Gavin: 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.

Host: What happens if there's like a massive supply shortage?

Gavin: 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.

Host: 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 re-industrializing America and it's awesome.

Gavin: Assume that you're right. There's not a physics reason why this can't work.

Host: 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 earth's crust.

Gavin: Every LP conversation that we have starts with like, how's this all going to go wrong?

Host: 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 trying to find someone to give you a bearish case, like to make your sentiment more negative. Have you found anybody?

Gavin: 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 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 in a quiet period, so maybe they've slowed down a little bit, but I do think the rest of the world has accelerated. OpenAI is clearly accelerated. Open source, I think, has accelerated more. And then I do think Grok, particularly after Grok 2, 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 can drown crossing a river that's on average 2 ft deep. And so there's not a lot of action at the index level,

Host: Right?

Gavin: 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.

Host: Yeah.

Host: Our friend Eric Vishria did a podcast with Patrick O'Shaughnessy and he said maybe everyone wins.

Gavin: Yeah. Anthropic wins, OpenAI wins, SpaceX wins, Meta wins. Google wins by selling a lot of TPUs. Open source wins, NeoClouds win, the inference clouds win on top of the NeoClouds.

Host: Applications win.

Gavin: Yeah. Probably maybe not all applications, but applications that I think execute well and navigate this. 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.

Host: Yes, definitely.

Gavin: You would rather do, yes.

Host: So you rebased and now you're comparable to OpenAI.

Gavin: Yeah. In terms of revenue added, like in terms of the definition, and now I think kind of revenue added.

Host: 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 OpenAI to release Astra.

Gavin: Yes.

Host: And then it's like the next...

Gavin: The next day.

Host: Here's Fable 5.1.

Gavin: Yes. Exactly.

Host: Magically and just happened to be available several hours after Astra.

Gavin: Yeah.

Host: So I think they're being thoughtful and heading into this IPO, and everyone is shooting at them.

Gavin: Yes. Everybody's shooting at them and they're in a quiet period so they can't really shoot back. And so there's a lot of gamesmanship, but I do think having OpenAI and Anthropic be public companies is going to be helpful for the market just because it's such a powerful force. And a lot of public investors, you know, you hear, "Oh, Sarah Friar said this at an all-hands meeting and it's on the cover of Wall Street Journal. Okay, we're going to put that into our model."

Host: Yeah. And 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?"

Gavin: Yeah.

Host: Because we're looking for people who are mission-aligned.

Gavin: Yeah. Mission, not mercenary. Yeah.

Host: 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 the equity goes to zero. Like I'm no expert, but I'm pretty sure on that.

Gavin: In that I do think they are...

Host: They're like the accidental enterprise company.

Gavin: Oh, for sure. Oh, yeah. They're kind of like the accidental everything.

Host: Enterprise is just a byproduct of like the mission, the objective at the end. Whereas I think OpenAI is a little more commercial, and obviously SpaceX 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,

Gavin: Which on a revenue payback basis would be like a one-year payback on a revenue basis, not a gross profit basis.

Host: Yeah. On a revenue basis. Yeah.

Gavin: And I've tried to use conservative numbers. You know, people seem to think Anthropic and OpenAI are both monetizing at $100 billion a gigawatt today.

Host: 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 8 gigs on training," and then your revenue just went from 480 to 120. And I actually think they would do that.

Gavin: Make that decision.

Host: Yeah. And this is just something that public markets are going to really have to get used to.

Gavin: Yeah.

Host: As you say, OpenAI may be a different animal. And I do think the realities, 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.

Gavin: Yeah.

Host: Where they price along this kind of Pareto curve and then how much they allocate between training and inference. So Meta and Google and these kind of internet companies, it was pretty smooth fundamentally even if the stocks were volatile.

Gavin: Well, there was no massive trade-off they had to make in terms of the cost or infrastructure to serve revenue side. Like they were totally separate.

Host: 100%.

Gavin: Yeah. It's fascinating. So if you go back to Eric's point of like it's all going to work, I actually think that's a great point. I describe it differently. I've had this conversation with LPs a lot because every LP conversation that we have, it's probably the same for you, starts with like, "How's this all going to go wrong?"

Host: And it's like what's going to crash, and I'm...

Speaker A: Like, this is... oh, are the large models screwed, or the labs screwed because of open source? And I'm like, this is all wrong. Like, this is not an "OR" thing, it's an "AND" thing, right? Frontier is going to work really well, N 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. Like, the clouds are probably going to be fine; they're probably going to work really well.

Speaker B: Like the five lab companies are probably going to do really well.

Speaker A: Yeah. And Nvidia is at the center of all of it.

Speaker B: Yes. Yes. They're probably going to do pretty well.

Speaker A: Yeah. The last 26 years have taught me not to bet against Jensen.

Speaker B: 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 the clouds, you know, cuz the internet companies and 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—maybe with the exception of Meta—some big long-term bet that's like a multi-year payoff.

Speaker A: Yeah, I think it's important to kind of be precise. I for sure 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, you know, GPUs, XPUs, whatever we're going to call them. Or maybe they subsidize heavily. Like we do know that that's happening at the labs.

Speaker B: Subsidize what heavily?

Speaker A: Their first-party products. So token consumption of their first-party products. So like they're doing all this research and they're spending a lot on data, on compute...

Speaker B: And the first products that are like a heavy subsidy products today, right?

Speaker A: Yeah. So it's 8 gigs of inference, and 2 gigs is for internal research, and then you know 2 gigs is actually training.

