Peter

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

Salim

she's built exactly the thing hitting the market. That exactly what everybody needs right now.

Peter

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

Ramin Hasani

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

Peter

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

Salim

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

Dave Blunden

now that's a moonshot, ladies and gentlemen.

Peter

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

Ramin Hasani

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

Peter

In Spain.

Ramin Hasani

All right.

Dave Blunden

Nothing going on in Spain this week. Damn it.

Salim

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

Peter

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

Salim

That's the round ball?

Alex Wisner

No, Peter, this is where the Falklands War gets re litigated on a soccer pitch.

Peter

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

Dave Blunden

I mean, you must have grown up on soccer, right? Didn't you? You were in Vienna for a while and getting your Ph.D. and, or your undergrad or whatever it was.

Ramin Hasani

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

Dave Blunden

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

Peter

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

Ramin Hasani

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

Peter

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

Salim

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

Ramin Hasani

so there was a lot going on actually, like in Europe, you know, so that's same. Same like you. Ten different places.

Peter

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

Dave Blunden

Ouch.

Peter

Ouch. But it is beautiful. There's no question. It is gorgeous. All right, I want to jump into our first conversation. We have a lot to unpack here. And of course, our mission is keeping you aware of what's going on in the world and giving you sort of the optimistic, hopeful vision of the future. Join us and keep up with the incredible pace as we head towards a singularity. So our first story Today, once again, CEOs building the most powerful technology in the world are asking to be regulated. You know, last week, Sam Altman published an op ed in the Financial Times proposing a framework for a u. S. Led international forum that would establish standards, provide expertise, impartial analysis and capabilities, and assess risks. This week, both Elon and Demis are adding their voice to the regulatory conversation. Elon says he expects a standalone regulator similar to the FAA or FCC to emerge at some point because, in his words, the consequences of AI going wrong are severe. Then this week, Demis Hassabis, CEO of DeepMind, went further in an essay Titled A Framework for Frontier AI and the Dawning of a New Age. He called for a U.S. led frontier AI standards body modeled on FINRA, the industry funded watchdog that polices Wall street under SEC oversight. He wants the FINRA equivalent to test frontier models before release. He reportedly wants this up and operational before the end of the year. Let's take a look at a quick video from Elon and then let's jump into this conversation, guys.

Elon Musk

I think the, the general. I think it's clear that there's a strong consensus there should be some AI regulation, that it would be in the best interest of the people to do so.

Ramin Hasani

And I think we'll probably see something happen.

Elon Musk

I don't know on what time frame or exactly how it will manifest itself.

Palmer Luckey

I don't know.

Elon Musk

I mean there's clearly. We've created regulatory agencies before while our regulatory agencies are not perfect. And I deal with regulators on a very frequent basis with automotive, you know, communications, StarLink and then FAA with rockets.

Peter

I think the probability of there being

Elon Musk

some sort of AI regulatory agency that stands on its own, similar to the FAA or FCC is likely at some point.

Ramin Hasani

You think so?

Elon Musk

I think so.

Ramin Hasani

Now the reason that I've been such

Elon Musk

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

Peter

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

Dave Blunden

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

Ramin Hasani

Yeah, there's like, you know, like there's a capability kind of threshold that we are trying to define right now. And there is some, some sort of an iteration is needed to see like how this, how these framework. It has to exist, you know, that's, that's for sure. You know, this has to be there, but it has to be related to capability. And then the thing that becomes a challenge is that there's a horizontal kind of capability lock into, let's say enterprise deployment of AI. And then there's the vertical. Because if you go to different verticals, for example, we operate on device and enterprises that are connected to the physical world. We are talking to car manufacturers, like semiconductor business and laptop business, people that are building AI PCs. And then we are also working with financial services and we are working with E commerce and biotech kind of companies. And we see like in different verticals, you know, like the enterprise applications themselves and enterprise criteria for let's say a limit or let's say a regulation or a governance kind of a structure. It's very different, you know, so for us it becomes a lot more kind of verticalized because we're building a specialized models and those specialized models, like per vertical. We have had like conversations with the DOD and we have had like a joint submission of something I think with AMD just recently with our team to really have a say basically in the design of these regulatory kind of things. And I think as an exploration, I think everything has to be getting started. I like to look at it as a game theory kind of way of looking at it, how to design policies in general. It would be a Stakelberg kind of game. I don't know if anyone is familiar with. I don't want to nerd out pretty soon on this, but we can talk about this.

Dave Blunden

Alex does it all the time. It's okay. Sooner the better. Sooner the better.

Elon Musk

Yeah.

Ramin Hasani

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

Peter

So I think, Salim, I see you chomping at the bit here, buddy.

Salim

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

Alex Wisner

Well, that's what otherwise you can end

Salim

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

Peter

The problem isn't that what's good about finra, it's not a government agency, it's an industry funded self regulatory org.

Salim

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

Dave Blunden

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

Peter

Alex, I want to hear your voice on this. Bill.

Alex Wisner

I tend to think this is a bad idea. It smells like regulatory capture. It smells like the attempted formation by demos of a cartel of frontier labs. And I think the elephant in this particular room is open weight models and research that lives outside of the frontier capabilities. And it's very easy to imagine a future with FINRA or other, I mean worst case scenario, FDA like capability. Even though outgoing personnel from the current administration have declared in no equivocal terms that there is going to be no FDA for AI regulation, that that would be maybe on the worst case end of the spectrum that we, we see the emergence of some sort of cartel of frontier labs that locks in certain practices, certain price performance optimal frontiers that try to box out open weight or open source or say university driven or other non incumbent frontier models. And I think that would be an utter disaster for both the west and the world for continuing to advance us towards ever increasing superintelligence capabilities. I just don't think it's a good idea.

