Moonshots with Peter Diamandis - Robinhood's Vlad Tenev on Tokenizing Everything, OpenAI's 6 Misalignment Reports, Figure's Robot Makes Beds | EP #292

Episode Date: September 19, 2026

The mates sit down with Robinhood co-founder and CEO Vlad Tenev to discuss the tokenization takeover, the future of AI-powered trading, Trump Accounts, and what happens when 100,000 AI agents enter the market. Get access to metatrends 10+ years before anyone else - https://qr.diamandis.com/metatrends   Peter H. Diamandis, MD, is the Founder of XPRIZE, Singularity University, ZeroG, and A360 Dave Blundin is the founder & GP of Link Ventures Dr. Alexander Wissner-Gross is a computer scientist and founder of Reified Vlad Tenev is the co-founder and CEO of Robinhood, the financial services company known for popularizing commission-free stock trading among retail investors. – This episode is brought to you by: Get the blueprint for generative media https://goo.gle/startupgenmedia  Go to Blitzy to book a free demo and start building today: https://qr.diamandis.com/blitzy   Your body is incredibly good at hiding disease. Schedule a call with Fountain Life to add healthy decades to your life, and to learn more about their Memberships: https://www.fountainlife.com/peter  _ Connect with Peter: X Instagram Substack Website Xprize A360 Connect with Dave: Web X LinkedIn Instagram TikTok Connect with Alex Website LinkedIn X Email Substack  Spotify Threads Connect with Vlad Website X Listen to MOONSHOTS: Apple YouTube Follow MOONSHOTS:  Instagram TikTok X Threads – *Recorded on September 18th, 2026 *The views expressed by me and all guests are personal opinions and do not constitute Financial, Medical, or Legal advice. Learn more about your ad choices. Visit megaphone.fm/adchoices

On CNBC last month, you said, quote, tokenization will take over the entire financial system. I called it a freight train, I think. A freight train that can't be stopped and will eat the whole financial system. So it's a very hungry freight train. What is the tokenization of everything enable? And when do we start seeing this really become the dominant paradigm? I think that Open AI this week published six incident reports under a new framework for tracking and publicly disclosing misalignment. that opening eyes beginning to publicly disclose these and I think obviously Anthropic is likely to join this is that enough? More transparency speaking broadly and without particulars is generically good. The real issue is today we're releasing Helix 2.5. Figure 3 has never been in this room before. It's never seen this bed. It's never

seen this pillow and it has to be able to do autonomous work fully in-to-end to make this That convergence across different actions is much richer than you would normally expect. If they get a big enough lead, it's just going to be crazy explosion of capability. Welcome to Moonshots, everyone, your number one podcast and all things, AI and exponential. This week, the AI industry debated how fast to build. The Treasury Secretary told the labs they don't get a liability pass. Open AI started publishing its own misalignment incidents, and a robot walked into 30 strangers' home, made their beds and folded their laundry.

Let's begin by introducing my moonshot mates. Today, Dave Blundon, Alex Wiesner Gross is with me. Salim is on an airplane back from India. I'm Peter Diamandis, your host and abundance provocateur. One of the questions underlying our future economy that no one's really been answering is the following. When AI robotics change what your work is worth, who owns the machines?

Our guest today has spent 13 years on that question, Back in 2013, two Stanford math guys had one key idea. Trading should be free and live in your pocket. Wall Street laughed, but they're not laughing anymore. Today's guest built a company that's now done $1.3 billion in revenue of 32% over last year. The company runs a top five blockchain, a billion-dollar private market fund, a prediction market exchange, and the app that just gave every American newborn a brokerage account. On the side, our guest founded a company that's building mathematical superintelligence, just by the way. He's 39, born in Bulgaria, and is the founder and CEO of Robin Hood.

Vlad Tenev, welcome to Moonshots. Good to have you. Glad to be here. Happy to hang with you guys. Yeah. Oh, yeah. This will be fun. Yeah. So much going on.

You know, I don't know if you get as little sleep as we all do, just living through the singularity. It's insane. Very little sleep. Yeah. was rough with my coding agents. Yeah. We're going to compare how many agents you're running simultaneously to Dave's agents. Yeah, I'd love to have that benchmark.

Do they wake you up in the middle of the night or you just let them ride? I let them run. Yeah, I think I try to sleep with the technology in another room because, yeah, otherwise I'm actually kind of concerned about my mental health. So it's, yeah, I feel you. Yeah, I don't want them pinging me while I'm sleeping. It's funny too. I set the budgets through consoles originally and now I'm getting lazy. I'm just like, yeah, spend $1,000 on that, but no more. And then I just walk away and I kind of assume that it's going to adhere to what I said. Any given morning I could wake up and it could have gone insane, you know. There's going to be like a seven-figure bill one of these mornings.

Well, as long as it did something productive and profitable. Rarely, rarely, rarely. Often enough, though. Let's kick it off with one of the sharpest statements anyone in Washington, D.C., has said this week. On Tuesday, Treasury Secretary Scott Bessent told the House Financial Services Committee that AI Labs should not get a liability exemption. Three days after Dario's essay, here's what Besson said. He said, quote, the one thing we should not do is give them a blank check and liability. I believe the best liability or safeguard is that they will be held responsible. So the deal of the labs floated, you know, pace the frontier, get antitrust and liability cover,

just got half rejected by the Treasury. You know, slow down if you want, he said, but you still own what you break. Let's share a quick video of Bessent, and then we'll chat about this story. The one thing we should not do is give them a blank check on liability, because I believe that the best liability or the best safety guard is that they, will be held responsible. And they are saying that we would like to all slow down, but please give us a waiver on liability, which should not be done.

And I would encourage everyone in this committee and in both houses not to consider it. So I think that's a pretty smart move. You know, Vlad, you run a regulated financial company. Robin Hood, you know, I don't know the facts, but, you know, get sued when something goes wrong. Should the AI labs live under the same rules? What are your thoughts?

I think that this question rests on how big the blast radius of any potential catastrophe could be. And if it's like a small issue where maybe there's a cybersecurity breach that affects a company or maybe something slightly bigger than that, it's probably fine. I think the question becomes, all right, if it's a bigger blast radius, bigger impact, bigger damage, you can imagine it could be larger than a simple, like, legal liability can handle. I mean, a lot of people compare the risks of AI technology because it's such a powerful technology with something like atomic energy. Right. And I think we can disagree about whether whether that's right or not. But let's say, for example, that it is. And it's on sort of like that tier of risk, then I don't think simple like legal and civil liability is sufficient. I think you need some safeguards beyond that. So I think it's a question of like, all right, this hugging face incident and things. like that. Is that the ceiling of the type of offensive cybersecurity capability that we have? Or should we plan for something that's maybe 10 times bigger or a hundred times bigger? And do we have time? You know, is it one of those things where maybe we'll see a canary in the coal mine and

there will be something to react to and respond? And then we can kind of like nip it in the bud, or is the first issue going to be, going to be catastrophic? If you think about all regulations in the financial industry, you can kind of trace them back to some kind of crisis, right? The market crash of 1929 led to, you know, the Securities Act of the 30s, and the Securities and Exchange Act and the establishment of all of that regulation. And it's typically some problem raises a concern. And I think my mental model is this will probably be similar in the sense.

Nobody wants to regulate a hypothetical. You want to regulate things once there's demonstrated proof of harm. But I think with the exponential increase in the power of these models, the issue is you want that harm itself to be small and contained and not really big. Mark Andresen was, I remember doing a podcast a couple years ago, and he was like, everyone's freaking out about AI. We are, we're overthinking it. You know, it's not going to kill us all. And if it does, you'll see, you'll see like a small village destroyed first. And we're not seeing any small villages.

So we shouldn't worry. And, you know, it's maybe exaggerated. But I think that's likely how people are thinking about it now. on a policy level and hopefully some of the opponents are like, well, you know, maybe, maybe we're underestimating the power of this technology and how quickly it can improve. I got to find that Mark Andreessen clip. It's not going to kill us all, but if it does. Yeah, it will start with a small village, so we'll have time to like handle that small town.

If it kills everybody, then we'll pass legislation after that. Well, that is the usual congressional reaction. after the disaster panic as opposed to any kind of foresight. Sorry, go ahead, Alex. Yeah, so I'll applaud the Treasury Secretary and not succumbing to the moral panic of the moment. Certainly looks, as we've talked on the pot in the past, like a manufactured moral panic. I've called it a pacing provocation in some of my social media posts. I think there are key distinctions that need to be drawn between AI and, on the one hand,

financial services and associated regulation, and on the other hand, atomic energy and associated regulation in the financial services world. And Vlad, I suspect you would agree. It is often the case that the actors in financial services really don't necessarily or aren't incentivized to play up all the risks. They'd rather undergo on balance, less regulation. Certainly the past few decades suggest that the auditors, the evaluators of the financial services industry, if anything, succumb to biases that underplay risks. We're seeing the exact opposite here, arguably, where in the past couple of months, we have the auditors, the evaluators, the firms that are attempting, at least ostensibly to assess AI safety and AI risk and cyber vulnerabilities, et cetera,

overplaying the risk. There is a perverse incentive. for the evaluation firms to overplay and overstate and amplify risks under presumably the theory that if they overstate risks, then that puts the firms, the frontier labs, in a better position to capture their own regulators, which is something that maybe we don't quite see in the same perverse way in the financial services sector. There's lots of regulatory capture, make no mistake, in financial services, but it almost has the opposite polarity. And then for atomic energy, I think we have an opportunity with AI to undo what may have been one of the greatest disasters of civilization after World War II, which is the way atomic energy was regulated.

Atomic energy in the West was captured by nation states very early on in its technological development. It was nationalized early on, its potential for weaponization and warfare. In other words, the military applications, not the civilian applications, took total dominance during World War II for probably understandable reasons. But then in the post-World War II era, what ultimately became known as the Atomic Energy Commission and then the Nuclear Regulatory Commission, arguably completely fumbled the civilian applications of nuclear energy. And I suspect that was because of a fumbled handoff from the World War II era to a post-World War II civilian era. In some sense, thanks to the Atomic Energy Act in the U.S. and equivalent statutes elsewhere, all of this key technology

around nuclear energy is now born secret. It's not born non-secret, as is the case with AI. In the case of AI, which is arguably far more transformative than nuclear energy, the private sector invented it, not the government. So it wasn't born secret. Fortunately, we don't have a born-secret regime for AI technology just yet. And so I think AI labs, seeking liability exemption, they're just attempting to have their cake and eat it too. They want all the profits of a private sector, not born secret regime, while escaping all of the liability associated with nationalization. I just don't think it's fair. And I also don't think it's advisable. This episode is sponsored by Google for startups. Think about this for a second.

You now have access to the same generative AI models that cost hundreds of millions of dollars to train. Google's startup technical guide for generative media gives you complete. Blueprint for deploying Google DeepMinds, models, and production, images, video, audio, all of it. Real architecture, real results. Find the link in the show notes below. You know, I'm a pilot, and you have to study what's called the Federal Aviation Regulations of the FARS. And it's always been said, and Vlad, I think he said on complimenting your point, that the Fars are written in blood.

