谷歌 AI 人才流失、SpaceX 的丰收季、Airtable 暴跌九成、美国数据喂养中国 AI

Jason Calacanis (00:00:00): Alright, everybody. Welcome back to your favorite podcast. It's the all in podcast. It's the summer. It's August 6. Having a hard time getting a quorum here on the podcast, but David Friedberg is here. David Friedberg is back, our sultan in science. How are doing, brother? David Friedberg (00:00:15): Great to be with you. Jason Calacanis (00:00:17): It's great to be with you. And everybody loves Brad Gerstner is here. He's your Bruce Wayne. If markets are your game, he brings that namaste to your payday. Buy his glasses at discount, and he'll get Brad Gerstner (00:00:31): you one Jason Calacanis (00:00:32): of those fancy Trump accounts. Alright. Welcome back to the program, Brad. David Sacks (00:00:36): I love it. I love it. You bring the rhymes back. Jason Calacanis (00:00:38): I bring a little intro back. We've been try Chamath is on the road right now. Chamath is on the road, but we will get a field report from Chamath. And I I called Daniel somehow Saxe is gonna 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 text there. Brad Gerstner (00:00:59): Oh. Oh, there he is. He made it. Jason Calacanis (00:01:00): Guys. He made it. Speaker 5 (00:01:02): How do you like my beautiful summer gelee? Jason Calacanis (00:01:05): It's incredible. It fits perfectly. You look warm. Speaker 5 (00:01:09): I don't know how Jamoth does this. Brad Gerstner (00:01:22): Fans, and they've just gone crazy with it. Love you, West Ice. Queen of Kenwa. Going all in. Jason Calacanis (00:01:29): Well, here's the report, everybody. As everybody knows, Shammoth is on the road. He oh, here he is. He this is a photo of Saks. He went he went to check his data center progress. I think that's in Colorado or Nevada where he was building a data center. Speaker 5 (00:01:42): I think that's on Dune. Jason Calacanis (00:01:44): Oh, it's on Dune. Yes. Dune 4. Ah, yes. There he is admiring himself. Brad Gerstner (00:01:48): Oh, Jason Calacanis (00:01:50): Here's Nat. 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, Brad, you were on CNBC David Sacks (00:02:02): with Andrew Ross working. Oh, there you go. David Friedberg (00:02:05): I wasn't sure if it was my Twitter feed that was just selecting into it, but it nearly hit everyone. Right? This was a viral thing. Jason Calacanis (00:02:11): This has hit everything. Alright. Listen. We got a lot to get to. Enough with the shenanigans and small talk. Google had two major shakeups to its AI staff on Wednesday. Demis Hassabis has moved to chair of DeepMind and chief scientist at Google. Reports describe 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 Axios' quote explaining the shake up. Quote, Google's Gemini 3.5 pro is months behind with some company sources telling Axios 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, Freiberg, 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, from what I understand continuously, for twenty seven years. Discovery Loop's gonna be focused on deep scientific breakthroughs in AI. Google shares down 4% on the news of Dean leaving. So 200,000,000,000 in lost market cap if you wanna correlate those two things. Freiberg, 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? David Friedberg (00:03:42): 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 200,000,000,000 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 ChatGPT equivalent internally a year before ChatGPT came out from OpenAI. Google chose not to release it for fear of cannibalizing search and so on. That's when Sergei stepped in, and there was this whole kind of revitalization. But as time has gone on and as everyone has 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 in 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. David Friedberg (00:06:02): They can be model agnostic. They can work with Anthropic. They can work with OpenAI. They can work with SpaceX. They have a significant ownership stake in SpaceX and in 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, you're Demis, you're Jeff Dean, you're this whole crew, and you're inside of Google, 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 Gerstner and a couple other people and raise a couple billion dollars at a multibillion dollar pre money with a PowerPoint deck because I'm the greatest in the world at doing this, that might be a better path for me. And I think that that's the moment. So I 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 in compute infrastructure, less capital into model development, That's what I think is going on. Jason Calacanis (00:07:09): Brad, what's David Sacks (00:07:10): your take on this? I think David nails it. I mean, listen. The same thing's going on at Microsoft. Right? Satya is out this week saying you know, citing Morgan Stanley's report and saying they're seeing over a 30% return on invested capital in tokens as a service, so in the infrastructure business. So I think David's exactly right. Those are such good businesses. Right? You you you you deploy capital. Everybody's running it from you. But the scientists who wanna be involved in superintelligence, who wanna cure cancer, who wanna 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 Anthropic at the same time they're trying to build their own model with Grok 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. Meta's talking about getting into the infrastructure as a service game. And then at Anthropic and OpenAI, 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 clarifying view as we look forward that we may in fact not have those companies on the frontier of of of model development if all these people leave. David Friedberg (00:08:56): 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 you get right back. Yeah. Jason Calacanis (00:09:12): Yeah. Hey, Saxe. Let me have you comment on, this as well, Polymarket, which companies will have the number one AI model by the end of this year on December 31. 