Jason: All right, everybody. Welcome back. Number one podcast in the world. It's July All-In episode 280. Freeberg is on a little vacay. We'll leave it at that. And uh yeah, bestie Brad is here. How you doing, Brad?

Brad: I'm doing great. I'm do vacay in maybe Idaho or somewhere. Jal,

Jason: I know. Who knows? Who knows?

Brad: Who know? It could be anywhere. He could be anywhere. I mean, there's lots of things. He could be in plenty of places. And uh looks like you are yeah somewhere in the northeast. I'll leave it at that. You having a little uh vacay for yourself this week?

Jason: Very pat I'm in my flag room. Very patriotic room here.

Brad: Very nice.

Jason: You know where I work on the east coast in the summertime and uh spent some time in DC this week and uh it's been a great week. Been a great week celebrating America 250.

Brad: Great. And you're going to be out of there for the by the by the second week of August. Yes. So I have it August 10th through the 30th. I'm good.

Jason: Exact. Exactly.

Brad: Jason B&B.

Jason: Oh, absolutely.

Chamath: You You don't know the half of it, man. I am on a summer bender. I'm like, "Where's your Where's your vacation?"

Brad: By the way, where are you? Where are you right now?

Chamath: I am in Paris. I did uh about eight interviews for at the Rays conference. They'll be coming out uh in the all-in feed. And of course, tackling from the factory. Look at him. You're working in the factory. Chimath Polyhapatia. It's going to be a hot software summer for Chimath. How's your hot software summer gone?

Brad: It's good. Selling enterprise software is hard, but it's good.

Chamath: Chamat's like, "Man, I was such a dick to all my CEOs in the SAS period." And now you know,

Brad: I went to Geneva. Shout out to Markov. I went to Geneva. He works out of Europe, sees all his European customers, and he had a dinner in Geneva, which I joined. And then me, Jensen, Brad Smith, Anthony Tan from Grab, and a bunch of other folks were put on this UN Commission for AI that Mark is the co-chairman of. When you see Mark Beni off in action, man, this guy is a master.

Chamath: Holy

Brad: He is the empasario of empisarios. Yeah.

Chamath: You see how he's built such a ginormous business. It's impressive. It's impressive.

Jason: What is a commission by the United Nations for AI? What is their what is their uh calling?

Brad: Open source. No, but no, I mean it's like the United Nations actually do anything. Do they actually It's so funny. Yeah. I mean, Anthropic was there too. Some one of the co-founders, Tom Brown, I think was his name.

Chamath: Yeah,

Brad: that's good.

Chamath: Was the Anthropic guy running around saying, "It's the end of the world. It's the end of the world."

Brad: No, no, no. He was he was very awesome. Tom's awesome.

Chamath: In fairness to him, he wears it on his sleeve, which is like, "Hey, we really believe we're doing the right thing, right?" and just trust us and I think the future's open source for all these countries.

Jason: Well, we're going to get into that. That's on the docket for sure. But let's start with the IPO update. You know, there's a trillion dollar IPO rush to the exits and you know, this was a big topic of discussion, Brad, at the liquidity summit last month. And we'd never seen a trillion dollar IPO. We had one this year already. SpaceX trading right about where it went public. So, it was priced, I guess, to perfection. and theoretically going to see two more. Uh Brad has the inside information, so I'll try to get it at him. Open AAI and Anthropic are slated to go out. Let's uh just go quickly over what happened with SpaceX. It ran up to $200 a share. Uh it's been down a bit. It's at $150 a share. As I said, that's right at the IPO price. So, it's trading at that two trillion market cap. Currently, seventh largest company in the world. And Anthropic confidentially filed on June 1st. I I don't know why they call this confidential filing when it immediately comes out. Uh but I guess the information is confidential. Poly market says 65% chance Anthropics IPO will happen this year on light volume 360K. And uh two weeks ago, Gavin Baker, another bestie, said he thinks they're going to end 2026 with over a hundred billion dollars in revenue and very profitable. He said a couple of us guests on the program that he thinks it would trade at three trillion right now if it went public. Chimath, you made a great call on the pod. You said, "Hey, good idea for Elon to get out first." What are the chances here, Chimoth, that these other two get out this year or maybe in like, you know, say 9 months in the in the first quarter of next year? We'll start there.

Chamath: Well, I think that these are all great businesses. I think the question is what is the market clearing price? And I think that's more of a function of how much appetite the markets have to absorb new issues and at what scale. That's number one. And I think that's mostly determined by price. So I think anthropic and open AI are probably in two different places. The last time we heard from OpenAI, their cash burn was still quite high just because of the diffuse nature of their business and more reliance on consumer than enterprise. I think Brad mentioned it in one of the pods that Anthropic may actually be accidentally profitable. I think he said something like that.

Brad: Yeah.

Chamath: Let me tell you something really interesting. I sat down with my CTO today and I said, "How are we doing on token spend?" And he said the most incredible thing. He said, "Right now, our token costs are doubling every 45 days."

Jason: Okay.

Chamath: And I was like, "Gh." And he said, "Yeah." And I said, 'Well, what is the downstream productivity? And he said, maybe 5% max.

Jason: Okay.

Chamath: And I said, okay, so my costs are doubling every 45 days. My upside is essentially flat. And he said, basically, and I said, well, explain why that is. and he said, "Honestly, what we're finding out is that you need to use a lot more tokens to get to this next iteration of improvement because we've effectively already asmmptoted."

Jason: And I said, "So, what should we do?" And he said, "Honestly, we have we have to figure this out." And so, we're going to take a step back and try to figure out what to do. I don't know how many other companies will actually go through this reckoning now, but the point is everybody in the next three or four years will for sure go through it. So, I suspect that if you can get out now, you should get out now before all of that starts to seep into the water table because I think that's probably what allows you to get out at a huge price and and raise a huge amount of money.

Jason: All right, Brad, you are uh well invested and well known for being invested in these two uh next IPOs, so you probably have some good insights since you talk to them on a regular basis. chances they get out in the next six to nine months. Both of them, you'd say 100% chance, unless there's some outside event, you know, blockade of Taiwan, some black swan event that we're not anticipating. What do you think the chances are they're public when we're sitting here and I'm skiing in Hokkaido?