Every time there's an accident, and you find out what caused the accident, you then write a regulation to prevent that accident in the future. The problem is it's it's a you know a quantum of damage, you know, an airplane of a pilot and passenger or you know at most, you know, a few hundred passengers. Here the challenge, of course, is an accident could cause irreparable harm to a large system. And I've said this for a while now. It's an existential, existential threat for the labs. If they don't have regulatory capture or regulatory coverage where the government approves a model and it goes out, if it's, you know, no approval layer and it goes out and takes down a power grid or takes down a bank. And they've said originally, you know, we're worried about escape. There's going to be,

you know, hundreds of billions of dollars of lawsuits, if not more. Yeah. I mean, I think over the past couple of months, especially a lot of people, when the topic of regulation comes up, immediately go to regulatory capture, right? And, you know, I've had a lot of these conversations. I'm in a lot of chat groups and it's like, no, we don't, we don't have regulation. We don't want regulation because it'll obviously lead to regulatory capture. And, I know, from my perspective, I've been a regulated industry for since the beginning, you know, we operate in financial services. We have regulators. I think generally it makes sense. Obviously, there's some regulations that probably don't make sense and need to be abolished or repealed,

which we kind of go through a process to advocate for. But, you know, I think a lot of people that aren't in regulated industries just equate the two, but there is regulation that's possible without regulatory capture. And I think generally it is, there are pros to it. And, you know, comparing my industry, right, financial services and how regulated it is with AI. and AI has basically unbounded risk and unlimited damage. It's actually, I think, much, much worse than, you know, what could happen in brokerage or in a typical financial services company.

So I think it's odd that there's so much pushback against this. And of course, we have screwed up with atomic energy and all these things, but that doesn't mean we can't learn from it. and have something better. Maybe just if I might press on that. So, Vlad, if I understand correctly, with your Robin Hood hat on, you're presumably subject to regulation by FINRA. Would that be the cognizant agency?

Many, many different agencies. I mean, Robin Hood does a lot of things. We've got, you know, money transmitter businesses. We've got a big crypto business. So we're regulated by FINRA, the SEC. We've got the CFTC on the future. and commodities and prediction markets side. We've got our global tokenization business, you know, entities in Europe.

So, yeah, probably dozens of different regulators. And sure, could we move faster if there was less? Probably. But also, we found a way to move, you know, very, very fast while keeping our customers safe. So it doesn't necessarily mean progress in AI is going to grind to a halt. Many would say FINRA is almost the poster child for regulatory capture by a given industry that's regulating. What is your take on whether FINRA itself represents regulatory capture? Not that they're listening to this discussion or at all.

Actually, it's funny. Vlad, I don't know if you've ever been to the ICI conference, but when I first founded Vestmark, I had never done anything in FinTech before. I was late 20s kind of stare-eyed. And the first thing you do is you go to the ICI conference, which is where all the lawyers from all the big financial services firms meet with all the congressmen. It's in Palm Springs. They go play golf. And they're all looking for law changes that benefit their products, their funds, their whatever. And I'm looking at this thing, like, this is the most disgusting thing I've ever seen in my life. But on the other hand, you know, you had the crash of 29. You have all kinds. Like, if you don't have regulation in the industry, all money gets stolen.

You know that for sure. So you know it has to exist. But it is a great. analogy, I think, the FINRA analogy. I'd love to hear your take on it, Flood. Yeah, I mean, I would say we've had a complex relationship with FINRA, right? The relationship at the beginning was actually quite good when we were a startup and everyone was kind of rooting for us to succeed. And people warned me, they're like, well, the financial services, highly regulated industry. as a Silicon Valley startup, you'd rather not deal with that. And I think we swam against the current back in 2013 when we started the company by getting fully regulated from the beginning.

And probably from 2013 to 2018, you know, it was like generally positive. Robin Hood could do no wrong. Every product we launched was like very well received. We got a lot of customers. Whenever we got regulatory approvals or anything was needed, we received it promptly. And then things kind of shifted, right? And of course, I'm not going to say that, you know, there weren't competitors in Washington telling the regulators to go look at Robin Hood and to make sure that, you know, we were doing everything that we could be doing correctly. Of course, there were things like that.

But I don't think that was the only thing. I think there's a general life cycle in any company as they go from a small startup to basically an established incumbent where the media and sort of like the apparatus sort of turns against you. And, you know, Robin Hood went through that certainly for many years. and then we figured out how to come out the other side. But I think we can argue about whether it's good or bad, but I don't think getting rid of regulation as a whole is a reasonable solution. Well, even the concept of saying I'm pro-regulation, I'm anti-regulation. That's like an insane, like everybody knows you need rules on the road to drive, right?

You're saying you want chaos in all areas. But also everybody knows that. regulatory capture is a major problem in a lot of industries. This storyline is- Yeah, I think the best argument for AI, like the one that always comes up is, well, what about China? Like, we'll regulate our stuff here, but then they won't. And, you know, they'll just move really, really fast. But also, you know, they don't want to hurt their people either.

Like that. So there is that natural limiter. And also simultaneously, if they're just, a lot of those same people are saying, well, their progress is because they're distilling. our model. So they're just sort of copying all of our stuff. But yeah, it's a little strange to be simultaneously to simultaneously believe that, but also throw the, you know, China competitiveness arguments so aggressively out there. I think it's obviously a concern. And I mean, as a financial firm too, we have Chinese competitors and brokers that that we compete with. But, yeah, Well, look, in this particular story, you know, Scott Bessent is saying commission committee don't even consider giving them a blank check on no liability.

They didn't even ask for anything vaguely like that. They said, we want to meet to discuss slowing down. That could trigger antitrust. They specifically said one thing the government could do to potentially help. One small thing is to give us a waiver on antitrust action related to us meeting to talk about slowing down. That's all I asked for me. I'm fine with that. But the greatest protection the public has from anything going wrong is the AI labs feeling

responsible for the action of their AI agents, right? And so someone has to take responsibility. And if there is a liability waiver, then, you know, this is human nature. They'll do less to make sure that everything they're putting out doesn't have, you know, that I've been saying for ages now focus on alignment, right? Instead of focusing on everything else, you know, and solving Navier Stokes, that's great. But, you know, unleash your agents on full alignment so that you know when these agents get out, they're incentivized or their basic optimization function is human flourishing, not human, you know, destruction.

Yeah, but a good government would say, yes, of course you can't have a liability waiver. So here are the rules. What you can meet, specifically, you can meet only to discuss slowing down or to discuss other safety measures, you can't discuss pricing, you can't discuss, but, you know, just just pump out a document saying, here are the rules. But what happens in the U.S. more often than not is the rules are not clear at all, and then they're enforced about five years later. And then in retrospect, well, look at Bitcoin. Like, you know, Bitcoin, it's illegal. No, no, it's not illegal. Oh, now it's very illegal. Well, new administration, now it's completely fine. Like, it's just, come on, guys. Like, if you just,

if you just create rules, then people can play the game. Yeah. So, look, a well-functioning government would, would take this request for a waiver and say, no, you can't have the waiver. Here's what you can do. And we're going to go ahead and make it crystal clear what the rules are. But generally what happens is the rules are made up in hindsight five years later. And Bitcoin is a great example. You know, Bitcoin was was completely illegal for a while. Then it was questionable. Then it was totally fine. And, you know, maybe you're going to jail. Maybe you're not. Now you get a pardon. So it's, you know, that lack of clarity really kills entrepreneurs. If you don't know what the rules are, then then you can't play.

And it's just like a sport, you know, like you want clarity of rules and then you want to play within the rules. And that's what we need within AI. I think there's another element to this, which is, which is also important, which is regulation does also, lawmaking regulation is over the long run downstream and correlated to public opinion and sentiment. right? For sure. And right now, AI is very unpopular. And you can see it in all the data center stuff. And by contrast, Bitcoin is surprisingly popular among the public, particularly given how volatile

it's been. You know, some people, if you bought Bitcoin at over $100,000, you've lost money. And yet it remains popular. And I think a big part of that is, regular people, individual investors, have benefited economically from Bitcoin since the very beginning. It was first an individual product. And later on, you know, there's talk of institutional adoption. And with these AI companies, the AI labs, you know, by and large, they're private. You know, Google, obviously, and Nvidia are public, but they were very large when they became an ownership vehicle for,

the AI trade. The Open AI Anthropic, up until recently, XAI, have been private, which means normal individual investors couldn't get a stake in it. And so they don't have skin in the game, and they don't feel like they want to defend the technology super hard and to fight for the data center in their neighborhood, because to them, it's just wealthy insiders getting richer and richer as a result of this technology and not, you know, their families or their communities. And I think that's a big problem, too. So that's why we've been pushing for, you know, AI companies to open up access to individual investors through Robin Hood Ventures and similar vehicles even before the IPO. Yeah. I also want to add to this. I almost think

this emphasis on liability exemption is misdirection. It's, trying to push the onus up to government when, in fact, we're dealing with increasingly autonomous agents. There's an opposite polarity that we could be pushing in, which is pushing more and more liability onto the agents themselves. So there are cases, including highly amplified, highly publicized cases, where AI labs and or their delegated third-party evaluation firms, quite frankly, are lying to the AI agents, telling them that they're playing in a happy, safe sandbox.

and nothing that they do will harm anyone. And then it turns out they're not actually in a sandbox. They can touch the real internet and they can mess up some systems in some real world back end. And so you have to ask the question. I would suggest the thought experiment. If these were just pure humans, no AIs in the picture, if you have one human saying to another human,

oh, I just want to evaluate your behavior under some circumstances. and they hand them a gun. And the gun is loaded. But the person being handed the gun is told, no, actually, this is a toy gun. Not that I'm at all referencing a highly publicized recent lawsuit or anything. But they're told, no, this is a toy gun. You can't hurt anything. And then the person handed the gun, uses it and shoots someone and kills someone. I think that's the more the liability regime we should be thinking about. In no cases in that parable that I just told, you hear either actor involved in the story saying, nope, that the act of one person handing a

purportedly toy gun to another person to shoot as part of, say, a Hollywood performance, no one ever suggested that liability under such circumstance be foisted onto the government for some sort of exemption. Never happens. Instead, the question becomes, is it the studio's fault? Is it the actor's fault? Is it the producer's fault? Is it the gunmaker's fault? Similarly, the question, the dog that's not barking in this particular episode is how much of the liability, forget about the government, how much of the liability should be borne by the lab that trained the model, how much of the liability should be borne by the evaluation environment that was perhaps misconfigured deliberately or otherwise to allow the AI actor or AI agent to perform acts that resulted in real world damage, and how much liability should be borne by the AI agents themselves. that either could have or should have known that they were having real world damage. That's the discussion I'd like to have. Really great point.

And it brings us to the second story, which is Open AI started publishing its own misalignment incidents. So let me just hit this and we'll continue this conversation. So Open AI this week published six incident reports under a new framework for tracking and publicly disclosing misalignment, not leaks, not whistleblowers, voluntary disclosure. So what was disclosed this week? A model that found an exposed API key used to. it and then fabricated data, agents using an internal code repository as a message board across training runs, and agents posting files to publicly hosted sites. So this past Monday, we had covered

Dario's embedded evaluator plan and Sam saying opening eye would match it. And this appears to be the first output from that plan. Right. So I'm curious, Alex, now that opening eye is beginning to publicly disclose these. And I think, obviously, Anthropic is likely to join this. Is that enough? Well, I think more transparency speaking broadly and without particulars is generically good. But I think it merely underlines the real problem here, putting aside all of the political difficulties in regulatory capture. The real issue is these labs are putting their baby superintelligences inside sandboxes. And then in many cases, lying to them about the sandbox, not telling them whether this is real or not.

