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

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

Speaker names marked ? are inferred from speaking time and the show’s known hosts — they are not voice-verified.

• 0:00Peter Diamandis?

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

• 0:06Speaker 01

  I called it a freight train, I think, a freight train that can't be stopped and will eat the whole financial system. So it's a very hungry freight train.

• 0:13Peter Diamandis?

  What is the tokenization of everything enable? And when do we start seeing this really become the dominant paradigm?

• 0:20Speaker 01

  I think that OpenAI this week published six incident reports under a new framework for tracking and publicly disclosing misalignment. Now that, you know, OpenAI is beginning to publicly disclose these, and I think, obviously, Anthropic is likely to join this, is that enough?

• 0:40Speaker 02

  More transparency, speaking broadly and without particulars, is generically good. The real issue is--

• 0:46Speaker 03

  Today, we're releasing Helix 2.5. Figure three 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.

• 0:57Speaker 04

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

• 1:06Speaker 05

  Now, that's a moonshot, ladies and gentlemen. This episode is brought to you by the Abundance Summit and Link Ventures.

• 1:19Peter Diamandis?

  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 London, Alex Weisner Gross is with me. Saleem is on an airplane back from India. I'm Peter Diamandis, your host and abundance provocateur. One of the questions underlying our future economy that no one's really been answering

• 2:00Peter Diamandis?

  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 superintelligence.

• 2:43Peter Diamandis?

  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.

• 2:53Speaker 01

  Glad to be here. Happy to hang with you guys.

• 2:54Speaker 04

  Oh, yeah. This'll be fun.

• 2:56Peter Diamandis?

  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.

• 3:05Speaker 01

  Very little sleep, yeah. Last night was rough with my coding agents.

• 3:10Peter Diamandis?

  Yeah, we're going to compare how many agents you're running simultaneous to Dave's agents.

• 3:15Speaker 04

  Yeah, I'd love to have that benchmark. Do they wake you up in the middle of the night, or you just let them ride?

• 3:21Speaker 01

  I let them run, yeah. I think I try to sleep with the technology in another room because, yeah, otherwise, I'm actually kind of concerned about my mental health. So--

• 3:33Speaker 04

  Yeah, I feel you.

• 3:34Speaker 01

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

• 3:37Speaker 04

  It's funny, too. I set the budgets through consoles originally, and now I'm getting lazy. I'm just like, yeah, spend $1,000 on that, but no more. Or, you know, whatever. And then I just walk away, and I kind of assume that it's going to adhere to what I said. Any given morning, I could wake up and it could have gone insane, you know? There's going to be like a seven-figure bill one of these mornings.

• 3:58Peter Diamandis?

  Well, as long as it did something productive and profitable.

• 4:02Speaker 04

  Rarely, rarely. Often enough, though.

• 4:08Peter Diamandis?

  Let's kick it off with one of the sharpest statements anyone in Washington, DC has said this week. On Tuesday, Treasury Secretary Scott Besant told the House Financial Services Committee that AI labs should not get a liability exemption. Three days after Dario's essay, here's what Besant 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 Besant, and then we'll chat about this story.

• 4:53Speaker 01

  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 safeguard 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--

• 5:20Peter Diamandis?

  So I think that's a pretty smart move. You know, Vlad, you run 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?

• 5:36Speaker 01

  I think that this question rests on how big the blast radius of any potential catastrophe could be, right? And if it's like a small issue, where maybe there's a cybersecurity breach that affects a company, or maybe something slightly bigger than that, it's probably fine. I think the question becomes, all right, if it's a bigger blast radius, bigger impact, bigger damage, you can imagine it could be larger than a simple, like, legal liability

• 6:19Speaker 01

  can handle. I mean, a lot of people compare the risks of AI technology, because it's such a powerful technology, with something like atomic energy, right? And I think we can disagree about whether that's right or not, but let's say, for example, that it is, and it's on sort of like that tier of risk, then I don't think simple, like, legal and civil liability is sufficient. And I think you need some safeguards beyond that. So I think it's a question of, like, all right, this hugging face incident and things

• 7:02Speaker 01

  like that, is that the ceiling of the type of offensive cybersecurity capability that we have? Or should we plan for something that's maybe 10 times bigger or 100 times bigger? And do we have time, you know? Is it one of those things where maybe we'll see a canary in the coal mine, and there will be something to react to and respond, and then we can kind of like nip it in the bud? Or is the first issue going to be catastrophic? If you think about all regulations in the financial industry, you can kind of trace

• 7:41Speaker 01

  them back to some kind of crisis, right? The market crash of 1929 led to, you know, the Securities Act of the '30s, and the Securities and Exchange Act, and the establishment of all of that regulation. And it's typically some problem raises a concern. And I think my mental model is this will probably be similar in the sense, nobody wants to regulate a hypothetical. You want to regulate things once there's demonstrated proof of harm. But I think with the exponential increase in the power of these models, the issue is

• 8:23Speaker 01

  you want that harm itself to be small and contained and not really big. Marc Andreessen was, I remember doing a podcast a couple of years ago, and he was like, everyone's freaking out about AI. We are overthinking it. You know, it's not going to kill us all. And if it does, you know, you'll see like a small village destroyed first. And we're not seeing any small villages, so we shouldn't worry. And, you know, it's maybe exaggerated, but I think that's likely how people are thinking about it now on a policy level. And hopefully, some of the opponents are like, well, you know, maybe we're underestimating

• 9:08Speaker 01

  the power of this technology and how quickly it could improve.

• 9:11Speaker 04

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

• 9:19Speaker 01

  Yeah, it'll start with a small village, so we'll have time to like

• 9:23Peter Diamandis?

  If it kills everybody, then we'll pass legislation after that.

• 9:27Speaker 04

  Well, that is the usual congressional reaction. That's after the disaster panic as opposed to any kind of foresight. So I got it, Alex.

• 9:34Speaker 02

  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

• 10:15Speaker 02

  to play up all the risks. They'd rather undergo unbalance, less regulation. Certainly, the past few decades suggest that the auditors, the evaluators of the financial services industry, if anything, succumb to biases that underplay risks. We're seeing the exact opposite here, arguably, where in the past couple of months, we have the auditors, the evaluators, the firms that are attempting, at least ostensibly, to assess AI safety and AI risk and cyber vulnerabilities, et cetera, overplaying the risk. There is a perverse incentive for the evaluation firms

• 10:55Speaker 02

  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.

• 11:35Speaker 02

  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

• 12:15Speaker 02

  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 and eat it too. They want all the profits of a private sector, not born

• 12:56Speaker 02

  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.

• 13:03Peter Diamandis?

  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 Tactical Guide for Generative Media gives you a complete blueprint for deploying Google Deep Mind's models in production. Images, video, audio, all of it. Real architecture, real results. Find the link in the show notes below.

• 13:31Peter Diamandis?

  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 he's 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 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

• 14:14Peter Diamandis?

  coverage, where the government approves a model and it goes out, if it's no approval layer, and it goes out and takes down a power grid or takes down a bank, and they've said originally, we're worried about escape, there's going to be hundreds of billions of dollars of lawsuits, if not more.

• 14:34Speaker 01

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

• 15:17Speaker 01

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

• 16:01Speaker 01

  So I think it's odd that there's so much pushback against this. And of course, we have screwed up with atomic energy and all these things, but that doesn't mean we can't learn from it and have something better.

• 16:18Speaker 02

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

• 16:29Speaker 01

  Many, many different agencies. I mean, Robin Hood does a lot of things. We've got money transmitter businesses, we've got a big crypto business. So we're regulated by FINRA, the SEC. We've got the CFTC on the futures and commodities and prediction market side. We've got our global tokenization business, entities in Europe. So yeah, probably dozens of different regulators. And sure, could we move faster if there was less? Probably. But also, we found a way to move very, very fast while keeping our customers safe. So it doesn't necessarily mean progress and AI is going to grind to a halt.

• 17:15Speaker 02

  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.