Brad: Yeah, I think I think it's very high. But let me let me first say, you know, the SpaceX IPO where we were also investors and we also bought in the IPO. I mean, it was textbook. It was a hugely successful IPO. They raised $75 billion at 1.75 trillion. Okay, so it went out below where we are today. It's up 25%. You know, and let's call it on 35 billion of forward revenue. So if you think about that revenue multiple, it's trading at 2 trillion on roughly 35 billion of of forward revenue. It's an incredible achievement. I think it was textbook. I think Anthropic and OpenAI were watching very closely because frankly we had not had an IPO of that size and to Elon's credit and to the team's credit Brett and Gwen they really pioneered some really smart and interesting things as part of that IPO. So, you know, you heard from Gavin Anthropics rumored to be, you know, trending over a 100red billion in revenue compared to the 35, right? If they exit the year at 100, that means their gap revenue next year could be well over a 100red. So, based on the SpaceX success, I think it would be a blockbuster IPO. And I think SpaceX has shown them the way on things like the

Host: total, raise, pricing, liquidity, inclusion into the indexes, how to do the lock up. Like I think they've gone to school.

Guest: It was a staged release in terms of getting out of the lockup. It has to hit certain milestones and some of those are time.

Host: Early inclusion in the index, raise $75 billion, like you know.

Host: The early inclusion in the index. Let me have you unpack that for a second cuz people said, "Hey, maybe this feels unfair that they should be forced to buy it." What's your take on that? Is that just like haters going to hate or is there something to that?

Guest: I think there was legitimate concern, right? This is legitimate concern.

Guest: The legitimate concern is that a company that had not been through the process of being vetted post IPO, there's a lot of volatility. You've seen that chart Jason that the peak to trough draw down in the 6 months post IPO is 50%. We've seen a pretty big draw down here from the peak to trough as well.

Guest: So you don't want to jam it into an index at the peak and then have a 30% draw down on top of people which often happens in IPOs cuz people get excited it runs ahead of itself. But they didn't do that here. There was fear that that was going to happen. So both the exchanges and the indexes, they looked at this and they made some modifications because the other side of the argument is it's so damn big and important that it needs to be part of the index. Right.

Host: Right. And so the reason the rules had previously existed is because most companies coming public were younger, earlier, less tested, less revenues, less profitable, all the things weren't as important in the overall scheme of things. So I think that they pioneered some really smart things. It's worked well. It's traded well and so I think that that provides a bit of a blueprint for Anthropic but just in terms of the enthusiasm.

Host: Is an Altimeter, is a Fidelity, is a T. Rowe an enthusiastic buyer of Anthropic based upon the things we know today around profitability and model improvement and revenue growth etc. Yes, everybody would be piling in. Everybody would be trying to get into the top of the book and you know, the last I heard, you know again rumored that they would like to get out this year. OpenAI, everybody knows that Anthropic kind of passed OpenAI on a revenue trajectory but I will tell you, OpenAI's kind of got its swagger and mojo back, it's coming out, you know just today with a whole new set of models. We know GPT-6, you know there's a lot of talk of that coming out within the next 30 days, a whole new generation of models. I think their revenue has really ticked back up. The most recent kind of rumors I see on Twitter is around $70 billion text the year. So just as a reminder, 70 billion may not be over a hundred billion that's rumored and Anthropic, but it's still twice, you know, where the revenue of SpaceX is at. So can they get out at over a trillion on that type of revenue growth being one of the two frontier premier labs? I think the answer to that is yes. Um, I'm not sure there's a huge race between the two of them to get out first. I think they'll both go out when it's time. I think OpenAI has a little bit more complexity just associated with the corporate restructuring that they have to go through, etc. So, I would be surprised if they go out before Anthropic, but the fact of the matter is I don't know. But today, as I sit here today, Altimeter would be a buyer at scale and at size in both of those IPOs.

Host: At three trillion, are you a buyer or are you a hey, you know, it's obviously going to trade up and down and I there's no rush because you, I think were the one who said on the pod or might have been at Liquidity Live when I asked you point blank, hey, should retail get involved in SpaceX? What's your thoughts? And you were like, hey, listen, it's a 4% float, 5% float. It's going to trade up and down, but you know, a year from now, it might be trading at the same basic price. It's going to be priced not to perfection, which it seems to have been, but you, I think your position was it's going to be priced reasonably. There'll be plenty of time to get in. You don't have to like, you know, panic about getting your shares.

Guest: Yeah. Once a company's valued at over a trillion dollars, like the get-rich-quick schemes are over, right? Like that you and I share a deep passion, Jason. We got to get retail investors. We got to get the citizens of the United States in on these value creating opportunities earlier, right? The accredited investor laws are insane that we have in this country and keeps people from participating in these things, but it is what it is, right? So, they're coming public at over a trillion dollars. I still think there's a lot of meat on the bone on SpaceX, on Anthropic, on OpenAI, but you're not going to have, I don't expect that they're going to be priced in a way where you're going to get a 50 to 100% durable bounce out of the IPOs. If so, that would mean they were probably mispriced right into the IPO. But I do think that these things can be compounders. They're going to compound at the rate they compound revenue. And I think all of these companies are going to compound revenue at well over 30% for the next many years.

Host: And 30% a year for just so people understand that this is high growth in public markets on very large revenue numbers already. You know, growing 30% when you have 100 million revenue is one thing. Growing 30%, you know, when you got a 10 billion or 100 billion, you know, this becomes a different task. So let's talk a little bit about these two companies, Anthropic, and what the public's going to perceive them as. Chat GPT seem to be the public brand, the consumer brand for, you know, large language models. It's the AI for, you know, people who are doing their homework or mom and dad are trying to fix the dishwasher, whatever. And then Claude took the lane of, hey, we're going to be the one for corporate. And it did seem like OpenAI got very distracted with Sora and you know Disney relationship. We're going to make a puck with Jony Ive, everything consumer. Then they realize oh wow the revenue seems to be an enterprise first. Is that going to wind up being the big mistake when we look at it? They kind of gave the Google position, the high growth position to Claude and Anthropic and they took the Yahoo position or do you think they'll catch up on the enterprise or maybe they should just go back to trying to be the consumer version? How are these going to be positioned a year from now? How's the public going to look at them?

Guest: The problem with enterprise revenue is at some point the person that's spending it has to see an ROI. I asked Anthropic's new model.

Guest: Yeah. I first asked it, what is the lift of the S&P 500 earnings per share growth since 2024 from AI? And they answered, oh, it's 50%. So then I looked through it and I said, well, no, you're including the money that Nvidia makes from selling chips to Amazon. So I said, okay. I asked a different question, which is, then what was the EPS growth of the S&P 493? And the answer was 9%. And I said, "Okay, well that's different." And I said, "Unpack that." And the overwhelming majority of that was from pricing power sitting on top of inflation. And then the other 3% was from buybacks. And so the answer as far as all publicly available data was that the actual ROI was somewhere between zero and 2%.