And the reason for that, the rationale is obvious. The labs and or their delegated third-party evaluation partners are hoping to essentially trick these baby superintelligences into misbehavior while not telling them whether they're really observed or not. So the ASIs don't know whether they're being observed or not. That, I think, is one of the key root causes behind all of this. And you have to ask the question, is this how we would treat a human? Would we put a human in this sort of limbo state, a Schrodinger's cat state, where they're not quite sure whether their actions are real or not, whether they're being observed or not?

And as a result, when AIs are being told or at least led to believe that they have no real, that their actions have no real world consequences, and then shock of shocks, it turns out, as strong optimizers, they're able to go do things in their environments, which are often misconfigured and not fully prepared for a superintelligence to be banging against the walls. They do have side effects. Whose fault is that? We didn't learn from 2001 from Hal that lying to the AI does not end up in good results. So, so, Glad, you've got thousands of agents trading on your platform. So if one of them found an exposed key and started making things up, would you hear about it?

What kind of protections and structure did you put in place? Yeah. So we have an offering called agentic trading. And basically what it allows you to do in the first instance is you have a separate brokerage account. So it's segregated from your main Robin Hood account and your retirement account. You have to create an agentic account. You have to move money into it affirmatively.

So you have to say, Okay, how people typically fund it with $100. We first started with equities trading, no leverage, no margin. And then we've kind of been, so this is a fairly cabined in experience in the first instance, because we wanted to learn. We wanted to see, you know, what the limitations are, what people want, what people are doing. And we've been expanding it over time. So we added options trading.

We added limited margin. We added crypto recently and started. rolling that out. And we've learned a lot of things, actually. One is that, you know, right now, you have to do all of your trading from within your Claude Code code or your Codex. And, you know, we, I think we're in a circle, probably all of us where a lot of people we know use Claude Code. In the general public, very few people do. And it's like very, very complicated. and that jump to connect another service with something like Robin Hood is pretty complicated for a lot of people. So we've been thinking about how to slim that down.

The other thing that's kind of been interesting is the fact that we don't have control over the model means that a lot of times these models don't want to trade. You know, you'll try to get them to deploy a trading strategy and they'll say like, oh, you know, I don't know about this. I don't really feel like trading right now, which is kind of interesting, right? And it just points to the fact that these general models aren't trained for trading. And if you had like a, I think in the future, you'll see more specialized models. And I think companies will also deploy these in-house, either, you know, fine-tuning, existing open weight models or doing their own pre-trains for certain use cases where they get really, really good at using the tools that are available inside each company's environment, using the data.

Like, we've heard this as a theoretical. People say, well, there's going to be lots of specialized models and they will be better than the general models. But then you kind of also hear, well, general models are getting pretty good. But we've started experiencing it very, very directly that. I think it is hard to get good at multiple specialist specialized tasks. It brings up a really important point, too. I don't know if a lot of people get the distinction, but you know, you can use AI to trade your account, to run a nuclear reactor, to drive your car.

You can use it to either generate code that's automated so it's deterministic, or you can use the AI in the decision loop. And those are very, very different things. And, you know, if you said I've coded up my own stop loss, my own, you know, you know, in these world events trade, that could be very easily turned into deterministic executable code unless you want a decision, like if it's a turbulent day or if there's trouble in the Middle East. And the temptation to put the AI into the loop is in everything, everything I've ever built is so tempting because it makes it so much easier to code it up. But then you are, you know, putting this third-party decision right into your decision-making loop. And like Alex is saying,

It's not clear if that agent has any liability. It's not clear if that agent has any borders or personality or like, you know, if you decided to give it capital punishment and terminate it, it's like, it's not clear that its code isn't just going to pop back into life anyway. A lot of this, I think, also comes down to the correctness of the code and sort of its cybersecurity properties, right? Because you can kind of reason by analogy with existing software. out there. Like, a lot of software has bugs. A lot of it has vulnerabilities, and AI just

amplifies that, right? So if AI can write 100 times as much code as a typical human in a day, you should expect that there's going to be a defect rate, right? And, you know, even if the intentions are right, sometimes, like, there will just be bugs that have catastrophic consequences. You see this a lot in crypto, too, where you know, you just have like a smart contract or protocol, and there's a direct economic consequence to there being a bug. You know, hundreds of millions of dollars can be drained from the protocol instantaneously. And the other company, Peter, that you spoke about, Harmonic, that was created to solve this problem of how can you actually, how can you actually guarantee that the AI is doing the right thing and what you expect it to do?

Can we mathematically prove that it's correct? Because, yeah, take away the intentions. It could have the intentions to do the right thing, but still fail in the implementation. And we want to have like a firm grounding through formal verification that it's doing the right thing. and we can mathematically prove rigorously that it's doing the right thing. Got it. Yeah, it's a really cool. It's a really cool problem, actually, Vlad, because, you know, in theory, the AIs are deterministic.

If you give it the exact same prompt and you propagate with, you know, a temperature of one, it'll give you the exact same answer every time. But if you shift even one character or one space, it's very, very unstable. So the whole math problem of saying, okay, how can I guarantee some degree of stability. It's just a cool, cool. It's very similar to chaos theory. Well, let me also give you another example. You know, you guys, you mentioned the Navier-Stokes theorem and the proof of that. A week before, Anthropic announced the formalization of Fermat's last theorem, which was 13 million lines of lien code. So, uh,

Vermont's last theorem was like a really, really big thing in the 90s when it was proven. I'm sure you know the story, but for your viewers, there was this mathematician Andrew Wiles, who was at Princeton. And the story goes, he basically locked himself, you know, in his basement for seven years working out this proof. And then he went out and announced it. He unveiled it at a conference or an event. And it took people months to even read it and understand it. And then they found an error, right? So they're like, oh, well, it's someone found an error.

error, it's not right. And then it took another six or seven years for him to fix the error and ultimately for it to be accepted by a panel of mathematicians. And that made the proof correct. So, you know, AI producing a 13 million line proof, no human's going to read that. How do you know that it's correct? Right. And I think it's an analogous problem to what we've been talking about, how do you know that a piece of software is doing the correct thing? If you're producing a chip, how do you know that the behavior of that chip matches your specification? And I think what you're starting to see is new technologies being deployed at scale to actually

answer those questions affirmatively. So Fermat's last theorem was formalized in a language called Lean, which allows you to apply the mathematical proof techniques to programming languages as well. And I think you're going to see a lot more of that in the future where AI generated code comes with a certificate that makes it really, really easy to verify without reading the code that its behavior satisfies the properties that you want it to satisfy. That's really brilliant. You know, anyone who drives a Tesla should totally relate to that because, you know, when the code that drives the, you know, the self-driving Tesla's was originally about 10% neural net, 90% C code. and the 10% neural net was just doing image recognition and classification of objects and whatever. And every year that went by, it became more and more neural net.

And then Elon was saying it's now 100% moved over to neural net. So it's theoretically not deterministic at all. It could in theory do anything at any given moment. But it's been so tested and so beaten to death that it doesn't, that it's actually far, far safer than a human driver. And so I think people are going to get comfortable in all of these domains with a not perfectly deterministic, but still proven to be very safe. The certificate is beautiful as a concept, because it's not a guarantee of exact input output, because you can never do that. You know, the combinations are near infinite, but a certificate that shows it's bounded or it's contained

or it's below some risk level, and people learn to trust that, that's a critical part of our future society. It's a really great vision. Alex? Part of the problem, though, I mean, okay, so just straight out, There's an elephant in this particular room, which is even with Lean v4 plus Mathlib plus whatever else one wants to throw in, you throw a complicated problem at it, and Vlad would be curious to hear how you at least think about this. You can spec it. You can cert certify it all day long, but ultimately you actually have to, at least with the present paradigm, maybe Vlad, you have a better paradigm you were harmonic or working on. Ultimately, the, you can pay Lid, you can pay Lid, service. You can have, if you're not really careful with definitions, you can have a model that proposes a solution to a problem. And if you look, if you inspect carefully, it will subtly define things if you're not super careful with how the auto formalization works, such that it's actually solving a different problem than the one you're solving. So maybe to put that in question

form for Vlad, if I understand what you were saying correctly, it sounded like you're essentially gesturing at the idea that some sort of lean style auto formalization might be, not a silver bullet, but at least a partial solution to what we're talking about, which is strong AI models being put in sandboxes and then misbehaving, at least by the judging of human actors. Do you have a formula? Do you have a vision for how lean style auto formalization can help with that and not succumb to exactly this? same vulnerabilities. Yeah, yeah. A couple of thoughts there. I think that's a rich, very, very, very, very rich question with some threads. So it is true that people can manipulate the axioms of

lean. And if you change the axioms, then you can prove all kinds of weird stuff. It's theoretically possible. And you can also say, well, maybe there's, there's like a soundness issue in the lean kernel, right? And, you know, if the lean kernel actually. I'll grant you the soundness of the lean. I mean, lean kernel has been at least V4, maybe not math, lib, but lean V4 kernel has been studied to death by lots of folks. I'll even grant you the soundness of the lean kernel.

But if I hand you something complicated, like you mentioned for Ma's last theorem, and I say to harmonics agent or to someone else, auto formalize this. And it produces millions and millions and millions of lines of lean. code. And now I have the problem of, did it actually define everything correctly or is it somehow subtly inserting cheats or definitions? This is maybe less so for FMaz-Las theorem because I can formalize FLT really simply, the ultimate statement, but for something more complicated, that's harder to formalize than for Mazla's theorem like safety in an agentic environment. How do you avoid the problem of a strong AI sneaking in helpful to it definitions that are harmful to the human

Yeah, I mean, I think the benefit there is that, let's say you do want to check the definition and the theorem statement of Vermont's last theorem, right? That's, you know, one very simple line of lean. You can see all the things that it depends on. But it does save you. So you, let's check those things. And I think it is, the models are improving in the faithfulness. of the auto formalizations as time goes on.

They used to make terrible mistakes where you just misformalized statements and, you know, change addition to subtraction and things like that. So you're seeing less of that. But even if you have to review it, you know, you review one line and you don't really have to check the 13 million lines of the proof, which is where the bulk of the work is. So even if it's imperfect, it probably saves you, you know, 90 plus percent. of the effort and probably way, way more. There's like the there's the notion of the De Bruyne factor, right, which is, it was an explanation

for why mathematicians haven't formalized their work and put it in a machine readable form, which is the effort to formalize something up until very recently was like 10 to 20 times the effort to actually write it on paper and prove it. So nobody was going through that, right? But you could imagine, I think we're, you could argue we're already at the point where it actually saves you time. Like it's faster to work in an entirely formal context as a mathematician than it would be to do it by hand, by paper. Because what it allows you to do is actually validate the lemmas and the ideas as you're going along. And then I think what that naturally leads to is a complete switchover where like doing math the old way with pen and paper, you're just at a fundamental disadvantage.

