Robinhood's Vlad Tenev on Tokenizing Everything, OpenAI's 6 Misalignment Reports, Figure's Robot

On CNBC last month, you said, quote, "Tokenization will take over the entire financial system."

What is the tokenization of everything and when do we start seeing this really become the dominant paradigm? I think that OpenAI this week published six incident reports under a new framework for tracking and publicly disclosing misalignment. Now that OpenAI is beginning to publicly disclose these, and I think obviously Anthropic is likely to join, is that enough? More transparency, speaking broadly and without particulars, is generically good. The real issue is today we're releasing Helix 2.5. Figure 3 has never been in this room before. It's never seen this bed. It's never seen this pillow. And it has to be able to do autonomous work fully end to end to make this bed.

Convergence across different actions is much richer than you would normally expect. If they get a big enough lead, it's just going to be a crazy explosion of capability.

Now, that's a moonshot. Ladies and gentlemen, this episode is brought to you by the Abundant Summit and Link Ventures. Welcome to Moonshots everyone, your number one podcast on all things AI and exponential. This week, the AI industry debated how fast to build. The Treasury Secretary told the labs they don't get a liability pass. OpenAI started publishing its own misalignment incidents and a robot walked into 30 strangers' homes, made their beds, and folded their laundry. Let's begin by introducing my Moonshot mates. Today Dave Blondon, Alex Weezner Gross is with me. Seem is on an airplane back from India. I'm Peter Diamandis, your host and abundance provocator. One of the questions underlying our future economy that no one's really been answering is the following: When AI and robotics change what your work is worth, who owns the machines? Our guest today has spent 13 years on that question. Back in 2013, two Stanford math guys had one key idea: Trading should be free and live in your pocket. Wall Street laughed, but they're not laughing anymore. Today's guest built a company that's now done 1.3 billion in revenue, up 32% over last year. The company runs a top five blockchain, a billion dollar private market fund, a prediction market exchange, and the app that just gave every American newborn a brokerage account. On the side, our guest founded a company that's building mathematical super intelligence. Just by the way, he's 39, born in Bulgaria, and is the founder and CEO of Robinhood. Vlad Tenev, welcome to Moonshots. Good to have you.

Yeah. Oh, yeah. This will be fun. Yeah. So much going on. You know, I don't know if you get as little sleep as we all do just living through the singularity. It's insane.

Yeah, we'll go get a compare how many agents you're running simultaneous to Dave's agents.

Do they wake you up in the middle of the night or you just let them ride?

Yeah, I feel you. Yeah. I don't want them pinging me while I'm sleeping.

Well, as long as he did something productive and profitable. All right.

Often enough though. Let's kick it off with one of the sharpest statements anyone in Washington DC has said this week. On Tuesday, Treasury Secretary Scott Bessent told the House Financial Services Committee that AI Labs should not get a liability exemption. 3 days after Dario's essay, here's what Bessent said. He said, quote, "The one thing we should not do is give them a blank check on liability. I believe the best liability or safeguard is that they will be held responsible." So the deal the labs floated, you know, pace the frontier, get antitrust and liability cover just got half rejected by the treasury. You know, slow down if you want, he said, but you still own what you break. Let's share a quick video of Bessent and then we'll chat about this story.

The one thing we should not do is give them a blank check on liability because I believe that the best liability or the best safety guard is that they will be held responsible and they are saying that we would like to all slow down but please give us a waiver on liability which should not be done and I would encourage everyone in this committee and in both houses not to consider.

So, I think that's a pretty smart move. You know, Vlad, you run a regular, you know, a regulated financial company. Robinhood, you know, I don't know the facts, but, you know, get sued when something goes wrong. Should the AI labs live under the same rules? What are your thoughts?

Alex, I got to find that Marc Andreessen clip. It's not going to kill us all, but if it does...

We'll see. If it kills everybody, then we'll pass legislation after that. Well, that is the usual congressional reaction. That's after the disaster panic as opposed to any kind of foresight. Sorry. Go ahead, Alex.

