Mira Murati's 975B Open Model, Ramin Hasani on Post-Transformer AI, and Demis' AI FINRA | EP #271

The mates discuss Mira Murati’s 975B Open Model, Ramin Hasani speaks on Post-Transformer AI, and Demi’s AI FINRA. Get access to metatrends 10+ years before anyone else - https://qr.diamandis.com/metatrends Peter H. Diamandis, MD, is the Founder of XPRIZE, Singularity University, ZeroG, and A360 Salim Ismail is the founder of Open ExO, a GP at Exponential Venture Capital/The Organizational Singularity Fund and a sought after global speaker and thought leader. Dave Blundin is the founder & GP of Link Ventures Dr. Alexander Wissner-Gross is a computer scientist and founder of Reified Ramin Hasani is the Co-founder and CEO of Liquid AI – My companies: Apply to Dave's and my new fund:https://qr.diamandis.com/linkventureslanding Go to Blitzy to book a free demo and start building today: https://qr.diamandis.com/blitzy Your body is incredibly good at hiding disease. Schedule a call with Fountain Life to add healthy decades to your life, and to learn more about their Memberships: https://www.fountainlife.com/peter _ Connect with Peter: X Instagram Substack Website Xprize A360 Connect with Dave: Web X LinkedIn Instagram TikTok Connect with Salim: LinkedIn X Join Salim’s 10X Shift Subscribe to Salim’s YouTube channel Exponential Venture Capital Connect with Alex Website LinkedIn X Email Substack Spotify Threads Connect with Ramin Website X LinkedIn Listen to MOONSHOTS: Apple YouTube – *Recorded on July 16th, 2026 *The views expressed by me and all guests are personal opinions and do not constitute Financial, Medical, or Legal advice. Learn more about your ad choices. Visit megaphone.fm/adchoices

• 0:00Speaker 00

  Mira Murati, the former OpenAI CTO, just shipped her first model. It's called Inkling. Customization over leaderboard dominance is what's going to win her the day.

• 0:11Speaker 01

  She's built exactly the thing hitting the market, that exactly what everybody needs right now.

• 0:16Speaker 00

  I want to pivot to a discussion of Liquid AI, the small language models, what they are, what they mean.

• 0:22Speaker 02

  Our mission has always been building efficient, general-purpose AI at every scale. That explores the computational graphs of intelligence beyond transformer, and then figure out what should be that architectural design that brings the same level of intelligence than a frontier model into, let's say, on a CPU.

• 0:42Speaker 00

  CEOs building the most powerful technology in the world are asking to be regulated. Demis Hassabis, CEO of DeepMind, he called for a US-led frontier AI standards body modeled on FINRA. When the incumbents ask for the rules and they set the standards, they set up a barrier for all the entry-level labs coming in.

• 1:02Speaker 01

  Let's just be real, AI moves way, way too fast for any kind of traditional bureaucracy. How quickly can you do this is going to be a huge, huge challenge because...

• 1:18Speaker 00

  All right, everybody, welcome to Moonshots, your number one podcast in all things AI, your front row seat to the Singularity. I'm here with my magnificent Moonshot mates, our original quartet, AWG, DB2, and Saleem, and a special guest, Ramin Hasani, co-founder and CEO of Liquid AI, and a pioneer in small language models, which we'll dive into. Ramin, welcome. Where are you this morning, pal?

• 1:43Speaker 02

  Thanks so much for having me. I'm actually in Spain right now.

• 1:46Speaker 00

  In Spain, all right.

• 1:48Speaker 01

  Nothing going on in Spain this week.

• 1:52Speaker 02

  Yes.

• 1:52Speaker 01

  I'm struggling from yesterday because I'm a long-suffering England supporter. It was a very difficult game to watch. God, they were like, just had it with six minutes to go and they blew it.

• 2:03Speaker 00

  Yeah.

• 2:04Speaker 01

  And Messi's a genius.

• 2:05Speaker 00

  That's the round, that's the round ball, right? Is that?

• 2:09Speaker 03

  Now, Peter, this is where the Falklands War gets re-litigated on a soccer pitch.

• 2:13Speaker 00

  Oh, God. You know, I just flew in last night from Zurich and I had the most painful experience, right? I don't know why every airline doesn't have Starlink. You know, I'm suffering on some, you know, some meager, thin pipe connection and you're flying over the poles, over the, you know, the northern territories, and there's nothing. And I'm trying to get ready for this pod. I was like, please, give me, give me some bits. So, anyway, challenge.

• 2:40Speaker 03

  Ramin, you must have grown up on soccer, right? Didn't you, you were in Vienna for a while and getting your PhD and, or your undergrad or whatever it was?

• 2:48Speaker 02

  Yeah, yeah. I mean, soccer has been like a big thing, you know? I'm Persian and Austrian like at the same time. You know, like it's a big thing for us. So, yeah, like competition is something that, you know, it's extremely core to what we do even today, you know? So, and I feel like that's like one of the main drivers, like sports and everything, like has been part of our lives like from day one. And then getting into science, the same thing, you know? Now getting into ventures, same things, you know? And that's, that's what we're doing.

• 3:18Speaker 03

  Just compete, compete, compete, compete. I love it.

• 3:20Speaker 00

  Well, are you there for a little bit of time or are you coming back soon?

• 3:23Speaker 02

  No, I'm coming, I'm flying tomorrow, actually back to San Francisco.

• 3:27Speaker 00

  And Saleem, are you, are you jealous of everybody of him being in Europe? Or are you happy to be home?

• 3:32Speaker 01

  No, no, three weeks bouncing around in 10 different spots. I'm very happy to be home right now. I was just in Spain, Ramin, myself, so.

• 3:40Speaker 02

  There was a lot going on, actually, like in Europe, you know? So that's, same, same like you, 10 different places, then.

• 3:48Speaker 00

  You know, Alex and I were just reminiscing the fact that Europe's sort of major advantage in the future is it's going to be a museum of the way the world used to be.

• 4:01Speaker 00

  Ouch. But it is beautiful, there's no question, it is gorgeous. All right, I want to jump into our first conversation. We have a lot to unpack here. And of course, our mission is keeping you aware of what's going on in the world and giving you sort of the optimistic, hopeful vision of the future. Join us and keep up with the incredible pace as we head towards the Singularity. So, our first story today, once again, CEOs building the most powerful technology in the world are asking to be regulated. You know, last week, Sam Altman published an op-ed in the Financial Times proposing a framework for a US-led international forum that would establish standards, provide expertise in partial analysis and capabilities and assess risks.

• 4:45Speaker 00

  This week, both Elon and Demis are adding their voice to the regulatory conversation. Elon says he expects a standalone regulator, similar to the FAA or FCC, to emerge at some point because, in his words, "The consequences of AI going wrong are severe." Then this week, Demis Hassabis, CEO of DeepMind, went further. In an essay titled "A Framework for Frontier AI and the Dawning of a New Age", he called for a US-led frontier AI standards body modeled on FINRA, the industry-funded watchdog that polices Wall Street under SEC oversight. He wants the FINRA equivalent to test frontier models before release. He reportedly wants this up and operational before the end of the year.

• 5:29Speaker 00

  Let's take a look at a quick video from Elon and then let's jump into this conversation, guys.

• 5:35Speaker 04

  I think the general, I think it's clear that there's a strong consensus, there should be some AI regulation, that it would be in the best interest of the people to do so. And I think we'll probably see something happen. I don't know on what timeframe or exactly how it will manifest itself. I don't know. I mean, this clearly, we've created regulatory agencies before. While our regulatory agencies are not perfect, and I deal with regulators on a very frequent basis, with automotive, you know, communications, Starlink, and then FAA with rockets. I think the probability of there being some sort of AI regulatory agency that stands on its own, similar to the FAA or FCC, is likely at some point.

• 6:15Speaker 00

  You think so?

• 6:16Speaker 04

  I think so.

• 6:20Speaker 04

  The reason that I've been such an advocate for AI safety in advance of sort of anything terrible happening is that I think the consequences of AI going wrong are severe. So we have to be proactive rather than reactive.

• 6:35Speaker 00

  Amazing. So, this is a conversation we've seen over and over again. And I think the government, the public, and now the CEOs want to be leading this. I like the approach that Demis laid out, right? But the challenge I have to discuss is when the incumbents ask for the rules and they set the standards, they set up a barrier for all the entry-level labs coming in. Saleem or Dave, do you want to jump in first?

• 7:04Speaker 03

  I'd be very curious to know, Ramin, if, do they reach out to Liquid AI and say, hey, join this? You know, we're going to create a FINRA-like regulatory body. I mean, the reason FINRA works fundamentally is because people from the industry who know what they're doing are willing to join it. They're definitely not willing to join the government in general, but they're willing to do a year or two in a regulatory body. It's actually kind of a badge of honor. So for this to work in AI, it would have to be something cool and people like Ramin or maybe, you know, some of the people on your team would need to come into your office and say, hey, boss, you know, I'd love to do this for a year. I think it's really good for the world, will you let me do it? And then you would also have to be like, yeah, this is a functional organization, go for it. So if it passed those two hurdles, Ramin, it might, it might actually work. I don't know, what do you think?

• 7:50Speaker 02

  Yeah, there's like, you know, like there's a capability kind of threshold that we are, we are trying to define right now. And there's some, some sort of an iteration is needed to see like how these, how these frameworks, it has to exist, you know, that's, that's for sure, you know, it has, this has to be there. But it has to be related to capability. And then the thing that becomes a challenge is that there's a horizontal kind of capability lock into like active, like, let's say, like enterprise deployment of AI. And then there's the vertical. Because if you go to different verticals, like for example, we operate on, on, on device and we're enterprises that are connected to the physical world, you know? Like we are connected, we are talking to car manufacturers, like semiconductor business, you know? And laptop business, you know, like people that are building like AI PCs.

• 8:33Speaker 02

  And then we are also working with financial services and we are working with like e-commerce and biotech kind of companies. And we see like in different verticals, you know, like the app, the enterprise applications themselves and enterprise criteria for, let's say, a limit or let's say a regulation or a governance kind of a structure is very different, you know? So for us, it becomes a lot more kind of verticalized because we're building specialized models. And those specialized models, like per vertical, we have had like conversations with the DoD and we have had like a joint submission of something, I think with AMD, like pretty, like just recently, like with our team, to really have, have a say, basically, like in, in, in the design of like these regulatory kind of things.

• 9:17Speaker 02

  And I think as an exploration, I think everything has to be like getting started. I like to look at it as a game theory kind of way of looking at it, like how to design like policies in general. Like it would be a stakeholder kind of game. I don't know if anyone is familiar with, I don't want to nerd out like pretty soon on this, but we can, we can talk about this.

• 9:34Speaker 03

  Alex does it all the time, it's okay.

• 9:35Speaker 00

  Sooner the better.

• 9:36Speaker 01

  Sooner the better.

• 9:37Speaker 02

  Yeah, so I mean, stakeholder games, like essentially like where two policies, like basically, like there's like a, you know, like you have like a policymaker and then you have agents or bodies that are working in that kind of game theory, kind of, they are trying to find an equilibrium. You know, what is the optimal policy and what is basically, which is good for both, right? And then, so there is the frequency of action. Usually policymakers are slower than the agents in the society, you know? So if you think about like, you can, you can really model it like that, right? And then you can, you can figure out like an equilibrium. This is not a Nash equilibrium because everything doesn't happen simultaneously. Regulations happens and then you, agents react and then you iterate kind of accordingly.

• 10:22Speaker 02

  And then you change those regulations, basically.

• 10:25Speaker 00

  Saleem, I see you chomping at the bit here, buddy.

• 10:29Speaker 01

  Yeah, so I think, I think what Ramin is saying is exactly right. The problem is we have no mechanism for that, right? Like, if you go down the path, Ramin, that you're talking about, you end up with the appropriate structures that are adaptive and API-based or like driven by benchmarks or something. But the mechanism that people have today is just static law. And the minute you pass the law, the law is going to be out of date, right? The, I, I found that the FAA and FCC analogy is pointing in the right direction. But, hey, let's just be real, AI moves way, way too fast for any kind of traditional bureaucracy, right? So you're going to need, you're going to need a standards body, you're going to need real-time audits, and you're going to need open evaluation suites.

• 11:12Speaker 01

  Otherwise, you can end up, otherwise, you can end up in political gatekeeping and then you're in a mess.

• 11:18Speaker 00

  Saleem, isn't that what's good about FINRA? It's not a government agency, it's an industry-funded self-regulatory org?