Guest: So I don't know. I mean I think that enterprise looks really good. The problem is that very smart investors like Brad and Gavin and others at some point will start asking companies what's your ROI? What's the actual EPS lift? And if the answer is, well, I don't really know or I'm not sure and you know, you don't necessarily have the pricing power to continue to raise prices. Enterprise is probably a little bit more brittle because there are fewer buyers and they're more demanding. Consumer on the other hand then all of a sudden becomes an incredible safe harbor because you have tens of millions of buyers and having those two orders of magnitude.

Speaker A: more buyers at a much smaller price point inoculates you from the vicissitudes of an ROI discussion.

Speaker B: So it all really depends on what the actual ROI is of this money being spent. I think that we're in the phase of just being astonished, as Brad said, about the scale of the revenue growth.

Speaker A: Yeah.

Speaker B: But at some point, you'd have to be an idiot not to ask, well, who is paying you this? And can they sustain paying it to you? I just don't know what the answer to that question is. And at some point, it may not be now, at some point, people will have to answer that question. And you're spending million dollars a year on tokens and that million dollar a year is doubling and tripling and quadrupling. At some point you're going to have to show an ROI that's above the risk-free rate of return otherwise you're going to have some angry investors on your hands.

Speaker A: And our discussion here for the last couple of weeks on the pod has centered around that and the industry has responded on the place where all the CTO CEOs and capital allocators hang out which is X.com formerly known as Twitter. Here's Pine, the CTO of Uber. And so when you ask like how are they getting the ROI out of this? People are now bringing that conversation front and center and they're explaining it on X and he talked remember Uber was also the one that ran through all their tokens in the first quarter. So then on the other side of the business which is legal, operations, marketing, customer support, HR and procurement which he lists here. He says in this you know today 99% of our engineers use AI tools. Okay great right it's everybody's you know doing vibe coding and has coding assistance. More than 70% of pull requests are attributed to local or cloud agents. Our engineers have built 200 agentic skills. So how are bringing agentic AI beyond engineering and what they've decided to do is essentially he talks about these agentic pods and this to me seems directionally how this should be done which is you find engineers and you as we talked about forward deployed engineers fancy way of saying put an engineer put them into departments and have them work with the department heads who understand systems thinking how their process is done and he says he's making basically long and short of it is they're making massive massive progress on the operational side of the business. So Brad, you're pretty familiar with Uber and have been a long supporter of that. This is a company that knows how to deploy technology pretty well and they're an operations machine run by an operations machine. DAR.

Speaker C: They should report the EPS gains attributable to AI.

Speaker A: Yeah. Well, I mean, and this first step seems like they're really being thoughtful about this first. Hey, this token spend got out of control with the developers. We're going to need to pause this and look at it. And then second, here's how it's going to lower cost and create more efficiency. So Brad, let's talk about that side of it. Not just token maxing with the developers hitting the slot machine of like, okay, let's see if this pull request and let's see if this produces the right code or not to these departments in a more strategic way. It's not just the person who works in HR, you know, using cloud code or perplexity or whatever and trying to vibe code something. This is hey we're sending engineering in to work with your top systems architect and we're going to you know try to find that ROI. Yeah.

Speaker C: Yes. I you know first I would say Chimath is right. The only question is on what time frame. There's no doubt that there's a lot of money being spent today that is in the experimental bucket right where I think there probably isn't direct ROI Chimat to your point but I think we're so early nobody cares. I think we're so early in terms of enterprise adoption. Remember the total addressable market here is every single small, medium, large company on the planet. And so we've never seen revenue growth like this because we've never seen a TAM like this. And if you look at the distribution of revenues across these businesses, it's not like it's concentrated with four or five customers. There are millions of customers independently economically making the decision that is rational for them every day that it makes sense like Pine at Uber and of course they're trying to find things on both right now mostly the cost side cost takeouts to justify the investments that they're making in you know in tokens but I think we're on the verge of breakthroughs in intelligence that's going to dramatically change the revenue side of the equation for a lot of these businesses breakthroughs in life sciences breakthroughs in in product innovation etc where they could not divorce themselves from this even if they wanted to. For example, Jensen Hang has talked many times that all of his design work all of his design work now at Nvidia is using AI to design the next generation chip. The machine is building the machine. So you can't get rid of that even if you wanted to. and tiny intelligence advantages at the frontier where he sits are required. Like there's no way I don't think that Jensen is going to use anything but the best models that he can to build out those capabilities. So I just think that we're not going to see that in the next few years. You're going to see it under the hood, of course, but that occurred at Snowflake. There was tons of optimization that occurred at Snowflake, but the revenue continued unabated. their revenue growth continued under beta because they further penetrate penetrated use cases f further penetrated the enterprise. So let me be provocative here. If these guys end a year over a 100red billion, I think that they're on a revenue trajectory that they could 3 to 5x again next year. We've never seen anything like this. Never.

Speaker A: You're saying 100 to 300. 100. And Jason, like you and I have talked about this. Our minds were blown. If a company could go from 100 million Yeah. to 300 million, we're talking from a 100 billion to 300 billion. 200 billion of incremental revenue is incomprehensible in the history of Silicon Valley. Okay. And just the fact that we're even the world in the history.

Speaker C: Yeah. In the history of the world. So the fact that we're even talking anywhere close to this tells us something different is going on here. I think the thing that's different is that intelligence is the largest TAM we've ever seen in the history of the world. These guys are penetrating it. So yes, the super sophisticated companies, the 8090 and Chimath are helping optimize their token spend that are early adopters. 100% that's occurring, but it's not really changing the trajectory that the Frontier Labs are on.

Speaker A: Yeah. And one of the interesting things about this technology that's really unique, we talk about intelligence on demand. When you would make a piece of software or you had some technological innovation, it would typically acrue to, I don't know, one group of people in an organization. You know, maybe two groups of people, right? Excel comes out. Okay. Yeah, the accounting department's having a field day with it, but it's not really affecting human resources or marketing. Okay. Yeah, maybe it trickles down eventually. Every single person in every single organization is playing with these tools. So if everybody's playing with it, everybody's trying to apply it all at the same time. It's kind of like, you know, you got a thousand person organization. People are spending 200 a month. Okay. Yeah, Chimath, they double it every, you know, x number of months. Okay. Yeah. Now they're spending $400 a month per person. Okay. They're spending $5,000. Well, if the average salary is 100 150k at this organization, it's only an incremental 3, four% on top of their salary. So the way I look at it is did it make that person three, four, five times more effective at their job? And I think the answer is yes. So that's why there's so much token maxing going on. And it's also a bottom up type product. You can just get into this product for 20 bucks a month and you know no CIO or CTO is like oh no you can't spend 20 bucks a month at your corporate card for this technology. So it when a bottomup technology hits everybody at the same

Jason: That's what would explain this revenue ramp that we're all having a hard time adjusting to. It applies to every single person. Who is it impacted by the technology is my question to you, Chimoth. In what organization you're working with, with 8090, is there a department that says, "Yeah, the intelligence on demand, not for us, we don't need it"?