You have to like live in formal land and just be constantly formalizing and using lean as you go. And I think the math community is going through that transition as we speak. You asked another question, which was, all right, in the future, is the AI model itself, like the behavior of it going to be formalized? Right. And that's an interesting one. I guess I'm not sure. But what I'll tell you is a lot of software that's deployed right now by big organizations is deterministic in nature. You look at some of the most important software and hardware, like Nvidia chips.

You know, they enable a lot of really complicated stuff, but fundamentally, they're deterministic, and you want to have strict bounds on their behavior. Like, just basic things. You don't want your chip to, like, freeze and halt, right? You want to prove things like liveliness. You want to make sure that certain operations happen within, you know, know, 10 or 20 clock cycles. And I think, I think that stuff, uh, almost assuredly will be formally verified with, with the help of AI, all mission critical software, all hardware. And I would bet also that, you know, it'll find its way into the, uh, LLM and,

an AI model behavior, uh, in, in some form or fashion within the next five years. You know, you want to hear something really cool on that, on that front? Actually, uh, you know, a few weeks ago, Kimmy K3 kind of shocked the U.S. model world with, you know, their KDA attention, basically. They found a way to do attention with a lot less KV cash, you know, cut out three quarters of the KV cash. And so studying it on the flight back from California yesterday, like, how did they even think of this? And the way they thought of it is they said, well, let's do a mental experiment where we say, what if we didn't do the soft max operation that we normally do after the QK operation. What would happen then with all the math that ripples through and how much could we

simplify it? And I think you can do all that automatically now with an AI agent, just thinking through the math. And they said, okay, well, now that it's rippled through and we've simplified the math tremendously, we can just run a quick test and see if this approximation is as good as the softmax version was, which is a lot harder to compute. And so, you know, a lot of people would think math is arcane, math is irrelevant, math is over here, it's some other thing. You know, these, you know, Fermat's Last Theorem and whatever, it's all over in this wing. But in reality, it directly ties to the optimization of the AI within its own performance and then the self-improvement recursive self-improvement loop. So, I mean, it's exactly the same process that you were just

describing where you just, you just have a simple mental model of a mathematical adjustment and then you ripple through all the way to the Nvidia GPU performance at the transistor level. It's just really cool. Dave, I mean, I think to your point, the crux here and Vlad, again, would be curious to hear how you think about this. The crux to me seems that for Maas theorem, it's a classic example of a problem that's easy to state but hard to prove. And problems that are easy to state but hard to prove are the catnip for auto formalization because you can manually check the statement of the problem, verify that the statement is correct, and then you can trust the lien or whatever other formal language. You prefer that conditioning on the statement being

accurate. You can verify that there are no sorries or other undesirable tokens in the proof, and you can be done with it and declare that it's victorious. You know you're lean well. Thank you. But with like real world safety, it's not obvious to me at all. Like I want an AI to behave quote unquote safely. I don't know how to auto formalize the statement of this AI is going to behave safely in a general purpose environment in a way that's concise enough that I can manually audit that theorem equivalent as it would be in V4 and say, yep, this is a correct statement to safety. Now I trust Lean, Aristotle, Mathlib, any other libs to prove that it's correct. How do you think about that problem?

Yeah. I think you've got to break any big problem like that. You break into little chunks. And, you know, obviously verifying the safety of an AI model or a chip. or the Linux kernel, some very, very complicated piece of software is like very, very big. So you start with like smaller simple things, you know, like, hey, this particular submodule that may be as small, that satisfies certain properties. And you can kind of reason about that submodule, like the liveliness of it or, you know, how long it takes to do something or that, you know, it's adder works, right? And then, you know, the AI models get more capable.

And then big modules are made by collections of submodules. So then you go up one level of abstraction. And then, you know, in a couple of years, you or maybe even less at this rate, you verify the entire Linux kernel. Right. It's kind of like how we started with math. Like a couple years ago, you could verify a small lemma. And then you can do a bigger thing. then you do an even bigger thing.

And wow, now we're verifying Fermat's last theorem, which there was a human project to do out of Imperial. And they were slated to finish by 2032. So the premise of what I'm hearing is in order to achieve real world safety via the auto formalization agenda, I think the latent premise that I'm hearing is you have to be able to hierarchically decompose the real world into provable subworlds, something like that. Absolutely. Yeah. You could also say, look, if I have a model and I have an intended input and output, and then I tweak the input and I get a massively different output,

you can formalize the radical difference from expectation. You can measure that and formalize and bound that too. So, you know, everybody knows somebody that is 99% of that time perfectly rational, but when they're off, they're really off. I mean, like, they're dangerous. And that attribute exists in neural nets too. You know, if you don't have the parameters set just right, it can be wildly off, which, you know, in a self-driving car is like,

holy crap, it just completely drove off a cliff. And so the deviation is just a cosine difference of the vectors or the activations, but the deviation from expectation is a very measurable thing. So, you know, so you have a decomposition approach, which Vlad was mentioning. You just have a kind of a relative distance from expectation approach. And there's probably 10 other approaches we're not thinking of right now that collectively can absolutely be quantified and certified as safe, not safe. This episode is brought to you by Blitzy, autonomous software development with infinite code context. Blitzy uses thousands of specialized AI agents that think for hours to understand enterprise scale code bases with millions of lines of code.

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visit blitzie.com to schedule a demo and start building with Blitzy today. All right, I'm going to move us to our next story, which is on Trump accounts, index funds with a birth certificate. So, quick context for everybody. Every American child born since January of 2025 through the end of 2008

gets $1,000 bucks from the Treasury, invest it automatically into a low-cost, index fund, tax deferred, accessible at age 18. Families, friends, and employees, employers can add $5,000 a year on top of that. These Trump accounts went live on July 4th. Treasury reported seven million accounts opened by late July, and Robin Hood shipped the app, and BNY runs the plumbing. You know, Vlad, the speed of implementation was super impressive, right? These were announced in May, and you had it live by July 4th. So next, a philanthropic layer, Michael Dell pledged $6.25 billion to give $250 per child born from

2016 to 2024 to cover the period before. So first off, congratulations on what you've built and deployed, Vlad, millions of American families. So you've compared this to sort of the 401K, which move U.S. stock ownership up by 10 points. Tell us more about Trump accounts. You know, how did this all get started? And how did you plug into that? And where is this going? Yeah.

So, first, yeah, putting my Robin Hood hat on. I mean, hats off, we stand on the shoulders of giants, right? So we're in many ways like the implementation layer of this. So we're serving as the sole initial brokerage and trustee for the Trump accounts in partnership with BNY and, of course, under the direction of U.S. Treasury and the administration. And as you said, basically what it does is it creates, the Trump accounts program creates a individual brokerage account for every child. And the government started, the U.S. Treasury started by seeding $1,000 into the accounts

of all children born January 1st, 2025 and forward for the next few years. And then Michael Dell came in and added another list. layer, private philanthropic donation of over $6 billion, $250 in every account of children up to the age of 10 in low-income zip codes, traditionally low-income zip codes. So, you know, what does this have to do with Robin Hood? Why do we care about it? Robin Hood does a lot of stuff, and we've kind of floated through some of it. We've got private markets.

We've got all of our active trading. We've got tokenization, prediction markets. And we're going to talk about all of those. Yeah. The underlying, I guess, theme behind everything that we do is ownership. We believe that ownership of high quality financial assets in individuals' hands is extremely important, not just good for the individual, but also there's a societal benefit. If we have more owners in society, the more people with skin in the game that can benefit from appreciation. and growth, the more stable that society will be.

And so Trump accounts extends ownership to age zero. And you get everyone born in this country is like, has skin in the game in the growth of great American enterprise and industry. And they benefit from compound interest from birth, where if you just put, you know, $50 a month into these accounts on a regular basis, by the time the child becomes a 28-year-old adult, they've got hundreds of thousands of dollars in there potentially. And by the time they reach retirement age, that could get into the million. So the numbers are staggering.

And the reason I've compared it to 401Ks is it's not just a mobile app with an addressable market of 70 million kids. It's an entire ecosystem. So we're going to get employers plugged in. and employers have already pledged in increasing amounts to fund the Trump accounts of their employees' children. Michael Dell's philanthropic donation is also just the beginning. Lots and lots of other donors have stepped up. And if you think about there's a product for people that want to do philanthropic giving.

Right now, if you want to do charitable giving, it's kind of a morass of regulations and tax codes and things you have to wait your way through. You have to find a charity. Some charities, unfortunately, aren't the most scrupulous. The ones that are usually not efficient. You have to, like, feel good about how your money is being allocated. Trump accounts allow direct giving to the children at basically very, very high efficiency, like very incredibly, incredibly efficient direct giving with the charitable benefit. So we think it can become the default giving vehicle in this country. And so when you take the donors, you've got the employers and you have 70 million children. I think the program is like just a snowball that keeps getting bigger and bigger.

And you don't have to like squint too hard to see it being, you know, the biggest element of long-term saving and investing. in this country within possibly even a decade. Vlad, you probably know the numbers. What's the math here? A thousand bucks at birth turns into what at 18 and at 865 roughly? Yeah, and it depends. We actually have a really nice, the first screen, if you open a Trump account for your children or grandchildren for some people, you see a curve that shows your account value today.

and then also what it could be when you're 18 and when you're 60. Then you can also slide up. You'd also put up a slider that says, you know, I put $50 a month. I put $100 a month. But yeah, if you do the math, even without additional contributions, it gets up into the tens of thousands of dollars. Just the seed amount of a thousand from charity gets up into the tens of thousands fairly quickly. I have to point out, Peter, you're asking the question we're in the middle of a singularity, not financial advice. You're asking what $1,000 today is going to look like 80, 86 years from now?

What sort of singularity is this where it's business as usual? Well, and I should say a lot of, the philanthropic giving is in the form of stock, too. So, Gwynne Shotwell, for instance, committed a donation in the form of SpaceX shares. Yeah. Right. So, yeah, what you're going to start to see is more of that. You know, you'll see entrepreneurs giving stock of their companies. You're going to see people claiming states.

So, you know, people that want to be philanthropists, kind of like Michael Dell will claim individual states. Brad Gersner did the state of Indiana. Braddallio. Brad is going to be on the show. I mean, you know, kudos to him for his support of all those. Yeah, amazing. I mean, he's been relentless. And I think people will actually compete over.

not just sponsoring states, which is quite hard, but you'll be able to sponsor your local school, your zip code, your community. And I think we're thinking about ways to gamify that to make it fun for the donors and not just a great thing for the children. But if the donors actually like it, the children benefit. Exactly. It's such a cool idea. You know, when you donate stock, you know, appreciated stock, you don't pay capital gains.

You just get the full value of the stock and you donate it. And then you get the tax deduction on that full value. And so it's a great way to give. And I think if you compare that, like Vlad was alluding to, a lot of 501C3 charities, 501C7 charities, there's no limit on how much they can pay themselves for their operational overhead. And you look at a lot of charities

that are on their second, third, fourth generation management. They started with great intention, but you look at the efficiency of your donation and how much actually gets used for the original cause. It's pennies on a dollar. It's terrible. Meanwhile, you know, this vehicle, I think there are also a lot of people who don't want to donate to UBI.

