a16z Podcast - Gavin Baker: Why AI Demand Is Outrunning Compute Supply

Episode Date: August 31, 2026

a16z’s David George sits down with Gavin Baker to unpack the state of the AI boom, why demand for intelligence may still be dramatically underestimated, and why the outcome doesn't necessarily have to be winner-take-all. David and Gavin explore the possibility that frontier labs, open-source models, applications, clouds, and NVIDIA can all capture significant value as AI adoption expands. They dig into the economics of the infrastructure buildout, why compute investments can have unusually fast payback periods, and what happens when today's relatively small group of heavy AI users expands to hundreds of millions of people. They also debate the risk of an AI bubble versus an AI shortage, the backlash against data centers, orbital compute, the rise of multi-model architectures, and NVIDIA's position at the center of the AI supply chain. Gavin makes the case that the AI buildout could help reindustrialize America, while David explores whether the bigger near-term risk is not overbuilding, but failing to build enough.   Resources: Follow Gavin Baker on X: https://x.com/GavinSBaker Follow David George on X: https://x.com/DavidGeorge83 Stay Updated:Find a16z on YouTube: YouTubeFind a16z on XFind a16z on LinkedInListen to the a16z Show on SpotifyListen to the a16z Show on Apple PodcastsFollow our host: https://twitter.com/eriktorenberg Please note that the content here is for informational purposes only; should NOT be taken as legal, business, tax, or investment advice or be used to evaluate any investment or security; and is not directed at any investors or potential investors in any a16z fund. a16z and its affiliates may maintain investments in the companies discussed. For more details please see a16z.com/disclosures. Hosted by Simplecast, an AdsWizz company. See pcm.adswizz.com for information about our collection and use of personal data for advertising.

When the history of the 21st century is written, you know, there was like the Victorian age. I think this will be like the age of Elon and Jensen because they are fundamentally altering the fabric of human society and civilization. What happens if there's like a massive supply shortage? Every time you've had a real profound new technology, you get a bubble because the markets get really excited and they get ahead of themselves. Things get overvalued. That overvaluation leads to an overbuild. One of the things that I think has been correct but ineffective is this idea that we need to stay ahead of China.

You're opposed to data centers. Well, you know what? It's probably the best thing that has ever happened to working class Americans. We are reindustrializing America, and it's awesome. Assume that you're right. There's not a physics reason why this can't work. An increasing fraction of the world's compute is going to be in orbit. This sounds crazy, but asteroid mining is going to be a very real thing.

It has more gold, silver, platinum, every precious metal in it that exists in the risk crust. Every LP conversation that we have starts with like, how's this all going to go wrong? Gavin Baker has spent the summer asking AI leaders one question. Can you give me a single quantitative data point in your business that's getting worse? So far, the answer has been no. In this episode, 816Z general partner David George

sits down with Gavin to take a fresh look at the economics of the AI boom. They discuss why AI may be a positive. some market where from tier labs, open source, applications, clouds, and chip companies can all win. And what today's compute economics tell us about the sustainability of the buildout. They also tackle the bubble question head on. Every major technology shift has produced overinvestment at some point. But with compute already constrained, and AI usage still concentrated among a relatively small number of people, what happens when that demand spreads across the broader economy?

From data centers and reindustrialization to orbital compute, open source, and invidia, this is a wide-ranging look at what happens if AI demand keeps outrunning supply. Gavin, you've been out here hanging out on the West Coast over the summer, and you've been talking about the fact that you're like trying to find someone to give you a bearish case, to make your sentiment more negative. Have you found anybody? No, and I ask everyone. My standard question is, can you tell me, one?

quantitative data point in your business that's getting worse. Just one. That's my standard question. And it's at least in July and August, I haven't been able to find a single person. Now, if we're being honest, you know, Anthropic is, you know, in a quiet period. So maybe they've slowed down a little bit. But I do think the rest of the world has accelerated. Open AI is clearly accelerated. Open source, I think, has accelerated more. And then I do think GROC, particularly after Grock Bot has had a pretty dramatic acceleration. And so AI overall, it accelerated in July. It accelerated in August.

And it can't keep accelerating forever, but it's just kind of wild that public stocks have kind of fallen out of bed over the last two months. And I mean, you know, you can drown crossing a river that's on average two feet deep. And so there's not a lot of action at the index level. Right. But some of these AI names are in pretty significant. drawdowns. And they bounced a little bit in August, but still pretty big drawdowns. And things are broadly accelerating. Yeah. Our friend Eric Fishery did a podcast with Patrick O'Shaughnessy,

and he said, maybe everyone wins. Anthropic wins, Open AI wins, SpaceX wins, Meadow wins, Google wins by selling a lot of TPUs, open source wins, Neo-clouds win. The inference clouds went on top of the neoc clouds. Applications win. Yeah. Maybe not all applications. Applications that I think execute well and navigate this. But that feels like a very possible scenario to me. And there's so much zero-sum thinking in the world. And by the way, on Anthropic, what is my hypothesis would be, if you're anthropic, one, I think they probably trued up and cleaned up some accounting.

Yes, definitely. You'd rather do that. Yes. So you rebased and now you're comparable to Open AI. Yeah. in terms of revenue added, in terms of the definition and now, I think, kind of revenue added. Exactly. So you kind of rebased, and then they did their testing the waters. And then I would hypothesize because they've executed well. Probably the next disclosure is a reacceleration.

And then there's always this kind of funny game between the frontier model companies. They always have more advanced checkpoints. Anthropic is clearly waiting for Open AI to release Astra. Yes. And then it's like the next day, here's Fable 5.1. Yes, exactly. magically, it just happened to be available several hours after Astra. Yeah. So I think they're being thoughtful in heading into this IPO, and everyone is shooting at them.

Yes. Everybody's shooting at them. And they're in a quiet period, so they can't really shoot back. So there's a lot of gamesmanship. But I do think having Open AI ananthropic be public companies, is going to be helpful for the market, just because it's, you know, it's such a powerful force. And a lot of public investors, you hear, oh, Sarah Fryer said this in an all hands meeting and it's on the cover of Wall Street Journal.

Okay, we're going to put that into our model. Yeah. And it's just, I think it'll be better for them to be public. I am a little, you know, anthropic is now in their culture interviews saying, how would you feel if the equity went to zero? Yeah, because we're looking for people who are mission aligned. Yeah, mission not mercenary. Yeah. And that's great.

We want missionaries, but we also want people. to make money. And at the end of the day, you can't afford the compute, you want for your mission if you go if the equity goes to zero. Yeah. Like, I'm no expert,

but I'm pretty sure on that. And that I do think they are... They're like the accidental enterprise company. Oh, for sure. Oh, yeah. They're kind of like the accidental everything. Enterprise is just a byproduct of the mission, the objective, the end.

Yeah. Whereas I think O'BiniI is a little more commercial and obviously SpaceX is a little more commercial. But all of these companies, let's just say they have 10 gigs of power. And... They're allocating eight to inference, and let's just say they're monetizing that inference at whatever, $60 billion a year. So $480 billion a year in revenue.

Which is like on a revenue payback basis would be like a one-year payback on a revenue basis, not a gross profit basis. Yeah, on a revenue basis. Yeah, yeah. And I've tried to use conservative numbers. People seem to think anthropic and opening I are both monetizing it $100 billion a gigawatt today. Yeah, yeah. Let's say they have a big research breakthrough, and they decide, wow, it is to our long-term advantage to go from eight gigs allocated to inference,

two gigs allocated to training, to eight gigs on training, and then your revenue just went from 480 to 120, and I think your annualized revenue, and I actually think they would do that. They would make that decision. Yeah. And this is just something that public markets are going to really have to get used to. Yeah. As you say, Open AI may be a different animal.