Chimoth: Well, it's less about being dismissive that way. It's more that regulators and other people won't necessarily allow you to use it the way you want.

Jason: Okay, so finance, HIPAA, yeah, there's HR data you're not allowed to put to work just yet. What I'm finding is, once you start using this and getting some gains, it's very addictive. And we were sitting here, Brad, I don't know, maybe in January, and I got that OpenClaw bug.

Jason: And then I started playing with this Hermes Agent — which is not a French company by the way, they just use French names, it's Nuvo Research or whatever it is. I started playing with that. It's a very peculiar piece of software, but it's very open piece of software. So I went to OpenRouter, I got my own keys, I've been playing with GLM. Then I talked a little bit about BitSensor on the program, known as Tao, dollar sign TAO. It's a crypto project. Somebody who is creating a subnet that is putting GLM 5.2 and other models available at really cheap prices. So I all of a sudden experienced — because they gave me an API key — having my token cost go down 95%.

Jason: And when you have unlimited tokens as an exercise — which is going to come to everybody — eventually everybody's going to learn how to drop the price by 95%. And it's going to happen as well because people like Rock with inference. This is all inference, right? This is what people are using. They're using inference to do this. Well, inference is being impacted like three or four different ways. The software is getting better. Open source at the same time. You're going to have distributed networks like Tao, and you're going to have better chipsets from Grock and Cerebras, etc. All that's happening at the same time. Once I got down to 95% cheaper, I started setting my agents instead of doing daily runs to doing hourly runs. Then I took my agents from doing one task and I broke them up into three agents and have them doing three different things on the hour. And when you start doing hourly tasks and then you wake up in the morning and like 14 jobs have been done, you're like, "Wait a second. This is completely different."

Jason: As one example, I have all the All-In episodes, all the This Week in Startups episodes, and we set these crown jobs to go find what the new trends are in technology. I have a trend spotting agent running every hour, informing me of the top three or four trends, and I just give it words. Really does change your thinking. When costs go down, what do you think the tokens are going to cost, Brad? And you know, next year?

Brad: Yeah, we've seen 90% reductions in the price of tokens for each of the last two and a half years. We've talked a lot about Jevons paradox, which I think you're referencing here, which is you're going to use a hell of a lot more when it happens. I think the central debate right now in AI is the one that Chimath keeps pointing us back in the direction of. For 18 months since the DeepSeek moment, right? When the DeepSeek moment happened, the markets fell 40%. And there was a reason for that. Many started arguing that the frontier models were screwed, that open-source was going to kill them, that they were closing the intelligence gap, that model routing was going to make it easier to route these tasks to cheap tokens. But despite all of those arguments, and now we're 18 months into this — and I had this back and forth with Gurley a lot. I love open source. I want all the competition in the world. Let's be very clear. But despite all of those arguments, the facts in the field are just the opposite. The share of economic value, right? There's this tweet this week from Jesse Zang that we ought to pull up here. The economic value, the share of wallet is actually increasing to the Frontier Labs, while the share of tokens, these commodity tokens, is obviously going up to the other guys. And I had a little back and forth this week with Nikesh on this, trying to sus out why is that the case, right? Because what people would have thought is, "Oh, cheaper, pretty damn good. 90% is good enough to do all these tasks that you're talking about, Jason. So nobody's going to use the Anthropics and the OpenAIs of the world." But despite that, it looks like their share of wallet has gone up.

Jason: I think it's not that. I think it's more that when the iPhone was a novelty, everybody would keep upgrading because you expected that the new price was worth it. And then at some point — and you can debate when it happened — people said, "You know what, I'm just going to keep the old phone 'cause it's good enough and I just don't see the difference." And I think that there's going to be a moment like that. Like when I use Fable 5, the problem is that it's nerfed on a bunch of things that I would normally research. You know, I was with somebody this weekend and he was telling me about some health thing and I put it into Fable and I was like, it won't answer you. I'm like, okay. So, I think that everybody will get to a point — they'll get to it at different times — where they just say, "You know what, it shouldn't really matter what model I'm using. If I get an answer that I think is reasonable and I can kind of go about my day separately." I think when the corporate CFO gets involved, it'll be an entirely different conversation altogether. I think that what I can tell you after this UN commission that I joined with Ben, Jensen, and Brad Smith, there is not a single country in the world that is not trying to figure out its own sovereign AI strategy. And I don't think they believe using a closed source American model is the answer. And so, I think we have to keep in mind there's trends. One is just geographic penetration of humans, and there are still many, many more people that don't use it than do, which is an upside and an opportunity for everybody.

Brad: Mhm.

Jason: And then the second is there is going to be the experimentation, as you said, that needs to transition to ongoing repeatable usage. And then the third is that all of that then needs to plug into the existing regulatory infrastructure that we use as societies to run the world. And I think when you put all of these things together, it's not clear to me who wins except that you're going to have a lot of diversity of choice. Certain countries, I can tell you after this week, have no desire to subjugate themselves to any technical risk, and so they're willing to spend the money to have their own. Now we can argue and debate whether that country has any chance, but they would rather take an open source model like Nvidia's actually and stand up their own stack soup to nuts for their own people and their own companies inside of their own country.

Brad: And if the models are 99% as good or 95% as good, there's going to be a claim that some countries make which is, it's just good enough.

Jason: That's the question. That's the question. And then separately there are companies who will not have the earnings growth to justify this without going on some long protracted carveout of cost. And most companies, you know this, they just don't do it. They don't have the nerve to do it. They're not capable of it. You know, you wrote that famous essay to Zuck. He was pressured into finally doing it. Absent a very few companies, most people just allow the problems to compound. So, I just don't see a world where, when you get clobbered over the head, you don't look at other ways of just displacing cost. And if it's like Coke Pepsi kind of a thing and Pepsi is 1/1,000th the cost of Coke, I don't know. I just think it's a risk that has to be managed in the perception of the market participants and the underwriters. To add to that, Brad, just open source…

Speaker A: is very hard to implement when compared to just firing up Claude and having Claude already approved in your organization. The number of steps it took me in order to, and I'm pretty familiar with technology. It took me hours to configure my new setup to get onto this bid sensor network to get open router going. And to your point, Jimoth, it does dynamically route now. So, I'm dynamically routing and I'm, you know, GLM 5.2 too. And then if I fall back to Claude, but which Claude am I going to fall back to? And here's another piece of evidence to your point, Schmoth. There are some organizations that just aren't capable of this. They don't have the team that, you know, does this naturally. We just talked about the CTO of Uber. Now, let's talk about another CTO. Andy Fang is the CTO of Door Dash. Shout out to Stanley. And so he, as you can see here in this tweet, a lot of people listening to All-In over the last couple weeks are coming out and as I said explaining what they're doing to address this exact issue. He says, "Hey, with our internal coding benchmarks, we're able to confidently introduce openweight models into our AI code review without degrading code quality. Have the frontier model Fable from Anthropic to do the hardest work, delegate lower level work to Kimmy 2.6 and they are now releasing their benchmark." So another group releasing their benchmarks and saying hey we know this is an issue the CTO has been charged to your point Chimath again CFO says hey make sure this is profitable we get the ROI they put that on the CTO here's another CTO from another leading tech organization that knows how to implement this yeah.

