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

23:15Peter Diamandis:For the next newsletter, that was like two newsletters ago. Okay, fine. That's out already. But thank you. Yeah, I can just imagine where a U.S. Frontier Lab CEO calls DeepSea and say, would you please accelerate your next model release? We want to get ours out as well. 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 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.

23:57Peter Diamandis:That could happen in AI as well as disaster. 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 still working for US companies. 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. Yeah, it's a new company. Look, this is a huge problem, right?

24:30Because we've seen throughout history that open ecosystems always win. And this is not open versus closes, which open ecosystem wins. And the U.S.'s historical strength has been open ecosystems with permissionless innovation. Abandoning that would be the weirdest strategically bizarre thing we've ever seen.

24:51Peter Diamandis:Yeah. The other, I mean, there's even a meta worry I have, which is how do we even define capabilities? 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 case scenario, which is we freeze in or otherwise lock in the benchmarks for how we measure capabilities. And that would be, I think, maybe even worse than just locking in the incumbents as labs, Because 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.

25:33Peter Diamandis:What's a frontier model versus not? I worry that could so distort model capabilities, like they'll overexercise certain capabilities deliberately and perversely under incentivize or under benchmarks others, that it'll just totally distort, maybe topiarize the future landscape of super intelligent capabilities. All right. Well, again, this is a story that we'll be following. On this news of OpeWayt models, so in the past - There's our topiary right there. In the past, we've been discussing how open-weight models in the U.S. have been lagging. In China, we have NVIDIA's NemoTron 3. We've got Google Gemma 4.

26:12But that changed last night with some breaking news. Mira Maradi, 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 openweight foundation AI model that can be downloaded by anyone fine-tuned and run on-prem on your own hardware. 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 the model going fast and cheap.

26:54It was trained on 45 trillion tokens of text, image, audio and video, and very importantly, reasons 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 Kuen, that have dominated the open weight leaderboards. Now, interestingly enough, Maradi, her bet is contrarian here. She's not claiming it's the best model on Earth. Her own blog say so. She's betting that AI companies can adapt her models for themselves, that customization over leaderboard dominance is what's going to win her the day.

27:33You've hit there, Peter, on the really big thing. What's that? She's pushing on the customization lever. Yeah. 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 is she's built exactly the thing hitting the market that exactly what everybody needs right now. 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 do hope this begins the race for powerful openweight models in the United States.

28:05Peter Diamandis:Well, 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 seems to be, at least according to the evals, that Thinky has released stronger than Nematron, which is great. So 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.

28:47Peter Diamandis:So it's not one of the strongest open weight models overall in the world. It's obviously weaker than the closed weight Western frontier models. But I think, point one, it's great to have better, stronger Western open weight models. Point two, 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.

29:24Peter Diamandis:And we see Anthropik about to IPO at a trillion dollars, and we see OpenAI planning to eventually IPO at a trillion dollars. And in China, which 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 incentivized to make money from the integrations between AI upstack on applications like robots and downstack 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, 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.

30:32Peter Diamandis:You get what you incentivize. Exactly. 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 Openweight. What happened to Meta? Now it's closed. No, they have a new model out and 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 there's a real possibility 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.

31:11Peter Diamandis: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 a well-worn path. So I wouldn't say this is necessarily a religion at Thinking Machines that they're going to stick with. The trend has been the opposite of that in the past. Ramin, what's your take on it? 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? Yeah, it's a matter of like the business of the company.

31:40You know, like Thinking Machine can do three more iterations of their pre-training or post-training kind of RL kind of environments and benchmarks like those numbers that you see on the benchmarks and release like a better model. But what their business is, their business is fine-tuning. This is kind of the place where customization has been something that the whole market around customization has been very empty. If you look at the first attempts, OpenAI released the OpenAI tuning, fine tuning, three years ago or something. It never took off. So they took a really good approach on designing the base for fine tuning larger instances of the models for enterprises.

32:19Because as you see, like the model layer is not anymore like, you know, like 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 I think their business strategy around what they're doing and this release is genius because they're deliberately releasing, they're leaving some room for fine tuning so that people can come in and using their business, their API business.

32:57Because that's even generating, I think in the order of one to two orders of magnitude more tokens as well, you know, on the customization side. So that would be like even printing money at a larger speed, like in the absolute best case, right? So I think that's the business entry.

