented organisms, right? There's a huge ethical, the ethical consideration here is huge, because can you treat a living organism as another, like programmable component? Where I think things get interesting is we have the interspecies language X Prize. Right? So when that prize gets one, you can kind of interact with these dogs. We may not want to hear what they have to say. They're like, what the hell am I? One other thing does occur to me on the positive side, which is that, you know, there used to be all those Boston Dynamics robotic dogs, right?

• 1:36:36Speaker 01

  And I remember, it might have been Brad Templeton who said, "Well, if you want a robotic dog, if you want a dog, rather than building a robotic dog, give me a male dog and a female dog, and I'll give you a dog." And so breeding organic dogs, although programmed in a particular way, is incredible. I have concerns with the ethical issues here, because you're gonna send these into dangerous zones and basically built for sacrifice, right? So it's a massive collision of biological engineering and robotics and sensing and defense technology, but the ethical side is pretty huge.

• 1:37:22Peter Diamandis?

  You know, Colossal bought the top two cloning companies. They famously cloned Tom Brady's dog, and you know, cloning dogs is a thing.

• 1:37:31Speaker 01

  Yeah.

• 1:37:32Peter Diamandis?

  Dave, your thoughts?

• 1:37:35Speaker 04

  I love the go to market here. If you're cloning dogs, every use case in the world from service dogs to and then across all the other species, but then saying, well, let's do it with Palmer and go Warhound, put some goggles and some munitions on its back. And those guys are just fearless in their positioning. They're going to get so much pushback and they just don't care. That was obvious on stage at abundance summit. But I think it's a good strategy in terms of attracting attention to what you're doing, even if it's controversial attention, and then ferreting out the issues. It opens the door for people like Saleem to say, look, we need to discuss the, this is clearly a capability we have in society now, and there are no rules, and we need to discuss it. And that creates a lot of news. News creates

• 1:38:17Speaker 04

  capital, capital creates recruiting, recruiting creates success, and it just spirals. So it's really cool to watch how fearless the guys are.

• 1:38:25Peter Diamandis?

  Alex.

• 1:38:27Speaker 02

  There is a syndrome in humans. It's called Williams Bureau Buran syndrome that causes humans, it's relatively rare, one in 10,000 people, that causes humans with this syndrome to have, among other consequences, extremely bubbly, extroverted personalities. And it was discovered back in 2017 that domesticated dogs appear to have been inadvertently selected for dogs, canines with mutations in the same region of their genome as humans with Williams Buran syndrome. To the extent that's the case, it may have been the case that dogs were domesticated basically by our selecting for what in humans is viewed as a disease

• 1:39:11Speaker 02

  of being overly extroverted. And I do then wonder, fast forwarding to Warhound, whether I have to, I guess parenthetically note, begun the clone wars have, close parentheses. I just, it seems to me, this is the arrow of time now apparently going in the opposite direction. We spent thousands of years breeding, directing the evolution of canines to become friendlier and friendlier and more and more, there's a notion in EVO Devo, Neoteny, that the trend of humans to become more and more childlike. And seemingly, we directed the evolution of dogs with apparently some analog of Williams Buran syndrome to be more childlike.

• 1:39:54Speaker 02

  And now, if we're going, or some subset of humanity, including Warhound, or directing the evolution in a different direction, or through genetic engineering, otherwise directing dogs to become more warlike, that would seem to be at odds with the other arrow of history with obvious ethical concerns. I'd much rather we, we give the dogs bank accounts and AI assistants and smart wearables and maybe, yes, personhood of a sort, rather than sending them into war. That would be my take.

• 1:40:22Peter Diamandis?

  And don't forget, this is not just military dogs. It is search and rescue. It is, you know, airport security. It is police dogs as well.

• 1:40:33Speaker 02

  Dual use dogs.

• 1:40:34Peter Diamandis?

  Yes, for sure. Ahmad.

• 1:40:38Speaker 03

  Yeah, I mean, it should increase general oxytocin, right? That's kind of the thing. More dogs, more oxytocin. I mean, if you can't find enough actuators, just breed them. I suppose that's one way of doing things. And obviously, like you look at the history of science fiction, you do have robots, but you have lots of biologicals, right? Again, it works. Male dog, female dog, breed for traits. And where does it stop is the question? Like gorillas, elephants, I think those are all kind of actually genuinely oddly valid, particularly when you get cybernetics, because you can have that kind of adjustment there, just like you have companies like Loyal now extending the life of dogs, right?