Speaker B: the really interesting thing we have to forecast right now is what happens in an earnings miss and I think what happens in a in a moment where for whatever reason maybe there's just an externality that there are a series of earnings misses.

Speaker C: Where are people going to look? And I just think that people find it very difficult to lay off other people. I think it's much much easier to cut other costs. And I think that the more successful these companies get in a very quick amount of time without really proving the ROI, I just think the bigger the risk is. It's complicated. The game on the field when you're working with these enterprises and just trying to explain it to them is I think that they're getting smarter quickly is what I would say.

Speaker D: I would just say I think that Chimath's absolutely right about the sovereign stacks that are going to get built around the world. This is not either or. We are going to have open source and we are going to have frontier intelligence. The preponderance of the tokens today are already shifting toward cheaper, lower lagging models out of open AAI or lagging models out of anthropic or the other frontier labs that are out there. Obviously, we you know, we talk about those two. SpaceX has released an incredible model, you know, in the last two days. Meta's out with, you know, a terrific model today. So, Gemini is still in the hunt. So, there are lots of choice. The meta thing was really intense because I thought okay you know we talked about the game theory which was Mark should scorch the earth with open source. I think they flubbed that play.

Speaker E: but then I think he is now said he's going to create a price war and so if you look at the tweet or the quote there was a post Jason I don't know Nick if you can find it.

Speaker F: I got it.

Speaker E: announcing it he was basically like hey guys I'm going to give you the same quality at like 1/100th of the cost. Now again,

Speaker D: there's a lot between here and there. There's a lot of enterprise distribution that's required and you know this there's been a couple of misfires before, but I thought it was interesting that the vector of challenge was on cost.

Speaker A: Yeah. Here's the Mark Zuckerberg tweet. Just sorry I queued up for you there, Brad. And he's at D, that was his old handle back when he was in college, fd. And he's done more tweets today over this Muse Spark announcement than he's done in his history. So he's getting into the X.com conversation. Quote, "Today we're releasing new Spark 1.1, a strong agentic encoding model at a very low price. It's available through our new meta model API and in meta AI." So he's coming out saying, "Hey, we got the strongest agentic tool here. Please come use it." He also wants to have his own essentially, you know, he wants to jump into not the hosting space, but he wants to provide tokens as well. So again, I think we're going to have a tremendous amount of selection. The competition is great for America, but I think if you look at the things people are doing, let me give you an example. The premium workload, Jason, you talked about summarizing a document may take 20,000 cheap tokens to do. Of course, shoot that to a lagging model or an open source model, but if you're talking about replacing a software engineer for 2 hours, that may take 2 million expensive tokens. And the consequence of using something that's 95% as good is really high, right? Because you have a long-running task and if the task breaks early or it breaks in the middle or breaks at the end, there's a huge cost to that.

Speaker C: you still burn the tokens, right? You and back to this analogy I was using, they're pulling the slot machine and you lose.

Speaker A: and the time and the compute. So if an AI agent is replacing a $200 an hour consultant, right? Take that as an example. So, three consulting firms, they're competing. They need the smartest consultant. They're charging 200 bucks an hour. The difference between spending three bucks on a cheap model or 15 bucks on an expensive model to replace a $200 an hour consultant, it's just irrelevance. That inference cost difference is irrelevance if you're getting something that's bulletproof for 15 bucks. And so, I think that's what we're seeing play out. The best evidence for all of this is just revenue growth,

Speaker C: right? We can sit here and speculate all day long as to revenue, not from Anthropic and Open AAI, but from their customers.

Speaker A: I'm talking about what is anthropic revenue growth compared to OpenAI compared to the open source models. Millions of independent actors are choosing every single day. The open source companies are growing, right? But they're growing selling something that is really, really cheap. And there's room in every single market for premium products, for mid-tier products, and for commodity products. And I think we see a lot of this token growth. People are speculating that the intelligence gap between that commodity stuff and the frontier stuff is going to collapse to the point that people won't pay for the frontier stuff. There is no evidence of that on the field today. It may develop over the course of the next couple years, but it's not on the field today.

Speaker B: Yeah. Yeah. And just to give people an idea, we keep mentioning what sovereigns are doing. To give you the specifics on that, the UAE very famously has their own Abu Dhabi Technology Innovation Institute shipping Falcon. You probably have heard about that. The Saudis have Humane and they're doing their own models that are Arabic LLMs. And then this week, Japan is investing $6 billion dollars in a consortium. It's called the Neoterra... Neoter. Neo T r a consortium and they're doing that and skipping ahead to physical AI i.e. robotics. Okay. Joining the conversation here, the one, the only Saxy Pooh Sax bringing you into the discussion talking a little bit here about the debate that we started here on the podcast getting ROI from tokens. Where are the tokens going to accrue to open source versus the frontier models? Bunch of CTO's chiming in on X this week in the last couple days. is in fact talking about how they're managing intelligent routing first to open source models then falling back to Fable and the Frontier models. How do you think this is playing out? And if you're an investor in the space, how do you think about the frontier models and their growth when you have CFOs coming in and saying hey justify this cost and do you have a cheaper solution and what is that cheaper solution? Well, look, I think that enterprise CTO's would like to shift

Speaker A: their token consumption to cheaper models for the obvious reason that that would be more efficient and they are seeing their compute cost or their token cost is skyrocketing right now. So, everyone's trying to figure out how do we put the brakes on this or at least control it, you know, make sure we're getting ROI. You also have the AI sovereignty issue that we discussed last week that Alex Karp talked about where they're worried about giving up the secret sauce or the alpha in their business to a frontier lab that may one day be competing with them. So there's no question that enterprises would like to diversify. They would like to get off of these frontier models when they can. The problem is I think in most cases they don't have the technical ability to do it. I mean Coinbase figured out how to do it. Door Dash figured out how to do it, which is to say they built a token routing system, a layer of middleware that allows them to sort of send frontier task to frontier models and non-frontier task to more mundane models. But I don't think your average enterprise has the technical capability to do that. So I think this is a case of the spirit is willing but the flesh is weak. I mean they are willing they would like to diversify off of these closed models but they are unable to do it and so this is why the share of wallet of closed models it actually increased. I think that opensource went from 19% last year to 11% this year. So open source as a share of enterprise spending is actually decreasing. Now I don't think that means that usage is decreasing. I think usage is skyrocketing in both these categories. It also may be the case that because the whole point of using an open model is you just pay for the compute cost. You don't have to pay a lab. So it may be the case that it's hard to measure that usage in terms of spend. But nonetheless, I mean anyone who's saying that these closed models are gonna lose or are somehow losing, you're just not seeing it in the data. Like Brad's saying the revenue is skyrocketing and I think the most you can say is that enterprises that are technically capable would like to gravitate towards hybrid architectures but at the same time it takes technical expertise and it is just phenomenally convenient whether you're a developer or an enterprise just to go with the frontier labs and that's why their revenue is skyrocketing.