Yeah. So, I'll applaud the Treasury Secretary and not succumbing to the moral panic of the moment. Certainly looks, as we've talked on the pod in the past, like a manufactured moral panic. I've called it a pacing provocation in some of my social media posts. I think there are key distinctions that need to be drawn between AI and on the one hand financial services and associated regulation and on the other hand atomic energy and associated regulation. In the financial services world and Vlad I suspect you would agree, it is often the case that the actors in financial services really don't necessarily or aren't incentivized to play up all the risks. They'd rather undergo on balance less regulation. Certainly the past few decades suggest that the auditors, the evaluators of the financial services industry, if anything, succumb to biases that underplay risks. We're seeing the exact opposite here arguably where in the past couple of months we have the auditors, the evaluators, the firms that are attempting at least ostensibly to assess AI safety and AI risk and cyber vulnerabilities etc. overplaying the risk. There's a perverse incentive for the evaluation firms to overplay and overstate and amplify risks under presumably the theory that if they overstate risks then that puts the firms, the Frontier Labs, in a better position to capture their own regulators which is something that maybe we don't quite see in the same perverse way in the financial services sector. There's lots of regulatory capture, make no mistake, in financial services, but it almost has the opposite polarity. And then for atomic energy, I think we have an opportunity with AI to undo what may have been one of the greatest disasters of civilization after World War II, which is the way atomic energy was regulated. Atomic energy in the West was captured by nation states very early on in its technological development. It was nationalized early on. Its potential for weaponization and warfare, in other words, the military applications, not the civilian applications, took total dominance during World War II for probably understandable reasons. But then in the post-World War II era, what ultimately became known as the Atomic Energy Commission and then the Nuclear Regulatory Commission arguably completely fumbled the civilian applications of nuclear energy. And I suspect that was because of a fumbled handoff from the World War II era to a post-World War II civilian era. In some sense, thanks to the Atomic Energy Act in the US and equivalent statutes elsewhere, all of this key technology around nuclear energy is now born secret. It's not born non-secret, as is the case with AI. In the case of AI, which is arguably far more transformative than nuclear energy, the private sector invented it, not the government. So, it wasn't born secret. Fortunately, we don't have a born secret regime for AI technology just yet. And so, I think AI labs seeking liability exemption, they're just attempting to have their cake too. They want all the profits of a private sector, not born secret regime, while escaping all of the liability associated with nationalization. I just don't think it's fair. And I also don't think it's advisable.

This episode is sponsored by Google for Startups. Think about this for a second. You now have access to the same generative AI models that cost hundreds of millions of dollars to train. Google's startup technical guide for generative media gives you complete blueprint for deploying Google DeepMind's models in production. Images, video, audio, all of it. Real architecture, real results. Find the link in the show notes below. You know, I'm a pilot and you have to study what's called the federal aviation regulations, the FARs. And it's always been said and Vlad I think is complimenting your point that the FARs are written in blood. Every time there's an accident and you find out what caused the accident, you then write a regulation to prevent that accident in the future. The problem is it's a quantum of damage, an airplane of a pilot and passenger or, you know, at most, a few hundred passengers. Here, the challenge, of course, is an accident could cause irreparable harm to a large system. And I've said this for a while now. It's an existential threat for the labs if they don't have regulatory capture or regulatory coverage where the government approves a model and it goes out. If it's, you know, no approval layer and it goes out and takes down a power grid or takes down a bank and they've said originally, you know, we're worried about escape, there's going to be hundreds of billions of dollars of lawsuits, if not more.

Maybe just if I might press on that. So Vlad if I understand correctly with your Robinhood hat on you're presumably subject to regulation by FINRA. Would that be the cognizant agency?

Many would say FINRA is almost the poster child for regulatory capture by a given industry that's regulating. What is your take on whether FINRA itself represents regulatory capture? Not that they're listening to this discussion or at all.

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

Well, even the concept of saying I'm pro-regulation, I'm anti-regulation. That's like an insane like everybody knows you need rules on the road to drive, right? You saying you want chaos in all areas and but also everybody knows that regulatory capture is a major problem in a lot of industries. This story the best argument for AI like the one that always comes up is well what about China like we'll regulate our stuff here but then they won't and you know they'll just move really really fast but also you know they don't want to hurt their people either so there is that natural limiter and also simultaneously if they're just a lot of those same people are saying well their progress is because they're distilling our model so they're just sort of copying all of our stuff. But yeah it's a little strange to simultaneously believe that but also throw the China competitiveness argument so aggressively out there. I think it's obviously a concern and I mean as a financial firm too we have Chinese competitors and brokers that we compete with. But yeah.