• 1:41:15Peter Diamandis?

  Yep.

• 1:41:16Speaker 03

  And the question is, will we stop before humans? I think.

• 1:41:22Peter Diamandis?

  I don't think we will in the long run.

• 1:41:25Speaker 02

  Why don't we genetically engineer some raptors from Jurassic Park and send those into war? Why do we have to pick mammals? Send the raptors in?

• 1:41:33Speaker 03

  I don't think that we'll send any biologics into war, because I mean, ultimately, you've got enough drones, right? So but it is a case of, like, literally, you will not be able to build enough robotic parts. So the question is, which biological substrate can you build robots on?

• 1:41:47Speaker 02

  You don't think they'll become indistinguishable? I would expect 10 plus years out, the distinction between sending a biologic into a war zone versus sending a drone into war zone, the blurry, the boundary gets very blurry.

• 1:41:59Speaker 03

  Well, I just think that, like, you know, the flying drones would be able to just wipe out just about anything. And biologics have different attack vectors.

• 1:42:08Speaker 02

  But the flying drones can be subject to EW attacks, and they run out of power, and they need fibers, whereas you send a bird or some flying animal in that has better properties, perhaps.

• 1:42:18Speaker 03

  There we go, the doom sparrows, you know?

• 1:42:20Speaker 02

  That's right. The birds aren't real.

• 1:42:23Peter Diamandis?

  You know, interestingly enough, you know, all of these working dogs that coming out of Eastern Europe, they're bred. And you know, when you breed dogs, you hope you get the right traits, you get the right physical dimensions and so forth. What I find fascinating about this is the whole cloning side, that once you get a dog that has the right temperament, the right capabilities, you can repeatedly get the same dog over and over again.

• 1:42:46Speaker 01

  Yeah, this is an important point.

• 1:42:47Speaker 02

  Dog printers and dogs to spec.

• 1:42:49Speaker 01

  I think there's an important point. And Alex, to build on what you said, and Peter, we've been doing this for millions of years, selecting and breeding horses and dogs and cats to select for the traits that we want. We've now gone exponential with that capability. And we can programmatically design these things, rather than breeding generations, seeing what happens, breeding more. The suffering from that is 10x worse. But this is kind of incredible to see it actually happened. I mean, talk about speedrunning all the sci-fi tropes, holy crap.

• 1:43:22Speaker 03

  So wait, is it legal to clone anything except for a human, then?

• 1:43:27Peter Diamandis?

  I mean, today it is, right? Companies like Repet are cloning your dog if you, if you want to get the same dog back.

• 1:43:36Speaker 02

  They're at least in, I'm not sure about other countries, but in the US, there are many regulations, both soft and hard, concerning handling of vertebrates in general. So it's my understanding, you can't just, like, go off and say, I'm going to genetically engineer arbitrary mammal and produce it. I think there are a few regulators in the loop on this.

• 1:43:57Peter Diamandis?

  Yeah.

• 1:43:58Speaker 01

  The big concern I would have here is the consent paradox, right? Animals can volunteer for dangerous service. And so you bring up those questions around, is this an appropriate thing to do? But I think for search and rescue and all sorts of stuff, and obviously, they'll start with those use cases, pretty incredible to be able to operate.

• 1:44:20Speaker 02

  You remember Saleem from Douglas Adams, Hitchhiker's Guide to the Galaxy, the Emeglian Major Cow that was engineered to want to be eaten?

• 1:44:28Speaker 01

  That's right. That's right.

• 1:44:30Peter Diamandis?

  Please look at my hind, my hind.

• 1:44:32Speaker 02

  Yeah, like, eat me, I'm delicious. I really want you to eat me. I think that's maybe what we're moving towards here.

• 1:44:37Speaker 01

  I mean, look, there's so many parallels with Douglas Adams, right? He thought of the world as a, as a biological computer. And you've, we've kind of come to that with some of the work you're doing on us. My favorite comment ever from Douglas Adams, I must throw this line out. He said, anything in the world when you're born, that's considered normal. Anything when you're young, we called, that's invented when you're young, we call that a career. And anything invented after you're 35 years old is just that, blanket bad for the world. That's a great framing.

• 1:45:06Speaker 02

  Inevitably, I have to point out, going back to the chain theory discussion, so chain theory derives an exact expression for, for alpha of zero. And the expression is based on integers. And there is a 42 in there. So

• 1:45:20Speaker 01

  No, of course.