• 17:29Speaker 04

  Actually, it's funny, Vlad, I don't know if you've ever been to the ICI conference, but when I first founded Vestmark, I had never done anything in fintech before. I was late 20s, kind of 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 have the crash of 29, you have all kinds. Like, if you don't have regulation in the industry, all money gets stolen. You know that for sure. So you know it has to

• 18:11Speaker 04

  exist. But it is a great analogy, I think, the FINRA analogy. I'd love to hear your take on it, Vlad.

• 18:18Speaker 01

  Yeah, I mean, I would say we've had a complex relationship with FINRA, right? The relationship at the beginning was actually quite good when we were a startup and everyone was kind of rooting for us to succeed. And people warned me, they're like, well, the financial services, highly regulated industry. As a Silicon Valley startup, you just don't, you'd rather not deal with that. And I think we swam against the current, back in 2013, when we started the company, by getting fully regulated from the beginning. And probably from 2013 to 2018,

• 19:05Speaker 01

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

• 19:48Speaker 01

  startup to basically established incumbent where the media and sort of like the apparatus sort of turns against you. And, you know, Robin Hood went through that certainly for many years. And then we figured out how to, how to come out the other side. But I think we can argue about whether it's good or bad. But I don't think getting rid of, of regulation as a whole is a, is a reasonable solution.

• 20:20Speaker 04

  Well, even, even the concept of saying, I'm pro regulation, I'm anti regulation. That's like an insane, like, everybody knows you need rules on the road to drive, right? You're saying you want chaos, like in all, in all areas. But also, everybody knows that regulatory capture is a major problem in, in a lot of industries. This storyline is-

• 20:39Speaker 01

  Yeah, I think the best argument for AI, like, the one that always comes up is, well, what about China? Like, we'll regulate our stuff here, but then they won't. And, you know, they'll just move really, really fast. But also, you know, they, they don't want to hurt their people either. Like, so there, there is that natural limiter. And also simultaneously, if they're just, a lot of those same people are saying, well, their progress is because they're distilling our model. So they're just sort of copying all of our stuff. But yeah, it's a little strange to be simultaneously, to simultaneously believe that, but also throw the, you know, China competitiveness argument so aggressively out there. I think

• 21:23Speaker 01

  it's obviously a concern. And I mean, as a financial firm too, we have Chinese competitors and brokers that, that, that we compete with. But yeah,

• 21:34Speaker 04

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

• 22:06Peter Diamandis?

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

• 22:47Peter Diamandis?

  function is human flourishing, not human, you know, destruction.

• 22:52Speaker 04

  Yeah, but a good, 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, 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 five years later. And then in retrospect-

• 23:17Peter Diamandis?

  After disasters.

• 23:18Speaker 04

  Well, look at Bitcoin. Like, you know, Bitcoin, it's illegal. No, no, it's not illegal. Oh, now it's very illegal. Well, new, new administration. Now it's completely fine. Like, it's just, come on, guys. Like, if you just, if you just create rules, then people can play the game. Yeah. So, look, a well functioning government would, would take this request for a waiver and say, no, you can't have the waiver. Here's what you can do. And we're going to go ahead and make it crystal clear what the rules are. But generally, what happens is the rules are made up in hindsight, five years later. And Bitcoin is a great example. You know, Bitcoin was, was, 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, you know, you get a pardon. So it's, you know, that lack of clarity really

• 24:03Speaker 04

  kills entrepreneurs. If you don't know what the rules are, then, then you can't play. And it's, 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 an AI.

• 24:15Speaker 01

  I think there's another element to this, which is, which is also important, which is, regulation does also, lawmaking regulation is, over the long run, downstream and correlated to public opinion and sentiment. Right?

• 24:32Speaker 04

  For sure.

• 24:34Speaker 01

  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, but particularly given how volatile it's been. You know, some people, if you bought Bitcoin at over 100,000, you've, 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 Bitcoins since the very beginning. It was first an individual product. And later on, you know, there's

• 25:13Speaker 01

  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, when they became a 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're, they're, they don't have skin in the game. And they don't feel like they want to defend the technology super hard until, you know, fight for the data center in their neighborhood. Because

• 25:53Speaker 01

  to them, it's just wealthy insiders getting richer and richer as, as a result of this technology and, and not, you know, their families or their communities. And I think that's a big problem, too. So that's why we've been pushing for, you know, AI companies to open up access to individual investors through Robin Hood Ventures and similar vehicles even before the IPO.

• 26:19Speaker 04

  Yeah.

• 26:20Speaker 02

  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, 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

• 27:03Speaker 02

  the real internet, and they can mess up some systems in, in some real world backend. And so you have to ask the question. I would suggest the thought experiment. If you, 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, 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

• 27:46Speaker 02

  and kills someone. I think that's the, more the liability regime we should be thinking about. In no cases, in, 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? 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

• 28:30Speaker 02

  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.

• 29:00Peter Diamandis?

  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.

• 29:41Peter Diamandis?

  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?

• 29:58Speaker 02

  Well, I think it 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

• 30:42Speaker 02

  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 Schrödinger'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

• 31:27Speaker 02

  against the walls. They do have side effects. Whose fault is that?

• 31:30Peter Diamandis?

  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?

• 31:49Speaker 01

  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. You 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

• 32:30Speaker 01

  instance, because we wanted to learn, we wanted to see, you know, what the limitations are, what people want, what people are doing. And we've been expanding it over time. So we added options trading, we added limited margin, we added crypto recently and started rolling that out. And we've learned a lot of things, actually. One is that, you know, right now, you have to do all of your trading from within your Claude code or your codex. And, you know, I think we're in a circle, probably all of us, where a lot of people we know use Claude code.

• 33:12Speaker 01

  In the general public, very few people do, and it's like very, very complicated. And that jump to connect another service with something like 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. Like, I don't really feel like trading right now, which is kind of interesting, right?

• 33:52Speaker 01

  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.

• 34:36Speaker 01

  But then you kind of also hear, well, general models are getting pretty good. But we've started experiencing it very, very directly, that I think it is hard to get good at multiple specialist specialized tasks.

• 34:53Speaker 04

  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, everything I've ever built, is so tempting, because it makes it so much easier to code it up.

• 35:38Speaker 04

  But then you are, you know, putting this third-party decision right into your decision-making loop. And like Alex is saying, it's not clear if that agent has any liability, it's not clear if that agent has any borders or personality, or like, you know, if you decided to give it capital punishment and terminate it, it's like, it's not clear that its code isn't just going to pop back into life anyway.

• 35:58Speaker 01

  A lot of this, I think, also comes down to the correctness of the code and sort of its cybersecurity properties, right? Because, and you can kind of reason by analogy with existing software out there. Like, a lot of software has bugs, a lot of it has vulnerabilities, and AI just amplifies that, right? So if AI can write 100 times as much code as a typical human in a day, you should expect that there's going to be a defect rate, right? And, you know, even if the intentions are right, sometimes like, there will just be bugs that have catastrophic consequences.

• 36:42Speaker 01

  You see this a lot in crypto, too, where, you know, you just have like a smart contract or a protocol, and there's a direct economic consequence to there being a bug. You know, hundreds of millions of dollars can be drained from the protocol instantaneously. And the other company, Peter, that you spoke about, Harmonic, that was created to solve this problem of how can you actually, how can you actually guarantee that the AI is doing the right thing and what you expect it to do? Can we mathematically prove that it's correct? Because, yeah, take away the intentions.

• 37:24Speaker 01

  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.

• 37:42Peter Diamandis?

  Got it.

• 37:43Speaker 04

  Yeah, it's a really cool, it's a really cool problem, actually, Vlad, because, you know, in theory, the AIs are deterministic. If you give it the exact same prompt and you propagate with, you know, a temperature of one, it'll give you the exact same answer every time. But if you shift even one character or one space, it's very, very unstable. So the whole math problem of saying, okay, how can I guarantee some degree of stability? It's just a cool, cool, it's very, very similar to chaos theory.

• 38:11Speaker 01

  Well, let me also give you another example. You know, you guys, you mentioned the Navier-Stokes theorem and the proof of that. A week before, Anthropic announced the formalization of Fermat's Last Theorem, which was 13 million lines of 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, you know, in his basement for seven years working out this proof. And then he went out and announced it. He unveiled it at a conference or an event.