And I do think the realities, you know, everybody has these ideals about how they're going to manage their business. Then they go public. And the stock is volatile. And it really impacts employee morale, recruiting, retention. So I'd be surprised if they did such a dramatic cut. But a lot of the revenue is kind of under their control based on what checkpoint they release. Yeah. Where they price along this kind of Pareto curve.

and then how much they allocate between training and inference. So it's going to be meta and Google and these kind of internet companies. It was just, it was pretty smooth fundamentally, even if the stocks were volatile. Well, there was no massive tradeoff they had to make in terms of the cost or infrastructure to serve revenue side. Yeah. They were totally separate. A hundred percent. Yeah.

It's fascinating. So if you go back to Eric's point of it's all going to work, like I actually think that's a great point. Like I describe it differently. I've had this conversation with LPs a lot because every other. conversation that we have, it's probably the same for you, starts with how is this all going to go wrong? Yeah, it's like, it's a bubble. What's going to crash? And I'm like, oh, are the large models screwed or the lab screwed because of open source? And I'm like, this is all wrong. This is not an or thing. It's an and thing, right? This is an and thing. Frontier's going to work really well. And minus one models are going to work really well. Open source is going to work really well. There's going to be a bunch of application companies that work really well. The clouds are probably going to be fine. They're probably going to work really well. The five lab companies are probably going to do really well. Yeah, and Vindia is sent it. Is it the center of all of it?

Yes, yes. They're probably going to do pretty well. Yeah, the last 26 years have taught me not to bet against Jensen. Yeah, he's in a pretty good position here. I want to come back to that. The point that you made about training versus inference is an interesting one. It seems to me like the labs will decide to take all incremental profits and probably much more than their profits and invest them in training for a long period of time.

Would you think that's fair? Like this is very different than like the clouds, you know, because like the internet companies in the clouds, they just end up being supply demand driven and they generate tons of profit and they can still grow a certain amount. But they don't have some, maybe with the exception of meta,

like some big long-term bet that's like a multi-year payoff. Yeah, I think it's important to kind of be precise. For sure, I don't think they will generate free cash flow anytime soon. I think they're going to generate a lot of operating cash flow and then they'll use that to buy a lot of GPUs, XBUs, whatever we're going to call them.

Or maybe they subsidize heavily. We do know that that's happening at the labs. Subsidize what heavily? Their first-party products. So token consumption of their first-party products. Oh, yeah, yeah, yeah, yeah. They're doing all this research and they're spending a lot of data on compute.

And the first-party products are like a heavy subsidy products today, right? Yeah, so it's 8 gigs of inference and 2 gigs is for interdiscs. internal research, and then, you know, two gigs is actually training. Yeah, exactly. And, you know, including probably the inference that goes into post-training. Yeah, I don't, I think given the belief systems that they all seem to have about scaling laws, which continue to hold, I don't think any of them are going to be that focused on generating free cash flow. And you've seen, right, we saw Satya Blake.

Yes. And Satya really regrets that. I think. Yeah, yeah. You know, he kind of blinked. I think it was last year, you know, he gave that great interview for Davos and they asked him about all the CAPX. And he said, I know I'm good for my 80 billion. Right.

And I think they blinked a little. They slowed down. They regret that. And then Dario famously, he went on a podcast and he made, and he said, listen, some people are being super irresponsible with their spending. And it's a hard decision because if you don't spend enough, you can. lose a lot of share, but if you spend too much, you could go bankrupt. And like, those are both bad things, but bankruptcy is worse than losing share, so I'd rather be conservative. And he was conservative. And open AI was aggressive. And now opening eyes back in the game. And SpaceX was

aggressive. And SpaceX was aggressive. And so, you know, like, there are clear high ROIs on those independent of supply demand mismatches that are happening. Like, clearly that seems to be the right decision, short term and long term. Yeah, absolutely. I mean, we, we calculate, you know, Nebius and Corweave both gave some interesting disclosures, but you can kind of get to a nine to 10 month payback for Nebius because, you know, okay, you bring on a gig, it costs $50 billion, you can get an upfront payment for 50 to 60 percent of that for customers. Yeah.

So now, you know, you're talking about $25 or $30 billion, and then you can monetize it if you put it into the spot market. At a spot, spot paybacks are probably much faster than $9.00. or 10 bucks. Yeah, you got to assume like a smoothed out level like two, two bucks, three bucks, even with that. It's very high payback. Now you can get like five bucks or eight bucks. And then SpaceX, because they build these really big clusters. And I think at a really important point is they bring them on fast. Yes. They have an even faster payback and they can monetize it, you know, higher. I have tried to shift, you know, to think of pricing and, you know, per megawatt

rather than per GPU. Because it seems like where the world is. But like, like, Like, SpaceX, the payback feels well inside of that. Yes. And I just, in my career as an investor, there haven't been that many opportunities where you have companies that could deploy tens, hundreds of billions of dollars, and get sub one-year paybacks.

Yes. And it's kind of crazy. And then also, like we should also talk, particularly if you're buying Nvidia GPUs to a lesser extent TPUs, you can finance these. Yes. And there's a very sophisticated, you know. Yeah, it's very low cost of capital to finance them today.

Yeah, and everybody's, you know, worked up about, you know, circularity. And it's like, well, I don't know. I know a lot of smart people who work at Blackstone and KKR and Apollo. And they're the ones that are financing it. They're the ones you're financing it at a relatively low cost. At a relatively low cost. And I think one reason that's happening is useful lives just keep getting extended. and has these models get better and better and better,

and the ROI on token spend goes up. You know, the monetization rate per gigawak goes up. So, I mean, the true equity payback like might be way inside of a year. Yeah, exactly, exactly. Yeah, and look, there's a case you could make that the prices actually of all the stuff go up, which could make the supply side economics even more compelling, right?

Like, you know, so on the supply side, like, that's the dynamic today. Like, it just is what it is. Like, there's a ton of data points out there that paybacks are within a year. Yep. I think it's actually interesting to think about the demand side, too, because the knock would be, well, in all these cycles, you get some overbuild, and then that, you know, destroys the economics of the supply side. The demand side today, like, what do we monetize? Like, the monetization of these companies, which are doing, call it 180 billion of revenue. or something in that direction, is on the back of what, like 30 million actual heavy paying users,

like getting real value. I'm talking about like developers. I might take the under on 30 million, man. Yeah, actually, what we see inside our companies is, you know, obviously there's a power law in which companies are spending a lot on tokens. Like old banks are probably spending 1%. Very tech forward companies are spending high single digits. But if you actually look at the sort of.

the power law of what's happening of the actual engineers in those companies. The highest spending engineers are spending 10 or sometimes 100 X more than the median engineer. And so, yeah, your 30 million is probably way overstated. It might be sub 10. And so there's this question of like,

where are we at in diffusion? There's a one and a half billion knowledge workers. Like it feels like we're nowhere on the demand side and we're massively supply constraint. And what are, I'm just curious, across the A16Z portfolio, what are your best companies spending on tokens per month relative to human compensation? What rough range?

Oh, high single digits, some at 10%, like some of the very AI-native ones, like 10% plus. And so, you know, and then old economy companies are spending the ones that are probably doing a good job like 1%. So it feels to me like when I look at the supply demand characteristics, it's like supply stuff, people say is that sustainable. Well, like, when you pair it with the demand stuff, it feels, it feels, it feels, like, there could be things that disappoint us in terms of, like, diffusion into the real economy,

but it feels like over a 10-year stretch, like we're nowhere. Yeah, absolutely nowhere. And I just, my, so at a trade is, our internal token consumption has gone up 100x from the month of March, March through August, 100x, our token spend. and we just got access to Grockbot Enterprise

and with two people using it like it looks like a token spend my 10 or 20x in a month from August. Yes. Like I think, but it's actually extremely valuable. Like we have some heavy Grogbot users here and like it is very productive use.

Like this is not like wasteful tokens, but. Yeah, I was in and listen like I try. I try super hard. You know, I always, when I use AI, I just remember when my parents, like, I was trying to get them to shift to an iPhone and an iPad and like, you know, get them used to it. And like, you know, they did a good job.