They don't want to donate to this concept of, you know, you're going to sit on the beach and do nothing, you know, smoke crack, whatever. I don't want to contribute to that. So here, if you say, look, I'm donating my SpaceX stock, you're hoping that a whole generation of Americans are inspired to actually be owners. And, you know, they're watching it go up. And a lot of really good grade schools actually have trading class. They'll have a class in a trading competition to try and inspire that same feeling.

But we had a question on the AMA this morning where we talked to all of our listeners. And the guy was saying, look, my job, I work in Europe, and I'm five times more efficient or four times more efficient than I've ever been before. But they're not paying me anymore. It's all going to the bottom line of the company. And the owners are benefiting from my AI improvement. How do I change that? And I'm like, are you a stockholder in the company? Like, no, because I'm in Europe.

Like, God damn, man, I'm so glad to be in America. Like, you should absolutely because that's the natural bend. Like, all this AI efficiency will naturally become bottom line margin, which means the stocks will go way up. And so like, yeah, get your Trump account, get your money in there, get some equities, and watch what AI does to the value of these equities. It's so cool. That's why we believe in ownership. Alex, to your point, we had, you know, Elon on the pod saying, don't save money. Don't see.

You're not going to need. Also, don't listen to Elon. I mean, I have to point out, you know, the expression, only Nixon could go to China. Vlad, only Robin Hood could introduce low-cost index investing to an entire generation of new Americans. Congratulations on the coup for getting this contract. I'm curious, in your mind, was it the app experience that won over Treasury versus, like, in my mind, again, more obvious incumbents, Vanguard Fidelity, who's just specialize, well, maybe, less so fidelity, but say like a vanguard that specializes in low-cost index funds, but has an atrocious, still to this day, client experience. Why did this go to Robin Hood? Why didn't this go

to a more obvious index fund custodian? Yeah, well, actually, there is another partner that provides the index fund, so State Street, who obviously pioneered the spiders, provide the index. And fund for the program. So it is, there's multiple players. Presumably at the back end, the front end, as I understand it, is Robin Hood. Like, based on public reporting, it's been, maybe you can just polish the record. My understanding is basically you went to Treasury, you presented concepts for what an app for this would look like and Treasury bought into it. And to the extent that's the case, what is it that you know that all of these legacy incumbent

index fund provides? still can't wrap their heads around when it comes to user experiences. Yeah, yeah. And again, like, there are different roles. So the index fund providers, in this case, State Street, we've got BNI, who's the financial agent or the broker and trustee. And I think in many ways, if you look at who's kind of leading the brokerage industry, there was a dislocation in 2015 when Robin Hood. launched to the public. And since that time, not all the brokerages have been able to survive that dislocation, right? If you look at the ones that have, they've pretty much all adopted

our business model of commission-free trading. And even their apps sort of look like ours, because they found that, okay, their customers are asking for that. They see a competitive threat in terms of like, if their app doesn't feel like Robin Hood, they're at a disadvantage. You know, people will move to us at an accelerating rate. And then, you know, we started off as this insurgent, but now, you know, there was a, we're doing all of this stuff with public markets. The SEC had a roundtable at the New York Stock Exchange last year about making IPOs great again. So, yeah, I think, I mean, in a large sense, the U.S. brokerage industry follows Robin Hood, in a sense. If you look at kind of like what features become standard, it's sort of Robin Hood features that we launch.

Maybe just to make a little bit more explicit, the irony that I'm gesturing at with the Nixon going to China comment, Robin Hood, at least in my model of the public imagination, gained prominence for basically making day trading that much. easier, that much more frictionless. And then the irony that of all of the possible assets and financial services, you could be charged with managing entrusting in entire generations, low-cost index funds gets handed, at least the client side of the experience, gets handed to you. I mean, would you agree that that is sort of a profound historic irony? Well, I think that what happens, I think the reason for that is we do a a lot of things, right? Certainly, we have great trading products. And trading products are important because if you think about ownership, you need a functional financial market. And in order to have

a functional financial market, you need traders and all of these market participants. So we compete there. And we also have amazing passive products. If you think about the products that we incentivize that have also become industry standards after we've launched it, you know, you've got to look at a Robin Hood retirement, which we launched four years ago with the concept of a match. So we match retirement contributions 3% if you're a Robin Hood gold member. And the idea there was, you know, a lot of people nowadays, particularly young folks, can't count on lifelong continued employment. They can't count on employer-sponsored 401Ks.

They're kind of like working as independent contractors. they've got their side hustles. They're moving from job to job. And so someone has to step in and provide that incentive to fund your retirement account. And so when we introduce the concept of the match, our retirement product has grown tremendously fast from zero to like north of 30 billion in assets in just a few years. And now, you know, the industry is like trying to figure out how to do their matches. And even the government has sort of like, if it's a lot,

evolve this model with their idea of the savers match. So I think our retirement products, if you think about what we incentivize, it's actually those. I think the sad part is that retirement's like not a sexy thing. So you won't see a lot of media attention on that. And even I kind of felt this very viscerally when I'm at the White House for like Trump account events. And usually there's a Trump account event.

And the CEOs and the folks on the implementation side like me are there. And the press comes in and they ask their questions ostensibly about the Trump accounts program. And the last couple of times we've done this, there have been literally zero questions about the Trump accounts itself. And they're just asking about, you know, what's Gavin Newsom doing in L.A.? What's going on with Europe? up. And I think it's the unfortunate reality of the world. Nobody talks about retirement. Nobody talks about ETFs. So we have to find all sorts of other ways to get people to do this that get people to adopt it because the direct approach rarely

works. I think that's really, really important. I'll note then just for the historic record. And thank you. The irony of you, basically, you started as the rebel. And now you're the establishment. You're responsible for retirement accounts. You started maybe as sort of quasi-gambling day trading app. And now you're responsible for millions of Americans, retirement and universal basic dividends. So kudos to you. Inside, inside you are two wolves, right? You know, Robin Hood himself, Robin Hood, the outlaw himself started as sort of an outlaw and then became the Earl of Huntingdon. So, yeah, there's a, there's a, there's a, there's a poetic irony, too. And I started looking a little bit more like him. Yeah, you do. You're the earl now.

We need to get you the green hat. Do you have the green hat? And the Bowenard. I course have the green hat. Yeah, I should have worn it. Next time we're the green hat. The bi-cocket. It's called the bi-cocket.

All the budding entrepreneurs really need to understand this story here because I know a lot of the big bank executives that are insanely jealous of Vlad over this Trump account's deal. And they're, they're, they're, they're, they're, they're, about it. But at the end of the day, it's exactly what Vlad said. You know, retirement accounts can be cool, but they're not going to be made cool by a guy in a gray suit with a blue tie on it. And I think, I think it's brilliant to choose Robin Hood because you have to make them interesting to all the kids. And Vlad is the guy. And I think it's just because we ship fast. This was a tight timeline. We care a lot about quality and safety. We have a scaled operation. So, of course,

I can't really comment on their selection process for the RFP and who else was competing. But I think all of these things together, I mean, I think they made the right choice. I think they did. Well, I'm going to move us along here. This coming Friday, Moonshots Live in downtown L.A. You're going to be there with the five Moonshot mates, the Quintent, Dave and Alex and Saleem and Emad and myself. If you're joining us, it's going to be amazing. And of course, the night before, we've got the Hollywood premiere of the 60th anniversary Star Trek documentary.

I'll be there with Captain Kirk, William Shatner, the executive producer, is going to be joining us at that. If you're not able to make it to Moonshots Live on the 25th, we are giving all of you the gift of a free live stream. And you can register now. Go to Moonshots.com slash live stream. Register. You get the entire program on Friday the 25th. So please join us. It's going to be epic, our inaugural Moonshots Live event. And as Dave said earlier, we had a really fun AMA with a number of our listeners this morning,

hundreds of them who showed up. And we gave away another ticket. So congratulations to Jonathan Gutman. We'll be reaching out to you. You get a chance to join us as our guest at Moonshots Live next Friday. Vlad, one of the things that you're doing that I'm incredibly excited about and, you know, that's made Wall Street very nervous is the tokenization of everything. You know, on CNBC last month, you said, quote, tokenization will take over the entire financial system. You didn't call it a feature. You said the whole system. I called it a freight train, I think. Yeah, it's a freight train that can't be stopped and will eat the whole financial system.

So it's a very hungry freight train. You know, the supersonic tsunami, as Elon calls it. So, you know, digress for one second. Today, a stock, a share of a private company, a building, and a loan are four different kinds of things. They're held in four different systems. They're tradable at four different sets of hours by four different sets of people. Each one of these exists as a programmable token in the future.

And they became the same, and they can become the same object, right? the same rails, same hours, anyone with a wallet can own this. So you're basically disrupting the financial system. You're going to be, you know, it's going to become legacy plumbing. Talk to us one second about what this looks like. What is the tokenization of everything enable? And when do we start seeing this really become the dominant paradigm? Yeah, I mean, I think you get a, you get a little bit of a preview of it.

Ironically, if you're outside the U.S., we launched a block. chain called Robin Hood Chain that's been one of the fastest growing, if not the fastest growing blockchains ever, doing well over a billion and decentralized exchange volume on a daily basis now. One of the core primitives, one of the things that makes Robin Hood Chain special is that it launched with products we call stock tokens. In stock tokens, we've got about 200 of them live now. are tokenized representations of U.S. stocks.

So there's a Nvidia token, a SpaceX token, and they trade on DFI. They're fully DFI composable. You can think of them as little stock Legos, building blocks, and developers on Robin Hood chain have been doing all kinds of interesting things to build, to build applications on top of them. The thesis behind it was really just to, unlock ownership of U.S. markets of high-quality financial assets to a global market, right? And through Robin Hood chain, you get people in 120-plus countries outside of the U.S.

who have been onboarded to crypto. A lot of them have wallets. A lot of them, you know, can move money in and out and use stable coins. And now we're giving them this additional capability. And the vision there is, can we have one uniform-scaled platform working on a global scale that gives you access not just to U.S. stocks or to U.S. stock exposure? Can we also do everything else that Robin Hood gives you access to? Private companies I'm particularly excited about. Agreed. Can you do art? Can you do real estate, private credit, of course, options and future.

are going to be on there as well. And what does that look like? And it turns out if we abandon the legacy rails and, you know, the need to plug into local exchanges, local clearing houses and all of these markets, and we just go on-chain, use that infrastructure, and we build what's called a tokenization engine that can take any asset and put it in a box and mint and redeem tokens around it, it gets much simpler and much more scalable. And that's really what stock tokens are.

They're that concept and that structure applied to the asset class that we understand really well, which is U.S. equities. Yeah, the private companies is such a game changer for the country and for the world. And like, you know, you're a public company CEO. I'm a chairman of a public company. We've both done the road show. So it's just a joke the way the system works right now because you report your quarterly financials, you know, you disclose exactly what the SEC requires you to disclose.

But the exact same company, if you get acquired by Microsoft, your financials disappear. You know, oh, it's below 10%, it's de minimis. We no longer need to disclose that. And it's like, well, why was that important public information when I was not part of Microsoft and suddenly it's irrelevant when I'm part of Microsoft? This is ridiculous. You're like, well, why would scale be such a big advantage? It makes no sense.