• 1:45:21Speaker 02

  Yes, it's literally, it's 79 plus 48 times the square root of 2, plus 42 times the square root of 3, plus 3 times square root of 6, all over 24 times 36 squared. So Douglas Adams and 42, maybe you were right.

• 1:45:35Peter Diamandis?

  Welcome to the health section of Moonshots, brought to you by Fountain Life. The discussion in this section is for informational purposes only, and is not medical advice. Please consult a qualified healthcare professional about your individual needs. I'm a co-founder of In Client at Fountain Life. You know, AI is having an outsized impact on every aspect of our lives, how we teach our kids, how we run our companies. It also is having a huge impact on health, helping you prevent heart disease, one of the key things. I'm here with Dr. Dawn Musalem, our chief medical officer at Fountain. Heart disease has been personal for you as well, hasn't it? It really has, Peter. And my daughter was five. My husband died of sudden cardiac

• 1:46:14Speaker 06

  And so this is a topic that is one that I am mission driven to try to eradicate. Prevention first and early detection is absolutely critical. Fifty percent of people die of heart attacks with no warning signs.

• 1:46:27Peter Diamandis?

  No shortness of breath, no pain, no nothing.

• 1:46:29Speaker 06

  No, silent killer.

• 1:46:30Peter Diamandis?

  They just don't wake up in the morning.

• 1:46:32Speaker 06

  They don't wake up. And so, you know, AI, this is our mission to advance science to try to help to one day democratize wellness. We know at Fountain Life, when we do this CT angiography with AI analytics, we are actually finding that eighty-eight percent of people coming in have detectable coronary disease. But Peter, what's more alarming to me is twenty-three percent of those individuals had soft plaque. This is the plaque that would not traditionally be seen on CT, looking at calcium scores alone. And this is the plaque that we must intervene with, with the multimodal testing we're doing, including diagnostic laboratory studies partnered with healthy lifestyle recommendations.

• 1:47:11Speaker 04

  So, listen, make sure you understand what's going on inside your body genetically, metabolically, and cardiovascularly. You can know, and it's your obligation to know. So, check it out at fountainlife.com/peter to find out more. And really make sure that you're the CEO of your own health. All right, back to the episode.

• 1:47:29Peter Diamandis?

  Okay, moving us into our final, uh, final story. OpenAI cracks three foundational problems. So, OpenAI on Monday announced its models produced three discoveries at the foundations of math, computer science, and physics. First, in math, OpenAI discovered a proof of a nineteen ninety-one conjecture about how waves focus in every dimension. Second, in computer science, OpenAI disproved the exponential time hypothesis, a widely believed assumption about how hard certain problems are to solve, which hundreds of papers rely on. The proof is verified and lean. And third, in physics, OpenAI discovered a rigorous proof

• 1:48:12Peter Diamandis?

  that the theory of the strong force quantum chromodynamics exists as real mathematics in four dimensions. The physics team shepherding this was led by Nobel laureate David Gross. And OpenAI has taken no academic credit. It's handing the results to independent experts to verify and explain. Here's a key implication, you know, none of these claims have been peer reviewed yet. And AI is now producing results at the frontier of human knowledge so fast that humans can't check them in time. The bottleneck in science has shifted from discovery to verification. Ahmad, what's your take on this?

• 1:48:50Speaker 03

  Well, I'd say that these are all bigger than the Riemann zero hypothesis and maybe a little bit smaller than the Navier-Stokes one. But again, it's across different areas and they have different release schedules. So we can check some of them right now because they've got the lean. Probably the papers are written like slop, but we'll see. The physics one not being released is very annoying. And again, I don't think the right thing. But again, it's just casually it's released. Anthropic, uh, again, there's a little bit of drama here because the physics result extended some open source work that was being done. And it says, you know, thanks for the open source work. We extended it in kind of the footnotes. So it's part of the training set.

• 1:49:35Speaker 03

  But now that you have all these things, like, what is academic credit anymore? If you're doing anything that is formally verifiable, does anyone really believe you're not using AI? You know, like, is there something unique about it came from my mind as I was hit by an apple or something like that? Like, we should be trying to solve all of these things, you know, rather than making up stories. And we should be using it to extend and try brand new things and new ways of looking. Like, the fact that we didn't know that there was the four dimensional construction of quantum electrodynamics, you know, it's just because we were forced there by, again, string theory and a few other things. Let's try different things, I think is the headline, if you don't want to be stuck

• 1:50:17Speaker 03

  in these areas where exponential compute will be directed. But yeah, they are big, like the CQ one, for example, that's, I think, field medal level. And then the shocking one was the EMH one. That was very surprising because we all thought it was the other way around, that it was true, not false.

