Urgent Update- AI Sputnik Moment: Kimi K3 Released w/ Emad Mostaque | Ep. 272

24:15Dave Blundin:But AI doesn't work that way. If you have state-of-the-art AI and you give it to a foreign government, they can use it to actually catch up to you and create state-of-the-art AI. And that became clear to the government, what, a month ago, a month and a half ago, that Fable 5 was over that line. And so they stopped it. But the reality is that Opus 4.8 was over that line. And people in China could use Opus 4.8 to create Kimi K3. And so that recursive self-improvement line was actually crossed earlier than Fable 5. And that's going to be obvious to the world now because all you need to do is have an AI that's capable of improving its own kernel.

24:54Dave Blundin:It doesn't have to. This is a point I made on a podcast like months ago. People think that RSI is going to trigger when it's Einstein-level intelligence. But all it has to be able to do is improve its own kernel and get a 10x step up in speed, which nobody perceives that as being true AGI. But that's all it needs to accelerate itself by 10x. and then the 10x smarter or 10x higher parameter model will be some level of intelligence higher. A lot of people in academia were saying, well, look, we're getting diminishing returns with the parameter count. So a 10x faster model won't natively be 10x smarter.

25:28Dave Blundin:But that turned out to be wrong. We're seeing slowing, but we're not seeing flattening of the intelligence curve. So all the evidence now is that if you boost the raw speed by another 10x, you're going to see genius level AI. And then that genius level AI will boost its speed again. So I think when we look back on this in history, we'll say right around Opus 4.8 was the point where the little spark was enough to ignite a flame. And then a flame can become a fire and then a fire can become a sun. And that's, I think, the way we'll look back on this moment in time. So the cat is definitely out of the bag.

26:02Dave Blundin:The current policy, the current U.S. policy of constrain the next model, there's no way that's going to contain global and corporate proliferation of frontier AI. Do you think the U.S. government starts a strategy of constraining, in some fashion, Chinese open-weight models from being used in the U.S.? Well, you know, in two weeks, these weights are supposed to be open-sourced. And then it's out of the way. Like this, probably if they're rational at the White House right now, they're spending every minute in a debate on do we negotiate with China immediately and not release those open weights?

26:38Dave Blundin:And I really doubt they'll move quickly enough. I'm sure they'll, well, I'm not sure. We'll see what happens in two weeks. Fascinating. Can I merge two ideas here? Yeah, of course, please. You know, Peter, you talked about exponentials and the law of accelerating returns, right? I think it's worth drilling into that because if you connect that to what Dave just said, this is why we've been saying forever and a day on this podcast that this is unstoppable. Ray's original observation was once you have an information-based paradigm, you just keep hopping across multiple technologies. So we had vacuum tubes, relays, and then vacuum tubes and computing.

27:15At some point, you can only fit so many vacuum tubes into a room, but that architecture was used to design transistors. transistors were used to design integrated circuits and you get these nested s curves and so what dave is talking about is as these architectures the uh all the various pieces of the puzzle get all reinforcing loops inside them each of those is like an s curve that starts accelerating the collective and it's unstoppable and so it doesn't there's no limit to where this goes and this is why people are so kind of freaked out about the upper end limit of this so important to connect those two dots.

27:51Emad Mostaque:Yeah, for sure. Can I just say something, Peter? I'm just following on from Dave. So there was an important speech by Xi Jinping a couple of days, yesterday, God, time flies, at the World AI Conference in Shanghai, where he basically said we are going to fully back open source as a public good for humanity and they're not going to regulate and stop it. This is their plan. It's great for China for a variety of reasons, from From the fact they have a billion people whose IQ is about to increase, you know, by having these tools. From the fact they need robots to solve their demographic thing and the soft power from putting a Chinese educated brain, a Xinhua graduate, into every critical system in the world.

28:33Emad Mostaque:But they're going to keep on doing that because they actually have a regulator. And from talking to some of the Chinese labs, it used to take 60 days for a model to be approved. Now it's like a week. Amazing. You know, and Xi Jinping just also announced a regulatory body that they've created, which includes Brazil, different parts of Asia and Africa. I don't know if you guys saw that.

28:56Peter Diamandis:I mean, it's obviously the new Belt and Road is now focused on AI coming out of China. It's a bizarre future where the Chinese Communist Party is saving American capitalism from itself. It's so true. Let's also note that Yang Jilin was a CMU graduate, and we could have given him a visa to stay. Yeah, we're going to get to that story in a second, Salim. This is an interesting chart here that shows the valuation. So Kimi's valuation, or Moonshot's valuation, Moonshot AI valuation is at$20 billion as compared to Anthropic at a trillion, and OpenAI basically at a trillion as well. If they were public companies today, I think you would have seen like a 30 % stock valuation drop.

29:43Peter Diamandis:I'll ask again, what are the American frontier labs doing with all of their capital? Yeah, what are they spending their money on?

29:49Dave Blundin:Well, actually, if you go into the buildings and talk to them and you have any idea at all, they'll give you the capital. They're desperate for more smart people to help because they're trying to deploy and change the world at this insane pace no one's ever experienced before. and they want to deploy that capital much more quickly than they can find smart people who have good ideas to use the capital. But it's a great point. You're sort of saying it in an accusing way, like, what are you guys doing with your capital? But no one in the history of the world has ever had this much money pour into their building this quickly with no prior business experience.

30:20Dave Blundin:We're talking about CEOs that have never run a company before. It's like they're trying, but, I mean, seriously, can any human being really rise to the occasion of AI that quickly? So my point there, though, is if you're smart and you have good ideas, get into those buildings and propose your ideas. This applies to XPRIZE, too. You know, they are desperate to move that money out the door into something productive that gives them a sustainable barrier to entry.

30:46Peter Diamandis:I also think that the frontier labs are also asking themselves that question and asking the U.S. regulatory apparatus that question. And Anthropic regularly is sending out smoke signals, accusing various Chinese frontier labs of distillation attacks. And maybe in Anthropic's public mind, that's how the Chinese labs are able to do it, through distilling and capturing reasoning traces. But honestly, like looking at the K3 performance, I'm not at all convinced that Moonshot is achieving their performance purely or even substantially through distillation attacks on Claude. It just doesn't smell right.

31:19Dave Blundin:No, no, I totally agree. I think, though, that there's a tendency to underweight or undervalue the existence proof. Like, just purely the knowledge that a highly scaled transformer running MOE with a Muon optimizer and simplified data, knowing that that works, gives you a much more refined roadmap. You don't have to copy. You don't have to cheat. You don't have to steal every trace. You just have to know that that formula works. And that cuts your R &D costs by 90%, 95%. So I think it's just that simple. There's nothing sneaky or cheaty about it. It's just knowing you're on the right path. I have the greatest value creation idea for ourselves ever.

31:58Okay. Which is we, in nine days when they drop their open source weights, we release an open source model called Kimi4 under the Moonshots podcast name and IPO it.

32:13Peter Diamandis:And instantly we'll be billionaires. So you're saying, Salim, what's better than one moonshot? Moonshots, plural. Why not copy the copiers? Let's go.

32:24Emad Mostaque:Let's go. Actually, I've got a good analogy for you, Dave. Why do Americans pay more for drugs than everyone else? All the R &D happens in America. You pay the premium, just like tokens premiums. And then what happens? You have generics elsewhere.

32:40Dave Blundin:That is a good analogy because that's like a 99 % cost cut, which is much more akin to AI. than cars. That's a great analogy. You know, Gavin Baker, our friend Gavin Baker, wrote a brilliant post, you can find it on X, about the implications of this for businesses. Must read. And essentially, must read, absolutely. But essentially, all businesses, all stocks, other than the Foundation AI Labs, are huge beneficiaries of this. And then the, like you said earlier, the Foundation Labs are like, well, what's your future? What's your revenue model? Why are you worth a trillion dollars? I don't quite get it.

33:18Dave Blundin:So you should see a really big reshuffling of valuations in the next week based on that observation. And then any corporation that has its technical act together, there aren't very many of those, but if you're a bank, but you happen to be a very good bank with brilliant IT and technical skills, or you have great partners and great vendors, you now have a clear roadmap to controlling your own destiny with your own AI, your own like JP Morgan AI. And so I suspect the markets will react to that if you put your hand up and say, hey, we have a way to do this internally. We know how to do this with our partners or however you get it done.

33:53This is why we call it the organizational singularity. And we're still seeing everybody who's using or trying to use Tableau 5 getting downgraded every time they mention biology or mention something that is potentially on the edge. And why would you tolerate that? You know, so in nine days, what do we see? Do we see every I mean, I'm as soon as it's available, going to upgrade. I'm running Kimmy 2.7 on my Mac Studios. I'll upgrade it to Kimmy 3. Everybody will. So do we start to see sort of the wholesale U.S. entrepreneurial base of capabilities on K3?

34:31Emad Mostaque:Well, I think I can give an analogy of this, which is stable diffusion. When we released Stable Diffusion, God, four years ago, time flies, you had these really restricted image generators that were a bit better, but they were restricted and they had all sorts of arbitrary restrictions because obviously it's a bit dangerous to have it. You couldn't have likenesses. There was no way to get IP in there, even if it's your own IP and more. And what happened? 100 million, 200 million downloads and a whole ecosystem that built around that and accelerated generative media. As you said, why are you going to have this model?

35:02Emad Mostaque:I can't even talk about philosophy with it. It downgrades me, right? Like when you can have the fully open variant of it, even a fraction of the price that you can then customize, a whole ecosystem will build around this and other models, and it has already been doing so. And that's a real danger versus being locked into the single vendor. Totally. Which is why I think the labs will go vertically integrated. Like all their customers are now going to be their competition, and they're going to be like, okay, I'm going to take you all on.

35:30Dave Blundin:Well, and that directly ties to Mira Mirati and Inkling. Are we going to talk about that story, too? That's huge this week. Well, we talked about it in the last pod, which was, oh, so two days ago. Okay. I mean, and Mira just released Inkling, which is fantastic to see a U.S. open source lab. But the question is, how many more will we get? How many more open source sort of shocking Sputnik moments are we going to see? I mean, we have a lot of Chinese labs pursuing beyond just moonshot. Yeah, so Inkling, it's just really telling about where things are going to go because it's designed for you to pick it up as a corporation and fine tune it within your corporate walls to whatever your use case is.