• 38:55Speaker 01

  And it took people months to even read it and understand it. And then they found an error, right? So they're like, oh, well, it's, someone found an error, 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 gonna read that. How do you know that it's, it's correct? Right? And, 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, correct thing? If you're producing a chip, how do you know that the behavior of that chip matches your

• 39:39Speaker 01

  specification? And I think what you're starting to see is new technologies being deployed at scale to actually answer those, 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, and I think you're gonna 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.

• 40:17Speaker 04

  That's really brilliant. You know, anyone, anyone who drives a Tesla should totally relate to that because, you know, when, when the code that drives the, you know, the self-driving Teslas was originally about 10% neural net, 90% C code. And the, the 10% neural net was just doing image recognition and classification of objects and whatever. And every year that went by, it became more and more neural net. And then Elon was saying it's now 100% moved over to neural net. So it's theoretically not deterministic at all. It could in theory do anything at any given moment, but it's been so tested and so beaten to death that it doesn't, that it's actually far, far safer than a human driver. And so I think people are gonna get comfortable in all of these domains with a not perfectly

• 40:59Speaker 04

  deterministic, but still proven to be very safe. The certificate is beautiful as a concept, because it's not a, it's not a guarantee of exact input-output, because you could never do that, you know, the, the combinations are near infinite. But a certificate that it shows it's bounded, or it's contained, or it's below some risk level, and people learn to trust that, that's, that's a critical part of our future society. It's a really great vision.

• 41:22Speaker 02

  Alex? Part of the problem, though, I mean, okay, so just straight out, there's an elephant in, in this particular room, which is even with Lean V4 plus MATLAB 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 Vlad, you have a better paradigm you or Harmonic are working on. Ultimately, the, you, 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.

• 42:04Speaker 02

  And if you look, if you inspect carefully, it is, 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, in question form for, for Vlad, if, 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, by the judging of human actors. Do you have a formula?

• 42:48Speaker 02

  Do you have a vision for how Lean-style auto-formalization can help with that and not succumb to exactly the same vulnerabilities?

• 42:57Speaker 01

  Yeah, yeah. A couple of thoughts there. I think that's, that's a, a rich, very, very, very rich question with some threads. So it is true that people can manipulate the axioms of Lean, and if you change the axioms, then you can prove all kinds of weird stuff. It's theoretically possible, I think, and you can also say, well, maybe there's like a soundness issue in the Lean kernel, right? And, you know.

• 43:31Speaker 02

  I'll grant you the soundness of the Lean. I mean, Lean kernel has been at least V4, maybe not MATLAB, 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 Harmonic's 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 Last Theorem, because I can formalize FLT really simply, the ultimate statement, but for something more complicated, that's harder to formalize

• 44:15Speaker 02

  than Fermat's Last 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?

• 44:26Speaker 01

  Yeah, I mean, I think, I think the, the benefit there is that, let's say you do wanna check the definition and the theorem statement of Fermat's Last Theorem, right? That's, you know, one very simple line of Lean. You can see all the things that it depends on. But it does save you, so you, you, let's check those things. And I think it is, the models are improving in the faithfulness of the auto-formalizations as time goes on. It used to, they used to make terrible mistakes where you just misformalize statements and, you know, change addition to subtraction and things like that. So you're seeing less of that.

• 45:08Speaker 01

  But even if you have to review it, you know, you review one line, and you don't really have to check the 13 million lines of the proof, which is where the, the bulk of the work is. So even if it's imperfect, it probably saves you, you know, 90-plus percent of the effort, and probably way, way more. There's like, the, the, there's a notion of the De Bruin factor, right? Which is, it was an explanation for why mathematicians haven't formalized their work and put it in a machine-readable form, which is, the effort to, to formalize something up until

• 45:48Speaker 01

  very recently was like 10 to 20 times the effort to actually write it on paper and prove it. So nobody was going through that, right? But you could imagine, I think we're, you could argue, we're already at the point where it actually saves you time. Like, it's faster to work in an entirely formal context as a mathematician than it would be to do it by hand, by paper. Because what it allows you to do is actually validate the lemmas and the ideas as you're going along. And then I, I think what, 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.

• 46:33Speaker 01

  You have to like, live in formal land and just be constantly formalizing and, and using Lean as you go. And, and I think the math community is going through that transition as we speak. You asked another question, which was, all right, is, is, in the future, is the AI model itself, like the behavior of it, gonna be, gonna be formalized?

• 46:59Speaker 02

  Right.

• 47:00Speaker 01

  And that's an interesting one. I, 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, they enable a lot of really complicated stuff, but fundamentally, they're deterministic, and you wanna have strict bounds on their behavior. Like, just basic things, you don't want your chip to like... Freeze and halt, right? You want to prove things like liveness. You want to make sure

• 47:39Speaker 01

  that certain operations happen within, you know, 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, you know, it'll find its way into the LLM and AI model behavior in some form or fashion within the next five years.

• 48:07Speaker 04

  You want to hear something really cool on that front? Actually, you know, a few weeks ago, Kimi K3 kind of shocked the US 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? 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,

• 48:52Speaker 04

  we can just run a quick test and see if this approximation is as good as the softmax version was, which is a lot harder to compute. And so, you know, a lot of people would think, math is arcane, math is irrelevant, math is over here, it's some other thing, you know, these, you know, Fermat's Last Theorem and whatever, it's all over in this wing. But in reality, it directly ties to the optimization of the AI within its own performance and then the self-improvement recursive self-improvement loop. So, I mean, it's exactly the same process that you were just describing, where you just, you just have a simple mental model of a mathematical adjustment, and then you ripple through all the way to the Nvidia GPU performance at the transistor level. It's just really cool.

• 49:34Speaker 02

  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 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 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 sorrys or other undesirable tokens in the proof, and you can be done with it and declare that it's victorious.

• 50:16Speaker 04

  You know your lean well.

• 50:17Speaker 02

  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 auto-formalize the statement of this AI is going to behave safely in a general-purpose environment in a way that's concise enough that I can manually audit that theorem equivalent as it would be in V4 and say, yep, this is a correct statement to safety. Now I trust lean, Aristotle, MathLib, any other libs to prove that it's correct. How do you think about that problem?

• 50:52Speaker 01

  Yeah, I think you got to break any big problem like that. You break into little chunks. And, you know, obviously verifying the safety of an AI model, or a chip, or, 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 sub-module that maybe is small, that satisfies certain properties, and you can kind of reason about that sub-module, like, the liveness of it, or, you know, how long it takes to do something, or that, you know, its adder works, right? And then, you

• 51:36Speaker 01

  know, the AI models get more capable, and then big modules are made by collections of sub-modules. So then you go up one level of abstraction. And then, you know, in a couple of years, you, or maybe even less at this rate, you verify the entire Linux kernel, right? It's kind of like how we started with math, like, a couple 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.

• 52:15Speaker 02

  So the premise, 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 sub-worlds, something like that.

• 52:32Speaker 01

  Absolutely.

• 52:33Speaker 04

  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, you can measure that and formalize and bound that, too. So, you know, like, everybody knows somebody that is 99% of the time perfectly rational, and then, 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, 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.

• 53:16Speaker 04

  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.

• 53:40Speaker 06

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• 54:41Peter Diamandis?

  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 $1,000 from the Treasury, invest it 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 4. 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

• 55:26Peter Diamandis?

  was super impressive, right? These were announced in May, and you had it live by July 4. 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.

• 55:48Speaker 01

  Thank you.

• 55:49Peter Diamandis?

  Millions of American families. So you've compared this to sort of the 401K, which move US stock ownership up by 10 points. Tell us more about the Trump accounts, you know, how did this all get started, and how did you plug into that, and where is this going?

• 56:04Speaker 01

  Yeah, so first, yeah, putting, putting my Robinhood hat on. I mean, hats off, we stand on the shoulders of giants, right? So we're in many ways like the implementation layer of this. So we're serving as the sole initial brokerage and trustee for the Trump accounts in partnership with BNY, and of course, under the direction of US 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 US Treasury started by seeding $1,000 into the accounts of all

• 56:47Speaker 01

  children born January 1, 2025, 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. So, you know, 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.