Speaker B: Yeah. Exactly.

Speaker A: And you know, including probably the inference that goes into post-training.

Speaker B: Yeah. 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 blink.

Speaker A: Yes.

Speaker B: And Satya really regrets that, I think.

Speaker A: Yeah. Yeah. 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 capex and he said, "I know I'm good for my 80 billion."

Speaker B: Right.

Speaker A: And I think they blinked a little, they slowed down, they regret that. And then Dario famously went on a podcast 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 could lose a lot of shares. But if you spend too much, you could go bankrupt. And those are both bad things, but bankruptcy is worse than losing shares. So I'd rather be conservative." And he was conservative. And OpenAI was aggressive. And now OpenAI is back in the game.

Speaker B: And SpaceX was aggressive.

Speaker A: And SpaceX was aggressive.

Speaker B: 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.

Speaker A: Yeah, absolutely. I mean, we calculate... you know, Nebius and CoreWeave both gave some interesting disclosures, but you can kind of get to a 9 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% of that from customers.

Speaker B: So now you're talking about 25 or 30 billion, and then you can monetize it if you put it into the spot market.

Speaker A: The spot. Yeah. Spot paybacks are probably much faster than 9 or 10 months.

Speaker B: Yeah, you got to assume like a smoothed-out level, like two bucks, three bucks, even with that. It's very, very... Now you can get like five bucks or eight bucks.

Speaker A: And then SpaceX, cuz they build these really big clusters and I think a really important point is they bring them on fast.

Speaker B: Yes.

Speaker A: They have an even faster payback and they can monetize at, you know, higher. I have tried to shift, you know, to think of pricing per megawatt rather than per GPU, because it seems like it's where the world is.

Speaker B: Where the world is. But like SpaceX, the payback feels well inside of that.

Speaker A: Yes.

Speaker B: 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.

Speaker A: Yes. And it's kind of crazy. And then also we should talk, particularly if you're buying Nvidia GPUs, to a lesser extent TPUs, you can finance these.

Speaker B: Yes.

Speaker A: And there's a very sophisticated, you know...

Speaker B: Yeah, very low cost of capital to finance them today.

Speaker A: Yeah. And everybody's worked up about 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 who are financing it at a relatively low cost. And I think one reason that's happening is useful lives just keep getting extended. And as these models get better and better, and the ROI on token spend goes up, the monetization rate per gigawatt goes up. So the true equity payback might be way inside of a year.

Speaker B: Yeah. Exactly. Exactly. 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? So on the supply side, that's the dynamic today. It just is what it is. There's a ton of data points out there that paybacks are within a year.

Speaker A: 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 destroys the economics of the supply side." The demand side today: the monetization of these companies, which are doing call it 80 billion of revenue or something in that direction, is on the back of what, like 30 million actual heavy paying users getting real value. I'm talking about like developers...

Speaker B: I might take the under on 30 million.

Speaker A: So call it... yeah, actually what we see inside our companies is 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 power law of what's happening of the actual engineers in those companies, the highest spending engineers are spending 10 or sometimes 100x 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 where are we at in diffusion? There's one and a half billion knowledge workers. Like, it feels like we're nowhere on the demand side and we're massively supply constrained.

Speaker B: And 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?

Speaker A: Oh, high single digits, some at 10%, like some of the very AI-native ones like 10% plus. 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...

Speaker A: stuff, I it feels it feels specific. 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.

Speaker B: Yeah. Absolutely nowhere. And I just 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 Grokbot Enterprise and with two people using it, it looks like token spend might 10 or 20x in a month.

Speaker A: From August?

Speaker B: Yes.

Speaker B: But it's actually extremely valuable. Like we have some heavy Grokbot users here and like it is very productive use. Like this is not like wasteful tokens,

Speaker A: yeah, and listen, like I try super hard. 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 get them used to it and they did a good job. I give them loads of credit. But you know I'm 50 years old, like how old are you David?

David: 42.

Speaker A: 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, like we got Claude Code, I built some stuff, did some cool stuff, and in like I don't know, three minutes of creating Grok bots, I had much better versions of everything I created. So I went on this Patrick O'Shaughnessy podcast like 5 months ago and I said like I love having a podcast summarizer. Everybody's like, "How'd you do it?" I was like, "Well, just use AI and do it."

David: Yes. Pretty simple.

Speaker A: It takes 10 seconds and Grokbot.

David: Yes.

Speaker A: It's amazing and it's so good. And then, a Substack summarizer, an X summarizer, an X sentiment tracker for topics and stocks.

David: Yeah. And all of those would have taken me I don't know hours working with Claude Code and they each took 7 to 12 seconds with Grokbot and it's better.

Speaker A: So to me Grokbot does feel like another at least for me like kind of ChatGPT moment because Claude Code

David: like I can see in the data was it was powerful. I did some really cool stuff with it that was like empowering and this is neat. You know, like family calendar apps, things like that. But this is just 10 seconds and it's way better than what I was able to do.

Speaker A: Yeah. Yeah, the Claude Code thing was obviously the shift in coding and our most sophisticated engineers were doing whatever 20% of their code with AI to like whatever 90 plus% and so now I think everything you described in what you built with Claude Code or Codex is still kind of reactive in a way right like it's still 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.

David: Yeah. And now you have a Grok bot that says, "What are the recommended actions?" Yes. Exactly. Based on everything the other bots have learned today.

