Google's AI Brain Drain, SpaceX's Huge Quarter, Airtable's 90% Collapse, US Data Fuels China AI

All right, everybody welcome back to your favorite podcast. It's the all-in podcast. It's the summer. It's August 6th. Having a hard time getting a quorum here on the podcast, but David Friedberg is here. David Friedberg is back our Sultan of Science. How you doing, brother? Great to be with you. It's great to be with you. And everybody loves Brad Gersner is here. He's your Bruce Wayne if markets are your game. He brings that namaste to your payday. Yeah, I'm his glasses at discounting. That's you one of those fancy Trump accounts. All right. Welcome back to the program, Brad. I love it. I love it. You're bringing the rhymes back. I bring a little intro back. We've been trying. Chimoff is on the road right now. Chimoff is on the road, but we will get a field report from Chimoff and I call Daniel. Somehow, Sachs is going to be here, but you know how he is. He's always late because you know, you get a phone call from very important people, but he will break in at some point. Oh, wait, I see in the texture. Oh, there he is. You made it. How do you like my beautiful summer in July? It's incredible. He fits perfectly. He looked forward. I don't know how Chimoff does this. Well, here's the report everybody. As everybody knows, Chimoff is on the road. He, oh, here he is. He, this is a photo. Sachs, he went. He went to check his data center progress. I think that's in Colorado and Nevada where he's building a data center. I think that's on Dune. Oh, it's on Dune. Yes. Dune four. Oh, yes. There he is in marrying himself. Oh, look, here's me. You know when a meme has reached its peak when your wife starts dunking on you, there it is. And here we are. This was at the Christmas party, I think. Oh, you were on CND. With Andrew Raskarck. No, there you go. I wasn't sure if it was my Twitter feed that was just selecting into it, but it's not really good. Everyone, right? This was the viral thing. This has hit everything. All right, listen, we got a lot to get to the number of the shenanigans in small talk. Google had two major shakeups to its AI staff on Wednesday. Demis has sabbis has moved to chair of deep mind and chief scientist at Google reports described this as Demis stepping down or being kicked upstairs. We'll get into that. But Google framed it as a promotion and says he was stepping up. Here's exeos is quote explaining the shakeup. Quote. Google's Gemini 3.5 Pro is months behind with some company sources telling exeos that it's in part due to low morale. Interesting. Several top researchers including Gemini's co lead have left the firm for competing AI labs. Chef Dean plus three other AI superstars are leaving Google to start a company called Discovery Loop. Dean is a legend freeberg. And I think you worked with him at Google. One of the world's great AI engineers. He was employee number 30 joined in 1999 and has worked there for what I understand continuously for 27 years. Discovery Loop is going to be focused on deep scientific break. There was an AI. Google shares down 4% on the news of Dean leaving. So 200 billion in loss market cap if you want to correlate those two things freeberg. This is your alma mater. What are your thoughts here? Is this creative destruction? Maybe these people weren't delivering and they wanted fresh blood or is this just the siren call of doing a startup in an age of unlimited capital for AI and unlimited opportunity just being too much for the OGs at Google to not take advantage of. Maybe it's the third bucket which is if you're the board and the management you're having a debate about how to best deploy capital. Google has made a commitment to deploy 200 billion dollars in CapEx this year in AI infrastructure data center build out because of the CapEx and accelerated depreciation making an investment in AI compute in the US right now is hugely tax advantaged and because of the extreme demand for compute it's a pretty obvious kind of ROIC model return on invested capital. So if you make this sort of an investment you have significant demand for that compute infrastructure you're very good at running the compute infrastructure that capital can deliver massive profit returns for you with very high confidence in some forecasted period building the most advanced frontier lab driven model also takes tens of billions of dollars of capital and the question really is can you deliver the profits from the model and in a world where open source is becoming so good and open weights models are catching up so quickly and all the frontier labs are catching up to each other so quickly does it really make as much sense to deploy tens of billions of dollars against building a model and I think that the scientists that we're seeing transition out are the scientists that have been at the core of model development of making these frontier models and they were certainly first out the gate you can look at some of the early interviews with Jeff Dean from a couple years ago where they actually had a chat GPT equivalent internally a year before chat GPT came out from open AI Google chose not to release it for fear of cannibalizing search and so on that's when surgae stepped in and there's this whole kind of revitalization but it's time has gone on and as everyone is competed on models as we've talked about many times on the show I think it's pretty obvious that it is very hard to get the same sort of return on capital invested in model development as it is and capital invested on compute infrastructure and being model agnostic what Google has is probably one of the greatest install enterprise bases in the world for compute so they have the most enterprise customers they have the most consumers and in both cases they don't necessarily need to have the best model to make an incredible business they can be model agnostic they can work with anthropic they can work with open AI they can work with SpaceX they have a significant ownership stake in SpaceX and an anthropic and they can work with all the open weights models they can host them all so now if you're one of the great computer scientists your demis your Jeff Dean you're this whole crew and you're inside a Google and they're allocating capital not to your models not to the things that you're most interested in but they're allocating capital to infrastructure and data centers and supporting the broad ecosystem of models you start to say well given the fact that I can go down the road and visit Brad Gerson and a couple other people and raise a couple billion dollars at a multi billion dollar pre-money with a power point deck because I'm the greatest in the world doing this that might be a better path for me and I think that that's the moment so the way I would frame it is CapEx is high alpha low beta in data center infrastructure that capital and model development theoretically could be high alpha but it's very high beta it's a very risky way to deploy capital so so if I'm the board I'm the management I'm deploying more capital and compute infrastructure less capital into model development that's what I think is going on Brad what's your take on this I think David nails it I mean listen the same things going on a Microsoft right Sasha is out this week saying you know Sidney Morgan Stanley's report and saying they're seeing over a 30% return on invested capital in tokens as a service right so in the infrastructure business so I think David's exactly right those are such good businesses right you you you deploy capital everybody's running it from you but the scientists who want to be involved in super intelligence who want to cure cancer want to be on the frontier of these models right they're sitting there dealing with this channel conflict at Google because you know Google Cloud wants all of the compute in order to rent it out to anthropic and those for those building the frontier models internally want that compute in order to compete with anthropic so you have this inherent channel conflict between those wanting to build the models I think David said it really well and I think that's a that that's a big challenge for them it looks like it's being resolved in favor of being more of an infrastructure company so where does you know telescope out for a second SpaceX also reported this week they also have channel conflict they're running out their compute to an anthropic at the same time they're trying to build their own model with rock and cursor you have that channel conflict at Google you have that channel conflict at Microsoft although I don't even really see them pushing the frontier anymore in terms of models met us talking about getting into the infrastructure as a service game and then at an anthropic and open AI you don't have any of that channel conflict they say we're not in the infrastructure business we're only in the model business so I think it's a you know a clarifying view as we look forward that we may in fact not have those companies on the frontier of model development if all these people leave by the way thanks to the law passed on capex depreciation if you assume a 26% corporate tax rate every dollar you deploy in capex because you get to write it off in this year you're basically getting 26% off you know that's money to get right back yeah yeah hey uh sacks let me have you comment on uh this as well polymarket which companies will have the number one AI model by the end of this year on December 31st um now of course in the last time they did this anthropic one so they're not on the list they're the winner but who will have it going forward open