33:15Peter Diamandis:To add to Ramin's point, I think the situation maybe 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 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. It was amazing. We used it all the time. It was incredibly cool for its time. 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.

33:54Peter Diamandis:The fine-tuning API has been shut off. So, 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's the paradigm? They may be right. They may be wrong. There's an alternative vision where RFT just dies. And 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. It's a really, really great point. You talked about the Alex Karp rant, right? Yes.

34:31The result of that was don't use a model that 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 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? And is the U.S. going to start to regulate against Chinese open-weight models, in which case a dominant U.S. open-weight model is going to have an advantage? And so is that the bet Mira is going after? We're going to probably see, my guess is Google, step up in this area as well very shortly, take Gemma 4 to the next level.

35:19And 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 U.S., hopefully we'll see that competition give birth to, you know, very strong opiate models here as well.

35:33Peter Diamandis: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 Quinn 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. So let's back up one second. Dave, explain what fine-tuning a model is for those who don't know. Well, 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.

36:10Peter Diamandis:You know, like what hours were 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 or here, drive this car for me because it's already in there.

36:47Peter Diamandis:But then when you get into biotech research or you get into aeronautical or the Mercedes, you know, like Ramina 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, OK, here it is in prompt form. But that's hugely inefficient. And you dump it into OpenAI and you dump it into Anthropics, you know, model, which now makes it accessible to everybody else as well. I mean, yeah, yeah. I mean, Sam, 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.

37:20Peter Diamandis:They're stealing your alpha. What it really means is they're looking at your most proprietary, your company payroll, your company secrets, your chemical research. It's all going right over the wire to these foundation labs. Is that what you want? And of course, 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 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.

37:53Peter Diamandis:We've hit a real tipping point. And Alex Karp, I think, is right about it, too. 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. And so this is aiming squarely at organizational use. This fits perfectly into the on-prem proprietary data model where you take your data, customize and fine tune, as you said, Dave, and that will be the future. A couple of historic notes, again, for those definitionally not tracking the full sorted history of fine tuning.

38:31Peter Diamandis: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 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.

39:14Peter Diamandis: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. Or only be an Accelerando flavor output. Well, no comment. 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 gray area between, you know, what's the distinction between them.

39:58Peter Diamandis: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 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 seems to work pretty well at the moment.

40:34Peter Diamandis: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 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. And we tend towards ASI. Let me bring up another key point here on this story, which is in the context, which is it's great to see a woman CEO in the AI frontier lab area.

41:17I 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 Daniela Roos, right? Ramin. Yeah, Fei-Fei too. And Fei-Fei Li. Yeah. But again, we're talking about what single digit percent of the AI industry is women. And we need more. So a call out to all the women out there, please jump into this industry. We need more balanced thinking. Yeah, for sure. I mean, I do think that's an important point to pull out here. This episode is brought to you by Blitzy, autonomous software development with infinite code context.

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42:55Ready to 5x your engineering velocity? Visit Blitzy.com to schedule a demo and start building with Blitzy today. 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.

43:48It's sort of the theoretical engine behind the hard takeoff scenario of the singularity. So this week, a startup called Weco AI with researcher Zheng Yao Zhang 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.

44:27So, Alex, what do you make about this? Is it the first? Is it significant?

44:32Peter Diamandis:Very significant. Highly unlikely that this is anywhere close to first. So a few bits of additional context. One, this is actually, Wico 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 co-scaling. So to the extent we talk about alignment, AI alignment on the pod, and I'm always banging the drum of defensive co-scaling as the ultimate alignment strategy. What does that mean?

45:12Peter Diamandis:So defensive of co-scaling 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.

45:50Peter Diamandis:Same idea with AI. The way we keep AI aligned with humanity, a key way is we make sure that we have enough good AIs policing any bad AIs in terms of raw capabilities that they defensively co-scale. So 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 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, and this is an emergent property, better results from the inner loop by preventing the inner loop from cheating and reward hacking.

46:47Peter Diamandis:And so in some sense, the outer loop is defensively co-scaling 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, all of those who would say, okay, we need to pause AI capabilities and throw all of our resources to AI alignment until something preposterous in my mind, like 2040. Stop the race to superintelligence. Stop it all. Focus the next 14 years on alignment research. It's going to backfire because every alignment capability, I would argue, is actually just new capability in sort of in disguise, in a trench coat.

47:32Peter Diamandis:Same idea here. We need stronger white hats to police the black hats. 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, Weco, 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 Society of Automotive Engineers has their, like, five levels of autonomy for autonomous vehicles.