• 11:26Speaker 01

  It is, but then the teeth go to the SEC, which is essentially being dismantled right now. So there's all sorts of issues here. I think the trend is correct, but how quickly can you do this is going to be a huge, huge challenge because forget passing a law, passing a structure where you have a new construct like this takes a long time and it takes forever in Europe.

• 11:49Speaker 03

  I think Ramin nailed two things that are very different from FINRA right out of the gate. One of them is, you know, at Vestmark, if somebody on our executive team said, hey, I want to be part of FINRA for a couple of years, we would say, sure, put on your suit and tie, go to, you know, go to the meetings, come back in two years, we'll still be here. You're not going to do that. Like if Alexander Aminy or Matthias Lechner came into your office or I mean, said, hey, I'm going to check out for three weeks, you'd be like, no, you can't do that right now. So the difference, number one is nobody's going to carve out the time to do something for years like they do at FINRA. The other big difference is AI can help regulate itself and FINRA, there's no equivalent to that in FINRA. It's all people just chatting for long periods of time. But, you know, when you start talking

• 12:31Speaker 03

  about Nash equilibrium and other ways to automate the process of regulation, that's a big, big difference as well. So the FINRA analogy has some legs, but you know, the differences are bigger than the similarities.

• 12:42Speaker 00

  Alex, I want to hear your voice on this.

• 12:45Speaker 05

  I tend to think this is a bad idea. It smells like regulatory capture. It smells like the attempted formation by Demis of a cartel of frontier labs. And I think the elephant in this particular room is open weight models and research that lives outside of the frontier capabilities. And it's very easy to imagine a future with FINRA or other, I mean, worst case scenario FDA like capability, even though outgoing personnel from the current administration have declared within no, no equivocal terms that there is going to be no FDA for AI regulation, that that would be maybe on the worst case end of the spectrum,

• 13:28Speaker 05

  that we, we see the emergence of some sort of cartel of frontier labs that locks in certain practices, certain price performance, optimal frontiers that try to box out open weight or open source or say university driven or other non incumbent frontier models. And I think that would be an utter disaster for both the West and the world for continuing to advance us towards ever increasing super intelligence capabilities. I just don't think it's a good idea.

• 13:59Speaker 00

  You know, and the other elephant, the other elephant in the room here is the CEOs who are asking for some level of regulation, I think are looking for a backstop. You know, if things go wrong, they want to be able to point at someone else. Now, I mean, we're all super fans of the optimistic vision of AI, but there's going to be issues that materialize. There are going to be rogue AIs that take down a power grid or take down, you know, stock market or something like that for some period of time. And I guess they, you know, there are going to be lawsuits flying as a result of that unless there's a regulatory body that, that backstops these large, these large models and these large frontier labs.

• 14:40Speaker 05

  Maybe. There are, I think, at least two different frames that one can look at the liability side from. There's regulate the inputs. That is to say, like have something that's FINRA like or FDA like that regulates the raw capabilities of the models at model construction time. That that's one end of a spectrum. The other end of the spectrum is regulating the actions of the models. Like you let the lawsuits fly if a model takes down a stock market or does something else that otherwise harms third parties. That's the other end of the spectrum. It's not obvious to me that we should be in the business of regulating super intelligence at super intelligence time. That's, that's maybe tantamount to

• 15:21Speaker 05

  thought policing the AIs. And I'm not generally a fan of that notion of let's thought police the AIs, but not thought police the humans. We don't, at least in the West, have a practice of regulating what's in our minds. We don't have a practice or a tradition of regulating an upper limit, say, or via some sort of regulatory code saying humans, natural persons can't be above some level of intelligence. It's not obvious to me why we would create a new tradition of regulating or otherwise coordinating the upper intelligence of non natural entities, perhaps soon to be persons, but regulating the actions that in at least the Western legal canon that we do do, and that I'd be much more supportive of.

• 16:04Speaker 00

  So do you, Alex, let me ask you a pointed question here. Do you think that this, you know, sort of outcry for regulation by the large frontier labs is, is regulatory capture that they're just trying to build a moat against further players coming in? Or do you think they actually want to provide some level of safety? What's their underlying driver here?

• 16:24Speaker 05

  I worry that it's more regulatory capture and creating moats for themselves in a hyper competitive landscape. And it is, I mean, it is a rat race at this point, the frontier. And I do worry that it's more regulatory capture than it is some notion of protecting the future.

• 16:41Speaker 00

  I'll take a poll here. Saleem, what do you think?

• 16:43Speaker 01

  Not workable.

• 16:46Speaker 00

  Well, I know that, but do you think, do you think it's regulatory capture? Or do you think that the, that the CEOs are trying to just make sure we've got a safety, a safety net of some type?

• 16:54Speaker 01

  I, I'd say it's like 50 50, but I think there's a bigger problem. There's an elephant in the room here. Some.

• 17:01Speaker 05

  There's already an elephant in the room, Saleem.

• 17:03Speaker 03

  Lots of them. We have a stampede.

• 17:04Speaker 05

  The room has to accommodate so many elephants. We need some other non-human animals.

• 17:07Speaker 01

  Better get a bigger room. You've got non-state actors and other folks that won't listen to this structure and you're back to square one. What's the, what's the point? I'm going to say it again. I've said this repeatedly. I see no mechanism to regulate AIs moving way too quickly. Any regulatory is static.

• 17:27Speaker 05

  Well, there are, I'll take a different position on that one, just if I may, Peter, narrowly on that. I mean, there are definitely hypothetical mechanisms that I'm not supportive of for regulating AI. Like we, Royal We, the US and China, if going back to, I think we gestured at it in a past pod, but past proposals to say, regulate the foundries, regulate the chip outputs, regulate the data centers, establish mutually assured destruction type schemes where the US is monitoring Chinese data centers and vice versa. Like there are, there are schemes. Yeah, there are schemes at chokeholds, as Peter says, in the supply chain by which one could imagine doing this. I don't think it's a good idea.

• 18:10Speaker 01

  The only mechanism, it's going to be like a pandemic style threat detection that would be globally agreed. And I don't see how we get there.

• 18:19Speaker 05

  Well, you don't need global. You just need US and China, right? The rest of the world is, is basically outside those blocks or inside those blocks.

• 18:26Speaker 00

  All right. Well, I think my guess, and there's probably a poly market out there we can, we can look at if someone wants to search on it, you know, the question of will we have a regulatory body by the end of the year, right? We have Demis saying by the end of this year, you know, Elon stepping up and Sam obviously trying to, on his own, on the side, trying to push for this. So when the three largest labs are pushing for it, my guess is the government will latch on and will do this. I don't think it's a matter of if, it's only a matter of when and what the structure will be.

• 19:02Speaker 05

  Well, I should also note that Elon clip, I think is from three years ago, which is interesting. And you know, it's from three years ago because Elon had his sort of like painted on Iron Man goatee when he was in that, that phase. So Elon's been forecasting this for at least three years. Others have been forecasting it for decades. We still don't have it. We have like subdivisions, orgs within NIST that are working on standards, but that's not really regulatory body. We have executive orders that are creeping towards a regular regulatory body. But, you know, at what point do we sort of, are we frogs boiling in water where there's just like a creeping rollout of increased standards,

• 19:43Speaker 05

  expectations of early reviews, but it never quite reaches a regulatory agency level before we achieve whatever escape velocity we're headed towards.

• 19:53Speaker 00

  Well, we're going to monitor this one closely for everybody. I think my guess is we see this before the end of the year. And the question is, can we see something that's intelligent? Let's go to the next story, which is related. And this is a wild one. Comes from the Washington Post. The White House is reportedly weighing a capability framework that would clear US models, open or closed, as long as they stay at or below the level of China's best open weight model. What's the translation? So the proposed ceiling for what American companies can openly release is pegged to what China has already put out on the internet for free. So here's the logic. Chinese open weight models reportedly trail

• 20:34Speaker 00

  US models on average of seven months. I think that's been closing over time. So if anything is at or below that, it's already out there. It's an implicit admission that open weight models cannot be unshipped. Models like DeepSeek have already been downloaded millions of times. So once China releases a model freely, banning it is impossible. So the US response is to define a permissible ceiling rather than a wall. The implications, we're tying our open release ceiling to China's pace of release, effectively giving, you know, Beijing control. If they push their open weight models higher, then the US can release higher models as well. If China holds back, then they throttle us. And it's a

• 21:19Speaker 00

  very strange mechanism. I was surprised to see this. Alex, let's go to you first on this one. What do you think of this?

• 21:25Speaker 05

  I mean, the, the obvious note here is this creates the perverse incentive to let China win the race to ever greater super intelligence so that Western models and Western labs can escape regulation. I'm not a fan of this. Ramin gesturing at you from a game theoretic perspective. This is the, I think this would be the moral equivalent to throwing the steering wheel out the window in a game of chicken. Not such a great idea. Not supportive of this.

• 21:52Speaker 00

  I love that. Oh my God. Saleem, what do you make of this? Is this just perverse Washington DC logic?

• 21:58Speaker 01

  Yes. This is like trying to uninvent the printing press. I mean, you're throwing the kitchen sink of things, trying to, to solve something that's already a problem. The, you have to move from like prevention and whatever to adaptation. You have to go to that. And we, we don't have the mechanisms for that.

• 22:16Speaker 00

  I, Ramin, you know, I mean, would you even listen to this? I mean, what?

• 22:23Speaker 03

  If it's a law, you might.

• 22:27Speaker 02

  Well, I mean, if I just look at the progression of the technology itself, it's getting into the place where like you, AIs are designing AI, like you're doing the same things and all of the labs are doing this. And the pace, it's just the pace of model development is like getting so, so, so much smaller, you know, that is, is becoming like exponentially more, more difficult to really like impose any, any of these type of constraints. I know like they had these type of conversations, but it's just at the level of conversations, you know, like these are the things that are getting leaked outside of White House.

• 23:03Speaker 00

  It seems like they're groping for ideas.

• 23:07Speaker 01

  Let me, let me give a headline for Alex for, for his next newsletter. The singularity is becoming a trade dispute. For the next newsletter, that was like two newsletters ago. Okay, fine, whatever. That's out already. But thank you.

• 23:23Speaker 00

  Yeah, I can just imagine where a US Frontier Lab CEO calls DeepSeek and say, would you please accelerate your next model release? We want to get ours out as well.

• 23:32Speaker 05

  Or you see worst case scenario, I mean, there's actually an even worse scenario, which is you start to see the best, if not Western labs, unlikely, the best Western researchers move to China to escape this regulatory framework. That would be a disaster, I think. And, and we've seen this, by the way, there's precedent for this. We saw this in biotech, where China now exceeds the West in terms of number of trials. Like China is experiencing a biotech boom. That could happen in AI as well. It would be a disaster.

• 23:59Speaker 03

  It's much more specific than that. If you look at all the quantization research, all the best stuff came out of Microsoft research in China. All those people now are at Chinese labs. They're not, they're not still working for US companies.

• 24:11Speaker 05

  China ran away with ternary and one bit quantization. You see a little bit of Western research. I don't think we're talking that much about it in this episode. You see a little bit of encouraging Western research on like one bit or 1.58 bit quantization, but China ran away with it due to constraints.

• 24:26Speaker 03

  Yeah, it's a new company.

• 24:27Speaker 01

  Look, this is a huge problem, right? Because over, we've seen throughout history that open ecosystems always win. And this is not open versus closes, which open ecosystem wins. And the US's historical strength has been open ecosystems with permissionless innovation. You like abandoning, that would be the weirdestly strategically bizarre thing we've ever seen.

• 24:51Speaker 05

  Yeah. The, the other, I mean, there's even a meta worry I have, which is how do we even define capabilities? And, and I worry a little bit, not just about regulatory capture of the labs themselves. I think there's actually, so sorry to be like a meta doomer here. There's a worst, worst, worst case scenario, which is we freeze in or otherwise lock in the benchmarks for how we measure capabilities. And that, that would be, I think, maybe even worse than just locking in the incumbents as labs. Because if, if someone somewhere ratifies, all right, like whatever index of evals, this is going to be the rubric going forward for how we measure what's above the threshold for frontier versus below. What's a frontier

• 25:33Speaker 05

  model versus not? I worry that could so distort model capabilities, like they'll over exercise certain capabilities deliberately and perversely, under incentivize or under benchmarks others, that it'll just totally distort, maybe topiarrize the, the future landscape of super intelligent capabilities.

• 25:53Speaker 00

  All right. Well, again, this is a story that we'll be, we'll be following on this news of open weight models. So in the past-

• 26:00Speaker 05

  There's our topiary right there.