AI 32% Google 20% Alibaba 14 and then you got Moonshot XAI meta by dense all that about 10% so sacks your thoughts here on what's the better business is the better business being in the language model frontier model or is that getting quickly commoditized and really you want to be in the token sale business or is that also going to be a commodity you just need to be on the application layer here's what I think is going on in terms of the the market structure is when I saw this Google News. My reaction was, and then there were two. Because, like Brad was saying, we used to have five major companies in the hunt to be the leading frontier lab, the leading frontier model just a year ago. Now we're really down to just anthropic and open AI. So the market for frontier intelligence has become a duopoly. Now, Elon is still in the hunt. I'm sure Google would say they're still in the hunt. But like Brad is saying, they may have contradictory incentives there because they can actually do quite well just with their compute. So I think that the market for frontier intelligence has become a duopoly. I think it's a very powerful duopoly. I don't think it's being commoditized. I think that what we're evolving to is a two-tier market structure where there's a market for frontier intelligence and there's a market for, let's call it kind of commodity or lagging intelligence, whatever you want to call it. That's six to 12 months behind. There is a market for those tokens, those models. But the reality is you can't charge anything for the weights. You can charge for the compute. You can charge for the inference that you're providing. You can charge for essentially consulting services to help put the whole thing together. But if you're not at the frontier, you can't charge for the model layer itself. If you are at the frontier, you can charge a premium. And that's where anthropic and open AI are. And I think the proof for this is just you look at the growth rates of these companies. The latest we heard is anthropic is now over 80 billion of ARR. Start of the year at 10, it had forecast 100 billion as exit ARR for the year. And most people said that that would be impossible to achieve. Now it looks like they're going to do it with a couple of months to spare. So their estimates are going up. I mean, 110, 120, or higher for end of your ARR. Open AI seeing acceleration. So I think what you're seeing now is a very clear bifurcation in the market. You've got a frontier model duopoly that can charge a premium. I think of it like Apple. You know, Apple is competing against Android. It's open source. Android actually has more users in the world. But all the monetization goes to Apple because people are willing to pay for the premium experience. I think in a similar way, people are willing to pay a premium for true frontier intelligence. If it's really at the leading edge. But if you're not the leading edge, there's a huge market for that too. But it's highly commoditized. People are just willing to pay you for the compute. So I mean, that's what I see happening right now. >> Jason, what do you think? Jason, what do you think? >> Well, if you look at Google Cloud, they posted 82% year over year revenue growth, which is something we've never seen in the history of these cloud providers. Elon Musk and XAI just had the SpaceX earnings were going to get into that. But they also had massive uptick in their Elon web services as I've dubbed it. And if you look at Google, I still think Google will be the number one AI company because they have so many people using AI inside of their products already. They have five products now with over 3 billion monthly users each. Android search, Gmail, Chrome, YouTube, all have over 3 billion. If you've used any of these products recently, they are becoming AI first products. YouTube especially, but obviously Chrome and Gmail, you're seeing tools pop up there for AI. And then, Freeberg, you kind of alluded to this. They have 13 products total with over a billion. And that now includes Gemini. In Q2, Gemini had over 950 monthly active users tripling year over year. They will be the number one AI company in terms of consumer usage, by far, I think this year. That doesn't mean that the frontier models are not great businesses. They obviously are. But I have been using exclusively non frontier models. And for 95% of the jobs I'm doing, SACS, it's good enough. And I just posted about this. And Elon and I got into it a little bit here. And I think you referenced this in our group chat. I tweeted just the other day, the difference between the open source models I'm using in frontier is negligible already. I believe that to be a true statement for the work I'm doing. And he said, you know, I'm responding back to me. It's actually a world of difference. If you're doing something other than making a copy of a video game, or you have incredible speed needs, the frontier models are not necessary anymore. They're just not necessary. The people using the frontier models are doing it because their company set it up. And it's too hard to implement open source right now. But it's going to get easier and easier to implement it. So I'm still going with open source in Gemini, being the leaders in this case. - Yeah, look, I think it's true for your use cases that let's say the cheaper commodity intelligence that middle of the market is good enough. Look, an Android phone would be good enough for me. I could get by on a cheap Android phone. You know what? I still pay a premium for this because I use it so much. So if you're a business that let's say you are a hedge fund and you're in a highly competitive industry, you don't want to take the chance to say you're not getting the best intelligence to power your models, you know? And there's a lot of industries like that where the competitive dynamics will drive you to pay for the best intelligence. There's also situations goes back to the blog post that Decagon posted, which is if you're looking for use cases, you also want to use the true frontier. Because again, when you're dealing with immature use cases, you don't know where the value is going to be. And you're searching for opportunity to use AI. You just want to use the best. Because again, there are turn on finding those use cases. It's going to be so much greater than the small premium you're paying at the token level. So I think there's a lot of examples like that when the use cases immature where you're in a competitive industry where you're just deploying AI, you want the convenience of the full style. So why not go frontier models? Yeah, I look at your own. You know, unless your employees are doing something stupid like you create a leader board and they're token maxing, I don't think the cost is that great. And again, the benefit that you're getting is huge. So a lot of people just like, give me the best. I'm willing to pay a premium for the best. I'll take a slightly different take. I think that it's not necessarily, do you take the best model or the open source model? I think that there's a blend that's happening. At least that's what I see. For example, we'll use open sorts, open weights for a vast majority of simple workflow applications. But when it comes to specialized applications, where we really need to have high quality model proficiency, for example, in life sciences and genomics modeling, I am going to go for the premium model. If I'm working at a media company and I'm trying to do AI rendering of video, I'm going to use Gemini's model that does video. It is the best model or Sora or whatever the best model is for that particular application. So I think the idea that there's kind of a model that you pick for everything, I think, is the false assumption. On the consumer side, it is likely the case that the consumers are not going to be using some open weight model because they can pay 20, 40 bucks a month and get chat GPT or Gemini or Claude and be very happy paying 40 bucks a month. And they'll basically be able to minimize their cost to run that for consumers. For enterprise, I think the enterprise is going to be very active in selecting a blend of models that are going to make the most sense. Very cheap open weight model for simple workflow applications, individual employees spinning up an app, whatever. And then more complex models for those really key workflow tasks and then specialized models. And I will say it is way too early to count Gemini out on building incredible specialized models. They have the best video data. They have the best life sciences data. They've been working on this for far longer than anthropic or open AI in the life sciences side. They're very well ahead on that front. I mean, Demis is still going to be running isomorphic labs. So when it comes to be specialized models, verticalized specialized models like video, life sciences, protein folding, I think these are the things where you're really going to see Gemini shine. And then every enterprise is going to have a mixture. But hey, if you can be the cloud service provider with that mixture of models, which is what Google GCP can now be, I'm going to sign up for working with GCP versus working just with anthropic. One of the things this has created is downward pressure on the pricing. We start open AI and cloud do massive price cuts for tokens. So they are reacting. They're not taking it sitting down. And the orchestration between these models is being built into a lot of harnesses inside of enterprises. So what's your take on the downward pressure on token pricing? Or is it just great for consumers and enterprises? Listen, we've