I give them loads of credit. And, but, you know, I'm 50 years old, you know, like, how do you, David? 42. 42. And you see these like 23-year-old kids and just the way they use AI. They're just fluent and native in it. I just feel like maybe in a way that no matter how hard I try I will never be and I'm trying really hard.

But, you know, like we got Cloud Code. I, you know, I built some stuff, did some cool stuff. And in like, I don't know, three minutes of type creating GrockBots, I had much better versions of everything I created. You know, so I went on this Patrick O'Shaughnessy podcast like five months ago. And I said, you know, like I love having a podcast summarized. Everybody's like, how'd you do it? I was like, well, just use AI.

you do it. Yes, pretty something. It takes 10 seconds and Grockbot. Yes. It's amazing and it's so good. Yeah. And then, you know, a substack summarizer, an X summarizer, an X-Simimit tracker for topics and stocks. Yeah.

And like that, all of those would have taken me, I don't know, hours working with Claude Code. And they each took seven to 12 seconds with Grockbot. Yeah. Yeah. Yeah. So to me, Grockbot does feel like another, at least for me, like kind of chat GPT moment, because Claude, like, I could see the data, it was powerful. I did some really cool stuff with it. It was like empowering and this is neat.

You know, like family calendar apps, things like that. Yeah. But this is just 10 seconds and it's way better than what I was able to do. Yeah. Yeah. The cloud code thing, like, was obviously the shift in coding and, you know, our most sophisticated engineers, you know, we're doing whatever 20% of their code, you know, with AI to like, you know, whatever 90 plus percent. And so now I think everything you described in what you built with cloud code or codex is still kind of reactive in a way, right? Like it's still, you know, it's like summarizers, preparation.

It's all like knowledge enhancing, which is part of your job. but it's not actually doing the work for you. Yeah, and now you have a GROC bot that says, what are the recommended actions? Yes, exactly. Based on everything the other bots have learned today. Yeah. What recommendations do you have for me today?

And that for sure is like, and it was so easy to build. I now have it. I'm like horse racing all these, which is like I have Grockbot doing it, codex doing it, all the like action taking forward. Yeah, yeah. I want to know, make me better at my job, look at everything I do.

Give me recommended automations you can do. I have town doing it as well, which is one of our companies. Early stage we're very good at it. And we're like kind of on the bleeding edge of trying to do this stuff. Just wait until everyone does this stuff. And then when we actually click like yes, go,

just automate this. Yeah. It feels like that's sort of endless token. Yeah. But I do, we should acknowledge, like the history of financial markets, you know, dating kind of back to like the South Sea bubble is whenever you get this,

this transformational new technology. Actually, went on a podcast. I said I thought the sussie bubble was connected to like the invention of longitude and the ability of sale. Turns out it was not. It was just, it was kind of like a more of a tulip episode. But like every time you've had a real, you know,

profound new technology, you know, whether it's the automobile, the TV, the radio, internet, the PC, railroads, you always get, steel mills. You get a bubble because the markets get really excited. and they get ahead of themselves. Things get overvalued. That overvaluation leads to an overbuild. And then particularly if you're funding it with debt.

And even today, a majority of this is still being funded out of operating cash flow, which I think is really helpful. You know, debt funded buildouts, they demand immediate ROI, not an ROI in two years. Yeah. You can't be off in the time. You can't be off in the time. But I'm just more, you know, like I've, um, I, um, I, You know, I talked to jazz about how Watson wafers, jazz, I guess, and Patrick, Watson wafers are these fundamental constraints. And just the buildout is so big and we're so early that we are, it's like impacting the raw productive capacity of so many industries, you know.

Yeah. Now, you know, everybody in copper. There's like an AI thesis. And like, we're going to have to like think about it to like fill the, you know, if 10% of what. what we just talked about comes true. You know, we're in this acute shortage with, I don't know, several million people are driving a crazy global compute shortage. What happens when that's 500 million?

And, you know, how many copper mines do we need to build to, like, support this? Yeah, yeah, yeah. It's kind of a wild thought. And so, like, these fundamental constraints, I think, are slowing us down. And I think that's good. I actually think that's good for society. And I would now say rates and regulation, you know, real rates are going. Mountain. Yes. And it just is what it is, which makes sense because we're like investing a lot.

So it makes sense that real rates are going up. And then regulation, man, it's, it is like I'm kind of shocked at what's happening in America. We're in a really bad place. Yeah. And just, you know, I had this exchange with, with Schulte for Anthropic and Dario on X last weekend. And, you know, Dario said, hey, I don't think I'd been negative. You know, I've written. I've written two essays. One was positive. One was negative. So being 50% negative, and particularly when it's like a terrifying negative.

Like an existential. An existential negative. Everybody might be out of a job like that L.E.S. Or Yudkowski guy says, if we build it, everyone will die. And it's like, how about if we build it, like we're going to cure cancer? We're all going to live forever. I thought one of the best things Dario said was,

like, what we need to do is stop talking about curing cancer and actually cure cancer. actually cure cancer and actually make breakthroughs. But just somebody, like my favorite line in the Bible is the truth shall set you free. Yes. But the only person who can, the only group that can tell the AI industry's truth is the AI industry, they need to just start telling the truth. Hey, when we, okay, you're opposed to data centers. Well, you know what?

It's probably the best thing that has ever happened to working class Americans. Yeah, exactly. You know, it's like going to college. be significantly in PV negative now because you can go learn how to be an electrician, a plumber, an HVAC tech, and make ungodly amounts of money. Yeah. So this has been amazing for working class Americans. We now have a lot of data that particularly with behind the meter power generation,

when a data center goes in, it transforms a town like tax revenue. It doesn't double. It like 10 axes. And it is revitalizing all of the. like dying small towns all over America. And listen, we're getting much better at addressing the environmental stuff. Generally, they use natural gas,

which is a pretty clean fuel. The water consumption thing is totally debunked. The water is nothing. It's totally debunked. It's nothing. It's nothing. So these are like really, really, really good and they're having a really positive impact on the world.

That's without even considering things like curing cancer, but somebody needs to tell that story. And I think the problem with it now, it's like the burden of proof is on not curing cancer, but actually delivering some real tangible everyday American benefits beyond using chat, you know, or grok to like answer your questions or substitute for a search engine, right? It does feel like we're pretty close to that.

Yeah, it does. And by the way, like one of the things that I think has been correct but ineffective is this idea that we need to stay ahead of China. Like it's like, it is true. Like, I'm very much, like, I'm a patriot. Like, I believe that. But it's way too abstract. Yeah.

The abstract, for the average American. Nobody's worried about China invading America. Yeah, exactly. I'm pretty sure the Pacific Ocean is really big. Yeah, like affordability and, like, how is this going to change my life for the better of course, right? And so I think there's a pretty immediate impact you could feel. Like, my favorite is, you know, Loud County, Virginia, which is like the highest per capita

income, zip code in the U.S., or county in the U.S. and it has the highest density of data centers. Yeah. And they make a tremendous amount of tax revenue from data centers. Like we should do this everywhere. Yeah, well, it's actually very funny. Someone very opposed to data centers said, oh, you're for data centers.

I'd like to see them put in the highest income zip code and the highest income county. They're like, actually, the highest income zip code in America and the highest income county has the highest per capita concentration of data centers. So we've done that. And it worked out really well.

It worked out really well. Yeah, but you know, hey, don't bother me with the details. I'm on to my next talking points. That's good. That's good. And all those talking points, it's tragic. Like, there is an organized CCP-funded campaign. I think against data centers here in America, like, I think a lot of it gets laundered through TikTok.