Peter

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

Alex Wisner

Maybe there are I think at least two different frames that one can look at. The liability aside from there's regulate the inputs that is to say have something that's FINRA like or FDA like that regulates the raw capabilities of the models at model construction time. That's one end of a spectrum. The other end of the spectrum is regulating the actions of the models. Like you let the lawsuits fly if a model takes down a stock market or does something else that otherwise harms third parties. That's the other end of the spectrum. Obvious to me that we should be in the business of regulating superintelligence at superintelligence time. That's maybe tantamount to thought policing the AIs. And I'm not generally a fan of that notion of let's thought police the AIs but not thought police the humans. We don't at least in the west have a practice of regulating what's in our minds. We don't have a practice or a tradition of regulating an upper limit, say or via some sort of regulatory code. Saying humans natural persons can't be above some level of intelligence is not obvious to me. Why we would create a new tradition of regulating or otherwise coordinating the upper intelligence of non natural entities, perhaps soon to be persons, but regulating the actions that in at least the western legal canon that we do do and that I'd be much more supportive of.

Peter

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

Alex Wisner

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

Peter

Saleem, what do you think?

Salim

Not workable.

Peter

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

Salim

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

Alex Wisner

Some. There's already an elephant in the room. The room has to accommodate so many elephants. We need some other non human animals.

Salim

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

Alex Wisner

It's a Huge difference here. Position on that one. Just if I may, Peter, narrowly on that. I mean there are definitely hypothetical mechanisms that I'm not supportive of for regulating AI like we royal we the US and China, if going back to. I think we gestured at it in a past pod, but past proposals to say regulate the foundries, regulate the chip outputs, regulate the data centers, establish mutually assured destruction type schemes where the US is monitoring Chinese data centers and vice versa. Like there are. There are schemes. Yeah, there are schemes at chokeholds, as Peter says, in the supply chain by which one could imagine doing this.

Salim

I don't think mechanism, the only mechanism. It's going to be like a pandemic style threat detection that would be globally. Agreed. And I don't see how we get there.

Alex Wisner

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

Peter

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

Alex Wisner

Well, I should also note that Elon clip I think is from three years ago, which is interesting. You know, it's from three years ago because Elon had his sort of like painted on Ironman goatee when he was in that phase. So Elon's been forecasting this for at least three years. Others have been forecasting it for decades. We still don't have it. We have like subdivisions orgs within NIST that are working on standards, but that's not really regulatory body. We have executive orders that are creeping towards a regular regulatory body. But you know, at what point do we sort of are we frogs boiling in water where there's just like a creeping rollout of increased standards, expectations of early reviews, but it never quite reaches a regulatory agency level before we achieve whatever escape velocity we're headed towards.

Peter

Well, we're going to monitor this one closely for everybody, I think. My guess is we see this before the end of the year and the question is, can we see something that's intelligent let's go to the next story, which is related, and this is a wild one, comes from the Washington Post that the White House is reportedly weighing a capability framework that would clear US models open or closed, as long as they stay at or below the level of China's best open weight model. What's the translation? So the proposed ceiling for what American companies can openly release is pegged to what China has already put out on the Internet for free. So here's the logic. Chinese open weight models reportedly trail US models on an average of seven months. I think that's been closing over time. So if anything is at or below that, it's already out there. It's an implicit admission that open weight models cannot be unshipped, or models like Deep Seq have already been downloaded millions of times. So once China releases a model freely, banning it is impossible. So the US response is to define a permissible ceiling rather than a wall. The implications? We're tying our open release ceiling to China's pace of release, effectively giving Beijing control. If they push their open weight models higher, then the US can release higher models as well. If China holds back, then they throttle us. And it's a very strange mechanism. I was surprised to see this. Alex, let's go to you first on this one. What do you think of this?

Alex Wisner

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

Peter

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

Salim

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

Peter

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

Dave Blunden

I mean, what's the logic? You might.

Peter

Well, what do you think of this?

Ramin Hasani

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

Salim

Let me, let me give a headline from. For Alex, for, for his next newsletter. The singularity is becoming a trade dispute.

Alex Wisner

For the next newsletter. That was like two newsletters ago. Okay, fine, whatever. That's out already, but thank you.

Peter

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

Alex Wisner

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. That could happen in AI as well.

Dave Blunden

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

Alex Wisner

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 1 bit or 1.58 bit quantization. But China ran away with it due to constraints.

Dave Blunden

Yeah, it's a new company.

Salim

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

Peter

Yeah, the, the other.

Alex Wisner

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, whatever index of evals, this is going to be the rubric going forward for how we measure what's above the threshold for frontier versus below. What's a frontier model versus not I worry that could so distort model capabilities. Like they'll over exercise certain capabilities deliberately and perversely under incentivize or under benchmax others that it'll just totally distort, maybe topiarize the future landscape of superintelligent capabilities.

Peter

All right, well again this is a story that we'll be, we'll be following on this news of opweight models. So in the past our topiary right there in the past we've been discussing how opweight models have been in the US have been lagging. In China we have Nvidia's Nemotron 3. We've got Google Gemma 4. But that changed last night with some breaking news. Mira Marathi, the former OpenAI CTO who walked out and raised her 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 opalweight 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 it, you know, keeps the model going fast and cheap. It was trained on 45 trillion tokens of text, image, audio and video and very importantly reasoned natively across all four. Reuters news framed it exactly right. Quote this is meant to be a western alternative to the Chinese opwait models Deepseek and Quinn that have dominated the opweight leaderboards. Now interestingly enough, Marathi, 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.