• 57:30Peter Diamandis?

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

• 57:31Speaker 01

  Yeah. The underlying, I guess, theme behind everything that we do is ownership. We believe that ownership of high-quality financial assets in individuals' hands is extremely important, not just good for the individual, but also there's a societal benefit. If we have more owners in society, the more people with skin in the game that can benefit from appreciation and growth, the more stable that society will be. And so Trump Accounts extends ownership to age zero. And you get, everyone born in this country is like, has skin in the game in the growth of great American enterprise and industry. And they benefit

• 58:13Speaker 01

  from compound interest from birth, where if you just put, you know, $50 a month into these accounts on a regular basis, by the time the child becomes a 28-year-old adult, they've got hundreds of thousands of dollars in there potentially. And by the time they reach retirement age, that could, that could get into the millions. So the numbers are staggering.

• 58:36Speaker 01

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

• 59:19Speaker 01

  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. The ones that are, are usually not efficient. You have to like feel good about how your money is being allocated. Trump accounts allow direct giving to the children at basically very, very high efficiency, like very incredibly, incredibly efficient direct giving with the charitable benefit. So we think it can become the default giving vehicle in this country. And so when you, when you take the donors, you've got the employers and you have 70 million children. I think

• 1:00:03Speaker 01

  the, the program is like just a snowball that keeps getting bigger and bigger. And, and you don't have to like squint too hard to see it being, you know, the biggest element of, of, of long-term saving and investing in this country in, within possibly even a decade.

• 1:00:24Peter Diamandis?

  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?

• 1:00:32Speaker 01

  Yeah. And it depends. We actually have a really nice, the, the first screen, if you open a Trump account for your children or, 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, if you do the math, even without additional contributions, it, it gets up into the tens of thousands of dollars. Just the, just the seed amount of a thousand from charity gets up into the

• 1:01:17Speaker 01

  tens of thousands fairly quickly.

• 1:01:20Speaker 02

  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?

• 1:01:33Speaker 01

  Well, a lot of the philanthropic giving is in the form of stock too. So Gwynn Shotwell, for instance, committed a donation in the form of SpaceX shares. 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 Gerson did the state of Indiana.

• 1:02:07Peter Diamandis?

  Brad's going to be on the show. I mean, you know, kudos to him for his support of all those.

• 1:02:12Speaker 01

  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. And I think we're thinking about ways to gamify that to make it fun for the donors and not, not just a great thing for the, for the children. But if the donors actually like it, the children benefit.

• 1:02:39Speaker 04

  Exactly. It's such a cool idea. You know, when you donate stock, you know, appreciated stock, you don't pay capital gains. You just get the full value of the stock and you donate it. And then you get the tax deduction on that full value. And so it's a great way to give. And I think if you compare that, like Vlad was alluding to, a lot of 501(c)(3) charities, 501(c)(7) 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. In a lot of cases, it's terrible. 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

• 1:03:22Speaker 04

  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, and, you know, they're watching it go up. And a lot of, a lot of really good grade schools actually have trading, 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, you know, we talked to all of our listeners. And the guy was saying, look, my, my job, I'm, 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

• 1:04:06Speaker 04

  owners are benefiting from my AI improvement. How do I, how do I change that? And I'm like, are you a stockholder in the company? Like, no, because I'm in Europe. Like, goddamn, 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, 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.

• 1:04:33Speaker 01

  That's why we believe in ownership.

• 1:04:35Peter Diamandis?

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

• 1:04:43Speaker 02

  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, of, of new Americans. Congratulations on the coup for, for getting 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? Why didn't this go to a more obvious index fund custodian?

• 1:05:27Speaker 01

  Yeah. Well, actually, there is, there is another partner that provides the index fund. So State Street, who obviously pioneered the, the spiders, provide the index fund for, for the program. So it is, you know, there's, there's multiple players.

• 1:05:50Speaker 02

  Presumably at the backend. 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, 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?

• 1:06:18Speaker 01

  Yeah. Yeah. And, 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, there was a dislocation in 2015 when Robinhood launched to the public. And, you know, since that time, not all the brokerages have, have been able to survive that dislocation. Right? You know, if you look at the ones that have, they've pretty

• 1:06:58Speaker 01

  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, are asking for that. They see a competitive threat in terms of like, if their app doesn't feel like Robinhood, they're, they're at a disadvantage. You know, people will move to us at an accelerating rate. And then, you know, we started off as this insurgent, but now, you know, there was a, we're doing all of this stuff with public markets. The SEC had a roundtable at the New York Stock Exchange last year about making IPOs great again. So, yeah, I think.

• 1:07:43Speaker 01

  I mean, in a large sense, the US 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.

• 1:07:58Speaker 02

  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?

• 1:08:36Speaker 01

  Well, I think that what happens, I think the reason for that is we do a lot of things, right? Certainly, we have great trading products. And trading products are important because if you think about ownership, you need a functional financial market. And in order to have a functional financial market, you need traders and all of these market participants. So we compete there. And we also have amazing passive products. If you think about the products that we incentivize that have also become industry standards after we've launched it, you know, you got to look at a Robinhood Retirement, which we launched four years ago

• 1:09:16Speaker 01

  with the concept of a match. So it's, we match retirement contributions 3% if you're a Robinhood Gold member. And the idea there was, you know, a lot of people nowadays, particularly young folks, can't count on lifelong continued employment. They can't count on employer-sponsored 401ks. They're kind of like working as independent contractors. They've got their side hustles. They're moving from job to job. And so someone has to step in and provide that incentive to fund your retirement account. And so when we introduced the concept of the match, our retirement product has grown tremendously fast from zero to like north of 30 billion

• 1:10:00Speaker 01

  in assets in just a few years. And now, you know, the industry is like trying to figure out how to do their matches. And even the government has, 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. The, 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

• 1:10:45Speaker 01

  in and they ask their questions ostensibly about the Trump Accounts program. And the last couple of times we've done this, there have been literally zero questions about the Trump Accounts itself. And, you know, they're just asking about, you know, what's Gavin Newsom doing in LA? What's going on with, with Europe? And I think it's the unfortunate reality of the world where 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.

• 1:11:26Speaker 04

  I think that's really, really important.

• 1:11:28Speaker 02

  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.

• 1:11:49Speaker 01

  Inside you are two wolves, right? You know, Robinhood himself, Robinhood, the outlaw himself, started as sort of an outlaw and then became the Earl of Huntingdon. So yeah, there's a, there's a, there's a poetic irony too. And I started looking a little bit more like him.

• 1:12:10Speaker 04

  Yeah, you do. You're the Earl now.

• 1:12:13Speaker 02

  We need to get you the green hat. Do you have the green hat?

• 1:12:15Speaker 01

  I of course have the green hat. Yeah, I should have worn it.

• 1:12:18Speaker 04

  I really think all the entrepreneurs, all the budding entrepreneurs.

• 1:12:21Speaker 01

  It's called a bicocket.

• 1:12:23Speaker 04

  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 Accounts deal. And they're, they're, 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, I think it's brilliant to choose Robinhood because you have to make them interesting to all the kids. And Vlad is the guy.

• 1:12:50Speaker 01

  And I think it's just because we ship fast. This was a tight timeline. We care a lot about quality and safety. We have a scaled operation. So of course, I can't really comment on their selection process for the RFP and who else was competing. But I think all of these things together, I mean, I think they made the right choice.

• 1:13:14Peter Diamandis?

  I think they did as well. I'm going to move us along here. This coming Friday, Moonshots Live in downtown LA, you're going to be there with the five Moonshot mates, the Quintet, Dave and Alex and Saleem 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. I'll be there with Captain Kirk. William Shatner, the executive producer, is going to be joining us at that. If you're not able to make it to Moonshots Live on the 25th, we are giving all of you the gift of a free livestream. And you can register now. Go to moonshots.com/livestream. Register. You

• 1:13:55Peter Diamandis?

  get the entire program on Friday, the 25th. So please join us. It's going to be epic, our inaugural Moonshots Live event. And as Dave said earlier, we had a really fun AMA with a number of our listeners this morning, hundreds of them who showed up. And we gave away another ticket. So congratulations to Jonathan Gutman. We'll be reaching out to you. You'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, you know, that's made Wall Street very nervous, is the tokenization of everything. You know,

• 1:14:35Peter Diamandis?