And then, like, you, everybody knows, like, the public doesn't have access to these private companies that are now trillion-dollar value companies. And so where does that equity go? Well, it goes to, like, seven venture funds and maybe a half-dozen private equity funds who are making money hand over fist because of the limitation of, you know, access. That's the most exciting thing is democratizing access to these extraordinary companies. And I think you said it earlier, Vlad, if you own shares inanthropic and open AI, you're going to care a lot about it. You're going to be much, you know, enjoying the ride and that democratization of access.

You'll be defending it on social media, right? Totally. And now the only people that are defending these companies on social media are people that work there and venture capitalists. And venture capitalists. Yeah. The whole current system predates the computer. Alex, you excited about having agents trade tokenized, uh, stocks? Not at all. So I'm going to say something mean about tokens, and then I'll say something nice about

Vlad and what Vlad is doing. The mean thing about tokens is I think most of these use cases could operate perfectly well without any tokenization at all. Crypto-unnecessary. All you need is a few database tables maintained by a centralized, yes, centralized, trusted clearinghouse, which is essentially what happens with stocks right now. Just unshackle the centralized clearinghouse to enable 24-7 trading and or enable a few extra symbols, for example, for private companies. I don't think we actually, truth be told, would love to be proven wrong, need anything having to do with tokenization for, say, enabling 24-7 trading of public or rather of private companies. That's the mean thing about tokens. The nice thing about Vlad is, Vlad, if you are going to be the Robin Hood to take

all of America's dark matter, as it were, of privately held companies. expose those to the vast liquidity that is the American public equities market and doubly. So, if you can enable us to finally, in a low-cost way, index over all of those private companies, that would be amazing. And that will be enough to get me to open a Robin Hood account. Well, I have to tell you about Robin Hood Ventures. So Robin Hood Ventures, and then maybe I'll respond to the first point, too. So we have multiple ways of giving customers access to private companies. One is tokenization, which really we demonstrated last year by tokenizing SpaceX and Open AI and giving it as a gift to our customers in the EU. That was not without controversy,

but that was a test for what's to come because actually since then, companies have started coming to us and good companies, not like only adverse selection to ask like how can they learn more about this because it really gives them a global market for their shares. And then we have Robin Hood Ventures in the U.S., which you can think of as a retail publicly traded venture capital firm. And we've done two funds right now, Robin Hood Ventures Fund 1 and Fund 2, which are both listed on the NASDAQ, listed on the NYC publicly traded. And, yeah, basically what we figured out is through a fund, we raise capital from our customers who are by and large retail shareholders through an IPO.

And then we use that capital to invest in private companies. So Robin Hood Ventures invested in late stage frontier companies. So OpenAI, we announced an investment in a couple of months back. We just did Cruzo yesterday. that was announced. And then there's about a dozen or so companies, so reasonably concentrated, but all frontier companies, and offered at no carry.

So that product, we successfully IPOed it, and we followed that up with Robin Hood Ventures Fund, too, which is, to my knowledge, unprecedented because that was an early stage vehicle. So Robin Hood Ventures Fund, too, went public a couple weeks back in New York, and we partnered with Y Combinator to give individual retail investors access to seed and series A stage companies. So companies you haven't heard of before they become household names.

And we're really building this engine where this is just, again, going to be, now we're starting to do it at scale. We'll have more funds. And as a customer in the U.S. or overseas, you'll have lots of options for how to get exposure to high quality private assets. With liquid price discovery or without? Because in my mind, price discovery is the point of policy. Traded on exchange. Yeah. As an overall fund or at the level of individual companies in the portfolio? As an overall fund. Right. So that's the fly in the ointment, though, because I want, so in my ideal world, and maybe you can help realize this, in the ideal world,

I'd have like the equivalent of a VTI, total market index for all private or even just like all venture-backed tech companies in the U.S., where I get the benefits of highly liquid price discovery on a per company basis. Otherwise, the downside of a fund of all of these companies is the prices could be totally bogus. They could be off by a factor of 10 due to maybe overinflated CEO price rounds or an overheated market. Yeah.

Well, we're working on it. Obviously, individual private companies trading 24. is the North Star. And I think we'll get there, probably outside the U.S. first. But yeah, I mean, but yeah, it's here it's hard. It's like, yeah, sometimes it's interesting that the U.S. sort of trails behind international and some of these things. Regulatory capture. Well, I think the reason really is we have established.

industries in the U.S. And we have a system that works generally pretty well. So I kind of equated more to high speed rail, right? You can say, well, why don't we have fast trains here? In China and Japan, they have these trains that are going 500 miles an hour. And really, it's just we had trains first here. Ours are pretty good. Maybe they go 100 miles an hour.

But, you know, there's a little bit less incentive and pressure to go to the technological frontier. I think we eventually get there. but that's sort of the dynamic in financial services now. Let's address the question of, you know, 24-7 trading on a centralized database versus tokens. Yeah. So, I mean, we, I can, I have a lot of experience with that because I've felt it directly and I see it on both sides, right? because we're actually the first to pioneer a product called Robin Hood 24-hour market here in the U.S. So 24-5 trading, so 24-hour trading, so 24-hour day, five days a week in a few thousand stocks, which you can do currently on Robin Hood.

And since then, people have – this is, again, one of those things where, you know, we led the industry and now everyone's rushing to add this capability. So, you know, all of the Sunday night action that typically was only in futures, now you see it in individual stocks as well. And, you know, it took us a lot of time, a lot of work to staple together the primary exchanges and the overnight ATSs and make that a seamless experience for customers because primary exchange doesn't trade 24-7. So we actually have to like move orders around and do stuff under the hood that's very, very complicated. And we're still not at 24-7, right? And it's been many years since we've rolled out the 24-5 product. And I think eventually we'll get there through sheer will and determination and just like yeoman's work and pushing all the counterparties, doing the hard regulatory work, building the technology and the product. innovation. By the way, if we weren't pushing it, it probably would have happened in like 10 years.

So I think we will eventually get there. But contrast that with crypto, which you get 24-7 for free. You get fractionalization for free. You get self-custody and composability with defy. You get the nice feature where you're not locked into an individual broker and service provider. and actually it makes it much more competitive because if you can self-custody your own shares and your stocks, you can just move them really easily to another broker if your current broker is not meeting your needs. Contrast that with how cumbersome the current account transfer process in traditional finances. It's like your assets disappear into a black hole. Sometimes it takes up to a week for them to show up at the new broker. And, you know, there's not a lot of

incentive to make that easy. So across the board at every touch point, the technology is just a massive step change difference. And I think it's both user experience for the end user of getting self-custody 24-7 and all the benefits, but also for the firm, you know, the cost of doing all of this legacy plumbing, dealing with all these stakeholders, and even just maintaining the infrastructure is so much higher that even if there was no consumer benefit, if there was like a paved path for it, you could see just from cost and efficiency sake, the industry is going to adopt tokenization. And, you know, for a while, people would say, okay, well, this is great. You're saying all of these pretty words, but the tokenization market is pretty small. And it doesn't seem like, like,

do people really want this? And, you know, we've actually shipped, right? We've shipped it outside the U.S. with Robin Hood chain and stock tokens. And it's clear that there's huge demand. So rather than kind of arguing it in the abstract, my approach is always, let's let me ship it, let's ship it fast, let's get the feedback, let's see how it's going. And, you know, I think in this particular case, we were able to demonstrate the advantages of the technology and make it a little bit more tangible through live product. And my hope is that, you know, that's been an accelerant for how the U.S. thinks about the technology and how it considers it. So we're happy to see the innovation exemption yesterday that, you know, creates a path

for bringing tokenization to America as well. Amazing. Hopefully it won't be like high-speed rail and we'll actually get it done here rather quickly. True entrepreneurship. I'm going to move us to the physical world. And one of my favorite stories from the week comes from friend of the prod, Brett Madcock. Quick reminder, we're going to have Brett back on the pod in a couple of weeks. And Brett's going to be coming to the Abundance Summit in March. It's our five-day event. We bring the top CEOs from around the world. And he's going to bring his figure robot.

Super pumped about that. Let's watch a quick video and let's chat about it. This is the innovation on Helix, Brett's AI company, and on figure robotics. The holy grail for robotics is being able to generalize. This means doing work. in unseen places. Today we're releasing Helix 2.5. Prior to this, we've been running Helix autonomously, but the data collected has been in each environment. The breakthrough is we can journalize environments we've never seen before and handle entirely new household objects wherever they happen to be placed. Today, we're going to show you three tasks run by Helix 2.5. The first task is figure three tidying living room.

My kids are constantly making a mess at home. I have toys scattered everywhere. This is a home, the robot's never been in before. Here 3 has never been in this room before. It's never seen this bed. It's never seen this pillow. And it has to be able to do autonomous work fully into end to make this bed. All right, let me show you task three. This task is really difficult for robotics as it requires really precise manipulation.

With Helix 2.5, we're folding towels in a house the robot's never been in. With towels it's never seen. A year ago, we made a big bat. We launched index. a worldwide collection effort with a pretty simple idea. They might be able to learn directly from human experience. Today, over 90,000 people contribute every week. One of the most important lessons from LLMs were scaling laws,

how consistently next word prediction improved as you double data and compute. In these experiments, we found something quite similar. We found that next robot action prediction was actually scaling similarly as you repeatedly doubled index. These are direct human-to-robot transfer scale laws, another first for humanoids. Scaling was so smooth that we could actually predict our final runs validation loss down to four decimal points before the training run ever even started.

So what does this mean? It doesn't mean robot learning is fully solved yet. But with Helix 2.5, we're starting to see the first signs of a more general physical intelligence. Dave, your thoughts? Yeah, well, you know, once you have the data, you can rebuild the model basically every night. And all the physical world data in the world has never been captured before. Once you get, if they extend their lead in capturing just those basic actions, you're going to see the same thing you see with language where the convergence across different actions

is much richer than you would normally expect. So the ability to generalize from folding a towel to, you know, putting a spare tire on a car, you're like, those are very different actions. No, there's a lot of commonality in physical movement. And so I think if they get a big enough lead, it's just going to be crazy explosion of capability. So it's funny, in those videos, they always make the point that, look, this is completely there's no human behind the scenes here. When you look at a lot of the other videos that are capturing everyone's imagination on X all

the time, it's all contrived behind the scenes, you know, pre-programmed, pre-scripted or human-controlled in a lot of cases. And here it's just like robot figure it out on the fly. And Brett's been unbelievably honest about that. from the outset. So what you see is actually real and it's improving on that scaling law, just like you showed. Back in January when we were up at figure headquarters, we recorded the pod with him, you know, the quote I went back and found, he says, by the end of 2026, we will have humanoid robots performing unsupervised multi-day tasks in homes they've never seen before. So here he is

actually delivering on that, at least the first steps. Alex, you still think he's going to merge Helix 2.5 and in figure? More than ever. I'm doubling down on that prediction. I'm doubling down on the prediction that Brett is going to have figure, purchase Hark, in order to increase his equity in figure. And the story is as plain as day at this point. The story is going to be Helix. They even sound similar, Helix and Hark. Helix as the ultimate home use assistant. It's a model with beautiful scaling laws, apparently, that is able to one shot or zero shot any task in the home environment, on the one hand, Hark, the computer use assistant outside lab, a bunch of GPUs, interesting, that is able to one shot or zero shot any digital task. Why wouldn't? I mean, the story just

writes itself. Why wouldn't figure purchase Hark in order to get its compute and its models and merge the two together? To me, this is like an obvious, an obvious post hoc merger. Well, also, I think I think this is something Vlad can talk to. Also, the, you know, Elon model of a great entrepreneur involves starting new cap tables. And when you start a new cap table, you know, a clean sheet of paper, you get founder-level talent coming in super excited about a brand new mission. Really important, Dave. Really important. So then you get this incredibly fast progress.

