Speaker 1When 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 Ilan 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.
Speaker 2One of the things that I think has been correct but ineffective is this idea that we need to stay ahead of China.
Speaker 1You'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. 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 earth's crust.
Speaker 3Every 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 thing. 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 build out. 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 re-industrialization, to orbital compute, open source, and NVIDIA, this is a wide-ranging look at what happens if AI demand keeps outrunning supply.
Speaker 2Gavin, 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?
Speaker 1No, and I ask everyone. My standard question is, can you tell me one? 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 has clearly accelerated. Open source, I think, has accelerated more. And then I do think Grok, particularly after Grok, Grok bought, 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 Fisher did a podcast with Patrick O'Shaughnessy, and he said, maybe everyone wins. Yeah. Anthropic wins. Open AI wins. SpaceX wins. Meta wins. Google wins by selling a lot of TPUs. Open source wins. Neo clouds win. The inference clouds win on top of the neo clouds. Applications win.
Speaker 4Yeah.
Speaker 1Maybe 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.
Speaker 2Yeah. In terms of revenue added. In terms. In terms of the definition and now I think kind of revenue added. Exactly.
Speaker 1So 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 bit. The next bit. 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 and anthropic be public companies is going to be helpful for the market just because it's, you know, it's such a powerful. It's such a powerful force. And a lot of public investors, you hear, oh, Sarah Fryer 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. 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 then I do think they are. They're like the accidental enterprise company. Oh, for sure. Oh, yeah.
Speaker 2They're kind of like the accidental everything. Enterprise is just a byproduct of the mission, the objective. Yeah. Yeah. Whereas I think open AI is a little more commercial and obviously SpaceX is a little more commercial.
Speaker 1But all of these companies, let's just say they have 10 gigs of power and they're allocating. Right 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.
Speaker 2Which 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.
Speaker 1Yeah, yeah, yeah. And I'm trying to use conservative numbers. People seem to think Anthropic and open AI are both monetizing at $100 billion a gigawatt, right? 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. I do think the realities, you know, everybody has these ideals about how they're going to manage their business. Then they go public. Then 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 messy. Yeah. And Google and these kind of internet companies, it was just, it was pretty smooth fundamentally, even if the stocks were volatile.
Speaker 2Well, there was no massive trade-off they had to make in terms of the cost or infrastructure to serve revenue side. Yeah. They were totally separate.
Speaker 1A hundred percent.
Speaker 2Yeah.
Speaker 1Yeah.
Speaker 2It'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 LP conversation that we have, it's probably the same for you, starts with how's this all going to go wrong. Yeah. And it's like, what's going to crash? And I'm like, oh, are the large models screwed? Are the labs 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 is going to work really well. N-1 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.
Speaker 1Yeah. And NVIDIA is at the center of it all. All of it. Yes. Yes.
Speaker 2They're probably going to do pretty well.
Speaker 1Yeah. The last 26 years have taught me not to bet against Jensen.
Speaker 2Yeah. 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.
Speaker 1Yeah, 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 GPU. They'll use XPUs, whatever, whatever we're going to call them, or maybe they subsidize heavily.
