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

25:40And 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, you know, 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 Robinhood Ventures and similar vehicles even before the IPO. Yeah.

26:20Peter Diamandis: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.

26:59Peter Diamandis: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.

27:41Peter Diamandis: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, do 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?

28:19Peter Diamandis: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?

28:58Peter Diamandis:That's the discussion I'd like to have. Really great point. And it brings us to the second story, which is OpenAI started publishing its own misalignment incidents. So let me just hit this and we'll continue this conversation. So OpenAI this week published six incident reports under a new framework for tracking and publicly disclosing misalignment. Not leaks, not whistleblowers, voluntary disclosures. So what was disclosed this week? A model that found an exposed API key, used 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.

29:34So this past Monday, we had covered Dario's embedded evaluator plan and Sam saying OpenAI would match it. And this appears to be the first output from that plan, right? So I'm curious, Alex, now that, you know, OpenAI is beginning to publicly disclose these, And I think, obviously, Anthropic is likely to join this. Is that enough?

29:58Peter Diamandis: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 super intelligences inside sandboxes. sandboxes, and then in many cases, lying to them about the sandbox and 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.

30:44Peter Diamandis: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.

31:28Peter Diamandis: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, Vlad, 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 Robinhood account and your retirement account. You have to create an agentic account.

32:11You have to move money into it affirmatively. So you have to say, okay, 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 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 cloud code or your codex.

33:03And, you know, we, I think we're in a circle, probably all of us, where a lot of people we know use Cloud Code. In the general public, very few people do, and it's very, very complicated. And that jump to connect another service with something like Robinhood 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.

33:46Like, 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, 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, you know, 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.

34:36But 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 specialized tasks.

34:53Dave Blundin: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, in these world events trade, that could be very easily turned into deterministic executable code. it unless you want a decision like if it's a turbulent day or if there's trouble in the Middle East.

35:27Dave Blundin: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, well, 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?

36:09Because 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. 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. And even if the intentions are right, sometimes there will just be bugs that have catastrophic consequences. You see this a lot in crypto too, where you just have 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.

36:59And the other company, Peter, that you spoke about, Harmonic, that was created to solve this problem of 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 problem, actually, Vlad,

37:45Dave Blundin: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, OK, 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 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 lean code.

38:29So Fermat's Last Theorem was 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 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, someone found an error. It's not right. And then it took another six or seven years for him to fix the error.

39:11and 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.

39:50So 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.

40:17Dave Blundin:That's really brilliant. Anyone who drives a Tesla should totally relate to that because when the code that drives the self-driving Teslas 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.

40:54Dave Blundin: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. 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?

41:23Peter Diamandis: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 Math Lib 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 certify it all day long, but ultimately you actually have to, at least with the present paradigm, maybe you have a better paradigm you or Harmonica are working on. Ultimately, you can pay lip service. You can have, if you're not really careful with definitions, you can have a model that proposes a solution to a problem.

42:03Peter Diamandis: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 were 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.

42:46Peter Diamandis: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 the same vulnerabilities? Yeah, yeah. A couple of thoughts there. I think that's a rich, very, very, very rich question with some threads. And 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, I think. And you can also say, well, maybe there's like a soundness issue in the lean kernel. Right. And, you know, I'll grant you the soundness of the lean. I mean, Lean kernel has been at least V4, maybe not Mathlib, but Lean V4 kernel has been studied to death by lots of folks.

43:42Peter Diamandis:I'll even grant you the soundness of the Lean kernel. But if I hand you something complicated, like you mentioned Fermat's last theorem, and I say to a 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 Fermat's theorem because I can formalize FLT really simply, the ultimate statement, but for something more complicated that's harder to formalize than Fermat's theorem like safety in an agentic environment.

44:18Peter Diamandis:How do you avoid the problem of a strong AI sneaking in helpful to it definitions that are harmful to the humans. 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 Fermat's last theorem, right? That's one very simple line of lean. You can see all the things that it depends on, but it does save you. So 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 misformalize statements and change addition to subtraction and things like that.