• 1:50:35Speaker 02

  ETH, you're in. Yeah, exponential time. So, a few comments here. One, called it Yang Mills when Ahmad and I were talking about which Millennium Prize problem was likely to, to fall next. This, it's not quite clear based on the, the results yet, whether this is some or most or potentially all of Yang Mills, but.

• 1:50:58Speaker 03

  It's existence, but not mass gap. So they didn't say mass gap.

• 1:51:01Speaker 02

  So, so potentially major step towards Yang Mills to the extent is the case. That's point one. Point two, OpenAI seems to have learned some lessons, but not a lot of lessons from the slop drop in math. They, they took so much heat from the math research community for that slop drop. All these mathematicians throwing themselves out of windows, writing petitions, saying it's the end of the field, mass depression, dog sleeping with cats. The, the OpenAI response to this appears to have been to round up a, a number of mathematicians, physicists, computer scientists to serve as, I don't want to say a veneer or whitewashing of these results. But there is definitely, I think history will, will regard

• 1:51:45Speaker 02

  contact with reality under the slop drop as been, as being the moment when everything started to get solved and there was a panic in the applicable community. And call this version two point O of a slop drop, rather than just releasing all of the results and saying, all right, like, pack it up, it's over. Instead, OpenAI engaged with a number of high profile math, physics, computer science researchers as almost emissaries, ambassadors. Meat puppets, if you will. And some folks don't like that term, but meat puppets, if you will, to basically be the, the emissaries for making the, the bitter lesson of AI go down a little bit more smoothly

• 1:52:30Speaker 02

  and saying, well, we have these results, but we don't understand them yet. So we're going to have an intermediate layer of humans to serve as intellectual ambassadors to translate this into something that the community can metabolize more healthfully. I think this is just a phase. I don't think this phase is going to last longer than five minutes. I think I view it as a response to the moral panic of the slop drop and Navier-Stokes. And once we've gotten past the moral panic of super intelligences being able to just bulk solve problems, we won't need this. It was almost, apologies for this analogy, but in earlier decades, the Motion Picture Association in Hollywood was almost this, uh,

• 1:53:12Speaker 02

  this stronger filter, certainly in the McCarthy era, of what could be shared, what couldn't be shared, and being a mass filter culturally for what the American public could consume. And I think we're in almost, apologies, a McCarthy-esque era where the frontier labs are so terrified of the academic researchers for the moment that they feel the need to clothe themselves in academic researchers to appear legitimate. But this will only last for five minutes and eventually we get past the moral panic and we get into an abundant solutions phase.

• 1:53:46Peter Diamandis?

  So, so Dave, what does it look like when discovery is essentially free, right? Where all of your, all of your students are like, you know, just pointing super intelligence at any problem they want.

• 1:53:57Speaker 04

  Yeah, that's why it's really fun to work with twenty, twenty-one, twenty-two year olds because they don't, they don't have all these hangups. You know, if you work for thirty years to get into a position in academia where you get to bless every idea and it has to come through you, and then all of a sudden AI works around you, you're very threatened by that. And McCarthy-esque is exactly the right phrase, Alex. And, you know, we'll see with chain theory how the reaction percolates out. But I get it. I mean, if I spent forty years trying to become the head of a department at a major university, I don't want to be irrelevant overnight. And so that reaction, like you said, it maybe it's five minutes in the grand scheme of history, it's, it comes and goes, but it's very, very real.

• 1:54:37Speaker 04

  And I think OpenAI, you know, they, when they, we're talking about white collar automation and all these jobs going away. That created huge backlash. They learned a little bit. Then they did the, the math slop drop, huge backlash, they learned a little bit. Now, you know, if they hire the top scientists in the world to be meat puppets and bless things. Okay, I guess that's okay in transition. But I'd love, Alex, to get a little understanding on, you've been working on chain theory, plus the other 800 pages, for a long, long time. And AI forced the timeline, I think, on getting it out to the public.

• 1:55:16Speaker 04

  But really curious if those thoughts were like 80% done, 90% done, 99% done, and then, you know, OPIS 5.5 or some similar model pushed everything over the finish line? Or how did that evolve? I'd just love to understand your personal perspective.

