All-In with Chamath, Jason, Sacks & Friedberg
00:12:43 · Jason Calacanis
How much does Nvidia spend to make the chips, and then how much do they sell the chips for? So for every dollar of COGS, every dollar that NVIDIA spends, that turns into $4 of revenue for NVIDIA, 75% margin. And then that gets sold to SpaceX, and then SpaceX turns around, and they're making, you know, an 83% margin on that chip spend because they're kind of looking at a five year depreciation. So they spent 10,000,000,000 on Colossus one, five year life, 2,000,000,000 a year depreciation. And so that's kind of the cost per year, but then they're making 1,250,000,000 a month on Colossus. And then Anthropic is showing in this s one pretty remarkably to Sacks' point that they're making about 85% incremental gross margin on every incremental dollar coming in. It's obviously a baseline of compute spend, but 85% is kind of the incremental gross margin. So at the end of the day, a dollar that NVIDIA spends turns into $18 that Anthropic is making selling to customers. And the question is, are customers making money? So if we're seeing the customer ramp as it is and the spend ramp as it is, and the customers are making, call it, on the order of two to 10 times the money they're spending because, otherwise, there there would be probably a retention issue and a decline in spend. Maybe I'm wrong. Maybe it's early days of exuberance and people are losing money, and we could debate that point a bit. But generally speaking, if AI is delivering value to the customers, I think this shows that there really is true productivity arising from AI, that across the whole value chain, there is such margin to be had. There's so much value being created all the way through to the customers, and we're only 1% or Anthropic is only 1% penetrated on their customer potential and probably less than 10% of the spend potential per customer. That this really is what I think Eric Schmidt a long time ago in the early days of Google used to say Google's total addressable market is global commerce. And I think for the first time ever, we're seeing a business actually potentially starting to realize that theory that if you can create enough productivity gain for your customers, that your TAM is commerce. And it really highlights that maybe, you know, we're almost growing the equivalency of the human working population by a 100 x or a thousand x or a million x with this technology stack.
00:15:09 · Jason Calacanis, Chamath Palihapitiya, David Sacks
So I I just thought, like, to me, was so striking how early we are, but also how incredibly profitable everyone is, which indicates there's quite a bit of productivity being produced across the whole value stack. Those are my biggest takeaways kind of from the s one. I don't know if you guys agree with this, but, you know, it all felt pretty remarkable to me. I'd be curious to know if you guys think I'm I'm I'm a little loopy on being too exuberant in my reading of this stuff. [00:15:39] S2: I think the thing that that is probably the most important is the last box that you said where it... You wrote in assumption, where you said assumption. [00:15:48] S0: Right. Yeah. Totally. [00:15:49] S1: Well, how do they measure that? But by the way, I agree with you.
00:15:49 · David Sacks, Jason Calacanis
[00:15:49] S1: Well, how do they measure that? But by the way, I agree with you. [00:15:51] S0: But The last box I made up. I said assume two to 10 x. So I'm just I have no idea. But I'm a user of Claude. I think we get a lot of value for Claude. I think our people are able to do more because of Claude than they would otherwise be able to do. So our business grows faster. We can get more done in a shorter period of time. I think I tangibly experience that pretty regularly. I've talked a lot about moving to open source, and we have our own cluster installed with our own kind of open source now. But I think what Claude is showing is that they they have such a high margin. They actually have good elasticity on pricing. So they could push price down, and you could start to see them start to segment the market a little bit where they could start to be more competitive perhaps with cheaper tokens for cheaper, easier tasks. And then premium tasks charge a higher price for intelligence and start to stratify the value of the intelligence that they're providing by changing the price points. And I'm... I envision that's where everyone's gonna go.
00:16:44 · Chamath Palihapitiya, Jason Calacanis
[00:16:44] S2: Nick, I'm gonna show you... This is to Friedberg's point about what is the potential downstream productivity gains that everybody can have theoretically with AI. Just put that on the screen. This is from Microsoft's earnings. My research team put this together. It's really quite incredible. What this shows you is that Microsoft's revenue went from 198,000,000,000 to 332,000,000,000 in the last four years while staff only increased by 2,000 people. I mean, isn't that stunning? Like, you would have thought before AI that if somebody said, great, we're gonna, you know, get close to doubling revenues at this kind of scale. [00:17:29] S0: Right.
00:17:29 · Chamath Palihapitiya
[00:17:29] S2: You probably would have thought that headcount would have doubled too. And the reality is headcount stayed flat, and we don't know how many people will take this offer that Microsoft just gave for, you know, to to leave because I think they offered it up to 60,000 people, but this number could be under 200,000 depending on how many take it. And so just the scale and the impact on the productivity, to your point, this idea that it creates job loss, don't think it's true. It's more that you're seeing productivity gains at the same number of human input, and I think that's interesting. I went through the s one. You want me to give you my little s one breakdown? Mhmm. This is the most incredible business I've ever seen. I hope that they go public after November 11 because it will force them to update with q three, and then we'll get even more insight into... This is like some contraption that I've never seen before. I've never seen a business like this. It's really, really, really incredible. So let's cross our fingers because post November 11, you'll see q three drop. What people have been whispering is that the revenue growth kind of moderated to sort of like the 50% range. Q4 is TBD, but let's break this down in a bunch of charts because I think it's interesting. Okay, so Nick, start with chart number one and we'll just go through them one by one. Okay, so the first thing that I always read in an S1 is what are the risk factors? And what I had my team do is I said go put together a word cloud. This is great, is cool. First tell me what are they afraid of, okay? And when you look at it, the share of words in the risk factors that are allocated to different risks are very important, and what this tells you is the single biggest risk to the ant business is compute. They mention it more than anything else. And specifically inside of compute they talk about data centers, power, chips, grid, electricity, hardware, etcetera. Then a close second but still second is regulation. Okay? They care about government policy. They care about sanctions. They care about export controls. Then it's this idea of zero data retention and privacy and IP. Then it's AI safety. Then it's just making money. Then there's their competitors. Then there's governance. What you notice is what are they not afraid of?
00:19:50 · Chamath Palihapitiya
They're not afraid of customer traction because they they rarely mention it. They're not really afraid of employees leaving because you'd be an idiot to leave if you were inside, and they don't really care about taxes and any of these other things. So this is a business that is very much on the forward foot that understands the binding constraint. So now if you know that the the compute is their biggest risk factor, then you double click into that. And I had my team run a further analysis, which is to say, okay, how do they describe these risks, right? How many times do they mention them? How many words are you spilling talking about this? And what you find is that inside of the compute problem, their biggest issue is really data centers. And then further, it's the financing commitments that they have and it's the power. All of these are really important because, again, it helps us highlight what the binding constraint is to this business. So as goes data centers, as goes power, and by the way, this is a great setup for the politicians because it's very obvious if you're a Democrat as you read this, and I'll speak about it in a second, what you should be trying to do to win in '28 versus what Republicans have to do in '28. Right? Basically, the takeaway from all of this is I think antisemblematic of the entire AI complex, which is to say it's all about data centers, which means it's all about power. If you're a Democrat, the best thing you wanna do is slow all of this down so that there's a recession going into '28. The only thing you need to do is slow down power and slow down data centers. On the other side, if you're a Republican and you wanna have the best chance of winning in '28, this is the one thing that must be done right. You must get the power, electricity, data center thing right.