And it's just tragic because the other thing that's happening is this is re-industrializing America. The combination of having the straight-of-form most closed, which is amazing for America. Yeah. You know, natural gas here is two or three bucks. It's now 25 bucks. in Europe and Asia or 20 bucks or whatever it is. And natural gas is an important input to the cost of electricity, which is an important input to almost all manufacturing processes.

And so we have a huge cost advantage for that basic input now. And you have that happening. And you have this kind of data center boom happening. We are reindustrializing America. And it's awesome. This is what everyone in both parties has wanted for a long time. Yeah, exactly. Like bring industry back.

Small towns that were left behind by the steel most closing. Well, data centers are bringing them back. Yeah. But somebody has to tell that truth. I mean, I tried to do it on every podcast, but like I'm just a dude. Yeah. And like your audience is the tech audience that already believe you're reaching the choir, if you will. But yeah, the story, the story.

Meta's probably doing the best job of telling that story, I would think. Yeah, it seems. You know, and I think one reason, it's really wired into meta's DNA. So one of the first things they started doing, as a public company. I remember if it was on their first journey's call, but Cheryl would run through, Cheryl Sandberg, would run through 10 or 15 very specific small businesses

that had started using META's advertising products and the impact it had on that business. You know, this cake bakery in Des Moines started, you know, worked with META. And, you know, it was two women who were single mothers working by themselves, and now they have 15 locations. They employ 50 people,

and this has been amazing for Des Moines and it's been transformative for them. And they would just run through that every time. And I do think the entire AI industry, like I'd love to see, you know, everybody, SpaceX, Anthropic, Open AI, Google, meta, say, hey, here are real businesses and real Americans,

and like either name the business or get permission to, if you can, name the American or anonymize it. This is a really positive thing it had on their life. Already very tangible, yeah. Yeah, same. NVIDIA, AMD, Broadcom, all of them. Yeah.

Just run through specifics because the truth shall set you free, but only if you tell it. Yeah, exactly. Yeah. So it seems more likely than given that fact pattern, if you go back to just the sort of macro situation that we're in, that we underbill on the supply side. Oh, yeah, for like through 28.

And by the way, like, there's no capacity available with all the forecast builds that will happen through 28, which are probably now going to be delayed, given the political dynamics they have. So everybody's worried about oversupply. I'm, like, more worried about... Massively undersupplied. Yeah, exactly. Which, okay, so then if that's the scenario, like, you could see a scenario where you see, you know, big price increases. Oh, yeah.

To access the intelligence. Yeah, well, which is the opposite direction of where everybody thinks this is going to go. Yeah, well, Dorcasch had a wild point. I forget what it was, but he was positing, I forget the... Like the cost of a token could go up 10x or something like that. Yes. Yes. Which is crazy, but like we do live in a supply demand world. Like, it's conceivable if the demand goes massively.

And by the way, the whole premise of this that's happening so far is that there's a massive amount of consumer or user surplus being generated. Yeah. So like, why do people select the frontier tokens when they could use the cheaper tokens to do most tasks? there's many reasons why but like the biggest one is because there's a tremendous amount of surplus even if you're using the frontier tokens.

Yeah, absolutely. And so yeah, what happens if there's like a massive supply shortage? Well, I think that would be the kind of funny, the consequence of like these like data center degrowthers may be like real

compute inequality where big companies and wealthy people can afford compute. And then, you know, two years from now, they'll be on about that. And it's like, well, that happened because of you. Yeah. You know, that happened because you wouldn't let us build data centers.

Yeah. And by the way, we've seen this, right? Like the path to a low-cost product delivered to consumers in a mass market is advertising. It takes a long time to build an advertising business. Yeah. As we've seen with all the, you know, consumer internet businesses that we've invested in over the years. And so there may be a disconnect in the period where you can't. to actually offer that.

Yeah. And that would be a terrible outcome. That would be a terrible outcome for the world. Nobody wants that. We need to build a lot of data siters. Yeah, exactly. Yeah. Like a compute inequality, like future.

That's not a good, that's not a good future for anyone, which is another reason open source is so important. And just one of the things, you know, I had a, Grock make me like a meme of that, like, three-headed dragon. And one of the heads is, like, kind of confused about, like, all of the really like stupid, bearish AI narratives. But people have this idea

that open source tokens are free. They're not. And it's like, it takes the exact same amount of compute. Yeah. All else equal to make an open source token has a, you know, frontier token for a comparably sized model.

Now, there's a lot of nuances there, but that's broadly true. It's just a question of what are the margins that are charged on top of that. And even then, the Kimmy license, something that I don't think a lot of people appreciate, is the Kimi license stipulates a 30% share of any revenue. Yeah. Yeah, yeah. So, like, Kimi has taken a 30% cut of all the revenue generated on its, and this is because it's open weights, not open source.

Yeah, exactly. Yeah. But it's also extremely token hungry, too, right? Oh, yeah. It's far, even we're talking on a token basis, but on a task basis, it's far more inefficient. Absolutely. So it's very costly. Yeah.

And I just always like, Jensen, he's a great patriot, great American. Like, we're so lucky to have, we're lucky to have him and Elon. Like, and I think like, you know, kind of when the history of the 21st century, 21st century is written, you know, there was like the Victorian age. I think this will be like the age of Elon and Jensen. Yeah. Because they have, they are fundamentally altering kind of like the fabric of human society and civilization with AI, SpaceX, making humanity, multi-planatory, Starlink, you know,

bringing low-cost internet access to the poorest communities in the world, which is amazing, which is, you know, something that people don't talk about, but it's like an amazing, you know, you talked about consumer surplus. That is an amazing surplus. There was never going to be an economic case to build internet access in those places because of the cost. Yeah. And now, the willingness to pay. And now you could. No, well, now it's there. And any incremental internet capacity, like, is not going to be built in a traditional sense on it. Yeah. It's going to come from space. And so that is a huge, that is a huge unlock. I agree. It's a good thing. But we're, you know, we're like, you know, we should, we should all be grateful for them because I do

think that, you know, they're making the future as exciting and inspiring as possible. Say we are in this supply crunch. It's so funny. Whenever I talk about SpaceX and it's, it's obviously near and dear to both our hearts, you know, I say like, first of all, the orbital data center stuff, It's not like big buildings in space. Like it's helpful to actually think of it. It's like the size of an airplane. Yeah, it's like a big rack. People are picturing like the Death Star.

Yeah, exactly. It's not that. Yeah. Floody around its base. That's not what it is at all. Yeah. It's a, you know, whatever, the size of an airplane, right? Yeah.

Yeah. It's like five of us standing together is kind of roughly the rack. And the airplane is like the wings. You have the solar wings. Yeah. And then you keep it in a sun-secretous orbit. So you have the. radiator that's always in the shadow of the rack. That's how you cool it. And it's like I can't,

it's very hard for me to engage. Now there's all these people on X and they're like, I am a physics PhD. And I, this is impossible. And actually there's there's a friend who's another investor who actually is a physics PhD who had many arguments with him. And he's like, I am a PhD and this is impossible. And then he goes to the SpaceX day and he talks to the SpaceX engineers he's like, well, I was wrong. And so like, if, let's say you're an astrophysics PhD, you're brilliant, you're hanging 100 IQ points on me.

Have you thought about this for an hour? Have you thought about it for 10 hours? Have you thought about for five hours? Because you have 10,000 of the world's smartest engineers at SpaceX who've thought about this each for hundreds, if not thousands of hours. and the sum of that, working with like very sophisticated, you know, engineering tools is it's a solved problem. And in their minds, it's dramatically simpler and easier than a Starlink satellite because the Starlink cast out of the phased rays and move around.

I think it's like, so, okay, so assume that you're right, I say it's like physics, there's not a physics reason why this can't work. cost-wise, it seems really imposing, but kind of the history of the Elon companies is the cost curve gets dramatically better. Like when we first invested in SpaceX, you know, Starlink, like, was not commercially available. And like, we had all these questions about how the economics would proceed over time,

the same on the launch side, the same with the Model 3. Like, I just have to think that that will get solved paired with the fact that we're going to have massive undersupply self-inflicted on Earth. It feels clear to me at a minimum it will be swing capacity. Yeah. And, you know, in the fullness of time,

maybe it will be larger. Well, no, it's really simple. Like, if we use 50, and it is the people, the question people should be asking about orbital compute, which is the one SpaceX is focused on, is Starship reusability.