got massive competition. That's the thing. America's winning. This is exactly what you want. We have massively competitive market. We have Chinese open source, domestic open source, frontier national labs that are doing what they're doing. We have downward pressure on pricing. David referenced to do opily. I think it's hard to call it a do-opily when you're only a few years into this. And you have giants like Amazon, Microsoft, and Google. I do think he's right. I do think they've emerged as the pure plays. Their revenues would suggest that they're gaining share of wallet. But there are two points I want to make here, because I think they're non-consensus views that were spoken this week. One was Elon's response to you, Jason. Over the last two weeks, everybody's been saying that the Chinese have caught up, that open source tokens have caught up in intelligence, that they're much cheaper, et cetera. And Elon comes out and says, not so fast. We're entering the singularity and the frontier models are way further ahead than people think. I believe that to be true. I think for your use case, they're very similar. But I don't think that's the most sophisticated use case that people are trying to train on and trying to experience. And then Jensen came out this week and said, closed models are actually cheaper. If you don't have to build it for yourself, if you don't have to-- the training costs and a lot of expertise to fine tune and maintain and guard rail and keep it safe. So it's basic. make in the argument that not only are the frontier models further ahead, but that the cost differential between the two is not what everybody's making it out to see, to be, which I think explains why they continue to run away with it on the revenue side of the equation. But I think we have healthy competition. You're right, J-Cal, for the vast majority of use cases. I think token consumption is going up for the open source, guys, while share of economics is going up for the frontier labs. I think that's what we want to see. Yeah, and it's just Android versus iPhone all over again. One platform makes the profit, one gets the majority of users, at least globally in usage. All right, let's talk space decks here. They had their first earnings report as a public company, shares dropped 13%. I think because people were a little concerned about the surging AI CapEx, it's down 30% since going public in June, but it's now trading ad, it seems to have settled in at a $1.4 trillion valuation when public, obviously, above $2 trillion. Q2 results were spectacular. It's the only way to put it. $7.8 billion in revenue, up 92% euro per year, let that sink in. And 67% quarter over quarter AI revenue, you want web services, more than tripled quarter over quarter to $2.6 billion. That's not cursor. That hasn't closed yet, but that's going to be one of the great purchases in history. This is from you on web services, renting out compute specifically to AMP Propic and Google from the Colossus collection of servers. But CapEx was up 18.4 billion in the quarter. That's six x year of a year. Obviously, you can do the math there for a run rate of about 75 billion dollars. I'll stop there and get your reaction brad to the SpaceX IPO. I know you've been tracking this and commented on it. I mean, listen, I think that one first, let's start off $1.4 trillion value creation for this company is extraordinary. So the fact that from peak to trough, it's down 40 or 50% from the IPO. We had that chart out a few weeks ago. Remember that within six months of the IPO, almost all these tech stocks are down 50% peak to trough. We see it again here with SpaceX. I thought it was a really solid quarter. I thought his guides were pretty extraordinary. 100 billion in ARR by the end of the year. And he pulled forward the $1 trillion target in ARR by a year from 2031 to 2030. Now to just put that in perspective, Morgan Stanley's 2030 revenue estimates $325 billion, which is also extraordinary. Remember, this company did $18 billion in revenue last year. So whether you're taking Morgan Stanley's numbers or Elon's numbers, clearly the market is not pricing that in. At 2 trillion, we were pricing ahead a couple years. I think now it's the value reflects kind of where we are. The market has questions about a few things. Here's what they are. Number one, on the rental business, the rental of compute business, he rented out a huge block of compute to anthropic. It's the question that we've been talking about here. Are you going to use the compute to build your own frontier model, or are you going to rent it out? And if you rent it out, are you going to be able to find those people who have the capital to offtake that compute? He's talking enormous numbers, 10 to 20 gigs, and people are wondering how they're going to be able to finance that. And remember those businesses, the GPU rental businesses, tend to trade at very low multiples. Look at CoreWeave, et cetera. On the frontier model business, I think this is the sleeper. I think he said on the call that Grock tripled tokens in the month of July. That doesn't include cursor. Cursor was already on a path to go from 3 billion to 10 billion by the end of the year. Cursor plus Grock could be at 10 to 20 billion by the end of the year. That would be an extraordinarily valuable asset going to trade at a much higher multiple than the data center business. And then, of course, we haven't even talked about Starlink and what he's going to do, you know, I think going to run the table on mobile. So this is the normal consolidation. We have funds like a cross-illicum valley that are distributing their shares. The stock is traded down a bit. Nothing's surprising to me here. Now it's all about execution. I think the most important thing to watch, the two most important things to watch are number one, how do the Grock and cursor revenues end the year? And number two, you know, the traction they get on, you know, continuing to replace traditional mobile carriers with Starlink. The distribution started. I think today or yesterday, I got my first distribution from a fund. I'm in a couple of funds that are in SpaceX, seems like everybody's in that. And that will obviously create down pressure if you are amongst the people who want to cash out and been in it for a long time. But I'm holding these for my grandkids. Sacks your take on these spectacular, I guess, is the only way to describe them results coming from a vertical that was in part of SpaceX's business, but nine months ago. Yeah, look, I thought it was a very bullish earnings call. I was a little bit surprised that the stock went down after the earnings call because not only was it a beaten race, but also, I think Elon spoke to a lot of their plans. The only thing I would add to what Brad said was around Starship. Elon basically said, we all saw it, right? That the Starship test flight was successful. The Starship floating in the ocean, the heat shield worked. That's going to enable more flights of Starship now at a more accelerated rate. That pays the way for the V3 satellite, which enables much more bandwidth for the Starlink network, which then powers the whole direct to cell play. So you have that piece of it. I mean, just the whole telecom aspect seemed very on track and they're very bullish about that. And then you've got the whole AI data center play. Now, on the data centers, I think what they said is that they expected to go from 1.4 gigawatts to compute to about two by the end of the year. And Elon said that the spot price for computes in the $30 to $50 per watt range. So you do the math that gigawatt is a billion watts. So $30 to $50 per watt means $30 to $50 billion per gigawatt. And I think they're at the high end of that range right now. So when Elon says, look, we're going to end the year at 100 billion of ARR. All you have to believe is that they're at two gigawatts of compute running for $50 a watt to hit that that doesn't include Starlink or the launch business or the Grock cursor piece or any of these things. So I think that's why they're so multiple ways to win is what you're saying. There's multiple ways to win with the stock. I think Starlink's just an unbelievable juggernaut cash machine. If you look at the financials, there's segment reports, space connectivity and AI. And on the connectivity side, the Starlink side, they generated $2.6 billion in adjusted EBITDA. You can kind of approximate that to be kind of operating cash flow. Space was kind of, you know, negative 200 million to call it break even. And AI was plus 1.1 billion. But AI to Brad's point, it's unclear whether the pricing they're getting on compute rental today is temporary and out of premium because of the lack of compute available in the market today and people that need compute are paying you on a premium for that cute and cute. So I think there's a question mark where that goes. But the connectivity piece on Starlink, 4.3 billion in the quarter and 2.6 billion in adjusted EBITDA. He's got 12 million subscribers. That's doubled year over year. $66 arpoo per month, what people are paying per month. And he grew 20% quarter of a quarter. So if you extrapolate this out, he's pretty close to being at a 24 million subscriber run rate on this multiple and assuming this enterprise stuff, which is like airlines and other things scale, which they seem to be scaling with the consumer business, Starlink alone could be generating on the order of $40 billion of revenue top line with a huge amount of that flowing to free cash. That could be a $30 billion free cash flow within the year. That alone provides the cash flow to fund much of what Elon's doing. And if you just put a 30X multiple on that, which I think you can, because the subscription businesses are very high renewal rate, very low-cacked, I think you could probably get a 30X just on the Starlink business. The Starlink business alone could be a trillion dollar market cap within two years within 18 months, let's say. That I think funds all of the rest of this is kind of science projects and upside. So I'm kind of making a bull case. It's crazy to me how well the Starlink business performs. And you can see it in AT&T and Verizon, QSnet, VESAT. I mean, these companies have been decimated. I used to have a QSnet satellite dish on my Sonoma County ranch in order to get internet. That's what we had to use. It was like, you know, 200 bucks a month or something. Terrible. Because those are terrible service. Terrible service. And they take forever to terrible service. And that market got decimated by Starlink. And if you launch the handset thing, that subscriber growth is going to go right now. He's adding two million subscribers on the consumer side a quarter. You could see that going to four to five million a quarter. You could actually see an acceleration in the consumer's description. 