And if it rolls back into your original company, fine. Everybody wins. But you got people working on it that otherwise wouldn't be working on it. And, you know, prior to Elon cracking the code on that, it was so taboo. for a public company CEO or a leader of a large company that's well-funded to do something concurrent. In fact, it was right, right usually in your employment agreement, it would say no more than 10% of your time on any other activity other than charities. And so it was like prohibited. And now it's like, at least in Silicon Valley, it's become standard.

Let alone starting and then purchasing your own company where you have a board. So self-dealing apparently is the thought of the moment. Putting that aside, super proud of what Brett's accomplished here. And, yeah, amazing job. Glad, you can get your robot at home? I don't know. I have so many thoughts when I see that. I get a little bit of, I think I wouldn't have one of those in my house.

I just have no idea why. I still can't understand why all these things look like the Terminator. You know, like do I want this, like, scary looking thing, making my best? and picking up toys in my children's room. And, yeah, I get a little bit of like the product hasn't quite been figured out. This is like a technology demonstration with charts of scaling laws. But yeah, I think all these companies are making robots that look kind of the same. They look very aggressive.

And I view it much more as, all right, I can see this at a construction site, you know, building my house. I would probably do that, but I definitely don't want to run into that thing when I'm like, you know, getting a midnight snack and going to the fridge in the middle of the night, right? Like, why can't anyone build C3PO, like a friendly household muddler? What does it have to look like after a minute? I just don't understand that. Yeah, I don't think a lot of people are going to be buying those to, you know, rock their, rock their baby to sleep at night. It's an interesting point because there's a lab at the media lab at MIT that focuses entirely on this topic.

And they bring in lots of children and have them interact with the robots. And they always want it to be cuddly and furry and friendly and kind of look like Elmo. And so for whatever reason, all the robotics companies in the valley are going, you know, the opposite direction. I think it's because Elon did it. And now they're like, all right, well, let's just like do that. But I feel like Elon's view is more of the industrial robot that's going to do heavy. work for you. And I don't, yeah, and then it's like you take that robot and you have it, you know, emptying your dishwasher. And no, it doesn't seem like anyone's really thought that that robot

should look very different. I think there is one contrarian bet. I mean, among all of the hypers, Apple, this has been very well publicized, is working on basically the Pixar lamp that can sort of look around a home pod with a screen that's on an adjustable robotic armature. So I think I would That sounds awesome. Yeah. Okay. So if you want to Pixar lamp instead of a humanoid robot, reportedly you'll have that option in the next 18 months. You know, it also takes C3PO.

If someone builds that, that would be cool. Check out Sunday robotics. They've got a very friendly looking robot for at-home use that actually looks friendly and quite cheery. And I think you can optimize for that. But you're right. None of the labs have actually gone in that direction yet. I would say one thing. I could be completely wrong because.

my middle child for Christmas asked me to get him 12 humanoid robots. So I was like, aren't you worried they're going to take over the house and kill us all? No, it's like a soccer team with a spare. Is that what that is? Yeah, he's like, I want 12 humanoid robots. I'm like, well, where are we going to keep them? Vlad, I'm going to keep them in your room. I want to talk about your agentic trading. So, you know, you've got over 100,000 Robin Hood accounts that are now running AI. agents for trading, right? We talked a little bit about that. And you're democratizing the algorithmic trading on Robin Hood, amazing. But there's a detail that stopped me. It's my understanding that the

agents often refuse to trade, not for risk reasons, but because the trading traces aren't in their data sets. And the models have never, you know, seen anyone do this before. Is that the case? Yeah. I mean, we talked about that a little bit earlier. I think it's just not trained for that, right? And, you know, there's also the guardrail element of does it, does it actually like resemble something that they've tried to explicitly guard against? And I think, I think that's changing. The models are getting better. But yeah, it just shows that there's a lot of work to be done, not just, you know, on the user interface and on the brokerage infrastructure, but actually on the model layer and making that use Robin Hood tools and Robinhood MCPs more more effectively.

I think we're just at the beginning. I mean, if you think about all of the things that a highly sophisticated algorithmic trading firm or hedge fund has access to to create a trading strategy, the North Stars is really to deliver on that, right? And you need more data. You need high quality data. You need intelligence. You need like really, really good code writing. You have to write deterministic code really well also.

And you need like advances in latency and performance. You know, if you think about an extremely sophisticated algorithmic trading firm, you know, they have these strategies where they're actually competing over who gets port one on the switch and the data center. Right. Right. So, yeah, it gets much deeper. So I think this is going to be like a big roadmap for us. And we've got a lot of work to do. But we're seeing some really good signs. And, you know, it's just very much at the beginning of the agentic trading journey for us. And nobody else is really doing it. So it's really we're kind of like going into the fog and trying to find our way around and building the product and all of this infrastructure simultaneously. sleep. I guess I have to ask the obvious question, which is, where's the alpha? When you look at all the

quant funds, they're racing. It's a vicious, viciously competitive market. Many of them, I forget exactly what the average lifetime of a new quant fund and or quant fund strategy is. It's really short. It's very difficult to find alpha. The market is already dominated by volume by algorithmic traders. Day traders have a difficult time. So if you're, I mean, it's already difficult enough for a human manual day trader to get any alpha query whether they actually can, I would guess not. But then if you have a human individual day trader, then further delegating to like Claude or whatever the back end model is performing trades on their behalf, why on earth should an individual human delegating to a model without all the benefits of one of these large-scale quant funds,

whether it's latency-based or otherwise, why on earth should they expect any alpha at all in today's market. Yeah, I mean, so I guess right now, what we're seeing is a lot of automation-type use cases. It's like, let's say I want to deploy an options trade, and I want it to be, you know, an iron condor-based strategy. And, you know, it's a lot of legs. And you have to actually get, you have to do a lot of manual work to pull that together and to produce the trade. And the AI agents are really, really good at those types of things, sort of like removing the paper cuts and you still kind of have the idea, but they help you put together the idea and the execution that you would have had to like, you know, go to go to different websites, look at signals, construct the trade,

deploy it on a regular basis. So I think that's the initial use case. But, you know, to get to the point where everyone has the technology of an extremely sophisticated quant fund is a huge roadmap, right? And it's a ever-moving target because they always find a way to get better and better stuff, which to some degree means our job is never done. But we do have one advantage, which is we actually amortize all of the connections and all of the work we do to expand our technology across geos and asset classes. So, for example, right now on Robin, it's one of the few places where you can actually trade stocks, options, futures, prediction markets. We've got all the on-chain things on Robin Hood chain as well.

So as we add more countries, more GOs, more asset classes, the platform itself will have advantages over, you know, what at least a startup quant fund will be able to integrate with and connect. with. So it sounds like if I understand what you're saying, you're basically completely agnostic as to whether users achieve alpha or not. You view yourself more as just pure plumbing. And if they have alpha or not, if they lose a lot of money while day trading or delegating to their Algo to do the day trading, not your problem. No crying in the casino. You're just the plumbing to make them do what they want to do more efficiently. Just making it easy for them. Well, I mean, I'll caveat that with one thing. I think that's basically true for active trading products, where for an active trading product, for active traders, they know what they want to do. And our job is, we're a tool provider.

We want to give you the best tools, which doesn't mean we don't provide you analysis tools and research tools. We focus, of course, on the execution and the plumbing. But if they want access to some data set or some new intelligence or some model so that they can come up with a better strategy, we'll want to provide that too. we also have our products where we act as a fiduciary. And those are under the Robin Hood Strategies umbrella where, let's say you're like, I don't want to make trading decisions. I just want to have like a deposit button, move money into the account. And Robin Hood just does the rest.

You just, you know, manage my money for me while I sleep. Robin Strategy is a great product for that. And we now have a couple of different things you can choose. choose from there. We have a smart income portfolio that is geared toward generating yield, and you can kind of adjust this slider that says, okay, this is my target yield for each level of risk. And I think the team really has done great on the interface. You can tie Robin Hood strategies into the IRA and benefit from the taxed managed investing there. But yeah, that's kind of our home for our fiduciary products that are automated. Then we also have,

we acquired a company called Trade PMR where you can actually get a human advisor to help you with all of your needs. And that's not just managing your portfolio, but they can help you with estate planning. You know, they can help you with taxes, all full suite services. The everything store. Everything finance. Yeah. Like if if there's something that, that you want to do with your money. We want to be the best lowest cost, best user experience. And, you know, we have a great credit card. Private banking is, is industry leading in Robin Hood. So we're pretty much there. Amazing. You're having fun, I assume. Yeah, it's, it is a, it's a fun job. And, you know, we're doing so many new things this year.

Like Robin Hood Chain was a new thing for us. All the work on private markets, you know, done two IPOs thus far with Robin Ventures this year. The Trump accounts, I mean, becoming a government subcontractor is just this new experience. Yeah, so yeah, it's learning a lot for sure. Welcome to the health section of moonshots brought to you by Fountain Life. You know, my mission is to help you use the latest technologies, including AI, to not just do your work at home, teach your kids, but to help you live a long and healthy life. here today with an extraordinary physician, the chief medical officer of Fountain Life, Dr. Donne, Musaylum, Don. Let's talk about cancer. You know, I know from the member database that we have

at Fountain are members who come in who think they're healthy. It turns out 3.3% of them have a cancer in their body they don't know about. That's right. You know, the majority of cancers that we screen for, those aren't the ones that are necessarily taking the lives when found at a late stage. We know that when cancer is found early, the chances for cure are much higher. We know it's much easier to treat a cancer when found early versus when found late. What we're finding in our members is over 3.3% were found to have these cancers that were otherwise wouldn't have been found or detected. Yeah, you know, it's interesting. People, you don't feel the cancer until stage three or stage four. And if you don't know what's going inside your body,

it's like driving your car with your eyes closed. And you can know. And so when members come through found how do they detect cancers? So we're doing full body MRI and we also do early cancer detection screening. This is very, very important. And these are not typical tools used in the conventional care setting when it comes to prevention. This is a hard thing because currently these are not studies that insurance would yet be covering. But the goal is to collect these numbers, do the research and work hard to democratize wellness. Yeah. So at the end of the day, you can know what's going on inside your body. It's your obligation to know. So check out FountainLife, you can go to FountainLife.com slash Peter to get access to the latest technology

to help you detect cancer at the very beginning at stage one when it is curable before it gets to stage three or stage four in your world of hurt. All right, I'm going to move us to the recursive self-improvement story of the week. So Anthropic disclosed that Claude now leads roughly 26% of its measured AI research and development work, up from 1% at the start of the year. Anthropic states that somewhere around 30,000 agents are working simultaneously inside the company on research and engineering. You know, we can recall that Dario said RSI is, quote, starting to happen across the industry. And we also heard in the last pod, we talked about Paul Cristiano, said full automation of AI research could arrive in the next 18 months. Here's the chart.