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? OpenAI 32%, Google 20%, Alibaba 14% and then you got Moonshot, xAI, Meta, ByteDance all at about 10%. So Saks, 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 gonna be a commodity and just need to be on the application layer? Speaker 5 (00:09:54): 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 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 on 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 wanna call it, that's six to twelve 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 OpenAI 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 80,000,000,000 of ARR. Started the year at 10. It had forecast a 100,000,000,000 as exit ARR for the year, and most people said that that would be impossible to achieve. Now it looks like they're gonna do it with a couple of months to spare. So their estimates are going up. I mean, one ten, one twenty, or higher for end of year ARR. Speaker 5 (00:11:50): OpenAI seeing acceleration. So I think what you're seeing now is a very clear bifurcation of 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. David Sacks (00:12:31): Yeah. Jason, what do you think? Jason, what do you think? Jason Calacanis (00:12:34): 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 x AI just had the SpaceX earnings. We're gonna 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 3,000,000,000 monthly users each. Android search, Gmail, Chrome, YouTube, all have over 3,000,000,000. 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 Freiberg, 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 nine fifty 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, you know, 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, Elon responded back to me, it's actually a world of difference. You know, 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. Jason Calacanis (00:14:31): They're just not necessary. The people using the frontier models are doing it because their company set it up and they it's too hard to implement open source right now, but it's gonna get easier and easier to implement it. So I'm still going with open source and Gemini being the leaders in this I Speaker 5 (00:14:46): 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 cheaper 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 wanna take the chance that 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 this goes back to the blog post that Decagon posted, which is if you're looking for use cases, you also wanna use the true frontier. Because, again, when you're dealing with immature use cases, you don't know where the value is gonna be, and you're searching for opportunity to use AI. You just wanna use the best because, again, the return on finding those use cases is gonna 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, you know, the use case is immature, where you're in a competitive industry, where you're just deploying AI, you want the convenience of the full Jason Calacanis (00:15:56): So why not go frontier models Speaker 5 (00:15:57): to Yeah. Brad Gerstner (00:15:58): Summarize Again, Jason Calacanis (00:15:58): your Speaker 5 (00:15:59): you know, unless your employees are doing something stupid like you create a leaderboard 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 are just like, give me the best. I'm willing to pay a premium for the best. David Friedberg (00:16:11): 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 source, 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 gonna be using some open weight model because they can pay $20.40 bucks a month and get ChatGPT or Gemini or Claude and be very happy paying $40 a month, and they'll basically be able to minimize their cost to run that for consumers. For enterprise, I think the enterprise is gonna be very active in selecting a blend of models that are gonna 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 For sure. 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 OpenAI on the life sciences side. They're very well ahead on that front. I mean, Demis is still gonna be running isomorphic labs. So when it comes to these 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. David Friedberg (00:18:00): 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 gonna sign up for working with GCP versus working just with Anthropic. Jason Calacanis (00:18:13): One of the things this has created is downward pressure on the pricing. We saw OpenAI and Claude do massive price cuts for tokens. So they are reacting. They're not taking and 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 this just great for consumers and enterprises? David Sacks (00:18:37): Because Listen. Jason Calacanis (00:18:37): We've got massive competition. David Sacks (00:18:40): That's the thing. America's winning. It's 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 a duopoly. I think it's hard to call it a duopoly 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, you know, as the pure plays. Their revenues would suggest that they're, you know, they're gaining share of wallet. But there are two points I wanna make here because I think they're nonconsensus views that were spoken this week. One was Elon's response to you, Jason. Right? Over the last two weeks, everybody's been saying that the Chinese have caught up, that open source tokens have have caught up in intelligence, that they're much cheaper, etcetera. 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. You know, if you don't have to build it for yourself, if you don't have to you know, the training costs and a lot of expertise to fine tune and maintain and guardrail and keep it safe. So he's basically making the argument that not only are the the the frontier models further ahead, but that the cost differential between the two is not what everybody's making it out to see to 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. I you're right, J Cal, you know, 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 wanna see. Jason Calacanis (00:20:31): 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 SpaceX 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 at, it seems to have settled in at a $1,400,000,000,000 valuation, went public obviously above 2,000,000,000,000. Q2 results were spectacular is the only way to put it. 7,800,000,000 in revenue, up 92% year over year, let that sink in, and 67% quarter over quarter AI revenue, Elon web services, more than tripled quarter over quarter to $2,600,000,000. That's not Cursor. That hasn't closed yet, but that's gonna be one of the great purchases in history. This is from Elon Web Services, renting out compute specifically to Anthropic and Google from the Colossus, collection of servers. But CapEx was up 18,400,000,000 in the quarter. That's six x year over year. Obviously, you can do the math there for a run rate of about $75,000,000,000. I'll stop there and get your reaction, Brad, to the SpaceX IPO. I know you've been tracking this and commented on it. Yeah. David Sacks (00:21:52): I mean, listen. I think that one first, let's start off. $1,400,000,000,000 of 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. 