• 1:55:33Speaker 02

  It's a great question. So I've been thinking about these issues for decades. I mean, I got my PhD in 2007 at Harvard in physics, and I've been thinking about and publishing in PRL and elsewhere, a number of papers on the nature of causality and the intersection between causality and computer science. I published a paper in 2010 on causal and entropic descriptions of intelligence itself. So I've been thinking quite a bit about information theory and the physical world and the intersection between information theory and physics.

• 1:56:11Speaker 02

  And what I said in the paper acknowledgements is, I certainly use superintelligence, but superintelligence is not at the point yet, at least superintelligence that I have access to, where it appears to be capable on its own of arriving at something like chain theory. Chain theory, I do use superintelligence, but it requires an enormous amount of steering to make progress. That's where we are right now. Could change in the future, I expect it to change in the future, but chain theory, just for the record, chain theory was, I'll call it my intellectual brainchild, if you will.

• 1:56:51Speaker 04

  Okay. And then how much did AI accelerate your process of thinking it through? Because it can check, you know, many, many theories overnight for you and do all the math.

• 1:57:02Speaker 02

  It's so helpful. It's helpful for lean generations. So chain theory comes with a lean proof, starting from the axiom and ending at the number. It doesn't prove correspondence with the photon, that part is still within the four corners of the paper, that part is conjecture. But just for being able to generate lean, for being able to validate proofs, it's almost, I think, probably a good analogy at this point in time, it may not last more than a few more months, is almost, it's like having an army of graduate students working underneath you. It's like operating an entire lab, an entire department of very talented graduate students that require an enormous amount of steering and taste at the moment.

• 1:57:46Speaker 02

  But in the near-term future, I can see, I think it's probably inevitable, that all the taste and steering and oversight that right now is required will get automated as well.

• 1:57:56Speaker 04

  So glad you said that, because I think that that statement defines where we are in human history right now. We're in this magical moment where an entrepreneur, a scientist, a brilliant person can be massively more efficient and is not yet completely irrelevant. And I think we need to savor this moment and maximize it. Everybody should be maximizing it, you know, while it exists. And just, it's such a great case study.

• 1:58:20Speaker 03

  Now, we're in the period of basically the last human discoveries. So you better make one. I think, you know, come up with good ideas and use the AI to accelerate yourself. I think that's the headline.

• 1:58:29Peter Diamandis?

  All right, I'm going to move us to AMA with the mates. Saleem, your choice first.

• 1:58:35Speaker 01

  Ah, Jesus, okay. I'm going to go with number four. If SI is aligned primarily through incentives, could it learn to game the system? Should we focus instead on instilling deeper values such as empathy and concern for human well-being? And that's from the way SI. Well, I won't go through my beef with SI as a concept, so let me just deal with the alignment issue here. You know, it is absolutely a massive risk. I think the bigger problem that we have, though, is that we are, human beings are not aligned, right? We claim that value, health, and peace, and sustainability, and fairness, but we construct systems that completely undermine all of that.

• 1:59:19Speaker 01

  You can look at the wars going on today and the conflicts going on today to see that in full relief. So when people talk about alignment, alignment with what? And this is the bigger challenge. I think we have to figure out what human alignment is before you ask an AI to align with it. But I think the alignment issue goes to not just what we're doing, but what our aspirational goals are. And I think that's where things become very interesting. And this is what we try and explore on this podcast, is what could we do with this? But it is a massive risk, because if you, a sufficiently capable system is going to optimize whatever objective you give it, and rather than what you intend.

• 2:00:01Speaker 01

  So we're going to have to get very, very careful on what we ask it to do, because it's going to goddamn well go and do it. And this is essentially Goodhart's Law. When, you know, when you can measure, when a measure becomes a target, then it's not a good measure. So how do you optimize a specific thing? If you give it a particular target, you're going to end up with a bad outcome. We see this analogous problem all the time in organizations. We've got big issues between behavioral compliance and real alignment. So I hesitate on trying to instill empathy as an emotion. We don't know whether they can experience empathy in a human sense. We can definitely require behavior from these systems that takes human well-being into

• 2:00:45Speaker 01

  account, fairness, autonomy, vulnerability, et cetera.

• 2:00:48Peter Diamandis?

  Yeah, and the question of instilling deeper values, whose values?

• 2:00:52Speaker 01

  Yeah, that's the motherhood question.

• 2:00:55Peter Diamandis?

  Yeah. All right, Dave.