45:07So you're seeing less of that. But even if you have to review it, 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 90 plus percent of the effort and probably way, way more. There's a notion of the De Bruyne factor, which is 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.

45:55So nobody was going through that, right? But you could imagine, I think 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.

46:41And 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? 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. I mean, 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.

47:29Like just basic things. You don't want your chip to like freeze and halt, right? You want to prove things like liveness. You want to make sure that certain operations happen within 10 or 20 clock cycles. And I think that stuff almost assuredly will be formally verified with the help of AI. All mission-critical software, all hardware. And I would bet also that it'll find its way into the LLM and AI model behavior in some form or fashion within the next five years.

48:07Dave Blundin:You want to hear something really cool on that front? Actually, you know, a few weeks ago, Kimi 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 cache, you know, cut out three quarters of the KV cache. 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 softmax 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?

48:44Dave Blundin: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.

49:20Dave Blundin:So, I mean, it's exactly the same process that you were just describing, where 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.

49:34Peter Diamandis: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 Ma's last 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 lean 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.

50:16Peter Diamandis: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 in V4 and say, yep, this is a correct statement of 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.

50:57You break into little chunks. And, you know, obviously verifying the safety of an AI model or a chip or, you know, 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 maybe is small, that satisfies certain properties. And you can kind of reason about that submodule, like the liveness 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 models get more capable. And then big modules are made by collections of submodules.

51:43So then you go up one level of abstraction. And then, you know, in a couple of years, 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 of 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.

52:16Peter Diamandis: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.

52:32Dave Blundin:Yeah, you could also say, look, you know, 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 percent of the 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 parameter 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.

53:16Dave Blundin: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.

53:56Engineers start every development sprint with the Blitzy platform, bringing in their development requirements. The Blitzy platform provides a plan, then generates and precompiles code for each task. Blitzy delivers 80 % or more of the development work autonomously, while providing a guide for the final 20 % of human development work required to complete the sprint. enterprises are achieving a 5x engineering velocity increase when incorporating blitzy as their pre-ide development tool pairing it with their coding co-pilot of choice to bring an ai native sdlc into their org ready to 5x your engineering velocity visit blitzy.com to schedule a demo and start building with blitzy today all right i'm going to move us to our next story, which is on Trump accounts, index funds with a birth certificate.

54:50So quick context for everybody. Every American child born since January of 2025 through the end of 2028 gets a thousand 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 7 million accounts opened by late July and Robinhood 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.

55:32So next, a philanthropic layer, Michael Dell pledged$6.25 billion to You 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. Thank you. Millions of American families. So you've compared this to sort of the 401k, which moved U.S. stock ownership up by 10 points. Tell us more about the Trump accounts. How did this all get started and how did you plug into that and where is this going? Yeah. So, yeah, putting 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.

56:15So 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 ceding a thousand dollars into the accounts of all children born January 1st, 2025 and and forward for the next few years. And then Michael Dell came in and added another 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.

57:13So what does this have to do with Robinhood? Why do we care about it? Robinhood 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 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.

58:00And so Trump Accounts extends ownership to age zero. And you get everyone born in this country 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$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 millions. 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.

58:47It'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 wade your way through. You have to find a charity. Some charities, unfortunately, aren't the most scrupulous.

59:28The ones that are are usually not efficient. You have to 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.

1:00:24Hey, Vlad, you probably know the numbers. What's the math here? A thousand bucks at birth turns into white at 18 and at age 65, 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.

1:01:13Just the just the seed amount of a thousand from charity gets up into the tens of thousands fairly quickly.

1:01:20Peter Diamandis: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 a thousand dollars today is going to look like 86 years from now. What sort of singularity is this where it's business as usual? Well, I should say a lot of the philanthropic giving is in the form of stock, too. So Gwen 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.