100,000,000,000 in ARR by the end of the year, and he pulled forward the $1,000,000,000,000 target in ARR by a year from 2031 to 2030. Now to just put that in perspective, Morgan Stanley's 2030 revenue estimate is 325,000,000,000, which is also extraordinary. Remember, this company did 18,000,000,000 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 2,000,000,000,000, we were pricing ahead a couple years. I think now it's you know, the 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 gonna 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 off take 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, etcetera. On the frontier model business, I think this is the sleeper. I think he said on the call that Grok tripled tokens in the month of July. That doesn't include Cursor. Cursor was already on a path to go from 3,000,000,000 to 10,000,000,000 by the end of the year. Cursor plus Grok could be at 10 to 20,000,000,000 by the end of the year. David Sacks (00:23:48): That would be an extraordinarily valuable asset gonna 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 gonna run the table on mobile. So this is the normal consolidation. We have funds like across Silicon Valley that are distributing their shares. The stock has traded down a bit. Nothing 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 Grok and Cursor revenues end the year? And number two, the traction they get on continuing to replace traditional mobile carriers with Starlink. Jason Calacanis (00:24:27): The distribution started, I think, today or yesterday. I got my first distribution from a fund I'm in. I'm in a couple of funds that are in SpaceX. Seems like everybody's in that. And that will obviously create downward 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. Sax, your take on these spectacular, yeah, I guess is the only way to describe them, results coming from a vertical that wasn't part of SpaceX's business but nine months ago. Speaker 5 (00:24:57): 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 beat and raise, but also I think Elon spoke to a lot of their plans. The only thing I would add to to what Brad said was around Starship. Elon basically said we all saw it, right, that the Starship test flight was successful. The Starship's floating in the ocean. The heat shield worked. That's gonna enable more flights of Starship now at a more accelerated rate. That paves the way for the v three satellite, which enables much more bandwidth for the Starlink network, which then powers the whole direct to cell play. So you had 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 of compute to about two by the end of the year. And Elon said that the spot price for compute is in the $30 to $50 per watt range. So you do the math, a gigawatt is a billion watts, so $30 to $50 per watt means $30 to $50,000,000,000 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 $100,000,000,000 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 Grok cursor piece or any of these things. So I think that's why they're Jason Calacanis (00:26:30): And multiple ways to win is what you're saying, Sachs. There's multiple ways to win with the stock. David Friedberg (00:26:34): 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, it they generated $2,600,000,000 in adjusted EBITDA. You can kind of approximate that to be kind of operating cash flow. Space was kind of, you know, negative 200,000,000 to call it breakeven and AI was plus 1,100,000,000. But AI, to Brad's point, it's unclear whether the pricing they're getting on compute rental today is temporary and at a premium because of the lack of compute available in the market today, and people that need compute are paying Elon a premium for that compute. So I think there's a question mark where that goes. But the connectivity piece on Starlink, 4,300,000,000 in the quarter and 2,600,000,000 in adjusted EBITDA. He's got 12,000,000 subscribers. That's doubled year over year. $66 ARPU per month, what people are paying per month. And he grew 20% quarter over quarter. So if you extrapolate this out, he's pretty close to being at a 24,000,000 subscriber run rate. On this multiple, I'm 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 $40,000,000,000 of revenue top line with a huge amount of that flowing to free cash. That could be a $30,000,000,000 free cash flow within the year. That alone provides the cash flow to fund much of what what Elon's doing. And if you just put a 30 x multiple on that, which I think you can because these subscription businesses are very high renewal rate, very low CAC, I think you could probably get a 30 x just on the Starlink business. The Starlink business alone could be a trillion dollar market cap within two years, within eighteen months, let's say. That, I think, funds all of the rest of this as kind of science projects and upside. David Friedberg (00:28:32): 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 and T and Verizon, HughesNet, ViaSat. I mean, these companies have been decimated. I used to have a HughesNet 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 a month or so. Terrible. Jason Calacanis (00:28:52): Terrible service. Orbit. Right? And they take forever to David Friedberg (00:28:54): Terrible service. And that market got decimated by Starlink. And if he launches the handset thing, that subscriber growth is gonna go right now, he's adding 2,000,000 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 subscription. David Sacks (00:29:13): Mobile sub 400,000,000 mobile subs just in The United States. Just in The David Friedberg (00:29:18): United States. You can make the bull case on Starlink alone. And then the rest of it is like, hey. Is Elon gonna do well with investing the excess capital that's spitting off of Starlink? How's Elon gonna do with that money? Well, I don't know who else I give it to to, like, you know, do what he's doing with Starship and with AI compute and the TeraFab. Oh Jason Calacanis (00:29:35): my god. This is a science fiction. Mean, if you want to talk about county tax. You know, David Friedberg (00:29:41): 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 gonna be the greatest semiconductor fabrication site on planet Earth. David Sacks (00:29:54): Well, you know, I I would say something, you know, David, to your point. You know how many CEOs or founders would just take that Starlink business, which is such an exceptional business, trillion dollar business going to 2,000,000,000,000, and they would not take any of these other