• 26:02Speaker 00

  In the past, we've been discussing how open weight models have been, in the US, have been lagging. In China, we have Nvidia's Nematron 3, we've got Google's Gemma 4. But that changed last night with some breaking news. Mira Murati, the former OpenAI CTO, who walked out and raised her in one of the largest seed rounds ever. It was incredible financing she pulled off in the background, just shipped her first model for her startup called Thinking Machine Labs. It's called Inkling. It's an open weight foundation AI model that can be downloaded by anyone, fine tuned and run on prem on your own hardware.

• 26:42Speaker 00

  The specs are serious. It's a mixture of experts model with 975 billion total parameters, only fires 41 billion at any one time. So it keeps it, you know, keeps the model going fast and cheap. It was trained on 45 trillion tokens of text, image, audio and video, and very importantly, reasoned natively across all four. Reuters News framed it exactly right, quote, "This is meant to be a Western alternative to the Chinese open weight models, DeepSeek and Quen, that have dominated the open weight leaderboards." Now, interestingly enough, Murati, her bet is contrarian here. She's not claiming it's the best model on earth. Her own blogs say so. She's betting that AI, that AI companies can adapt her models for themselves, that

• 27:26Speaker 00

  customization over leaderboard dominance is what's going to win her the day.

• 27:32Speaker 01

  You've, you've hit there, Peter, on the really big thing. She's making this, she's pushing on the customization lever. And this, because it's not going to be the future is the raw power. It's going to be the adaptability that's going to win. And this, she's built exactly the thing hitting the market that exactly what everybody needs right now.

• 27:51Speaker 00

  And people owning their own models, working on prem and not giving their, you know, their controls to the large frontier models. I mean, I, I do hope this begins the race for powerful open weight models in the United States.

• 28:06Speaker 05

  Well, it's, it's worth looking at the raw capabilities. So if you believe the evals, hopefully that Thinking Machines, AKA Thinky, has released, it's stronger than Nematron, which is great. Like Nematron, you'll recall from past pod where we were discussing Alex Karp's rant on sovereignty of models. Nematron is one of the incumbents, at least on the American side, for open weight frontier models. So this, this seems to be, at least according to evals, that Thinky's released stronger than Nematron, which is great. So the, the West now has a new frontier open weight model. It's weaker than GLM 5.2, which is arguably the strongest or one of the strongest Chinese open weight models and open weight models overall. So it's not, it's not one of the strongest open weight models overall in the

• 28:51Speaker 05

  world. It's obviously weaker than the closed weight Western frontier models. But I, I think point one, it's great to have better, stronger Western open weight models. Point two, I, I think it raises the question, why has the West been so bad at releasing frontier open weight models? And why has China been so good at it? And I think it comes down to, you show me the incentives and I'll show you the outcomes. I think the West has been poorly incentivized to release strong open weight models because these API based frontier models are just such a good business model. And we see Anthropic about to IPO at a trillion dollars, and we see OpenAI planning to eventually IPO at a trillion dollars. And in China, which

• 29:34Speaker 05

  has been GPU and compute deprived on the one hand, and on the other hand, has the CCP declaring five-year AI plus plans to integrate AI into the rest of society, has all of the incentives, a different incentive structure than what the West has. China has been much more incentive, incentivized to make money from the integrations between AI up stack on applications like robots and down stack into the chips than the West has, which is more horizontally stratified. So to the extent that Thinky has been incentivized in the West due to competition and due to just a saturation of the frontier by the closed weight models into looking a little bit more, dare I say, Chinese in terms of their outlook and their incentive structure,

• 30:20Speaker 05

  I think this is very helpful to finally have enough competition in the West that's creating ways to monetize open weight models other than just per token sales, namely selling them into enterprises.

• 30:32Speaker 00

  You get what you incentivize.

• 30:34Speaker 03

  I agree. I agree wholeheartedly. But also, you have to note that OpenAI started open source, open weight, and then went closed, big revenue. And Meta also was the leader of-

• 30:48Speaker 00

  Yeah, Llama. What happened to Meta?

• 30:50Speaker 03

  Now it's closed. No, they have a new model out and it's, it's closed API. I mean, it's exactly what Alex said. If you throw your model out there as open source, what's your revenue model? So I think, you know, there's a real possibility that, that you put a data point on the map with a really solid open source release that's not quite on the frontier, you generate news, then you have a data point on the line, then you do another, then you do another. And then when you have something really groundbreaking, then you go closed source and you launch an API into corporate America. And so that's, that's a well worn path. So I wouldn't, I wouldn't say this is necessarily a religion at Thinking Machines that they're going to stick with. You know, the trend has been the opposite of that in the past.

• 31:27Speaker 05

  Ramin, what's your take? They're leaning into fine tuning as a service. If fine tuning as a service becomes like something at scale, revenue generation wise, I think maybe this has legs, but who knows?

• 31:37Speaker 02

  Yeah, it's a matter of like the business of the company, you know, like Thinking Machine can do three more iterations of their pre-training or post-training kind of RL, kind of environments and benchmark stack those numbers that you see on the benchmarks and release like a, like a better model. But what they, what, what their business is, their business is fine tuning. Like this is kind of the place where customization has been like something that everything, the whole, the whole market around customization has been very empty. Like if you look at the first attempts, like OpenAI released their OpenAI tuning, like fine tuning kind of three years ago or something, it never took off. So they took like a really good approach on designing the base for fine tuning larger instances of the models for enterprises. Because as you see, like the model layer

• 32:22Speaker 02

  is not anymore like, you know, like the, the place where you can actually extract value, especially if you're not hitting the maximum frontiers, you know, like, and even the open weight kind of models, when you're talking about sovereign AI and integration of these models into enterprises, you need to leave some room for, let's say, fine tuning these models. And what they have, what, what I think their business and strategy around what they're doing, and this release is genius, because they're deliberately releasing, they're, they're putting, they're leaving some room for fine tuning so that people can come in and using their business, their API business, because that's even generating, if, I think in the order of one to two orders of magnitude more tokens as well, you know, on the, on

• 33:05Speaker 02

  the, on the customization side. So that would be like even printing money at a larger speed, like in the, in the, in the absolute best case, right? So I think that's a business they're entering.

• 33:15Speaker 05

  To add to Ramin's point, I think the situation maybe is, is even more extreme. So a couple of points. One, OpenAI was the first, to my knowledge, to launch reinforcement fine tuning, RFT as a service, and no one used it. The, the whole tech world, everyone I speak with, no one used it. It was barely advertised by OpenAI. Second point, OpenAI shut off their fine tuning API. OpenAI was one of the earliest, if not the first, to offer fine tuning as a service.

• 33:43Speaker 03

  It was amazing. We used it all the time. It was, it was incredibly cool for its time.

• 33:48Speaker 05

  And they've just recently, in the past few months, they announced it has either already been wound down or about to be wound down. The fine-tuning API has been shut off. So that, I mean, it raises the question, is thinking machines bet explicitly contrarian? Are they thinking that we're going to end up in a world where reinforcement fine-tuning and RL fine-tuning in general and fine-tuning, that that's the paradigm? They may be right, they may be wrong. There's an alternative vision where RFT just dies. And we, the baseline models are so generalist in terms of their capabilities that all you need is prompt engineering and there's no need for RFT at all.

• 34:27Speaker 00

  Alex, we talked about the Alex Karp rant, right?

• 34:31Speaker 05

  Yes.

• 34:32Speaker 00

  And the result of that was, don't allow, don't use a model that is, has all of your data open to your competition. And I do think we're going to see a real push over the next months to years where people want to use fine-tuned open weight models that they own on their own hardware in their, you know, on-prem. And if that's the case, then the question is, who are they going to use? Which models are they going to use? Or, you know, and is the US going to start to regulate against Chinese open weight models? In which case, a dominant US open weight model is going to take, is going to have an advantage? And so is that the bet Mira is going after? You know, we're going to probably see, my

• 35:13Speaker 00

  guess is Google step up in this area as well very shortly, you know, take Gemma four to the next level. And hopefully, we get some, you know, two or three major, in the same way we have a closed, you know, the closed model frontier labs competing and dominating in the US, hopefully, we'll see that competition give birth to, you know, very strong open weight models as well.

• 35:33Speaker 03

  Just to build on something, you know, Alex and Ramin were saying, you know, if I compare today to a month ago, you know, we've been fine-tuning Quen all week. And the idea of using Inkling sounds really compelling to me. And, you know, our companies are using Liquid as well. A month ago, to fine-tune these things with some huge engineering effort that required AI experts, now with Fable 5, it's just a prompt.

• 35:54Speaker 00

  So let's back up one second. Dave, explain what fine-tuning a model is for those who don't know.

• 35:59Speaker 03

  Well, you know, back when GPT-2 and GPT-3 came out, you could actually very easily fine-tune by uploading text right into a window and say, look, you're pretty smart, but you don't know anything about my laundromat. You know, like what hours we're open, who our employees are, our entire payroll. Let me dump that data in too, and retrain the model with that knowledge. And if you didn't do that, you couldn't do anything useful because it didn't have this holistic, I know everything capability back then. So without the fine-tuning, it was borderline useless to use the models. Then the models got so smart that they're pre-trained with now 45 trillion tokens, which is basically every word ever written by humanity, has already been trained into the model. So people tend to use them in their vanilla form today and just say, here, write this code for me,

• 36:44Speaker 03

  or here, drive this car for me, because it's already in there. But then when you get into biotech research, or you get into aeronautical, or the Mercedes, you know, like Ramin is doing, there's a whole bunch of proprietary company knowledge that actually isn't in the model. So right now, we dump it into the prompt field and say, okay, here it is in prompt form. But that's hugely inefficient.

• 37:02Speaker 00

  And you dump it into OpenAI, and you dump it into Anthropic's, you know, model, which now makes it accessible to everybody else as well.

• 37:11Speaker 03

  Yeah, yeah. I mean, Sam and Dario can see everything, all your proprietary information. They're looking right at it. That's what Alex Karp was ranting about when he said, they're stealing your weights, they're stealing your alpha. What he really means is they're looking at your most proprietary, your company payroll, your company's secrets, your, your chemical research, like it's all going right over the wire to these foundation labs. Is that what you want? And of course, you know, for defense and for banking, of course, that's not what you want. And so now the ability to bring the model in-house and fine-tune it with your local data is a huge, is a huge unlock. But the higher level point is now the technological capability to do it relatively easily is hugely better today than it was a month ago. So I think Mira may be on to something here. We've hit a real tipping

• 37:54Speaker 03

  point. And Alex Karp, I think, is right about it, too.

• 37:57Speaker 01

  I think there's two things that also that I saw that were really interesting here. One is a very big context window, like a million tokens, because that means you can do a lot with it. And the second is multimodality.

• 38:07Speaker 00

  Yes.

• 38:08Speaker 01

  And so this is aiming squarely at organizational use. This fits perfectly into the on-prem proprietary data model, where you, you take your data, customize and fine-tune, as you said, Dave, and that will be the future.

• 38:23Speaker 05

  A couple of historic notes, again, for those definitionally not tracking the full sorted history of fine-tuning. So fine-tuning is this notion that you start with a model, model consists of billions usually these days of weights of parameters that are frozen. And if you want to customize the model for your purposes, you can conduct a so-called fine-tuning process that usually makes relatively small, hence the fine changes to some, usually a tiny subset of the weights in order to customize the model for your end application. That's fine-tuning. There's actually now decent literature out there that suggests

• 39:03Speaker 05

  that conventional fine-tuning, like supervised fine-tuning, LoRa-style, low-rank adapter, one class of fine-tuning architectures, doesn't result in increasing the capabilities of your model at all. And at most, it results in like a style transfer, like you could fine-tune a language model to only speak in Shakespearean verse, for example. That's not really increasing its capabilities.

• 39:25Speaker 01

  Or only be an accelerando flavor output.

• 39:28Speaker 05

  Well, no comment. But, but I would say, historically, fine-tuning didn't have a history of increasing capabilities. Then along came reinforcement fine-tuning, where for the first time, via large amounts of synthetic data and giving access to all of the weights and not just like a subset that's convenient to train, we gained the ability. And, you know, fine-tuning post-training, there's a, there's a gray area between, you know, what was the distinction between them. But with reinforcement fine-tuning, RFT, and the release of the first generation of reasoning models, we saw fine-tuning actually start to increase the capabilities of the models. Now, the problem with Thinking Machines'

• 40:12Speaker 05

  business model, as I understand it, is it's a bet on the flavor of the moment that reinforcement fine-tuning is going to be a paradigm in the future. Right now, it's obviously the paradigm of the moment, that you could take an off-the-shelf model and RFT your way to customization with proprietary data and proprietary environments and other things that, that seems to work pretty well at the moment. But in some sense, if that is like the permanent long-term plan of Thinking Machines, it's fundamentally a bet that we're not going to ever move beyond the reinforcement fine-tuning paradigm, which I think is probably wrong. I think probably RFT is the scaling of the moment. But in the future, I can totally

• 40:53Speaker 05

  imagine a generalist-based model that is just so generally capable that it doesn't actually benefit from any further reinforcement fine-tuning on any internal data sets.