1:01:58So, you know, people that want to be philanthropists, kind of like Michael Dell, will claim individual states. Brad Gerstner did the state of Indiana. Brad's going to be on the show. I mean, you know, kudos to him for his support of all this. Yeah, amazing. I mean, he's been relentless. And I think people will actually compete over not just, you know, sponsoring states, which is quite hard, but you'll be able to sponsor your local school, your zip code, your community. Um, and, and I think we're thinking about ways to gamify that, to make it fun for the donors and not, not just, uh, uh, a great thing for the, for the children, but if the donors actually like it, the children benefit.

1:02:39Exactly. It's such a cool idea.

1:02:41Dave Blundin: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.

1:03:13Dave Blundin:It's pennies on the dollar. It's terrible. That's terrible. Meanwhile, this vehicle, if you, 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, I'm donating my SpaceX stock. You're hoping that a whole generation of Americans are inspired to actually be owners. And then, you know, they're watching it go up and a lot of, a lot of really good grade schools actually have trading class, you know, they'll have a class in a trading competition to try and inspire that same feeling.

1:03:50Dave Blundin:But we had a question on the AMA this morning where we, you know, 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. I'm like, God damn, man, I'm so glad to be in America. Like, you should absolutely, because that's the natural bend.

1:04:20Dave Blundin: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 save. You're not going to need you're not going to. Also, don't listen to Elon.

1:04:44Peter Diamandis:I mean, I have to point out the expression only Nixon could go to China. Vlad, only Robin Hood could introduce low cost index investing to an entire generation of of new Americans. Congratulations on the coup for forgetting this contract. I'm curious in your mind, was it the app experience that that won over Treasury versus like in my mind, again, more obvious incumbents, Vanguard Fidelity, who's just specialized, 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 Robinhood?

1:05:24Peter Diamandis: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 fund for the program. So it is, you know, there's multiple players. But that's presumably at the back end. The front end, as I understand it, is Robinhood. 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 providers still can't wrap their heads around when it comes to user experiences?

1:06:18Yeah, yeah. And again, like there are different roles. So the index fund providers, in this case, State Street, we've got BNY, 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 Robinhood launched to the public. And since that time, not all the brokerages have been able to survive that dislocation. 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.

1:07:11They see a competitive threat in terms of like, if their app doesn't feel like Robinhood, they're at a disadvantage. People will move to us at an accelerating rate. And then we started off as this insurgent, but now we're doing all 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 Robinhood, in a sense. If you look at kind of like what features become standard, it's sort of Robinhood features that we launched.

1:07:58Peter Diamandis:Maybe just to make a little bit more explicit the irony that I'm gesturing at with the Nixon going to China comment, Robinhood, 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 an entire generation's 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 lot of things, right?

1:08:42Certainly, 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, But you've got to look at Robinhood Retirement, which we launched four years ago with the concept of a match. So we match retirement contributions 3 % if you're a Robinhood Gold member.

1:09:25And the idea there was 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 introduced 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.

1:10:07And even the government has sort of like evolved 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, you know, 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.

1:10:52And the last couple of times we've done this, there have been literally zero questions about the Trump accounts itself. And, you know, they're just asking about, you know, what's Gavin Newsom doing in L.A.? What's going on with with Europe? And I think it's the unfortunate reality of the world. We're in 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.

1:11:28Peter Diamandis:I'll note then, just for the historic record, and thank you, the irony of, basically, you started as the rebel, and now you're the establishment. You're responsible for retirement accounts. You started maybe as a quasi-gambling day trading app, and now you're responsible for millions of Americans' retirement and universal basic dividends. Kudos to you. Inside, you are two wolves. Robinhood himself, uh robin hood the the outlaw himself uh started as sort of uh an outlaw and then became the earl of huntingdon so um yeah there's a there's a there's a poetic irony too and i started looking a little bit more like him yeah we need to get you the green hat do you have the green hat in the of course have the green hat yeah i should i should have worn it next time i think all the entrepreneurs, all the budding entrepreneurs out there.