Speaker 2We do know that that's happening. happening at the labs subsidize what heavily their first party products so token consumption of their first party products oh yeah they're doing all this research and they're spending a lot of data on compute and their first party products are like a heavy subsidy products today right yeah so it's
Speaker 1eight gigs of inference and two gigs is for internal research and then you know two gigs is actually training yeah exactly um and you know including probably the inference that goes into post-training yeah i don't i 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 blink yes and satya really regrets that i think yeah yeah um 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 capex and he said i know i'm good for my 80 billion right and 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 could 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 open ai is back in the game and spacex was
Speaker 2aggressive 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
Speaker 1decision short term and long term yeah absolutely i mean we we calculate you know nebious um and core we've both gave some interesting disclosures but you can kind of get to a nine to ten month for nebious because you know okay you bring on a gig it costs 50 billion you get you can get an upfront payment for 50 to 60 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 the spot yeah at a spot spot paybacks are probably much faster than nine or ten yeah you gotta assume like a smoothed out
Speaker 2level like two two bucks three bucks even with that it's yes it's a really good payback now you
Speaker 1yeah and then spacex because they build these really big clusters and 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 um you know to think of pricing and you know per megawatt rather than per gpu because it seems like where the world world is but 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 if 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 a 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 um i know a lot of smart people who work at blackstone and kkr and apollo and they're the ones that are financing it we'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 monetization rate per gigawatt goes up so i mean the true equity payback like might be way inside of a year
Speaker 2yeah 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 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 the paybacks are within a year yep um 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 monitor like the monetization of these companies which are doing call it 180 billion of revenue or something in that direction um is on the back of what like 30 million actual heavy paying users like getting real value i'm talking about like a lot of people are getting real value i'm talking about like a lot of people are getting real value i'm talking about like a lot of people are getting real value i'm talking about like a lot of people are getting real value i'm talking about real value i'm talking about like a lot of people are getting real value i'm talking about real value i'm talking about like a lot of people are getting real value i'm talking about like developers it like i might take the under on 30 million so call it 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 one percent 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 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 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
Speaker 1supply constrained and what are i'm just curious across the a16z portfolio if what are your best companies spending on tokens per month relative to human compensation what rough range i'm not
Speaker 2oh 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 one percent 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 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
Speaker 1my so at a trade is our internal token consumption has gone up 100x from the month of march march through august 100x yeah our token spend and we just got access to uh grok bot enterprise and with two people using it like it looks like a token spend might 10 or 20x
Speaker 2in a month yes from august yes like like but it's actually extremely valuable like we have some heavy grok bot users here and like it is very productive use like this is not like wasteful
Speaker 1tokens but yeah i was in and listen like i 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 i was like i was like i was like i was like i was they did a good job i give them loads of credit and but you know i'm 50 years old you know like how old are 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 try i you know i built some stuff did some cool stuff and in like i don't know three minutes of type creating grok bots 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 summarizer everybody's like how'd you do it i was like well just use ai and do it yes it takes 10 seconds and grok bot yes it's amazing and it's so good yeah and then you know a substack summarizer an x summarizer um an x sentiment tracker for topics and stocks yeah and like that all of those would have taken me i don't know hours working with cloud code and they each took 7 to 12 seconds with grok bot yeah it's better yeah so to me grok bot does feel like another um at least for me like kind of chat gpt moment because quad code like i could see the data it was it was powerful i did some really cool stuff with it it was like empowering and this is neat um you know like family calendar apps things like that yeah um but this is just 10 seconds and it's way better than what i was able to do yeah yeah the
Speaker 2cloud code thing like was obviously the shift in coding and you know our our most sophisticated engineers you know we're doing whatever 20 of their code you know with with ai to like you know i think everything you described in what you built with cloud code or codex is still kind of reactive yeah way right like it's it's still you know it's like summarizers yeah preparation it's all like knowledge enhancing which is part of your job but it's not actually doing the work for you
Speaker 1yeah now you can actually have a grok 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 - It was so easy to build.