36:15Dave Blundin:So if you're a biotech lab and you're researching and you don't want everybody to see your proprietary data, you take Inkling and you tune it internally. But the reason that's telling is because Miramarati came from OpenAI. So if she didn't believe that pathway was viable, she wouldn't start thinking machines around that thesis. So it tells you that the people that are inside the best frontier labs believe that this process can catch up to the frontier. So you combine that with Kimi K3 proving it, and it's a different world next week. You know, the other thing that was weird in the market at the end of the week is that things started to reshuffle pretty dramatically toward the end of the week.

36:55Dave Blundin:But in the downdraft, the semiconductor companies also came down. But they're actually going to go the other direction. And this is the point Gavin Baker was making, that this drives up the need for silicon, not down. It changes the whole software landscape tremendously. But silicon is going to be more in demand than ever before and completely sold out, as we know. Can we talk a second about the NVIDIA embargo that we put for China? So here we see highest performance models. Was the whole NVIDIA sort of regulatory embargo unnecessary? Did it do what we've always done before, which is just spark China's need to develop their own capabilities, Huawei?

37:42Peter Diamandis:Of course that's what happened. Of course we did everything that the embargo only incentivized the Chinese frontier labs to develop and cultivate new efficiencies. that, by the way, were always there. To Dave's point earlier about the nano GPT speedrun, there's this enormous overhang that isn't fully exploited in terms of leveraging algorithmic and computational and hardware efficiencies to train larger and more capable models. And all these export controls do, I think, is incentivize the Chinese labs, which are already feeling plenty of demand pull to compete with Western frontier models, to leverage those efficiencies sooner And maybe on balance, although it's superficially bad for the West now that we've incentivized this new generation of much more efficient Chinese frontier models, in the end, I think it's net good for not just the world, but also for the U.S.

38:36Peter Diamandis:to have this fire lit underneath them by Chinese competition that's much more efficient, much more capital efficient, more weight efficient, probably more bit efficient. This is all a net positive as long as the U.S., in my mind, does not set up or fall into some ultimately protectionist regime of trying to prevent what may be construed as Chinese superintelligence dumping on the U .S. As long as we avoid that, it's great.

39:04Dave Blundin:It's exactly what happened. That's exactly right. And I think the U.S. learned a really important lesson in the Vietnam War. And then, you know, because that's over 50 years ago now, it's been forgotten again and you have to be reminded again. But in the Vietnam War, it was really clear that either you go to war and you win quickly or you don't. But what you don't do is send in a few troops and then send in a few more and then creep in. And like nothing good comes of that at all. The embargo of chips on China was totally harebrained because it was enough to irritate, but not enough to actually work.

39:39Dave Blundin:It's just the worst case scenario. and it sparked, exactly like Alex said, a huge amount of quantization research, which is critically important and under-discussed, that allows faster performance on cheaper chips. And those innovations don't go away. That's going to be around forever now. Let's go to Imad and then Salim.

39:58Emad Mostaque:Imad? I think we've got a completely self-contradictory, but it has some interesting outcomes. So the total amount of compute used for Kimi K3 is the same as Inkling. Wow. And you can tell that because it's the amount of dense weights. And roughly, we assume about twice the number of tokens trained because we don't have it. We're like, but how does that work? Well, you look at their architecture and it's a two and a half times in data to intelligence conversion through the advantages and data mix that they have because they've had to operate in these constraints. And we see that because the first model isn't as good as the second model.

40:34Emad Mostaque:And for Inkling, you're going from a trillion parameter model to a 300 billion parameter model about to be released, which is actually better performance. So you see this with the labs. And these labs have had to deal with the constraints. But here's something really interesting, I think. If you look at that slide that Alex loves, and we kind of chuck it up on the screen. so what they've had to do is they've had to optimize their inference for huawei 910 ascent chips for the new alibaba chips and others 64 nodes in one because this is a big model like you're gonna have to buy another mac studio or two peter to serve this you know it needs like two terabytes of wrap so you see where kimmy k3 is there that's because they can only use chinese silicon to run it they don't have blackwells they don't have vira rubens vira rubens and blackwells are designed for these really large models that have really small things because it's 50 billion active parameters against three trillion total american companies like modale like fireworks like base 10 will be able to serve this model 10 times cheaper than their chinese competitors because they have access to the nvidia and amd big chips and so like i said it's a bit ironic the development r &d suddenly has gone there but there's going to be a 10 to 100 times price drop once this is optimized for the next generation via rubin well that also amad you're saying

42:04Dave Blundin:essentially the same thing but that also unleashes a bunch of chips that aren't currently in circulation. They're underpriced. And also a bunch of fabs that can't make a GB300, but they can make an inference time chip that'll run the cheaper Chinese or the lower granularity Chinese model. So a lot of capacity for compute gets unleashed through that same process you just described. If I could go up a level and go a little bit woo-woo, right? We've had this mantra in the internet world.

42:36We've had this mantra in the internet world that information wants to be free, right? Basically, intelligence also wants to be free. And essentially, we've gone over the course of evolution from biological intelligence, where you had evolution built in recursive improvement. And then we broke through that to individual intelligence, to the person of a species to collective intelligence like markets or networks. Now we have AI, which can scan across all the data to create a whole other level of intelligence. So this is not stoppable. And so any entity or domain or government or whatever that tries to constrain it always, always, always, always fails.

43:20And so it's just a fundamental law of nature that you cannot constrain this. And it's just not possible. Why people bother is what really blows my mind. It's a very scarcity mindset to try and think about it this way. The faster we get to better intelligence, the faster we get to abundance, the faster we don't need to fight over anything.

43:38Peter Diamandis:I can't disagree with you, Salim. I wrote an entire paper arguing intelligence manifests in the physical world as maximizing future freedom of action. So here's to the frontier liberation front.

43:51Dave Blundin:Well, you don't want to slow it down anyway. You remind me of the Monty Python thing where there's the popular people's front and the people's popular front of Judea. We need T-shirts. This episode is brought to you by Blitzy, autonomous software development with infinite code context. Blitzy uses thousands of specialized AI agents that think for hours to understand enterprise-scale code bases with millions of lines of code. Engineers start every development sprint with the Blitzy platform, bringing in their development requirements. The Blitzy platform provides a plan, then generates and precompiles code for each task.

44:31Blitzy delivers 80 % or more of the development work autonomously, while providing a guide for the final 20 % of human development work required to complete the sprint. enterprises are achieving a 5x engineering velocity increase when incorporating Blitzy as their pre-IDE development tool, pairing it with their coding co-pilot of choice to bring an AI native SDLC into their org. Ready to 5x your engineering velocity? Visit blitzy.com to schedule a demo and start building with Blitzy today. Let me bring up a related subject to the story here that I have a pet peeve about, and it's this one. So, you know, the founder and CEO behind Moonshot AI, Yang Zhilin, you know, didn't learn his craft in Beijing.

45:23You know, he earned his PhD at Carnegie Mellon, you know, one of the best computer science programs in the world, in Pittsburgh. And we basically trained him up. We admitted him, we trained him up at one of our best institutions. And then when he gets his PhD, you know, he doesn't get a green card. He goes through the hassles of trying to get a visa and he goes back to China and he builds moonshot AI there. Just a moment and talk about, you know, I've stated publicly so many times that I think when anybody gets a PhD, they should get a green card stapled to the back of it. Why are we sending the most brilliant people who come here to get educated back home, whether it's to China, whether it's to India, whether it's to Brazil, why don't we enable them to stay here and build?

46:09Gentlemen, comments on that.

46:11Peter Diamandis:Okay, so I did some research on this, and I think the story is not what it seems to be. So a little bit of chronology first. So Yang Zhurlin, according to my research, he starts his PhD after undergrad in China, starts his PhD at CMU in 2025, fall of 2015. 2015. Okay. Then approximately one year later, he founds a startup while a PhD student at CMU. The startup is named Recurrent AI. Where is Recurrent AI based? It's based in China. It's not based in the US. So one year into his PhD program, he starts a Chinese AI startup while still doing his PhD at CMU. That's interesting. And that's a problem.

46:57Peter Diamandis:This also runs counter the sort of a narrative violation for, oh, we wouldn't staple his visa or whatever, and then he goes back to China. No, actually, one year into his American PhD program, he starts a Chinese AI startup. Then he graduates in 2019, is my understanding. My understanding is he had offers from Google, Facebook, Huawei, and others upon graduation in 2019. But he goes back to China because that's where his startup, Recurrent AI, was actually incorporated a few years earlier. And so I don't think necessarily this is the case where either the U.S. was unwilling to retain him or even President Trump somehow through some policy was driving away this particularly talented Chinese graduate.

47:45Peter Diamandis:He started his company during the tail end of President Obama's term in China. Yeah. And Alex, I appreciate the deeper dive that you did. Thank you for that. The point still stands. And, you know, Salim, you and I have seen this so many times, right, at Singularity University. Dave, you may have seen this at MIT. I mean, the fact of the matter is a lot of the most brilliant students aren't given the opportunity to stay and develop here. Dave, or actually Imad, what are your thoughts on that, being someone not in the U.S.?

48:20Emad Mostaque:so if I can just give my two cents yeah I completely agree with it and here's this crazy thing the math and the numbers are all there what is the value of a PhD staying in America it's actually quantifiable and there'd be multiple studies on that you know Dave does a great job obviously of converting them into startups into innovation and then there's the other thing that shoots in the foot which is American companies can't invest in Chinese companies because of regulations and other things as well. Some of those are Chinese, but look at the trouble that Benchmark got in for investing in Manus, for example.

48:51Emad Mostaque:So I think it's kind of twofold, but I completely agree that if you've created or contributed to creating a valuable asset, most foreigners stay in America after they do their PhDs, but too many don't have a very direct path despite the math proving that they will add value to the American economy. Yeah. I mean, another point just to make here is, you know, the AI race isn't only about chips and compute. It is about people. You know, key people are still driving the greatest value, at least for the moment.