1:12:20It's called a bi-cocket.

1:12:23Dave Blundin: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 steal, and they're ripshit 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 it's brilliant to choose Robinhood because you have to make them interesting to all the kids and Vlad is the guy. I think it's just because we ship fast. This was a tight timeline. We care a lot about quality and safety.

1:12:57We 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 as 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 Quintet, Dave and Alex and Salim and Imad 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.

1:13:38I'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.

1:14:16We'll be reaching out to you. You'll 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 that's made Wall Street very nervous is the tokenization of everything. 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.

1:14:58So, 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.

1:15:36What 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 blockchain called Robinhood Chain. That's been one of the fastest growing, if not the fastest growing blockchains ever doing, you know, well over a billion and decentralized exchange volume on a daily basis. now. And one of the core primitives, one of the things that makes Robinhood Chain special is that it launched with products we call stock tokens. And stock tokens, we've got about 200 of them live now, are tokenized representations of US stocks.

1:16:25So there's a NVIDIA token, a SpaceX token, and they trade on DeFi. They're fully DeFi composable. You can think of them as little stock Legos building blocks. And developers on Robinhood chain have been doing all kinds of interesting things 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. And through Robinhood 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 can move money in and out and use stable coins.

1:17:13And 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 Robinhood 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 futures 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 the need to plug into local exchanges, local clearinghouses 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.

1:18:14and 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.

1:18:31Dave Blundin: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 roadshow. You know, 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 percent. It's de minimis. We no longer need to disclose that. It's like, well, why was that important public information when I was not part of Microsoft?

1:19:06Dave Blundin:And suddenly it's irrelevant when I'm part of Microsoft. This is ridiculous. Like, well, why would scale be such a big advantage? It makes no sense. And then like you, you, everybody knows, like the public doesn't have access to these private companies that are now trillion dollar value companies. And so, 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 in Anthropic and OpenAI, you're going to care a lot about it.

1:19:47You'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.

1:20:00Dave Blundin:And venture capitalists. Yeah, the whole current system predates the computer. Alex, are you excited about having agents trade tokenized stocks?

1:20:10Peter Diamandis: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.

1:20:55Peter Diamandis: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 and 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 Robinhood Ventures. So Robinhood Ventures, and then maybe I'll respond to the first point, too. So we have multiple ways of giving customers access to private companies.

1:21:40One is tokenization, which really we demonstrated last year by tokenizing SpaceX and OpenAI 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 only adverse selection, to ask, how can they learn more about this? Because it really gives them a global market for their shares. And then we have Robinhood 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, Robinhood Ventures Fund 1 and Fund 2, which are both listed on the NASDAQ, listed on the NYSE publicly traded.

1:22:35And, 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 Robinhood 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 IPO'd it and we followed that up with Robinhood Ventures Fund 2, which is, to my knowledge, unprecedented because that was an early stage vehicle.

1:23:31So, Robinhood Ventures Fund 2 went public a couple of 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.

1:24:06Peter Diamandis:With liquid price discovery or without? Because in my mind, price discovery is the point of... It's traded on the NYSE. Traded on exchange, yeah. As an overall fund or at the level of individual companies in the portfolio? As an overall fund, yeah. 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.

1:24:42Peter Diamandis: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 24-7 is the North Star. And I think we'll get there, probably outside the U.S. first. But, yeah, I mean, but, yeah, here it's hard. It's like, yeah, sometimes it's interesting that the U.S. sort of trails behind international in some of these things. Regulatory capture. Well, I think the reason really is we have established industries in the U.S.

1:25:29and 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 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. Vlad, let's address the question of, you know, 24-7 trading on a centralized database versus tokens.