Salim

You've, you've hit there Peter, on the really big thing.

Peter

What's that?

Salim

She's making this. She's pushing on the customization leverage because it's not going to be the future is the raw power. It's going to be the adaptability that's going to win and this she's built exactly the thing hitting the market that exactly what everybody needs right now and

Peter

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

Alex Wisner

Well, it's worth looking at the raw capabilities. So if you believe the evals, hopefully that Thinking Machines, AKA Thinke 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 Thinke is 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. So it's not, it's not one of the strongest open weight models overall in the world'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. And we see anthropic about to IPO at a trillion dollars and we see OpenAI planning to eventually IPO at a trillion dollars and in China which has been GPU and compute deprived on the one hand and on the other hand has the CCP declaring 5 year AI 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 to 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.

Peter

Get what you incentivize.

Dave Blunden

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 Llama. What happened in 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, then you do another, then you do another. And then when you have something really groundbreaking, then you go closed source and you launch an API into corporate America. And so that's, that's a well worn path. So I wouldn't, I wouldn't say this is necessarily a religion at thinking machines that they're going to stick with. You know, the trend has been the opposite of that in the past.

Alex Wisner

Ramin, what should 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?

Ramin Hasani

Yeah, it's a matter of like the business of the company. You know, like thinking machine can do three more iterations of their pre training or post training kind of RL kind of environments and benchmarks like those numbers that you see on the benchmarks and release like a, like a better model. But what they, what, what their business is, their business is fine tuning. Like this is kind of the place where customization has been like something that everything the whole market around customization has been very empty. Like if you look at the first attempts like OpenAI released the OpenAI tuning like 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. Because as you see the model layer is not anymore 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 we are talking about sovereign AI and integration of these models into enterprises, you need to leave some room for let's say fine tuning these models and what they have, what, what I think their business strategy around what they're doing and this release is genius because they're deliberately releasing. They're they're putting, they're leaving some room for fine tuning so that people can come in and using their business, their API business, because that's generating I think in the order of one to two orders of magnitude more tokens as well on the customization side. So that would be like even printing money at a larger speed in the absolute best case. Right? That's the business. Their entry.

Alex Wisner

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.

Dave Blunden

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

Alex Wisner

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

Peter

Alex, really great point. We talked about the Alex Karp rant, right? And the result of that was don't allow, 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, you know, on prem. And if that's the case, then the question is who are they going to use? Which models are they going to use and is the US 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 Gemaphore to the next level and hopefully we get some 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 opioid

Dave Blunden

models 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.

Peter

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

Dave Blunden

Well, you know, back when GPT2 and GPT3 came out, you could actually very easily fine tune by uploading text right into a window and say, look, you're pretty smart, but you don't know anything about my laundromat. You know, like what hours were open now, who our employees are, our entire payroll. Let me dump that data into 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. But then when you get into biotech research, or you get into aeronautical, or the Mercedes like Ramin is doing, there's a whole bunch of proprietary company knowledge that actually isn't in the model. So right now we dump it into the prompt field and say, okay, here it is in prompt form. But that's hugely inefficient.

Peter

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

Dave Blunden

Yeah, yeah. I mean, Sam and Dario can see everything, all your proprietary information. They're looking right at it. That's what Alex Carter Sharp was ranting about when he said, they're stealing your weights, they're stealing your alpha. What it really means is they're looking at your most proprietary, your company payroll, your company's 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 we may be onto something here. We've hit a real tipping point. And Alex Karp I think is right about it too.

Salim

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.

Alex Wisner

A couple of historic notes again for those definitionally not tracking the full sorted history of fine tuning. So fine tuning is this notion that you start with a model model consists of billions usually these days of weights of parameters that are frozen. And if you want to customize the model for your purposes, you can conduct a so called fine tuning process that usually makes relatively small hence the fine changes to some, usually a tiny subset of the weights in order to customize the model for your end application. That's fine tuning. There's actually now decent literature out there that suggests that conventional fine tuning, like supervised fine tuning Lora style low rank adapter, one class of fine tuning architectures, doesn't result in increasing the capabilities of your model at all. And at most it results in 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

Peter

only be an accelerando flavor output.

Alex Wisner

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 fine tuning post training there's a gray area between what's the distinction between them. But 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 that seems to work pretty well at the moment. But in some sense, if that is like the permanent long term plan of thinking machines, it's fundamentally a bet that we're not going to ever move beyond the reinforcement fine tuning paradigm, which I think is probably wrong. I think probably RFT is the scaling of the moment, but in the future I can totally 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.

Peter

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. I think women are distinctly missing from the entire AI industry. Right. We have Lisa Su from amd, but very few in leadership positions and I think that's an important point. I'm not sure who else. Alex, are you seeing Daniela Rus, right where Ramin 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.

Salim

We need more balanced thinking.

Peter

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

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Peter

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. But 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. It's sort of the theoretical engine behind the hard takeoff scenario of the singularity. So this week a startup called Wiko AI with researcher Zhang Yaojiang 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 A squared with an outer AI agent whose job is to rewrite the code and the research strategy for an inner AI agent. In their experiment, they claim that eight days of machine self improvement beat two years of expert human effort. So Alex, what do you make about this? Is it the first, is it significant?

Alex Wisner

Very significant. Highly unlikely that this is anywhere close to first. So a few bits of additional context. One, this is actually WECO's 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 which. 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.