Yes. Because the math is, like, let's just say it's 50 billion a gig, and let's just say 35 of that is IT. Yep. So that's the same. And maybe it grows a little, because it's going into space.

The rest is power, cooling, labor, all sorts of things that you don't need in space because you have the solar panel and the big radiator. And that's $15 billion, and it's probably inflationary here on Earth. Yeah. Because labor fundamentally feeds into that. We just talked about what's happening to, you know, electrician.

Yeah, comp, yeah. Yeah, electrician. Materials are all going to go off. Yeah, all of it. Yeah, we're going to have, yeah, We're going to run out of, you know, the copper bowls are, you know, focused on, like, copper shortages. Yeah, optics, you all of it. Yeah, so that $15 billion is inflationary.

And so what you have to compare it to is the cost of launch. And with Starship reusability, that goes to under a billion. So the economics just instantly flip. Now, you're always going to train on Earth. There will always be advantages to having, you know, GPUs right next to each other. Like, there are, you know, speed of all. light limitations are a real thing. Latency matters.

So data centers on Earth, they're not going anywhere. I think they're going to continue to be very, very valuable. But an increasing fraction of the world's compute is going to be in orbit. And, you know, Elon said that he and Jensen have co-designed a Ruben Rack. And it's going to launch in the fourth quarter of 27. Yeah. And let's just say, let's just say he's off by two quarters. Yeah.

I mean, that's still fun. 2008. Yeah, that's still okay. That's pretty soon. You know, as Brad Gersner says, like, nobody's really paying attention to this. And it's like kind of happening in plain sight. I mean, it kind of, to me, solves for something, you know, mid-segledger's billions today. Which, by the way, you know, is like, that's just like keeping share constant.

Yeah, exactly. You know, of like what's happening with coding. Not presuming to taking any share on GrogBod. Yeah, well, from 3 billion. And by the way, man, I would just, I'd probably take the over with Grockbot. I bet it's like changing by the day, just based on my own usage. And a number of people who are hitting their usage limits. And then you are starting to get from, you know, Grockbot like, hey, we here, servers are overloaded every once in a while.

And like, they have a lot of compute. So it's just like, okay, you don't want to debate orbital data centers. No problem. Well, like Starlink Mobile, like they have a pretty clear, credible plan for how that's going to work. And that, you know, wireless is, you know, call it another $900, $900 billion of revenue that they address. So, yeah, your mobile plus your broadband, whatever, it's called it like close to $2 trillion of a market. And then you have a really rapidly growing AI error base. Yeah, AI error.

You've got the cloud, you know, the sort of cloud business. Yeah. So I don't think, great, you're an orbital compute skeptic. No problem. It doesn't matter. Yeah, exactly. We don't even need to. We could just look at.

things that are happening today with terrestrial compute, with cursor, with grok, with Grokbot. By the way, I think X ads are, you know, we have telemetry. Yeah, they're also growing. You know, I would expect at some point you'll have like a Starlink Grokbot, ex-advertising bundle, you know, kind of one of the ways Google built their pod business as they bundled it with ads and like, hey, we're, you know, maybe you're bundling the ads with AI, but why not do that? Yeah.

Yeah, I actually like the AI. position that they're in because it's like heads you win tails you win in the sense that their first party business is growing very fast and they they caught up to the frontier like very quickly yeah um and so they've made the very aggressive compute investments to enable that first party work um and that's the kind of heads you win and like tails you win say they overbuilt their capacity for what they need for inference or training they have a sub six month payback on the compute side, you know, with like massive scarcity of supply. And so I think that's a really good setup.

And there was a bear case that, hey, okay, well, in a, in the open AI anthropic maximalist view where they're the only two companies and they're designing their own chips, then like, where what's the room for anyone else? Well, like, I don't think they're going to have a reusable starship and multiple spaceports anytime soon. And if the economics are of computer such that orbital is where it makes sense, increasingly going forward because starship should be deflationary, you know, terrestrial cooling, you know, power should be inflationary. Well, like, even in a world where they fumble the ball with their first party AI applications, like they do still have. Yeah, then they're a massive infrastructure business.

Yeah. Yeah, I'm so fired up about the Star Base, Louisiana. Oh, yeah. I can't wait to visit, man. So cool. Yes. I was reading about it last night, and yeah, it's sort of like, they now have the infrastructure for, you know, thousands of launches a year. Yeah.

And eventually, I think you'll see like these star bases in multiple places, multiple coasts all over the world. Yeah. Like, you know, at some point you'll probably see one somewhere in the Middle East. You'll see, you know, whatever European country is like the least bureaucratic at the time. You'll see one there, you know, you'll push, I think you'll see probably one in. You know, whether it's Japan, South Korea, who knows. Yeah, yeah, yeah. Yeah, that's pretty exciting.

Yeah. Yeah. Yeah, the capability to do, call it, you know, whatever, 5,000 launches a year. Like, that feels very futuristic. Yeah. I mean, it's wild. And I do think a distinction that, you know, SpaceX really tried to kind of hammer home during their IPO is there's a difference between reusability. And China, they did catch kind of a rocket using this.

it was actually kind of ironic. It was this kind of jury rigged system of kind of wires that had actually been suggested on the SpaceX subreddit. Yes. Like seven or eight or nine, no, no, it was before they landed the first falcon. So it's like more than 10 years ago. And like China's clearly paying close attention to SpaceX subreddit. But that's very different catching that thing from what they're trying to do with

Starship where, you know, the booster gets caught with the things and then it gets moved. and then the starship gets caught and then it gets stacked, it gets fueled, and just sent right back up. Yeah, two a day, two a day per pad. Like, those numbers add up pretty fast.

And they're, and I do think, I think they're engineering the pads for more than two a day. Yeah, I think that's a conservative. Yeah, yeah, yeah. Yeah. But I mean, yeah, what's the,

okay, so SpaceX, like, again, you and I talked a ton about SpaceX. What's like the most futuristic thing that you think about with SpaceX? Like the 10-year-killer. Okay, so you and I were at this conference together, and there was this whole debate about among a small group of public investors

of like what's going to be the first $10 trillion company. And I think what you said was, like, I have no idea, but I know which one's going to be the first $20 trillion company. Like, what's the most futuristic, like, product or market or technology think about SpaceX that you can think of? Look, I mean, this sounds crazy, but asteroid mining is going to be a very real thing. We're going to capture, you know, there's asteroid psyche.

It has more gold, silver, platinum, you know, every precious metal in it that exists in the Earth's crust. At some point, particularly with Starship, you will be, you know, and we may need that lunar base to make this happen. You'll be able to capture these asteroids. You'll bring them into a stable kind of geosynchronous orbit over some, you know, American-owned, at all in the middle of the Pacific you know no humans within whatever 50 miles you'll you know you can imagine like optimist robots you know um

yeah doing the work yeah doing the work and then you know delivery to earth is free and for sure some of it's going to burn up but I think that's going to happen and I always think um Jeff Bezos said something very interesting he said I think in the future, Earth is going to be zoned residential.

And, you know, somebody asked him, it was like 15 years ago. What do you mean by that? He's like, all heavy industry will take place in outer space. And then this addresses the pollution concerns. It addresses everything. You know, people always get, like, really worried about, oh, you know, we still be able to see the stars. And it's just like, I think it's hard for like the human mind to understand how big space is. Yeah.