48:11Peter Diamandis: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 &D. Level one, net positive, where the AIs beat human R &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. So they rate themselves as a level one here?

48:48Peter Diamandis:Yeah, they rate themselves as level one. but reading between the lines, they're like, this is like sparks of ignition, literally and figuratively. Okay, so maybe I can jump in and say a couple of words. I'm not as excited as Alex is on the topic, and I see this is an impressive engineering kind of work that has been done. Just to tell you a little bit about 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 some code patches like changing.

49:32Because that gives you the assumption that every single AI model that 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's 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 models, like changes in the system prompt, because I will give you like fundamental reasons why this is actually limiting.

50:12Because if you just run it, like how, I want to tell you how hard of a problem is recursive self-improvement. For us, recursive self-improvement means that you have an AI system or an army of AI systems that they can also retune themselves. They can adapt, very similar to how humans do it. If you think about it, the core competencies of these models that we have right now, they're fixed weight models, and the capabilities are within a certain kind of threshold. And the frameworks that they actually designed, 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.

50:53But I wouldn't go so much to say this is the first breakthrough in the entire AI industry or something. In fact, about three years ago, we published a paper ourselves. We talked about automatic design of model architectures. As Liquid AI, we didn't want to put a bet on a single architecture. We have basically designed self-improvement 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 retuning the weights of the system are getting automated.

51:36So we are talking about AIs or designing AIs. So that's what I would be like calling it like the holy grail where 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, competition intractable to actually performing this job. Yeah, exactly. Training an AI model, being able to customise an AI model and training an AI model on a meaningful number of tokens for adaptation, or let's say the core competence of the model changing, core architecture of the model changing, core learning algorithm itself changing, all of those matters adds more and more complexity on the situation.

52:18I can give you also one numerical example of this. There is a scaling loss called Chinchilla law. You know, like chinchilla is like the scaling laws of neural networks. And like it is unproven, like we have actually unproven, but still like 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 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?

52:53So 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 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's a lot of computational complexity goes into nested learning systems, metal learning systems. These are the kind of problems that the last four years of, at least at my company, we have been heavily focused on. And I know friends at OpenAI and Entropic has been focusing on this recursive self-improvement.

53:40And Entropic has been having a lead on all of these things because they thought about this before everybody else. That's what I can put out there. Dave?

53:49Peter Diamandis:Yeah, brilliantly said. And actually, just so the audience can get the analogy there, when a baby is born and then learns, you know, that happens over about a 20-year timescale. And after 20 years, you've got an adult that's capable. Recursive self-improvement is like evolution on top of that, where you're changing the DNA and creating a new species. Changing the neuronal structure of the brain along the way. Exactly. So that happens over, you know, about a 10 million year timescale. So you go from 10 years to 10 million years to go from learning to recursive self-improvement or recursive evolution.

54:22Peter Diamandis: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, 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.

54:52You know, 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 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? 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 hit? Is ASI going to be that point or is it post-AGI, whatever that means?

55:37Salim, I say that for you. I'll let Ramin go first. Answer that exists. I've got several comments. But Ramin, go ahead. When do you think we can see this happening? The thing is I can tell you like the early evidence of 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.

56:13And then what I would say is that like the cybersecurity kind of threads that we are seeing coming out of these type of pipelines of recursive self-improvement. They're real. I've always been pro open source and I want to open source technology all the time. We are doing it all the time. 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 see the value of open source going forward. But some of these concerns that Peter, you brought up, they're very real, like the cybersecurity aspect of things. That's why I feel like a degree of at least enterprises themselves having some degree of self-control.

56:59Before mass release of their models, there has to be always a certain degree of self-check. And I think Antropic took it very seriously. The reason behind it is because they're seeing the impact of recursive self-improvement. So I know this for a fact because I know what is happening. And we are seeing it at a smaller scale. You can do reward hacking, but you can also avoid reward hacking to a certain extreme and push a model to actually discover some stuff that are out of norm. And we see that on a small model, like at a certain capabilities emerging. And then I can only imagine what kind of capabilities could emerge from, let's say, larger and larger systems thrown more and more computing them.

57:48When do you think we, when do we have a pod?