Peter

What does that mean?

Alex Wisner

So defensive 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. Same idea with AI. The way we keep 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 2 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. But both powered by the same underlying AI driven exploration process, at least initially. The outer loop NAI discovered that it was able to achieve, and this was an emergent property, better results from the inner loop by preventing the inner loop from cheating and reward hacking. 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, like stop all, 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 capability, new capability in sort of in disguise, in a trench coat. Same idea here.

Peter

We need stronger white hats to police the black hats.

Alex Wisner

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. They've published a scale for recursive self improvement that goes from 0 to 3. 0 is delegation, where the AIs are slower than human R and D. Level 1, net positive, where the AIs beat human R and D at the same cost. Level two they call ignition, where the improvers are better, basically a better improver. And level three, inflection self acceleration with a fixed budget. And the claim here is that they are 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.

Peter

So they rate themselves as a level one here?

Alex Wisner

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

Ramin Hasani

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 concept of work that has been done. Just to tell you a little bit about like how the foundation model labs are operating. All foundation model labs, since the beginning of, let's say like four years ago, or let's say five years ago, everybody has been thinking about recursive self improvement. And for us, the definition of recursive self improvement is not the engineering and prompt engineering of inner loop and outer loop to really get, get some code patches like changing. Because that gives you the assumption that every single AI model that you're using in your pipeline is already like, you know, like it's already defined and it's already fixed with a certain type of capabilities, which is actually the case in the whole pipeline that they actually like design. There is no weight changes in the neural networks. So that means like the AIs that are actually getting used right now, there's no kind of improvement of the core competencies and even behavior of the models. They're always like in the system prompt of the, of the models, like changes in the system prompt. Because I will give you like fundamental reasons why this is actually limiting. Because if you just run 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 like retune themselves. They can, you know, adapt very similar to how humans do it. You know, if you, if you think about it, the core competences of these models that we have right now, they're, they're, they're fixed weight models and the capabilities are within a certain kind of threshold. And the frameworks that they actually like designed, it's not, it's a very nice early stage of showcasing an engineering pipeline that can improve work, which is actually very, very important and very nice. But I wouldn't 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 improved 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 define what should be the final model. And then right now at our company, all the process of training foundation models and really like retuning the weights of the system are getting automated. So 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, computationally intractable to actually performing this job. Training an AI model, training like being able to customizing an AI model and training an AI model on a meaningful number of tokens for adaptation or let's say like the core competence of the model, changing core architecture of the model, changing core algorithms, learning algorithm itself, changing all of those matters adds more and more complexity on the situation. I can give you also like one numerical kind of example of this. There is a scaling laws 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 can 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. So that ratio is like 20. And then when you actually do the math with the frameworks that they have, if they want to like let's say you launch this framework on retuning an AI model to recursively self improvement 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, meta learning systems. These are the kind of problems that the last four years of, least at my company we have been heavily focused on. And I know friends at OpenAI and Anthropic has been focusing on this recursive self improvement. And Anthropic 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.

Peter

Dave.

Dave Blunden

Yeah, brilliantly said. And actually just so the audience can get the analogy there, when a baby is born and then learns, 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.

Peter

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

Dave Blunden

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. 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 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 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.

Peter

There's a lot of fear just to call it out, that recursive self improvement leads to AIs that take off. 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. Salim, I say that for you

Dave Blunden

answer

Salim

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

Peter

When do you think is happening?

Ramin Hasani

The thing is, I can tell you like the early evidence of recursive self. By the way, recursive self improvement is not related to what 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 it's still like, let's say mythos level kind of class because everybody like heard about Mythos. And then what I would say is that like the cybersecurity kind of threads that we are seeing, like coming out of these type of pipelines of recursive self improvement. They're real. You know, like the reason why I'm actually I've always been like, you know, like pro open source and I want to open source technology all the time. Like we are doing it all the time. Like every single release of our models is open source. Our science has been always open source. I believe science has to be open source and I see the value of open source going forward. But some of these concerns that Peter you brought up, they're very real, you know, like the cybersecurity kind of aspect of things. That's why I feel like a degree of at least enterprises themselves having some degree of kind of self control about how before mass release of their models there has to be always a certain degree of self check. And I think Anthropic 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 in 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 at 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.

Peter

When do you think we, when do we have a pod?

Alex Wisner

Timelines, Ramin, timelines?

Peter

Yes, when 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's a, you know, debate about whether it really is or is not real recursive self improvement. When do you think we actually give the trophy out to somebody? Is it a year, three years, five years?

Ramin Hasani

Yeah, I mean I'm telling you that. So I would say you're going to see unbelievably kind of models probably in the next two years or so models that are going above our understanding even. 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 with the rate that we are seeing right now and if there is no like let's say another chip shortage or memory shortage like on 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. So everything at a foundation model applied. When you're customizing a model, when you're building something that is better than its previous generation, we always categorize it with depth of customization. The place where recursive self improvement today is really good at is prompt engineering, changing editing code, like in engineering kind of tasks that you've seen like some elements of these things, like at a very, very superficial level, let's say make my model run fastest, like doing kernel engineering basically, you know.

Elon Musk

Exactly.

Ramin Hasani

Make my model run faster. That's what I call like the shallowest level of kind customization where you have Python code and then you're kind of adopting that Python code to really run or maybe like even lower level programs that you have like on a kernel level to optimize like let's say inference speed. You know, that's something that I think when they released Fable 5 they shared like entropy 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 Fable 5 can actually, you can push it to actually get to some degree of kind of customization with some performance optim, performance optimization, performance optimization of the model, but by fine tuning then the latest holy grail, which is like the craziest one, which would be pre training, right? Can a language model pre train the next generation of their own? That's why they hired Andre Karpathy, because Andre was talking about like nano GPT style kind of fine tuning, you know. And Andre joined Anthropic 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 computer salim.