How big outer space is. Yeah. You know, you don't have to worry so much about emissions up there. Yeah, yeah. So I think that is, that's probably the most futuristic thing. But in terms of an economic application, but it does, I mean,

I do think in the next few years, you're going to have a fleet of starships, land on Mars next few years. I mean, I don't know. Let's just say at the outside, this is eight years away. Yeah. They're going to land on Mars. I'm going to have like, you know,

Our little ramps are going to come out of the Pez dispenser, and it's going to be a modified starship, the Mars Colonial Transporter. And it's going to be wild. You're going to have Optimus robots holding American flags, like walk down, and then, you know, they're going to pull out a bunch of solar panels and batteries and racks of compute, and they're going to set all of that up. They'll be dropping Starlink, you know, and maybe the orbital mechanics don't allow

this. But I think, you know, they will figure out a way to have, you know, capacity. So just think how crazy it is to watch, like, the views from Pathfinder, you know, or, you know, whatever these different, you know, Mars. Yeah, rovers and stuff. Rovers are. And like, you know, 4K video through Optimus robots all over Mars. And then after that, there will be humans.

You can inhabit it. Yeah. Yeah. Yeah. Yeah. Yeah. That is crazy to think about. And that's going to be an amazing moment for America.

Yeah. I mean, think about the moon landing. Yeah. You know, this is a little bit bigger, yeah. Yeah. Yeah. So that seems cool. That's a good one.

That's a good. That's a good one. Yeah, there's not a lot of chatter about that one out there. Yeah, but I think it's highly likely to happen. Yeah. Yeah. So you mentioned Microsoft. Yeah.

And the bet that they made, which is like a little bit of, you know, like Apple's the extreme kind of bet against the future kind of bet they made. And like Microsoft is kind of a gradient of that. Yeah. Like, what's your outlook for their decisions? Well, I do think the world has gotten a lot friendlier for their strategy. You know, they clearly tried to make a frontier model. They failed. Yeah.

You know, Satya said, we're going to have our own models that are very competitive. Like, I think he said that 18 months ago, they don't have their own models that are competitive. But what you're seeing with, I think the future is an ensemble of models. You know, there's a Pareto curve. No one model's going to be. to be the best at everything. And I think the future for certainly, you know, kind of the global 1,000 biggest companies is you're going to take whatever the best open source model is, I think probably in the very near future, that's going to be an Nvidia model.

Yep. The labs making ASICs create very interesting incentives for each to get into each other's business. Incentives for Jensen. And everybody's, well, oh, in a world where open source wins, who funds the training? Well, the chip companies could fund the training. Yeah. It's trivial to do a $50 to $100 billion training run, you know, for Jensen. And maybe soon, I do wonder if this is kind of Google's, like, super long-term play. Like, they seem to, like, maybe have opted out of the frontier race for now. We're going to monetize our compute at high rates. And we're going to sell TPUs externally.

But that generates so much cash flow. And open source is getting closer and closer and closer to the frontier. and it just maybe the winner is ultimately just who has kind of the most cash flow to fund these big training runs. But I do think you're going to see American Open Source, led by Nvidia, get really close to the frontier. Like they paid that Pulside acquisition was made for a reason. Pulside actually had a lot of really good American open source talent. I think they're doing a lot of smart things. But that is really good for Microsoft.

And at some level, almost every. application software company. Because what you can do now is you can take a base model and Nematron to date has not had a lot of post-training. It's kind of been a good pre-trained model

that you can do with what you want. So if you take a really good pre-trained base model and then instead of sharing your own kind of enterprise context, that's truly your IP, that's truly the value

you know, of your company is like, you know, the context embedded in all of your data. And like sharing that with a frontier lab, you know, that may be hazardous for your financial health. Yeah, certainly with like the shift in the ZDR policy, like, yes. Yes. And so you take a really capable open source model and you do a lot of RL and supervised fine-tuning on your own data. So you own it and it's your model. Yeah. And then if intelligence is like a super important input into your business, you want to own and control your intelligence, its capabilities, is cost.

And then what we've seen from a lot of companies, and, you know, Grock Bot, my understanding is, you know, I think it's Gemini 3.7, Flash, Grock 4.6, and some opus. Yep. And what you, and behind a router. Yeah. And you will, and I'm sure Elon is very focused on having it all grogby.

Yeah, yeah, of course. But I think what you'll see these companies do is they'll have their own model on their data and it will work with one or two other frontier models. Not necessarily, but just checking each other. It'll be kind of transparent to the most frontier for planning and then have execution run by everything else that's lower cost. Yeah, absolutely. And so I think that feels like a very likely future to me.

And that is a much Microsoft-friendlyer future than one in which there's just only two dominant frontier models. And it certainly looks like there's going to be at least three with Grock. I do think you've got to give meta a lot of credit. They've done a great job. Yeah. And I mean, they were out of the game and they got back in the game. And it's just kind of amazing.

Who could have imagined a year ago, you know, when it was like Jim and I was ascendant. Yeah, exactly. That this is the scenario that we're in. The Jim and I wouldn't even be in the conversation. Yeah, they're not in a conversation. And Muse and Mehta would be significantly ahead of them from a capability perspective. So it's just, you know, this is kind of like, the highest stakes game of like corporate chess ever played.

And, you know, people, you know, some people have made bad moves. They've made good moves. You seem some people come out of the game. Others come back in. But a future where, that future where it's a, you know, I don't know if we're going to call it multimodal, a hybrid model. I don't know what terminology the world is going to settle on. But I think that's the future.

Yeah. And I'm actually surprised. I think the best broad instantiation of that. today outside of Grockbot, outside of cursor, outside of, you know, like Harvey's done some cool things. Yeah, yeah, yeah, yeah, where they've done it is actually just the fireworks Nexus product. Yeah, yeah, yeah, exactly. Yeah, you can choose your frontier model.

Let us take whatever open source model you want, RL it for you, for your data, for Goldman Sachs, for Morgan, for JPMorgan, for Fidelity, for A16C. You have all your own data. You control your intelligence. and we make it transparent behind a router. I think that is like a very plausible future. And that's clearly what Lynn from Fireworks. She was the first one to say it. And then Alex Carp and Satya, they both kind of like...

Yeah, they've taken their own version of it. Yeah. But, you know, Satya, as I say, specialized intelligence. Like, I think it's very plausible. But this stuff is really hard to do. Like, that sounds easy. I was... It sounds easy to describe, like the way I describe it to people is like

who gets to be the abstraction layer to the organization and the users with intel, like, of intelligence. It's like the most whatever, vied after space or position that you could imagine in business, like in the history of business. Yeah, for sure. Right?

I think it's like the answer is and again. Yeah, yes, and for sure. It's, yeah, who's the arbiter of intelligence for global enterprises and probably consumers? How's a retail analyst? And, you know, everybody kind of thinks running one of these big chains is easy and there's a lot into it. And it's like, well, it's really easy. I start an American retailer in any category because America is so big that's worth over $50 billion.

Almost any category. Yeah. All you have to be able to do is have a fleet of 1,000 stores in 50 different states that have very different climates, consumer preferences, you need to have them stocked with the right products at the right time for that region, at the right prices. They need to be staffed by friendly and knowledgeable employees

who don't steal from you. You turn over at 100% a year. Turn over at least 100% a year. The stores need to be clean and well lit. And if you can do that, press show, $50 billion. Yeah. And like, in the history of American business,

like, you can, I mean, it's more than one hand, but you don't have to go through many. It's really hard to do. And having that abstraction layer, having it work, having it seamless, is I think way harder to do than people think. And I do think, something I think is very interesting about Cursor. I love your opinion on this. It's like everybody else in the lab space, you know,

had this like we're creating a digital deity, you know, and AGI and ASI, like, we're, and the cursor guys were just like, we want to make great product. Yes, exactly. And in a strange way, of everybody at the frontier, probably cursor, and it was the most product focus. Yes. Yeah, I'd say, and now they're part of SpaceX,

but that suits Elon and his mindset really, really well. Yeah. Let's make it an engineering problem, you know, create the model factory, and then we need to have a really good, product. Yeah. You know, the, you know, the, the, the Tesla cars, they're amazing. I mean, it's, I don't know if you drive on, but it drives everywhere.