400 million mobile sub 400 million mobile subs just in the United States. I think you can make you can make the bull case on Starlink alone. And then the rest of it is like, Hey, is Elon going to do well with investing the excess capital that's spitting off of Starlink? How's Elon going to do with that money? Well, I don't know who else I give a two. You know, do what he's doing with Starship and with AI compute and the tariff. Oh my god. This is the science fiction. I mean, like if you want to talk about how the US gets off of this dependency with Taiwan and China from semiconductors, if Elon takes this on his shoulders and he delivers what he's showing as a vision here today, this is going to be the greatest semiconductor fabrication site on planet Earth. Well, you know, I would say something, you know, David to your point. know how many CEOs or founders would just take that starlink business, which is such an exceptional business, trillion dollar business going to two trillion, and they would not take any of these other risks. They would not do tariffab. They would not try to build out the days, they would not try to build their own model that is highly risky, but highly important investments that are being made. I mean, it is heroic and important that we have this level of, I just think, I'm bridled enthusiasm for innovation on the frontier that Elon's doing. And I wish we saw more CEOs, more public companies willing to take this level of risk. We just got done talking about, you know, some CEOs maybe that were taking less risk because the safe bet was, was easier to make. Elon refuses just to take the safe bet. He's taking all the dollars from this thing where he has an extraordinary business and plowing them back into these things that are critically important to the United States. And by the way, Brian, such a good point, because if you look at other CEOs and other management teams, they're getting in on this. They're starting to realize that buying back your shares, giving dividends is not as important as betting on the future. DoorDash got taken to the woodshed because they're investing too much in CapEx. Obviously, Google got smacked with their CapEx spend. So that keeps happening over and over again. And just on the headwinds that SpaceX is going to face the arguments that I think will turn out to be wrong, but they're valid to talk about here are, hey, is this demand for tokens and compute going to keep up or does on-prem and desktops and open source models getting smaller, better? Does that actually mute at some point demand? I don't think it does. I don't know. There's an upper, I don't know. There's an upper bound for on-demand intelligence. The second one obviously is Starlink is for people who are in a rural neighborhood. If you've got Verizon fiber-tier building or spectrum, you're not putting, nor can you put a Starlink on your building. So the piece there that's going to be explained probably in the next year or two is every single Tesla sold's going to have Starlink in it. When they get that merger done, what that means is you're going to have Wi-Fi networks connecting any phone to any Tesla, say, all those robotaxies out there, you'll be able to connect also directly with the next generation of Starlink. So your phone will be able to direct if it's got clear line of sight. It's going to be able to connect to any Tesla on the road, which there are many. That all future ones will have a Starlink built into them. So those are super promising. And then finally, it has been a lot of speculation about the valuation. Brad, you brought it up. I think that liquidity, when people were asking, and I heard you talk about it, hey, private companies, venture capital, we are a voting mechanism. And then when it goes public, it becomes a weighing mechanism. And sometimes you'll have this moment in time where there's hand-ringing about those valuations. And the hand-ringing peaked in the last quarter. You had 160 times price-to-sales ratio for Tesla when it first came out. 160 times, right? You take this $2 or $3 trillion market cap and you put it against a smaller revenue number. But well, if you look at the revenue number increasing, now we're down to a 45 times price-to-sales ratio. So some kind of balance is occurring here. Yeah, Brad, between these private and public markets as well as the increase in revenue. Yeah, I mean, honestly, I think this is all super healthy. I think the SpaceX IPO is extraordinary. I think the consolidation here is perfectly predictable. And now you have a company at 1.4 trillion that I think if you take a three or four year view, you can see yourself tripling your money in this business at a very reasonable valuation on the Morgan Stanley numbers or on the Elon numbers or whatever. But that's always been the bet. Do you believe that Elon is the greatest innovator and great allocator of capital? But the price of entry matters, right? When you get carried away on day one of an IPO and you buy this thing over two trillion, you got to know that this is going to happen. I was on CNBC the day of the IPO and I said, I would want to own this company, but I'm not sure today's the day I would buy the company, right? And so entry price matters. And entry price is just fundamental. But let me give you another one. We've talked about the interopic IPO or a lot of people have talked about it later this year. I hear a lot of people saying 1.5 or two trillion dollars. David just talked earlier that it's going to be run rating over a hundred billion maybe by the end of the year. That's like 10 to 15 times revenue. That is not that much for a company that just grew 10x and is rumored to be profitable and Q2. And so I look at the market, the consolidation we saw in the month of July, we put in the Leopold bottom, hopefully in July that a lot of semi stocks were down. Listen, the guy's doing great. He's apparently still up 80% for the year, just made another big private investment. I think he's done an extraordinarily good job building a firm in short period of time. But the market did panic around that as he had to cover, I think all of that is really good. So as I look ahead, marching to these IPOs later in the year on the back of the SpaceX IPO, I think we're in really good shape, particularly if these revenues continue to pace. You know what Elon's really good at? It's just building stuff. Factory physical physical physical sites that is such a core advantage in this world where everyone's competing for data centers and fabs. The software layer needs hardware in the physical world in order to deliver their software services. And there is no one better than Elon at actually doing that. Look at how gigafactories have been stood up around the world. This is his core competency. So Brad, like when you put Elon up against Adario and a Sam, even an alphabet which has 27 years of doing this, I mean, man, Elon's got a core advantage of this is what this world comes down to. He's asking me like that on the call where he said, look, putting up data centers is nothing compared to the difficulty of putting up a rocket, right? It's like, you know, creating data centers is not rocket science. So they take some of those hardware expertise that they have from SpaceX and they put them into data centers and that's why they've been able to stand up, you know, more data centers or bigger data centers faster than all the competitors. A couple of points there. Is it clear why starship is so important to Starlink? Okay, let me just explain this quickly. So basically, SpaceX has developed a new V3 satellite that has 10X the bandwidth of its V2 satellite. So currently the Starlink network is powered by V2 satellites. They deploy them on the Falcon 9 rocket and they launch about 27 satellites per launch and that adds about 2.6 terabytes per second of total network capacity. Starship deploys 60 of these V3 satellites per launch that would add 60 terabytes per second of total network capacity per launch. So over 20 times more capacity per launch. That's the power of it. So if they get Starship working, by the way, the last test, not only did it prove that the heat shield worked, my understanding is they actually launched or rather they deployed 20 V3 satellites as a test and they were able to make connection with those satellites and prove that it worked. They even had cameras on it. The