Speaker 2- I now have it, I'm like horse racing all these which is like i have uh uh crockbot doing it codex doing it all the like action taking yeah just i want to know make me better my job look at everything i do give me give me recommended automations you can do i have town doing it as well which is one of our companies very good at it um but and we're like kind of on the bleeding edge of trying to do this stuff just wait till everyone does this stuff yeah and then 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
Speaker 1acknowledge like the history of financial markets you know dating kind of back to like the south sea bubble is whenever you get this transformational new technology um 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 it was kind of like a more of a tulip episode but like every time you've had a tulip episode you've had a tulip episode and you've had a tulip episode a real you know profound new technology you know whether it's the automobile the tv the radio internet the pc um 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 overvalue overvaluation leads to an overbuild and then particularly if you're funding it with debt um and and even today a majority of this is still being funded out of operating cash flow which i think is really helpful um you know debt funded built build outs they demand immediate roi not an roi in two years yeah you can't be off in the time you can't be off off in the time but i'm just more you know like 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 the build out 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 and everybody in copper there's like an ai thesis and like exactly we're gonna have to like think about it to like fill the you know if if 10 percent of 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 the economy yeah yeah it's kind of a wild thought and so like these fundamental constraints i think are slowing us down and i 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 up yes and it just is what it is which makes sense because we're like investing a lot so it makes sense um 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 just you know i had this exchange with with um sholto from anthropic and and and dario on x last weekend and you know dario said hey i don't think i've 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 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 actually cure cancer and actually make breakthroughs like 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 might 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 x's and it is revitalizing all of these like dying small towns all over america and listen we're getting we're getting much better at addressing the environment environmental stuff generally they use natural gas which is a pretty clean fuel there is the water the water consumption thing the water is nothing debunked it's totally debunked yeah it's nothing it's nothing it's 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
Speaker 2story 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 or substitute yeah search engine right it does feel like we're pretty close to that um yeah it does and 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
Speaker 1for the average american nobody's worried about china invading america yeah exactly like what
Speaker 2they care about is really big yeah like affordability and like how is this going to change my life for the better yeah of course right and so i think there's a pretty immediate impact you could feel like i my favorite is you know loudon county virginia which is like the highest uh highest per capita income uh zip code in the u.s or county in the u.s and it has the highest density of data centers yeah and they and they make a tremendous amount of tax revenue from data
Speaker 1centers like we should we should do this everywhere yeah it was actually very funny a 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 you know you're 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 yeah but you know hey don't bother me with the details i'm on my next talking point 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-industrialized re-industrializing america the combination of having the straight of foremost closed which is amazing for america yeah you know natural gas here is two or three bucks it's now 25 bucks if you're up in asia or 20 bucks or whatever it is and natural gas is an you know 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 re-industrializing america and it's awesome this is what everyone in both parties has wanted for a long time yeah exactly bring industry back small towns that were left behind by the steel mills 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
Speaker 2i'm just a dude yeah and like your audience is the tech audience that already believe you're you're reaching the choir if you will um but yeah the story the story i met is probably doing the best job i've ever done in my life i've been doing the best job i've ever done in my life i've been doing the best job i've ever done in my life i've been doing the best job i've ever done in my life i'm
Speaker 1telling 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 don't remember if it was on their first earnings 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 yeah you know this cake baking business is going to be a in des moines started you know worked with meta and you know it was it was it was two women who are 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 yeah and they would just run through that every time and and i do think the entire ai industry um like i'd love to 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 did 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
Speaker 2will set you free but only if you tell it 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're we under build on the supply side oh yeah for for like through 28 and 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 yeah so um everybody's worried about oversupply i'm like more worried about massively massively undersupplied yeah yeah which 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 access the intelligence yeah well So the opposite direction of where everybody thinks this is going to go?
Speaker 1Yeah. Well, Dworkash had a wild point. I forget what it was, but he was positing, I forget the-
Speaker 2Like the cost of a token could go up 10x or something like that. 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, 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. Yeah, 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,
Speaker 1the consequence of like these like data center de-growthers may be like- 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 2Yeah.
Speaker 1You know, that happened because you wouldn't let us build data centers.
Speaker 2Yeah. 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 4Yeah.
Speaker 2As we've seen with all the, you know- Yeah. 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. Yeah. And that would be a terrible outcome.
Speaker 1That'd be a terrible outcome for the world. Nobody wants that, so we need to build a lot of data centers. Yeah, exactly. Yeah. Exactly. Yeah. Like a compute inequality like 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. They're not. And it's like, it takes the exact same amount of compute.
Speaker 5Yeah.
Speaker 1All 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. It's just a question. It's a question of what are the margins that are charged on top of that. And even then, the Kimi license, something that I don't think a lot of people appreciate, is the Kimi license stipulates a 30% share of any revenue. Yeah.
Speaker 5Yeah, yeah.
Speaker 1So, 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.
Speaker 2But it's also extremely token hungry, too, right? Oh, yeah. We're talking on a token basis, but on a task. On a task basis, it's far more inefficient. Absolutely. And so, it's very costly.
Speaker 1Yeah. 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, 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. Yeah. Because they have, they are fundamentally altering kind of, like, the fabric of human society and civilization with AI, SpaceX. Making humanity multi-planetary. 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.