• 41:04Speaker 00

  And we tend towards ASI. Let me bring up another key point here on this story, which is in the, in the context, which is, it's great to see a woman CEO in the AI frontier lab area. I think women are distinctly missing from the entire AI industry, right? We have Lisa Su from AMD, but very few in leadership positions. And I think that's an important point. I'm not sure who else, you know, Alex, are you seeing?

• 41:33Speaker 03

  Daniela Rus.

• 41:35Speaker 00

  Daniela Rus, right? Where, Ramin? Yeah, Fei-Fei too. And Fei-Fei Lee, yeah. But again, we're talking about, what, single-digit percent of the AI industry is, is women. And we need more. So a call out to every, all the women out there, please jump into this industry. We need.

• 41:53Speaker 01

  We, we need more balanced thinking.

• 41:55Speaker 00

  Yeah, for sure. I mean, I, I do think that's an important point to pull out here.

• 42:00Speaker 06

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• 43:04Speaker 00

  All right, let's move on to our next story here. It is a fun one. Alex, I was walking in the streets of, where was I yesterday? Zurich, and I saw this come up and I said, hey, let's talk about this tomorrow. And you said, yes. So here is the story. We've talked about the holy grail of AI is recursive self-improvement. It's sort of like the holy grail of the launch industry was reusable rockets. This, you know, RSI is a holy grail for AI. It's the idea that AI makes itself smarter. And then you use that smarter AI to create the next generation of AI.

• 43:49Speaker 00

  It's sort of the theoretical engine behind the hard takeoff scenario of the singularity. So this week, a startup called Weko AI with researcher Zhang Yalzhang published what they call experimental evidence for the first recursive self-improvement. Whether they're first or not, Alex, I'll ask you about that. They built a system called AI-driven exploration squared, AID squared, with an outer AI agent whose job is to rewrite the code and the research strategy for an inner AI agent. In their experiment, they claim that eight days of machine self-improvement beat two years of expert human effort. So Alex, what do you make about this? Is it the first? Is it significant?

• 44:33Speaker 05

  Very significant, highly unlikely that this is anywhere close to first. So a few bits of additional context. One, this is actually, Weko is a startup that's based in London, interestingly. It's not based in the US, but still Western sphere, so great. This is a startup built by a bunch of, as I understand it, UC London grads. Secondly, a few points that I love about this story. One, it's an example of defensive coscaling, which, so to the extent we talk about alignment, AI alignment on the pod, and I'm always banging the drum of defensive coscaling as the ultimate alignment strategy.

• 45:11Speaker 00

  What does that mean?

• 45:12Speaker 05

  So defensive coscaling is the idea borrowed by analogy from human alignment, human-to-human alignment, that rather than hoping for, call it the great man theory of alignment, that someone somewhere is going to discover the perfect algorithm for keeping AI safe. Instead, the solution for AI safety is AI policing AI in proportion. The way we keep cities safe is we have police forces, police forces that scale according to some scaling law in proportion to the population of the city. So we have the good guys and the bad guys, and the way we keep the bad guys in check is with making sure that we have enough good guys to police them. Same idea with AI. The way we keep AI aligned with humanity, a key way is we make sure that we have enough

• 45:56Speaker 05

  good AIs policing any bad AI AIs in terms of raw capabilities that they defensively coscale. So what, one of the things I love about this AID two story is that the outer loop, so the way this recursive self-improvement process worked was they had an outer loop and an inner loop. The outer loop was tasked with the, with improving the inner loop. The inner loop was tasked with improving software development processes in general according to some benchmark. The outer loop discovered, and both powered by the same underlying AI-driven exploration process, at least initially, the outer loop and AI discovered that it was able to achieve,

• 46:38Speaker 05

  and this was an emergent property, better results from the inner loop by keeping, by preventing the inner loop from cheating and reward hacking. And so in some sense, the outer loop is defensively coscaling with and policing the inner loop. All the while, this is reaching toward greater and greater capabilities. And I think this is also parenthetically an example of a case, you know, all of those who would say, okay, like, we need to pause AI capabilities and throw all of our resources to AI alignment until something preposterous in my mind, like 2040, like, stop all, stop the race to superintelligence, stop it all, focus on, focus the next 14 years on alignment research.

• 47:21Speaker 05

  It's going to backfire because every alignment capability, I would argue, is actually just capability, new capability in sort of in disguise, in a trench coat. Same idea here.

• 47:33Speaker 00

  We need stronger white hats to police the black hats.

• 47:37Speaker 05

  Yes, but the beauty, yes, agree with that. And also, the beauty is the so-called white hats were emerging organically on their own just from the outer loop policing the inner loop towards greater capabilities. That's first point. Second point, quickly, the same startup, Weko, has published a scale of recursive self-improvement, which is, I think, something the world has been missing. So we have like for autonomous cars, we have the, the Society of Automotive Engineers has their like five levels of autonomy for autonomous vehicles. They've published a scale for recursive self-improvement that goes from zero to three. Zero is delegation, where the AIs are slower than human R and D.

• 48:20Speaker 05

  Level one, net positive, where the AIs beat human R and D at the same cost. Level two, they call ignition, where the improvers are better, basically a better improver. And level three, inflection, self-acceleration with a fixed budget. And the claim here is that they're touching, just starting to touch on ignition. They call it level one rather than level two, but the claim here is like, this is a pre-ignition event, which I think is super exciting.

• 48:45Speaker 00

  So they rate themselves as a level one here?

• 48:47Speaker 05

  Yeah, they rate themselves as level one, but reading between the lines, they're like, this is like sparks of ignition, literally and figuratively.

• 48:54Speaker 02

  Okay, so maybe I can, maybe I can jump in and say a couple of words. I'm not as excited as Alex is like on the, on the topic, and I see, I see this is an impressive engineering kind of work that has been done. Just to tell you a little bit about like how the foundation model labs are operating. All foundation model labs, since the beginning of, let's say, like four years ago, or let's say five years ago, everybody has been thinking about recursive self-improvement. And for us, the definition of recursive self-improvement is not the engineering and prompt engineering of inner loop and outer loop to really get, get some code patches like changing, because that gives you the assumption that every single AI model that

• 49:36Speaker 02

  you're using in your pipeline is already like, you know, like it's already defined and it's already fixed with a certain type of capabilities, which is actually the case. In the whole pipeline that they actually like design, there is no weight changes in the neural networks. So that means like the AIs that are actually getting used right now, there's no kind of improvement of the core competences and even behavior of the models. They're always like in the system prompt of the, of the models, like changes in the system prompt, because I will give you like fundamental reasons why this is actually limiting. Because if you just run the, like how, I want to tell you how hard of a problem is recursive self-improvement.

• 50:18Speaker 02

  For us, recursive self-improvement means that you have an AI system or an army of AI systems that they can also like retune themselves. They can, you know, adapt, very similar to how humans do it, you know? If you, if you think about it, the core competences of these models that we have right now, they're, they're fixed weight models, and then the capabilities are within a certain kind of threshold. And the frameworks that they actually like designed, it's not, it's a very nice early stage of showcasing an engineering pipeline that can improve work, which is actually very, very important and very nice. But I wouldn't, I wouldn't go so much to say like, this is like the first breakthrough in, in, in the entire AI industry or something like, in fact, like about three years ago,

• 51:02Speaker 02

  we published a paper ourselves, like we talked, we talked about automatic design of model architectures, you know? Like, you know, as Liquid AI, we didn't want to put like a bet on a single architecture. We have basically designed self-improvement, like meta-AI systems that are actually defining their architectures and then going through scaling laws for various types of architectures, and then trying to figure it out based on the criteria that you find, what should be the final model. And then, right now at our company, all the process of training foundation models and really like retuning the weights of the system are, are getting automated. So we are talking about AIs or designing AIs. So that's, that's what I, what I would be like calling it like the holy grail, where

• 51:44Speaker 02

  you can actually do automatic kind of tuning of a model. And I'll tell you with the frameworks that they kind of structured, it would be extremely exhaustive, computationally intractable to actually performing this, this job. Training an AI model, training, like being able to customizing an AI model and training an AI model on a meaningful number of tokens for adaptation, or let's say like the core competence of the model changing, core architecture of the model changing, core algorithm, learning algorithm itself changing, all of those matters adds more and more complexity on the, on the situation. I can give you also like one numerical kind of example of this. There is a scaling law called Chinchilla law, you know, like Chinchilla is like the scaling laws of neural networks.

• 52:27Speaker 02

  And like, it is unproven, like we have actually unproven, but still like it's, it gives you a good sense. It says, when you're training a neural network, let's say of a given size, if the size of the model is 2 billion parameters, you need 20 times of more tokens, number of tokens to train these models so that you have, we have compute optimality. Given a compute budget, how many tokens do you have to train a model so that you have like a general purpose kind of system? So that ratio is like 20. And then when you actually do the math with the frameworks that they have, if they want to, like, let's say you launch this framework on retuning an AI model to recursively self-improve

• 53:07Speaker 02

  with this framework that is getting introduced, it takes us 350 years to really fine tune a 2 billion parameter model with this framework. So there are, so, so, so there is, there's a lot of, there's a lot of computational complexity goes into nested learning systems, nest metal learning systems, you know, like, these are the kind of problems that the last four years of like, at least at my company, like, we have been heavily focused on. And I know friends at OpenAI and Entropic has been like focusing on this recursive self-improvement. And Entropic has been having a lead on all of these things because they thought about this before every, everybody else. That's, that's what I can put out there.

• 53:48Speaker 00

  Dave?

• 53:48Speaker 03

  Yeah, brilliantly said. And actually, just, just so the audience can get the analogy there. When, when a baby is born and then learns, you know, that happens over about a 20-year time scale. And after 20 years, you've got an adult that's capable. Recursion self-improvement is like evolution on top of that, where you're changing the DNA and creating a new species.

• 54:09Speaker 00

  You're changing the neuronal structure of the brain along the way.

• 54:12Speaker 03

  Exactly. So, so that happens over, you know, about a 10 million year time scale. So you go from 10 years to 10 million years, to go from learning to recursive self-improvement or recursive evolution. And so the big foundation model labs, like Ramin said, are all doing it. It's the most important moment in human history. But there's no, you know, little guy out there that's going to come up and say, hey, I've got a breakthrough in recursive self-improvement. My Mac Mini suddenly became conscious and now it's improving itself. Just computationally, it doesn't even come close to fitting. So it's happening, but it's happening with big compute and big budgets. And, and, you know, there's a lot of room for efficiency improvement. A lot of breakthroughs will happen, but it's not going to just pop up on some, you know.

• 54:52Speaker 00

  You know, there's a lot of, there's a lot of fear, just to call it out, that, you know, recursive self-improvement leads to AIs that take off a hard, you know, we've discussed the hard takeoff, and without our understanding of that black box. I, I guess the two questions need to be asked is, is there a concern that recursive self-improvement, once we hit level two, level three, by that definition, runs away in a way that causes an uncontrolled AI that is misaligned with humans? And the second question I have is, when do you think we'll see this? When do you think we'll actually see recursive self-improvement

• 55:31Speaker 00

  hit? Is ASI going to be that point? Or is it post AGI, whatever that means? Salim, I say that for you.

• 55:40Speaker 01

  I'll let Ramin go first.

• 55:41Speaker 03

  Answer that, answer that, exist.

• 55:42Speaker 01

  I've got, I've got several comments, but Ramin, go ahead.

• 55:45Speaker 00

  When, when do you think we can see this happening?