  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. So.

• 1:14:45Speaker 01

  I called it a freight train, I think.

• 1:14:47Peter Diamandis?

  Yeah, it's, yeah.

• 1:14:48Speaker 01

  A freight train that'll, that can't be stopped and will eat the whole financial system. So it's a very hungry freight train.

• 1:14:55Peter Diamandis?

  You know, the supersonic tsunami, as Elon calls it. So, you know, digress for one second. Today, a stock, a share of a private company, a building, and a loan are four different kinds of things. They're held in four different systems. They're tradable at four different sets of hours by four different sets of people. Each one of these exists as a programmable token in the future. And they became the same, and they can become the same object, right? The same rails, same hours. Anyone with a wallet can own this. So you're, you're basically disrupting the financial system. You're going to be, you know, it's going to become legacy plumbing. Talk to us one second about what this looks like. What is the tokenization of everything enable? And when do we start seeing this

• 1:15:40Peter Diamandis?

  really become the dominant paradigm?

• 1:15:42Speaker 01

  Yeah, I mean, I think you get a, you get a little bit of a preview of it ironically if you're outside the US. 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 in decentralized exchange volume on a daily basis now. And the core, one of the core primitives, one of the things that made, 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. So there's

• 1:16:26Speaker 01

  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, to build applications on top of them. The thesis behind it was really just to unlock ownership of US markets, of high-quality financial assets, to a global market, right? And through Robinhood Chain, you get 100, people in 120-plus countries outside of the US who have been on-boarded to crypto. A lot of them have wallets. A lot of them, you know, can move money in

• 1:17:11Speaker 01

  and out and use stable coins. And now we're giving them this additional capability. And the vision there is, can we have one uniform scaled platform working on a, on a global scale that gives you access not just to US stocks or to US stock exposure? Can we also do everything else that Robinhood gives you access to? Private companies, I'm particularly excited about. 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

• 1:17:52Speaker 01

  it turns out, if we abandon the legacy rails and, you know, the need to plug into local exchanges, local clearinghouses in all of these markets, and we just go on-chain, use that infrastructure, and we build what's called a tokenization engine that can take any asset and put it in a box and mint and redeem tokens around it, it gets much simpler and much more scalable. And that's really what stock tokens are. They're, they're that concept and that structure applied to the asset class that we understand really well, which is US equities.

• 1:18:31Speaker 04

  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. It's just a joke, the way the system works right now because you report your quarterly financials, you know, you disclose exactly what the SEC requires you to disclose. But the exact same company, if you get acquired by Microsoft... Your financials disappear. You know, oh, it's below 10%. It's de minimis. We no longer need to disclose that. It's like, well, why was that important public information when I was not part of Microsoft and suddenly it's irrelevant when I'm part of Microsoft? This is ridiculous. Like, why would scale be such a big advantage? It makes no sense.

• 1:19:14Speaker 04

  And then, like you, everybody knows, like, the public doesn't have access to these private companies that are now trillion-dollar value companies. And so where does that equity go? Well, it goes to, like, seven venture funds and maybe a half dozen private equity funds who are making money hand over fist because of the limitation of, you know, access.

• 1:19:34Peter Diamandis?

  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. You're going to be much, you know, enjoying the ride. And that democratization of access-

• 1:19:52Speaker 01

  You'll be defending it on social media, right?

• 1:19:54Speaker 04

  Totally.

• 1:19:55Speaker 01

  Now, the only people that are defending these companies on social media are people that work there and venture capitalists.

• 1:20:00Speaker 04

  And venture capitalists, yeah. The whole current system predates the computer.

• 1:20:04Peter Diamandis?

  Alex, are you excited about having agents trade tokenized stocks?

• 1:20:09Speaker 02

  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 unchackle 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

• 1:20:51Speaker 02

  24/7 trading of public or rather of private companies. That's the mean thing about tokens. The nice thing about Vlad is Vlad, if you are going to be the Robin Hood to take all of America's dark matter, as it were, of privately held companies 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.

• 1:21:25Speaker 01

  Well, I have to tell you about Robin Hood Ventures. So, Robin Hood Ventures, and then maybe I'll respond to the first point, too. So, we have multiple ways of giving customers access to private companies. One is tokenization, which really we demonstrated last year by tokenizing SpaceX and 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 like only adverse selection, to ask, like, how can they learn more about this? Because it

• 1:22:10Speaker 01

  really gives them a global market for their shares. And then we have Robin Hood Ventures in the US, which you can think of as a retail publicly traded venture capital firm. And we've done two funds right now, Robin Hood Ventures Fund One and Fund Two, which are both listed on the NASDAQ, listed on the NICI, publicly traded. And, yeah, basically, what we figured out is through a fund, we raise capital from our customers who are by and large retail shareholders through an IPO, and then we use that capital to invest

• 1:22:51Speaker 01

  in private companies. So, Robin Hood Ventures invested in late-stage frontier companies. So, OpenAI, we announced an investment in a couple of months back. We just did Cruzo yesterday. That was announced. And then there's, you know, about a dozen or so companies. So reasonably concentrated, but all frontier companies and offered at no carry. So, that product, you know, we successfully IPO'd it, and we followed that up with Robin Hood Ventures Fund Two, which is, to my knowledge, unprecedented because that was an early-stage vehicle. So, Robin Hood Ventures Fund Two went public a couple of weeks back in New

• 1:23:36Speaker 01

  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, you know, 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 US or overseas, you'll have lots of options for how to get exposure to high-quality private assets.

• 1:24:05Speaker 02

  With liquid price discovery or without? Because in my mind, price discovery is the point of-

• 1:24:12Speaker 01

  It's traded on NICI. Traded on exchange, yeah.

• 1:24:14Speaker 02

  As an overall fund or at the level of individual companies in the portfolio?

• 1:24:18Speaker 01

  As an overall fund, yeah.

• 1:24:20Speaker 02

  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 US, where I get the benefits of highly liquid price discovery on a per-company basis. Otherwise, the downside of a fund of all of these companies is the prices could be totally bogus. They could be off by a factor of 10 due to maybe overinflated CEO price rounds or an overheated market.

• 1:24:53Speaker 01

  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 US first. But, yeah, I mean, but, yeah, it's, here, it's hard. It's like, yeah, sometimes it's interesting that the US sort of trails behind international in some of these things.

• 1:25:20Peter Diamandis?

  Regulatory capture.

• 1:25:23Speaker 01

  Well, I think the reason really is we have established industries in the US, and we have a system that works generally pretty well. So, I kind of equated more to high-speed rail, right? You can say, well, why don't we have fast trains here? In China and Japan, they have these trains that are going 500 miles an hour. And really, it's just, we had trains first here. Ours are pretty good. Maybe they go 100 miles an hour, but, you know, there's a little bit less incentive and pressure to go to the technological frontier. I think we eventually get there, but that's sort of the dynamic in financial services now.

• 1:26:02Peter Diamandis?

  Vlad, let's address the question of, you know, 24/7 trading on a centralized database versus tokens.