57:52Peter Diamandis:Timelines, Ramin, timelines. Yes, when do you have a pod that said, yes, this is recursive self-improvement? Because while, you know, while the data released by WECO is interesting, it's their own self-reported data. It hasn't been confirmed by anybody else yet. And, you know, there is a, you know, debate about whether it really is or is not real recursive self-improvement. When do you think we actually, you know, give the trophy out to somebody? Is it a year, three years, five years? Yeah. I mean, I'm telling you that. So I would say like you're going to see like unbelievably kind of models like probably in the next two years or so.

58:28You know, like models that are like going above our understanding even like that. That's 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 the rate that we are seeing right now. And if there's no like, let's say another chip shortage or memory shortage like on compute 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.

59:04So 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. Exactly. 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.

59:50You know, that's something that I think when they released Fable 5, they shared like, and Tropic actually shared that this was one of the tests that they have been performing, you know. But 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 Favo 5, you can push it to actually get to some degree of kind of customization. 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, Can a language model pre-train the next generation of their own?

1:00:35That'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. that you're chomping. 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.

1:01:18It's accelerating experimentation, right? Because the system doesn't need, 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 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 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.

1:01:55So this is the first glimpse So the organizational singularity is happening at the research level, because AI is not just doing tasks in a workflow. It's redesigning the workflow that makes it better for doing future tasks, right? So this is proof now for the whole thesis we've had. We predicted this, but it's great to see it actually happen, 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 of the baby changing the DNA is fantastic. That's such a great visual around this. What the hell does it become over time?

1:02:32So really, really, I'm beyond excited about this. 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, 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 by an adversary, but a sanctioned official AI clone of a national leader. We've seen this before, Selene. We've talked about 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 a presidential decree became the first AI system in the world named at a cabinet-level role.

1:03:26So 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. Yeah, I met the former Prime Minister when I was there helping them open a university. and Anwar Ibrahim is a really, really good guy as a follow-on. There's a risk here. The risk is that the authenticity kind of collapses because people need, you know, you could launch a bunch of deep fakes with this and have a huge issue.

1:04:09Is this the actual leader? That kind of question can come up. But I love the general approach because if you can do it 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 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 about this. Do you remember the reason why Albania put their AI cabinet minister in place?

1:04:47Yeah, corruption. Corruption, exactly. It was about to fight corruption. Yeah. Now, Malaysia is a pretty decent place, but definitely you have that issue. But I think 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. I love the fact that we had this conversation with the president of Argentina going full out here. And it's interesting to see which countries are sort of experimenting on the edge.

1:05:16Peter Diamandis:Alex, do you want to weigh in? 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. AI Trump is coming, is that what you're saying? High personality leaders 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.

1:05:56Peter Diamandis: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 also don't think it's just going to be governments or government leaders that adopt this. I think it's likely that corporations, corporate CEOs will do this. We already see Zuck and others creating digital twins of themselves. We had Dara on the abundance stage last year. 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.

1:06:33Yes.

1:06:34Peter Diamandis: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 see like lots of religious institutions, organizations already creating basically living versions of their founding documents and making those interactive. But I think the biggest twist, and 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 some sort of like organized religion actually themselves become the leader. That's at some point the digital twin, to the extent it's interfacing much more with the populace, the proletariat, as it were, of an organization.

1:07:21Peter Diamandis:At some point, It's actually the digital twin of the leader running the company and not the actual behavioral origin that's running the company. And I think that's one way in which, Salim, to your exo point, this is, I think, potentially a pathway towards not just uploading individuals like natural persons or non-human animals, but uploading entire organizations into cyberspace, into the cloud, if we create a digital twins of the leaders, and those are the ones actually running the organization. It could lead to a true democracy. Dave, where do you come out on this? I mean, we saw just one quick point.

1:07:56We saw Sam Altman talk about in the future, if I believe enough in what we're building 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 Link Ventures?

1:08:14Peter Diamandis:Absolutely going to create an AI Dave Blunden. And I'm shocked that there isn't already a Peter Diamandis. Well, there is one. It's just inside the Abundance ecosystem. I mean, anybody – it was funny. I 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 blew my mind. You've got your own big brother, Peter? It was like he goes, I want to introduce you to someone, Peter. And he spins him up. And, you know, it's interesting to have a conversation with your AI self.

1:08:52It is very compelling. I mean, I have enough books and tweets and sub-stack posts out there that it does a damn good job. We should effectively, you know, moonshots.com is 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.