Yeah, yeah, yeah, yeah. But like, what cursor figured out is they're, they had, I would say, a similar in-state vision as what those other guys had. Yeah. It was just a different path to get there. And it's sort of like a practical meet the customer with what, with where they are, meet the technology where it is. And I think, you know, they'll sort of, they have already demonstrated that they kind of

leg their way up into autonomy from, from, for, you know, from, what, with where they're from that starting point. Coding is unique compared to everything else in knowledge work. This would be in support of the point that Microsoft is in a good position because it is verifiable and perfectly documented. And like nothing else in Enterprise is verifiable and perfectly documented. And so it will be messy. Like that leads you to a good, you know, bulkase for something like Microsoft,

that abstraction layer. If they execute, but it's really, really hard to make it really simple for, oh, you know, click my co-pilot, link to all my stuff, train a model on our data, convince me that you're not going to share it with anyone else, and then put it behind a router that's seamless for me and continuously upgrade that open source model. It's not just some middleware.

Like, it's very hard to do. Yeah. And by the way, they're going to be competing with not only the labs to be that abstraction layer, but Databricks. Oh, yeah. Palantier, the inference. the inference providers, the application companies, right? So, like, Harvey has done an incredible job of this.

Yeah. And, you know, like, legal has sort of in takeoff. And I think they can see the future of how to be that abstraction layer and do the work. But, like, legal is also unique because it's very documented. Yeah. And it's somewhat verifiable. We'll see tax. We'll see things like that.

But, like, the one and a half billion, the really appealing broad pie is going to be very messy to go get. Yeah. Although I do always think, and, you know, I think probably in their heart of hearts, Harvey and Lagora think, oh, if we solved this, we could be that obstructionally are for everyone. I think probably in their heart of hearts cognition things, something like that too. I think everybody thinks it. And by the way, there's like massive validation of the category. Yeah.

Because Kirkland and Ellis said, we're going to spend 500 million bucks to build this ourselves. Like, first of all, you know, like, good luck. That's going to be very hard. Yes. But that actually tells you that the pie is really big. Oh, huge. Yeah, it's massive. And that's, it's, and I'm sure they have a very smart head of AI,

but it's not like a $500,000 one-time build. That model has to be continuously updated, switching out the base model. Then all of that has to happen transparently. But I think you're going to have this huge collision between, you know, products like fireworks nexus, these legal agents, coding agents, big companies like Microsoft. Data bricks and Snowflake coming up. You know, for sure, you know, Salesforce, I think, is going to, you know, Salesforce and Workday and all these companies, this is like everybody's going to go after it.

It's just going to come down to who executes the best. And this is just, you know, who has the lowest costs? Yes, exactly. And it's going to be very hard, I think, over time unless you're, if you're not vertically vertically integrated, you have to be so. good to emerge as that abstraction. Yeah, yeah, yeah, to be the low-cost provider. Yeah, because, yeah, we're just simply not going to be the low-cost provider if you're not vertically integrated, if you don't own your own compute over the very long, long-term.

And, you know, it's another reason like I, you know, I increasingly look at these hyperscalers on EV to net PPNE. Yes. Because net PPD is compute and that is just what the market thinks you're going to monetize your fleet of compute at. And you can kind of look at them. and there's some pretty obvious inefficiencies to me. Yeah, yeah, yeah.

Yeah, kind of an AI version of price to book. Yeah, I like the price to book. Okay, that's good. It's okay, you mentioned Jensen. You know, I share your sentiment. Like, he's, like, carrying this industry forward. Like, tell me your thoughts on a video. So I think he's in a very, very good position

and his strategy of being vertically integrated, but horizontally open. And it's like, okay, like, let's just say, You know, let's say there's some accelerator that emerges that is really, really, really, really good. Almost certainly it will be better if it can plug into, and this is why, like, I know you have an accelerator investment. My number one thing is if you're a semiconductor CEO, the only thing you should ever say is thank you, Jensen. Thank you for creating this opportunity. Thank you.

How can we work with you? We want to enable you. sure, we're going to compete with you on the edges. Yeah. But, you know, my rule of thumb for accelerators, every 1% share today is probably worth $100 billion. Yes. So there's no need to go head on with Nvidia.

Yeah. Just pick a niche. Get your 1%. Make sure that, you know, he has nine chips. Yeah. You know, he's got multiple flavors of accelerators. He's got CPUs. He's got, you know, Ethernet switches.

He has two kinds of DPUs. You know, he's got. you know, we've gone from just scale out networking being a thing. We have scale up, scale out, scale across, now scale in. Yeah. So just try to find a way to plug into his ecosystem. By the way, this is not foreign. Like, his biggest customers all have competing products with various of those nine chips.

Yeah. And just try to find a way to plug in, but just be nice to him. Be nice. Be nice. It's all personal. Yeah. You know, and it's just like sometimes like, you know, you hear some. of these and it's like have you ever seen game tape of the Chicago Bulls when Jordan was is you know

game 50 of the season yeah and he's a little bored yeah and the Bulls are down because you know they're up eight games you know they're up eight games over the number two person in their conference and he's a little bored and then somebody somebody talks shit somebody who's kind of young decides I'm gonna talk shit to him because we're beating him and then he just looks and it's like it's the best that's my favorite yeah it's amazing yeah it's amazing yeah Yeah, we've all seen, you know, whatever the last dance. Just don't do that. Yeah, exactly.

You know, just like, hey, Michael, man, I'm so happy to be on the court with you. Like, that's, that's, that's, that's, that's, that's, that's, that's, that's, that's, that's, but the reason it's particularly important is because Jensen's data centers are financeable. Yes. Yes. And it goes back to that point. Like, let's say it's $50 billion. Um, for an invidia data center, you need. need a $15 billion equity check.

Yeah. Okay? You can finance the other $35 billion. Yeah. And it's not circular financing. I have a lot of respect for the people I have met from Blackstone and KKR and Apollo. Yeah. And they're underwriting each of those.

Yeah. And they finance it. And then there's a residual value guarantee, which as long as that residual value guarantee is less than the gross profit dollars he's getting from selling the chips into that data center, it's like essentially. it's super NPV positive with very little risk for him. And then he gets a revenue share. So if you're and his data centers are the most financiable.

Yes. And like let's just say a good case for probably TPUs are the second most financiable. It probably takes, I don't know, double the equity check at least. And then the rates on the rest of it are higher. Yeah, exactly. And so cost of capital is a huge advantage, and that's why you just want to be part of his ecosystem. And you can see he has all these chips, he is acquiring land power and shell companies now, matchmaking them with offtake agreements. I think one reason he's doing these RVGs is if he doesn't do them, it's kind of an anthropic and open-AI dominated world because they can pay the most for compute.

He can effectively help other people compete with anthropiates. and open AI. Yeah, and the same way that he stood up the neoclods in the first place. Yeah. It's just democratizing compute, which is good for the world. Again, I think he's a patriotic American.

His interests are aligned with that, though, with the patriotic American ones, right? Of course, yeah. Fragmentation, right. Yeah, fragmentation, no dominant AI. Yeah, exactly. Which is really good because he's like a, he is a ruthless competitor,

and it's awesome that his incentives around fragmentation of AI, fragmentation of models, and, you know, fragmentation of power, are completely aligned with what's good for America. And just going back to open source, just like, I just can't take it that people think that Jensen is like the world's biggest advocate for open source

and it's somehow a giant risk to his business. Yeah, exactly. No, it's great for his business. It's great for his business. Because it means that instead of, you know, having a 90% margin on top of a token made with an Nvidia GPU, maybe it's a 40% margin, so more of those tokens are going to be consumed,

which means you need more compute. Yeah, exactly. In a supply-constrained world. In a supply-constrained world. And, you know, let's just, what percentage of the world's supply has he locked up? 70, 80, somewhere in there. And then...