Speaker A: Yeah. What recommendations do you have for me today? And that for sure is like

David: and it was so easy to build. I now have it. So I'm horse racing all these which is like I have Grokbot doing it, Codex doing it. All the like action taking for just 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 very good at it.

Speaker A: but and we're like kind of on the bleeding edge of trying to do this stuff. Just wait till everyone does this stuff and then and then when we actually click like yes go just automate this. It feels like that's sort of endless token

David: and but we should acknowledge the history of financial markets dating kind of back to like the South Sea bubble is whenever you get this transformational new technology. I actually went on a podcast, I said I thought the South Sea bubble was connected to like the invention of longitude and the ability to sail. Turns out it was not. It was just kind of like a more of a tulip episode. But like every time you've had a real profound new technology, whether it's the automobile, the TV, the radio, internet, the PC, railroads, steel mills, you get a bubble because the markets get really excited

Speaker A: 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. Debt funded buildouts demand immediate ROI not an ROI in three years.

David: Yeah. You can't be off on the time, but I'm just more, you know, I talked to Jazz and Patrick about how Watson wafers are these fundamental constraints and just that the buildout is so big and we're so early that it's like impacting the raw productive capacity of so many industries. Everybody in copper, there's like an AI thesis and we're going to have to think about it to fill the If 10% of what we just talked about comes true, we're in this acute shortage with several million people are driving a crazy global compute shortage. What happens when that's 500 million? And how many copper mines do we need to build to support this?

Speaker A: Yeah, it's kind of a wild thought. And so these fundamental constraints, I think, are slowing us down.

David: And I think that's good. I actually think that's good for society. And I would now say rates and regulation, real rates are going up. And it just is what it is, which makes sense because we're investing a lot. So it makes sense that real rates are going up.

David: And then regulation, man. It is like I'm kind of shocked at what's happening in America.

Speaker A: We're in a really bad place.

David: Yeah. And I had this exchange with from Anthropic and Dario on X last weekend. Dario said, "Hey, I don't think I've been negative. I've written two essays. One was positive, one was negative." So being 50% negative and particularly when it's like a terrifying negative

Speaker A: like an existential

David: an existential negative everybody might be out of a job like that Eliezer Yudkowsky 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.

Speaker A: Actually cure cancer and actually make breakthroughs like

David: but my favorite line in the Bible is the truth shall set you free. Yes. But the only group that can tell the AI industry's truth is the AI industry. They need to just start telling the truth. Hey, 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.

Speaker A: Yeah, exactly.

David: You know, it's like going to college might be significantly NPV negative now because you can go learn how to be an electrician, a plumber, an HVAC tech, and make ungodly amounts of money. 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. Tax revenue doesn't double. It like 10xes and it is revitalizing all of these dying small towns all over America. And listen, we're getting much better at addressing the

Speaker A: ...environmental stuff. Generally, they use natural gas, which is a pretty clean fuel.

Speaker B: The water consumption thing is totally debunked. It's totally debunked. Yeah.

Speaker A: 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.

Speaker B: It's now and I think the problem with it now is 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...

Speaker A: ...or substitute for a search engine, right? It does feel like we're pretty close to that.

Speaker B: Um 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.

Speaker A: 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. Too abstract for the average American.

Speaker B: Like nobody's worried about China invading America.

Speaker A: Yeah. Exactly. Like the ocean is really big.

Speaker B: Yeah. Like affordability and like how is this going to change my life for the better or worse, right? And so...

Speaker A: I think there's a pretty immediate impact you could feel. Like my favorite is, you know, Loudoun County, Virginia, which is like the highest per capita income zip code in the US or county in the US...

Speaker B: ...and it has the highest density of data centers.

Speaker A: Yeah. And they make a tremendous amount of tax revenue from data centers. Like we should do this everywhere.

Speaker B: Yeah. It was 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, you know, income county." And 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...

Speaker A: ...and it worked out really well.

Speaker B: Yeah. But, you know, hey, don't bother me with the details. I'm on to my next talking point.

Speaker A: That's good. That's good.

Speaker B: 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 Strait of Hormuz closed which is amazing for America. You know natural gas here is two or three bucks.

Speaker A: It's now 25 bucks in Europe and Asia or 20 bucks or whatever it is.

Speaker B: 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.

Speaker A: And you have that happening and you have this kind of data center boom happening. We are re-industrializing America. And it's awesome. This is what everyone in both parties has wanted for a long time.

Speaker B: Yeah. Exactly.

Speaker A: Like bring industry back. Small towns that were left behind by the steel mills closing—well, data centers are bringing them back.

Speaker B: Yeah. But somebody has to tell that truth. I mean, I try to do it on every podcast, but like I'm just a dude.

Speaker A: Yeah. And like your audience is the tech audience that already believes you—you're preaching to the choir, if you will. But yeah, the story... Meta is probably doing the best job of telling that story, I would think.

Speaker B: Yeah, it seems.

Speaker A: 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 don't remember if it was on their first earnings call, but Sheryl would run through—Sheryl 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.

Speaker B: Yeah.

Speaker A: 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.

Speaker B: Yeah.

Speaker A: And this has been amazing for Des Moines and it's been transformative for them.

Speaker B: Yeah.

Speaker A: And they would just run through that every time. And I do think the entire AI industry—like I'd love to see everybody, SpaceX, Anthropic, OpenAI, 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 did, it had on their life."

Speaker B: Already very tangible. Yeah.

Speaker A: Yeah. Same. Nvidia, AMD, Broadcom, all of them.

Speaker B: Yeah. Just run through specifics because the truth will set you free, but only if you tell it.