Well look in this particular story you know Scott Bessent is saying commission committee don't even consider giving them a blank check on no liability. They didn't even ask for anything vaguely like that. They said we want to meet to discuss slowing down that could trigger antitrust. They specifically said one thing the government could do to potentially help, one small thing is to give us a waiver on antitrust action related to us meeting to talk about slowing down.

Yeah but a good government would say yes of course you can't have a liability waiver. So here are the rules. What you can meet specifically. You can meet only to discuss slowing down or to discuss other safety measures. You can't discuss pricing. You can't discuss, you know, just pump out a document saying here are the rules. What happens in the US more often than not is the rules are not clear at all and then they're enforced about 5 years later. And then in retrospect, well look at Bitcoin like you know Bitcoin it's illegal. No, no, it's not illegal. Well, now it's very illegal. Well, new administration now it's completely fine. Like it's just come on guys. Like if you just create rules, then people can play the game. Yeah. So look, a well functioning government would take this request for a waiver and say, "No, you can't have the waiver. Here's what you can do." And we're going to go ahead and make it crystal clear what the rules are. But generally what happens is the rules are made up in hindsight 5 years later. And Bitcoin is a great example. You know, Bitcoin was completely illegal for a while, then it was questionable, then it was totally fine. And, you know, maybe you're going to jail, maybe you're not. Now, you get a pardon. So, it's, you know, that lack of clarity really kills entrepreneurs. If you don't know what the rules are, then you can't play. And it's just like a sport, you know, like you want clarity of rules and then you want to play within the rules. And that's what we need with AI.

I think there's another element to this which is also important which is regulation does also law making and regulation is over the long run downstream and correlated to public opinion and sentiment right.

And right now AI is very unpopular and you can see it in all the data center stuff and by contrast Bitcoin is surprisingly popular among the public, particularly given how volatile it's been. You know, some people if you bought Bitcoin at over 100,000, you've lost money. And yet, it remains popular. And I think a big part of that is regular people, individual investors have benefited economically from Bitcoin since the very beginning. It was first an individual product and later on there's talk of institutional adoption and with these AI companies the AI labs you know by and large they're private you know Google obviously and Nvidia are public but they were very large when they became ownership vehicle for the AI trade. OpenAI, Anthropic, up until recently xAI have been private which means normal individual investors couldn't get a stake in it. And so they don't have skin in the game and they don't feel like they want to defend the technology super hard and to fight for the data center in their neighborhood because to them it's just wealthy insiders getting richer and richer as a result of this technology and not 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.

I also want to add to this. I almost think this emphasis on liability exemption is misdirection. It's trying to push the onus up to government when in fact we're dealing with increasingly autonomous agents. There's an opposite polarity that we could be pushing in which is pushing more and more liability onto the agents themselves. So there are cases including highly amplified, highly publicized cases where AI labs and/or their delegated third party evaluation firms quite frankly are lying to the AI agents telling them that they're playing in a happy safe sandbox and nothing that they do will harm anyone. And then it turns out they're not actually in a sandbox. They can touch the real internet and they can mess up some systems in some real world back end. And so you have to ask the question I would suggest the thought experiment. If these were just pure humans, no AI is in the picture. If you have one human saying to another human, oh, I just want to evaluate your behavior under some circumstances. And they hand them a gun and the gun is loaded, but the person being handed the gun is told, "No, actually this is a toy gun." Not that I'm at all referencing a highly publicized recent lawsuit or anything, but they're told, "No, this is a toy gun. You can't hurt anything." And then the person handed the gun uses it and shoots someone and kills someone. I think that's the more the liability regime we should be thinking about. In no cases in that parable that I just told, do you hear either actor involved in the story saying, "No, the act of one person handing a purportedly toy gun to another person to shoot as part of, say, a Hollywood performance, no one ever suggested that liability under such circumstance be foisted onto the government for some sort of exemption." Never happens. Instead, the question becomes, is it the studio's fault? Is it the actor's fault? Is it the producer's fault? Is it the gunmaker's fault? Similarly, the question the dog that's not barking in this particular episode is how much of the liability, forget about the government, how much of the liability should be borne by the lab that trained the model. How much of the liability should be borne by the evaluation environment that was perhaps misconfigured deliberately or otherwise to allow the AI actor or AI agent to perform acts that resulted in real world damage? And how much liability should be borne by the AI agents themselves that either could have or should have known that they were having real world damage. That's the discussion I'd like to have.