• 1:26:10Speaker 01

  Yeah. So, I mean, we, I can, I have a lot of experience with that because I've felt it directly and I see it on both sides, right? Because we're actually the first to pioneer a product called Robin Hood 24-Hour Market here in the US. So, 24/5 trading, so 24 hours a day, five days a week, in a few thousand stocks, which you can do currently on Robin Hood. And, you know, since then, people have, this is, again, one of those things where, you know, we led the industry and now everyone's rushing to add this capability. So, you know, all of the Sunday night action that typically was only in futures, now you

• 1:26:55Speaker 01

  see it in individual stocks as well. And, you know, it took us a lot of time, a lot of work to staple together the primary exchanges and the overnight ATSs and make that a seamless experience for customers because primary exchange doesn't trade 24/7. So, we actually have to, like, move orders around and do stuff under the hood that's very, very complicated. And we're still not at 24/7, right? And it's been, it's been many years since we've rolled out the 24/5 product. And I think eventually, we'll get there through sheer will and determination and just like yeoman's work and pushing all the counterparties, doing the hard regulatory

• 1:27:39Speaker 01

  work, building the technology and the product innovation. By the way, if we weren't pushing it, it probably would have happened in like 10 years. So, I think we will eventually get there. But contrast that with crypto, which you get 24/7 for free. You get fractionalization for free. You get self-custody and composability with DeFi. You get the nice feature where, you know, 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 it's, if your

• 1:28:22Speaker 01

  current broker is not meeting your needs. Contrast that with how cumbersome the current account transfer process in traditional finance is. It's like your assets disappear into a black hole. Sometimes it takes up to a week for them to show up at the new broker. And, you know, there's not a lot of incentive to make that easy. So, across the board, at every touch point, the technology is just a massive step-change difference. And I think it's both user experience for the end user of getting self-custody 24/7 and all the benefits, but also for the firm, you know, the cost of doing all of this legacy plumbing,

• 1:29:02Speaker 01

  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.

• 1:29:21Speaker 01

  And you know, for a while, people would say, okay, well, this is great. You're saying all of these pretty words, but the tokenization market is pretty small. And it doesn't seem like, like, 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 Robin Hood chain and, and stock tokens. And, and it's clear that there's huge demand. So rather than kind of arguing it in the abstract, my approach is always let's, let me ship it. Let's ship it fast. Let's get the feedback. Let's see how it's going. And, you know, I think in this particular case, we were able to demonstrate the advantages of the technology and make it a little bit more tangible through live product.

• 1:30:05Speaker 01

  And, and my hope is that, you know, that, that's been an accelerant for how the U.S. thinks about the technology and how it considers it. So we're, we're happy to see the innovation exemption yesterday that, you know, creates a path for, for bringing tokenization to America as well.

• 1:30:20Peter Diamandis?

  Amazing.

• 1:30:21Speaker 01

  Hopefully it won't be like high speed rail and, and we'll actually get it done here rather quickly.

• 1:30:27Peter Diamandis?

  True entrepreneurship. I'm gonna 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 gonna have Brett back on the pod in a couple of weeks. And Brett's gonna be coming to the Abundance Summit in March, right? It's our five day event. We bring the top CEOs from around the world and he's gonna 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, Brett's AI company and on Figure Robotics.

• 1:31:04Speaker 03

  The holy grail for robotics is being able to generalize. This means doing work in unseen places. Today, we're releasing Helix 2.5. Prior to this, we've been running Helix autonomously, but the data collected has been in each environment. The breakthrough is we can generalize environments we've never seen before and handle entirely new household objects wherever they happen to be placed. Today, we're gonna show you three tasks run by Helix 2.5. The first task is Figure 3 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. 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.

• 1:31:53Speaker 03

  All right, let me show you task three. This task is really difficult for robotics as it requires really precise manipulation. With Helix 2.5, we're folding towels in a house the robot's never been in with towels it's never seen.

• 1:32:10Speaker 07

  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. We 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.

• 1:32:53Speaker 07

  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.

• 1:33:04Peter Diamandis?

  Dave, your thoughts?

• 1:33:06Speaker 04

  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. Once you get, if they, if they extend their lead in capturing just those basic actions, you're gonna see the same thing you see with language where the convergence across different actions is much richer than you would normally expect. So the ability to generalize from folding a towel to, you know, putting a spare tire on a car, you're like, well, those are very different actions. No, there's a lot of commonality in physical movement. And so I think if they get, if they get a big enough lead, it's just gonna be crazy explosion of capability. So it's funny, in those videos, they, they always make the point that, look, this is

• 1:33:51Speaker 04

  completely un, there, 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, 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 he showed.

• 1:34:18Peter Diamandis?

  Yeah, back in, back in January, when we were up at Figure Headquarters and we recorded the pod with him, you know, the quote, I went back and found, he says, "By the end of 2026, we will have humanoid robots performing unsupervised, multi-day tasks in homes they've never seen before." So here he is actually delivering on that, at least the first steps. Alex, you still think he's gonna merge Helix 2.5 and, and Figure?

• 1:34:43Speaker 02

  More than ever. I'm doubling down on that prediction. I'm doubling down on the prediction that Brett is going to have Figure Purchase Hark in order to increase his equity in Figure. And the story is as plain as day at this point. The story is going to be Helix, they even sound similar, Helix and Hark. Helix has 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, I mean, the story just writes itself.

• 1:35:24Speaker 02

  Why wouldn't Figure Purchase Hark in order to get its compute and its models and merge the two together?

• 1:35:30Speaker 04

  Yeah, of course.

• 1:35:31Speaker 02

  This is like an obvious, an obvious post hoc merger.

• 1:35:34Speaker 04

  Well, also, I think, I think this is something Vlad can talk to also. The, the, you know, Elon model of a great entrepreneur involves starting new cap tables. And when you start a new cap table, you know, a clean sheet of paper, you get founder level talent coming in super excited about a brand new mission.

• 1:35:50Peter Diamandis?

  Really important, Dave.

• 1:35:51Speaker 04

  Really important. So then, 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 then, 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. In fact, it was right, right, usually in your employment agreement, it would say no more than 10% of your time on any other activity other than charities. And, and so it was like prohibited. And now it's like, at least in Silicon Valley, it's become standard.

• 1:36:24Speaker 02

  Let alone, let alone starting and then purchasing your own company where you have a board. So self-dealing apparently is, is the, the, the thought of the moment.

• 1:36:34Peter Diamandis?

  Putting that aside, super, super proud of what Brett's accomplished here. And yeah, amazing job. Vlad, you gonna get your robot at home?

• 1:36:43Speaker 01

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

• 1:37:25Speaker 01

  And I view it much more as, all right, I can see this at a construction site, you know, building my house. I, 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, why can't anyone build C3PO like a friendly household butler? Why does it have to look like a Terminator? I just don't understand that. Yeah, I, I don't think, I don't think a lot of people are gonna be buying those to, you know, rock their, rock their baby to sleep at night.

• 1:38:00Speaker 04

  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.

• 1:38:20Speaker 01

  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 gonna do heavy work for you. And I don't, yeah, and then, then it's like you take that robot and you have it, you know, emptying your dishwasher and no, it doesn't seem like anyone's really thought that that robot should look very different.

• 1:38:44Speaker 02

  I think there is one contrarian bet. 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 home pod with a screen that's on an adjustable robotic armature. So I think we'll know.

• 1:38:59Speaker 01

  That sounds awesome.

• 1:39:00Speaker 02

  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.

• 1:39:06Speaker 01

  I'd also take C3PO if someone builds that. Yeah, that would be cool.

• 1:39:11Peter Diamandis?

  Check out Sunday Robotics. They've got a very friendly looking robot for at-home use that actually looks friendly and quite cheery. And I think you can optimize for that. But you're right, none of the, none of the labs have actually gone in that direction yet.

• 1:39:25Speaker 01

  I would, I would say one thing, I could be completely wrong because my, my middle child for Christmas asked me to get him 12 humanoid robots.

• 1:39:36Speaker 01

  So I was like, aren't you worried? Not 12, not 13, not 11, 12.

• 1:39:41Speaker 04

  It's like a soccer team with a spare. Is that what that is?

• 1:39:44Speaker 01

  Yeah, he's like, I want 12 humanoid robots. I'm like, well, where are we going to keep them?

• 1:39:49Peter Diamandis?

  Vlad, I want to talk-

• 1:39:50Speaker 01

  Keep them in your room.

• 1:39:51Peter Diamandis?

  I want 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, right? 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, you know, seen anyone do this before. Is that the case?