49:21Peter Diamandis:I would argue not just people, but also to my earlier point, it's about where the startups get domiciled. There is an alternative world where he, through whatever immigration oriented regs, was deterred from starting his first AI startup in China while still an American Ph.D. And we incentivized him to start recurrent here in the U.S. And I think there's maybe an alternative counterfactual world where Recurrent was American, and then its arguably intellectual successor, which is Moonshot AI, also remained domiciled in the U.S., and then he followed his own startup to stay here.

49:57Dave Blundin:Yeah, one thing that came out of the story is when people come from India to get educated in the U.S., they overwhelmingly stay. When people come from China, about 80 % of the time they go back. And it's just a difference in the local economy. You know, going back to India to start your company is a nonstarter. It's just so unlikely to catch. But going back to China, it's a thriving ecosystem, lots of support. So going back to China to start your company is actually not a bad plan for a lot of people. And so I didn't realize that until this report came out. But, you know, as Peter was alluding to, we have tons of friends from MIT that came from China.

50:32Dave Blundin:And I don't want to put them all in one bucket because there's a really clear distinction to me between people from China that are Hong Kong, Taiwan, whatever, that come over that don't really align with the Chinese Communist Party at all. The fact they kind of hate it. And then you've got, you know, Chinese people that come over for an education. And in one case, it'd be you. A very good friend of ours is the dean of computer science at BU. And there was this massive crisis because there's a concerted effort by the CCP to plant specific students into BU to gather specific knowledge. And they were given tasks.

51:07Dave Blundin:You have to go study this, learn it, and then send it back. And they didn't know what to do at BU. It's like, you know, these are effectively trained spies that got into our PhD program, but we weren't ready for it. What are we supposed to do? And, you know, they want to be highly ethical, so they don't want to just dismiss the students. So I don't know how they resolve that. So you got this really, like, that's a very different thing from the bulk of Chinese students who are, you know, they don't align with the CCP, and they just want to thrive in the world, And they're happy to start their company here or anywhere else.

51:39And don't forget, don't forget when we looked at the frontier labs, I mean, originally in the early days of XAI, for example, and in Meta, like 50 percent of the research staff of their research PhDs were were Chinese Americans. An extraordinary. And the Chinese, you know, the Chinese every year in the, you know, the math Olympiad are at the top of the scoreboard. There is an incredible wealth of capability here that I think most companies desire to retain inside. Salim, you were going to say? Yeah, two things here. One is, you know, the asymmetry of the talent, I think, is the really important part here.

52:23I made this point a couple of podcasts ago. 70 % of the elite AI researchers are not U.S. citizens. They're in order Chinese, Indian, Taiwanese, and U.K. And so that's a huge problem. Stapling in a green card is the easiest thing we could do with zero friction to then give them incentive to stay here and build here. The U.S.'s massive asymmetric advantage for the rest of the world, it was better to build here than anywhere else in the world. And that's starting to become less true. And that's why people are going back to China, increasingly back to India even, to do things, despite the kind of the friction that exists trying to do something in India.

53:02That is the part, the failure of the U.S. to fix immigration is one of the biggest problems this country has right now. Amen. All right, I'm going to move us forward here. I just want to put up this slide. You know, since mid-April, we've seen 13 new frontier models launched, an average of one every 10 days. Just comparing this to 2025, we had eight frontier releases over the course of a year, one every 50 days. A year earlier in 2024, we had six releases, one every 60 days. And it doesn't seem to be slowing down. And then, Imad, you sent me this morning this tweet from Elon. Thank you. I'll put it up here.

53:45This is Elon's tweet. Our$2 trillion model, which is better than our$1.5 trillion in every way, will finish initial training next week. It might be able to exceed Kimi, but with speed and token efficiency close to our$1.5 trillion, a.k.a. Grok 4.5. So, I mean, this is the number one piece of evidence that we're living in the singularity. The speed at which this intelligence is accelerating is insane.

54:12Peter Diamandis:Peter, it gets better. If you take Suhail's list of frontier models and the dates and you regress an exponential curve to the predicted frequency or time period between model releases, which I did just as an exercise, you find that at the present rate, we're going to get to daily frontier model releases. Sure. By, wait for it, January. By January. By January, we're going to see daily new frontier model releases if this exponential trend continues, which basically implies continuous versioning. So I guess the question is, what does that really mean? Right. What does it mean to have a new release if it's a continuous process?

54:56Peter Diamandis:I mean, maybe it means that we'll have to do our daily moonshots episodes about something other than point releases from the frontier labs. We'll need something new to talk about because it'll just be updated behind the background. Like my son Jet said, okay, so another release, a little bit better. I mean, like, Dad, come on, like, what's reeling you here?

55:17Dave Blundin:Well, actually, yeah, we'll see later in the pod some use case demos, but I think those will take over because it's much more exciting when you see a tick up in the intelligence. You're like, yeah, so what? Like, well, look what it made. That's what really gets people's attention. Let's take a second and just look at that because I skipped over it. But I think one of the things that's interesting here, and I'll just play these, you know, is what we're seeing is, I mean, like recreate your favorite game. And in on the right hand side of the equation here, we're seeing a web browser, web browser based web app simulating an Apple desktop.

55:58I think this is, you know, we haven't talked about what the implication to the gaming industry, which is huge. Right. You I'll stop that noise here.

56:09Dave Blundin:I think it took over your computer there. It won't stop now. But, I mean, what was fun the last 24 hours was seeing everybody sort of show their use of Kimi K3. And it's impressive. Everybody becomes a creator. Everybody becomes a maker. One warning, though. One warning. One-shotting a game is very different from building the entire ecosystem and the customer service and the marketing that goes around with it, et cetera, et cetera. So you really have to be passionate about that domain. But the friction of getting a game launched per your personal interest or your fascinations or your particular type of game that you want is near zero now, and that becomes really interesting.

56:53Dave Blundin:Yeah. Yeah, it's actually, it's mentally taxing because if you take it to the limit, which is very soon, I can one-shot prompt to create anything. and then you're sitting with your corporate exec staff saying, well, what do we want? Well, we've never had the ability before. We never really think this way. So then you have to kind of stretch your brain to like, well, what's the purpose of our organization in the first place?

57:18Peter Diamandis:Here's a thought. Historically on this pod, we've done calls to action to submit outro music videos. What about a call to action to submit an outro video game that people have just casually created? That's cool. Yeah. Yeah. So going to your point, Dave, I think having taste, having imagination, understanding what the public wants, I think these become the scarce elements. And, you know, for entrepreneurs out there, as you're seeing this capability, I think the entrepreneurial mindset and the ability to imagine something even greater. I mean, what happens when you're unleashed in what you can make?

58:01Dave Blundin:Yeah, and visualizing happiness is, you know, we're not used to trying, but, you know, a lot of people don't manage their own happiness particularly well because they have to suffer through their daily job. They have to suffer through whatever, you know, mosquito bites and geography. Like, you have no choice. Given choice, what would you do? And that's so liberating for the mind, but because we're not used to thinking that way, we're not ready for, like, there must be an infinite number of things. The one that's easy for everybody is medicine and biotech. Like, at least I want to be healthy. You know, that's an obvious one.

58:32Dave Blundin:What about all the other things that make humanity happy? Have we really thought through what we could voice, you know, prompt tonight? And it's really like we are godlike in our abilities. Hence the name of your book. Yeah. Well, I mean, it's the idea is being being a being a creator or a maker. Right. Versus a consumer or a taker. Salim, I saw your eyebrows go up. No, I'm I'm just agreeing with all of this. I think it's such a magical time to be alive. Everybody listening to this podcast, please think up some business idea, project, impact project, whatever, and use AI to go build it.

59:12Peter Diamandis:I just want to get one thing. Just briefly, Nick Bostrom speaks about this a bit in Deep Utopia. Peter, you and I speak about this quite a bit in Solve Everything. I'll just outright suggest folks, if listening, I'm speaking just for myself. I'd love to see an outro video game that you casually create, maybe something in the theme of the Moonshots pod, since evidently we've completely solved and cooked music video creation. It'll be a first shooter game where we get to take aim at AWG. Oh, no, please. No, no, no. Ideally, a nonviolent outro video game. Be careful when you're on. Nonviolent.

59:48Emad Mostaque:You need a civilization tech tree. That's what you need. Civilization tech tree game would be great. I want to hit one point. You know, everybody watching and listening here, you have two options when you hear about this extraordinary ascent of Kimmy K3. Fear might be one. And the other might be, oh, my God, what an extraordinary time to be alive, right? Hope and excitement and, you know, just an abundance mindset. And rather than fear, realize you are being unleashed. Your creativity, your ability to do whatever you want, the ability to create. Your passion. your purpose, right? Find, you know, what Salim and I talk about so much is finding your massive transformative purpose, right?

1:00:30Just to distinguish between two, a passion is something you love doing. A purpose is something you love doing that actually benefits the world, right? And so if you can connect with that and realize that you can without any background, I mean, I think this is one of the most important things. You don't have to be a computer scientist. You don't have to be an expert. You have to be purpose-driven. And if you use these tools, you can make a dent in the universe. So you can improve, you know, humanity at an awesome scale. And that's what entrepreneurship is. Three steps. Yeah. Read the Alex and Peter's paper.

1:01:03Solve everything. Pick the biggest problem you dare to pick. Go download the organizational singularity cloud skill, which is free, and start building.

1:01:14Dave Blundin:Yeah. Yeah. Awesome. Yeah. And note for our production team, too, here, it's so cheap and easy now to do things like Alex suggested, you know, make a video game we should we should collect and post some examples for the audience uh so that they can say oh that's what alex was talking about but it's you know just a little road map is all people need it it can be this long if the audience doesn't send in amazing moonshots

1:01:35Peter Diamandis:oriented video games as outros i i promise i will create a cyberpunk fps but it'll be a non-violent fps if you can imagine that oriented around moonshot what are you shooting you'll be tickling bunny rabbits. It'll be a cyberpunk FPS where we're cooking every problem. How about that?

1:01:56Emad Mostaque:It's a first-person solver. Not a first-person shooter. First-person solver, love it.