• 55:47Speaker 02

  The thing is, I can tell you like the early evidence of recursive self-improvement. By the way, recursive self-improvement is not related to one single agent. It's a social kind of character as well. You can imagine like, you know, you have societies of agents. So this defining kind of structure for societies of agents itself, self-improving, these are the places where actually mythos level kind of class of models, like I hate this analogy, but still like, let's say mythos level kind of class, because everybody like heard about mythos. And then what I would say is that like the cybersecurity kind of threads that we are seeing, like coming out of these type of pipelines of recursive self-improvement, they're real, you know, like, like the reason why I'm actually, I, I've always been like, you know, like pro open source and I want to open source technology all the time. Like

• 56:32Speaker 02

  we are doing it all the time. Like every single release of our models is open source. Our science has been always open source. I believe science has to be open source. And I, I see the value of open source going forward. But some of these concerns that Peter, you, you brought up, they're, they're very real, you know, like the cybersecurity kind of aspect of things. That's why I feel like a degree of, at least, enterprises themselves having some degree of kind of self-control, like about like how, before mass release of their, their models, there, there has to be always a certain degree of self-check. And I think Entropic took it very seriously. The reason behind it is because they're seeing the impact of recursive self-improvement. So I know, I know this for, for a fact, because

• 57:17Speaker 02

  I, I know what is happening, like in, in, and we are seeing it at a smaller scale, you know, you can do reward hacking, but you can also like, you know, like avoid reward hacking, like to, to a certain extreme and push a model to actually discover some stuff that, you know, like are, are out of norm, you know? And, and we, we see that on a small models, like at a, at a certain capabilities, certain capabilities emerging. And then I can only imagine like what kind of capabilities could emerge from, let's say, larger and larger systems thrown more and more computably.

• 57:48Speaker 00

  Ramin, when do you, when do you think we, you know, when do we have a pod?

• 57:52Speaker 03

  Timelines, Ramin, timelines.

• 57:53Speaker 00

  Yes. When do you have a pod that said, yes, this is recursive self-improvement? Because while, you know, while the data released by Wiko is interesting, it's their own self-reported data. It hasn't been confirmed by anybody else yet. And, you know, there's a, you know, debate about whether it really is or is not real recursive self-improvement. When do you think we actually, you know, you know, give the trophy out to somebody? Is it a year, three years, five years?

• 58:21Speaker 02

  Yeah. I mean, I, I'm telling you that, that, so I, I would say like you're going to see like unbelievably kind of models, like probably in the next two years or so, you know, like models that are like going above our, our understanding even, like that, that's, that's what, what I would imagine to get. The reason behind it is because the time to developing the next generation of the models is reducing, especially if the compute grows, like at foundation model companies, like with the rate that we are seeing right now. And if there is no, like, let's say, another chip shortage or memory shortage, like on computer or anything like around the globe, and they have access to abundant compute, we are going to see those things like happening faster and faster. Now, in terms of model development, there's a concept that we have, we call it depths of customization.

• 59:04Speaker 02

  So everything at a foundation model lab, when you're customizing a model, when you're building something that is like better than its previous generation, we always categorize it with depths of customization. The place where recursive self-improvement today is really good at is prompt engineering, changing, editing code, like in engineering kind of tasks that you've seen, like some elements of these things, like at a very, very superficial level. Let's say, make my model run fastest, like doing kernel engineering, basically, you know?

• 59:34Speaker 00

  Exactly.

• 59:34Speaker 02

  Make my model run faster. That's what I call like the shallowest level of kind of customization, where you have Python code, and then you're kind of adopting that Python code to really run, or maybe like even lower level programs that you have like on a kernel level, to optimize like, let's say inference speed, you know? That's something that I think when, when they released Fable 5, they, they shared, like, and Tropic actually shared that this was one of the tests that they have been performing, you know? But they, they don't share like the next level depths of customization. The next level depths of customization is that can a model fine tune a small language model to a production-grade capability, or a smaller version of itself to a certain capability? Today, like Fable

• 1:00:16Speaker 02

  5, can actually, you can push it to actually get to some degree of kind of customization with some.

• 1:00:23Speaker 00

  Performance optimization.

• 1:00:23Speaker 02

  Performance optimization of the model, but by fine tuning. Then the latest holy grail, which is like the craziest one, which would be pre-training, right? Can a language model pre-train the next generation of their own? That's why they hired Andre Karpathy, because Andre was talking about like nano GPT style kind of fine tuning, you know? And Andre, like, joined Entropic, and now he's working on pre-training automation, like basically automation of automation. So which is, which is a very, very important kind of element that we don't have yet, because the scale of these problems goes beyond human imagination in terms of the scale of compute.

• 1:00:59Speaker 00

  Salim, you're chomping.

• 1:01:01Speaker 01

  I've got, I've got, for me, this is by far the most important story or slide we're going to cover today. I'm beyond excited for a couple of reasons. The, you know, I'm not really focused on the self-awareness or the loop that will go there, but this is self-accelerating, it's accelerating experimentation, right? Because the system doesn't need, it's, it's improving the process by which it searches and evaluates and selects improvements. And the innovation loop begins to compound. That, for me, is the key. Why? Because the, this whole thing we've been doing called the organizational singularity relies on one thing, which is can you get to recursive self-improvement at the workflow level? Here

• 1:01:43Speaker 01

  we're talking about the model and we're talking about like, can you, but you don't need that level. The bar can be much, much lower to improve invoice approval at a company, right? That's a very low bar to improve that process. So this is the first glimpse of the organizational singularity. It's happening at the research level, but because AI is not just doing tasks in a, in a workflow, it's redesigning the workflow that makes it better for doing future tasks, right? And so this is proof now for the whole thesis we've had. We predicted this, but it's great to see it actually happened, because now I can kind of tick that box off and go, this is there. Because now you have meta-improvement. And I think Dave's analogy

• 1:02:24Speaker 01

  of the baby changing the DNA is fantastic. That's such a great visual around this. What the hell does it become over time? So I really, really, I'm beyond excited about this.

• 1:02:35Speaker 00

  I've got to move us along. There's a lot that happened this week. Our next story here is the Malaysian Prime Minister, Anwar Ibrahim, has, is preparing to debut an AI-generated digital double of himself, trained to sound like him for public communications and outreach. So this is one of the most prominent cases yet of a sitting head of government officially adopting an AI likeness as a communications tool. Not a deep fake Biden adversary, but a sanctioned official AI clone of a national leader. We've seen this before, Salim. We've talked about it in the past, where Albania in 2025 announced Delia, an AI avatar that was formally appointed the Minister of State for Artificial Intelligence, and following

• 1:03:19Speaker 00

  a presidential decree, became the first AI system in the world named at a cabinet-level role. So one leader, in this case, Prime Minister of Malaysia, can personally address millions in their own languages. It's worth noting that Malaysia has 135 spoken languages. So it's a big deal, especially in a nation like that. Salim, I'm going to go to you first on this one. We've been talking about this for a while.

• 1:03:46Speaker 01

  Yeah. I, I met the, the former Prime Minister when I was there helping them open a university. And Anwar Ibrahim is a really, really good guy to, as a follow-on. The, there's a risk here. The risk is that authenticity kind of collapses, because people need, you know, you could, you could launch a bunch of deep fakes with this and have a huge issue. Is this the actual leader? That kind of question can come up. But I love the general approach, because if you can do it from a, with a watermarking or something and say, this is the actual avatar, then it gives every citizen a voice to plug into and gives huge props

• 1:04:29Speaker 01

  to the civics of all of this. Because now you're scaling civic engagement. And I think that's a very powerful thing to do. It's one of the biggest challenges we have with democracies all over the world is civic engagement. And this allows you to scale that. So I'm very excited.

• 1:04:42Speaker 00

  Do you remember the reason why Albania put this, their AI Cabinet Minister in place?

• 1:04:47Speaker 01

  Yeah, corruption.

• 1:04:48Speaker 00

  Corruption, exactly. It was to fight corruption.

• 1:04:51Speaker 01

  Yeah. Now, Malaysia is pretty decent, is a pretty decent place, but definitely you you have that issue. But I think the this is more of a PR thing and more him trying to figure out ways of connecting with the ordinary citizenry, which is all great.

• 1:05:04Speaker 00

  I I love the fact that we, you know, we had this conversation with the president of Argentina, you know, going full out here, and it's interesting to see which countries are sort of experimenting on the edge. Alex, do you want to weigh in?

• 1:05:18Speaker 05

  Yeah, so many thoughts here. First, I think we're going to see more of this in the West as well, especially with like extra high alpha personality leaders that want to amplify themselves and touch the citizenry.

• 1:05:32Speaker 00

  AI Trump is coming, is that what you're saying?

• 1:05:35Speaker 05

  High personality leaders that that want to touch the citizenry. And in some sense, I think it's a generalization of social media. So, social media enables direct outreach from the leader or the influencers to everyone, but it's sort of broadcast one to many, it's not interactive. This generalizes, in some sense, social media to make it a lot more bidirectional, since if you're touching a million or a hundred million or a billion people, it's very difficult to interact bidirectionally with everyone all at once. Now, if you create a digital twin of the leader or the influencer or the organization, now it can be bidirectional. So, I I also don't think it's just going to be governments or government leaders that adopt this. I I think it's likely that corporations, corporate CEOs, will do this.

• 1:06:18Speaker 05

  We already see Zuck and others creating digital twins of themselves.

• 1:06:22Speaker 00

  We had Dara on the Abundance stage last year, we were discussing this, that the employees made a Dara clone that they could go and practice their pitches on and get feedback before they pitched to him.

• 1:06:33Speaker 05

  Yes, and it won't just be, I think, corporations, religious leaders and religious institutions. If you're Catholic, imagine having like a digital twin of the Pope. And you you see like lots of religious institutions, organizations already creating basically living versions of of their founding documents and making those interactive. But I think the biggest twist, and we we've seen variants of this movie before, are going to be in cases where what start as digital twins of the leads or the avatars of an organization or an some sort of like organized religion actually themselves become the leader. That that's at some point, the the digital twin, it to the extent it's interfacing much

• 1:07:15Speaker 05

  more with the the populace, the the proletariat, as it were, of an organization, at some point it's actually the digital twin of the leader running the company and not the actual behavioral origin that that's running the company. And I think that's that's one way in which, Salim, to your to your XO point, this is, I I think, a potentially a pathway towards not just uploading individuals like natural persons or non-human animals, but uploading entire organizations into into cyberspace, into the cloud, if we created digital twins of the leaders, and those are the ones actually running the organization.

• 1:07:51Speaker 00

  It could lead to a true democracy. Dave, where do you come out on this? I mean, we saw, just one quick point, we saw Sam Altman talk about in the future, if I believe enough in what we're building with with ChatGPT, it should be the CEO of OpenAI eventually. Dave, are you going to create an AI Dave Blunden that's going to run Link Studios and and Link Ventures?

• 1:08:14Speaker 03

  Absolutely going to create an AI Dave Blunden, and I'm shocked that there isn't already a Peter Diamandis.

• 1:08:20Speaker 00

  Well, there is, there is one, it's just inside the abundance ecosystem. I mean, anybody, it was funny, I went to, went up to Calgary and met with one of my dear friends and abundance member, and on his wall, I kid you not, he had a giant screen of my AI avatar that he has all of his tech employees talk to, to sort of get their moonshots, and it was, it blew my mind.

• 1:08:43Speaker 05

  You've got your own big brother, Peter?

• 1:08:45Speaker 00

  I was like, he goes, I want to introduce you to someone, Peter, and he spins them up. And I, you know, it's interesting to have a conversation with your AI self. It is very compelling. I mean, I have enough books and tweets and and and Substack posts out there that it does a damn good job. We should effectively, you know, moonshots.com is our our platform we're building out. I think we should have AI avatars of all of us there, where people can go and do AMAs.

• 1:09:11Speaker 05

  Now, I think in some cases, Peter, I think that might be redundant.

• 1:09:16Speaker 00

  Ah, well, hey, in other words, you're already an AI, but we can have an AI of the Alex AI.

• 1:09:22Speaker 01

  Sure. It would be so much better than the real person, because we'll have access to everything we've ever said, all our memories, all our thinking, the context will be much broader. Go for it.

• 1:09:30Speaker 03

  This whole area, this whole area is about a year behind where it should be, largely because, you know, Noam Shazer was doing character AI, and and we had Steve Brown, Peter, that was two years ago now, we had Steve Brown make the debate between AI Peter and.

• 1:09:47Speaker 00

  AI Dumar.

• 1:09:48Speaker 03

  Yeah. Yeah, and and so it's been, it's been possible for a while now, but all the key talent working on it got sucked back into the big foundation labs. And, you know, there's so many big, big, big, you know, core technological breakthroughs going on, that the people that were working on this just got absorbed back into those things and not into the the avatar. But my my mom would always tell me when I was a kid that John F. Kennedy beat Richard Nixon in the election because he looked good on TV and TV was the new medium, and the prior medium was radio, and Nixon was still using radio voice when TV had taken over. So then, you know, elections go by and suddenly it's the Internet, it's it's social media, now it's YouTube.