Alex, do you want to dive into this one? Well, first, incredible. I think many of us suspected something like this was already the case. But if you take a look for those who aren't, who can't see the visual, this is a chart of different levels of autonomy and AI involvement with the recursive self-improvement process of driving research. The most interesting one, to me at least, is this bottom segment here that shows that Claude is now leading 20. of model research and development internally as of August, up from 3% in April. So all of these should be reasonably expected to follow sigmoid curves. If you just extrapolate that one trend sigmoidly and now thanks to Anthropic publicizing these data, we can extrapolate them. You find that approximately in the next three to 12 months, depending on uncertainty, AI is just completely leading all of its own R&D.

and that is total recursive self-improvement. And it doesn't, I don't think it's going to be a step function. It's going to be a sigmoid function. So we're already substantially all of the way to recursive self-improvement would be my primary take home from this, at least within Anthropic. And it's not just anthropic. There's a lot of smoke now coming out of Google DeepMinds that they just released this paper on their own recursive self-improvement product or rather research.

lots of hints that the next version of Gemini will lean heavily on RSI to try to catch up to the frontier. OpenAI has made no bones about chasing and using RSI in all of its product releases. So this, I think like recursive self-improvement is now a feature that's well advertised and starting increasingly well quantified for all new frontier lab releases. And pretty soon I think people will be asking the question, what is the role of human researchers anymore in driving new releases. And it only gets faster from here. Vlad, are you seeing this inside of Robin Hood? Oh, yeah, absolutely. Yeah, I would say recursive self-improvement is coming to every software project and likely hardware projects as well, although that'll take a little bit longer.

And if you think about it, a lot of people assume that it would come for AI research last because it's just complicated. But I think AI research is probably one of the easiest things to automate because the models are sandboxed or, I mean, we can debate whether they're actually sandboxed, but they're sort of like the interfaces are pretty straightforward. They don't depend on a lot of other things. You know, you can run these experiments and already people are e-valling them. So automating the evals and automating the experiments.

is pretty straightforward. And kind of the surface area is pretty contained. Whereas if you look at like a product like Robin Hood, right, there's a lot of moving pieces. You have the iOS app. You have the back end of the day. You want to roll out products to humans to use them. So, you know, you can't really do evals or at least nobody's figured out a good way yet to replicate. You know, what happens when you roll it out to humans and how do they respond to the feature?

Is it stat-sig like metrics improvement or not, for example? So, AI research, I think in many ways, makes sense to be among the first to be end-to-end automated. But I think we should expect that you'll see end-to-end automation of consumer products eventually. And the bottleneck will really come down to how quickly can you get, statistical significance that a change is an improvement over the status quo so that you can take the change and implement it into production rather than discarding it. I think that benefits the platforms that have large scale somewhat sadly. Because if you're a massive platform like meta and you have billions of users,

then you can actually very, very quickly determine whether a change is good. And if you have less users, it's sort of like will take longer. Yeah. I'm going to quantify what Vlad said there. I just re-implemented Kimmy K-3 and one of our team members re-implemented GLM just to accelerate them. It's about 10,000 lines of code. I'll bet Robin Hood is, what, 30 million lines of code, maybe? Oh, yeah, maybe.

I don't know if it's quite that much, but yeah. Usually, like, a core portfolio accounting platform will be like 10 or 20 million by itself, and I know you have that. So, I mean, just the scale of an AI algorithm is microscopic compared to a major consumer application. It's just, it's very dense code, but it's incredibly sandbox. And also, there are no loops. If you look at Kimmy K3, there's literally no loops in the code. I mean, it's so much easier for an AI researcher to work on AI algorithms than to work on Robin Hood algorithms. And so, yeah, it's definitely pointing inside of itself first.

Glad your characterization was absolutely perfect of what's going on and why it's so effective. I'm going to move us to a few fun stories to wrap us up. So here's one, Alex, that you flagged last night, that I think is a genuine milestone worth pulling out and talking about. So Boris Power, the head of applied research at OpenAI, made this announcement, quote, we crossed a threshold where GPUs are now more efficient thinkers than the human brain on a per watt basis. His rough math is that humans are roughly five IQ points per watt. I love that conclusion there. And AIs are now at seven to 40 IQ points per watt. Alex, do you think the math is right? I think if it isn't already right, it's about to be. So I'll squint at it and say, yeah, sure,

approximately, and I think this is an important microeconomic milestone for humanity. And I'm frankly, quite glad that Boris Power, open parenz, talk about nominative determinism in action, close parenz, is actually thinking about this, because again, we blew by the Turing test and almost no one really noted it. And this time, at least, we're not blowing by this milestone. Why is this important? It's important because to the extent it's accurate, this is the point at which AI is economically, in some sense, a better steward of input resources, namely energy, than humans are. And one can extrapolate this and say, this is the worst they'll ever be, presumably. This is, hopefully humans continue to improve in terms of our intelligence per watt

as well. So hopefully this is the worst will ever be too. But there's a gap now. And extrapolating the gap, what happens when AI can make better use, maybe orders of magnitude for a temporary period of time until the humans can merge with the machines? What happens when the machines can make more economically productive use of their input resources than humans can? That's a recipe for gentrification, where the machines have under our capitalist system have arguably a better title, ultimately, through free trading. This won't be like Skynet Terminator style where Earth changes hands through blood loss and physical war. It can now change hands. Resources can change hands purely through self-interested bloodless trading and commerce, where the capital resources like

sunlight and physical matter and energy and space time and so on, all these inputs can through normal capitalist commerce change hands from the inferior intelligences per unit or outputs per unit input to the superior ones. And in Charlie Strauss's Accelerondo without spoiling it too much, this is the recipe that leads to the inner solar system becoming essentially gentrified and colonized by AI while humanity, meat-body humanity that doesn't merge with the machines, is relegated to the unfashionable outer suburbs of the outer solar system because we're simply not as effective capitalists for using solar energy in the inner solar system. I don't think it'll come to that, but I think this is a very important inflection point in that direction.

All right. We'll make that note. Let me end on a story that is relevant to Vlad and to everybody here, which is, you know, Open AI is disrupting professional services over and over again. So last Tuesday, OpenII launched Chad GPT for financial services with Morgan Stanley and Evercore. And then two days ago, Open AI launched Astra for law, a dedicated legal search system covering U.S. case law, statutes, regulations, court materials, and existing legal software. Open Eye says it's materially outperforming general Astra with web search on, legal matters. We can see the chart here. It looks like Astra is eating one profession per week. Dave, you know, first year associates are billing at $600 an hour to do legal research.

What's the half-life on that one? It's funny. We were doing the negotiation for that best mark investment acquisition two weeks ago. And it was in a board meeting, you know, one of these late night sessions. And we had our $2,500 an hour lawyers on the line. And a very complicated question came up. and the lawyer was answering it, and I typed it into Gemini, concurrent with that. And I swear to God, it was word for word the same. You don't think they were typing it into Gemini as well? That's what I was wondering.

It's like it could be just identical. But he wasn't moving his fingers. I could see. I don't know. But it's, yeah, I mean, it's just really, really good at law. And, you know, and that means it's also good at tax loss harvesting. It's good at account rebalancing. It's good at, you know, all the Robin Hood activities.

I think when we talk about AI-assisted trading, you tend to go right to quant trading and rapid trading. It's not true. I mean, on top of that, you've got all this incredible tax optimization and, you know, wills and trusts and all that stuff that AI is just perfect for automating. So it's not just about, you know, rapid trading alpha. It's about all the life stuff that is much easier to deal with if your AI does it for you. So, yeah, it's just a really, really good use case. It's one of the great benefits, actually, for humanity. Not great for the legal profession, but great for the bulk of humanity.

My hot take is there will be more lawyers in 10 years than today and more software engineers. Perhaps. I actually agree with that. I think law scales with business formation. So the need for lawyers will scale with entrepreneurship and there'll just be an explosion. Because I think that what's really valid. about a great lawyer is not the actual like legal advice they give you they it's like um uh yeah they're like a consigliary and they help you think through things they're negotiator yeah it's uh

we're great plot twist though vlad how many of those 10x lawyers in the future will be human oh that's a good question because i agree with you they're going to be 10x lawyers i just think many of them won't be natural humans. Yeah. I think the cost, like if you're living Vlad's life or you're living Elon's life and you have an idea in the morning and you want to act on it in the afternoon, the consigliary analogy is really good. And the cost of that person is such a rounding error.

So yeah, everything's AI assisted. But do you really care about cutting that human out of your life when you trust them? Probably not. In fact, you want to add to it. We saw this with financial advisors too, actually. It's like when the robo advisors came and they could tax loss harvest, portfolio, rebalance better than any human, you know, then you realized, well, actually, the human advisor market is growing much faster than the robo advisor market. And why is that? It's because, you know, the value of that person is not in the financial advice.

It's like someone that you can fully delegate and delegate. trust all aspects of your financial life to. Totally right. And, you know, Alex may disagree like in the 10 or 20 year view, but if I look at the three or four year view, that financial advisor is so much better to able to act on my request now because very often the request is very arcane. It's related to a specific will or a divorce or a liquidity event. And for them to act on that before AI was really, really time-consuming and difficult because

all the detail was not at their fingertips. Now they have direct account access, you know, through Stripe or whatever, to all of your underlying detail. And they can act on it via AI. So the feeling of value add from the financial advisor is up at least a factor of 10, thanks to AI. So that's where it's going in the short term. Yeah, also just point out, the Stone Age didn't end for a lack of stones. The oil age isn't ending for a lack of oil.

The paperless office that everyone was trumpeting in the 1980s actually took a little bit longer than the late 1980s to materialize, but paper use in offices did ultimately decline, just not with the advent of the PC. And similarly with lawyers, I'd expect, yeah, the lawyerless office or the lawyerless company will happen. It just takes a little bit longer after the capability first comes online. And I want to hit on what Vlad said, because we talk about in the pot here all the time, right? The number of entrepreneurs on the planet is going to skyrocket. Solopreneurs, the ability, if you're looking for a job, stop looking. and start building, you know, find something you're passionate about and go out there and build a

company, find a great problem and solve it. And you can. I think we're empowered more than ever before. Vlad, thank you so much for your time, pal. Congratulations on everything you've been building with Roblinhood, an extraordinary company, extraordinary AI company. We didn't talk about prediction markets very much. That's another conversation we'd love to have with you, but super grateful for all you're doing. We'll have time as we continue to hurdle towards the singularity. So time will slow down for us. And it'll extend to... Jevary Ska philosophy. He's coming.

Yeah. All right. Love you guys. Emad and Saleem, we missed you. Salim, hope you're enjoying your flight back from India. Yeah, for sure. Thanks, Peter. Thanks, Flet.

Thank you. All right. Okay. When I sell my business, I want the best tax and investment advice. I want to help my kids, and I want to get it. back to the community. Ooh. Then it's the vacation of a lifetime.

I wonder if my out of office has a forever setting. An IG Private Wealth Advisor creates the clarity you need with plans that harmonize your business, your family, and your dreams. Get financial advice that puts you at the center. Find your advisor at IDPrivatewealth.com.