risks. They would not do TeraFab. They would not try to build out the data center. They would not try to build their own model. That's 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, unbridled 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. Jason Calacanis (00:30:56): And by the way, Brad, 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 gonna 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 gonna 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 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 to your building or spectrum, you're not putting nor can you put a Starlink on your building. So the piece there that's gonna be explained probably in the next year or two is every single Tesla sold is gonna have Starlink in it. When they get that merger done. What that means is you're gonna have Wi Fi networks connecting any phone to any Tesla, say all those robo taxis 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 gonna 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, you know, there's been a lot of speculation about the valuation. Brad, you brought it up. I think at liquidity when people were asking you 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 wringing about those valuations. And the hand wringing peaked in the last quarter. You had a 160 times price to sales ratio for Tesla when it first came out. Jason Calacanis (00:33:02): 160 times. Right? You you take their this 2 or $3,000,000,000,000 market cap, and you put it against a smaller revenue number. 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. David Sacks (00:33:24): Yeah, I mean, honestly, I think this is all super healthy. I think the SpaceX IPO was extraordinary. I think the consolidation here is perfectly predictable. And now you have a company at 1,400,000,000,000 that I think if you take a three or a 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 2,000,000,000,000, you gotta know that this is going to happen. I was on CNBC the day of the IPO, and I said, I would wanna own this company, but I'm not sure today's the day I would buy the company. Right? And so, you know, Entry that Jason Calacanis (00:34:11): price matters. I mean, fundamental. Just not David Friedberg (00:34:13): But but let David Sacks (00:34:14): me give you another one. You know, like, we've talked about the Anthropic IPO or a lot of people have talked about it later this year. I hear a lot of people saying one point five or two trillion dollars. David just talked earlier that it's gonna be run rating over a 100,000,000,000 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 10 x and is rumored to be profitable in q two. And so I look at the market, the consolidation we saw in the month of July. You know, we put in the Leopold bottom, hopefully, in July that, you know, a lot of semi stocks were down. Jason Calacanis (00:34:46): It's poor guy. Leopold bottom. Hey. David Sacks (00:34:49): Hey. Listen. The guy's doing great. He can he's apparently still up 80% for the year. Just made another big private investment. I I think he's done an extraordinarily good job building a firm in a short period of time, but the market did panic around that. Yeah. As as 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 in in really good shape, you know, particularly if these revenues continue apace. David Friedberg (00:35:18): You know what Elon's really good at? It's just building stuff. Jason Calacanis (00:35:22): Like Yeah. Factory specific. Speaker 5 (00:35:23): By the way, David Friedberg (00:35:24): is it is 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 a Dario and a Sam and even an Alphabet, which has twenty seven years of doing this, I mean, man, Elon's got a core advantage if this is what this world comes He down Speaker 5 (00:35:58): said something 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 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 more data centers or bigger data centers faster than all the competitors. A couple 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 v three satellite that has 10 x the bandwidth of its v two satellite. So currently, the Starlink network is powered by v two satellites. They deploy them on the Falcon nine rocket and they launch about 27 satellites per launch and that adds about 2.6 terabits per second of total network capacity. Starship deploys 60 of these v three satellites per launch. That would add 60 terabits 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 v three satellites as a test, and they were able to make connection with those satellites and prove that it worked. Jason Calacanis (00:37:30): They even had cameras on it. The reason we were able to see the Starship was because they're like, YOLO, let's put Brad Gerstner (00:37:37): some cameras HD cameras on them. Speaker 5 (00:37:39): Right. Now I think those satellites, basically, it was just a test and Jason Calacanis (00:37:41): they They burned Brad Gerstner (00:37:42): up. Speaker 5 (00:37:42): They burned up. So I think the next big milestone here will be when they launch Starship with, let's say, 60 of these v three satellites, put them in the correct orbit, make connection with them, add the bandwidth to the network. That's gonna be a big milestone. But you play this out to its logical conclusion, and the bandwidth available to the Starlink network goes up 10 x or eventually a 100 x times. And that's when they can do all the interesting things like direct to cellular. There were some interesting hints that Gwynne Shotwell talked about about with ground stations about what they could potentially do there. And I think Jason Calacanis (00:38:17): might buy T Mobile or something like that, Saks. It's easily within their range of purchases. Speaker 5 (00:38:22): And and I think Elon mentioned something about potentially the the Starlink network could eventually handle roughly half of Internet traffic. So, I mean, this this thing could get so much bigger than just 12,000,000 subscribers to your point, Freeberg. But look. I I wanna actually talk about the data centers for a second, Brad. I do have a couple of questions about this. So Elon mentioned that, okay, we're gonna be at two gigawatts by the end of the He said that we will be at five to 10 next year, closer to 10 than five. So let's just say eight. Okay? So Yeah. I'm just making that up, but it's on their range. So let's just say that's an add of six gigawatts. So they go from two to eight. Okay. To me, there's two questions there. One is, how do you know that the spot price is gonna stay where it is? You know, 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. So the market is constrained by whatever the bottleneck is at that time. Right now, the bottleneck is memory. So where do you see the spot price going? How do we know? How much risk is around that? And then the other question I would have is if you go from two to eight gigawatts that you have net as six, we know that a gigawatt power data center is, you know, 50,000,000,000 of CapEx. Right. So six incremental gigawatts of compute would be 300,000,000,000 of CapEx next year, assuming they build that. Right? I mean, they have optionality around that, I'm sure. So how do you finance that? You know? What's the most nondilutive way? They said their payback is a year or less. Speaker 5 (00:40:11): I'm sure that's tied to the spot price. 