• 1:10:28Speaker 03

  But this is another step function change in the way that you reach out to people, and it's underutilized, but it should be easily dominant two years from now in the next election. And so I'd be shocked if, because the technology is already there, and people are visualizing the medium right now as, oh, let me make an AI version of myself, I'm Alex Wisner Gross, here's my AI version, it's just like the real thing. That completely misses the point. The the AI version of it can in real time access any information and make it visual, graphs, charts, you know, it can morph its face, it can, it can teleport through space to make a point and point to atoms, it can shrink and expand. It has all these capabilities that the real human version doesn't have, and that's why

• 1:11:13Speaker 03

  it's going to be so compelling. It's the differences that make this new medium so exciting, not the, not the exact clone. And so, once people realize that, there's no going back, it's going to be huge.

• 1:11:23Speaker 01

  I think, Dave, that's such a great point that you make, it's the complementarity that is very powerful.

• 1:11:28Speaker 00

  Let me, let me close out on one thing here. If if to our audience here, if you've not sat down, if if you're lucky enough to have your mom and dad still alive, or your grandparents still alive, and you haven't sat down and interviewed them in video for hours at a time, please do that, right? You're gonna, you're gonna wish you had. So, I've done that with my mom, I miss doing that with my dad, and it's the ability for your kids and your grandkids and your great-grandkids to really have a great AI representation of your of your parentage and your your lineage. I think that's going to be super important. Ramin, I want to, I want to pivot to a discussion of Liquid AI and the the small language models, what they are, what they mean.

• 1:12:14Speaker 00

  Super excited, you know, just for full disclosure, you know, Liquid AI is a company in which, Dave, you played a important pivotal role as an early investor. Dave, want to give that backstory here a little bit?

• 1:12:29Speaker 03

  Actually, I got a call from Daniela Ruth over at CCL saying, the best students I've ever had.

• 1:12:34Speaker 00

  Who is Daniela?

• 1:12:35Speaker 03

  Daniela is, you know, one of the three, I guess, big shot women in AI. She runs CCL at MIT, the biggest AI lab in the world.

• 1:12:42Speaker 00

  Computer Science AI.

• 1:12:43Speaker 03

  AI lab. You know, it's, I don't know if you remember back in the day, there was the AI lab and then LCS lab for computer science, were the two biggest, you know, comp sci labs at MIT, they merged them together and made one mega lab, put it in the new State of Building, which is that crumpled looking, looking beautiful structure, you know, right on the edge of MIT's campus, and then Daniela is running that entire thing. So, I think it's like 1,500 researchers in the building, biggest AI lab in the world. And and so she has access to incredible talent, but she called and said, hey, best students I've ever had, had this incredible breakthrough, and then she completely lost me, she said, it's based on the nervous system of the worm, the C elegans 300 neuron worm. Like, what are you talking about?

• 1:13:27Speaker 03

  But it turns out that if you, you know, I I actually don't know of any successful foundation lab that has really rethought from the ground up the transformer and thrown it out, basically, and started over, which which, you know, humanity desperately needs, because everybody knows the transformer architecture and the whole attention mechanism is bloated, and if you really go back to founding principles and think again, you might be able to build something. Dramatically, like massively better. And so the team went from idea in a lab to billion-dollar valuation faster than any company out of MIT in history.

• 1:14:04Speaker 00

  And luckily, we were an investor in that company.

• 1:14:06Speaker 03

  Luckily, we were, yeah. I'm very, very thankful, actually. It was very competitive getting any money in at all. So, Ramin, we owe you a huge debt of gratitude for being invited to the party. But yeah, it's one of about 200 unicorns out of MIT all time, but the only foundation model company that I know of that reached unicorn status coming out of MIT. So it's a really unique and incredible achievement and in record time, too.

• 1:14:31Speaker 00

  So, Ramin, take us from there. You're doing your PhD under Daniela Rus at the Computer Science AI Lab, CSAL, and you're studying a 302-neuron worm, C. elegans. And so take us from there forward to what you're doing now. What is Liquid AI?

• 1:14:50Speaker 02

  Absolutely, absolutely. Like, before I start, like, I want to thank you guys, like, for the support throughout, like, these three and a half years of Liquid AI. You have been, like, great support, giving us, like, the kind of distribution that a company needs, you know, like, and at our scale, like, starting off of the East Coast. Thank you so much for doing that, both of you. And, yeah, so 2015, I was in Vienna. I started my PhD with a professor in Vienna, Professor Radu Rusu. There, he had the idea of, like, we don't understand a lot about human intelligence, let's start on a smaller animal. And then from first principles, like, if you understand how the neurons exchange information

• 1:15:30Speaker 02

  in the brain of the worm, the worm has 302 neurons in its nervous system, it is, its body is transparent, so you can actually see the body actually lighting up, like, so it is one of the best model organisms in the world. It won so far, like, four Nobel Prizes for humanity, like, you know, because it has 78% similarity, genome similarity to human genome, you know. So, and the way nervous systems compute in the brain of a little worm, which is 2mm, is basically analog, very similar to how artificial neural networks are actually computing. They are also like analog switches, like, they have like graded potential, they're not spiking. So in biological neural networks, usually in the brains, you see neurons spike.

• 1:16:14Speaker 02

  And when you have a spike, that's, there's an analog to digital kind of transfer of things are happening, and that's a natural development of nervous systems for, in the human beings and bigger animals, for propagation, for efficient propagation of information. In the brain of the worm, neurons behave very similar to how artificial neural networks react, but then the mechanisms are very interesting. So we wanted to add more complexity into the neural, like, every individual, single blocks of nervous systems and see, can we pack more information into, inside the smaller kind of units of compute, you know? And that's what we have done. So Daniela Rus, two years into basically discovery of these things that I was doing

• 1:16:55Speaker 02

  with my co-founder Matthias Lechner, Matthias was a master student in Vienna, Vienna University of Technology, and I was a PhD student. And then when Daniela heard from Radu that, you know, like, this project is going on, Daniela was like, oh my God, this is crazy, we should apply this in autonomy and robotics and all that sort of things, because you're showing, like, a handful of neurons can drive and control autonomous systems, you know, and can we scale this to vehicles, can we scale it to drones, to jets, to like predictive kind of places. So Daniela came in and said, would you guys consider coming to MIT? And we've been there since 2017, in the middle of my PhD, I actually joined CSAL.

• 1:17:34Speaker 02

  There, we continued working on this technology, which was, you know, like, from a base, it's a completely different things, and neuroscience inspired, the math behind, like, every single neuron in a liquid neural networks that became kind of my PhD thesis is very different than how attention works, you know? These are based on recurrent neural networks, these are based on continuous time processes, you know? Like, more and more kind of nature-inspired competition went into the design of, design of kind of AI systems. And then we applied these liquid neural networks as a completely new base, because we applied them to real-world scenarios like robotics, because you can pack a lot more information

• 1:18:18Speaker 02

  into smaller kind of processors. In the real world, in the physical world, you don't have the luxury of having abundant compute. Let's say a robot doesn't have, it doesn't have, like, a lot of GPUs or parallel data centers, like, attached to it. A robot has a CPU and a small, like, let's say GPU, and let's say an NPU, a custom ASIC. So you can actually take this type of, you know, intelligence that we design, that deliver, basically, intelligence at the level of, like, models that are 10 to 1,000 times larger than themselves. You can bring those things, like, directly running on CPUs, GPUs, and NPU's outside of data centers. So we thought that, okay, this format is going to open up an opportunity for us to bring in, like, alternative architecture.

• 1:19:00Speaker 02

  If we scale this technology to, let's say, into the regime of foundation models, which is kind of large language models and SLMs, as a whole, like, human understandable, like, making these liquid neural networks or architectures that we have also scalable, like the transformer architecture. And we built, like, a foundation model lab around the idea in 2020, 2023, beginning of 2023. I think at the very beginning, when we started, there was no foundation model lab apart from DeepMind and OpenAI, basically, like, when we started. And this notion of foundation model labs didn't exist, and everybody was betting on top of, you know, transformer architecture.

• 1:19:42Speaker 02

  And we came in and we said, okay, so why don't we explore this space of alternative architectures, starting from the priors that we have from nature, and then take a different approach, build a meta-AI system, again, basically, an automated AI system that allows us, an AI that designs AI, that explores the computational graphs of intelligence beyond transformer, and then figure out what should be that architectural design that brings the same level of intelligence than a frontier model into, let's say, on a CPU, that we can run, let's say, a physical system.

• 1:20:17Speaker 00

  All right. Take a second and walk us through, so these are small language models. Can you define an SLM and how it varies from an LLM?

• 1:20:25Speaker 02

  Definitely. So when you start developing kind of foundation models, you start, you run something called scaling loss, you know? Like, scaling loss is, like, basically, starting with a smaller models, and with these smaller models, you train them on a certain number of token budget, given amount of compute. You train these models to see how well they perform. Then you start systematically making the models larger and larger, so that, and we have seen scaling loss shows that the larger you make the models, the more token budget you spend, the more intelligence of a system you can get. And this has been, like, giving rise to large language models. Along the way of scaling, there are instantiation of the models, which are smaller, you know, like, on the scaling loss.

• 1:21:09Speaker 02

  But we have been doing as a lab, our mission has always been building efficient general purpose AI at every scale. So we started as a foundation model lab to really run the scaling laws on efficiency front, you know? And efficiency was a first-class citizen for us, you know, like, thinking about computational graphs of intelligence. Smaller models are models that are, you know, like, along the line of, like, scaling, they can solve, let's say, they don't have, like, the general capability to the level of the largest kind of language models, but they can be specialized to solve dedicated problems. They are general purpose, small language models are general purpose in the sense that they understand language, they can see and they can hear in a multimodal kind of format.

• 1:21:53Speaker 02

  But they don't, it doesn't mean that they can solve, let's say, a homework in physics, and at the same time, they can solve an enterprise problem. You usually specialize smaller language models.

• 1:22:03Speaker 00

  And what does small mean in this case?

• 1:22:06Speaker 02

  Small means, like, basically, I mean, now they come, like, now small would be, like, anything below 100 billion parameters, you know, like, that's kind of the regime that I would count. I mean, midsize, like, basically, is around that size, but I would consider, like, anything below 100 billion parameter is something that is not small and medium-sized kind of models, you know? There's no, there's no clear threshold of, like, let's say, what is the number of parameters. But for us, like, the notion of on-device AI is extremely important here to distinguish within this range of parameters. On-device AI is like models that you can actually deploy them on the, on an actual kind of device, physical device. This could be a phone.

• 1:22:46Speaker 00

  Let's make this concrete, because you've got a significant deal in Mercedes.

• 1:22:50Speaker 02

  Yes.

• 1:22:51Speaker 00

  And can you speak to that? And let's talk about, you know, these, these SLMs in terms of on-prem, basically, they're energy-efficient, you know, fast, offline. Let's dive into that, give people sort of a real understanding here.

• 1:23:08Speaker 02

  Absolutely. So, as I mentioned, you can specialize these foundation models. We work with a lot of enterprises that are building devices themselves, like automotive is a device, is a, is an environment where you have a lot of chips in there. And now... In a car, you don't have that much compute. So there is like one chip that is available for infotainment and in-car intelligence. You know, it's like that chip is very, very small. The Qualcomm chip, or let's say Samsung chip, like depending on like what company is providing the chip, like that chip is like very, very small. We are talking about two gigabytes to eight gigabytes of RAM. You know, like not more than that. So the model has to be very small and at the same time being able to perform. Because we want to bring this and enable a private space inside the car

• 1:23:52Speaker 02

  that powers the intelligence of the car. In the car, car is a safety critical environment. You don't want your car to be driven by an AI model that is sitting in the cloud. Why? Because connectivity is not always available, right? Then it is private because it's one of those spaces that people spend a lot of time in and you don't want those conversations to be like recorded. So we brought the intelligence, like, um, basically we brought, uh, uh, one of our multimodal foundation models that is only less than one gigabyte of in size. And it can go inside the car's chip, like very, very tiny chip. The chip could be as cheap as sixty dollars, you know, like that's what I'm saying. Like we are bringing that level of intelligence into that.

• 1:24:35Speaker 02

  That voice and it is going to power kind of the multimodal intelligence experience inside the car. We do that with all car manufacturers. We announced the Mercedes partnership as a first, uh, first kind of, uh, point of entry. Because automotive is like, it's very sensitive kind of a topic and they're, they're pretty slow. One of the things that Mercedes Benz actually enjoyed from this process was the speed of operations that we had for enterprises, you know? Like when we are bringing this type of technology in-house, this has been like one of those cornerstones of landing the deals, you know? Because we want to work, we are an enterprise company, we are a B two B company. We are bringing our full power to really like deploy the solutions and really have platforms that allows people to fine tune like their small models.