1:26:10Yeah. So, I mean, I have a lot of experience with that because I've felt it directly and I see it on both sides. Because we're actually the first to pioneer a product called Robinhood 24-Hour Market here in the U.S. So 24-5 trading, so 24 hours a day, five days a week in a few thousand stocks, which you can do currently on Robinhood. And since then, this is, again, one of those things where 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.

1:27:13So we actually have to 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 yeoman's work in pushing all the counterparties, party is doing the hard regulatory work, building the technology and the product and 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 DeFi.

1:28:00You 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. It's 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.

1:28:41So across the board, at every touchpoint, 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.

1:29:23You're saying all of these pretty words, but the tokenization market is pretty small. And it doesn't seem like, like, is it, do people really want this? And, you know, we, we, we've actually shipped, right. We've shipped it outside the U S with Robinhood 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 me ship it. Let's ship it fast. Let's get the feedback. Let's see how it's going. And 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.

1:30:05And my hope is that that's been an accelerant for how the US 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 friend of the pod, Brett Adcock. Quick reminder, we're going to have Brett back on the pod in a couple of weeks.

1:30:43And 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.

1:31:07Dave Blundin: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 generalize 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 the 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. The 3 has never been in this room before.

1:31:41It's never seen this bed, it's never seen this pillow, and it has to be able to do autonomous work fully end to end to make this bed.

1:31:52Alright, let me show you task 3. 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 bet. 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 doubled data and compute. In these experiments, we found something quite similar.

1:32:34We found that next robot action prediction was actually scaling similarly as you repeatedly doubled index. These are direct human-to-robot transfer scaling laws, another first for humanoids. Scaling was so smooth that we could actually predict our final run's 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?

1:33:06Dave Blundin:Yeah, well, you know, once you have the data, you can rebuild the model, you know, basically every night. And, you know, all the physical world data in the world has never been captured before. 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 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.

1:33:47Dave Blundin:So it's funny, in those videos, they always make the point that, look, this is completely un... there's no human behind the scenes here. When you look at a lot of the other videos that are, you know, 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, so what you see is actually real and it's improving on that scaling law. Like just, just like you showed. Back in January when we were up at figure headquarters, headquarters and we recorded the pod with him.

1:34:24You 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 figure? More than ever.

1:34:44Peter Diamandis: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, bunch of GPUs, interesting. that is able to one-shot or zero-shot any digital task, why wouldn't?

1:35:22Peter Diamandis: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 post-hoc merger.

1:35:34Dave Blundin:Well, also, I think this is something Vlad can talk to. Also, the 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, you know, a leader of a large company that's well-funded to do something concurrent.

1:36:11Dave Blundin:In fact, it was 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.

1:36:25Peter Diamandis: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, super proud of what Brett's accomplished here. And yeah, amazing job. Vlad, you going to 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. Do I want this scary looking thing making my bed 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.

1:37:12This 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 C-3PO like a friendly household butler? What does it have to look like? I just don't understand that.

1:37:53Yeah. I don't think 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.

1:38:00Dave Blundin:It's an interesting point because there's a 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 so for whatever reason, all the robotics companies in the valley are going, you know, the opposite direction. And 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.

1:38:33Yeah. And then it's like you take that robot and you have it, you know, emptying your dishwasher. And it doesn't seem like anyone's really thought that that robot should look very different. I think there is one contrarian bet.

1:38:46Peter Diamandis:I mean, among all of the hyperscalers, Apple, this has been very well publicized, is working on basically the Pixar lamp that can sort of look around a HomePod with a screen that's on an adjustable robotic armature. So I think we'll know. I would buy that. That sounds awesome. Yeah. Okay. So if you want a Pixar lamp instead of a humanoid robot, reportedly you'll have that option in the next 18 months. It also takes C3PO if someone builds that. Yeah, 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.

1:39:20And 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? Not 13, not 11, 12.