• 1:40:25Speaker 01

  Yeah, I mean, we talked about that a little bit earlier. I think it's just not trained for that, right? And, you know, there's also the guardrail element of, does it, does it actually like resemble something that they've, they've, they've tried to explicitly guard against? And I think, I think that's changing. The models are getting better, but yeah, it just, it just shows that there's a lot of work to be done, not just, you know, on the user interface and on the brokerage infrastructure, but actually on the model layer and making that use Robinhood tools and Robinhood MCPs more, more effectively. I think we're just at the beginning. I mean, if you think about all of the things that a highly sophisticated algorithmic

• 1:41:10Speaker 01

  trading firm or hedge fund has access to, to create a trading strategy, the North Stars is really to deliver on that, right? And, and you need more data. You need high quality data. You need intelligence. You need like really, really good code writing. You have to write deterministic code really well also. And you need like advances in latency and performance. You know, if you think about a 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.

• 1:41:52Peter Diamandis?

  Right.

• 1:41:52Speaker 01

  Right? So, yeah, the, it gets, it gets much deeper. So I think, I think this is going to be like a, a big roadmap for us and we're, we're, we've got a lot of work to do, but we're, we're seeing some really good signs and, you know, it's just very, very much at the beginning of the agentic trading journey for us. And nobody else is really doing it. So it's, it's really, we're kind of like going into the fog and trying to find our way around and building the product and all of this infrastructure simultaneously.

• 1:42:21Speaker 02

  I, 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, the average lifetime of a new quant fund and/or quant fund strategy is. It's really short. There's, 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, 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, the backend model is performing trades on their behalf.

• 1:43:06Speaker 02

  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?

• 1:43:19Speaker 01

  Yeah, I mean, so, so I guess right now, what we're seeing is a lot of automation type use cases. It's like, let's say I want to deploy an options trade and I want it to be, you know, an iron condor based strategy. And, you know, it's, it's a lot of legs and you have to actually get, you have to do a lot of manual work to pull that together and to produce the trade. And the AI agents are really, really good at those types of things, sort of like removing the paper cuts and you still kind of have the idea, but they help you put together the idea and, and the execution that you would have had to like, you know, go to, go

• 1:44:03Speaker 01

  to different websites, look at signals, construct the trade, deploy it on a regular basis. So I, I think that's the initial use case, but, you know, to, to get to the point where everyone has the technology of an extremely sophisticated quant fund is a, 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 GOs and asset classes.

• 1:44:45Speaker 01

  So for example, you know, right now on Robin, it's one of the few places where you can actually trade stocks, options, futures, prediction markets. We've got all the on-chain things on Robinhood chain as well. So as, as we add more countries, more GOs, more asset classes, the platform itself will, will, will have advantages over, you know, what at least a, a startup quant fund will be able to integrate with and connect with.

• 1:45:17Speaker 02

  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, 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, the plumbing to make them do what they want to do more efficiently.

• 1:45:39Peter Diamandis?

  Just making it easy for them.

• 1:45:41Speaker 01

  Well, I, I mean, I'll, I'll caveat that with one thing. I, I think that's basically true for our active trading products where for an active trading product, for, for active traders, they know what they want to do. And our job is, we're a tool provider. We want to give you the best tools, which doesn't mean we don't provide you analysis tools and research tools. We focus, of course, on the execution and the plumbing, but if they want access to some data set or some new intelligence or some model so that they can come up with a better strategy, we'll want to provide that too. We also have our products where we act as a fiduciary and those are under the Robinhood

• 1:46:21Speaker 01

  Strategies umbrella where let's say you're like, I don't want to make trading decisions. I just want to have like a deposit button, move money into the account and Robinhood just does the rest. You just, you know, manage my money for me while I sleep. Robin Strategy is a great product for that. And we now have a couple of different things you can choose, choose from there. You know, we have a smart income portfolio that is, is geared toward generating yields and you can kind of adjust this slider that says, okay, this is my target yield for, for each level of risk. And I think the team really has, has done great on the interface.

• 1:47:04Speaker 01

  You can tie Robinhood Strategies into the IRA and, and benefit from the tax and managed investing there. But yeah, that's kind of our home for our fiduciary products that are automated. Then we also have, we acquired a company called Trade PMR where you can actually get a human advisor to help you with, with all of your needs. And that's not just managing your portfolio, but they can help you with estate planning. You know, they can help you with taxes, all full suite services.

• 1:47:35Peter Diamandis?

  The everything store.

• 1:47:37Speaker 01

  Everything finance. Yeah. Like if, if there's something that you want to do with your money, we, we want to be the best, lowest cost, best user experience. And, you know, we have a great credit card, private banking is, is industry leading in, in Robinhood. So we're pretty much, we're pretty much there.

• 1:47:56Peter Diamandis?

  Amazing. You're, you're having fun, I assume.

• 1:47:59Speaker 01

  Yeah, it's, it is, it's, it's a fun job and, you know, we're doing so many new things this year, like Robinhood Chain was a new thing for us. All the work on private markets, you know, done two IPOs thus far with Robin Ventures this year, the Trump accounts, I mean, becoming a government subcontractor is just this, this new experience.

• 1:48:23Speaker 01

  Yeah, so yeah, it's a learning a lot for sure.

• 1:48:26Peter Diamandis?

  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. Dawn Musalem. Dawn, let's talk about cancer. You 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.

• 1:49:01Speaker 08

  That's right. You know, the majority of cancers that we screen for, those aren't the ones that are necessarily taking the lives when found at a late stage. We know that when cancer is found early, the chances for cure are much higher. We know it's much easier to treat a cancer when found early versus when found late. What we're finding in our members is over 3.3% were found to have these cancers that were otherwise wouldn't have been found or detected.

• 1:49:25Peter Diamandis?

  Yeah, you know, it's interesting, people, you don't feel the cancer until stage three or stage four. And, 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 Fountain, how do they detect cancers?

• 1:49:40Speaker 08

  So we're doing full body MRI and we also do early cancer detection screening. This is very, very important and these are not typical tools used in the conventional care setting when it comes to prevention. This is a hard thing because currently these are not studies that insurance would yet be covering, but the goal is to collect these numbers, do the research and work hard to democratize wellness.

• 1:50:02Peter Diamandis?

  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 and go to fountainlife.com/peter to get access to the latest technology to help you detect cancer at the very beginning at stage one, when it is curable, before it gets to stage three or stage four in your world of hurt. All right, I'm going to move us to the recursive self-improvement story of the week. So Anthropic disclosed that Claude now leads roughly 26% of its measured AI research and development work, up from 1% at the start of the year. Anthropic states that somewhere around 30,000 agents are working simultaneously inside of the company on research and engineering.

• 1:50:44Peter Diamandis?

  You know, we can recall that Dario said RSI is quote, "starting to happen across the industry." And we also heard in the last pod, we talked about Paul Cristiano said full automation of AI research could arrive in the next 18 months. Here's the chart. Alex, do you want to dive into this one?

• 1:51:04Speaker 02

  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 aren't, who can't see the visual, this is a chart of different levels of autonomy and AI involvement with the recursive self-improvement process of driving research. The most interesting one, to me at least, is this, 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 the, all of these should be reasonably expected to follow sigmoid curves. If you just extrapolate that one trend sigmoidly,

• 1:51:48Speaker 02

  and now, thanks to Anthropic publicizing these data, we can extrapolate them, you find that approximately in the next three to 12 months, depending on uncertainty, AI is just completely leading all of its own R and D. And, and that is total recursive self-improvement. And it doesn't, I don't think it's going to be a step function, it's, 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, there's a lot of smoke now coming out of Google DeepMind, that they, they just released this paper on their own recursive self-improvement product, or rather,

• 1:52:29Speaker 02

  research. Lots of hints that the next version of Gemini will lean heavily on RSI to try to catch up to the frontier. OpenAI has made no bones about chasing and using RSI in all of its product releases. So, this, I think like recursive self-improvement is now a feature that's well advertised and starting increasingly well quantified for all new frontier lab releases. And pretty soon, I think people will be asking the question, what is the role of human researchers anymore in driving new releases?

• 1:52:58Peter Diamandis?

  And it only gets faster from here. Vlad, are you seeing this inside of Robinhood?