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. David Sacks (00:40:22): It's a great framing, David. First, it's $50,000,000,000 per gigawatt to build minimum. Okay? So you're $300,000,000,000. 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 dilutive equity raise, neither of which they wanna 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 unlimited because the fear in the world is that that sprite 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 gonna 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 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 above 20% in terms of your returns. Right now, the shortage is so acute, and the willingness to pay from the front 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. Jason Calacanis (00:41:54): You're saying that they would be willing to overpay by a factor of up to five x. David Sacks (00:41:59): Well, that's the 50 that, you know, that's the $50 per watt that 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 offtake revenue that can afford to buy compute at this scale. Right? It wasn't the Chinese open source companies that were buying, you know, SpaceX's excess compute or building the 10 gigawatt, you you know, plant in in Ohio. That's OpenAI and Anthropic. So the vast majority of the offtake commitments are coming from Anthropic, OpenAI, and NVIDIA. Right? When you hear about the hyperscalers building all of this, you know, this compute out, they're building it out to sell to the people that we, you know, that we just mentioned. So, David, net net, if he builds six gigawatts next year and by the way, probably only Elon, you know, 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. You know, Google doesn't come close in terms of standing it up in that time frame. I think that he's gonna have a challenge, you know, 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 powered shell all in time? I think the off take is there. Right? But to put it in perspective, this year, Anthropic and OpenAI combined, their starting total compute was like five gigawatts. So he's talking about incrementally adding more than they had as combined companies. Right? Speaker 5 (00:43:32): 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? David Sacks (00:43:41): You The have demand exists in the world. The demand exists in the world today. I think it will exist in the world for, well, you you know, the next twelve to twenty four months, but there is a wall of worry in the market. The reason we saw the pullback in July is Kimi scared people into thinking, oh my gosh. They're gonna undercut the Frontier's 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. Jason Calacanis (00:44:14): Ways, the fact that there's a discussion going on that this next 10 gigawatts is gonna cost, you know, SACS $500,000,000,000 and you ask 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, David Sacks (00:44:51): our good friend Bill Gurley likes to remind us, he's like, can't believe that we're all just taking in 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 the next twelve to eighteen months, but, you know, you have these unknown unknown moments that certainly causes people to be fearful. Credit spreads are blowing, you know, have continued to to stay wide on these deals. So there is fear in the market about them. Jason Calacanis (00:45:44): Alright, everybody. The fifth annual. If it's September, you know it's time for the All 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. Jensen Huang, founder and CEO of NVIDIA. If you care about where AI is headed, you won't wanna miss this conversation. David Sacks (00:46:07): The best, the Oracle. Jason Calacanis (00:46:08): 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 Gersner and Bill Gurley, BG two coming back. SpaceX's Gwen Shotwell. My guy, Jake Paul. Nick Shirley. Lot of incredible people coming. Martin Schirle maybe is even coming. He's that's gonna be fun. Go to the allinsummit.com to apply today. Allin.com or the allinsummit.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. Casino night too. I heard it's gonna be a big casino night. David Friedberg (00:46:47): Biggest yet. And the concert to be announced, who will be performing at the concert, but it is gonna 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 every year in making it an amazing experience, not just cool content on a stage, which I think is what a lot of these other shows really deliver. But it's like, how do you actually come and have a have an experience for a couple days? It's gonna be awesome. Jason Calacanis (00:47:26): So It really is those three things that we focus on. One, you're gonna learn something. Right? You got these great people on stage. You're gonna learn something from them. You're gonna meet new people. You're gonna network, and then you're gonna have these great experiences. It's the trifecta folks. You're excited, Brad? You excited to David Sacks (00:47:39): be back? What what are the dates? What are the dates again? Jason Calacanis (00:47:42): Look at your calendar. You're speaking. David Friedberg (00:47:44): September thirteenth through fifteenth in LA. David Sacks (00:47:47): This couldn't be better dates for the summit. I mean, we're we're gonna be within sixty days of an election, midterm election. We're gonna be within thirty days of an IPO, you know, potentially of. I mean, like, it's it's gonna be heated. Jason Calacanis (00:48:01): The sass pocalypse, not the Sakspocalypse. This is the SaaS pocalypse 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, $480,000,000, $500,000,000 in annual revenue, growing 20% a year, respectable if it was a public company, with almost $1,000,000,000 in cash has been sold. It's been sold for 1,280,000,000, about 10% of its peak valuation, which was 11,700,000,000 in 2021. Now they did have a bunch of cash. So if you include the cash position, sale was 2,250,000,000. 