• 1:25:19Speaker 02

  And fine tuning small models is not that expensive. It's something that is extremely tangible. So they, we fine tune kind of the small models for the, the applications inside the car. We also have data flywheel kind of systems that allows the system always stay adaptable. Imagine some of the, some of the problems in enterprise AI has always been, let's download a GLM two, a five point two, like, you know, and, and let's say an open source model and put that in production. And then, so what happens after you put the system in production? What happens like when there's a drift from the use cases that is hitting this model? Inside, let's say a car and in the physical world, it becomes even more challenging. Because when you deploy an intelligence that is completely kind of disconnected from the cloud,

• 1:26:03Speaker 02

  how do you want to like maintain updates of the system? Because we have always thought about like intelligence in the format of liquid, you know? Like intelligence has to always stay adaptable. And, um, and that's, that's kind of a portion that we are also pushing on to really be able to collect the data and personalize models to the experience of every single user. With Mercedes, we are rolling this out first in North America as, as soon as basically this year. All the Mercedes Benz North America cars like from twenty twenty-two on, they're going to get an update, over the air update, because the size of the update is six hundred megabyte. So that's, that's like a, that's like a over here of like, it doesn't consume that much internet to really update your software. And that allows us to also further customization.

• 1:26:48Speaker 02

  Imagine if every update that you want to perform on the system is in the order of twenty megabytes? Because we are doing like some sort of lower adapters and let's say all sort of adapters that we can actually bring in inside the car. You would be able to have like a recursively kind of improving the experience of the user as well. So that's kind of the.

• 1:27:07Speaker 00

  Let's take it, make it more concrete for me. So what am I going to be? How am I using this model in my Mercedes next year? So right now my experience is using Grok in my Tesla, right? And it's over there. If I don't have connectivity, I don't have Grok. But, you know, what kind of, what kind of queries, what kind of capabilities does this all of a sudden enable in a Mercedes?

• 1:27:28Speaker 02

  It has access, it, it's, it's sitting below the, the, the operating system. So that means like it is basically, it is like basically have access to all the functions inside the car. You know, so there are seven hundred functions inside the car, seven hundred to like, I don't know, one thousand two hundred, depending on what, what you count as a function. You can, you can talk to your car, you can control like all the panels of your car. You can ask for, let's say manuals of the car, you know, like when, when you're like, let's say stuck somewhere, you know, like something pops up, you know, like you would be able to talk to the car. There are memory features that we are adding to the car. Like you basically can have conversations with that, with that system. Once the, like, one of the beauties of this system is that like it has full access to the,

• 1:28:10Speaker 02

  to all the functionalities of the car, plus all the apps. Because there are like function calls, there are one function calls away. You know, so if you want to control any other thing from this, from this intelligence unit inside the car, you would be controlling everything, all the ecosystem that is sitting on top of the,

• 1:28:28Speaker 02

  sitting on top of the operating system of the car.

• 1:28:31Speaker 00

  So basically, I mean, if I get you right, there, the advantage of the S L M's are, first of all, you know, the size of the model, I assume energy consumption, they're efficient and they can run on, on prem. Do, I mean, how do you avoid or reduce sort of overgeneralization of these models compared to L L M's?

• 1:28:53Speaker 02

  What do you mean overgeneralization?

• 1:28:55Speaker 00

  In other words, are the, do you have enough capabilities internal to them so that they are actually able to accurately answer the questions you're asking?

• 1:29:05Speaker 02

  Great question. So if you have, like, you know, I, I told you about the framework of foundation model development, which is depths of customization. We try to actually stay adaptable and have access to the tools across these customization stacks. Sometimes prompt engineering is enough. Sometimes you've got to fine tune the model. Sometimes you have to go and pre-train a model again, you know, for the core capabilities or specialization of intelligence. Now, we make systems that are, you know, our platforms are getting into the place where they are automatically identifying what depths of customization is needed for a certain solution. And the platform basically like it's, it's one of the products of the company that we sell to enterprises to allow them to fine tune kind of models, like, I, I don't want to call it fine tune,

• 1:29:47Speaker 02

  customize a model at a level that is needed for that, uh, for that system to actually operate, right? So for Mercedes Benz, we have, uh, let's say, like the framework that we have at, at in-house, we call it model plus X, you know? Model plus a platform that allows you to perform customization. It's not just the models that we are selling to enterprises, the static weights of a model. We sell them something that they can actually like retune and fine tune the system. Detecting how, how much, uh, generality like the base models have, it's something that, you know, like libraries of liquid models are coming out for many different applications. We have models that we are working with, for example, in silicone medicine, like, you know, Alex.

• 1:30:29Speaker 00

  I introduced you, I guess.

• 1:30:30Speaker 02

  And you introduced us, Peter, like, I remember. And, um, and through that kind of interaction, like it is getting big, you know? Because they discovered that liquid foundation models are actually pretty good, getting customized for a certain, obviously they're, they're basically RL-able, like really, really well RL-able. So, and, and, and that's something that they, they, they figured out that it comes handy for them. So now we have a state of the art biotech foundation models, like longevity foundation models. Like these are the kind of things that we're building in bio. And imagine like as a horizontal company that is building foundation models, we went to like fine tuning and that became like something that we have, we have managed to do. And then in terms of, um, you know, like some of the, uh, some of the other engagements,

• 1:31:11Speaker 02

  like we recently, with Shopify, we entered like, uh, one, uh, one billion kind of requests address inside the Shopify kind of framework. And there, like what we've done, we, uh, we deploy our liquid foundation models in production. They have been in production for the last six months and they are really serving clients, you know? And, and Shopify is like a huge, uh, uh, base, like we are touching hundred million kind of, hundreds of millions of kind of users, ten billion products and many different kind of, uh, places to, to integrate. We are working with Mercedes Benz, as I mentioned, like on the car kind of side of things. We are working with AMD and other cheap manufacturers to really bring AI, uh, uh, let's say, um, local AI experiences on PCs as well. So that's like another area that we enter.

• 1:31:56Speaker 02

  The focus of our company is to really, uh, make sure that we can bring, uh, basically intelligence outside of data centers. That's like something that we are focused on and I think our efficiency is actually allowing us to get.

• 1:32:09Speaker 05

  Preliminary matter, I have no financial interest in Liquid. Sorry, Ramin, have to ask the, the most obvious question. I have so many questions for you, which is the company Liquid was founded, as I understand it, and I, I remember reading the original, I think it was in Science or Nature paper on liquid neural networks. The premise is basically a neuromorphic premise that, that you could gain useful AI insights from looking at nematodes, a few hundred neurons, sort of the ultimate small neural network. But my perception, I'm hoping that you can either help me amend or revise my perception is that although Liquid started with a neuromorphic premise, if you will, like a post transformer, very recurrent oriented architectural premise or prior,

• 1:32:54Speaker 05

  that over time, again, just based on my perception of public messaging, Liquid looks more and more like either transformer or transformer plus MOE or transformer plus hyena plus MOE plus dot, dot, dot. It looks more and more like basically a conventional off the shelf architecture. It may be a good business selling sort of customized transformer derivatives to Mercedes at all. If so, great from the business side, but from the technical side, does Liquid still have anything that looks remotely like a trans, a post transformer architecture, either in production or under development? And can you speak to what if anything is post transformer or non-transformer oriented about the architecture that you currently use?

• 1:33:36Speaker 02

  Great, great question. So let me tell you like the space of kind of architecture. So Liquid Neural Networks in the original form, they are one of the most expressive formats of computes that you can actually create arguably, like in terms of our architecture. They have nested nonlinearities that are like, you cannot really like take them out. They are like completely physics inspired. They are like having like the neural O D Es and basically like irregularly sampled data can be handled by them. So they become like one of the very, very general class of architectures as a whole. Underneath these things, like when you want to scale this type of technology, these recurrences, like you know, like these nested kind of loops that they have.

• 1:34:18Speaker 02

  If you want to scale these systems, a lot of people have attempted, including ourselves, to linearize the dynamics so that you can actually like scale them. Space, you know, state space models are kind of basically like Mambas and those kind of variants falling into the same category of continuous time neural networks, but dumbed down into a linear kind of dynamical systems because you want to scale them. They are underneath this class of continuous time models that we have. Then there is like, there are variants of linear, linear attention, gated linear attentions that are coming out. They are also like gating mechanism is something like there's a special gating, input dependent gating mechanism that actually we got inspired by the, by how neurons actually exchanging information with each other.

• 1:35:01Speaker 02

  That gating mechanism is also something that is adding a lot more expressivity, like it is also descendant of the original formation of how neurons exchange information with each other. That gating mechanism still exists today in, in many different architecture and including ours. But the most important thing that I want to mention that you should know about the technology transformation of our company is that we really didn't want to bias ourselves towards one single architecture. One of the things that we did day one at Liquid AI, we designed a search algorithm to, let's say, like, you know, let the algorithm, instead of human biasing kind of the algorithm, let the algorithm run the scaling laws on, let's say, hundred different variations of operations that potentially can give you a general purpose computer.

• 1:35:47Speaker 02

  So we built a meta AI system. The paper around this is actually published like two and a half years ago. We published a paper about, about the topic. It's called STAR: Automated Design of Tailored Architectures. So read about STAR and STAR is a framework that brings all the dynamical systems with any format, including kind of variations of attention into one format for us to be able to search through. Okay. So to see like for four criteria, what is the most optimal neural architecture, let's say of choice for, let's say, a certain deployment? Number one criteria is memory, like how much memory are you consuming on a given

• 1:36:28Speaker 02

  processor? Number two was the efficiency of computation, how fast you can operate? Number three is latency of operations. And number four, do not lose accuracy on the performance. There are pure transformer models and then there are hybrid models that you can actually build. Hybrid models have like an essential component, like they have a little bit of transformers in them, but they're, but the, but the rest of the kind of dynamical system and most of the dynamical system for the purpose of these four objective functions that I mentioned would be, you would change that. And you can actually automate this whole framework to design foundation models. In-house, the, the technology stack of Liquid Foundation, like Liquid Foundation Models is called Automated Foundation Model Design kind of algorithms. We call it AFMD.

• 1:37:13Speaker 02

  This automated framework is the one that explores architectures for, for a given kind of hardware. And guess what came out of like the first generation of the architectures that we started optimizing? It came double gated convolution kind of mechanisms as 80% of the network being this. So when we run without a human bias, the gating mechanism that we had exactly in the Liquid Neural Networks' original paper, it actually shows up with this very, very similar kind of format in the final architecture that comes out of the search space.

• 1:37:46Speaker 00

  Everybody, welcome to the health section of Moonshots brought to you by Fountain Life. You know, we talk about AI on this Moonshot podcast all the time. One of the most important things AI is going to be able to do for you, besides educating your kids and helping you with your taxes, is making sure that you're living a healthy lifestyle, that you get a chance to get to 100 plus. I'm here today with Dr. Dawn Musalem, the Chief Medical Officer of Fountain Life and a part of my medical team. Dawn, a pleasure.

• 1:38:13Speaker 07

  Great to be here.

• 1:38:13Speaker 00

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

• 1:38:29Speaker 07

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

• 1:38:53Speaker 00

  Wow.

• 1:38:54Speaker 07

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

• 1:39:16Speaker 00

  And one of the things I love about Fountain is we're searching the world for the best therapeutics, the best approaches, and making sure we bring it to our members. So if having healthy brain function till 100, 120 is important to you, check out Fountain Life, go to fountainlife.com/peter. Make sure you become the CEO of your own health. All right, now back to the episode. All right, our next story comes from Palmer Lucky, the founder of Oculus and now the chairman of the defense giant Anduril. It's funny to call Anduril a defense giant, but it is. He's claiming that the modern patent system has become a national security liability. In his words, "The entire patent office could be downloaded every morning, ripped off, and used to fight a war against you."

• 1:39:58Speaker 00

  The core problem is baked into what patents actually do. Patents are a requirement, if you want to get a patent, you have to teach a, a person skilled in the art how to actually, you know, create and use your device. So this disclosure of your invention and the exact words in patent law is in such full, clear and concise and exact terms as to enable any person skilled in the art to make and use the same. So if you do that, you're effectively teaching the world how to use it and you're exchanging that, that sharing of your invention for roughly 20 years of exclusivity.