Speaker A: Yeah. Exactly. 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 underbuild on the supply side.

Speaker B: 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, um...

Speaker A: Everybody's worried about oversupply. I'm like more worried about...

Speaker B: ...massive massively undersupply. Yeah, exactly. Which, which... Okay. So, then if that's the scenario, like you could see a scenario where you see, you know, big price increases actually to access the intelligence. Yeah.

Speaker A: Which is the opposite direction of where everybody thinks this is going to go.

Speaker B: Yeah. Well, Dorcash had a wild point. I forget what it was, but he was positing...

Speaker A: ...like the cost of a token could go up 10x or something like that.

Speaker B: Yes. Yeah, which is crazy, but like we do live in a supply demand world.

Speaker A: 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, right? 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, right?

Speaker B: Absolutely. And so yeah, what happens if there's like a massive supply shortage? Well, I think that would be the, you know, kind of funny the consequence of like the 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."

Speaker A: You know that happened because you wouldn't let us build data centers.

Speaker B: 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.

Speaker A: Yeah.

Speaker B: 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 actually offer that.

Speaker A: Yeah.

Speaker B: And that would be a terrible outcome.

Speaker A: That'd be a terrible outcome for the world. Nobody wants that. So we need to build a lot of data centers.

Speaker B: Yeah. Exactly. Exactly. Yeah. Like a compute inequality future—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 Grok 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...

Speaker A: ...and it's like it takes the exact same amount of compute.

Speaker B: Yeah.

Speaker A: All else equal to make an open source token as a, you know, frontier token for a comparably sized model. Now there's a lot of nuances there, but that's broadly true.

Speaker B: It's just a question of what are the...

Speaker A: 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 Kimmy license stipulates a 30% share of any revenue.

Speaker B: Yeah. Yeah. Yeah.

Speaker A: So like Kimmy has taken a 30% cut of all the revenue generated on its and this is because it's open weights, not open source.

Speaker B: Yeah. Exactly. Yeah.

Speaker A: Yeah. But it's also extremely token hungry too, right? So it's more it's it's far even we're talking on a token basis, but on a task basis, it's far more inefficient. So it's very costly.

Speaker B: Yeah. And I just always like Jensen, he's a great patriot, great American. Like we're so lucky to have him and Elon. And I think 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.

Speaker A: Yeah, because they are fundamentally altering kind of like the fabric of human society and civilization with AI, SpaceX making humanity multiplanetary, Starlink 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 talked about consumer surplus—that is an amazing surplus.

Speaker B: There was never going to be an economic case to build internet access in those places because of the cost.

Speaker A: And the willingness to pay, and now you could.

Speaker B: Without—and any incremental internet capacity is not going to be built in a traditional sense on Earth. It's going to come from space and so like that is a huge unlock. I agree.

Speaker A: It's a good thing, but we should all be grateful for them because I do think that 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 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's like the size of an airplane.

Speaker B: People are picturing like the Death Star or the Pentagon floating around in space. That's not what it is at all.

Speaker A: Yeah. It's you know whatever the size of an airplane, right? Rack of 72 whatever chips.

Speaker B: Yeah. It's like five of us standing together is kind of roughly.

Speaker A: Is like the wings, solar wings.

Speaker B: Yeah.

Speaker A: And then you keep it in a sun-synchronous orbit.

Speaker B: So you have the radiator always in the shadow of the rack.

Speaker A: That's how you cool it. And it's very hard for me to engage. You know, there's all these people on X and they're like, "I am a physics PhD and this is impossible." And actually 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 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 are 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? Cuz 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 engineering tools is it's a solved problem. And in their minds, it's dramatically simpler and easier.

Speaker B: Yeah.

Speaker A: Than a Starlink satellite cuz a Starlink has to have the phased arrays and move around.

Speaker B: So, assume that you're right. There's not a physics reason why this can't work. Costwise, 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, Starlink was not commercially available and 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 under supply self-inflicted on Earth.

Speaker A: It feels clear to me at a minimum it will be swing capacity.

Speaker B: Yeah.

Speaker A: And you know in the fullness of time maybe it will be larger.

Speaker B: Well no it's really simple. The question people should be asking about orbital compute, which is the one SpaceX is focused on, is Starship reusability.

Speaker A: 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 call that 15 billion, and that's probably inflationary here on Earth.

Speaker B: Yeah. Because labor fundamentally feeds into that. We just talked about what's happening to electrician compensation.

Speaker A: Materials are all going to go.

Speaker B: Yeah. All of it. The copper bulls are focused on copper shortages. All of it.

Speaker A: 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 GPUs right next to each other. Speed of 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 Reuben rack.

Speaker B: And they're it's gonna launch in the fourth quarter of 27.

Speaker A: Yeah.

Speaker B: And let's just say he's off by two quarters.

Speaker A: Yeah.

Speaker B: I mean, that's 2028.

Speaker A: Yeah. That's still okay. That's pretty soon.

Speaker B: That, you know, as Brad Gerstner says, like nobody's really paying attention to this and it's like kind of happening in plain sight. And it kind of to me solves for something, you know, mids single digit billions today.

Speaker A: Which by the way, is like that's just like keeping share constant.

Speaker B: Yeah. Exactly.

Speaker A: You know, of like what's happening with.

Speaker B: Not presumably taking any share on Grokbot.

Speaker A: Yeah. From three billion. And by the way, man, I'd probably take the over with Grokbot. Yeah.

Speaker B: I bet it's like.