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, AOL's legacy business, Evernote, Eventbrite, 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 in at a high level. What can we take away from this transaction in Bending Spoons? Are they the buyer of last resort now? Speaker 5 (00:49:26): 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 talent 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 wanna focus on the new thing, the AI company. That's where the big value creation is gonna be in the future or the potential for it. So essentially, the talent is gonna 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 in the commentary on this, which is only 30% of Airtable 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, product led growth, and they were growing about 20% a year. But that was not good enough for its board. You know, these are investors, some of whom invested in an $11,000,000,000 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 it grow faster. So now what's the opportunity for the acquirer here? Bending spoons can go in here and do what Elon did at Twitter, eliminate 85, 90% of the cost structure. Speaker 5 (00:51:30): Don't do the 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 Jason Calacanis (00:51:43): to 80% profitable probably. Speaker 5 (00:51:45): Right? Probably. I mean 400,000,000 Jason Calacanis (00:51:46): to the bottom line, pace for David Sacks (00:51:47): the acquisition in Jason Calacanis (00:51:48): a couple years. Speaker 5 (00:51:49): People are saying they're only gonna generate 30% EBITDA margin. I think, like you're saying, it could be 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 know, you'll you'll be generating I don't know. You could probably generate 300,000,000 of EBITDA a year or 400,000,000, while growing, you know, 10 to 20%. So that's a play for bending spoons. Jason Calacanis (00:52:18): But look, venture investors here, Saks. They're Yeah. Happy to get their money back and move on to the next thing. It's a bit of a push for them, you know, in terms of at the blackjack table rather than they've gotta go 10 x just to catch up. And then they would have to go 10 x again to make their LPs happy. It's not gonna happen. Speaker 5 (00:52:35): I think the question is if Bending Spoons can basically take this business that's not making money and probably generate 400,000,000 a year of EBITDA and pay for the acquisition in just three years. Jason Calacanis (00:52:47): Amazing. Speaker 5 (00:52:48): Why isn't that something that the company could do on its own? And I think that's the structural problem is 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 gonna have to demolition what they've built. Right? They've got all this loyalty to the team. They don't wanna think about how do I eliminate 90% of the cost structure. It's just not what they do. I mean, what Of course. Founders wanna do and 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. Jason Calacanis (00:53:23): They're not built for it, Zach. Speaker 5 (00:53:24): 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 who have to get paid back who invested at this $11,000,000,000 valuation and and, you know, all the way up. Jason Calacanis (00:53:39): The the incentives are broken, Brad. Right. And you, you yourself at your firm Altimeter, you were pretty frisky in this period. You made a lot of bets. So I don't know if Airtable 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? David Sacks (00:53:59): Multiples of revenue can compress very quickly. Right? It works great when a company is growing greater than 50%. But remember, it's just a heuristic. It's just a very rough estimate used almost exclusively in Silicon Valley. You know? So people are saying, oh my god. This thing sold for two times revenue. But when you actually look at it on a look through basis, probably sold for maybe 30 times free cash flow. I don't think it's easy to get it to 400,000,000 in EBITDA. I think if it was, the board would have done that. I'm you know, we're involved in some of these companies. Once they slow down, the company morale goes to hell, turnover among your customers, you know, begins to spike. It starts to feed on itself. So I Jason Calacanis (00:54:39): think to go to work every day. Speaker 5 (00:54:40): Brian, what do you need to keep? What do you need to be? Because it looks very David Sacks (00:54:43): 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 has 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 wanna go work on AI. Speaker 5 (00:55:12): Agreed. But you don't need them for this product. David Sacks (00:55:14): I agree. Speaker 5 (00:55:14): I mean, the market's being efficient. I mean, look, this is where I think Bending Spoons has an advantage that the company's board and founders wouldn't have, which is they already have an infrastructure. Right? They have a core team at 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 David Sacks (00:55:37): Of course. Speaker 5 (00:55:37): You couldn't eliminate, like, all of the talent, the infrastructure is because you needed the institutional memory. You needed people who knew the code base. Now AI can learn the code base instantly. Jason Calacanis (00:55:47): That's interesting insight. And so yeah. Maintaining is easier with AI. Speaker 5 (00:55:51): I think maintenance mode becomes way easier with AI because you don't need the historical knowledge anymore. The AI can go in David Friedberg (00:55:57): and sort of Speaker 5 (00:55:58): reconstitute that that historical knowledge. Jason Calacanis (00:56:01): Let me get you in here, Freebert, if I may. When we look at the lessons from peak ZERP and SaaS, and then we look at, you know, this moment in time, this surging AI market, any parallels that we might find here or lessons between the two? David Friedberg (00:56:19): Between ZERP and AI era? Jason Calacanis (00:56:21): The ZERP SaaS era, we had a lot of very high valuations, a lot of enthusiasm, a lot of suspending disbelief. We're here in the AI era. We just talked about, you know, the price of compute and all these companies being at a 100 x price of sales ratio. Any parallels here or not? It's a kind of a softball question for you. David Friedberg (00:56:40): 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 into SaaS. It's like, oh, you get a 20 x 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 Yeah. Linked Brad Gerstner (00:57:10): to It was a softball question, to be honest. I was Jason Calacanis (00:57:12): letting you hit it out of the park. Speaker 5 (00:57:14): Look, I mean, obviously, SaaS 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 super bubble. 