Application-layer companies will always have a place because even with AGI, agents need somewhere to store work, pull information from, and reason about things; the 'labs are the last startups' narrative is incorrect.
Jesse pushes back on the dominant narrative that Anthropic and OpenAI are the last startups. Noting that humans are analogous to AGI yet still need databases and CRMs, he argues AI agents will likewise need software infrastructure, so application-layer companies persist—potentially becoming vertically specialized labs. ✦ AI generated
Jesse · a16z Podcast · 2026-07-31 · original ↗
starts at this moment · 15:08
“do you think about this potentially false dichotomy of app versus infrastructure company... does and you know uh does agent lab you know popular uh description uh floating around the last couple of weeks like does that describe it”
I'm not as bought into the the labs are less startup view of the world... in a way we human beings are kind of AGI right and human beings have needed to use software for lots of things you know you need databases to put stuff in you need CRM to track things and I think even once you have AGI all our AGI agents are going to need somewhere to store work and pull information from and reason about things. So... I don't think software as a whole in any meaningful way is going away
verbatim transcript · starts at 15:08
14:50and we know how to decompose a problem we know how to uh build train and deploy these models very very quickly all of a sudden that the cost aspect becomes a lot less pressing um but if you're still in the world where you're using frontier models for everything then I do thinking about toolics makes makes a lot of sense >> I just want to make this meta
15:08observation that a lot of the conversation even just now has in about things like training models, right? Uh we're talking about reinforcement learning and um it really does blow away what I think is, you know, a lingering misperception about what an AI application is. Um and just to get into that debate a little bit because I think it's sort of this narrative that dominated the first half of 2026, which
15:34is that anthropic open AI, they're the last startups. They're going to take over everything. applications, their thin UIs with FTEEs, you know, with implementation attached to it. Um, you know, we talked a little bit about Deck Gun Labs, but can you guys just share what is your like how do you think about this potentially false dichotomy of app versus infrastructure company? Um, clearly you guys are so much more than
16:00the UI or the implementation. Um and uh you know does agent lab you know popular uh description um floating around the last couple of weeks like does that describe it like how do you guys think of decagon? >> Um so I'll give a quick perspective just like from the from the POV of like an enterprise and then maybe we can talk about like broader the industry. So
16:21let's say I'm like a Fortune 100 company, right? and I'm looking out there on all my use cases and I have a choice of partnering [clears throat] with an application company or uh sort of using the labs and building from scratch. Um I think there is a lot of merit to partnering with the labs in certain cases. I think if you look at our case right like we we just talked
16:40about all this fine-tuning stuff. I think a common misconception that people have is you know fine-tuning is is a way to like customize it for that customer. In fact, most of the fine tetuning we do is like customizing it for our use case, like the customer service use case. >> Yeah. >> And it's worth it for us to do it because that's all we do, right? We do
16:58these agents across all of these different customers. And so it is worth it for us to put in a ton of time and research into like how do you tune this one model to be good at selecting customer service topics. But if you're the enterprise, is [clears throat] it really worth your valuable research resources to like tune a model for these like customer service behaviors? Probably not, right? So that that's like
17:19that's one reason why people um partner with applications. Another reason is >> let's say I do put in the engineering effort to build like a some agent myself using the frontier models. Um and you know to my earlier point you know I'm not fine-tuning for behavior but I'm I'm sort of teaching the AI my own procedures. And again that doesn't happen through finetuning that that happens like in context because if you