Speaker A: Changing by the day just based on my own usage and the number of people who are hitting their usage limits. And then you are starting to get from Grokbot like "hey, servers are overloaded" every once in a while, and they have a lot of compute. So it's just like, okay, you don't want to debate orbital data centers.

Speaker B: No problem. Well like Starlink mobile, they have a pretty clear credible plan.

Speaker A: For how that's going to work and wireless is another 800 to 900 billion of revenue that they address. So your mobile plus your broadband, call it close to a 2 trillion dollar market.

Speaker B: And then you have a really rapidly growing AI ARR base.

Speaker A: Yeah. AI ARR, you've got the sort of the cloud business.

Speaker B: Yeah. So great, you're an orbital compute skeptic, no problem. It doesn't matter.

Speaker A: Yeah. Exactly.

Speaker B: 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.

Speaker A: They're also growing.

Speaker B: You know, I would expect at...

Speaker A: At some point you'll have like a Starlink, Grok bot, X advertising bundle. You know, kind of one of the ways Google built their cloud business as they bundled it with ads and like, hey, maybe you're bundling the ads with AI, but why not do that?

Speaker B: 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 caught up to the frontier like very quickly. Yeah.

Speaker A: And so they've made the very aggressive compute investments to enable that first party work.

Speaker B: 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 very compelling sub six month payback on the compute side, you know with like massive scarcity supply, and so I think that's a really good setup.

Speaker A: And there was a bare case that hey okay well in the OpenAI Anthropic maximalist view where they're the only two companies and they're designing their own chips then like 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 of compute are such that orbital is where it makes sense increasingly going forward because Starship should be deflationary, you know, terrestrial cooling, power should be inflationary. Well, even in a world where they fumble the ball with their first party AI applications, like they do still have...

Speaker B: They're a massive infrastructure business.

Speaker A: Yeah. Yeah. I'm so fired up about the Starbase Louisiana.

Speaker B: Oh, yeah.

Speaker A: I can't wait to visit, man.

Speaker B: So cool. Yes.

Speaker A: 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.

Speaker B: Yeah. And eventually I think you will see like these star bases in multiple places, multiple coasts all over the world.

Speaker A: Yeah.

Speaker B: 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, I think you'll see probably one in, you know, whether it's Japan, South Korea, who knows?

Speaker A: Yeah. Yeah. Yeah. Yeah. Yeah. It's pretty exciting.

Speaker B: Yeah.

Speaker A: The capability to do to call it, you know, whatever 5,000 launches a year, like that feels very futuristic.

Speaker B: Yeah. I mean, it's wild. And I do think a distinction that 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 juryrigged system of kind of wires. Yeah.

Speaker A: That had actually been suggested on the SpaceX subreddit.

Speaker B: Yes.

Speaker A: Like seven or eight or—no no it was before they landed the first Falcon. So it's like more than 10 years ago.

Speaker B: And like China's clearly paying close attention to the 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. Yeah. Two a day. Two a day per pad.

Speaker A: Like those numbers add up pretty fast.

Speaker B: And I do think they're engineering the pads for more than two a day if I...

Speaker A: Yeah. I think that's a conservative assumption. Yeah.

Speaker B: Yeah. Um but I mean...

Speaker A: Yeah. What's the—Okay, so SpaceX, like again, you and I have talked a ton about SpaceX. What's like the most futuristic thing that you think about with SpaceX? Like the 10-year—Okay, so you and I were at this conference together and there was this whole debate 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 dollar company. So like what's the most futuristic like product or market or technology thing about SpaceX that you can think of?

Speaker B: 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, 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 geocynchronous orbit over some, you know, American-owned atoll in the middle of the Pacific. No humans within whatever 50 miles. You can imagine like Optimus robots doing the work. Yeah.

Speaker A: Yeah. Doing the work. And then, you know, delivery to Earth is free and for sure some of it's going to burn up.

Speaker B: But I think that's going to happen. And I always think Jeff Bezos said something very interesting. He said, "I think in the future Earth is going to be zoned residential." And somebody asked him, this is 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.

Speaker A: You know, people always get like really worried about, oh, you know, we still be able to see the stars.

Speaker B: And it's just like I think it's hard for like the human mind to understand how big space is, how big outer space is.

Speaker A: We don't have to worry so much about emissions up there. Yeah.

Speaker B: Yeah. Yeah. So I think that is probably the most futuristic thing 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.

Speaker A: Yeah. They're going to land on Mars. Going to have like, you know, a little ramp's 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 Starlinks, you know, and maybe the orbital mechanics don't allow this, but I think, you know, they will figure out a way to have capacity. So, just think how crazy it is to watch like the views from Pathfinder...

Speaker B: Or, you know, whatever these different Mars...

Speaker A: Rovers and stuff.

Speaker B: Rovers are and like, you know, 4K video through Optimus robots all over Mars and then after that there will be humans...

Speaker A: Who can inhabit it. Yeah. Yeah. Yeah. That is crazy to think about.

Speaker B: And that's going to be an amazing moment for America.

Speaker A: Yeah.

Speaker B: Oh, I mean, think about the moon landing.

Speaker A: This is a little bit bigger. Yeah.

Speaker B: Yeah.

Speaker A: Yeah.

Speaker B: Um, so that seems cool.

Speaker A: That's a good one. That's a good one. Yeah. Not a lot of chatter about that one out there. Yeah. But I think it's highly likely to happen.

Speaker A: Yeah. Yeah. Yeah. So, you mentioned Microsoft.

Speaker B: 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.