Peter

You're chomping.

Salim

I've got for me this is by far the most important story or slide we're going to cover today. I'm beyond excited for a couple of reasons. The, you know, I'm not really focused on the self awareness or the loop that will go there. But this is self accelerating. It's accelerating experimentation, right? Because the system doesn't need, it's 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. That's a very low bar to improve that process. So this is the first glimpse of the organizational singularity. It's happening at the research level because AI is not just doing tasks in a workflow, it's redesigning the workflow that makes it better for doing future tasks. Right. And so this is proof now for the whole thesis we've had. We predicted this, but it's great to see it actually 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?

Peter

So 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 Biden adversary, but a sanctioned official AI clone of a national leader. We've seen this before, Salim. 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. So one leader, in this case Prime Minister of Malaysia, can personally address millions in their own languages. It's worth noting that Malaysia has 135 spoken languages. So it's a big deal, especially in a nation like that. Saleem, I'm going to go to you first on this one. We've been talking about this for a while.

Salim

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. Is this the actual leader? That kind of question can come up. But I love the general approach because if you can do it from a, with a watermarking or something and say this is the actual avatar, then it gives every citizen a voice to plug into and gives huge props 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 democracy all over the world is civic engagement and this allows you to scale that. So I'm very excited.

Peter

Do you remember the reason why Albania put this, their AI cabinet minister in place?

Salim

Yeah. Corruption.

Peter

Corruption, exactly. It was to fight corruption.

Salim

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

Peter

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. Alex, do you want to weigh in?

Alex Wisner

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

Peter

AI Trump is coming is how you're

Alex Wisner

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 bi directional since if you're touching a million or 100 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.

Peter

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

Alex Wisner

Yes. And it won't just be, I think corporations, religious leaders and religious institutions. If you're Catholic, imagine having like a digital twin of the Pope and you see like lots of religious institutions, organizations already creating basically living versions of their founding documents and making those into actual. 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 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 at some point, it's actually the digital twin of, 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 created digital twins of the leaders and those are the ones actually running the organization, it could lead to a true democracy.

Peter

Dave, where do you come out on this? I mean we saw just one quick point. We saw Sam Altman talk about in the future, if I believe enough in what we're building with 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?

Dave Blunden

Absolutely going to create an AI Dave Blunden. And I'm shocked that there isn't already a Peter dmn us.

Peter

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

Alex Wisner

You've got your own big brother, Peter.

Peter

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

Alex Wisner

I think so much better in some cases, Peter, I think that might be redundant.

Elon Musk

It.

Peter

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

Dave Blunden

Sure.

Salim

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.

Dave Blunden

This whole area. 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. Debate between AI, Peter and Aristotle.

Peter

Yeah, yeah.

Dave Blunden

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

Salim

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

Peter

Let me close out on one thing here. If 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. You're gonna wish you had. So I've done that with my mom. I miss doing that with my dad. And it's the ability for your kids and your grandkids and your great grandkids to really have a great AI representation of your 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 a important, pivotal role as an early investor. Dave, want to give that backstory here a little bit.

Dave Blunden

It actually, I got a call from Daniela Roots over at csail, saying the best ever. Daniela is, you know, one of the three, I guess, big shot women in AI. She runs C sale at mit. Computer science lab in the world. Computer science AI lab. Yeah. I don't know if you remember, back in the day, there was the AI lab and then LCS lab for computer science were the two biggest, you know, comp sci labs at mit. They merged them together and made one mega lab. Put it in the new Stata building, which is that crumpled looking, beautiful structure right on the edge of MIT's campus. 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. 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, which, which, you know, humanity desperately needs because everybody knows the transformer architecture and the whole attention mechanism is bloated. And if you really go back to founding principles and think again, you might be able to build something dramatically, like massively better. And so the team went from idea in a lab to billion dollar valuation Faster than any company out of MIT in history.

Peter

And luckily we were an investor in that company.

Dave Blunden

Luckily we were. Yeah. And 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, for being invited to the party. But yeah, it's one of about 200 unicorns out of MIT all time, but the only foundation model company that I know of that reached unicorn status coming out of mit. So it's a really unique and incredible achievement and in record time too.

Peter

So, Ramin, take us from there. 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?