1:02:00Peter Diamandis:Oh, God. You gotta do the tickling bunny rabbits, too, though. Okay, I'll tickle bunny rabbits. I'm gonna move us to our next story. Please, let's stick with the music videos. They're great. And Imad, this is one you sent over the transom that I added here. So if Kimmy K3 is the frontier going big, trillions of parameters and a data center, this story is about frontiers going small, small enough to fit on your smartphone. So Bonsai 27B for a billion is the work of Prism ML. It's a U.S.-based AI startup out of Caltech. It's run by Babak Asabi, backed by Kusla Ventures, Cerebrus, and Google. It's the first 27 billion parameter class model to run entirely on a smartphone.

1:02:43Not a stripped-down version. It is built on Quinn 3.627B. Imad, tell us about this. Why is it important? We just talked about small language models with Liquid AI on our last pod.

1:02:57Emad Mostaque:Yeah, this is one of Dave's favorite topics, quantization, right? And Prism ML, and actually Tencent, which I'll talk about in a second, have had massive advances in being able to take a model that's been trained in a 16-bit architecture or an 8-bit architecture, like Kimmy is basically 8-bit, 4-bit, and take it down to ternary, which is three bits of information, or binary.

1:03:21Peter Diamandis:So ternary is three values, approximately 1.58 bits.

1:03:25Emad Mostaque:1.56, yeah. So three values. You geeks. This is really, really important.

1:03:33Dave Blundin:Pay close attention, geeks, because this is a really important topic.

1:03:37Emad Mostaque:Well, this is, again, the accuracy thing. So what Prisma ML managed to do is they managed to get the model down to ternary, which basically means, well, I think it was six gigabytes for the model. This 27B model, which is really performant. I think it's basically GPT-5 class from memory. Wow. With a 5 % drop in accuracy, they managed to get it down to six gigabytes. And with a 15 % drop in accuracy, down to four gigabytes.

1:04:02Dave Blundin:And you can get the accuracy back, too, by expanding the size of the network a little bit. Sorry, I'm out.

1:04:07Emad Mostaque:There are various things you can do. And so this is a big deal because it means you have 110, 20 IQ buddy that can work on your smartphone. Again, without an internet connection. Without an internet connection. It's smaller than a video game. You literally have this level of intelligence in your pocket all the time. Is it live? It's live. You can download the way it's right now. You can run it on your smartphone. Exactly. That way you go. But this is the super interesting thing. When you reduce the bits, it also increases the speed. So from 16 bits down to 3 bits, it's a five times improvement in the speed.

1:04:45Emad Mostaque:And there was another article or another release, which is Tencent's latest model. And this is actually the old Wizard LM team who had to leave Microsoft because Microsoft wouldn't give them compute. It's very ironic. They managed to get binary compression. So taking it all the way down for their Hi3 model. so it's now the best on a ggx spark or a big macbook to take a 300 billion parameter model the size of the new model that's coming out of inkling um to work on binary with a five percent

1:05:17Dave Blundin:drop in performance wow yeah and now i'm gonna sorry i'm out this is so important yeah i'm only gonna say this once on the pod because we're investing a lot of a lot of companies that are working on exactly this. I don't want to tip it too much. But the implications of what Ahmad just said have, there's one more step there, which is if I imagine like all of this intelligence under the covers, the computation going on has always been matrix multiplications. So I have a number, I multiply it by another number, and then I add two of those together. It's called a MAC, a multiply accumulate. If one of those numbers is just one zero or minus one, I think we can multiply a number by one zero minus one pretty damn efficiently.

1:06:01Dave Blundin:So that's the efficiency Ahmad's talking about, but it also opens the door for new ways to compute. And what Alex has been saying for a while, if anyone listens through it, we're going to discover new physics, but also new substrates on which we can compute, and we're going to discover that computation is possible virtually anywhere, in crystals, in liquids. But the computation we're looking for is simply one zero minus one. So it really narrows the focus on where we look for these computing substrates that'll take AI to, you know, ask. So what we're envisioning right now in the Dyson swarm is, you know, a bunch of GPUs from NVIDIA sitting in a satellite, you know, with a solar panel and a radiator.

1:06:44Dave Blundin:That's only going to last a couple of years. Something very different is going into space. something much more like, you know, Star Trek-y with crystals and holographs and things that are capable of doing the exact computation that I'm on. You heard it here first, guys. How efficient does this get? How compressed does this go? Oh, my God.

1:07:06Peter Diamandis:I homily Peter on that. So this is something I think about quite a bit. So the most quantized bonsai model that we were just talking about, I think, is approximately one and an eighth, 1.125 effective bits per weight. But you could ask the question, like, is one bit per weight the limit? And the answer is no. We can go below one effective bit per weight. How do we do that? We do that with sparsity and quantization and low-rank factorization. And by the way, that's what we're starting to see from some of the labs, in particular, like Samsung. For obvious reasons, Samsung wants to be able to host highly capable frontier class models on their own edge devices like smartphones.

1:07:52Peter Diamandis:Just in the past two months, Samsung published a model called NanoQuant that breaks the one bit, one effective bit per weight barrier. So it's sub one bit, which I think we're going to be talking quite a bit more about in the future, using a variety of tools. And so this is my extrapolation episode. I went through the exercise of extrapolating frontier quantization out. And Naive Extrapolation finds that sub-1-bit quantization is going to go mainstream sometime in the next year. And then overwhelmingly likely photonic, the speed of light in photonic will be the way we're computing in the future.

1:08:30You made a comment. Imad, you made a comment.

1:08:33Dave Blundin:Yeah, excellent. Imad, you made a tweet a couple of weeks ago that said, we're going to get Fable-level capability running on a normal MacBook in 18 months, right? This is essentially the path you're talking about.

1:08:46Emad Mostaque:Yeah, so... Sorry, please. Go ahead, go ahead. Yeah, so if you look at what NVIDIA did with their last Nemetron series, they took the big model and they actually distilled it with Logix, as they're called, down to a smaller dense model. What's the difference between a 27 billion parameter dense model and these really big sparse ones? When you have the model weights, you can actually do proper distillation, which is a bit different from the reasoning traces. And so what you're going to see is models like Kimi get distilled down to perfect data sets for smaller models that will be trained for bit and then cast down to ternary or binary or even lower in terms of the bit weights.

1:09:22Emad Mostaque:and when you actually look at like look at quen max versus quen 027b you can actually extrapolate what the sizes of these models will be as you move dense and you go through the whole process you end up with a model that works on 16 gigabytes of ram by the end of next year that is the level of kimmy k3 and you could even extract all the knowledge out of kimmy k3 because it will be open source. So that means every vehicle, every robot, every manufacturing device, every device in the world has their own built-in persistent intelligence and can make autonomous decisions at the edge for whatever task they can.

1:10:03So this decentralizes capability at the most infinite level.

1:10:07Emad Mostaque:And it could go even one step further when you get down to ternary or binary. Actually, ternary is better for many things. You could build custom photonic silicon or even etch onto the silicon itself. The zero is just it doesn't have a path on it. So you can etch the model weights once they're good enough. And that leads to an actual increase in the total speed. And you don't need to use the smaller silicon anymore. So the cost of intelligence is going to drop by 100 times anyway by the end of next year, just due to the new chipsets. Speed running Star Trek.

1:10:38Peter Diamandis:And I think this is what Dave was talking about earlier, that as we move potentially to Ternary or even sub one bit, it's far more ergonomic to adopt post CMOS type architectures underneath. There's plenty more room at the bottom. Yeah.

1:10:55Emad Mostaque:My bet there is 0.78 will be the bottom. So we're going to put that as a marker today.

1:11:01Dave Blundin:OK, we can do our end of year predictions on that one. That's a really very specific number. I mean, okay. I'm going to have to listen to this bit like four times over to just kind of figure out.

1:11:12Peter Diamandis:Do you want to go around quickly and ask everyone what their favorite quantization endgame is? Ahmad, it sounds like you have a bizarrely specific one.

1:11:21Emad Mostaque:I'll post the details of that soon. We'll let everyone else have a think about it first, and then we'll have a future episode.

1:11:27Dave Blundin:Dave, you were going to say, Dave? I was going to say that the most likely forecast, based on everything Ahmad and Alex just said, we're expecting 100 to 10 ,000 X within three years on just the raw compute through quantization and new compute methods. And that's multiplicative with the other algorithmic improvements. It's really hard to forecast. So realistically, a million X. I want to pause. Let's pause there one second, Dave, and just for folks to absorb that for a moment. We've seen this incredible speed in performance and intelligence. and we're about to see what is 10 ,000 or, you know, add algorithmic improvements, get you to a million.

1:12:09What does that feel like over the course of what the next three years?

1:12:14Dave Blundin:I mean, yeah, three years. Yeah. One thing it feels like for sure is that the AI is doing things that you really desperately want, but when it explains to you what it did, you just can't keep up. I'm already feeling this with Fable 5. You know, I've got so many Fable 5 agents running and they're doing, the outcomes are exactly what I want, but it's like, well, what did you do? and I can't get through it all. I had this conversation with Ray, you know, the point at which AI is asking and answering questions that you can't even grasp. Yeah. Yeah, that's very soon. So, you know, tonight back to our conversation, the idea of slowing it down is nutty.

1:12:46Dave Blundin:Like, there's no regulatory concept of slowing it down that makes any sense. All we need now is some kind of a global inspection and global, you know, partnership to monitor it and then just take advantage of all the abundance that's going to come from it. You know, all the new medicines, all the new capabilities, all the global happiness, it's imminent. We just need to unleash it. Don't slow it down, but inspect everything. You know, this whole mechanistic interpretability is going to become the most important thing that anyone can work on. And we just need global transparency and full throttle.

1:13:19I think this is one of the most important podcasts we've ever had, guys. Mind-boggling. Sputnik moment. Yeah, Sputnik moment. Moonshot's brought to you by Moonshot. Our new sponsor, yes. All right. I'm going to move us along to another fun story, one that I love talking about. It's called Predicting the Future. So there's a guy named Philip Tetlock. He's a political psychologist at University of Pennsylvania who authored a book called Super Forecasting, the Art and Science of Prediction. After he identified what he called a group of super forecasters, These are ordinary folks who, through disciplined reasoning, consistently out-predict even CIA analysts with classified information.