1:09:13Peter Diamandis:In some cases, Peter, I think that might be redundant. Ah, well, hey. In other words, you're already an AI, but we can have an AI of the Alex AI. 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. I was actually in trouble. Yeah. This whole area is about a year behind where it should be, largely because, you know, Noam Shazir was doing Character AI and we had Steve Brown, Peter, that was two years ago now, We had Steve Brown make the debate between AI Peter and Aristotle.

1:09:48Peter Diamandis:Yeah. Yeah. And so 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 there's so many big, big, big core technological breakthroughs going on that the people that were working on this just got absorbed back into those things and not into the avatar. But 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.

1:10:26Peter Diamandis:It's social media. Now it's YouTube. 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 AI version of it can in real time access any information and make it visual, graphs, charts. It can morph its face.

1:11:02Peter Diamandis: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 it's going to be so compelling. It's the differences that make this new medium so exciting, not the exact clone. And so once people realize that, there's no going back. It's going to be huge. I think, Dave, that's such a great point that you make. It's the complementarity that is very powerful. Let me close out on one thing here. To our audience here, if you've not sat down, 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?

1:11:47You're going to 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 parentage and your lineage. I think that's going to be super important. Ramin, I want to pivot to a discussion of Liquid AI and the small language models, what they are, what they mean. Super excited. Just for full disclosure, Liquid AI is a company in which, Dave, you played an important pivotal role as an early investor. Dave, want to give that backstory here a little bit?

1:12:29Peter Diamandis:Actually, I got a call from Daniela Ruth over at CSAIL saying the best students I've ever had. Who is Daniela? Daniela is one of the three, I guess, big shot women in AI. She runs CSAIL at MIT. Computer Science. It's the biggest AI lab in the world. Computer Science AI lab. I don't know if you remember back in the day, there was the AI lab and then LCS lab for computer science. We're 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 beautiful structure, you know, right on the edge of MIT's campus.

1:13:02Peter Diamandis:And then Daniela is running that entire thing. So I think it's like 1500 researchers in the building, biggest AI lab in the world. And so she has access to incredible talent. But she called and said, hey, best students I've ever had have 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? But it turns out that if you, you know, 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.

1:13:39Peter Diamandis: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. And luckily, we were an investor in that company. 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.

1:14:17Peter Diamandis:but yeah it's 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 so Ramin take it take us from there you're you're doing your PhD under Daniela Rus at the computer science AI lab CSAIL 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? Absolutely. Absolutely. Like before I start, like I want to thank you guys like for the support throughout like this three and a half years of Liquid AI.

1:14:58You have been like great support giving us like the kind of distribution that a company needs, you know, like 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 Grussu. 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 in the brain of the worm, the worm has 302 neurons in its nervous system. It is its body is transparent.

1:15:37So 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 two millimeter is basically analog. Very similar to how artificial neural networks are actually computing. They are also like analog switches. They have graded potential. They're not spiking. So in biological neural networks, usually in the brains, you see neurons spike.

1:16:14And when you have a spike, there's an analog to digital kind of transfer of things that are happening. And that's a natural development of nervous systems 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 every individual single blocks of nervous systems and see, can we pack more information inside the smaller units of compute? And that's what we have done. So Daniela Rousse, two years into basically discovery of these things that I was doing with my co-founder, Matthias Lechner.

1:16:58Matthias was a master student in Vienna, Vienna University of Technology, and I was a PhD student. And then when Daniela heard from Radu that 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. And can we scale this to vehicles? Can we scale it to drones, to jets, to like a predictive kind of process? So Daniela came in and said, would you guys consider coming to MIT? And we went there since 2017 in the middle of my PhD, actually joined C-cell.

1:17:34There, we continued working on this technology, which was, you know, like from a base, it's a completely different thing. The 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 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 into smaller kind of processors.

1:18:20In 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 a lot of GPUs or parallel data centers attached to it. A robot has a CPU and a small, let's say, GPU and let's say an NPU, a custom ASIC. So you can actually take this type of intelligence that we design that deliver basically intelligence at the level of models that are 10 to 1 ,000 times larger than themselves. You can bring those things directly running on CPUs, GPUs, and NPUs outside of data centers. So we thought that, okay, this format is going to open up an opportunity for us to bring in alternative architecture.

1:19:00If we scale this technology into the regime of foundation models, which is large language models and SLMs, as a whole, human understandables. 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.

1:19:42And we came in and we said, OK, 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. Take a second and walk us through. So these are small language models.