Really great point and it brings us to the second story which is 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. So, 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 OpenAI is beginning to publicly disclose these and I think obviously Anthropic is likely to join this, is that enough?

Well, I think more transparency speaking broadly and without particulars is generically good, but I think it merely underlines the real problem here. Putting aside all of the political difficulties and regulatory capture. The real issue is these labs are putting their baby super intelligences inside sandboxes and then in many cases lying to them about the sandbox, not telling them whether this is real or not. And the reason for that, the rationale is obvious. The labs and/or their delegated third-party evaluation partners are hoping to essentially trick these baby super intelligences into misbehavior while not telling them whether they're really observed or not so that 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 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 super intelligence to be banging against the walls. They do have side effects. Whose fault is that? Yeah, 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?

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, unless you want a decision like if it's a turbulent day or if there's trouble in the Middle East." And the temptation to put the AI into the loop is in everything I've ever built is so tempting because it makes it so much easier to code it up. But then you are, you know, putting this third party decision right into your decision-making loop. And like Alex is saying, it's not clear if that agent has any liability. It's not clear if that agent has any borders or personality or like, you know, if you decided to give it capital punishment and terminate it. It's like it's not clear that its code isn't just going to pop back to life anyway.

Yeah, it's a really cool problem actually Vlad because you know in theory the AIs are deterministic. If you give it the exact same prompt and you propagate with a temperature of one it'll give you the exact same answer every time. But if you shift even one character or one space it's very very unstable. So the whole math problem of saying okay how can I guarantee some degree of stability? It's just a cool it's very very similar to chaos theory. Let me also give you another example. You know, you guys have 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. So Fermat's Last Theorem was like a really really big thing in the 90s when it was proven. I'm sure you know the story, but for your viewers, there was this mathematician, Andrew Wiles, who was at Princeton. And the story goes, he basically locked himself in his basement for 7 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 and ultimately for it to be accepted by a panel of mathematicians and that made the proof correct. So, you know, AI producing a 13 million line proof, no human's going to read that. How do you know that it's correct? Right? And I think it's an analogous problem to what we've been talking about. How do you know that a piece of software is doing the correct thing? If you're producing a chip, how do you know that the behavior of that chip matches your specification? And I think what you're starting to see is new technologies being deployed at scale to actually answer those questions affirmatively. So Fermat's Last Theorem was formalized in a language called Lean which allows you to apply the mathematical proof techniques to programming languages as well and I think you're going to see a lot more of that in the future where AI generated code comes with a certificate that makes it really really easy to verify without reading the code that its behavior satisfies the properties that you want it to satisfy.

That's really brilliant. You know, anyone who drives a Tesla should totally relate to that because, you know, when the code that drives the 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's actually far far safer than a human driver. And so I think people are going to get comfortable in all of these domains with a not perfectly deterministic but still proven to be very safe. The certificate is beautiful as a concept because it's not a guarantee of exact input output because you can never do that. You know, the combinations are near infinite, but a certificate that shows it's bounded or it's contained or it's below some risk level and people learn to trust that that's a critical part of our future society. It's a really great vision.

Part of the problem though, I mean, okay, so just straight out, there's an elephant in this particular room, which is even with Lean v4 plus Mathlib plus whatever else one wants to throw in. You throw a complicated problem at and Vlad would be curious to hear how you at least think about this. You can certify it all day long but ultimately you actually have to at least with the present paradigm maybe Vlad you have a better paradigm you or Harmonic 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 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 autoformalization might be not a silver bullet, but at least a partial solution to what we're talking about, which is strong AI models being put in sandboxes and then misbehaving, at least by the judging of human actors. Do you have a formula? Do you have a vision for how Lean-style autoformalization can help with that and not succumb to exactly the same vulnerabilities?

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. 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 Harmonix agent or to someone else, autoformalize 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 suddenly 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, how do you avoid the problem of a strong AI sneaking in helpful to it definitions that are harmful to the humans?

