Matt argues that while closed-source models remain more widely used overall, the field passed a threshold roughly a year ago when leading application startups (Cursor, Decagon, Harvey) concluded they had to do their own training and deployment tricks — which requires building on open source rather than closed APIs. ✦ AI generated
Matt Bournestein · a16z Podcast · 2026-08-06 · original ↗
starts at this moment · 7:30
“at what point did you see this sort of transitioning from being, you know, a a muchbeloved open- source project to critical infrastructure and then a company?”
I do think we passed a threshold in like I want to say about a year ago where a bunch of smaller companies or like new application companies as they were trying to figure out how do I really build an AI without just being a wrapper on top of open AI. The answer to that question turned out to be open source. I mean this is what cursor did... and and a bunch of other like really really strong application level startups sort of made the determination we can't build just on closed source. We need to do our own mid-training our own post- training our own sort of inference and deployment tricks and all of that means it must be built on top of open source...
verbatim transcript · starts at 7:30
7:30really build an AI without just being a wrapper on top of open AI. The answer to that question turned out to be open source. I mean this is what cursor did. This is what sort of Decagon and Harvey are are in the process of doing now and and a bunch of other like really really strong application level startups sort of made the determination we can't build just on closed source. We need to do our
7:50own mid-training our own post- training our own sort of inference and deployment tricks and all of that means it must be built on top of open source like you know the the closed source vendors won't won't give you the access to do this. So my read is like kind of a yearish ago. Open source became really central in a way that's not always visible because it's it's deeply embedded in some of
8:08these products but you know some of the most innovative products and applications now um you know really depend on this very deeply. >> Yeah. Yeah. And can you talk about where VLM sits in that stack where where we do have these larger enterprise companies that are choosing to use open source models like where where does VLM sit in the stack for them? >> Yeah. I mean you should like just about
8:28everybody uses VLM. You should describe it. >> Just about everybody uses VLM. VLM is a inference engine. That means its job is to turn available GPUs into a running endpoint for intelligence. So that means it is kind of like databases and operating system other critical software to power uh this uh economy or power of the AGI that everybody really uses today to ensure they can have uh cost effectiveness,