reason we were able to see the Starship was because they're like, "Yolo, let's put some cameras on them." Right. Now, I think those satellites basically, it was just a test and they burned up. So I think the next big milestone here will be when they launch Starship with, let's say, 60 of these V3 satellites, put them in the correct orbit, make connection with them, add the bandwidth to the network. That's going to be a big milestone. But you play this out to his logical conclusion and the bandwidth available to the Starlink network goes up 10X or eventually 100X times and that's when they can do all the interesting things like direct to cellular. There were some interesting hints that Gwinshaw will talk about with ground stations about what they could potentially do there. I think they might buy T-Mobile or something like that, so it's easily within their range of purchases. I think Elon mentioned something about potentially the Starlink network could eventually handle roughly half of internet traffic. This thing could get so much bigger than just 12 million subscribers to your point, Freberg. But look, I want to actually talk about the data centers for a second, Brad. I do have a couple of questions about this. Elon mentioned that we're going to be at two Gwinshaw by the end of the year. He said that we will be at 5 to 10 next year, closer to 10 than 5. Let's just say 8. I'm just making that up, but it's on their range. Let's just say that's an ad of six Gwinshaw. They go from 2 to 8. To me, there's two questions there. One is, how do you know that the spot price is going to stay where it is? Can it stay at $50 per watt? How do we know? How do we track that? How much risk is there around that? I got the sense on the call that Elon thinks that number is going up because the market is memory constrained right now. I think he mentioned that we might see a 20% increase in memory production next year, but the demand is going up 200%. Plus, the market is constrained by whatever the bottleneck is at that time right now. The bottleneck is memory. Where do you see the spot price going? Then the other question I would have is if you go from 2 to 8 Gwinshaw that you have net of 6, we know that a Gwinshaw power data center is 50 billion of CAPEX. Six incremental Gwinshaw compute would be 300 billion of of CapEx next year. I'm assuming they build that, right? I mean, they have optionality around that, I'm sure. So how do you finance that? What's the most non-delutive way? They said their payback is a year or less. I'm sure that's tied to the spot price. So you only have to finance it for a year. And the question, do you think Nvidia gives them that financing? Or how will this play out, I guess is my question. - It's a great framing, David. First, it's $50 billion per giga lot to build minimum. Okay, so you're $300 billion. So in order to finance that, it seems to me, you either have to go into the market and borrow the money, or you have to do a delutive equity raise, neither of which they want to do. Or you get Nvidia to backstop it, which they've indicated that they're going to do more of, but the problem there is Nvidia shareholders don't want them backstopping on limited. Because the fear in the world is that that spot price, at some point, right, may go against you. And when it does, the payback period changes. Now, nobody thinks that the payback period is going to be one year, even though the spot price is suggesting that it is that today, right? Just a few years ago, people thought you would get paid, or not a few years ago, a few months ago. People thought you'd get payback over four years. So you basically spend 50. You then earn 10 to 15 per year. You get payback over four to five years. And then hopefully you get the six year, which really takes you up well, about 20% in terms of your returns. Right now, the shortage is so acute. And the willingness to pay from the frontier labs is so high because they all recognize they're on the verge of some massive breakthroughs that they're willing to pay three, four, five X market pricing in order to get at scale compute. And that's what happened with the Anthropic Deal with SpaceX. I think Anthropic would buy a lot more of that today if they could, same with OpenAI. You're saying that they would be willing to overpay by a factor of up to five X. Well, that's the 50, that's the $50 per watt. The David was referencing. They would be willing to pay this 30 to 50 if they could get at scale compute that would give them a competitive advantage over the other people in the market. And remember, there aren't a lot of people who have the off-take revenue that can afford to buy compute at this scale. Right? It wasn't the Chinese open source companies that were buying SpaceX's excess compute or building the 10 gigawatt plant in Ohio. That's OpenAI and Anthropic. So the vast majority of the off-take commitments are coming from Anthropic, OpenAI and Nvidia. Right? When you hear about the hyperscalers building all of this, this computer, they're building it out to sell to the people that we just mentioned. So David, NetNet, if he built six gigawatts next year and by the way, probably only Elon can actually stand up that much in that time frame. Like Jensen said to me on the pod, he's like, nobody comes close. Microsoft doesn't come close. Google doesn't come close in terms of standing it up in that time frame. I think that he's gonna have a challenge getting all of the componentry. Right? I know he can stand it up, but can he get the memory? Can he get the chips? Can he get the land powers shell all in time? I think the off-take is there. Right? But to put it perspective, this year Anthropic and OpenAI combined, they're starting total compute, it was like five gigawatts. So he's talking about incrementally adding more than they had as combined companies. Right? But that's not that much of an increase when Anthropic is growing 10X year over year and OpenAI is maybe at what, 4X or maybe higher now? So you have-- The demand exists in the world. The demand exists in the world today. I think it will exist in the world for, well, the next 12 to 24 months, but there is a wall of worry in the market. The reason we saw the pullback in July is Kimmy scared people into thinking, "Oh my gosh, they're gonna undercut the Frontiers revenues and if they undercut the Frontier Labs revenues, who the hell is gonna pay for all this compute?" That's why you saw a 40% trade down in the core weaves of the world and all of the semiconductor stocks and semiconductor related AI stocks. So in some ways, the fact that there's a discussion going on that this next 10 gigawatts is gonna cost, you know, $500 billion in US, the question, where does that come from? A secondary offering does Nvidia put it on their books? Do they create SPVs off their books? Like some people are doing, you know, the fact that we're having this conversation, everybody's aware of it. The market has been educated on it means I think people will be able to change in real time if it doesn't come to pass or if it slows down, which I suspect this cannot keep up at this pace, you know, more than another two years or so. Although, as our good friend Bill Gurley likes to remind us, he's like, "I can't believe that we're all just taking "and stride this level of seller financing." Right? He would call it circular revenues, right? But the market has gotten comfortable with this and remember, like we saw in July, if there is a scare about demand, the whole sector trades down. Yeah, everything will trade down, you know, together because that's just the leverage that you're pumping into the system. You're effectively backstopping people's ability to build ahead of their revenue. So it becomes much more violent if you ever see demand slippage, you know, famous last words, I don't see it today over the course of next 12, 18 months. But, you know, you have these unknown moments that certainly causes people to be fearful. Credit spreads are blowing, you know, and the top continued to stay wide on these deals. So there is fear in the market about them. All right, everybody, the fifth annual. If it's September, you know, it's time for the oil in summit, the fifth annual is happening. Yes, that's right, David Friedberg's been at work and we have an all star, all star list of people joining us. Chets and Wang, founder and CEO of Nvidia. If you care about where AI is headed, you want to miss this conversation. The best, the Oracle Satya Nadella, CEO of Microsoft Fan of the Pod will be coming on for the second time, Jared Isaacman from NASA, the one the only Brad Gerson and Bill Gurley BG2 coming back. SpaceX is going shot well. My guy, Jake Paul, Nick Shirley, a lot of incredible people coming. Martin Shrely maybe is even coming. He's that's going to be fun. Go to the all in summit.com to apply today. All in.com or the all in summit.com. Any of those will get you there. And we're taking over universal studios again. We'll have our own private playground, Dave Friedberg, great job on the summit. Cassino Knight too. I heard it's going to be a bit Cassino Knight. - Biggest yet. And the concert to be announced who will be performing at the concert, but it is going to be incredible. So I'll just say one of the things about the summit, we've had people come to the summit from over 60 countries. It's really incredible to meet all these people, entrepreneurs, investors, people that are just really interested in the topics that we talk about. We try and have the world's most important conversations. But it's really this amazing community experience. That's what