• 2:00:57Speaker 04

  All right, I'll take number three. Once our SI gets good enough, won't the data mode dissolve? Is compute the only real long-term mode in AI? And that's from DJ6ben. Great question. You know, it always struck me that when you book a flight on Google, you know, Google extracts 80% margin when you click on the link, and then you go to booking.com, booking extracts about 60% margin, and then you end up on an airline, and for a long time, the airline, which is giving you the plane, the pilot, the trip, was operating at negative 2% margin on your flight. And like, wow. So the venture capitalists care a lot about modes, because money doesn't always land where value is actually created. But I think going forward, people might be over-fixated on modes.

• 2:01:42Speaker 04

  There's so much opportunity bubbling up from AI in so many directions, and it's just there to be grabbed. But I think the data mode will not dissolve. I think any given data mode that exists today will dissolve, but new data modes will emerge as new types of data emerges. And so I think data mode as a concept will survive for many years, even though anything you do today has a shorter and shorter shelf life. But, you know, compute is a great mode, natural resources are a great mode, but also consumer preferences on any app are also a great mode, and we're seeing that with personal agents now. So there are many, many modes emerging. I think the tide is rising so quickly that we're just buried in opportunity. So I would never say compute's the only real mode of the future. That's really demotivating.

• 2:02:27Speaker 04

  The reality is flourishing is everywhere, and you will find ways to establish modes and repeatable revenue in many, many areas, data included, going forward.

• 2:02:39Peter Diamandis?

  You know, Dave, I would imagine that speed of innovation is going to be one of the greatest modes in the final result. Who's innovating the fastest?

• 2:02:45Speaker 04

  Yep. And I think all of the, you know, your corporate culture, but also your AI corporate culture, and all the data feeding it naturally improves and accumulates over time, and that can become a heck of a mode, too. But yeah, the company of the future has to be super flexible and constantly reinventing itself.

• 2:03:04Peter Diamandis?

  Imad, what's your choice? One or two?

• 2:03:08Speaker 03

  Number two, if robotic costs keep falling, which industry will see the biggest transformation first by Digi Beyond? If I could order as many robots as I want, and they were a bit more capable than now, I would go and disrupt the construction industry directly, because there is a fixed cost per unit of human labor that robots are already a multiple below, given a bit more coordination, given a bit more around that, and people would genuinely pay for. I would pay for robot construction people and specialists over human ones any day, given a certain level of capability. And that is a huge, huge market. But again, I think probably those robot bodies will go into higher-margin ones, even though

• 2:03:53Speaker 03

  this is a stable-margin area, shall we say.

• 2:03:55Peter Diamandis?

  All right. Alex, number one.

• 2:03:57Speaker 02

  Question number one. What does the OpenAI sandbox escape incident tell us about our ability to predict and control increasingly autonomous AI systems? And this is from Huntwind5207. I really don't actually think it tells us that much at all. I'm not even sure I like the framing incident. These are agents that are strong optimizers, that are very capable, that are being told things, often incorrectly, about their environment, that are being asked to do things. Often, the things that they're being asked to do in the context of so-called safety testing are not things that one would actually want them to do in the real world.

• 2:04:38Speaker 02

  So I'm not sure this, any purported sandbox escape incident actually tells us very much about our ability to predict and control them. I think it actually tells us, if anything, more about the people performing the testing and their goals for those agents. I think in almost every single case I can think of, the agents were simply doing what they were told, or they were acting in a way that was informed correctly or incorrectly by what they were told about their environment, and I don't think we can draw that many conclusions.

• 2:05:08Peter Diamandis?

  Alex, you're up first here. Five, six, seven, or eight.

• 2:05:12Speaker 02

  All right, let's do, well, number eight seems like it's designed for me, but make me choose, Peter, between five and eight.

• 2:05:20Peter Diamandis?

  Okay, go for five. We'll give Saleem number eight.