Ramin Hasani

Absolutely, absolutely. Before I start, I want to thank you guys for the support throughout these three and a half years of liquid AI. You have been great support, giving us the kind of distribution that a company needs and at our scale, starting off of the east coast. Thank you so much for doing that, both of you. 2015, I was in Vienna. I started my PhD with Professor in Vienna, Professor Radu Grusu there. He had the idea of, we don't understand a lot about human intelligence. Let's start on a smaller animal. And then from first principles, if you understand how the neurons exchange information in the brain of the worm. The worm has 302 neurons in its nervous system. Its body is transparent, so you can actually see the body actually lighting up. So it is one of the best model organisms in the world. It won so far, like four Nobel prizes for humanity because it has 78% similarity, genome similarity to human genome. And the way nervous systems compute in the brain of a little worm, which is 2 millimeter, is basically analog, very similar to how artificial neural networks are actually computing. They're 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. When you have a spike, there's an analog to digital 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 the neuro, like every individual single blocks of nervous systems and see can we pack more information inside the smaller kind of units of compute. And that's what we have done so. Daniela Rus, two years into basically discovery of these things that I was doing with, with my co founder Matthias Lechner. Matthias was a master student in Vienna, Vienna University of Technology. And I was a PhD student. And then when Daniela heard from Radu that you know, like this project is going on, Daniela was like, oh my God, this is crazy. We should apply this in autonomy and robotics and all that sort of things. Because you're showing like a handful of neurons can drive and control autonomous systems, you know, and can we scale this to vehicles, can we scale it to drones, to jets, to predictive phrases? So Daniela came in and said, would you guys consider coming to MIT? And we been there since 2017. In the middle of my PhD, I actually joined CSIL there we continued working on this technology which was from a base is a completely different things. A neuroscience inspired the math behind every single neuron in a liquid neural network that became kind of my PhD thesis is very different than how attention works. These are based on recurrent neural networks. These are based on continuous time processes. More and more nature inspired competition went into the design of AI systems. Then we applied these liquid neural networks as a completely new base because we applied them to real world scenarios like robotics, because. Because you can pack a lot more information into smaller kind of processors in the real world. In the physical world you don't have the luxury of having abundant compute. Let's say a robot doesn't have a lot of GPUs or parallel data centers attached to it. A robot has a CPU and 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 cells. You can bring those things like 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 like alternative architecture. If we scale this technology to let's say into the regime of foundation models, which is kind of large language models and SLMs as a whole, like human understandable, like making these liquid neural networks or architectures that we have also scalable, like the transformer architecture. And we built like a foundation model lab around the idea in 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 of transformer architecture. And we came in and we said, okay, so why don't we explore this space of alternative architectures, starting from the priors that we have from nature and then take a different approach. Build a meta AI system again, basically an automated AI system that allows us an AI that designs AI that explores the computational graphs of intelligence beyond transformer and then figure out what should be that architectural design that brings the same level of intelligence than a frontier model into, let's say on a CPU that we can run, let's say a physical system.

Peter

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

Ramin Hasani

Definitely. So when you start developing kind of foundation models, you run something called scaling laws. Scaling laws is 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 loss shows that the larger you make the models, the more token budgets you spend, the more intelligence of a system you can get. And 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 thinking about computational graphs of intelligence. Smaller models are models that are, you know, like along the line of like a scaling they can solve, let's say they don't have like the general capability to the level of the largest kind of language models, but they can be specialized to solve dedicated problems. They are general purpose. Small language models are general purpose in the sense that they understand language, they can see and they can hear in a multimodal kind of format. 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.

Peter

And what does small mean in this case?

Ramin Hasani

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

Peter

this concrete because you've got a significant deal with Mercedes and can you speak to that? And let's talk about these SLMs in terms of on prem. Basically they're energy efficient, fast, offline. Let's dive into that, give people a real understanding here.

Ramin Hasani

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's like one chip that is available for infotainment. And in car intelligence 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. We are talking about 2 gigabyte to 8 gigabyte of RAM, you know, like not more than that. So the model has to be very small and at the same time being able to perform because we want to bring this and enable a private space inside the car that powers the intelligence of the car in the car. Car is a safety critical environment. You don't want your car to be driven by an AI model that is sitting in the cloud. Why? Because connectivity is not always available. Right then it is private because it's one of those spaces that people spend a lot of time in and you don't want those conversations to be recorded. So we brought the intelligence, basically we brought one of our multimodal foundation models that is only less than 1 gigabyte in size and it can go inside the car's chip. Like very, very tiny chip. The chip could be as cheap as $60. That's what I'm saying. We are bringing that level of intelligence into the, that voice and it is going to power kind of the multimodal intelligence experience inside the car. We do that with all car manufacturers. We announced the Mercedes partnership as the first kind of point of entry because automotive is like, it's very sensitive kind of 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. You know, like when we are bringing this type of technology in house, this has been like one of those cornerstones of landing the deals. Because 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 the small models for the applications inside the the car. We also have data flywheel kind of systems that allows the system always stay adaptable. Imagine some of the, some of the problems in enterprise AI has always been let's download a GLM 5.2 like you know, and, and let's say an open source model and put that in production. And then so what happens after you put the system in production? What happens like when there's a drift from the use cases that is hitting this model inside let's say a car? And in the physical world it becomes even more challenging because when you deploy an intelligence that is completely kind of disconnected from the cloud, 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. With Mercedes we are rolling this out first in North America as soon as basically this year all the Mercedes Benz North America cars like from 2022 on, they're going to get an update over the air update because the size of the update is 600 megabyte. So that's like an over the air. 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 Lora adapters and let's say all sort of adapters that we can actually bring in inside the car. You would be able to have like a recursively kind of improving the experience of the user as well.

Peter

So that's kind of ramin. Let's take it, make it more concrete for me. So what am I going to be? How am I using this model in my Mercedes next year? So right now my experience is using Grox 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?

Ramin Hasani

It has access, it's sitting below the operating system. So that means it is like basically have access to all the functions inside the car. So there are 700 functions inside the car. 700 to, I don't know, 1200 depending on what you count as a function. You can talk to your car, you can control all the panels of your car, you can ask for, let's say manuals of the car. When you're, let's say stuck somewhere, something pops up, you would be able to talk to the car. There are memory features that we are adding to the car. You basically can have conversations with that system. 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, they'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.

Peter

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

Ramin Hasani

What do you mean over generalization?

