Martin Casado: evaluation

Transcript context

…Yeah. Um, yeah, I think it’s interesting, uh, I don’t know if you guys would agree with this, but it feels like the emerging strategy is, and this builds off of your other question, um. You raise money for compute, you pour that or you, you pour the money into compute, you get some sort of breakthrough. You funnel the breakthrough into your vertically integrated application. That could be chat GBT, that could be cloud code, you know, whatever it is. You massively gain share and get users. Maybe you’re even subsidizing at that point. Um, depending on your strategy. You raise money at the peak momentum and then you repeat, rinse and repeat. Um, and so. And that wasn’t true even two years ago, I think. Mm-hmm. And so it’s sort of to your, just tying it to fundraising strategy, right? There’s a, and hiring strategy. All of these are tied, I think the lines are blurring even more today where everyone is, and they, but of course these companies all have API businesses and so they’re these, these frenemy lines that are getting blurred in that a lot of, I mean, they have billions of dollars of API revenue, right? And so there are customers there. But they’re competing on the app layer. Yeah. So this is a really, really important point. So I, I would say for sure, venture and growth, that line is blurry app and infrastructure. That line is blurry. Um, but I don’t think that that changes our practice so much. But like where the very open questions are like, does this layer in the same way. Compute traditionally has like during the cloud is like, you know, like whatever, somebody wins one layer, but then another whole set of companies wins another layer. But that might not, might not be the case here. It may be the case that you actually can’t verticalize on the token string. Like you can’t build an app like it, it necessarily goes down just because there are no abstractions. So those are kinda the bigger existential questions we ask. Another thing that is very different this time than in the history of computer sciences is. In the past, if you raised money, then you basically had to wait for engineering to catch up. Which famously doesn’t scale like the mythical mammoth. It take a very long time. But like that’s not the case here. Like a model company can raise money and drop a model in a, in a year, and it’s better, right? And, and it does it with a team of 20 people or 10 people. So this type of like money entering a company and then producing something that has demand and growth right away and using that to raise more money is a very different capital flywheel than we’ve ever seen before. And I think everybody’s trying to understand what the consequences are. So I think it’s less about like. Big companies and growth and this, and more about these more systemic questions that we actually don’t have answers to. Yeah, like at Kernel Labs, one of our ideas is like if you had unlimited money to spend productively to turn tokens into products, like the whole early stage [00:09:00] market is very different because today you’re investing X amount of capital to win a deal because of price structure and whatnot, and you’re kind of pot committing. Yeah. To a certain strategy for a certain amount of time. Yeah. But if you could like iteratively spin out companies and products and just throw, I, I wanna spend a million dollar of inference today and get a product out tomorrow.…

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