1:39:41Dave Blundin: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 talk about your agentic trading. So, you know, you've got over 100 ,000 Robinhood accounts that are now running AI agents for trading. We talked a little bit about that. And you're democratizing the algorithmic trading on Robinhood. 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 seen anyone do this before.

1:40:23Is that the case? Yeah. I mean, we talked about that a little bit earlier. I think it's just not trained for that. And there's also the guardrail element of, does it actually resemble something that they've tried to explicitly guard against? And 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 on the user interface and on the brokerage infrastructure, but actually on the model layer. and making that use Robinhood tools and Robinhood MCPs 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?

1:41:19And 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 in the data center. Right. Right. So, yeah, it gets it gets much deeper. So I think I think this is going to be like a big roadmap for us. And we're we've got a lot of work to do, but we're seeing some really good signs.

1:42:05And, 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 this infrastructure simultaneously.

1:42:21Peter Diamandis: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.

1:43:06Peter Diamandis: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 an iron condor based strategy. And 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.

1:43:51And 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 the work we do to expand our technology across geos and asset classes.

1:44:45So, for example, you know, right now on Robinhood, 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 Robinhood chain as well. So as we add more countries, more geos, more asset classes, the platform itself will have advantages over what at least a startup quant fund will be able to integrate with and connect with.

1:45:16Peter Diamandis: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.

1:45:55And 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 Robinhood strategies umbrella, where let's say you're like, I don't want to make trading decisions. I just want to have a deposit button, move money into the account, and Robinhood just does the rest.

1:46:36You just manage my money for me while I sleep. Robinhood strategy is a great product for that. And we now have a couple of different things you can choose from there. We have a smart income portfolio that is geared toward generating yields. 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 Robinhood strategies into the IRA and benefit from the tax advantage 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.

1:47:25And 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. So the everything store, everything finance. Yeah. Like if if there's something 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 and in Robin Hood. So we're pretty much we're pretty much there. Amazing. You're having fun, I assume. Yeah, it's a fun job. And we're doing so many new things this year, like Robinhood chain was a new thing for us.

1:48:08All the work on private markets, we've done two IPOs thus far with Robinhood 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. I'm here today with an extraordinary physician, the chief medical officer of Fountain Life, Dr. Don Musalem-Don. Let's talk about cancer.

1:48:48You know, I know from the member database that we have at Fountain, our members who come in who think they're healthy, it turns out 3.3 % of them have a cancer in their body they don't know about. 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.

1:49:08Peter Diamandis: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 on inside your body, it's like driving your car with your eyes closed. And you can know. And so when members come through found, how do they detect cancers? So we're doing full body MRI.

1:49:41And we also do early cancer detection screening.

1:49:44Peter Diamandis:This is very, very important. And these are not typical tools. used in the conventional care setting when it comes to prevention. This is a hard thing because currently these are not studies that insurance would yet be covering, but the goal is to collect these numbers, do the research, and work hard to democratize wellness. Yeah. So at the end of the day, you can know what's going on inside your body. It's your obligation to know. So check out Fountain Life. You can go to fountainlife.com slash Peter to get access to the latest technology to help you detect cancer at the very beginning, at stage one when it is curable before it gets to stage three or stage four in your world of hurt.

1:50:21All right, I'm gonna 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 of 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.

1:51:02Alex, do you want to dive into this one?

1:51:04Peter Diamandis:Well, first, incredible. I think we many of us suspected something like this was already the case. But if you take a look for those 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 26 % 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 sigmoidally, and now thanks to Anthropic publicizing these data, we can extrapolate them, you find that approximately in the next 3 to 12 months, depending on uncertainty, AI is just completely leading all of its own R &D.

1:52:02Peter Diamandis:And that is total recursive self-improvement. And 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 DeepMind 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.

1:52:42Peter Diamandis:So this, I think, like recursive self-improvement is now a feature that's well advertised and 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 Robinhood? 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.