Speaker 2There was never, there was never going to be an economic case to build internet access in those places because of the cost. Yeah, and now. And the willingness to pay, and now you could. No, well, now, now it's there. And any incremental internet capacity. Any incremental internet capacity, like, is not going to be built in a traditional sense on if it's going to come from space. And so, like, that is a huge, that is a huge unlock. I agree.
Speaker 1It's a good thing. But, like, we're, you know, we're, like, you know, we should all be grateful for them because I do think that, you know, they're, you know, they're making the future as exciting and inspiring as possible.
Speaker 2Say 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. It's, like, the size of an airplane. Yeah, people are.
Speaker 1It's, like, a big rack. People are picturing, like, the Death Star. Yeah, exactly. It's not that. Or the Pentagon. Yeah, yeah. Floating around in space. That's not what it is at all.
Speaker 2Yeah, it's, you know, whatever, the size of an airplane, right? Rack of 72, whatever, chips, whatever.
Speaker 1Yeah, but even, 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 secret. 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. 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, you know, 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 were 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 it 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. 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 has to have the phased rays and move around.
Speaker 2I 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, Yeah. And, you know, in the fullness of time, maybe it will be larger.
Speaker 1Well, 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 up. Yeah, all of it. Yeah, we're going to run out of, you know, the copper bowls are, you know, focused on, like, copper shortages. Yeah. Optics, yeah, 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 light limitations are a real thing. Latency matters. So data centers on Earth, they're not going. 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 Rubin 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 2028. Yeah, that's still okay. That's pretty soon. You know, as Brad Gerstner 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-single just billions today. Which, by the way, you know, is like, that's just like keeping share constant.
Speaker 2Yeah, exactly.
Speaker 1You know, of like what's happening with coding. Not presuming taking any share on Grokbot. Yeah, from 3 billion. And by the way, man, I would just, I'd probably take the over with Grokbot. Yeah. I bet it's like 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, you know, Grokbot, like, hey, we hear 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 $800, $900 billion of revenue that they address.
Speaker 2So, yeah, your mobile. Plus your broadband, whatever. Let's call it like close to $2 trillion of a market.
Speaker 1And then you have a really rapidly. growing AI ARR base. Yeah, AI ARR, you've got the cloud, you know, the cloud. Yeah, the cloud. 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 can just look at things that are happening today with terrestrial compute, with cursor, with Grok, with GrokBot. By the way, I think XADS or, you know, we have telemetry. Yeah, they're going. They're also growing. You know, I would expect at some point you'll have like a Starlink GrokBot X advertising bundle, you know, kind of one of the ways Google built their cloud business is they bundled it with ads and like, hey, we're, you know, maybe you're bundling the ads with AI, but why not do
Speaker 2that? 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. And so they've made the very aggressive compute investments to enable that first party work. Yeah. 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 six-month payback on the compute side, you know, with like massive scarcity of supply. And so I think that's
Speaker 1a really good setup. And there was a bear case that, hey, okay, well, in the OpenAI entropic maximum less 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 of computers 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 a massive
Speaker 2infrastructure business. Yeah. Yeah. I'm so fired up about the Starbase Louisiana. Oh, yeah.
Speaker 4I can't wait to visit. It's so
Speaker 2cool. I was reading about it last night. And yeah, it's sort of like it's now the they now have the infrastructure for, you know, thousands of launches a year.
Speaker 1Yeah. And eventually, I think you'll see like these Starbases 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 for sure. I think you'll see probably one in, you know, whether it's Japan, South Korea. Who knows? Yeah.
Speaker 2Yeah. Yeah. Yeah. Yeah. That's pretty exciting. Yeah. Yeah. The capability to do to call it, you know, whatever, 5,000 launches a year. Like that feels very futuristic. Yeah.
Speaker 1I 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 is 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 the SpaceX subreddit. But that's very different catching that thing from what they're trying to do with Starship where, you know, the the booster gets caught with the things and then it gets moved and then the Starship gets caught and then it gets stacked. Yeah. It gets fueled and just
Speaker 2sent right back up. Yeah. Two a day. Two a day per pad. Like those numbers add up pretty fast. And there, and I
Speaker 1do think, I think they're engineering the pads for more than two a day. If I Yeah, I think that's a conservative. I think that's a conservative
Speaker 2assumption. 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 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. So, like, what's the most futuristic, like, product or market or technology thing about SpaceX that you can think of? Look, I mean,
Speaker 1this 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, 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 atoll in the middle of the Pacific. You know, no humans within whatever 50 miles. You'll, you know, you can imagine like Optimus robots, you know, 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 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, this 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, will 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. How big outer space is.