1:14:06He scores this on what's called a Breyer score, where lower is better. So now the benchmark that pits AI against these superforecasters is called the Forecast Bench, and it's been tracking a steady year-long climb as models close the gap. We've talked about this before on the pod. Well, the newest numbers have just come in, And according to the Forecasting Research Institute, for the first time, several AI models are now statistically indistinguishable from superforecasters. So the implications, you know, if an AI can forecast novel events at superforecaster level, then every decision that we make, right, in insurance, investing, policy, geopolitics, corporate strategy, gets a cheap, tireless, superhuman advisor always on.

1:14:53I find this fascinating, right? The data is out there and the ability for an AI to gather it and make predictions. So at the end of the day, every political decision is going to be modeled this way. Every investing decision is going to be modeled this way. And this becomes sort of the differentiator. So who wants to jump in on this one?

1:15:14Peter Diamandis:I'll jump in. I absolutely love this to pieces. First, a few additional pieces of context. So the number one AI super forecaster is from a British startup named Cassie, short for Cassandra, who, of course, made predictions but wasn't listened to. Interesting. What's founded by a British intelligence officer who served in Afghanistan and then advised the British government and then formed this in part inspired by super forecasters. What I think is really interesting, though, we've spoken when we've talked about these sorts of stories in the past about Isaac Asimov's psychohistory and other riffs.

1:15:49Peter Diamandis:I want to try a new riff here, which is an interesting thought experiment. What happens when hyperforecasting is not just superforecasting, hyperforecasting is connected to capital markets? What happens when the AIs, which are already AI algo traders, are already completely dominating by volume public securities markets? What happens when they have better internal autoregressive models of humanity than humanity does of itself? That's in some sense, it's in the same sense in which large language models were trained off of the autoregressive task of predicting the next token of Internet text better than humans can.

1:16:33Peter Diamandis:And now LLMs can predict, at least from a perplexity perspective, the next token I'm going to say in this sentence probably faster than I can generate it myself. What happens when these hyperforecasters are able to generate the next actions by humanity collectively faster than humanity can take it? That's sort of the ultimate market squeeze efficiency outcome where literally I think capital markets will be where this is maximally interesting, where the prediction is actually preemptively shaping the action of the market. And I think those who were so dismissive of the efficient market hypothesis, I think the EMH is going to be crowned king of the capital markets once hyperforecasters like this are ultimately plugged in, which seemingly is imminent.

1:17:22I think this leads to wisdom, right? I think this is one of the most important things. And I've written a substack on this. I've talked about it in the past. If you think about when you go to a wisdom council and you ask, you know, what should I do? You go to that wisdom council because they've had so many experiences in life. They can tell you go this path, it's not going to succeed. Go down this path, you have a higher probability. So imagine a world in which everything's being simulated to the point where an AI can tell you what is the maximal path to take for world peace or to find your spouse or to determine how to answer to your kids.

1:18:00I mean, if you can literally simulate society on a level, we have a godlike support structure to help us navigate the decades ahead. Well, if I make this practical to an organizational level, right? Think about most high-level management capabilities like budgeting, hiring decision, product launches, investments in various things. Each of those is essentially a forecast, right? But you never record the probability of that or score the accuracy of that. Once you have AI forecasting that approaches that capability, this means senior management essentially evaporates. Because most senior management is there because they have deep expertise.

1:18:50If you're the head of supply chain for BMWs because you ran supply chain for Spain or you ran supply chain for that engine, And over decades, you built up experience to manage that domain. Once that judgment, and hard to quantify, not an AI system essentially can reproduce that without your biases that you have that are inevitable for human systems. Really important point. That essentially wipes out all senior management expertise. So now you need to focus even more on purpose and what you're trying to accomplish and the objectives you have, et cetera. It completely changes the game for senior management in any company and any government.

1:19:30Yeah.

1:19:31Emad Mostaque:Imad? Yeah, so, you know, it's a topic close to my heart. In my best-selling book, The Last Economy, I actually describe how the mathematics of generative AI can apply to economics. And soon we'll have a paper coming out that derives all of economics from the same math of generative AI, every single equation. It's kind of crazy. But one of the nice things here is that… Even the incorrect ones? even the incorrect ones it shows them as limits and why they're incorrect which is fantastic um but one of the interesting things in in psychohistory in isaac asimov's foundation he says that entire groups and populations can be modeled like gas and the equations of gas are the equations of diffusion models which turn out to be better than humans at prediction and we are going to release a whole bunch of studies around that on economic prediction where they're outperforming but then this raises something very interesting you know peter you said the wisdom You know, Salim, you've said no senior management.

1:20:25Emad Mostaque:The way these models will start entering is second opinions, medicine, business, policy. But then the liability profile is going to go crazy. Matchmaking, matchmaking. Well, matchmaking. Yeah, we have some dark things there like Black Mirror and other things. But think about it this way. If you make a decision not approved by Dr. AI, your insurance premium goes up like that. You know, if you take drive and you don't drive according to FSD in a few generations, your insurance premiums go up like that. And that recursion is something that's super interesting because in foundation, you had three requirements for psycho history to hold.

1:21:05Emad Mostaque:One of which was that the population is sufficiently large, and that can be like driving a car or entire economies. The next thing is lack of technological advances of sufficient levels of technological stagnation, because that can change the entire landscape of what's new. And the final thing was ignorance. And so, you know, Alex just mentioned these things coming into the market change it. But these things coming into making a health care decision or a government decision or a company decision actually changes the way it's like, hey, you're my match made in heaven, according to the AI. How can you argue against the AI?

1:21:41Emad Mostaque:Worst pickup line ever right now. But who knows in a few years, right?

1:21:46Peter Diamandis:This is very meta, Imad, the sort of reflexivity in economics, I think many would call it, if the best predictor ends up being named after Cassandra and no one believes it.

1:22:00Emad Mostaque:They can make the money. It's OK.

1:22:02Peter Diamandis:You can slice the irony with a knife. David, have you seen any startups in this area?

1:22:07Dave Blundin:No, shockingly, no. And, you know, Safe Super Intelligence, Celia Sutzkever may be doing a version of this, but they're keeping it in-house, you know, and launching it toward markets and printing money internally. But the version I'd love to see very soon, I think a huge amount of human unhappiness comes from consumerism and consumer marketing. And, you know, like Homer Simpson comes home at 6 p.m., cracks open a beer, lies down on the couch and starts channel surfing. And then, you know, like naked and afraid is on, ends up watching it until falls asleep on the couch, wakes up the next morning with a hangover, having not brushed his teeth, kicks the dog and ends up with unhappy kids.

1:22:46Dave Blundin:like that chain of decisions is so bad, but there's no, there's no explicit decision to live that life in that chain, right? It's just, you just reacted to the beer ad and then you went down this chain. And I think AI is going to be an incredible coach to say, Hey dude, you know, what if you take this alternate path and here's the outcome you're going to get to love that. That to me is forecasting used correctly for just changing. Like, are we anywhere near optimal? And The answer is no. If you objectively look at your life, nobody's near optimal. But with a little AI assistance, you can get on a much better path.

1:23:21Dave Blundin:But what we do right now is it's massive consumerism. You're reacting to billboards. You're reacting to TV ads. It's telling you you need certain things. And people tend to get sucked into these pathways. I think we can get out of those pathways with AI. That's brilliant. You know, just to say, first of all, there is a rumor out there that ILIA SSI is going to be releasing something very shortly. I think everybody's feeling the pressure to release. We saw that, you know, with Mira coming out, so it's interesting to see. But the point you made, I think it's brilliant, is are these labs actually, you know, pulling their punches, holding on this capability to generate revenue on their own?

1:24:00I mean, if you had this super forecasting capability in the markets today, you would do that. I remember having a conversation with Eric Schmidt who said, you know, listen, if Google wanted to maximize its income, it knows exactly which companies are going to have a stock bump in the fourth quarter because everybody's Googling this product or that product. We have advanced information about where the sales are going to be and which products are going to peak. But if we could only do that once, then we'd be shut down. So interesting to see if these companies, and Alex, you and I have talked about the fact in Solve Everything, the notion that the greatest money, the greatest income these frontier labs are going to make is going to be as they solve scientific breakthroughs and superconducting and age reversal and so forth.

1:24:47Peter Diamandis:Exactly. And maybe just a footnote on the Google story. So I've had this conversation with Google execs many, many times over the years, totally agree with the premise that if Google were to attempt stock trading based on arguably insider or unfiltered insider information passing through the query stream, that that's a one and done type shutdown scenario. But there are other things that Google hypothetically could be trading besides public securities that would necessarily have the blowback. For example, again, hypothetically, foreign exchange rates.

1:25:17Emad Mostaque:Yeah, I think that you have to be careful here, though. Like, I think there's the there's the market side of things and you know like maybe maybe not i will launch a hedge fund based on our own stuff but there's the moral side of things maybe not uh okay of course but at any rate um but look there's the moral side of these things like it's fantastic that we can optimize ourselves but who controls these models and the advice they give can control vast waves of humanity and there needs to be a real discussion about this because it's like the people that follow their GPS into a lake. Yeah, that's the risk.

1:25:56Emad Mostaque:We're going to rely on these far too much. And again, how can you debate it in just a few years' time? Like, again, it'll be more expensive not to do this. You will be penalized for not listening. And if we're all watched over by machines of loving grace, we need to know whose grace that is. And again, that discussion needs to start now. Yeah.

1:26:14Dave Blundin:Salim just said beer. Just says beer. Homer drinking beer advised by AI was not on my bingo card for this episode. Welcome to the health section of Moonshots brought to you by Fountain Life. You know, my mission is to help you use the latest technologies, including AI, to not just do your work at home, teach your kids, but to help you live a long and healthy life. I'm here today with an extraordinary physician, the chief medical officer of Fountain Life, Dr. Don Musalem. Now, Dawn, let's talk about cancer. You know, I know from the member database that we have at Fountain, our members who come in who think they're healthy, it turns out 3.3 % of them have a cancer in their body they don't know about.

1:26:59Dave Blundin:That's right. You know, the majority of cancers that we screen for, those aren't the ones that are necessarily taking the lives when found at a late stage. We know that when cancer is found early, the chances for cure are much higher.

1:27:11Peter Diamandis:We know it's much easier to treat a cancer when found early versus when found late. What we're finding in our members is over 3.3 % were found to have these cancers that

1:27:21Emad Mostaque:were otherwise wouldn't have been found or detected. Yeah. You know, it's interesting. People, you don't feel the cancer until stage three or stage four. And if you don't know what's going on inside your body, it's like driving your car with your eyes closed. And you can know. And so when members come through found, how do they detect cancers?