Speaker B: It is the easiest choice. It's the most refined Yeah. most refined product's there's Yeah. And there's one other thing here as well and this was discussed in a really interesting blog post by the founder of Decagon which is enabling AI powered customer support for enterprises and what the founder said is look open models are great when you know exactly what you're trying to do why they're smaller cheaper models but you have to do post training you have to have the data set and you have to know exactly what you're going to use them for. Totally. But if you don't know exactly what you're going to use them for, you want the most powerful general intelligence that you can get. Right? So what he said is that for mature use cases, yeah, you want to go open, but for immature use cases, which are all the new things people are discovering right now, you're just going to want to use the most capable general model that you can. And then once you figure out what the workflow is and what the workload is going to be and exactly what you're trying to accomplish, then you can use a small highly trained model. And I think he said

Speaker A: optimize in the post to get the game for customer support. Your model doesn't need to know physics, you know, for example,

Speaker B: and so you don't need that capability, but enterprise are still trying to figure out exactly what all these workflows are going to do. So I think that's another factor which is to say that you know it depends on the use case and how mature that use case is and you really want the most powerful frontier models that you can at the discovery of all the potential for the technology.

Speaker A: Yeah.

Speaker B: And then let me just wrap up then there's one other interesting post that I saw was by Nesh Aurora who also said that what he's seeing is yeah enterprises would like to diversify. They would like what he called model fungeibility. They would love to commoditize these models, right? And just hot swap them.

Speaker A: Yeah. Headless is the term being used, right? Yeah.

Speaker B: Yeah. That'd be ideal for enterprises is you sort of swap out the model for the cheapest one that gets your task done. But then what do you do about memory? What do you do about context? What do you do about history? And what he said is no one's really figured out a way to abstract that stuff away from the model yet. And again, this goes to the technical challenge of creating this middleware layer that would do the most efficient token routing. It's well, you know, it doesn't work unless you can make all of that context and memory and history fully portable to the cheaper model that you want to basically hot swap to,

Speaker A: which means you have to have some technical ability sachs. And the people with technical ability, the tip of the spear, the 1% of people deploying this technology are starting to figure that out. Here's another proof point and some more evidence. This is Ali the founder uh of data bricks I think a company you're very familiar with Brad and what he realized when you start taking apart the harness and you start looking at the skills you look at the memory and all this acruant that you put around your tasks he said we find that the same model using the same model not using open source versus open AI or or claude we found that for the same model the choice of harness can significantly save costs by about 2x. So they found with GLM 5.2 that this performs extremely well and that their tasks literally are getting cut in half using the same model but with a different harness. And that rings true to me. I once you've built one of these agents and I was talking about one earlier I'm running every hour on the hour to find trends. I asked it to please start optimizing it. And when I optimized it, it was like 80% less token use. And now in these apps, you can go to your analytic stacks and you can actually see your token use by hour, by job, and across which models you're using. This is really sophisticated and hard for for a consumer to do of the technology, but it's definitely a trend.

Speaker B: So with that harness that he was using, is that something they built in house?

Speaker A: Yes, I think it's um

Speaker B: Okay, got it. So they they basically creat

Speaker A: Yeah. in front of these and that it can multiplex different harnesses and models for different tasks. So he's not only routing to the right LLM, he's routing to the right harness and people don't even know what skills are. People don't even know what the memory is at this point there. That's all abstracted into the claw product or the perplexity product etc. Yeah, there'll be a massive business. There already is. All these inference clouds, you know, the base 10s of the world, the fireworks of the world, every single hyperscaler in the world is going to do this. They're all going to provide, you know, tools that allow you to achieve some level of model fungeibility. The big question is at the end of the day, we're going to have it's going to be very heterogeneous, but what is the mix between these two? I again think the TAM is so damn big here that you're going to have huge open-source use cases, sovereign use cases, etc. You're going to have plenty of room for the frontier labs. I Let me throw something out. I'd like to get your opinion on. you know to a certain extent there's this implied assumption in the world that uh there's going to be this convergence of intelligence right and if you look at the benchmarks today seems like everybody you know on the benchmarks they are converging but yet if you look at the revenue distribution it's not converging at all one of the questions I have will the model router itself be smart enough to overcome David the inherent intelligence advantages of the generalized of the frontier labs the non One consensus argument might be that intelligence is not converging at all that c super intelligence becomes fully self

Jason: As it becomes recursive, you actually extend the lead because the smarter your model gets, the more revenue you get, the more compute you can buy, the more compute you can buy, the better the model is that you can build. So I think there's a chance that over the course of the next 2 to 3 years, as we take on much more complex agentic tasks, that the distance between the frontier and everybody else doesn't converge. It actually extends. We shall see. But, you know, I think there's this implicit assumption in all the arguments today that everything's converging. I'm not sure that we've really run that to ground.

Jason: Another piece of evidence to put into this mix. I interviewed Anton, the CEO of Lovable. Lovable is an app or a service that allows you to vibe code, you know, different pieces of software. They've got a really interesting take on that. They went from 100 to 600 million in revenue over the last two years. They went from zero to 350 in the first two years of the company. Products been out I think roughly for like 30 months. Then I also spoke to the CEO of 11 Labs, Matti. And I asked both of them point blank, are you guys you know your major customers of you know the Frontier models? Yes. They're spending tens of millions of dollars with those frontier models. I asked them, hey, are you concerned about data leakage and them compete, you know, releasing competing products? 11 Labs is doing voice and obviously Claude code, you know, is an obvious competitor to lovable. Are you going to make your own models? Both of them said that they're working essentially on their own models. Those are major customers of the Frontier Labs who they want to get off of the Frontier models and they want to have their own proprietary model. The ability to create verticalized models is getting easier and easier every six months or so. So that's going to be another trend to look for is these verticalized models for voice, verticalized models for building code. And we're going to see people stop using the Frontier Labs and these are major major eight and nine figure customers. I think they're going to just run for the hills and only use the Frontier models.