Speaker 2You don't have to worry so much about emissions up there. Yeah.
Speaker 1Yeah. 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. Going to have like, you know, our little ramps 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, 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 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. That is crazy to think about. And that's going to be an amazing moment for America. Yeah. Of course. I mean, think about
Speaker 4the moon landing. Yeah. This is a little bit bigger. Yeah. Yeah. Um, so that seems cool. Um, 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.
Speaker 2So, 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, what's your outlook for their decisions?
Speaker 1Well, I do think the world has gotten a lot friendlier for their strategy. Um, 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, um, I think the future is an ensemble of models. You know, there's a Pareto curve. No one model's going to be the best at everything. And I think the future for certainly, you know, kind of the global, you know, 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, uh, 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. Um, we're going to monetize our compute at high rates. And we're going to, um, 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 Poolside acquisition was made for a reason. Poolside actually had a lot of really good American open source talent. I think they're, you know, 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.
Speaker 2Yeah, certainly with like the shift in the ZDR policy, like, yes.
Speaker 1Yes. 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, its cost. And then what we've seen, from a lot of companies and, you know, Grok Bot, my understanding is, you know, I think it's Gemini 3.7 Flash, Grok 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 Grok as soon as possible. Yeah, yeah, of course. But I think what you'll see these companies do is they'll have their own model. Yeah. On their data. And it will work with one or two other frontier models. Not, you know, necessarily, but just, you know, checking each other. It'll be kind of transparent to the user.
Speaker 2The most frontier for planning and then have execution run by everything else that's lower cost.
Speaker 1Yeah, absolutely. And so I think that feels like a very likely future to me. 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. 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 Gemini was ascendant. Yeah, exactly. That this is the scenario that we're in. That Gemini wouldn't even be in the conversation. Yeah, they're not in the conversation. And Muse and Meta 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 see some people come out of the game, others come back in. But a future where that future where it's, you know, I don't know if we're going to call it multi-model, 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 Grokbot, outside of Cursor, outside of, you know, like Harvey's done some cool things. Yeah, yeah, yeah. Harvey's done great things with that. Where they've done it is actually just the Fireworks Nexus product. Yeah, yeah, yeah, exactly. 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. Yeah. 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 Karp and Satya, they both kind of like. Yeah, they've taken their own version of it. Yeah. But, you know, Satya, as I say, has specialized intelligence. Like, I think it's very plausible. But this stuff is really hard to do. Like, that sounds easy.
Speaker 2I 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 afterspace or position that you could imagine in business, like in the history of business.
Speaker 1Yeah, for sure.
Speaker 2Right? I think it's like the answer is and, again. Yeah, yes, and for sure.
Speaker 1It's, yeah, who's the arbiter of intelligence for global enterprises and probably consumers? I was 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 to start an American retailer in any category, because America is so big, it's worth over $50 billion. Almost any category.
Speaker 5Yeah.
Speaker 1All 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. Who 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, well, 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, and the Cursor guys were just, like, we want to make great product. Yes, exactly. In a strange way. In a strange way, everybody at the frontier, probably Cursor, was the most product focused. Yes. Yeah, I'd say, you know, 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.
Speaker 5Yeah.
Speaker 1You know, the, you know, the Tesla cars, they're amazing. I mean, it's, I don't know if you drive one, but it drives me everywhere.
Speaker 2Yeah, 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.
Speaker 4Yeah.
Speaker 2It 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 that starting point. Coding is unique compared to everything else in knowledge work. This, this would be, like, 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. Yeah. Is verifiable and perfectly documented. And so, it will be messy. Like, that, that leads you to a good, you know, bull case for something like Microsoft, that abstraction layer.