• 1:40:41Speaker 00

  Palmer argues that when a strategic adversary can simply harvest every file, ignore the legal protections and weaponize the disclosed knowledge, you've handed them a free instruction manual to your best ideas. So just for some numbers, the US Patent Office receives about 600,000 applications annually. It grants a little over half of those, 323,000. That's 2025 data. Interestingly enough, patents granted have increased 40% in the last five years. My guess is that is secondary to AI. Palmer's proposed fix isn't to abolish patents. It's to massively scale up a national security

• 1:41:23Speaker 00

  patent process, which goes back to the Secrecy Act of 1951. So this obscure mechanism lets inventors obtain classified patents in which you keep your exclusive rights, but you don't disclose it to anyone and neither can the government. So there are roughly 6,000 of these secure secrecy orders active in the US. Lucky wants that this edge case is turned into default mechanism. So here's the question, right? If we genuinely trade this openness, which has been sort of the basis for American entrepreneurial exceptionalism, for a secret system, are we trading safety of having our patents ripped off against really the innovative

• 1:42:08Speaker 00

  ecosystem that we've had? Let's watch a short video from, from Palmer and then we'll talk about it.

• 1:42:15Speaker 08

  Stop patenting everything. Patents are Chinese instruction manuals. The founding fathers never predicted a world where you would have a globalized economy where the entire patent office could be downloaded every single morning and then ripped off and then used to fight a war against you. We need to really fundamentally revisit the patent system. I think we need to massively expand the national security patent process. You can, you can obtain a classified patent. You can get a patent on something that you are not allowed to disclose to anyone, but you still maintain the exclusivity on those rights. We need to massively expand that program.

• 1:42:48Speaker 00

  So, you know, I've applied for and gotten a dozen patents. I know Alex, you have a, even a much larger number of them. So I'm curious, guys, how do you come out on this? Alex, do you want to kick it off?

• 1:43:02Speaker 05

  I think this is the episode of people, tech CEOs floating terrible ideas. I think this is a terrible idea. I think the, I would argue, the Invention Secrecy Act of 1951, which is, I think, what Palmer is gesturing at, has been, probably on balance, quite detrimental, not just to democracy, that if patents, so maybe a bit of context, the way the Invention Secrecy Act works is, it's not that you can just sort of file the patent in secret and not disclose, it's that, basically, it can only be practiced. The invention that, that is basically confiscated or eminent domained by the military can only be practiced for

• 1:43:47Speaker 05

  military reasons. It's not contra any construal otherwise, that the Invention Secrecy Act somehow offers legal cover for an individual to secretly disclose how their invention works under some confidentiality and then go practice it. In general, they can't. It's that the military exclusively can practice it, and then the inventor gets royalties from that practice. That may be good for Andrel's defense business, but I think, in general, terrible idea. Greater concern that I have is, these would be basically secret monopolies. I think it's bad enough that we have Invention Secrecy Act classification

• 1:44:26Speaker 05

  of inventions, query whether entire swaths of technology that could be completely transformative economically to the entire world from an energy perspective for other domains have somehow, without general knowledge, been swept up by the Invention Secrecy Act and basically confiscated by the Department of War for purely military reasons. That's, is very concerning to me, the idea of expanding it overall. I would argue, if anything, the Invention Secrecy Act regime should probably go away in total.

• 1:44:59Speaker 00

  We can have this debate. So Palmer's going to be joining us at the Moonshots Gathering on September 25 in LA. Everybody can go to moonshots.com. We have an amazing day with the Moonshot mates there. We'll be having these conversations with Palmer. Saleem, I mean, what makes America great is our open innovation policy, people building on top of other people's creations. What are your thoughts here?

• 1:45:23Speaker 01

  Look, we've seen this problem get bigger and bigger over the last 20 to 30 years, where the disclosure, especially in an age of AI where people can just run around it or replicate or learn from it, it's a huge challenge. The real mode is learning loops. That's going to be the real defense abilities. What are your feedback loops and can you learn in a proprietary way and then create trade secrets around that and action that in the marketplace? Continuous innovation is going to be the winning defense. It's not going to be ownership. The only people that win in this whole, in this particular model are the lawyers.

• 1:46:01Speaker 00

  Well said. Dave, any thoughts here?

• 1:46:04Speaker 03

  Yeah, I think, you know, if there's a flashpoint for a World War III, this is probably one of the most likely

• 1:46:11Speaker 00

  Seriously?

• 1:46:12Speaker 03

  Where, yeah, well, you know, look, Alex is right. We're going to discover new physics, new medicines at an incredible accelerating rate. And, you know, places like Europe respect intellectual property rights, and that creates a kind of a coherent economy where you can trade these things. China completely ignores intellectual property rights and just takes it and runs with it. So I think the likely outcome of that is the US will trade embargo anybody who doesn't respect intellectual property rights. And then you have to choose, are you part of the, you know, the free world or are you part of the alternate world? But I think that's the more likely outcome, and that's going to happen soon, like in the next couple of years, because the rate of innovation is going to go through the roof.

• 1:46:53Speaker 03

  But there's no science fiction future book I've ever read where there isn't massive amounts of intellectual property being created by AI at an incredible accelerating rate, and there's some vehicle by which innovators can profit from that. And if you don't have that, then you don't have the future. You know, a huge fraction of brilliant thinkers coming out of, you know, Cambridge and MIT and Harvard don't work on foundational technologies because there's no money in it. And that's got to change fundamentally, and protecting intellectual property rights is a kiki way to reverse that tide and get people working on really important things.

• 1:47:29Speaker 05

  Yeah. I think to Dave's point also, Palmer fundamentally appears to misunderstand the nature of patents. The whole point of a patent is that you disclose how it works in return for a state-granted temporary monopoly on it. And say, you know, sort of bellyaching that the Chinese are running away with the disclosure is really a quibble with enforcement of patent. It's not, you don't want to throw necessarily the baby out with the bathwater and say, we want to give away the patent trade of disclosure in return for a temporary monopoly. Really, what he should be asking is better enforcement of US patents in China.

• 1:48:06Speaker 00

  Agreed. All right, I'm going to move us into the world of healthcare abundance. So two stories this week are demonstrating an incredible impact of AI on healthcare abundance, demonetizing and democratizing diagnostics for billions of people. The first story is the performance of GPT-5.6 Sol, which was released a couple of weeks ago, on Health Bench Professional, which is OpenAI's hardest medical benchmark. So ChatGPT or GPT-5.6 Sol set a brang you all-time benchmark high. And then, the second part coming out here is in a blind test across roughly 20,000 individual physician judgments. In other words,

• 1:48:46Speaker 00

  you know, diagnosing for accuracy, safety, completeness, GPT-5.6 answers were compared to specialty-matched physicians, in other words, pulmonologists, pediatricians, whatever, who were given unlimited full access to the web and unlimited time to answer, and the doctors still lost. So we've got ChatGPT. We've known this for some time, that these AI diagnostic models are better than the best physicians, given all the tools that humans can use. The second part of this story comes from Meta. So OpenAI's own Health Bench Professional benchmark, which is 525 real clinical tasks, Meta's Muse Spark 1.1,

• 1:49:28Speaker 00

  again, released last week, beat ChatGPT's, or GPT-5.6 Sol on, across the marks, and it was seven times cheaper. But even better, I mean, important to note here, is that Muse Spark is free inside of all of Meta's products, you know, WhatsApp and Facebook. And Meta today serves 3.56 billion daily active users using their products. So here we've got a situation where the top medical AI capabilities are now free to over 3.5 billion people on the planet. And that's just extraordinary. I mean, this is the abundance thesis at large.

• 1:50:11Speaker 00

  And again, as people talk about the concerns of AI and so forth, please realize this. People who've never had access to the best diagnosticians now have them. Saleem?

• 1:50:21Speaker 01

  There is a, there's a model, an AI doctor in China that's being used in rural environments by 100 million people already, right? Basically, diagnosis has had massive cost collapse. The healthcare domain is particularly interesting because it's where abundance becomes actually morally urgent, right? If you can deliver way better first inline answers at like near zero cost, it's how quickly can you safely get it out there? That's the only question. And so it's absolutely, and right, that's recognize that in almost every country in the world, there's radical doctor shortage. So this is really, really critical.

• 1:51:02Speaker 05

  You see, like, this is such a July 2026 story where, think about it, Instagram now gives better medical advice than a human doctor. It's pretty wild. Cost of intelligence not just going too cheap to meter, cost of medical intelligence becoming too cheap to meter.

• 1:51:21Speaker 00

  Free, basically free. I mean, that's

• 1:51:23Speaker 05

  Well, the ultimate too cheap to meter is asymptotically free, right? But I would say probably, in all honesty, I suspect a little bit of mild benchmarking by Meta on this Meta Spark 1.1 is on, if you believe the AAI cost frontier analysis, it is on the optimal cost frontier, but it's not at the top. So if it's beating, say, Fable 5, which barely allows you to do anything biological, or GPT-5.6, which does allow you to do it, that does, to me, suggest, in all honesty, a little bit of mild benchmarking. But still, it's a great day when Instagram gives better medical advice than human doctors.

• 1:52:03Speaker 00

  I think that's our, that's our takeaway quote from today's pod. I'm going to move us to one more longevity story that I love. This is breaking news from yesterday, and it really got me excited here. I know you, Alex and I, were talking about this. So for decades, one of the fundamental problems of aging is the slow accumulation of what are called advanced glycation end products. I love the acronym. It's called AGES, A-G-E-S. And these are sugar molecules that cross-link and damage your proteins in your body over the course of time. So this chemical reaction is called glycation, and it happens slowly in our bodies as we age. It stiffens your arteries, it clouds your linges with cataracts,

• 1:52:48Speaker 00

  it damages kidneys, it wrinkles skins. And this idea is that it's always been irreversible until this week. And yesterday, in Nature Communications, a team from a new startup called Revel Pharmaceuticals demonstrated an engineered enzyme called CML ACE that acts like a molecular lawnmower. I love their description, a molecular lawnmower. It oxidizes away the glycation scars and restores the original healthy protein underneath. And amazingly, this isn't happening just in a test tube. They showed it worked in human tissue samples from elderly donors, reversing damage that accumulated over the lifetime. It's still early, but the significance of this cannot be overstated. A category in

• 1:53:32Speaker 00

  aging that we've always filed as permanent just became reversible. And again, we talk about longevity escape velocity, we talk about, you know, our ability to understand the 5 billion chemical reactions per second per cell in your 40 trillion cells. And when we talk about reaching LEV, you know, longevity escape velocity by 2033, it's tech like this. So congrats to Revel in doing this.

• 1:54:01Speaker 05

  And not just Revel. I mean, a couple of interesting notes here. It was Revel and Calico, the California life company that was one of the alphabet other bets that's been, I would say, like a lot quieter than, say, Waymo. They're still doing work. That's very encouraging to me that Calico is apparently deeply involved in this and has a heartbeat. A couple of other points, the broader process here, a class of chemical reactions are called Maillard reactions. It's also the reason why when you bake bread, the outer crust is usually brown.

• 1:54:37Speaker 00

  Or seared steaks.

• 1:54:37Speaker 05

  Chemical, or yeah, or it's why, as a vegetarian speaking, why everything purportedly tastes like chicken. It's the same class of reactions, but the sugar is reacting with the carbonyl functional group or carbonyl groups within sugars reacting with the amines in proteins to create broad class of molecules that look optically brown. So the same thing is going on in the human body. To me, this is very exciting because it's not quite unscrambling eggs, but it's halfway there. It feels almost, again, strictly speaking, it's not like reversal of the thermodynamic arrow of time, but it's the next best thing. If we can remove all

• 1:55:21Speaker 05

  of these unwanted sugar plus protein byproducts that are associated with inflammation and other correlates of aging with directed evolution of a protein that came from bacteria. Like, what else is there out there in the biosphere for us to mine in addition to all the obvious glib ones? Great potential for longevity, escape velocity. What other bacterial innovations can we use to turn back aging?

• 1:55:50Speaker 00

  It's human engineering. We're taking control. It's going from evolution by natural selection to evolution by human direction. And I love that. I'll be happy when I have Ramin's hair. That's all right. Well, there are lots of companies working on that, Saleem. So, gentlemen, grateful for our time today. I'm excited for Starship 13 launch later today. Wish Elon and the group there lots of luck. Ramin, congrats on the success of Liquid AI and excited to have you on the pod with us. Dave, great move investing in Ramin. Thank you. On behalf of all of our shareholders. Thank you. Yeah. Gentlemen, have an amazing week. I'm sure we'll be having an emergency pod

• 1:56:35Speaker 00

  very soon because the speed of the singularity waits for nobody.