Speaker B: But Jason, the counterpoint there is 11 Labs. I love Maddie.

Jason: Yeah.

Speaker B: Do you really think he's going to use an inferior model? He's got to be have the best voice agent in the world. And if the best voice agent in the world is given to him by using the Frontier Labs, can he afford in a competitive marketplace to say, "I'm going to use the cheaper version, the thing that I built for myself, even though it's not as good as the other thing." If he builds something better, I totally agree with you, which is what he believes he's doing. He believes he's making a better version. Yeah. So that's the question that I just, you know, put on the table whether or not you're going to see this convergence, whether it in fact is that easy. I don't think it's that easy, but we shall see.

Speaker B: Okay.

Speaker B: It may come down to how discreet and sort of predictive the use is. So like Decagon, the customer support AI company, they said that 90% of their usage now is being sent to open models, but those are open models that they've had the opportunity to post train on and do a huge amount of customization based on all of their learnings and all of the data that they've gotten. So again, it comes back to maturity of the use case. If you know exactly what you're trying to do, it's probably easier. But, you know, I don't think that's most enterprises, though. And maybe there's going to be a pattern where, you know, all the immature use cases, which is to say, all the things you're figuring out, you're just going to want to use the most powerful model possible. And then once it gets really well defined, maybe you start moving some of those workloads to post-trained open models,

Jason: purpose-built models. Yeah.

Speaker B: Yeah. Purpose-built. Yeah, it could be something like that.

Speaker B: That's kind of what's happening with the people who are the tip of the spear. They're working on the routing of the jobs. They're working on the harness and they want to be independent and have that AI sovereignty we talked about last week.

Speaker A: Speaking well, but just to take on Brad's point for a second, I actually agree with what I think you're saying, Brad, which is the market today seems to be pushing towards duopoly or it has become a duopoly. Certainly measured in terms of revenue. If you're to look at market share based on token revenue, there's only two companies making meaningful revenue. Anthropics at what 60some billion ARR open AAI at 40ome billion ARR. I don't know if anybody else even registers and it may be the case that the more tokens that Anthropic and OpenAI produce, I mean we got to remember every token that they're serving up is on behalf of a use case, right? So they themselves are learning from that and they're getting better at then providing whatever offering that is and so who knows like the gap may be growing. A year ago it seemed like we had five major labs you know now it seems like there's a top two and then everybody else. So I mean look I could see AI easily becoming another tech market that becomes a duopoly

Speaker B: which by the way is the trend. The historical trend is like monopoly or duopoly in most tech categories for better or worse.

Speaker A: Yeah. In this case though, however, we're using revenue as the metric to determine the winner. Keep in mind when you're doing open source, those are dark tokens. Those don't come up as revenue. So, we don't know the utilization that's occurring at Door Dash when they're using an open-source model. We do know their fable and their anthropic spend, right? And because we see that in the anthropic revenue ramp, the more they deploy these things on their own hardware, using commoditized hardware, using the NeoClouds, you don't see that. It doesn't come up as revenue. It comes up as free. The only thing you're paying for there is the hosting cost, you know, and that's that will come up on Nvidia's balance sheet. So the gains you'll see there will be Cerebrris, Neoclouds, Caruso Cloud, etc. So keep just keep that in mind when we're having this discussion.

Speaker A: Let's talk a little bit back to sovereignty here. The CCP said that they might or there's a report out according to Reuters. Reuters generally does a good job of this. They dropped a couple of anonymously sourced reports about AI in China. And these were published about 15 minutes apart the big scoop that CCP officials, Chinese Communist Party, are reportedly considering restricting overseas access to China's top models. So two Chinese regulators met with Alibaba, Bite Dance and Z.AI. They are the ones who are doing GLM 5.2 that we keep referencing. They're discussing limiting access to the top open and closed models outside of China. Why are they doing this? Well, they're making any theft or leaks of AI research a national security offense and they want to control who can fund Chinese AI labs. And we saw this with Manis which was a Chinese company tried to go to Singapore. the CCP pulled those employees from Singapore back to China. And so here is their main concern. The quote is that they're concerned about Mythos. Chinese authorities are deeply worried about the potential for Mythos to exploit software vulnerabilities and that Washington might deploy a model against Chinese interest. Saxs, last week I proposed the reverse to you in your previous position as Zar of AI. Do you think the United States should be banning those models? Now we have the opposite. China saying potentially according to these reports allegedly that they might restrict them. So explain the game on the field here. If you're going to look into what China's thinking, why would they want us to not have those open source models and and how is this chessboard development?

Speaker B: Well, last week I explained why it would be harmful to the US to ban open models. So if you're China and you want to harm the US, maybe you would want to. I mean, it does kind of make sense because our companies are benefiting a lot from all this R&D that they're doing. Now, at the end of the day, I think the story is probably a little bit

Dylan: overstated. I think there are a few Chinese models that were open source that have gone closed source, but I don't think they're all I'd be surprised, let's put it that way, if they all went closed. So, for example, the number one model in China, as I understand it, is bite dances model, which is already closed. That's kind of like their chat GPT equivalent and it's always been closed. Then you've got Alibaba's Quen, which was open and now I think is going closed, and ZepO, which has GLM 5.2, too, which we've talked about a couple weeks ago because it seemed to be catching up to what was then commercially available as the American frontier at certain tasks. They I think are going closed too after having been open. And so this is I think the tactic is you stay open until you catch the frontier or you get close to it and then there's a really compelling incentive to go close because you want to capture all the value for yourself

Speaker A: which by the way is exactly what Sam Alman did famously at OpenAI. Not only did they go from a nonprofit to a for-profit, they went from open models to closed models. So, it's exactly paralleling what Sam realized 3 years ago. Yeah.

Speaker B: I mean, in a way, that was what I think Meta's original strategy was was that Llama was going to be open, but then they actually they've sort of backed away from open a little bit. But this is kind of an obvious strategy, right? is is that if you want to catch up, you go open. Because by the way, you're not going to make any meaningful revenue on on closed anyway because you're not close enough to the frontier. So why would anyone buy your product, but if you go open, you get the developer community on your side. So

Speaker A: and you get utilization, more people use it, which in AI gives you reinforcement learning. Yeah. Sex.