You're talking about fab. All of it. All of it. You know, it's just because he saw this coming before everybody else. Yeah, and all the system supply chain. Yeah, he's got the... five capacity locked up.

He's got DRAM capacity locked up. He's got NAN capacity. He's got laser capacity. He has

capacitor capacity. He has, you know, what you need to make the racks. And it's just like, you know, he used to say

if I go back, you know, 15 years, he'd say, listen, I'm making a two or three billion dollar bet

every two years and I'm moving really, really fast. Now he's making these multi-hundred billion dollars

bringing the supply chain alongside him. He's bringing the financing alongside him by kind of standardizing it, making it easy for the very smart people at Blackstone, KKR and Apollo, and Goldman Sachs, and Morgan Stanley, JPMorgan to finance. And, like, that is hard to compete with. Yeah.

And, you know, it is, we, my firm, trainees, we have a pretty big portfolio, private portfolio companies. They're semiconductors. And it's just, You know, Elon said a lot of people are going to learn a hard lesson in hardware. And like, I would just say I've learned a lot of hard lessons in semiconductor investing.

Like you can bet on the best team and you tape the chip out. You feel great. Okay, we've taped it out. And that's happening faster than ever, right? Yeah, it's happening faster than ever. You feel great about it. And we're getting really good with the emulation and the simulations. And you feel great about it.

And then, you know, you'll experience this. the chip comes back from the lab. Everybody you get a FaceTime from the CEO. They plug it in. Yeah. You know, and like, and then sometimes it doesn't work. Yeah. It's just like.

Yeah, this famously happened with Cerebrus twice, right? Yeah. And they've powered through and like they've done great job. I think the chip, I think each Cerebris chip worked, it just struggled to find product market fit. Yeah, yeah, yeah, fair. For the first two generations. The chip worked.

Yeah, yeah, fair. And they've done great with it. Yeah. Yeah. thing between you plug it in. It doesn't work at all. And it doesn't work at all. And then it's like, if it doesn't work at all, you might be back to the drawing

board and, hey, we need another, you know, hundreds of millions of dollars, billion dollars. And we've learned a lesson. It's going to work the next time two years from now. Yeah. Assuming you can get financing. Yeah. Yeah, assuming you can get financing. So it's, you know, semiconductors are hard, like the real world is hard.

Like hardware is hard. And what he is doing at the scale he is doing at and the speed and bringing all of this alongside him. Because, you know, the land of the power has to come. Yeah. You know, the entire supply chain has to come. The financing has to come. Yeah.

And so given that he's, you know, 70, 80%, whatever we want to say, you just want to plug into that ecosystem. Yeah. Yeah. Yeah, part of why Elon made the decision made, right? Yeah. Yeah, which I also think was like, like a very high ELO move.

Yeah, totally. So you've had everybody else try and build their own ASIC. Yeah. They've gone up on stage. Sometimes they say negative things about, you know, Jensen or Nvidia or take shots. I did think it was pretty smart, you know, the Halapeno team last night, and we should give credit where credit is due. Halapeno is the, I would say, the first good ASIC other than TPU or Traneum I have seen. from internal.

In what seems to be a pretty short amount of time. It is very impressive. It's impressive. We should give credit where credit is due. They do have a good team working on it. They have a good team, yeah. So they had a really good team. I think they had a lot of advantages.

And I do think if you're a lab and you have the model and you see the direction of research, that's a big advantage for designing your own chip. But then you go back to Nvidia and they work with everyone. Yes. And everybody, you know, keeps thinking it's going to really standardize. And if you look at the three big. you know, Chinese open source models, Deep Seek, Kimmy, Quinn,

they're kind of all evolving in very different ways. Yeah, yeah. And they can, you know, they can all run on, you know, more general purpose chip, a GPU, but you're going to need, if you want to specialize. Yeah, you're going to need general purposes at a minimum

for the types of evolution to see from that, yeah. So, like, I think he's, I'm very happy his incentives has a C.E. are perfectly aligned with what's good for America. Yes. So I just make sure your semiconductor guys, do not talk trash about Michael Jordan. Be nice to MJ. Be nice to MJ.

Be nice to MJ. Yeah, exactly. Yeah. And then it's like, you know, sometimes it's like, you know, you tug on Superman's cape and you get confident. Yeah. You know, you get confident and you start to talk a little bit of trash. You know, Superman, sometimes you just flies away. Yeah.

Like that's what happened to the TPU team. Yeah. Yeah. And, you know, Halapeno, they're tugging on Superman's cape a little bit. Yeah, we'll see. We'll see. And it is kind of amazing that, like, Halapeno did something that none of the big, like, this is as competitive of a chip as IFC. Yeah.

But again, it's just competitive with one of his eight or nine chips. Yeah, one of his nine. Yeah, of course. And it's- They'll continue to work closely together. Yeah. They'll continue to work closely together. So it's like, hey, that's great.

You did the one thing. well, to actually be competitive with him at the system level, you did another eight chips. Yeah, exactly. Yeah. And he is at, you know, Dylan at some of the analysis talks about how he's the bank of AI. He's like, he's the central bank of AI. He's the Federal Reserve of AI.

Yeah. And so I actually think it was really smart for Elon instead of like competing, you know, with somebody who is fully aligned. Yeah, fully aligned. Mm-hmm. And I think that history is going to judge that to be a way. decision. In a world that is so supply chain constrained, it's actually really hard to tell what true customer preferences are. Right. Because like you come out. Yeah, they'll take anything. Yeah,

this is how you know that like very old, whatever, the price is held up of H100 is very high. Yeah. And if you have a TSM allocation, you're going to be sold out. Yes. Particularly if you can get the DRAM to pair with it. You're going to be sold out. So it's actually kind of hard to infer true customer preferences. And I actually think one of the best ways you can, like see true customer preferences is the kind of deals they cut with chip companies. So broadly speaking, you know, the first deal

is where the chip company invests in a customer and you saw TPU and Traneum, Amazon and Google do that with Anthropic. And that was to their immense advantage because it really helped their businesses, I think help those chips really level up because you kind of need to use a chip. There's a cold start problem.

And in that scenario, as long as the dollars you invest or less in the gross profit, can't lose money. And then there's a scenario where you do the RVG, Blackstone finances it or whoever, Blackstone Apollo, KKR, Golden Sachs finances it. And as long as that RVG is actually less than your growth for profit. You can't lose money.

And you have upside probably through a revenue share on top of it. Then there are deals where you give warrants away, but they're tied to like a fixed price per million tokens. And as long as the performance of your chip kind of outruns the performance of your stock, you're going to do good in that situation. If you just give warrants away, it could be negative in PV

because the better this stock does, the more value that's captured by the person, yeah. Yeah. And so you can kind of look at that hierarchy of deals and infer something about true customer preferences. Yeah, that's interesting. Yeah. So in video it is pretty good deals.

Like, yeah, I mean, there's a reason that people I consider smart are investing in their deals. Yeah. see it. Gavin. Thank you. Fun. Always fun to hang out with you. Thanks, David. This is great, man. Thanks for listening to this episode of the A16Z podcast. If you like this episode, be sure to like, comment, subscribe, leave us a rating or review, and share it with your friends and family.

For more episodes, go to YouTube, Apple Podcast, and Spotify. Follow us on X at A16Z and subscribe to our Substack at A16Z.com. Thanks again for listening, and I'll see you in the next episode. As a reminder, the content here is for informational purposes only. Should not be taken as legal business, tax, or investment advice, or be used to evaluate any investment or security, and is not directed at any investors or potential investors in any A16Z fund. Please note that A16Z and its affiliates may also maintain investments in the companies discussed in this podcast.

For more details, including a link to our investments, please see A16Z.com forward slash disclosures.