1:20:21Can you define an SLM and how it varies from an LLM? Definitely. So when you start developing kind of foundation models, you run something called scaling laws. Scaling laws is like basically starting with 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. And we have seen scaling laws shows that the larger you make the models, the more token budgets you spend, the more intelligence of a system you can get.

1:20:58And this has been giving rise to large language models. Along the way of scaling, there are instantiation of the models, which are smaller, like on the scaling laws. 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. And efficiency was a first class citizen for us, like thinking about computational graphs of intelligence. Smaller models are models that are along the line of scaling. They can solve, let's say, they don't have the general capability to the level of the largest kind of language models, but they can be specialized to solve dedicated problems.

1:21:43They 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. But 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. And what does small mean in this case? 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, mid-size, like basically is around that size.

1:22:18But 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 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. There's on-device AI is like models that you can actually deploy them on an actual kind of device, physical device. This could be a follow-up. Let's make this concrete because you've got a significant deal in Mercedes. Yes. And can you speak to that? And let's talk about these SLMs in terms of on-prem.

1:22:59Basically, they're energy efficient, fast, offline. Let's dive into that, give people sort of a real understanding here. 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 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, depending on what company is providing the chip, that chip is very, very small.

1:23:39We are talking about 2 gigabyte to 8 gigabyte of RAM, 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 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. 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 recorded. So we brought the intelligence, basically we brought one of our multi-modal foundation models that is only less than one gigabyte in size.

1:24:24And it can go inside the car's chip, a very, very tiny chip, the chip could be as cheap as$60. That's what I'm saying. We're bringing that level of intelligence into that voice, and it is going to power the multimodal intelligence experience inside the car. We do that with all car manufacturers. We announced the Mercedes partnership as a first point of entry because automotive is very sensitive topic, and 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. When we are bringing this type of technology in-house, this has been one of those cornerstones of landing the deals.

1:25:06Because we want to work, we are an enterprise company, we are a B2B company. We are bringing our full power to really deploy the solutions and really have platforms that allows people to fine-tune their small models. And fine-tuning small models is not that expensive. It's something that is extremely tangible. So we fine-tune kind of the small models for the applications inside the car. We also have data flywheel kind of systems that allows the system always to stay adaptable. Imagine some of the problems in enterprise AI has always been, let's download a GLM 5.2, like, you know, and let's say an open source model and put that in production.

1:25:45And then, so what happens after you put the system in production? What happens 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 disconnected from the cloud, how do you want to maintain updates of the system? Because we have always thought about intelligence in the format of liquid. Intelligence has to always stay adaptable. And 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.

1:26:25With Mercedes, we are rolling this out first in North America as soon as basically this year. All the Mercedes-Benz North America cars from 2022 on, they're going to get an update, over-the-air update, because the size of the update is 600 megabytes. Wow. That's like a over here. It doesn't consume that much internet to really update your software. And that allows us to also further customization. Imagine if every update that you want to perform on the system is in the order of 20 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.

1:26:59You would be able to have like a recursively kind of improving the experience of the user as well. So that's kind of the role. Let's 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 the air. If I don't have connectivity, I don't have Grok. But what kind of queries, what kind of capabilities does this all of a sudden enable in a Mercedes? It has access. It's sitting below 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?

1:27:40So there are 700 functions inside the car, 700 to like, I don't know, 1 ,200, depending on what you count as a function. 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 you're like, let's say stock 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 system. Once the found... One of the beauties of this system is that it has full access to all the functionalities of the car, plus all the apps, because they're like function calls.

1:28:15They're one function calls away. So if you want to control any other thing from this intelligence unit inside the car, you would be controlling everything, all the ecosystem that is sitting on top of the operating system of the car. So basically, I mean, if I get you right, the advantage of the SLMs are, first of all, the size of the model. I assume energy consumption, they're efficient and they can run on-prem. How do you avoid or reduce sort of overgeneralization of these models compared to LLMs? What do you mean overgeneralization? In other words, do you have enough capabilities internal to them so that they are actually able to accurately answer the questions you're asking?

1:29:05Great question. So if you have, like, you know, 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 the model again. for the core capabilities or a specialization of intelligence. Now, we make systems that are, our platforms are getting into the place where they are automatically identifying what depths of customization is needed for a certain solution.

1:29:38And the platform basically, it's one of the products of the company that we sell to enterprises to allow them to fine-tune models. I don't want to call it fine-tune, customize a model at a level that is needed for that system to actually operate. Right. So for Mercedes-Benz, we have, let's say, like the framework that we have 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're 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 much generality like the base models have.