1:27:38Peter Diamandis:So we're doing full body MRI and we also do early cancer detection screening. This is very, very important. And these are not typical tools used in the conventional care setting when it comes to prevention. This is a hard thing because currently these are not studies that insurance would yet be covering. But the goal is to collect these numbers, do the research and work hard to democratize wellness. Yeah. So at the end of the day, you can know what's going on inside your body. It's your obligation to know. So check out Fountain Life. You can go to fountainlife.com slash Peter to get access to the latest technology to help you detect cancer at the very beginning at stage one when it is curable before it gets to stage three or stage four in your world of hurt.

1:28:20So, Salim, you sent me an article, a chart. I'm going to put this up here right now. This is our constant debate. And we're seeing this, again, across data center wars in the United States. Data centers are sucking up electricity, driving up the cost for consumers, and also water. One of the loudest criticisms of AI right now is that data centers are guzzling drinking water to cool their servers. So this week, this particular chart I'm showing made the rounds, and it pairs two figures. On one side, every data center in the entire U.S., according to Lawrence Berkeley National Labs, is consuming 17 billion gallons of water on site.

1:29:03But what it shows is American golf courses that have soaked up 531 billion gallons of irrigation since 2024. That's 31 times as much. And so, you know, the posters I'm going to start seeing on the sides of the highways is forget data centers. We must ban golf courses immediately.

1:29:23Peter Diamandis:Yeah, Peter, where's the Chinese influence campaign to get America to shut down its golf courses? I tell you, I don't see it anyplace. But here's the shocking piece of data. Besides golf courses, California almond farming alone consumes one trillion gallons of water, 60 times all the data centers combined. I have one other stat, which is Amazon warehouses occupy 10 times more land in the US than all the data centers combined. So it's such a drop in the bucket compared to everything else in terms of land usage, water usage. The hue and cry is such a completely non-data-driven garbage bullshit. It's unreal.

1:30:10Dave Blundin:Exactly. That's the concern because the water use is such a non-issue. I mean, it's such a joke. But if we take that head on and say, guys, don't worry about water, you know that the angry crowd is going to move to something else equally irrational. So the underlying problem doesn't go away, which is, you know, the next issue is going to be something semi sane. This is completely insane, but something semi sane, but still wrong. And then that's going to create a populist movement. And, you know, the word moratorium, like, let's just stop. Like what kind of a decision, what kind of governance is let's just stop.

1:30:45Dave Blundin:But if you look at the history of nuclear and a whole bunch of other things, that's the actual outcome we get. And so, yeah, David Sachs. This is the pandemic of fear that I keep on speaking about that I'm very concerned about. There's an underlying sense that AI and robotics are going to combat humanity, are going to be our foes. And again, I'll just go back to it. It's, I blame to some degree, Hollywood, right, of all the dystopian movies out there. And if all you see is negative visions of the future, you're going to want to shut it down. And what do you want to shut down? How can you shut down AI?

1:31:19Well, you can shut down the data center in your state.

1:31:24Peter Diamandis:Yeah, and also, just the elephant in this particular room, the Dyson swarm. If the compute all moves to sun-synchronous orbit, you can do closed-loop liquids, including water and other coolants there. But it's not like it's going to be consuming on-margin additional water. And then to Dave's point, the complaints, which may or may not be in part the result of an influence operation from a foreign state actor, will move to something else. It'll be very low Earth orbit, SpaceX star mines and other competing Dyson swarms are polluting the atmosphere with their decay. or something else. The complaint will move on to something else.

1:32:02Dave Blundin:Did you hear the rant about the Starship rocket launches early? It was Falcon, actually. The pollution from the Falcon launches, Elon was just like, oh my God, I'm going to vomit right now. It was like 0.001 % of all emissions come from any form of rocket launch. It's like, but you have to actually answer these questions. You need to drive it in nuts. I hope those individuals who are complaining have thrown away their smartphones, don't use GPS, and are just basically going back to subsistence farming.

1:32:36Peter Diamandis:Yeah, as Elon likes to say, let them shake their fists at the sky.

1:32:41Emad Mostaque:I have a fun start. I was doing some numbers around the water thing. It's about 600 gallons of water per Big Mac, and McDonald's sells 2 billion burgers a year. So it's about twice the number of golf courses, the total amount of water that McDonald's uses.

1:32:57Peter Diamandis:That I can get behind. OK, so what you're saying, Ahmad, is Chinese influence ops should also be shutting down American Big Macs.

1:33:03Emad Mostaque:Well, there you go. It'd be a big, it'd be a stab to the heart of America. Yeah, that's right. Definitely improve the health of America as well. Shall we move to one of our favorite conversations, humanoid robots? This is so cool. Yeah. So China, as we've discussed before, has gone all in on humanoid robots. It's a national priority. Companies like Unitree and others are racing to commercialize. Last report, and Alex, we've talked about this, 150 humanoid robot companies in China under development. And part of their strategy is spectacle and something you're trying to bring, Alex, to America. They've been staging public robot combat events, literally MMA style.

1:33:48And we've got a video to show. Let me just go ahead and pull this up here of a recent MMA that went viral on the Internet. And it's a beautiful thing. It's just so cool.

1:34:23These are only going to get better.

1:34:25Dave Blundin:You got to watch the full video. It's just the way the fight ends is epically awesome. One of the robots kicks the other robots head off. You know, it's, you know, remember Rock 'em Sock 'em robots? Yeah, yeah, yeah. As a game, as kids. And so this goes viral. I mean, a lot going on in the robot world. We just saw Hyundai, all of the workers at Hyundai start to strike because they don't want robots brought in on their assembly line. That was fascinating. Alex, take it from here.

1:34:57Peter Diamandis:A few thoughts on this. Thoughts on many different levels. One is mild horror that if anyone who's seen Steven Spielberg's movie, AI, where there's, without spoiling it too much, I think Steven would call it the dark sandwich at the center of the movie, the flesh fare where humanoid robots are tortured and abused for human entertainment. I think utterly horrifying. So at one level, I'm mildly horrified that humanoid robots, no matter the extent to which they're being teleoperated here, are setting an inductive prior or bias for future more autonomous embodied intelligences to be basically trying to kill each other or at least otherwise abuse, physically abuse each other for human entertainment.

1:35:50Peter Diamandis:I'm concerned about that. But one level deeper. Now imagine that these robots are more autonomous, that they're running algorithms that are on the edge, so they're much more encapsulated. And now imagine that these humanoids are in the Chinese PLA infantry. Yeah. Because I think that's the future that we are almost certain to find ourselves in. The West needs to catch up in humanoids. That's why I've supported ProRL, which, Peter, you were gesturing at, which ran the first humanoid robot mini marathon in America in the Boston Seaport a number of months ago. Which you helped organize, right? Correct.

1:36:31Peter Diamandis:Yeah. Yeah, so the West needs something like this, hopefully less violent and more economically productive. I'd love to see people cheering on humanoid robots competing to iron clothing or perform some economically productive task and not just kicking each other's heads off. You prefer the humans to be doing that in the MMA matches? I prefer no one to be doing it. I'm not a fan of MMA. I think it's destructive to humans, and I worry about the message that we're sending to the future light cone by having robots doing instead of humans. I'd rather see people in a cage competing, if they must compete at all, to do something that's positive sum, not negative sum.

1:37:08Dave Blundin:Coding? Like a cage match coding? If anything. Or just sitting there, okay. I have a couple of thoughts. One is my normal commentary around kickboxing is not the greatest marketing demo for humanoid robots. But I will acknowledge something here. This is like unbelievably demanding engineering environment, right? You've got a stress test. It's stressing balance and impact resistance and recovery and locomotion and latence. There's about 20 things that they're doing. And it's kind of incredible to watch them navigate that. Of course, a four-armed robot would beat the two-armed robot. So I'll just leave it at that.

1:37:51So hello, there we go. So, you know, we're going to see this go to competitive sports. We'll see a version of the World Cup with robotics. The question is whether people will watch that or not. Yeah, I'll say that the real test is whether a human being can make that penalty shot under pressure at that top point in the game. Although, like, watching England implode the other day was really devastating for me. But still, it's really, I think the people much rather watch people in that environment rather than robots.

1:38:24Dave Blundin:Yeah, I think sports is going to thrive for many, many decades to come. Formula racing pushes the edge. And I think when we start to see robotic sports, it's pushing the edge. I think the point you just made, Salim, is important, right, that we're going to see this happening in a competitive fashion so that the top robots, And I can't wait to see figure versus optimus. I think that will be a fun competition, whatever form it takes.

1:38:52Emad Mostaque:I think that these robots are a little bit different, though. Like, I think probably you'll first see the real steel type of teleoperated robots, because robots can't actually respond fast enough. If you look at the latency of a VLA model, like this is impressive from some preoperated flying kicks. But why aren't they doing Kung Fu? When will robots do Kung Fu? That's when you move to things like etch silicon, when you move to teleoperation. And I think that'll be the next stage that comes next year. But I think there's a bigger issue that I have with this, although I love fighting robots and I can't wait to see Gundams and all that.

1:39:24Emad Mostaque:These robots are Engine AI T-800s. They weigh about 70 kilograms and they punch four times harder than Mike Tyson. So they could legitimately kill someone, us fleshy humans. Robots like that should not be allowed on the streets. and there's no regulation against that you know like again they could be in the pla people's liberation army or whatever but robots are about to enter our household i mean who here has a 1x robot on order you know like come on it's coming they will be walking around very soon we need to have regulations about safety of what the talks are on these things of how they operate and others because they represent a real threat to individuals because they are machinery.

1:40:08Emad Mostaque:Then beyond that, you will have the embodiments and others. And we need to have the discussion of what that looks like when they are autonomous because these things are delivering themselves by pushing a button on the door, you know, like ringing your doorbell. And the final thing is, Unitary has only made 11 ,000 robots humanoids total. We are literally at the very start of this. A few years from now, it will be 11 million a year from 11 ,000. So we've got to have this discussion fast as well. Lots of talking to do. Yeah. I mean, this is what the work you and I were doing, you know, in terms of how do governments sort of counsel their policymaking around these areas.