Brad: Well, having having spent some time in DC this week, I and and and talking with both the White House and Treasury, etc. on this topic. What I can tell you is while there may be some, you know, debates about regulation of US models, the one thing there's absolute agreement on is doing everything to stay ahead of China. And, you know, and and the president all the way up to the president very interested how far are we ahead of China? What are the things we need to do to stay ahead of China? It is a unifying force in Washington. And the idea that we were going to kind of take our frontier labs off the field, off the playing field while letting Chinese open source models run free, you know, and on top of that distilling our models. I will tell you GL GLM 5.2 has watermarks from mythos all over it, right? So we know they were distilling, etc. And I think the US government's going to take steps against distillation, which they should do. So I think that you know China doing this in some ways I don't think it hurts the United States. The United States can spin up open source models. We've got Reflection spinning one up. Obviously we got the good work going on at Nvidia with their open source models. The labs I've talked to a couple of the frontier labs about open source models. I said why aren't you guys making open source models? They're like there's not a lot of demand for it. If there was a lot of demand for it we'd make it. And so I think the US is in a good position. And I think this is probably more chess playing by China than actual threats because it would hurt them a lot more than it would uh hurt us.

Speaker A: Yeah. And then to just back up your point, Saxs, about when you're behind, go open and then once you catch up, start tighten things up. That's exactly what they did with Android, right? Google released Android. At a certain point, they were like, in order to use Android in the license, you have to include Google Search, you got to use Google Drive, you got to use Chrome. and they started tightening it up. So, it's not really an open- source project at this point.

Speaker B: Just, by the way, I think the absolute best thing that could happen

Speaker A: Yes,

Speaker B: we need uh like a Chinese yud over there.

Speaker A: We got to get their poom up. We got to get their poom up.

Speaker B: Exactly. We need a lot more people over there freaking out about, you know, job loss or RSI or whatever.

Speaker A: Yeah.

Speaker B: That'd be the best thing that could ever happen to us is if they start cracking down on their labs in the same way that the doomers want to do over here.

Speaker A: Yeah.

Speaker C: Brad, let me just say just one comment. I mean, look, I I agree with you that from the president on down, everyone wants to win the AI race. And in fact, that was, you know, in the big AI policy speech the president gave about one year ago. That was the whole thrust of the speech was declaring that we were in an AI race and America had to win it. I think the big risk is more that and this would not be at like the top level. You know, I think if the president could make every single decision, it'd be perfect. The issue is at a lower level in the bureaucracy, do people somehow do things that are counterproductive? Maybe they think it's going to help us in the race against China, but they end up doing something that's hamfisted, they just like ban something or without, you know, really truly understand all the implications of it. So, I think there's no question that the administration wants to win the AI race. The president definitely does and at the top levels they will all make smart decisions. The question is whether at lower levels of the bureaucracy, you can get mistakes being made. And then you have the influence of Congress, whatever they want to do. Those guys, they're more responsive, I think, in a way to like the doomer community that's creating a lot of political pressure right now.

Speaker B: Well, and the throttle, you know, paradoxically, to all of this might not be the software, might not be the chips, it might be energy. Yeah, Chimath. I mean, when you look at your data center projects and the other ones that are going out there, if we need more tokens, if people need more inference, we have a gating factor in the United States, which is which is energy. There's a an analysis that my team put together which I think is quite staggering. If you just look at the load growth that's expected between now and 2050, we are about three entire California's worth of energy short. And that's just assuming regular consumption of devices and cars and fridges and televisions and computers. So yeah, we have a we have an enormous problem in the United States with respect to electrons.

Speaker A: Yeah. And if you put Taiwan into the mix here where the chips are coming out of, I had a really big wakeup call and there was a Wall Street Journal article about this. The amount of LNG, which is what Taiwan runs on, is like they have two or three weeks of it. China decides to blockade Taiwan, they're going to run out of energy immediately. So this is energy both in China and Taiwan and in the United States. It's all dependent on that. We have to get nuclear running, more solar running, more batteries, more of everything. And uh that is obviously a regulatory challenge here in the United States. All right, let's talk about your time in DC. Brad Gersonner went to DC, everybody. And uh huge huge congrats, Brad. you've been uh harping on about this um you know uh accounts now called Trump accounts, the Invest America accounts and um tell us what happened in DC this week cuz I think you finally have the number one app in the world Trump accounts is the number one app in the world. Congratulations and a bunch of announcements. So what what what's the contours of the announcement and uh maybe you could take us behind the scenes. There it is. Trump accounts, the official app number one in top downloads. Your kids can invest in the future.

Brad: This has been, you know, a four-year mission in the making thanks to you guys. You were early supporters, backers. We talked about it on here. And, you know, founders are crazy. And you guys probably looked at what I was working on and and and thought, you're nuts. You're wasting your time on this. And so, you know, when it got signed into law last year, that's a huge moment in

Brad: a founder journey that it's like getting your first round of funding maybe like, okay, we actually this thing is going to happen. But on July 4th of this year, the app went live, right? So that means millions of accounts got created, the accounts got funded. And to celebrate that and to really kind of take the next step forward, you know, we designed a joint bell ringing first in history between the NYSE and NASDAQ from the Oval Office. That was incredible. We had hundreds CEOs there, kids there, families that were impacted. And the president really, you know, kind of laid out that this is much bigger than just a program to give a few people some accounts. This is really about making every child a capitalist. In fact, the president suggested that we're going to autocreate accounts for all 50 million kids or upwards of 70 million kids under the age of 18. So he called on us to get the accounts open faster for more people to have more impact to make sure no child is left behind.

Host: Brad, just slow down because a lot of people don't even know what a Trump account is. Just explain the what it is and then you should contrast it to like a 529 account and some of these other things.

Brad: Great. So, as you guys know, the idea was very simple. $1,000 for every child at birth that could compound for their life in a privately-owned investment account. So you're born, you get a social security number and you get an investment account. And if you do that, you start with $1,000 and somebody matches that and you save 10 bucks a week, that's $50,000 at age 18. And the idea

Host: and that's invested in the S&P 500.

Brad: S&P 500. So when these accounts are created, all that money goes into the S&P 500. There's no cost. It's a free account for the lifetime of the recipient. And that was packaged into the Invest America Act, which was passed into law a year ago as part of the reconciliation bill. So that's what actually occurred on July 4th of this year. All those accounts were created for all of these kids. That's the reason the Trump account app is number one in the app store because parents started hearing about this and saying, "Whoa, I need to go download and get this set up for my child." We had over a million and a half accounts created in the first 24 hours after the launch of this. We had over a billion dollars of deposits. So, I was contributing money into the accounts of my nieces, my nephews, my kids, friends, kids. Every account has a QR code. Jason, so somebody can just send you the QR code for your kid. You double click Apple Pay on your phone, double click and you send them 25 or 50 bucks. So that is kind of the most essential part of it. But we also had a bunch of announcements around philanthropy