Speaker 1If they execute, but it's really, really hard to make it really simple for, oh, you know, click my copilot, link to all my stuff, train a model. Yeah. On our data, convince me that you're not going to share it with anyone else, and then. And put it behind a router that's seamless for me, and continuously upgrade that open source model. Yeah, it's not just some middleware. Like, it's very hard to do.
Speaker 2Yeah. And, and by the way, they're going to compete, they're going to be competing with not only the labs to be that abstraction layer, but Databricks. Oh, yeah. So, it's like Palantir, the inference, the inference providers, the application companies. Yeah. Right? So, like, Harvey has done an incredible job of this. Yeah. And, you know, like, legal is sort of in takeoff. And, 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. Tax, we'll see that. We'll see, see things like that. But, like, the, the one and a half billion, the really appealing broad pie is going to be very messy to go get. Yeah.
Speaker 1Although, I do always think, and, you know, I think probably in their heart of hearts, Harvey and Lagora think, oh, if we solve this, we could be that abstraction layer for everyone. Yeah. I think probably in their heart of hearts. I think probably in their heart of hearts, cognition thinks something like that, too. I think everybody thinks it.
Speaker 2And, by the way, there's, like, massive validation of the category. Yes. 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.
Speaker 1Yeah, it's massive. And that's, it's, you know, just, and I'm sure they have a very smart head of AI, but it's not like a 500, $100 million one-time build. No. The 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. Databricks. Databricks 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. It's going to come down to who executes the best and, 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 integrated, you have to be so good to emerge as that abstraction layer. Yeah, yeah, yeah. To be the low-cost provider. Yeah. Very hard. Because, yeah, 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. You know, it's it's another reason like I, you know, I increase look at these hyperscalers on EV to net PPNE. Yes. Because net PPNE 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. Yeah. Yeah. Yeah. Yeah. Kind of
Speaker 2an AI version of price to book. Yeah. I like the price to book. OK. That's good. It's OK. You mentioned Jensen. You know, I share your sentiment like he's like carrying this industry forward.
Speaker 1Like, tell me your thoughts on the 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, OK, 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 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 one percent share today is probably worth 100 billion. Yes. So there's no need to go head on with NVIDIA. Yeah. Just pick a niche. Pitcher one percent. Make sure that, you know, is very big. He has he has nine chips. Yeah. You know, he's got he's got multiple flavors of accelerators. He's got CPUs. He's got, you know, Ethernet switches. He has two kinds of GPUs. 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.
Speaker 2By the way, this is not foreign. Like his biggest customers all have competing products. Absolutely. With various of those nine chips.
Speaker 1Yeah. 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, it's 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. Yeah. Yeah. And they're up 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 going to talk shit to him
Speaker 4because we're beating him. And then he just looks. And it's like. And it's like. It's the best.
Speaker 1Yeah. It's amazing. Oh, 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 the move. But the reason it's particularly important, is because Jensen's data centers are financeable. 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. 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. Yeah, it makes sense. Essentially, it's super NPV positive with very little risk. Yeah. For him. And then he, you know, he gets a revenue share. So, if you're, you're, and his data centers are the most financeable. Yes. And I, like, let's just say a good case for probably TPUs are the second most financeable. It probably takes, I don't know, double the equity check at least. Right. Yeah. 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's, he has all these chips. He's acquiring land power and shell companies now, matchmaking them with offtake agreements. And he's, he's, he's, 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. Yeah. Because they can pay the most for compute. He can effectively help other people. Yeah, exactly. Compete with anthropic and open AI. Yeah, in the same way that he stood up the neoclouds in the first place. Yeah. Yeah. It's just democratizing compute, which is good
Speaker 2for the world. Again, I think he's a patriotic American. His interests are aligned with that, though, with, with, with the patriotic American ones, right? Of course. Yeah. Fragmentation.
