Bitter Lessons in Venture vs Growth: Anthropic vs OpenAI, Noam Shazeer, World Labs, Thinking Machines, Cursor, ASIC Economics — Martin Casado & Sarah Wang of a16z
Episode
55 min
Read time
2 min
Topics
Productivity, Investing, Fundraising & VC
AI-Generated Summary
Key Takeaways
- ✓ASIC Economics Threshold: Once a training run exceeds $1 billion, building a custom ASIC becomes economically justified. Saving even 20% yields $200 million — enough to tape out a dedicated chip. In practice, efficiency gains closer to 2x are achievable, making custom silicon economics far more compelling than generic NVIDIA hardware at scale.
- ✓Capital Flywheel Risk: Frontier model companies are currently gross-margin positive on existing models but gross-margin negative when accounting for next-generation training costs. This means growth is structurally borrowed against future fundraising rounds. If a company cannot raise its next round, the model cycle breaks and market fragmentation likely follows rapidly.
- ✓Vertical Dominance Math: If a foundation model company can raise more capital than the aggregate of all companies building on top of its API, it can systematically expand into every application layer above it. Unlike prior tech eras, engineering bottlenecks no longer slow this expansion — capital converts directly into capability within roughly 12 months.
- ✓Cursor's Reverse Verticalization: Cursor built a near-state-of-the-art coding model at roughly one-hundredth the cost of frontier labs by starting at the application layer and moving downward, rather than the reverse. This demonstrates that companies with dense product usage data and a focused vertical can compete on model quality without frontier-scale compute budgets.
- ✓Boring Software Is Underinvested: Enterprise software companies growing 5x annually in large markets are being systematically ignored because they lack AI narrative momentum. From an LP returns perspective — targeting 3x net over a fund lifecycle — a focused, high-margin software company in a large market represents a structurally sound investment that current VC attention patterns consistently overlook.
What It Covers
Martin Casado and Sarah Wang of a16z join Latent Space to analyze how AI's capital flywheel is reshaping venture investing, blurring lines between infrastructure and applications, and creating structural dynamics where frontier model companies like Anthropic and OpenAI may outspend the entire ecosystem built on top of them.
Key Questions Answered
- •ASIC Economics Threshold: Once a training run exceeds $1 billion, building a custom ASIC becomes economically justified. Saving even 20% yields $200 million — enough to tape out a dedicated chip. In practice, efficiency gains closer to 2x are achievable, making custom silicon economics far more compelling than generic NVIDIA hardware at scale.
- •Capital Flywheel Risk: Frontier model companies are currently gross-margin positive on existing models but gross-margin negative when accounting for next-generation training costs. This means growth is structurally borrowed against future fundraising rounds. If a company cannot raise its next round, the model cycle breaks and market fragmentation likely follows rapidly.
- •Vertical Dominance Math: If a foundation model company can raise more capital than the aggregate of all companies building on top of its API, it can systematically expand into every application layer above it. Unlike prior tech eras, engineering bottlenecks no longer slow this expansion — capital converts directly into capability within roughly 12 months.
- •Cursor's Reverse Verticalization: Cursor built a near-state-of-the-art coding model at roughly one-hundredth the cost of frontier labs by starting at the application layer and moving downward, rather than the reverse. This demonstrates that companies with dense product usage data and a focused vertical can compete on model quality without frontier-scale compute budgets.
- •Boring Software Is Underinvested: Enterprise software companies growing 5x annually in large markets are being systematically ignored because they lack AI narrative momentum. From an LP returns perspective — targeting 3x net over a fund lifecycle — a focused, high-margin software company in a large market represents a structurally sound investment that current VC attention patterns consistently overlook.
Notable Moment
Casado reframes the "bitter lesson" concept for startups: a foundation model company that can raise three times more than the combined revenue of its entire API customer base can simply outspend and absorb every application built on top of it — something engineering constraints previously made structurally impossible.
Episode Transcript
Hey, everyone. Welcome to the Latent Space Podcast live from a16z. This is Alessio, founder of Colonel Lance, and I'm joined by Swyx, 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. It's Erwin. 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 it's still getting takes a while to get used to, but it reminds me of, like, sort of a callback to a more ambitious age, which I think is kind of It definitely makes a statement. Describe it. Yeah. Yeah. Yeah. Not quite sure what that statement is, but it makes a statement. Martina, I go back with you to Netlify. Yep. And, you know, you create a software defined networking and all all that stuff. People can read up on your back on. Yep. Sarah, I'm newer to you. You you sort of started working together on AI infrastructure stuff. That's right. Yeah. Seven seven years ago now. Best growth investor in the entire industry. Oh, Seymour. Hands down. Oh, Sarah is. Sarah is. I mean, when it comes to AI companies, Sarah, I think, has done the most kind of aggressive, investment thesis around AI models. Right? So she worked with Noam Shazir, Mira, Ilya, Fei Fei. And so just these frontier kind of, like, large AI models. I think, you know, Sarah's been the the broadest investor. Is that fair? No. I I well, I was gonna say, I think it's been a really interesting tag tag team, actually, just because the 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 So so I was gonna say the resources they, one, they just grow really quickly. But then, two, the resources that they need day one are kind of growth scale. So I 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 like no. It's a very interesting time in investing because, like, you know, take, like, the character route. 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, you know, more of a number 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, you know, stuff like BD, for example, you wouldn't usually need BD when you were seed stage trying to get a BizDev? …
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