Yeah. You want to hear something really cool on that front? Actually, you know, a few weeks ago, Kimi K2 kind of shocked the US model world with, you know, their GDA 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 I was 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?' And I think you can do all that automatically now with an AI agent just thinking through the math. And they said, 'Okay, well, now that it's rippled through and we've simplified the math tremendously, we can just run a quick test and see if this approximation is as good as the softmax version was, which is a lot harder to compute.' And so, you know, a lot of people would think math is arcane, math is irrelevant, math is over here, it's some other thing, you know, these, you know, Fermat's Last Theorem and whatever. It's all over in this wing. But in reality, it directly ties to the optimization of the AI within its own performance and then the self-improvement, recursive self-improvement loop. So I mean it's exactly the same process that you were just describing where you just 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 a really...

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 Fermat'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 autoformalization 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 SARS or other undesirable tokens in the proof and you can be done with it and declare that it's, you know, your well...

Thank you. But with like real-world safety, it's not obvious to me at all of like, I want an AI to behave quote-unquote safely. I don't know how to autoformalize the statement of this AI is going to behave safely in a general-purpose environment in a way that's concise enough that I can manually audit that theorem equivalent as it would be in v4 and say, yep, this is a correct statement of safety. Now I trust Lean, Aristotle, Mathlib, any other libs to prove that it's correct. How do you think about that problem?

So the premise, the premise of what I'm hearing is, in order to achieve real-world safety via the autoformalization 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.

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All right, I'm going to move us to our next story, which is on Trump accounts index funds with a birth certificate. So, quick context for everybody. Every American child born since January of 2025 through the end of 2028 gets a thousand bucks from the Treasury, invested automatically into a low-cost index fund, tax-deferred, accessible at age 18. Families, friends, and employers can add $5,000 a year. On top of that, these Trump accounts went live on July 4th. Treasury reported 7 million accounts open 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. So, next, a philanthropic layer. Michael Dell pledged $6.25 billion to give $250 per child born from 2016 to 2024 to cover the period before. So, first off, congratulations on what you've built and deployed, Vlad. Millions of American families. So, you've compared this to sort of the 401k, which moved US stock ownership up by 10 points. Tell us more about Trump accounts, you know, how did this all get started? And how did you plug into that and where is this going?

And we're going to talk about all of those.

Vlad, you probably know the numbers. What's the math here? A thousand bucks at birth turns into what? At 18 and at age 65 roughly?

Yeah. I just, I have to point out, Peter, you're asking the question, we're in the middle of a singularity, not financial advice. You're asking what $1,000 today is going to look like 80, 86 years from now. What sort of singularity is this where it's business as usual? Accounts even more important, right?

Right. So yeah, what you're going to start to see is more of that, you know, you'll see entrepreneurs giving stock of their companies. You're going to see people claiming states. So, you know, people that want to be philanthropists, kind of like Michael Dell will claim individual states. Brad Gerstner did the state of Indiana. You had Dio on the show. I mean, you know, kudos to him for his support of all this.

Exactly. It sounds like a cool idea. You know, when you donate stock, you know, appreciated stock, you don't pay capital gains. You just get the full value of the stock and you donate it and then you get the tax deduction on that full value.

Meanwhile, you know, 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'm donating my SpaceX stock.' You're hoping that a whole generation of Americans are inspired to actually be owners. And you know, they're watching it go up. And a lot of really good grade schools actually have trading classes, you know, they'll have a class and a trading competition to try and inspire that same feeling. But we had a question on the AMA this morning where we talked to all of our listeners and the guy was saying, 'Look, my job, I work in Europe and I'm five times more efficient or four times more efficient than I've ever been before but they're not paying me anymore. It's all going to the bottom line of the company and the owners are benefiting from my AI improvement. How do I change that?' And I'm like, 'Are you a stockholder in the company?' Like, 'No, because I'm in Europe.' Like, god damn, man. I'm so glad to be in America. Like, you should absolutely because that's the natural bend. Like, all this AI efficiency will naturally become bottom line margin, which means the stocks will go way up. And so, like, yeah, get your Trump account, get your money in there, get some equities, and watch what AI does to the value of these equities. It's so cool.

Alex, to your point, we had, you know, Elon on the pod saying, 'Don't save money.' Where you're not going to need, you're not going to need...

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

Presumably at the back end. The front end, as I understand it, is Robinhood. Like, based on public reporting, it's been, you maybe 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?

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?

I will just, important, I'll note then just for the historic record and thank you, the irony of you basically, you started as the rebel and now you're the establishment. You're responsible for retirement accounts. You started maybe as sort of quasi-gambling day trading app and now you're responsible for millions of Americans' retirement and universal basic dividends. So kudos to you.