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 retention. So this thing will just 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 SaaS space based on this one company, Airtable. I think there's some things about Airtable that make it very different than, I don't know, let's say a Salesforce or a Workday, Airtable was always a little bit of a quirky product. I remember at the peak hype for this company, people were saying like, oh, this is like a new Excel or a new Google Sheets. Jason Calacanis (00:58:22): New Microsoft Office, yeah. Speaker 5 (00:58:24): Yeah, it was basically a spreadsheet for words. That's how people were viewing it as this, like, this new kind of spreadsheet for 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. Airtable never got to that point. Most people still don't know what Airtable is. Again, it had its dedicated fans, but it was a hard product to explain to people. When do you use it? Jason Calacanis (00:58:52): It had a cult following. Speaker 5 (00:58:53): It had a cult following, but but it never 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 kinda get the marketing right because of that. Jason Calacanis (00:59:06): And to be honest, if you look at Claude Cowork, Complexity Computer, Agents, those things are now doing what Airtable did. So Speaker 5 (00:59:13): It never carved out, I think, a niche where it was super clear when you were always supposed to use Airtable. And and, really, it was part of this hodgepodge of of this grab bag, you should 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 SaaS right now because mean, what is Claude code really good at? I mean, that's the ultimate David Sacks (00:59:37): no It's Jason Calacanis (00:59:37): no code code. Lovable Claude code perplexity. I mean, Speaker 5 (00:59:40): the thing with Airtable 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 Airtable. You had to learn how to use Retul, 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 Claude and you just tell it what you want it to create. And so, you know, if you do wanna 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 SaaS is being impacted right now, but this has gotta 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 wanna 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. Jason Calacanis (01:00:32): I gotta be honest. My team, SACS, made I don't do you use, like, a portfolio, off the shelf SaaS tool for managing crafts, like, portfolios and everything? Speaker 5 (01:00:44): Well, we we vibe coded something actually. Brad Gerstner (01:00:47): So yeah. We just did the same too. Jason Calacanis (01:00:48): Yeah. So my team just built something that is so mind blowing that to buy with off the shelf software would have been a $250,000 in software and like a million dollars in integration over two or three years, and we built it in a month. And and now we have complete insight into the whole portfolio, the competitive set, the founders, everything going on. Speaker 5 (01:01:06): Keep in mind that one of the reasons why Leopold got blown out. Okay? I mean, it it is because he bet on the SaaSpocalypse. Remember, it wasn't just that he was super long, these chip stocks that had a correction. Jason Calacanis (01:01:18): Oh, is that right? He was short Speaker 5 (01:01:19): with SaaS? He was short Adobe and a whole bunch of other SaaS companies. And those trades also moved the wrong way on him. So, again, I just think that it's painting with too broad a brush to say that all of SaaS is gonna 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. Active Directory is 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 FedRAMP 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 gonna wanna spend tens of millions of dollars ripping out something that costs you a million dollars a year. It just that just doesn't make sense. And I noticed that Benioff just tweeted five minutes ago that 15 out of 15 cabinet agencies run on Salesforce. Look, the government is not gonna rip and replace Jason Calacanis (01:02:27): Salesforce with Speaker 5 (01:02:28): somebody vibe coded. So look, not all SaaS is equal in this dimension. Jason Calacanis (01:02:32): 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 I put Figma in that bucket. David Sacks (01:02:44): Just to wrap this this section. Sure, please. IGV is up 20% in the last six months. It's up 20% in the last five years. Jason Calacanis (01:02:51): Explain IGV, please. David Sacks (01:02:52): So the high growth software stock index, right? Snowflake's up 88 in the last six months. Jason Calacanis (01:03:00): That's an IGV is an ETF David Sacks (01:03:04): of of gross software companies. Right? So to to David's point, there was a panic about software companies. There was a big trade out. You know, honestly, they they performed pretty well. And and as he mentioned, in the month of July, they were up when a lot of the semiconductor AI stocks were down. And some of these companies, Databricks, Snowflake, Clickhouse, etcetera, are doing extraordinarily well. As I just mentioned, Snowflake's up 90% in the last six months, which puts it in the same category as the semiconductor AI stocks. So to David's point, you can't throw them all in the same bucket, but I do think that for these no code, a lot of these application software companies, they're realizing, like, the the, you know, the game is up. Sell the company. Get what you can get. You know, importantly here in the Airtable story, all the late stage investors. Right? We passed on this in the last three funding rounds, right, which I think were at 2,000,000,000, 5,000,000,000, 11,000,000,000. 