1:30:19It's something that, you know, like libraries of liquid models are coming out for many different applications. We have models that we're working with, for example, in silico medicine, like, you know, Alex. I introduced you. Alex, you introduced us, Peter, like I remember. 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 novel. They're basically RL-able, like really, really well RL-able. And that's something that they figured out that it comes handy for them. So, So now we have a state-of-the-art biotech foundation models, longevity foundation models.

1:30:55These are the kind of things that we're building in bio. And imagine as a horizontal company that is building foundation models, we went to fine tuning and that became something that we have managed to do. And then in terms of some of the other engagements, recently with Shopify, we entered 1 billion kind of requests address inside the Shopify framework. work. And there, like what we've done, 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 Shopify is like a huge base. Like we are touching hundreds of millions of kind of users, 10 billion products, and many different kind of places to integrate.

1:31:41We 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, let's say, low-cut AI experiences on PCs as well. So that's another area that we enter. The focus of our company is to really make sure that we can bring, basically, intelligence outside of data centers. That's something that we have focused on.

1:32:07Peter Diamandis:And I think our efficiency is actually allowing us to gain. Preliminary matter, I have no financial interest in liquid. Sorry, Ramin. I have to ask the most obvious question. I have so many questions for you, which is the company Liquid was founded, as I understand it, and 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 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, 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.

1:33:06Peter Diamandis:that looks more and more like basically a conventional, off-the-shelf architecture, and maybe a good business selling customized transformer derivatives to Mercedes et all? If so, great from the business side. But from the technical side, does Liquid still have anything that looks remotely like 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? Great, great question. So let me tell you the space 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, in terms of our architecture.

1:33:50They have nested nonlinearities that you cannot really take them out. They are completely physics-inspired. They are having the neural ODEs, and basically, 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. 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.

1:34:24Space, 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 dumped 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 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 how neurons actually exchange information with each other.

1:35:01That 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 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, let the algorithm, instead of human biasing kind of the algorithm, let the algorithm run the scaling laws on, let's say, 100 different variations of operations that potentially can give you a general purpose computer.

1:35:46So we build a meta AI system. The paper around this is actually published like two and a half years ago. We published a paper about the topic. It's called the STAR, Automated Design of Tailored Architectures. So read about style. 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 processor?

1:36:29Number 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 an essential component. They have a little bit of transformers in them. But the rest of the dynamical system, and most of the dynamical system, for the purpose of these four objective functions that I mentioned, you would change that. And you can actually automate this whole framework to design foundation models.

1:37:03In-house, the technology stack of liquid foundation models is called automated foundation model design algorithms. We call it AFMD. This automated framework is the one that explores architectures 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.

1:37:46Everybody, 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. Don Musalem, the chief medical officer of Fountain Life. And a part of my medical team, Dawn, a pleasure. Great to hear. 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.

1:38:23And the numbers about dementia are problematic. Can you share what you've learned?

1:38:29Peter Diamandis: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. And we know that when it comes to dementia, the conservative estimates are that 45 % are entirely preventable. What was amazing is with the advanced testing we're doing at Fountain Life, one quarter of our members had advanced brain age. Wow. But what was really awesome is, again, back to that prevention, when we partnered it with Healthy Living, this gives me chills, eating healthier, moving our bodies, sleep, optimizing sleep is so important.

1:39:04Peter Diamandis:You know what we saw? we saw that we improved that brain age by 26%. That is a big, big number to show that the majority of those individuals were able actually to improve the brain age. And one of the things I love about Fountain is we're searching the world for the best therapeutics, the best approaches, and making sure we bring it to our members. So if having healthy brain function till 100, 120 is important to you, check out Fountain Life. Go to fountainlife.com slash 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 Luckey, the founder of Oculus and now the chairman of the defense giant Andrel.

1:39:44It's funny to call Andrel 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. 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 person skilled in the art how to actually 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.

1:40:30So if you do that, you're effectively teaching the world how to use it and you're exchanging that sharing of your invention for roughly 20 years of exclusivity. 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 U.S. 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.

1:41:12My 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 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 U.S. 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 ecosystem that we've had?

1:42:09Let's watch a short video from Palmer, and then we'll talk about it.

1:42:15Peter Diamandis:Stop patenting everything. Patents are Chinese instruction manual. 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 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.

