Bitter Lessons in Venture vs Growth: Anthropic vs OpenAI, Noam Shazeer, World Labs, Thinking Machines, Cursor, ASIC Economics — Martin Casado & Sarah Wang of a16z
episodePreviously titled “Inside AI’s $10B+ Capital Flywheel — Martin Casado & Sarah Wang of a16z” — renamed by the publisher on Aug 2, 2026
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What is the new AI financing model where venture and growth rounds merge?
Hey, everyone. Welcome to the Latent Space podcast live from A16Z. This is
Alessio from the Kernel Labs, and I'm joined by Twix, editor of Latent Space. Hey, hey, hey. And we're so glad to be on with you guys. Also, a top AI podcast, Martin Casado and Sarah Wang. Welcome.
Very happy to be here and welcome.
Yes. We love this office. We love what you've done with the place. The new logo is everywhere now. It's still getting... It takes a while to get used to, but it reminds me of sort of a callback to a more ambitious age, which I think is kind
of... It
definitely makes a statement.
Yeah. Not quite sure what that statement is, but it makes a statement.
Martin, I go back with you to Netlify. And you create a software-defined networking and all that stuff that people can read up on your background. Sarah, I'm newer to you. You sort of started working together on AI infrastructure stuff.
That's right. Yeah, seven years ago now. Best
growth
investor in the entire industry. Oh, say more. Hands down. Yes, there is. I mean, when it comes to AI companies, Sarah, I think, has done the most kind of aggressive investment thesis around AI models. So she worked with Noam Chazir, Mira, Ilya, Fei Fei. And so just these frontier kind of like large AI models, I think Sarah's been the broadest investor. Is that fair?
Yeah.
No, well, I was going to say, I think it's been a really interesting tag team, actually, just because a lot of these big C deals, not only are they raising a lot of money, it's still a tech founder bet, which obviously is inherently early stage. But the resources, one, they just grow really quickly. But then two, the resources that they need day one. our kind of growth scale. So the hybrid tag team that we have is quite effective, I think.
What is growth these days? You know, you don't wake up if it's less than a billion or like
it's actually it's actually very like it's a very interesting time in investing because like, you know, take like the character around, right? These tend to be like pre monetization, but the dollars are large enough that you need to have a larger fund and the analysis You know, because you've got lots of users because this stuff has such high demand requires more of a number of sophistication. And so most of these deals, whether it's us or other firms on these large model companies are like this hybrid between venture and growth. Yeah, totally. And I think stuff like BD, for example, you wouldn't usually need BD when you were seed stage trying to get product marketing. Are we talking about BizDev? BizDev, exactly.
I'm not
familiar with what does BizDev mean for a venture fund because I know what BizDev means for a company.
Yeah, you know, so a good example is, I mean, we talk about buying compute, but there's a huge negotiation involved there in terms of, okay, do you get equity for the compute? What sort of partner are you looking at? Is there a go-to-market arm to that? And these are just things on this scale, hundreds of millions, maybe six months into the inception of a company, you just wouldn't have to negotiate these deals before. These large rounds are very complex now. Like in the past, if you did a Series A or a Series B, like whatever, you're writing a $20 to a $60 million check and you call it a day. Now you normally have financial investors and strategic investors. And then the strategic portion always still goes with like these kind of large compute contracts, which can take months to do.
And so it's very different ties. I've been doing this for 10 years. I've never seen anything like this.
Yeah. Do you have worries about the circular funding from some of these strategics?
No, listen, as long as the demand is there, like the demand is there, like the problem with the internet is the
demand
wasn't there.
Exactly. All right. This is, this is like the whole pyramid scheme bubble thing where like, as long as you mark to market on like the notional value of like these deals, fine. But like once it starts to chip away, it really,
as long as there's demand, I mean, you know, this is like a lot of these soundbites have already become kind of cliches, but they're worth saying it right.
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Chapters
8 chapters
1
What is the new AI financing model where venture and growth rounds merge?
0:00–6:11
2
How does the “raise → train → ship → raise bigger” capital flywheel work for model labs?
6:11–13:55
3
Can frontier AI labs outspend the entire ecosystem of apps built on their APIs?
13:55–21:33
4
What is the AGI‑vs‑product tension and how do companies allocate scarce GPU resources?
21:33–27:22
5
Why are talent wars and massive compensation packages reshaping early‑stage founder economics?
27:22–33:03
6
Why are “boring” enterprise software and traditional infrastructure under‑invested in the AI boom?
33:03–38:41
7
How are hardware, robotics, and generative 3‑D (World Labs) changing the compute economics of AI?
38:41–44:00
8
What are the two divergent futures for AI market structure – infinite fragmentation vs. oligopoly?
44:00–55:18
Speakers
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