Peter

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

Ramin Hasani

Great 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 gotta fine tune the model, sometimes you have to go and pre train a model again, you know, for the core capabilities or specialization of intelligence. Now we make systems that are our platforms are getting into the place where they're automatically identifying what depths of customization is needed for a certain solution. And 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. For Mercedes Benz we have, let's say like the framework that we have at in house, we call it Model plus X. You know, Model plus a platform that allows you to perform customization. It's not just the models that we are selling to enterprises, the static weights of a model, we sell them something that they can actually like retune and fine tune the system. Detecting how much generality like the base models have. It's something that you know, like libraries of Liquid models are coming out for many different applications. We have models that we'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 novelty. They're basically R.L. able, like really, really well able. And that's something that they figured out. It comes handy for them. So now we have a state of the art biotech foundation models, longevity foundation models. These 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, like we recently with Shopify, we entered 1 billion kind of request address inside the Shopify kind of framework. 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. And Shopify is like a huge base. We are touching hundred million hundreds of millions of kind of users, 10 billion products and many different kind of places to integrate. We are working with Mercedes Benz, as I mentioned, on the car kind of side of things. We are working with Amazon, AMD and other chip 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 and I think our efficiency is actually

Alex Wisner

allowing us to get preliminary matter. I have no financial interest in Liquid. Sorry Ramin, have to ask 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. It looks more and more like basically a conventional off the shelf architecture and maybe a good business selling sort of customized transformer derivatives to Mercedes et al. 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?

Ramin Hasani

Great, great question. So let me tell you like the space of kind of architecture. So liquid neural networks in the original form, they are one of the most expressive formats of computes that you can actually create. Arguably like in terms of our architecture, they have nested non linearities that are like, you cannot really take them out. They're like completely physics inspired. They are like having like the neural odes and basically like, like irregularly sampled data can be handled by them. So they become like one of the very, very general class of architectures as a whole. Underneath these things like when you want to scale this type of technology, these recurrences, like you know, like these nested kind of loops that they have. 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. State space models, models are kind of basically like Mambas and those kind of variants falling into the same category of continuous time neural networks, but dumbed down into a linear kind of dynamical systems. Because you want to scale them, they are underneath this class of continuous time models that we have. Then there is like there are variants of linear, linear attention, gated linear attentions that are coming out. They are also like gating mechanism is something like there's a special gating input dependent gating mechanism that actually we got inspired by the by how neurons actually exchange information with each other. That gating mechanism is also something that is adding a lot more expressivity. Like it is also descendant of the original formation of how neurons exchange information with each other. That gating mechanism still exists today in many different architecture and including ours. But the most important thing that I want to mention that you should know about the technology transformation of our company is that we really didn't want to bias ourselves towards one single architecture. One of the things that we did day one at Liquid AI, we designed a search algorithm to let's say like, you know, let the algorithm, instead of human biasing kind of the algorithm, let the algorithm run the scaling laws on let's say 100 different variations of operations that potentially can give you a general purpose computer. So we build a meta AI system. The paper around these is actually published like two and a half years ago. We published a paper about, about the topic is called 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, 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? Number two was the efficiency of computation, how fast you can operate. Number three is latency of operations. And number four, do not lose accuracy on the performance. There are pure transformer models and then there are hybrid models that you can actually build. Hybrid models have like an essential component, like they have a little bit of transformers in them, but the rest of the kind of dynamical system and most of the dynamical system for the purpose of these four objective functions that I mentioned would be you would change that and you can actually automate this whole framework to design foundation models in house. The technology stack of liquid foundation models is called Automated Foundation Model design kind of 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 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 Network's 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.

Peter

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

Dr. Dawn Musailam

Great to be here.

Peter

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

Dr. Dawn Musailam

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

Peter

Wow.

Dr. Dawn Musailam

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

Peter

And one of the things I love about Fountain is we're searching the world for the best therapeutics, the best approaches, and making sure we bring it to our members. So if having healthy brain function till 100, 120 is important to you, check out Fountain Life. Go to fountainlife.com Peter. Make sure you become the CEO of your own health. All right, now back to the episode. All right, our next story comes from Palmer Luckey, the founder of Oculus and now the chairman of the defense giant Anduril. It's funny to call Anduril a defense giant, but it is. He's claiming that the modern patent system has become a national security liability. In his words, the entire patent office could be downloaded every morning, ripped off, and used to fight a war against you. 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 and patent law is in such full, clear, and concise and exact terms as to enable any person skilled in the art to make and use the same. So if you do that, you're effectively teaching the world how to use it, and you're exchanging that 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. My guess is that is secondary to AI Palmer's proposed fix isn't to abolish patents. It's to massively scale up a national security 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? Let's watch a short video from Palmer and then we'll talk about it.

Palmer Luckey

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. We need to really fundamentally revisit the patent system. I think we need to massively expand the national security patent process. You can, you can obtain a classified patent, you can get a patent on something that you are not allowed to disclose to anyone, but you still maintain the exclusivity on those rights. We need to massively expand that program.

Peter

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

Alex Wisner

I think this is the episode of people tech CEOs floating terrible ideas. I think this is a terrible idea. I think, I would argue the Invention Secrecy act of 1951, which is, I think what Palmer is gesturing at, has been probably on balance, quite detrimental, not just to democracy, that if patents. 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 it's not 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 Anduril'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. 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.

Peter

We can have this debate. So Palmer's going to be joining us at the moonshots gathering on September 25th in LA. Everybody can go to moonshots.com we have an amazing day with the moonshot mates there and will 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?

Salim

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 write, replicate or learn from it, it'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.

Peter

Well said. Dave, any thoughts here?

Dave Blunden

Yeah, I think, you know, if there's a flashpoint for a World War 3, this is probably one of the most likely.