1:40:47And it's happening at a blinding speed. Crazy. Yeah. All right. I'm going to move us to the most important conversation we always have, which is the Dyson swarm. and let's take a look at a video from our friend Sam Altman. I honestly think the idea with the current landscape of putting data centers in space is ridiculous. It will make sense someday. But if you just do like the very rough math of launch costs relative to the cost of power we can do on Earth, to say nothing of how you're going to fix a broken GPU in space,

1:41:32Peter Diamandis:and they do break a lot still, unfortunately, we are not there yet. There will come a time, space is great for a lot of things, orbital data centers are not something that's going to matter at scale this decade. All right, we have the continuing MMA battle between Elon and Sam. Yeah. Yeah, so fascinating. I'm curious of reactions here. Alex, I'll go to you first. Yeah, I think there's an obvious conflict of interest. We saw similar messaging from Masa Sun regarding lack of purported promise for orbital data centers. Remember, OpenAI has retreated from its own data centers. Remember Project Stargate?

1:42:11Peter Diamandis:Project Stargate has been rebranded from OpenAI owning and operating its own data centers to just leasing terrestrial data center capacity from others. OpenAI is delaying its own IPO. So just not even at the object level, one has to look at OpenAI's messaging here and say perhaps it's not even in a financial or operational position at the moment to lean into orbital data centers, say the way Anthropic, which in their collaboration agreement, which was announced with SpaceX AI and for use of Colossus and Colossus 2, far friendlier to orbital data center-based compute. So I think the crossover is going to happen.

1:42:51Peter Diamandis:Elon's messaging regarding when this crossover is going to happen is two to three years. You see other analyses that suggest that the unit economics for orbital versus terrestrial data center. Costs are going to cross over sometime by the early 2030s. I'm not sure which is the case, but either way, I think there is an obvious conflict of interest. And just as we were discussing with Philip Johnston, barring some surprising left turn, I expect that OpenAI's tune is very conveniently going to change on ODCs sometime in the next two to three years, right on time. And of course, Elon's response to this is, we'll be launching them in two years.

1:43:29So just stay tuned and watch.

1:43:31Dave Blundin:Well, I think anyone listening to this video would say, OK, Sam says space data centers make no sense. Elon says they make sense. The two guys hate each other. But if you actually listen closely to Sam's words, they don't disagree at all. Sam is saying that space data centers will not be meaningful this decade. There will come a time. But this decade is only three and a half years left. And if you look at Elon's forecast of his launch rate, they agree, actually. So they're just hating on each other all the time. And it seems that way in this phrasing, but the truth is pretty clear. They both have the same numbers.

1:44:08Dave Blundin:So Alex is right. You know, they're going to space. It's going to take a while. I think a couple percent of all compute will be in space by the end of the decade because we're building out on land as quickly as we can, too. And the ocean. But then the lines cross. You know, Alex, you and I were going back and forth texting while the Starship attempt, Starship 13 flight was making an attempt a couple of days ago, and it's been rescheduled. When this pod comes out, we'll be seeing a next launch attempt on Starship 13 on Monday of this coming week. That launch was thwarted at T minus zero when two of these—

1:44:44Peter Diamandis:First time I've ever seen that, by the way. Yeah. Here's the point. Two of the 33 Raptor engines on the booster stage of Starship did not ignite and they're going to be replaced. But here's the extraordinary point. So, by the way, you know, SpaceX's stock dropped 5 percent on news of that failed launch, which is kind of ridiculous. The point people need to realize is that was an amazing demonstration of technology. The fact that you could shut down at T equals zero, safe the vehicle, unload the methane and the liquid oxygen. And that's, you know, I was part of the space industry in the 90s before it was a space industry.

1:45:27And those vehicles would have exploded on the spot. They would have failed on the spot. The ability we have to control them at that level of detail is evidence of the extraordinary engineering that SpaceX has done. I thought that was the most interesting part, which is how quickly the system diagnoses the problem and returns. It would have taken months and months to do this and fix it and recover everything and replan another launch. And you're like, yeah, problem, shut it down, redo it. Oh, we're starting Monday. I mean, it's amazing.

1:45:56Emad Mostaque:Yeah. Extraordinary. I think if you're serious about spending intelligence with what we know, you have to have a space play. OpenAI is going to buy like Planet Labs or something like that. you know like then the tune will change all right i'm going to go to some ama questions so imad you had suggested i post questions to x and we have a number of uh of questions coming about kimmy uh from our x audience let me go ahead and and show these uh and let's dive in so uh imad i'm going to give you first crack which of these questions do you want to answer um i think probably number four is an interesting one.

1:46:38Emad Mostaque:Given Kimi K3's lower token efficiency, is it actually as cost effective as advertised compared with Sol or Fable? So Kimi K3 is an expensive model relative to the other Chinese models. Like DeepSeek is now a dollar per million tokens. Kimi K3 is$15. Sonnet is$20. Opus is$40. And I think like Fable is$60. but that's because they're actually making money when you back out the numbers from the Chinese models and the chips they're running on they're probably making 80 90 margins now and that's with their Chinese chips which aren't that efficient for running this we will see the cost of K3 drop by 10 to 50 times I think in the next few months as it gets optimized and right now it uses twice the number of tokens for the same task versus GPT 5.6 again a frontier model that uses 37 % less tokens and 5.5 or fable again we're going to see that drop because everyone and their dog is going to optimize the crap out of this like you've seen fireworks just raised at a 17 billion dollar valuation others like modal at 10 billion base 10 at 10 billion these are the inference providers of open source models they've all raised a billion dollars that they're not going to spend to optimize the chinese model and make it more efficient and run it and so american labs who do the inference side of things are going to optimize the crap out of this, so we will see it catch up.

1:48:05All right. And by the way, I welcome the mates to lean in on these questions. But Salim, you want to go next? Given that I made the comment about number one, how much could Kimi K3 devalue U.S. frontier models? I'll stick with my original estimate of about 75%, 50 % from the U.S. regulating the front end. and then you've got lack of compute on the supply side plus the front open source models kind of within the release barely of where you are. That bleeding edge is such a perishable thing. I would say 75 % drop. So if OpenAI was worth a trillion bucks, I'd put it at 250 billion. You still have a very valuable business because now the competitiveness, you have to compete on reliability, security, integrated tools, ease of deployment.

1:48:59But the actual frontier cutting edge, it becomes one ingredient amongst the whole thing. You know, I would not want to be inside these frontier labs right now. It must be a frenetic code red 24-7.

1:49:13Peter Diamandis:It is a total rat race. I have so many friends at the frontier labs, friends who are jumping hypothetically from one frontier lab, Google, which is nowhere at this point missing in action to other frontier labs. It is a total rat race. Yeah, it's crazy. Dave? You have a choice for me? No, pick one. You got two and three, I think.

1:49:38Dave Blundin:Okay, I'll take two. What does the release of Kimi K3 do with due to the open source versus closed source race? Will this force the large companies to provide more product? I think they're implying more open source product. Yeah, it's a total game changer in the sense that anyone with resources can build an internal model that's tailored to a specific use case and then use it as a defensive moat. I don't think the large U.S. model providers will go open source. I think they're committed to their pathway. So if you were talking to Anthropic right now, they would say, look, Kimmy has caught up for a week, but Fable 5.1 is coming out in just a few weeks.

1:50:17Dave Blundin:When you look at the all-important enterprise use cases, so white-collar automation, drug discovery, people are going to use the best model no matter what. And it's like if you're using an AI to design a car or a rocket, a slight improvement in the design has massive payoff. So you're going to use the best of the best of the best model. So the Anthropic guys are going to scramble to stay a step ahead and keep their price point nice and high. The cost of the model itself is so small compared to the benefit that people will pay the price. So it does create, like Alex was saying, the rat race is incredible.

1:50:55Dave Blundin:But people aren't going to switch to Kimi unless it's proprietary data they want to keep in-house and they want to tune their own. Or Kimi actually bypasses Anthropic, which it hasn't done. You know, it's only caught up or not even quite caught up.

1:51:10Peter Diamandis:All right, Alex, number three. All right. Number three asks, and these are, I think these questions seem to all be variations on a theme, but it asks, how can U.S. models, I think this means U.S. frontier model providers, continue to justify their mass evaluations if China can leapfrog with an open-weight model at less than half the token cost? So I don't think the premise is quite accurate. There are so many elements, so many layers to superintelligence. And quite frankly, superintelligence itself is, as it fully develops, I think far larger than the total GDP of the entire world anyway. There's an enormous amount of pie that can be sliced.

1:51:49Peter Diamandis:But to the extent we're talking about, say, Google, which, as I was mentioning earlier, seems to be MIA at this point on the frontier. I can't find a single top Google model at this point on the cost frontier for capabilities. What does Google do? Well, they can continue to race, obviously, in terms of capabilities. But if I'm Google, I'm thinking, yeah, I want to become a hyperscaler. I mean, Google obviously is a hyperscaler, but a hyperscaler provider to other frontier labs. That's one obvious venue of differentiation. And we've seen that approach vector from SpaceX AI itself, which is now signed deals with Anthropic.

1:52:30Peter Diamandis:We're seeing it with Meta, interestingly, which on the one hand is offering Spark 1.1. And on the other hand, in the past two days, just as we were going to air, it was announced that Meta is exploring selling$10 billion of compute to Anthropic. So differentiating by going down stack and offering your compute up to other more competitive providers, whether Western, usually Anthropic, sometimes OpenAI, or Chinese models in a self-hosting model, that's one area. You can also go up stack. You can try to vertically integrate and offer applications that are benefiting from the commoditization of their complement, namely the model layer.

1:53:11Peter Diamandis:You can also, I think the premise that valuations somehow are going to net shrink just because Kimi K3 exists now is completely fallacious. We saw that incorrect thinking happen with the original deep seek shock, which was at the time also branded as a Sputnik moment. So we saw a bit of a hiccup in capital markets at the time. But as always, Jevons paradox kicks in and we see the value of chip stocks ultimately increase, not deflate. And we also see it's open weight. So there's absolutely nothing in Kimi K3 that OpenAI and Anthropic and other Western frontier labs can't just immediately reappropriate for their own internal models.