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. Jason Calacanis (01:04:06): 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 get our money back with the optionality, hey, maybe this would be a good investment. You could say the same thing about some AI bets. Speaker 5 (01:04:21): Well, is one of those cases where the liquidation preference actually mattered. You know? Absolutely. Normally, it doesn't matter, but it Well, I think Jason Calacanis (01:04:27): they got straight money here. I my understanding is this wasn't like they had, a 7%, you know, 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 and I couldn't Brad Gerstner (01:04:41): find it. David Sacks (01:04:42): 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. Jason Calacanis (01:04:48): Yeah. But if they had the I guess, Sacks, they we lived through moments in time where companies had to guarantee a one x. Right? You know, in a sale. Speaker 5 (01:04:57): One x liquidation preference is standard. It just means you get your money back before other people start to profit, which is appropriate. But all the common Jason Calacanis (01:05:05): rates were taken out, right, of these deals, I think during peak No. Speaker 5 (01:05:10): The standard terms, you know, what's known as clean terms, it's just a simple one x liquidation preference. Right. 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? Jason Calacanis (01:05:23): Yeah. But participating preferred is the double dip. Right? Speaker 5 (01:05:27): 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. It just doesn't make sense, right? Jason Calacanis (01:05:41): It screws up the alignment. Well, Speaker 5 (01:05:43): that's just a transfer of value from some people in the cap table to other people on the cap table. So the standard thing you do is you make sure that the investors get paid back and then everybody participating in the upside. Jason Calacanis (01:05:56): Okay. Fourth 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 startups, Surge AI and Mercor are both valued at over $20,000,000,000. They sell training datasets to people like OpenAI, Anthropic, federal agencies. They all sell the same data sets to top Chinese AI companies according to this report like Tencent, Byteance, Alibaba, Moonshot, etcetera. Top six AI labs in China according to this report are spending $500,000,000 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 MicroOne. The founder of Micro One didn't participate in selling to China. He made that decision, Sax. 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. Merricor, MicroOne, all these companies are providing it. Should they be providing the same data and selling it to Chinese open source companies or not? Speaker 5 (01:07:35): Well, look, I think we gotta decide what our objective is here. Are we trying to just get in like a full blown economic war with China? Are 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. Jason Calacanis (01:08:44): No dependencies. Speaker 5 (01:08:45): 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 tool Jason Calacanis (01:08:53): used? Yeah. It's not Speaker 5 (01:08:54): Does it have a military application? Jason Calacanis (01:08:55): Yeah. I don't think it has military. It's definitely not data labeling. This is like hiring PhDs, hiring super professionals to, you know, create unique datasets. So it's science. It's Speaker 5 (01:09:06): China can do that too, and I guarantee you they are. I I don't think this is gonna give us a decisive advantage in the AI race. It's gonna annoy. It's gonna create annoyance. It's gonna create friction. And how bad do you want our relationship with them to be? Do you wanna risk starting another trade war? Look. I'm not against restrictions when I think they're gonna 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. Jason Calacanis (01:09:46): 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? David Sacks (01:09:57): First, you know, I'm in absolute agreement with David that we want maximum competition. As we sit here today, The US is winning. We talked about it at the start. Our frontier labs are winning. Our open source is winning. And we have fairly limited regulations. 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 wanna 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, you know, it's the type of story, Jason, that I think will continue to muddy 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 advisers, 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've caught up. They've passed us, etcetera, 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. Jason Calacanis (01:11:20): I gotta say using Kimmy and Quinn and, you know, GLM five two for the last sixty days, my lord, these things are good. And I don't it's very patriotic to be giving them an advantage. I wouldn't do it. I'm glad the company Speaker 5 (01:11:33): Sorry. What's the advantage? What's the dataset that you you're worried about that's so proprietary? Jason Calacanis (01:11:37): Any of these datasets are, created by experts here in America who are given, like, the queries that have errors in them. So when you give, 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. Speaker 5 (01:12:15): But what what makes you think that China can't do this? They have tons of PhDs over there. Jason Calacanis (01:12:19): They would have to hire no. No. If 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, you know, 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. In line with distillation, you know, they're it's it's really very similar process. Speaker 5 (01:12:51): Look, if there's something truly proprietary here, I don't want us to sell our secret sauce to China. So, you know, I'd have to look into that and see, like, 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, Jason Calacanis (01:13:14): And especially we're graduating and kicking them out of the country. That's the other problem. We gotta get that fixed. Speaker 5 (01:13:19): Well, it's like there's a lot of different issues here. I don't know if you wanna conflate, 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. Jason Calacanis (01:13:35): Well, they're all proprietary by design, but I don't know about the dual use because I don't have the datasets here. Alright, folks. That's another amazing episode of your All In podcast. Thank you so much, Brad, for joining us. Shammoth, good luck on your world tour. Hope you're enjoying a little, rest and good luck, trying to buy a white turtleneck this season. They're sold out everywhere. So go to the allin.com store, allin.com/store. We have 1,000 signature chamath autographed white sweaters coming. You could sign up in advance for this. All proceeds go to charity. And by charity, I mean, Spots Yacht Fund. Alright. We'll see you next week, everybody. Bye bye. Brad Gerstner (01:14:19): We'll let your winners ride. Rain man David Sarkis. I'm going home. And it said we open sourced it to the fans, and they've just gone crazy with it. David Sacks (01:14:43): We Brad Gerstner (01:14:50): should all just get a room and just have one big huge orgy because they're all just useless. It's like it's like sexual tension that they just need to release somehow.