Speaker 1Yeah. Fragmentation. No dominant AI. Yeah, exactly. Which is, 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 the giant risk to his business. Yeah, exactly. No, it's great for his business. It's great. 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, 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 capacity? 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 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 $2 or $3 billion bet every two years, and I'm moving really, really fast. 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. Yeah. And, you know, it is... We, my firm, Tradies, we have a pretty big portfolio, 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 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, you know, and, like, and then sometimes it doesn't work. You know, it's
Speaker 2just like... Yeah, this famously happened with Cerebrus twice, right? And they've powered through
Speaker 1and, like, they've done a great job. Well, I think the chip, I think each, Cerebrus chip worked. It just struggled to find product-market fit. Yeah, yeah, yeah, fair. For the first two generations. The chip worked. Yeah, yeah, yeah, fair. It just didn't have product-market fit. And they've done great with it, yes. Yeah, but there's a different thing between you plug it in... Plug it in, it doesn't work at all. And it doesn't work at all. Yeah, 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. Yeah, assuming you can finance it, 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 and power has to come. You know, the entire supply chain has to come. The financing has to come. And so, given that he's, you know, 70, 80 percent, whatever we want to say, you just want to plug into that ecosystem. Yeah. Yeah, that's why Elon made... Be nice to Michael Jordan.
Speaker 2Part of why Elon made the decision he made, right? Yeah.
Speaker 1Yeah, which I also think was like a very high ELO move. Yeah, totally. So, you've had everybody else try and build their own ASIC. Yeah. They've gotten up on stage. Sometimes they say negative things about, you know, Jensen or NVIDIA or take shots. I did think it was... I 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 IFC from internal. You know, in what seems to be a pretty short amount of time. In a pretty short amount of time. Yeah. 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 were a lab and you have the model and you see the future 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, DeepSeq, 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 purpose at a minimum for the types of evolution you see from that. Yeah. So, like, I think he's, I'm very happy his incentives as a CEO are perfectly aligned with what's good for America. So, I just, make sure your semiconductor guys do not talk trash about Michael Jordan ever.
Speaker 4Be nice to MJ. Be nice to MJ.
Speaker 1Be 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.
Speaker 5Yeah.
Speaker 1You know, you get confident. And you start to talk a little bit of trash. You know, Superman, sometimes he just flies away.
Speaker 5Yeah.
Speaker 1Like, that's what happened to the TPU team.
Speaker 5Yeah.
Speaker 1Yeah, yeah, yeah. And, you know, Jalapeno, they're tugging on Superman's cape a little bit. Yeah, we'll see. We'll see. Yeah. And it is kind of amazing that, like, Jalapeno 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.
Speaker 2Yeah, of course.
Speaker 1Right? Yeah.
Speaker 2They'll continue to work closely together. Yes.
Speaker 1Yeah, 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. Yeah, exactly. Yeah. And he is at, in, you know, Dylan at Semi-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.
Speaker 5Yeah.
Speaker 1And 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. Yeah. 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.
Speaker 2Right. Because, like, you come out with. Yeah, they'll take anything. Yeah, this is how you know that, like, very old, whatever, the price that's all up of H100 is very high.
Speaker 1Yeah, 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. Yeah. You're going to be sold out. Yeah. So, it's actually kind of hard. It's 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. Yep. In a customer. And you saw TPU and Tranium, 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. Yeah. There's a cold start problem. Yeah. And. 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. Then there are deals where you give warrants away, but they're tied to, like, a fix. Yeah. And you have to give warrants away, but they're tied to, like, a fix. And you have to give warrants away, but they're tied to, like, a fix. And you have to give warrants away, but they're tied to, like, a fix. And you have to give warrants away, but they're tied to, like, a fix. And you have to give warrants away, but they're tied to, like, a fix. And you have to give warrants away, but they're tied to, like, a fix. 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 NPV because the better this stock does, the worse the deal is. The more value that's captured by the person, yeah. Yeah. And so, you can kind of look at that hierarchy of deals and, like, infer something about true customer preferences.
Speaker 2Yeah, that's interesting. Yeah. So, NVIDIA does pretty good deals.
Speaker 1Like, yeah. I mean, there's a reason that people I consider smart are investing in their deals. Yeah, I see it. Gavin. Thank you. Fun. Always fun to hang out with you. Thanks, David. This was great, man.
Speaker 3Thanks for listening to this episode of the A16Z Podcast. If you liked 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 Podcasts, and Spotify. Follow us on X, A16Z, and subscribe to our Substack at a16z.substack.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. It 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. 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.
PodcastTranscript AI