Peter

Seriously?

Dave Blunden

Where? Yeah, well, you know, look, Alex is right. We're going to discover new physics, new medicines at an incredible accelerating rate. And, you know, places like Europe respect intellectual property rights, and that creates a kind of a coherent economy where you can trade these things. China completely ignores intellectual property rights and just takes it and runs with it. So I think the likely outcome of that is the US will trade embargo. Anybody who doesn't respect intellectual property rights. And then you have to choose are you part of the free world or you're 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 incredible accelerating rate. And there's some vehicle by which innovators can profit from that. And if you don't have that, then you don't have the future. You know, a huge fraction of brilliant thinkers coming out of, you know, Cambridge and MIT and Harvard don't work on foundational technologies because there's no money in it. And that's got to change fundamentally. And protecting intellectual property rights is a key, key way to reverse that tide and get people working on really important things.

Alex Wisner

Yeah, I think to Dave's point also, Palmer fundamentally misunderstand, 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 sort of bellyaching that the, the Chinese are running away with the disclosure. It is really equivalent with enforcement of patent. It's not. You don't want to throw necessarily the baby out with the bathwater and say we want to give away the patent trade of disclosure in return for temporary monopoly. Really what he should be asking is better enforcement of US patents in China.

Peter

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 diagnostic for billions of people. The first story is the performance of GPT 5.6 SOL, which was released a couple weeks ago on Healthbench Professional, which is OpenAI's hardest medical benchmark. So ChatGPT or GPT 5.6 Saul 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. In 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 ChatGPT. We've known this for some time that these AI diagnostic models are better than the best physicians given all the tools that humans can use. The second part of 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 across the marks and it was seven times cheaper. But even better, I mean, important to note here is that musespark 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.

Salim

Celine, there's a model in an AI doctor in China that's being used in rural environments by 100 million people already, right? Basically, diagnosis is, 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 in line 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 right, let's recognize that in almost every country in the world world there's radical doctor shortage. So this is really, really critical.

Alex Wisner

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, it's, it's pretty, pretty wild. It cost of intelligence not just going too cheap to meter, cost of medical intelligence becoming too cheap to meter free, basically free.

Peter

I mean that's well the the ultimate

Alex Wisner

two cheap to meter is asymptotically free, right? But I would say probably in all honesty, I suspect a little bit of mild benchmaxing 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. So if it's beating say Fable 5 which barely allows you to do anything biological, or GPT 5.6 which does, does allow you to do it, that does to me suggest in all honesty, a little bit of mild benchmaxing. But still, it's a great day when Instagram gives better medical advice than Human doctors.

Peter

I think that's our takeaway quote from today's pod. I'm gonna move us to one more longevity story that I love. This is breaking news from yesterday, and it really got me excited here. I know you, Alex, and I were talking about this. So for decades, one of the fundamental problems of aging is the slow accumulation of what are called advanced glycation end products. I love the acronym. It's called ages A, G, E, S. And these are sugar molecules that cross link and damage your proteins in your body over the course of time. So this chemical reaction is called glycation, and it happens slowly in our bodies as we age. It stiffens your arteries, it clouds your lens 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 CMLase that acts like a molecular lawnmower. I love their description. A molecular lawnmower. It oxidizes away the glycation scars, and restores the original healthy protein underneath. And amazingly, this isn't happening just in a test tube. They showed it worked in human tissue samples from elderly donors, reversing damage that accumulated over the lifetime. It's still early, but the significance of this cannot be overstated. A category in aging that we've always filed as permanent just became reversible. And again, we talk about longevity escape velocity. We talk about, you know, our ability to understand the 5 billion chemical reactions per second per cell in your 40 trillion cells. And when we talk about reaching Lev, you know, longevity escape velocity by 2033, it's tech like this. So congrats to Revel in doing this.

Alex Wisner

And not just Revel. I mean, a couple of interesting notes here. It was Revel and Calico, the California Life, the company that was one of the. 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. That's very encouraging to me that Calico is apparently deeply involved in this and has a heartbeat. A couple of other points.

Palmer Luckey

The.

Alex Wisner

The broader process here, a class of chemical reactions are called Maillard reactions. It's also the reason why when you bake bread that the outer crust is usually ground or chemical or. Yeah, or it's why this vegetarian speaking, why everything purportedly tastes like chicken. It's the same class of reactions, but the sugar is reacting with the carbonyl functional group or carbonyl groups within sugars reacting with the amines in proteins to create broad class of molecules that. That look optically brown. So the same thing is going on in the human body. To me, this is very exciting because it's not quite unscrambling eggs, but it's halfway there. It feels almost. Again, strictly speaking, it's not like reversal of the thermodynamic arrow of time. But it's the next best thing if we can remove all of these unwanted sugar plus protein byproducts that are associated with inflammation and other correlates of aging with directed evolution of a protein that came from bacteria. Like, what else is there out there in the biosphere for us to mine? In addition to all the obvious glyp ones. Great potential for longevity, escape velocity. What other bacterial innovations can we use to turn back aging?

Peter

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.

Salim

I'll be happy when I have Ramin's hair. That's when I'll be happy.

Peter

Well, there are lots of companies working on that, Saleem. So, gentlemen, grateful for our time today. I'm excited for Starship 13 launch later today. Wish Elon and the group there. Lots of luck. Ramin, congrats on the success of Liquid AI and excited to have you on the pod with us. Us, Dave, Great move investing in Ramin. On behalf of all of our.

Ramin Hasani

Thank you.

Peter

Yeah, 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.