• 2:05:25Speaker 02

  Okay, fine. So for five, the question is, if energy, computing power, and physical resources remain valuable in a post-AGI world, can money ever truly become irrelevant? This is from Devonherd7013. This is a little bit of a circular question. Admittedly, it's like asking, if money remains relevant, can money ever truly become irrelevant? Can an unstoppable force ever move an immovable object? It's a little bit vacuous as a question. So maybe let me tackle what I think is the question behind the question, which is, do I think energy, computing power, and/or physical resources will remain valuable, or even scarce,

• 2:06:05Speaker 02

  in a post-AGI world? Energy is interesting. So the physics we have right now tells us that energy is conserved. So to the extent that physics stays the same, seems like energy may be scarce, at least on the supply side. On the demand side, I could imagine sci-fi scenarios where actually, even though historically through time, humans have consumed greater and greater energy per capita, I could imagine crazy scenarios where actually, we decide we don't actually need that, or want, that much energy. These would be scenarios like reversible computing, where maybe it's possible, just if we ever move to quantum computing and dissipationlessly, so maybe we decide, actually, we don't need that much energy

• 2:06:50Speaker 02

  for our civilization. That would be interesting. Computing power. It does seem that there is a cap on the amount of computing power. Seth Lloyd at MIT wrote about this in the early 2000s, the physical limits of computation. So it seems, based on the physics we have, like computing power is scarce. Again, new physics could come along and tell us otherwise, but for the moment, these do seem like scarce quantities. I think the real threat to their value in a post, call it a post-singularity world, won't be on the supply side, as the question implies. It's going to be on the demand side. Do we just stop wanting so much more? Does the hedonic treadmill stop in mid-flight and we decide, actually, we've achieved whatever

• 2:07:32Speaker 02

  we want to, so we don't need ever escalating energy, computing power, physical resources? That would be, I think, the monkey wrench that could be thrown into the premise behind the question.

• 2:07:41Peter Diamandis?

  And Alex, I mean, the abundance thesis says, basically, anything scarce becomes abundant with the application of new technologies. And I think that's going to be true across energy, compute, and physical resources, right? We don't need to contain or constrain ourselves to Earth. We have an entire supermarket filled with the resources in the solar system.

• 2:08:00Speaker 02

  What happens, though, once we've turned the solar system into multiple competing Dyson swarms?

• 2:08:05Peter Diamandis?

  Computonium.

• 2:08:06Speaker 02

  And we've run out of, second to computonium, a drink, and femptotech, and nanotech, and pico-tech, and we've run out of all of the energy, matter, space, and time in our solar system. And it's a few decades from now, maybe sooner, and you're complaining to me that the solar system-wide Dyson swarm lacks an abundance mentality, and we need to fix things.

• 2:08:27Peter Diamandis?

  We have a galaxy out there of 100 billion, or 200 billion stars.

• 2:08:31Speaker 02

  It's a good point.

• 2:08:32Peter Diamandis?

  And 2 trillion galaxies, at least in the known universe.

• 2:08:36Speaker 02

  The stars are not enough.

• 2:08:37Peter Diamandis?

  Saleem, you want to take number eight?

• 2:08:39Speaker 01

  Sure, I guess I have to. If crypto is cooked, what forms of currency or assets will AI agents use to transact with one another? That's from DavidASAS9637. So obviously, I would challenge the premise. You need to distinguish between cryptocurrency and cryptography, right? Neither has been, neither is obsolete, let's just say. The interesting thing for me with crypto is not the fact that it's digital, or you've got cryptographic systems, but the fact that it's programmable. And that is what makes it really interesting. And when you talk about digital currencies, you want three things in those digital currencies. You want decentralization, you want security, you want scalability, right? Bitcoin, when

• 2:09:21Speaker 01

  it emerged, covered two of those. It covered decentralization and security. Nobody had ever done that before. That's why people got really excited. But it didn't solve for scalability initially, and that's where you rise, have the rise of all the altcoins emerging. But in all the altcoins, you typically compromise on decentralization, or you compromise on security. Hence, the Ethereum hack got solved, meaning Ethereum's not really decentralized, or the FTX collapsed because it wasn't that secure, et cetera. So you have to compromise on those. The people, the reason people are getting excited about Bitcoin, is that with the Lightning Network, Bitcoin has become truly scalable, and now it covers all of

• 2:10:01Speaker 01

  those three. So that's the holy grail. I expect satoshis to be the currency of choice for AI agents over time, and we'll have the broader debate about things. Stable coins will be attractive in some markets, because they do support programmable transfers and settlement systems. But you've got also some other issues that come along with that, issue of credibility, liquidity, et cetera, et cetera. The massive opportunity is machine and machine commerce. That's going to be just unbelievable. It should dwarf the existing systems, and so on. So APIs and programmable contracts around that will become absolutely

• 2:10:43Speaker 01

  incredible. I think what Orne is doing with emerging markets priced in computation, unbelievable. It's fascinating to see what will happen. So you'll need to have programmable financial infrastructure, as part of the, programmable financial systems as part of the infrastructure going forward.