Speaker A: Yeah.

Speaker B: Like what's your outlook for their decisions?

Speaker A: 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.

Speaker B: Yeah.

Speaker A: 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...

Speaker A: ...of models. You know, there's a Pareto curve. No one model is going to be the best at everything. And I think the future for certainly, you know, kind of the global, you know, 10,000 biggest companies is you're going to take whatever the best open source model is, I think probably in the very near near future that's going to be an NVIDIA model.

Speaker B: Yep. The labs making ASICs create very interesting incentives for to get into each other's business...

Speaker A: ...incentives for Jensen. And everybody's, "Well, oh, in a world where open source wins, who funds the training?" Well, this—the chip companies could fund the training. Yeah, it's trivial to do a $50 to $100 billion training run, uh, you know, for Jensen.

Speaker B: 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 may be 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 Poolside acquisition was made for a reason. Poolside actually had a lot of really good American open source talent. They're doing a lot of...

Speaker A: ...and behind a router. And you will, um... And I'm sure Elon is very focused on having it all grow as soon as possible.

Speaker B: Yeah. Yeah. Of course. Um, 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. Um, not necessarily, but just, you know, checking each other. It'll be kind of transparent to you: the most frontier for planning, and then have execution run by everything else that's lower costed. Yeah, absolutely. And so I think that feels like a very likely future to me.

Speaker A: And that is a much Microsoft-friendlier 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 Grok. I do think you got to give Meta a lot of credit.

Speaker B: They've done a great job.

Speaker A: 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 Gemini was ascendant exactly, that this is the scenario...

Speaker B: ...Gemini wouldn't even be in the conversation, and Muse and Meta would be significantly ahead of them from a capability perspective.

Speaker A: Um, so it's just, you know, this is kind of like the highest stakes game of like corporate chess ever played. And, 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. Um, but a future where—that future where it's a, you know, I don't know if we're going to call it multi-model, a hybrid model, you—I don't know what terminology the world is going to settle on, but I think that's the future.

Speaker B: Yeah.

Speaker A: And I'm actually surprised. I think the best broad instantiation of that today outside of Grokbot, outside of Cursor, outside of, you know, like Harvey's done some cool things with that, where they've done it is actually just the Fireworks Nexus product.

Speaker B: Yeah.

Speaker A: Where you can, 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 Stanley, for JP Morgan, for Fidelity, for A16Z. You have all your own data. You control your intelligence and we make it transparent behind a router.

Speaker B: Yeah, I think that is like a very plausible future. And that's clearly what, um, Lin from Fireworks, she was the first one to say it, and then Alex Karp and Satya, they both kind of like...

Speaker A: Yeah, they've they've taken their own version of it. Yeah.

Speaker B: Yeah. But you know, Satya's essay of specialized intelligence, like I think it's very plausible...

Speaker A: ...but this stuff is really hard to do. Like that that sounds easy.

Speaker B: 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 intelligence? It's like the most whatever vied-after space or position that you could imagine in business, like in the history of business.

Speaker A: Yeah, for sure.

Speaker B: Right. Who's the arbiter of intelligence for global enterprises and probably consumers? I was a retail analyst and, um, 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 to start an American retailer in any category cuz America's so big it's worth over $50 billion almost any category."

Speaker A: Yeah. All you have to be able to do is have a fleet of a thousand stores in 50 different states that have very different climates, consumer preferences.

Speaker B: 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...

Speaker A: ...who turn over at 100% a year.

Speaker B: Turn over at least 100% a year. The stores need to be clean and well lit. And if you can do that, presto, $50 billion dollars. Yeah.

Speaker A: 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.

Speaker B: Yeah. It's really hard to do. And...

Speaker A: ...having that abstraction layer, having it work, having it seamless is, I think, way harder to do than people think. And I do think what—something I think is very interesting about Cursor, I'd love your opinion on this, is 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...

Speaker B: ...and the Cursor guys were just like, "We want to make great product."

Speaker A: Yes. Exactly. In a strange way, of everybody at the frontier, probably Cursor was the most product focused.

Speaker B: Yes. Yeah. I'd say, and you know, now they're part of SpaceX, but that suits Elon and his mindset really, really well. "Let's make it an engineering problem. You know, create the model factory and then we need to have a really good product." You know, the Tesla cars, they're amazing. I mean, I don't know if you drive one, but it drives everywhere. Yeah. But what Cursor figured out is they had, I would say, a similar end-state vision as what those other guys had. It was just a different path to get there, and it's sort of like a practical meet the customer where they are, meet the technology where it is. Um, and I think, you know, they'll sort of...

Speaker A: They have already demonstrated that they kind of led their way up into autonomy from that starting point. Coding is unique compared to everything else in knowledge work. This would be like in support of the point that Microsoft is in a good position...

Speaker B: ...because it is verifiable and perfectly documented, and like nothing else in enterprise...

Speaker A: ...is verifiable and perfectly documented. And so it will be messy, like that leads you to a good bull case for something like Microsoft, that abstraction layer...

Speaker B: ...if they execute, but it's really, really hard to make it really simple for...

Speaker A: ...oh, you know, click my Copilot link to all my stuff, train a model...

Speaker B: ...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.

Speaker A: Yeah. 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...

Speaker B: ...but Databricks.

Speaker A: Palantir, the inference providers, the application companies. Right, so like Harvey has done an incredible job of this. And you know, legal has sort of taken off, and I think they can see the future of how to be that abstraction layer and do the work. But legal is also unique because it's very documented and it's somewhat verifiable. Tax we'll see that, we 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.