brings folks back. So we try and invest more and more over your in making it an amazing experience. Not just cool content on a stage, which I think is one of these other shows really deliver. But it's like, how do you actually come and have an experience for a couple of days? It's going to be awesome. So we're excited. - And really is those three things that we focus on. One, you're going to learn something, right? You got these great people on stage. You're going to learn something from them. You're going to meet new people. You're going to network. And then you're going to have these great experiences. It's the trifect of folks. You're excited, Brad? You excited to be back? - What are the dates? What are the dates? - Look at your calendar. Here's me. - September 13th through 15th in LA. - This couldn't be better dates for the summit. I mean, we're going to be within 60 days of an election. Minterm election. We're going to be within 30 days of an IPO, you know, potentially of an enthrop. I mean, like, it's going to be heated. The SaaS apocalypse, not the SaaS apocalypse. This is the SaaS apocalypse is, I guess, winding its way out. The indigestion might be clearing. AirTable just got acquired for less than it raised. It's a profitable SaaS company, a great product. $480 million, half a billion dollars in annual revenue, growing 20% a year, respectable if it was a public company, with almost a billion dollars in cash has been sold. It's been sold for 1.28 billion, about 10% of its peak valuation, which was 11.7 billion in 2021. Now they did have a bunch of cash. I mean, the cash position sell was 2.25 billion. They were acquired by a firm called Bending Spoons. This is an Italian company, Milan based company. They buy challenged, but, you know, interesting businesses, AOLs, Legacy Business, Evernote, Eventbride, Vimeo, Meetup.com, and they just went public last month, shares that is Bending Spoons went public last month, shares jumped 15% on the AirTable News Sacks. When we look at this, this was a company that had done a lot of things right, had a massive amount of cash in their war chest, but rumors were maybe the founders were a little exhausted. Maybe some of the investors were exhausted, who bought it in a high level. What can we take away from this transaction in Bending Spoons? Are they the buyer of less resort now? Well, I think they're creating a great business for themselves, because I think this will end up being a fairly profitable acquisition for them. Let me just add a piece to this, which is AirTable spun out its AI agent business, which is known as Hyperagent, into a separate independent company, prior to this acquisition. So, I think what's going on here is that the founders and town of the company, they said, "Look, we don't want to have to make this legacy product work, that's basically a private equity play. I'll explain what that means in a second. We want to focus on the new thing, the AI company, That's where the big value creation is going to be in the first place. future or the potential for it. So essentially the talent is going to focus on the venture play and then they're selling the private equity play to bending spoons. Now, why do I think this could be a good acquisition for bending spoons? I think there was a really interesting data point that I saw on the commentary on this, which is only 30% of air table sales team was making quota. They had a 30% sales attainment number. And that told me a lot about this business. Okay, what it told me is, and I'm reading between the lines here. But this was a company that had a successful PLG motion. In other words, organic growth, pot-like growth, and they were growing about 20% a year. But that was not good enough for its board. These are investors, some of whom invested in an $11 billion peak valuation. So they're looking for a venture type outcome. So what happens? The board pressures the founders to do something that frankly is unnatural for them, which is they say, look, you should bolt on a traditional sales-led motion here to get the growth up faster. Does that work? No, they probably get a little bit of growth out of it, but they only get 30% attainment. So they've got hundreds and hundreds of sales reps here trying to push on a string, and it's not making a growth faster. So now what's the opportunity for the acquire here? Bending spoons can go in here and do what Elon did at Twitter, eliminate 85%, 90% of the cost structure. Don't do this sales-led motion. Just go back to your product-led growth roots. You'll probably keep most of that 20% growth, and it'll be a very profitable company. You'll be able to-- 80% profitable, probably, right? Probably, I mean, $400 million to the bottom line pays for the acquisition in a couple years. People are saying they're going to generate 30% EBITDA margin. I think like you're saying, it could be 80%, 90%. I don't think you need to keep most of this business, or most of the cost structure associated with this business. AirTable is a company that has its fans. I think they will probably stick with it. And you'll be generating-- I don't know, you could probably generate $300 million of EBITDA a year or $400 million while growing 10% to 20%. So that's a play for bending spoons. And the venture investors here, SACs, they're happy to get their money back and move on to the next thing. It's a bit of a push for them in terms of at the blackjack table. Rather than they've got to go 10x just to catch up. And then they would have to go 10x again to make their LPs happy. It's not going to happen. I think the question is, if bending spoons can basically take this business that's not making money and probably generate $400 million a year of EBITDA and pay for the acquisition in just three years-- Amazing. Why isn't that something that the company could do on its own? And I think that's the structural problem. I think it's very hard for both VCs who are on the board and the founders to shift into private equity mode. Why? Because they're going to have to demolition what they've built. They've got all those loyalty to the team. They don't want to think about how do I eliminate 80% of the cost structure. It's just not what they do. I mean, what founders want to do and the outcome that the board members are going for is a venture backed outcome. And I think they could have done this. They could do what bending spoons does. They're not built for it, SAC. They're not built for it. And moreover, the structure of the cap table is all wrong because they're sitting behind this giant liquidation preference. All these investors have to get paid back. We invested at this $11 billion valuation and all the way up as well. The incentives are broken, Brad. And you yourself, at your firm outtiminary, you were pretty frisky in this period. You made a lot of bets. So I don't know if air table was one of them, but you made some SaaS bets there. Some of them were at high valuations. How are you looking back at that time period? Any lessons that you take going forward? Multiple of revenue can compress very quickly. It works great when the company's growing greater than 50%. But remember, it's just a heuristic. It's just a very rough estimate. Used almost exclusively in Silicon Valley. So people are saying, oh my god, this thing sold for two times revenue. But when you actually look at a look through basis, probably sold for maybe 30 times free cash flow. I don't think it's easy to get it to $400 million in EBITDA. I think if it was, the board would have done that. I'm not-- we're involved in some of these companies. Once they slow down, the company morale goes to hell. Turn over among your customers begins to spike. It starts to feed on itself. So I think-- It sucks to go to work every day. Brad, what do you need to keep? What do you need to keep? It's a look very tricky. I don't know the core product and what's happening in terms of turnover in the core product, David. But my hunch is that the core product has started to really fizzle as the advances in the core product have slowed down. You're seeing a bunch of churn out of it on the product side. And now people are saying, listen, it's almost impossible for a software company today to keep any decent salespeople, to keep any different decent product development people. Because they all want to go work on AI. Agreed. But you don't need them for this product. I mean, the market's being efficient. I mean, look, this is where I think bending spoons has an advantage that the companies board and founders wouldn't have, which is they already have an infrastructure, right? They have a core team of bending spoons that's managing now-- I don't know, dozens of these properties. And so they can plug this in. I think AI in a way makes their job easier. Because in the past, the reason why-- Of course. --you couldn't eliminate all of the talent the infrastructure is because you need the institutional memory. You need people who knew the code base. Now AI can learn the code base instantly, right? That's interesting inside that. And so yeah, maintaining is easier with AI. I think maintenance mode becomes a way easier with AI. Because you don't need the historical knowledge anymore. The AI can go in and sort of reconstitute that historical knowledge. Let me get you in here, free bird, if I may. When we look at the lessons from peak zirp and sass, and then we look at this moment in time, this surging AI market, any parallels that we might find here, or lessons between the two? Between zirp and AI? There are. The zirp sass era. We had a lot of very high evaluations, a lot of enthusiasm, a lot of