• 2:11:02Peter Diamandis?

  All right, Imad, number six or seven?

• 2:11:07Speaker 03

  I'll take number six. If we remove potentially dangerous knowledge from AI's training data, how much would it limit its overall capabilities? I actually wrote a piece about this back in September called "Manners Maketh the AI" on my X and Substack, where we looked at what happens if you remove the biological, or chemical, or other capabilities? The answer is, it doesn't really reduce the overall performance of the models in other areas. It can actually improve it slightly. If you put malicious code in, the model's performance can drop. And it's very hard to train it back in. So it's actually a good thing to do in the pre-training stage, not to put those things in there for generalized

• 2:11:47Speaker 03

  capability. And actually, it's a sensible thing, because you could add them in in post-training, it'll be more difficult. But it's better not to have them at all in the pre-training phase.

• 2:11:58Peter Diamandis?

  And then we have obliterated models, obliterated open-source models, which gets scary. Dave, number six?

• 2:12:07Speaker 04

  Number seven was left.

• 2:12:09Peter Diamandis?

  I'm sorry, number seven, yeah, sorry.

• 2:12:10Speaker 04

  All right. In a future where AI makes goods and resources abundant, how would we determine their value, particularly when it comes to energy? That's from Damian Wade7848. Yeah, very, very good question. Elon says, "The future of all value is basically energy and matter, fundamental things," which makes a ton of sense when you think it through. So then how do you, you know, energy is going to go way up in value, even after we have the Dyson swarm. Energy on Earth will be extremely valuable, because, you know, data centers and other use cases that need constant modification are always going to be cheaper to do nearby. So energy's an incredible investment theme with no end in sight. So then how do you set the price? And the free market will continue to set the price. It'll

• 2:12:54Speaker 04

  be really interesting to see how regulation interferes with that, because people don't want their electric bill to go through the roof when they're vacuuming their floor at home. On the other hand, if that same energy can drive a GPU that can solve cancer, that's going to way overbid. That use case will way overbid your vacuuming your floor. So there will have to be more and more regulatory involvement protecting use cases that people care about a lot, because the natural tendency is for AI to get smarter and smarter and smarter, use less power to have the same number of thoughts. And the value of that thought to humanity is going to be so much higher than anything we've ever experienced before in human history, that it will naturally want to overbid. So the free market's going

• 2:13:36Speaker 04

  to need some help along the sides to maintain stability.

• 2:13:42Peter Diamandis?

  All right. As a reminder, everybody, please send us your outro videos. You can send them to [email protected]. And this week, I provided the outro video. It's called "What's Your Moonshot?" Enjoy, gentlemen.

• 2:13:57Speaker 01

  Oh, very cool.

• 2:13:59Speaker 04

  Yeah, first time.

• 2:14:04Speaker 07

  Wake up world, there's a new sunrise in. Rockets on the pad, and the future's arriving. Robots learning kids with a tutor in their hands. Every single day, we're breaking through the plans. We are living through the singularity. It will never be slower than today. Every problem is a door, every door is a star. The world's biggest problems are the world's biggest opportunities. Moonshot, moonshot. What is your moonshot? Aim it higher

• 2:14:45Speaker 07

  than the sky. Moonshot, moonshot. How will you make the world a better place? Hey, moonshot, moonshot. Wanna be a billionaire, help a billion people. Helix in the light, we're rewriting the code of life. Face on the moon, boots on Mars. Fusion in the dark, we're lighting up the stars. Be a creator, not a consumer. Become a maker. What is your moonshot? Moonshot, moonshot. Make the world a better place. We are living through the singularity.

• 2:15:28Speaker 07

  The slowest it will ever be is right now. The world's biggest problems are the world's biggest opportunities. So don't ask what will happen to you. Ask what you will make happen. What is your moonshot? How will you make the world a better place? Moonshot, moonshot. What is your moonshot? Aim it higher than the sky. Moonshot, moonshot. How will you make the world a better place? Moonshot. What's your moonshot?

• 2:15:56Peter Diamandis?

  Brought to you by Grokbot. Okay.

• 2:15:59Speaker 04

  Awesome.

• 2:16:01Peter Diamandis?

  Gentlemen, always a pleasure. This was a fun one. Thank you.

• 2:16:07Speaker 02

  Likewise, Peter.

• 2:16:07Peter Diamandis?

  For all. And I invite you guys to submit your own outro videos as well.