Speaker B: Yeah. Although I do always think, in their heart of hearts, Harvey and Leya think, "Oh, if we solve this..."

Speaker A: ...we could be that abstraction layer for everyone.

Speaker B: I think probably in their heart of hearts, Cognition thinks something like that, too.

Speaker A: I think everybody thinks... and by the way, there's like massive validation of the category, because Kirkland & Ellis said...

Speaker B: ..."We're going to spend 500 million bucks to build this ourselves." Like, first of all, you know, good luck, that's going to be very hard. Yes.

Speaker A: Um, but that actually tells you that the pie is really big, right? Huge.

Speaker B: Yeah, it's massive.

Speaker A: And I'm sure they have a very smart head of AI, but it's not like a $500 million 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...

Speaker B: ...Databricks...

Speaker A: ...Databricks, Snowflake coming up...

Speaker B: ...for sure. Salesforce, I think, 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...

Speaker A: ...and this is just, you know, who has the lowest costs.

Speaker B: Yes, exactly.

Speaker A: But it's going to be very hard, I think, over time unless you're... if you're not vertically integrated, you have to be so good to emerge as that abstraction layer.

Speaker B: Yeah. To be the low-cost provider is very hard...

Speaker A: ...because you'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 that's another reason like I increasingly look at these hyperscalers on EV to net PP&E.

Speaker B: Yes.

Speaker A: Because net PP&E 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, too.

Speaker B: Yeah. Kind of an AI version of price to book.

Speaker A: Yeah, I like the price to book. Okay.

Speaker B: So, okay, you mentioned Jensen. You know, I'd share your sentiment, like he's like carrying this industry forward. Like tell me your thoughts on Nvidia.

Speaker A: 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 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..."

Speaker B: ...but you know, my rule of thumb for accelerators, every 1% share today is probably worth a hundred billion. Yes.

Speaker A: So there's no need to go head on with Nvidia.

Speaker B: Yeah.

Speaker A: Just pick a niche, get your 1%. Make sure that...

Speaker B: ...is very big.

Speaker A: He has nine chips. Yeah.

Speaker B: You know, he's got multiple flavors of accelerators. He's got CPUs, Ethernet switches. He has two kinds of GPUs. You know, we've gone from just scale-out networking being a thing. We have scale-up, scale-out, scale-across, now scale-in.

Speaker A: Yeah. So just try to find a way to plug into his ecosystem.

Speaker B: By the way, this is not foreign. Like his biggest customers all have competing products with various of those nine chips.

Speaker A: 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, you hear some of these, and it's like, have you ever seen game tape of the Chicago Bulls when Jordan was... you know, it's game 50 of the season...

Speaker B: Yeah.

Speaker A: And he's a little bored. And the Bulls are down cuz, 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 who's kind of young decides, "I'm going to talk to him because we're beating him." And then he just looks...

Speaker B: ...and it's like... it's the best. Those are my favorite.

Speaker A: It's amazing. Yeah. We've all seen, you know, whatever, The Last Dance.

Speaker B: Just don't do that.

Speaker A: Yeah. Exactly. You know, just like, "Hey, Michael. Man, I'm so happy to be on the court with you." Like that's the move.

Speaker A: But the reason it's particularly important is because Jensen's data centers are financable.

Speaker B: Yes. And it goes back to that point, like let's say it's $50 billion for an Nvidia data center, you need a $15 billion equity check.

Speaker A: Yeah. Okay. You can finance the other 35 billion.

Speaker B: Yeah.

Speaker A: 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.

Speaker B: 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...

Speaker A: ...it's essentially super NPV positive with very little risk for him.

Speaker B: Um, and then he gets a revenue share. So his data centers are the most financable.

Speaker A: Yes. In a good case, probably TPUs are the second most financable.

Speaker B: It probably takes, I don't know, double the equity check at least. Yeah.

Speaker A: And then the rates on the rest of it are higher.

Speaker B: Yeah. Exactly.

Speaker A: 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's 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 OpenAI dominated world because they can pay the most for compute. He can effectively help other people...

Speaker B: ...compete with Anthropic and OpenAI.

Speaker A: Yeah. In the same way that he stood up the neo clouds in the first place. Yeah.

Speaker B: It's just democratizing compute, which is good for the world.

Speaker A: Again, I think he's a patriotic American. His interests are aligned with that though, with the patriotic American ones, right? Fragmentation, right?

Speaker B: Fragmentation, no dominant AI. Exactly. Which is really good because 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, 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.

Speaker A: Yeah, exactly. No, it's great for his business. It's great for his business.

Speaker B: It's amazing 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,

Speaker A: maybe it's a 40% margin. So more of those tokens are going to be consumed which means you need more compute.

Speaker B: Yeah, exactly. In a supply-constrained world...

Speaker A: in a supply constrained world and you know let's just what percentage of the world's supply has he locked up?

Speaker B: 70, 80 somewhere in there. And then you're talking about fab capacity.

Speaker A: All of it. All of it. You know it's just because he saw this coming before everybody else.

Speaker B: Yeah. And all the system supply chain.

Speaker A: Yeah. He's got the fab capacity locked up. He's got DRAM capacity locked up. He's got NAND 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."

Speaker B: Yeah. Now he's making these multi-hundred billion dollar bets, 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, JP Morgan to finance and like that is hard to compete with.

Speaker A: Yeah. And you know it is we my firm trades we have a pretty big private portfolio companies that are 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 will 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 now.