1:42:48So, you know, I've applied for and gotten a dozen patents. I know, Alex, you have 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?

1:43:01Peter Diamandis: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. 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 is basically confiscated or eminent domained by the military can only be practiced for military reasons.

1:43:48Peter Diamandis:It's not contra any construal otherwise that 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 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.

1:44:49Peter Diamandis:That's very concerning to me, the idea of expanding it overall. I would argue, if anything, the Invention Secrecy Act regime should probably go away. We can have this debate. So Palmer's going to be joining us at the Moonshots gathering on September 25th in L.A. Everybody can go to moonshots.com. We have an amazing day with the Moonshot mates there. We'll be having these conversations with Palmer. Salim, 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? Look, we've seen this problem get bigger and bigger over the last 20 to 30 years, okay, where the disclosure, especially in an age of AI where people can just route around it or replicate or learn from it.

1:45:39It's a huge challenge. The real moat is learning loops. That's going to be the real defensibilities. 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 particular model are the lawyers. Well said. Dave, any thoughts here?

1:46:06Peter Diamandis:Yeah, I think if there's a flashpoint for World War III, this is probably one of the most likely. Seriously? Yeah, well, look, Alex is right. We're going to discover new physics, new medicines at an incredible accelerating rate. And 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 U.S. will trade embargo anybody who doesn't respect intellectual property rights. And then you have to choose.

1:46:40Peter Diamandis:Are you part of 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. 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 incredibly 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.

1:47:20Peter Diamandis:And that's got to change fundamentally. And protecting intellectual property rights is a key, key way to reverse that tide and get people working on really important things. 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, it is really a quibble with enforcement of patent. 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 temporary monopoly.

1:48:01Peter Diamandis:Really, what he should be asking is better enforcement of U.S. patents in China. Agreed. All right. I'm going to move us into the world of health care abundance. So two stories this week are demonstrating an incredible impact of AI on health care 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 JAT GPT or GPT 5.6 SOL set a brand new all-time benchmark high. And then the second part coming out here is in a blind test across roughly 20 ,000 individual physician judgments.

1:48:45In other words, 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 chat GPT. 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 the story comes from Meta. So OpenAI's own health bench professional benchmark, which is 525 real clinical tasks. Meta's Muse Spark 1.1, again, released last week, beat ChatGPT's or GPT 5.6 Sol on across the marks.

1:49:36And 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 three and a half billion people on the planet. And that's just extraordinary. I mean, this is the abundance thesis at large. 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.

1:50:21Salim? 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 absolute. And let's recognize that in almost every country in the world, there is a radical doctor shortage. So this is really, really critical.

1:51:02Peter Diamandis: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. Free, basically free. I mean, that's... 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 bench maxing by Meta on this Meta Spark 1.1 is on, if you believe the AAII cost frontier analysis, it is on the optimal cost frontier, but it's not at the top.

1:51:46Peter Diamandis: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 bench-maxing. But still, it's a great day when Instagram gives better medical advice than human doctors. I think 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.

1:52:30I 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 lunges with cataracts. 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.

1:53:07I 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 aging that we've always filed as permanent just became reversible. And again, we talk about longevity escape velocity. We talk about our ability to understand the 5 billion chemical reactions per second per cell in your 40 trillion cells.

1:53:49And 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.

1:54:01Peter Diamandis: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. Or it's your steaks. It's chemical. Yeah, or it's why, as a vegetarian speaking, why everything purportedly tastes like chicken.

1:54:44Peter Diamandis:It's the same class of reactions, but the sugars 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 way there. It feels almost, again, strictly speaking, it's not like a reversal of the thermodynamic arrow of time, but it's the next best thing if we can remove all 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.

1:55:36Peter Diamandis:Like, what else is there out there in the biosphere for us to mine, in addition to all the obvious GLP-1s, great potential for longevity escape velocity. What other bacterial innovations can we use to turn back aging? 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 when I'll be happy. Well, there are lots of companies working on that, Salim. So, gentlemen, grateful for our time today. I'm excited for Starship 13 launch later today. I wish Elon and the group there lots of luck.

1:56:17Ramin, congrats on the success of Liquid AI and excited to have you on the pod with us. Dave, great move investing in Ramin on behalf of all of our shareholders. Thank you. Gentlemen, have an amazing week. I'm sure we'll be having an emergency pod very soon because the speed of the singularity waits for nobody.