suspending disbelief. We're here in the AI era. We just talked about the price of compute and all these companies being out of 100x-- No, I know. --price sales ratio. --any parallels here or not. It's kind of a softball question for you. No, this is a very different paradigm. The AI CapEx build out and model training, which is where the predominance of the capital is flowing, is not about some high multiple on revenue, which is where capital was flowing in the sass. It's like, oh, you get a 20x multiple, turn a dollar into 20. That's great. Let's do it all day long. This is a very different structure and strategy and capital allocation process. So I don't think that I would look at them as being a lengthy-- It was a softball question to me on as I was letting you hit it out of the park. Look, I mean, obviously, sass companies were overvalued during the Zerp era for two reasons. One is that we had artificially low interest rates. So we had a kind of a speculative asset superbubble. But the other is that people were treating these things like guaranteed annuities and actually growing annuities. They'd look at it and see, oh, 120% net dollar attention. So this single does grow 20% year over year forever as a base case, right? And they were then priced that way. But what we've seen with AI is obviously, there's disruption. And you can't-- to Brad said, I'm sure they're seeing elevated churn right now. And it's not an annuity. Things can change. So obviously, now these things are trading at a much greater discount. All of that being said, let me just say, I don't think you can extrapolate to the entire sass space based on this one company air table. I think there's some things about air table that make it very different than-- I don't know, let's say a Salesforce or a workday-- is air table is always a little bit of a quirky product. I remember at the peak hype for this company, people were saying, oh, this is like a new Excel or a new Google Sheets. It's basically-- New Microsoft Office, yeah. Yeah, it was basically a spreadsheet for words. That's how people were viewing it. It's like this new kind of spreadsheet for words as opposed to numbers. And it never achieved that kind of promise. It never achieved that kind of ubiquity. People understand how to use spreadsheets. Everyone uses them. Air table never got to that point. Most people still don't know what air table is. Again, it had its dedicated fans, but it was a hard product to explain to people. When do you use it? It had a cult following. But it never achieved that sort of level of acceptance. It was never self-explanatory. In terms of why you should use it, what the use cases are, they never were able to get the market right because of that. And to be honest, if you look at Cloud Code Work, complexity, computer, agents, those things are now doing what Air Table did. So-- Never carved out, I think, in niche where it was super clear, when you were always supposed to use the Air Table. And really, it was part of this hodgepodge of this grab bag. You could say, of no code tools. This is the category it was put in. And no code has to be the most impacted, the most disrupted area of SAS right now, because what is Cloud Code really good at? I mean, that's the ultimate, no code tool. It's no code. Loatable, Cloud Code, for complexity. Yeah, the thing with Air Table or Retool Things like this is it's true you didn't need to be a coder to use them, but you had to learn how to use Air Table. You had to learn how to use Retool, all these-- It was kind of these alternative programming languages, in a way. And you just don't need to learn any of that anymore. I mean, you use Cloud. and you'll see. just tell it what you want it to create. And so, if you do want to create some sort of new dashboard, some sort of, I don't know, like a verbal spreadsheet or whatever, you just tell Claude what you want. You don't have this learning curve. Look, all of this is being impacted right now, but this is got to be the most impacted area. So I don't know that you can totally extrapolate based on what's happening to AirTable. I don't necessarily think that you want to replace your CRM, your ERP, your HR system, with something that's been vibe coded, you want the certainty, you know, for anything that involves compliance. I got to be honest, my team, Sachs, made, I don't, do you use like a portfolio off the shelf, SaaS tool for managing crafts, like portfolios and everything? Well, we vibe coded something, actually. So, yeah, we just did the same too. So my team just built something that is so mind blowing that to buy it with off the shelf software would have been a quarter million dollars in software and like a million dollars in integration over two or three years. And we built it in a month. And now we have complete insight into the whole portfolio, the competitive set, the founders, everything going on. Keep in mind that one of the reasons why Leopold got blown out, okay? I mean, it is because he bet on the SaaS apocalypse. Remember, it wasn't just that he was super long, these chips stocks that had a correction. Oh, is that right? He was super. He was a short, Adobe and a whole bunch of other SaaS companies. And those trades also move the wrong way on him. So again, I just think that it's painting with two broad of brush to say that all of SaaS is going to get obliterated here. Yeah. And there was a really good post about this, let me just quote from this where they said, nobody buys Microsoft because Microsoft writes the best code. They buy Microsoft because Microsoft is the rail that everything else runs on. Actors for directories where your employee identities live, Excel is where your board decks numbers come from, teams is where the compliance recorded conversation happens, Azure holds a Fed ramp, high authorization and Department of Defense impact level five clearance, which means a defense contractor cannot casually swap it out for something cheaper and so on down the line. So there's a lot of really good compliance reasons why if you're a large enterprise, you're not going to want to spend tens of millions of dollars ripping out something that costs you a million dollars a year. That just doesn't make sense and I noticed that Benny off just tweeted five minutes ago that 15 out of 15 cabinet agencies run on Salesforce. Look, the government is not going to rip and replace Salesforce with some hype coded. So look, it not all SaaS is equal in this dimension. I just want some figma. I just think some of these SaaS companies with great founders who are in it for the long term and they have like passionate user bases. I think they will make the jump to AI first products and I put figma in that bucket. Just to wrap this, this actually, I GV is up 20% in the last six months. It's up 20% in the last five years. Explain I GV please. So the high growth software stock index, right? Snowflakes 88% in the last six months. That's it's an IGV is an ETF of the IGV is an ETF of gross software companies. So to David's point, there was a panic about software companies. There was a big trade out. And honestly, they performed pretty well. And as he mentioned in the month of July, they were up when a lot of the semi conductor AI stocks were down. And some of these companies, Databricks, Snowflake, Clickhouse, etc, are doing extraordinarily well. As I just mentioned, Snowflakes up 90% in the last six months, which puts it in the same category as the semi conductor AI stocks. So to David's point, you can't throw them on the same bucket. But I do think that for these no code, a lot of these application software companies, they're realizing like the game is up, sell the company, get what you can get. Importantly here in the air table story, all the late stage investors, right? We passed on this in the last three funding rounds, right? Which I think we're at $2,5 billion and $11 billion. But all those late stage investors, which were the most venerable of growth firms, they all got their money back. And the early stage investors ended up making a lot. So if this is a failure, this is a pretty good failure for Silicon Valley. This is one of the points that was made at that time, which is, hey, this is a strong enough company and team and revenue base that if we just got our money back with the optionality, hey, maybe this would be a good investment. You could say the same thing about some AI bets. Well, this is one of those cases where the liquidation preference actually mattered. Absolutely. Absolutely. It doesn't matter. Well, I think that's great money here. My understanding is this wasn't like they had like a 7% interest rate or they didn't have like a participating preferred where you get two times your money back and then they do the trade. Does anybody know? Because I looked deeply into this. They just say and I think that net of cash, they may have come in a little bit less than the total cash raise, but it seemed like everybody got made whole. Yeah. But if they had the, I guess, sacks, we lived through moments in time where companies had to guarantee a 1x, right? You know, one x liquidation preference is standard. It just means you get your money back before other people start to profit, which is appropriate. But also the interest rates were taken out, right? Of these deals, I think during the standard terms, you know, what's known as clean terms. This is a simple 1x liquidation preference. The preferred just gets their money back before the common starts to participate in a successful sale of the company. That just makes sense, right? Yeah. But participating preferred is the