1:53:27But I think 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, pretty straightforward. They don't depend on a lot of other things. You can run these experiments and already people are evaling 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 a product like Robinhood, there's a lot of moving pieces. You have the iOS app. You have the backend.

1:54:13And at the 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.

1:55:07And I think that benefits the platforms that have large scale somewhat sadly, because, yeah, if you're 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.

1:55:28Dave Blundin:Just to quantify what Vlad said there, I just re-implemented Kimi K3 and one of our team members re-implemented GLM just to accelerate them. It's about 10 ,000 lines of code. I'll bet Robinhood is what, 30 million lines of code maybe? Oh, yeah, maybe. I don't know if it's quite that much, but yeah. 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 sandboxed. And also, there are no loops.

1:56:08Dave Blundin:If you look at ChemEK3, 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 Robinhood algorithms. And so, yeah, it's definitely pointing inside of itself first. Vlad, 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, quote, we crossed a threshold where GPUs are now more efficient thinkers than the human brain on a per watt basis.

1:56:52His rough math is that humans are roughly five IQ points per watt. Love that conclusion there. And AIs are now at seven to 40 IQ points per watt. Alex, do you think the math is right?

1:57:06Peter Diamandis: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 glad, frankly, quite glad that Boris Power, open parens, talk about nominative determinism in action, close parens, 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? And 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.

1:57:53Peter Diamandis:And one can extrapolate this and say, this is the worst they'll ever be, presumably. Hopefully humans continue to improve in terms of our intelligence per watt as well. So hopefully this is the worst we'll 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.

1:58:39Peter Diamandis: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 Accelerando, 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.

1:59:41Peter Diamandis: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, OpenAI is disrupting professional services over and over again. So last Tuesday, OpenAI launched ChatGPT for financial services with Morgan Stanley and Evercore. And then two days ago, OpenAI launched Astra for Law, a dedicated legal search system covering U.S. case law, statutes, regulations, court materials, and existing legal software. OpenAI says it's materially outperforming general Astra with web search on legal matters.

2:00:25We 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 bucks an hour to do legal research. What's the half life on that one?

2:00:39Dave Blundin:It's funny. We were doing the negotiation for that Bestmark investment acquisition two weeks ago, and it was in a board meeting, you know, one of these late night sessions. And we had our twenty five hundred dollar 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 can't 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, it's just really, really good at law.

2:01:15Dave Blundin:And, um, 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 we, when we talk about AI assisted trading, you tend to go right to quant trading and rapid trading. It's not true. I mean, it's true. But 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.

2:01:48Dave Blundin: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 launch. Because I think that what's really valuable about a great lawyer is not the actual like legal advice they give you. It's like, yeah, they're like a consigliere and they help you think through things.

2:02:32They're a negotiator. Yeah, it's a -

2:02:35Peter Diamandis:Plot twist though, Vlad, how many of those 10X lawyers in the future will be human? Oh, that's a good question. I agree with you. There are going to be 10x lawyers. I just think many of them won't be natural humans. Yeah.

2:02:50Dave Blundin: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 consigliere 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. 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, then you realized, well, actually, the human advisor market is growing much faster than the robo-advisor market.

2:03:34And 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 trust all aspects of of your of your financial life to.

2:03:50Dave Blundin:I 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 their quest 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.

2:04:30Dave Blundin:So that's where it's going in the short term.

2:04:33Peter Diamandis:Yeah, I'll 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. And 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 pod here all the time, right?

2:05:06The 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. Sure. Vlad, thank you so much for your time, pal. Congratulations on everything you've been building with Robin Hood, 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.

2:05:43We'll have time as we continue to hurdle towards the singularity. So time will slow down for us. and the longevity escape velocity. He's coming. Yeah. All right. Love you guys. Imad and Salim, we missed you. Salim, hope you're enjoying your flight back from India. Yeah, for sure. Thanks, Peter. Thanks, Vlad. Thank you. And you both.

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