Speaker B: 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.

Speaker A: Yeah. you know, and like and then sometimes it doesn't work, you know, it's just like

Speaker B: Yeah, this famously happened with Cerebras twice, right? Like, and they've powered through and like they've done great.

Speaker A: Well, I think the chip I think each Cerebras chip worked, it just struggled to find product market fit.

Speaker B: Yeah. Yeah. Fair.

Speaker A: For the first two generations, the chip worked. It just didn't have product. And they've done great with it. Yes.

Speaker B: Yeah. But there's a different thing between you plug it in, doesn't work at all.

Speaker A: And it doesn't work at all. Exactly. 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 our lesson. It's going to work the next time two years from now.

Speaker B: Yeah. Assuming you can finance it. Yeah.

Speaker A: 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 cuz you know the land and the power has to come.

Speaker B: Yeah.

Speaker A: You know the entire supply chain has to come the financing has to come.

Speaker B: And so given that he's you know 70 80% whatever we want to say you just want to plug into that ecosystem.

Speaker A: Yeah. Part of why Elon made the decision he made right. Yeah.

Speaker B: Yeah. which I also think was like a very high elo move.

Speaker A: Yeah, totally.

Speaker B: So, you've had everybody else try and build their own ASIC.

Speaker A: Yeah, they've gotten up on stage. Sometimes they say negative things about, you know, Jensen or Nvidia or take shots.

Speaker B: Um you I did think it was pretty smart. You know, the jalapeno team last night and we should give credit where credit is due. Jalapeno is the I would say the first good ASIC other than TPU or tranium I have seen from internal

Speaker A: in a in a what seems to be a pretty short amount of time.

Speaker B: Pretty short amount of time. It's impressive. We should give credit where credit is due.

Speaker A: They do have a good team working.

Speaker B: They have a good team. Yeah.

Speaker A: Um so they had a really good team. I think they had a lot of advantages and I do think if you are 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.

Speaker B: Yes.

Speaker A: 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, Deepseek, Kimmy, Quinn, they're kind of all um evolving in very different ways.

Speaker B: Yeah.

Speaker A: And they can, you know, they can all run on, you know, a more general purpose chip, a GPU, but you're going to need, if you want to specialize,

Speaker B: Yeah. You're going to need general purposes at a minimum for the types of evolution you see from that. Yeah.

Speaker A: So, like I think he's I'm very happy his incentives as a CEO are perfectly aligned with what's good for America.

Speaker B: Yes.

Speaker A: Um so I just make sure your semiconductor guys do not talk trash about Michael Jordan.

Speaker B: Be nice to be nice to MJ. Be nice to MJ. Yeah. Exactly.

Speaker A: Yeah. And then it's like, you know, sometimes it's like, you know, you tug on Superman's cape and you get confident.

Speaker B: Yeah.

Speaker A: You know, you get confident and you start to talk a little bit of trash. Well, you know, Superman sometimes he just flies away like that's what happened to the TPU team.

Speaker B: Yeah. You know, and you know, Jalapeno, they're tugging on Superman's cape a little bit.

Speaker A: Yeah. We'll see.

Speaker B: We'll see. And it is kind of amazing that like Jalapeno did something that none of the big

Speaker A: like I this is as competitive of a chip as I have seen. Yeah.

Speaker B: But again, it's just competitive with one of his eight or nine chips.

Speaker A: Yeah. One of his nine. Yeah, of course.

Speaker B: They'll continue to work closely together. Yes.

Speaker A: 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 need another eight chips.

Speaker B: Yeah. Exactly.

Speaker A: Um and he is at and you know, Dylan at some 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.

Speaker B: And so I actually think it was really smart for Elon instead of like competing, you know, with somebody who is fully aligned.

Speaker A: Yeah. Fully aligned. Mhm.

Speaker B: And I think that history is going to judge that to be a wise decision. In a world that is so supply chain constrained, it's actually really hard to tell what true customer preferences are, right?

Speaker A: Because like you come out, they'll take anything. Yeah. This is this is how you know that like very old whatever the price is held up of H100 is very high.

Speaker B: Yeah. Yeah. And if you have a TSM allocation, you're going to be sold out. Yes.

Speaker A: Particularly if you can get the DRAM to pair with it. You're going to be sold out.

Speaker B: 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

David: speaking, you know, the first deal is where the chip company invests

Gavin: Yep.

David: in a customer. And you saw TPU and Trainium, Amazon and Google do that with Anthropic. Yep. And that was to their immense advantage because it really helped their businesses, I think, helped those chips really level up because you kind of need to use a chip. There's a cold start problem.

Gavin: And in that scenario, as long as the dollars you invest are less than the gross profit, you can't lose money. And then there's a scenario where you do the RVG, Blackstone finances it or whoever, Blackstone, Apollo, KKR, Goldman Sachs finances it. And as long as that RVG is actually less than your gross profit, you can't lose money and you have upside probably through a revenue share on top of it,

David: 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,

Gavin: you're going to do good in that situation. If you just give warrants away, it could be negative NPV because the better the [company] does the more value that's captured by the person. Yeah.

David: Yeah. And so you can kind of look at that hierarchy of deals and like infer something about true customer preferences.

Gavin: Yes. That's interesting.

David: Yeah.

David: So Nvidia does pretty good deals.

Gavin: Yeah. I mean there's a reason that people I consider smart are investing in their deals.

David: Yeah, I see it. Gavin, thank you. Fun. Always fun to hang with you.

Gavin: Thanks, David. This was great, man.