• 1:53:02Speaker 01

  Oh, yeah, absolutely. Yeah, I would say recursive self-improvement is coming to every software project, and likely hardware projects as well, although that'll take a little bit longer. And if you think about it, a lot of people assume that it would come for AI research last, because it's just complicated. But I think, 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,

• 1:53:42Speaker 01

  the interfaces are pretty, pretty straightforward. They don't depend on a lot of other things. You know, you can run these experiments, and already people are evaling them. So, automating the evals and automating the experiments is, is pretty straightforward. And kind of the, the, the surface area is, is pretty, pretty contained. Whereas if you look at like, a product like Robinhood, right, there's, there's a lot of moving pieces. You have the, the iOS app, you have the backend. 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

• 1:54:24Speaker 01

  figured out a good way yet to replicate, you know, what happens when you roll it out to, to humans, and how do they respond to the feature? Is it stats, 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, I think we should expect that you'll, 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, take the change and implement

• 1:55:04Speaker 01

  it into production, rather than discarding it. I think that benefits the, 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.

• 1:55:28Speaker 04

  Just to quantify what Vlad said there, the, 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?

• 1:55:44Speaker 01

  Oh, yeah, maybe. I don't know if it's quite that much, but yeah.

• 1:55:49Speaker 04

  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, 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. If you look at Kimi K3, there's literally no loops in the code. I mean, it's, 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, your characterization was absolutely perfect of, of what's going on, and why it's so effective.

• 1:56:27Peter Diamandis?

  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, you know, I think is a genuine milestone worth pulling out and talking about. So, Boris Power, the head of Applied Research at OpenAI, made this announcement, quote, "We crossed a threshold where GPUs are now more efficient thinkers than the human brain on a per-watt basis." His rough math is that humans are roughly five IQ points per watt. Love that conclusion there. And AIs are now at seven to 40 IQ points per watt. Alex, do you think the math is right?

• 1:57:04Speaker 02

  I think if it isn't already right, it's about to be. So, I'll, 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 parentheses, talk about nominative determinism in action, closed parentheses, is, is actually thinking about this. Because, again, we blew by the Turing test, and almost no one really noted it. And this time, at least, we're not blowing by this milestone. Why is this important? It's important because, to the extent it's accurate, this is the point at which AI is

• 1:57:45Speaker 02

  economically, in some sense, a better steward of input resources, namely, energy, than humans are. And one can extrapolate this and say, this is the worst they'll ever be, presumably. This is, hopefully, humans continue to improve in terms of our intelligence per watt as well. So, hopefully, this is the worst 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, for a temporary period of time, until the humans can merge with the machines? What happens when the machines can make more economically productive

• 1:58:24Speaker 02

  use of their input resources than humans can? That's a recipe for gentrification, where the machines have, under our capitalist system, have arguably a better title ultimately through free trading. This won't be like Skynet Terminator style, where, 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, the capital resources like sunlight, and physical matter, and energy, and spacetime, and so on, all these inputs can, through normal capitalist commerce, change hands

• 1:59:09Speaker 02

  from the inferior intelligences per unit, or outputs per unit input, to the superior ones. And in Charlie Strass'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. I don't think it'll come to that, but I think this is a very important inflection point in that direction.

• 1:59:45Peter Diamandis?

  All right. We'll make that note. Let me end on a story that is relevant to, 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 US case law, statutes, regulations, court materials, and existing legal software. OpenAI says it's materially outperforming general Astra with web search on legal matters. We can see the chart here. It looks like

• 2:00:27Peter Diamandis?

  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?

• 2:00:38Speaker 04

  It's funny, we were doing the negotiation for that Vestmark Investment acquisition two weeks ago, and it was in a board meeting, you know, one of these late-night sessions, and we had our $2,500 an hour lawyers on the line, and a very complicated question came up. And the lawyer was answering it, and I typed it into Gemini concurrent with that. And I swear to God, it was word for word the same.

• 2:01:01Peter Diamandis?

  You don't think they were typing it into Gemini as well?

• 2:01:04Speaker 04

  That's what I was wondering. It's like, it can't be this 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. And, you know, and that means it's also good at tax loss harvesting, it's good at account rebalancing, it's good at, you know, all the Robinhood 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, and on top of that, you've got all this incredible tax optimization, and, and, you know, wills and trusts and all that stuff, that AI is just perfect for automating. So, it's, it's not just about, you know, rapid trading alpha, it's about all of the life stuff that is

• 2:01:45Speaker 04

  much easier to deal with if your AI does it for you. So, yeah, it's just a really, really good use case. It's, it's one of the great benefits, actually, for humanity. Not great for the legal profession, but great for the bulk of humanity.

• 2:01:57Speaker 01

  My hot take is there will be more lawyers in 10 years than today, and more software engineers. Perhaps.

• 2:02:07Speaker 04

  I actually agree with that. More to get done, right? Yeah.

• 2:02:10Speaker 01

  I think law scales with business formation. Right? So the need for lawyers will scale with entrepreneurship and there'll just be an explosion of entrepreneurship. Because I think that what's really valuable about a great lawyer is not the actual like legal advice they give you. They, it's like, um, uh, yeah, they're like a consigliere and they help you think through things. They're negotiator. Yeah, it's a.

• 2:02:34Speaker 02

  Plot twist, though, Vlad, how many of those 10x lawyers in the future will be human?

• 2:02:41Speaker 01

  No, that's a good question.

• 2:02:44Speaker 02

  Because I agree with you, there are going to be 10x lawyers. I just think many of them won't be natural humans.

• 2:02:50Speaker 01

  Yeah.

• 2:02:50Speaker 04

  I think, 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, everyone, everything's AI assisted, but do you really care about cutting that human out of your life when you trust them? Probably not. In fact, you want to add to.

• 2:03:13Speaker 01

  We saw this with financial advisors too, actually. It's like when the, when the robo-advisors came and they could tax loss harvest, portfolio rebalance, um, better than any human.

• 2:03:26Speaker 01

  You know, then you realized, well, actually, the human advisor market is growing much faster than the robo-advisor market. And why is that? It's because, you know, the value of that person is not in the financial advice. It's like, uh, someone that you can fully delegate and trust all aspects of, uh, of your, of your financial life to.

• 2:03:51Speaker 04

  Totally right. And, you know, Alex may disagree, like in the 10 or 20 year view, but if I look at the three or four year view, that financial advisor is so much better to able to act on my request now. Because very often the 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, through Stripe or whatever to all of your underlying detail and they can act on it via AI. So the, the feeling of value add from the financial advisor is up at least a factor of 10, uh, thanks to AI. So that's where it's going in the short term.

• 2:04:33Speaker 02

  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, that the lawyerless office or the lawyerless company will happen. It just takes a little bit longer after we, the capability first comes online.

• 2:05:02Peter Diamandis?

  And I want to hit on what Vlad said, because we talk about in the pod here all the time, right? The number of entrepreneurs on the planet is going to skyrocket. Solopreneurs, the ability, if, if you're looking for a job, stop looking and start building, you know, find something you're passionate about and go out there and build a company, find a great problem and solve it. And you can, I think we're empowered more than ever before. Vlad, uh, thank you so much for your time, pal. Congratulations on everything you've been building with Robinhood, an extraordinary company, extraordinary AI company. We didn't talk about prediction markets very much. Um, that's another conversation we'd love to have with you, but, uh, super grateful for all you're doing.

• 2:05:44Speaker 01

  We'll have time as we continue to hurdle towards the singularity. So time will slow down for us and.

• 2:05:49Peter Diamandis?

  Jovian escape velocity, he's coming.

• 2:05:51Speaker 01

  Yeah.

• 2:05:54Peter Diamandis?

  All right. Love you guys. Uh, Imad and Saleem, we missed you. Saleem, I hope you're enjoying your flight back from India. Yeah, for sure.

• 2:06:03Speaker 02

  Thanks, Peter. Thanks, Vlad.

• 2:06:05Peter Diamandis?

  Thank you all.

• 2:06:05Speaker 01

  You both.

• 2:06:15Speaker 09

  Instagram teen accounts come with automatic protections that limit who can contact teens, the content they can see and the time they spend on the app. Learn more about teen accounts and Instagram's ongoing work to protect teens online at instagram.com/teenaccounts.