1:53:58You don't think that the amount of revenue these labs are going to make because it gets reduced as people start to use KimiK3 for their work instead of the API calls? No.

1:54:12Peter Diamandis:For example, so I spend, and my portfolio companies, spend an extraordinary amount on, let's say, Anthropik and OpenAI. And to my knowledge, my expectation is Moonshot would have to release like a 2x, 3x, 10x better model than, say, Fable 5 to have a massive diversion of that spend. Right now, what K3 buys at the moment, to the extent it's legal, query how much longer K3 will be legal to host within the U.S. But assuming it remains legal and regulatorily uninhibited, all it results is greater in-house self-hosting. But it's not at the top of the frontier. To Dave's earlier point, Fable 5 at the moment is.

1:54:58Peter Diamandis:So if you're trying to solve the frontieriest of problems, K3 is not causing you to divert your spend. Well, let me hit that point you just made, Alex, and ask you and the other mates a question here, which is, do you think it's possible that some legal policy in the United States prevents U.S. companies from downloading K3? It's going to be on the open Internet. It's going to be available through a multitude of sources beyond hugging face. Can it be shut down in the U.S.? It can effectively be. This is not prescriptive, and I'm not a fan of this policy, but I think it can effectively be shut down by requiring that every public corporation disclose any use of Chinese open weight models and subjecting them to scrutiny.

1:55:42Peter Diamandis:As we were going to air the latest, we talked in the last pod about Demis' proposal to create a FINRA-like entity that would regulate the frontier. Well, guess what? The reports are that the present administration is actually running with a proposal like that and is planning to, or at least exploring, creating a FINRA-like agency to regulate frontier AI that would live under the SEC, because the SEC already has statutory authority to operate FINRA-like industry-advised and funded entities. So it's a natural place. Self-regulated organizations. Yeah. Self-regulated governance, aka regulatory capture cartels, under the SEC.

1:56:26Peter Diamandis:And so I think it's completely plausible, albeit I think highly undesirable, that we get sometime in the future an SEC sub-org that looks like FINRA that basically makes it completely economically infeasible for corporations of any size, especially public corporations, to actively use Chinese open weight models. Any other comments on this? I've got comment on this. I mean, this is ridiculous in terms of trying to limit the use here. Because once you release the weights, right, stopping them, you can mirror them across jurisdiction. You can use peer-to-peer networks, hello VPNs, all you're going to do is deny American researchers and startups access to those models.

1:57:08And security experts, well, the rest of the world goes ahead on building on those models. I don't think there's a viable approach.

1:57:15Emad Mostaque:I mean, this is the same as denying Americans cheap insulin. I mean, it's, again, regulatory capture, right? Like, why can't you have generics? Because, again, you have the regulatory capture point. there's operation i think they're calling it gold eagle to approve access to frontier models you will have anti-token laundering regulations you will have know your prompter regulations like the u.s government's really realized that this technology is about to break through and i think that they're a lot more worried about it than china is you know like china again you look at that xi jinping speech i would urge everyone to kind of check it out they're like full-on open source we're going to do this america doesn't know what it's going to do but as you said there's a real chance that they might hobble american capitalism and oddly china's encouraging

1:58:04Peter Diamandis:capitalism it's going to get ccp saves american capitalism from itself it's a crazy future the world is so weird all right let's go back to you salim on next question okay which some of these They're a little bit duplicative. Yeah. Yeah. I'll take number five. Would you trust Kimi K3 to write your code for you without oversight or review? The answer is no. But I wouldn't trust a human being to put consequential untested code into production either. Right? The question is not whether we trust the modelers, whether we trust the development system around it. So, you know, AI-generated code needs to be run in a sandbox and pass automated tests and security scanning and all sorts of things before it goes into production.

1:58:58And then you do proportionate permissions based on the use case and on the potential impact. This is the same thing we talk about. Whatever the workflow is that AI is running, you're still going to need human review at the highest level and at the highest consequential inputs. So a lot of the routine can be automated, but the scalable model is not AI with no oversight. It's machine-generated plus verification plus human accountability combined. That's going to give you the real power.

1:59:32Emad Mostaque:All right. Imai. Yeah, I think what role, if any, did Distillation play in K3 development? They distilled data clearly from Opus and others. But to be honest, using KimiK 2.5 and KimiK 3 now quite intensely, it feels different. So I think they did a lot of their own data creation based in part from Distillation, but everyone's distilling from each other right now. The one area that it's clear that they've had a big leap ahead is in the front end development. Again, this isn't the best mathematician in the world, although it's quite a good general model. It's not the best cyber attacker from our benchmarks.

2:00:11Emad Mostaque:But they've kind of done something original and new on the front-end game consumer slash entertainment side of things, which I think is really interesting. Although that might be also because it's a multimodal model.

2:00:26Dave Blundin:Dave? Number seven, what are the reasons why Kimi K3 might not be as good as advertised or we shouldn't use it? The scenario where it's not as good as advertised is if it's benchmarked and, you know, in two weeks, you know, the open source will be out. We'll have beaten it to death. We'll know the answer if they benchmarked. So we're going to find out. I think it's unlikely that it's benchmarked to the point where every company in America right now should be in the world right now should be saying we need a crash program with our best possible advisors to decide. are we going to do our own model on our own on-prem hardware, or are we going to use Anthropic or OpenAI or Google and just trust that API?

2:01:12Dave Blundin:But we need to decide whether tuning and training on our own proprietary data gives us a long-term competitive advantage. And so there's going to be a desperate shortage of good advice on this and vendors and McKinsey consultants. And you've got to grab those resources quickly. Exactly. EXO consultants. Make seed stage investments. Get your network together. Find out who can answer that question for you internally on your business and your use case quickly, and then commit to the path. You can do something internally and still use the APIs, but if you don't start down the path of evaluating KimiK3 on your own, you can't really come back to it later.

2:01:52Dave Blundin:I think everybody's got to just get going on this question. We'll know in a couple of weeks, though, whether it was Benchmax to hell or not. But I think it's very, very likely that the open source path is a viable path for every U.S. and world company and government now. Can I just add to that real quick? Yes, of course. Very simple suggestion for every company. Implement two installations, KimiK3 and Inkling. fine-tune your own internal data because that learning loop is going to be the proprietary goal that you do not want to lose and start there. Alex, why don't you close us out here? You've sort of answered number six already, but perhaps you can expand on it.

2:02:32Peter Diamandis:Yeah, I'll say something new. So the question six asks, should the U.S. move to block loading the weights of the next Kimi release onto Hugging Face? I'll give a conditional answer. I think that if some party, presumably in the U.S., can prove to a cognizant court that the next Kimi release, presumably a reference to this Kimi release, was somehow obtained or derived illegally, maybe through copyright infringement or illegal distillation of traces or something like that, that would probably be grounds for blocking its release in the U.S. But if no one can prove that that Kimmy's parent, Moonshot, did anything otherwise wrong in creating it?

2:03:14Peter Diamandis:No, I don't think the U.S. should be blocking its release in the process. I think, if anything, quite the opposite. I think every U.S. frontier lab should be closely scrutinizing it and learning whatever they can so that we can leapfrog it. And I would like to see far more outward pressure from U.S. Labs creating the best in world open weight and open source models so that it's not the CCP with their new Belt and Road for AI initiative blanketing the world, some would even say dumping superintelligence on the rest of the world or the so-called global south. It should be the US, the canon of freedom, the arsenal of freedom that's also the arsenal of superintelligence showering the rest of the world with open weight and open source superintelligence, not China.

2:04:05Showering the rest of the world. I love that. And remember, we're moving towards intelligence too cheap to meter, but a million times more available and more powerful than ever before. Everybody listening, I'm grateful on behalf of the Moonshotmates here for your time. If you haven't subscribed, please do. We're going to be putting this out more and more often as we're starting to see the release dates move from months and weeks to days. And there's no time to sleep during the singularity. Gentlemen, what's in store for the week ahead? Imad, I'll go to you next, Salim. Yeah. Imad, what's news in your life?

2:04:42Emad Mostaque:Just getting a whole bunch of research papers ready to release. So finally, it's going to be exciting. Yeah. Again, acceleration. For Intelligent Internet, your company, yes? Yes. Salim, please. Tuesday I have my next Meaning of Life session 7pm Eastern For those that are interested Where do they go to find out? We'll have the link below But it's openexo.com So this is the second time I'm doing it If you've not participated in one of Salim's Meaning of Life sessions They are extraordinary They'll take you beyond the AI Into the realm of philosophy and theology Alex Are you coming up? What's going on with you?

2:05:24Peter Diamandis:I'm so focused at this point on literally solving everything. I'll say large swaths of the sciences at this point, I'm convinced, are so thoroughly cooked. More to come on that subject. Peter, you and I wrote Solve Everything About It, but now it's actually coming true. Yeah, I'm excited. You're going to be doing an AMA with my Abundance community coming up. That's going to be a fun deep dive. And of course, we're going to have you during the Moonshots gathering on September 25th doing it. In fact, all of us will be here. Imad, you're joining us in LA in September. Yeah, it's going to be fun to have all of us together again for the full day.

2:06:01Dave, you know, this has got to be the most exciting time to be in Link Studios.

2:06:07Dave Blundin:Oh my God, yeah. I think that discussion we had of quantization on this podcast that Imad kicked off, I think that now vaulted to my new best piece of media ever recorded passing Leopold Ashenbrenner. I got to go back and listen to that again in slow-mo. And also, you know, we had Vlad Bulevich from MIT Nano in this week. He's going to advise and help us on our new startup working on photonic computing. And he gave us a whole roadmap of people I need to meet next week. So we were looking to add two MIT people with our Princeton team to work on just the photonics, quantized photonics side of the equation.

2:06:45Dave Blundin:So I'll be working on that next week. But I think I can take that video we shot earlier and use it as a recruiting tool. It was just so freaking brilliant. You guys are incredible. Yeah. I love you guys so much. What a great week. Awesome conversation. We'll see what breaks tomorrow. Yeah. Over the weekend. Emergency pod.

2:07:03Peter Diamandis:We need emergency pods every day by January. All right. Be well, everybody. Thank you for tuning in to Moonshot, your front row seat to the singularity. Take care, guys. Awesome job as always. Thanks, Peter.

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