double-debt, right? Yeah. And look, we've never done that. You know, we believe in clean terms. No one's trying to be punitive towards founders. It's just, it doesn't make sense for some people in the cap table to be making money while other people are losing money. Yeah. This doesn't make sense, right? That's just a transfer of value from some people in the cap table to other people in the cap tables. So, the standard thing you do is you make sure that the investors get paid back and then everybody participating in the upside. Okay. So, what's the short story here? China is training on US data from US providers Forbes, published an investigation called, these American startups are making China's AI smarter. And I think this relates to a lot of your work in the early part of the administration SACs. They claim US data labeling startups are selling valuable training data to Chinese labs, which in turn is helping them catch up with the US frontier ones to start up Surge AI and Merco are both valued at over $20 billion. They sell training data sets to people like Open AI and Thropic, Federal agencies. They all sell the same data sets to top Chinese AI companies according to this report like Tencent by Tencent, Alibaba, Moonshot, etc. Top six AI labs in China according to this report are spending $500 million a year buying what Forbes calls secret sauce, PhD written content, reinforcement learning, knowledge pipelines, all that kind of great stuff. I have investments in a couple of these companies, including Micro One. The founder of Micro One didn't participate in selling to China. He made that decision. SACs, what do you think here about this new wrinkle in terms of really the secret sauce behind a lot of these models is the data we've run out of open data on the web. Obviously, we talked last week about the books being, you know, having the spines taken off of them and scanned in. I mean, people are looking for data. Merco or Micro One, all these companies are providing it. Should they be providing the same data and selling it to Chinese open source companies or not? Well, look, I think we got to decide what our objective is here. Are we trying to just get in like a full blown economic worth China? We just trying to prevent all of our companies from doing business over there. If that's our objective, then you can take that position. Historically, the rules have been that you want to be careful about technology transfer of technology that has a dual use, right? That it has a military application. My sense of data is that it's largely a commodity. I mean, data labeling certainly is. If you basically tell them that they can't use data labeling. I guarantee you there's no shortage of labor in China that they can use to do the data labeling. In fact, they probably are. What I'm saying is there's a lot of ways to get this data. So look, if we basically ban these companies from selling to China, we should expect reciprocal actions taken by China to ban companies over there selling to us. Maybe rare earths. These two countries are not completely independent of each other. By the way, I want us to be as independent and sovereign as possible. I don't want to have any dependencies. No dependencies. But we still at this moment in time do have some dependencies. So I think you have to ask the question, is this data really proprietary? Does it have a do use? It is. It does have a military application. Yeah. Well, I don't think it has military. It's definitely not data labeling. This is like hiring PhDs, hiring super professionals to create unique data sets. So it's science. Well, look, it's trying to do that too. And I guarantee you they are. I don't think this is going to give us a decisive advantage in the AI race. It's going to annoy. It's going to create annoyance. It's going to create friction. And how bad do you want our relationship with them to be? Do you want to restarting another trade war? Look, I'm not against restrictions when I think they're going to hack a punch. For example, I'm really glad that the first Trump administration limited the export of EUV lithography machines to China. You know, that was all the way back. I think in 2019. So that was a really important decision. And so look, I think targeted strategic controls make sense. I would just make sure that this one actually meets that bar. Brad, any thoughts here on this open source catch up the data being sold to China and our adversaries? Are you concerned about these open source models and then us providing data to them? First, you know, I'm an absolute agreement with you. David that we want maximum competition as we sit here today, the U.S. is winning. We talked about it at the start of frontier labs are winning, our open source is winning. And we have fairly limited regulations, right? She's coming here in September in a bilateral meeting to meet with the president. We're advancing relations on a variety of fronts. So I think everything looks good and you want to continue down that path. With that said, I will tell you that this will irritate people in Washington who feel that this, along with distillation and other things, could be the export of chips, all of which, at a certain level, makes sense, cause people to wonder whether or not we're making it too easy on the Chinese labs to catch up with American labs, you know, in the race to frontier intelligence. So it's the type of story Jason that I think will continue to money the waters that will continue to be monitored. The reason I don't think it will cause us to change our stance with respect to China is because we're winning. But if the president asks his advisors, you know, one of these days, six months down the line, are we winning against China? And all of a sudden he gets a response, no, we're no longer winning. They caught up. They passed us, etc. Then these things will get a lot more scrutiny than they're getting today. I think the only reason they pass muster today is because we're still leading the race. I got to say using Kimmy and Gwen and, you know, GLM 5/2 for the last 60 days, my Lord, these things are good. And I don't think it's very patriotic to be giving them an advantage. I wouldn't do it. I'm glad the company. Sorry. What's the advantage? What's the data set that you're worried about that's so proprietary? Any of these data sets are created by experts here in America who are given like the queries that have errors in them. So when you give them, you know, a thumbs down to a query that's highly technical. It could be code. It could be biology and science. These are, you know, PhDs going in there and putting in the latest and greatest content and then verifying it, double verifying it. And that's why we're getting better and better results out of the LLMs. So essentially, you're just helping them catch up. And this could be a big advantage for America if we weren't sending it there. I think a big reason these models are getting better is because data is being leaked to them. But what makes you think that China can't do this? They have tons of PhDs over there. They would have to hire. No, no. They were to do it at this scale. They would need to hire the best and brightest scientists and experts in the West. So basically, all the knowledge of the West is being put into packages for our LLMs to get better. They're sending those same packages and reselling them to Chinese companies, which means they catch up just as quick. I think it's a big part of why they're catching up. And why I'm with distillation, it's really a very similar process. Look, if there's something truly proprietary here, I don't want us to sell our secret sauce to China. So, I'd have to look into that and see, is there some real secret sauce here? But this idea that it would seriously disadvantage China. You know, they're graduating more math and science, graduates every year than the rest of the world combined. I mean, they don't have a shortage of smart people, especially. Yeah. And checking them out in the country has the other problem. We got to get that fixed. Well, it's like, there's a lot of different issues here. I don't know. I mean, you want to conflate. But this idea that they can't, but this idea that they can't recreate those data sets. I mean, look, if there's something truly proprietary here, if it has a dual use, if it's military related, but I don't know that that's what this is. Well, they're all proprietary. It might design, but I don't know about the dual use because I don't have the data sets here. All right, folks. That's another amazing episode of your all in podcast. Thank you so much, Brad, for joining us. Chimoff, good luck on your world tour. Hope you're enjoying a little rest and good luck trying to buy a white turtle neck disease in there. Sold out every way. All right. So go to the all in.com store all in.com slash store. We have 1000 signature. Chimoff autographed white sweaters coming. You could sign up in advance for this. All proceeds go to charity by charity. I mean, spots. Y'all fun. All right. We'll see you next week. Everybody bye bye. We'll let your winners ride. Brain man David Sadler. We open source it to the fans and they've just got crazy with it. I'm going to be a queen of kinwap. I'm going to win. What? What? What? What? What? What? What? What? What? Besties are gone. That's my dog taking it away. You should all just get a room and just have one big huge or two because they're all just like this like sexual tension that we just need to release them out. What? You're the bee. What? You're the bee. Be. What? We need to get my besties already. I'm going all in. I'm going all in.