红杉：企业自建AI的四大动因与四步路线图

Okay, good morning everyone. Thank you all so much for being here. We have about 80 portfolio company founders and AI leaders uh in the room to explore a very timely topic, owning your intelligence or sovereign AI. Uh today's event is meant to be half a rallying call and half technical how-to. And so we have stacked the agenda with I think high high substance technical talks and demos so that we don't just talk the talk of building your own AI but actually learn how to get there together.

Uh so thank you all for taking the time out of your mornings to join us. I know your time is incredibly precious and let's dive right in. So let's start with the opportunity. What is sovereign AI? Uh a sovereign is an independent state that has total self-governance. And so sovereign AI refers to companies owning their own intelligence without external dependencies down to the weights. An important nuance here is we are definitely not telling our companies to get off Opus or GBT.

That is definitely not the message. Uh for coding agents, for desktop work, uh for frontier level APIs, the closed model APIs are wonderful. But what we've observed is that more and more of our companies are going down the path of wanting to build their own AI capabilities in parts of their products and vertically integrating towards owning more of this intelligence. And so today's session is meant to equip companies that are starting to go down that journey.

We're obviously not alone in this idea. In the last month, uh, the rhetoric around sovereign AI has escalated sharply with folks like Alex Karp and Satia speaking up in support of companies owning their own intelligence. Just last week, Jensen led the charge in making sure that openweight models remain available in the US and it was awesome to see the near unanimous wave of support. I think the message is very clear that companies want to own their intelligence.

They want to own not rent their weights. And I think it's simply because intelligence is too core, too fundamental of a property to just outsource. We're glad that sovereign AI is in the zeitgeist right now. Uh because we think it's a good thing for the world. On one hand, you have centralized intelligence where a single all powerful AI powers more and more of the GD the world's GDP as a black box sucking in all the data exhaust, all the data flywheels from the rest of the world.

On the other hand, you have decentralized intelligence where the whole world builds on top of a solid core, but every individual person company builds their own intelligence on top bespoke to their own data, their own industries, their own personalization, uh their own way of working, their own taste and so the ecosystem flourishes and individuality triumphs. No single company swallows the rest. I think this is a much more optimistic view of the world.

And so we work with dozens of companies that are going down the journey of building their own AI. Um here are the biggest reasons that we've seen people move. Reason number one is cost. Uh especially for low zero negative margin companies, sovereign AI isn't a nice to have, it's a mustave. Um ironically, the more successful your AI product is, the higher your AI cogs tend to be. And so it's actually the companies that have been most advanced in their deployment of AI that have been the first to go on this journey of of owning their own models.

Reason number two is speed. And so in certain domains, coding is one of them. Uh security is another one. A small distilled custom model can beat a large general one because speed is so important. Num reason number three is performance. And this is a relatively newer one. I would say last year most companies were not choosing to own their intelligence to generate better performance. Uh but we're now at the point where open models can outperform closed ones on your domain and we're going to spend a lot of today's agenda talking about how to get there.

And then reason number four, controlling your own destiny. Uh anthropic and open AI, I actually think to their credit, they've been really wonderful partners to a lot of the ecosystem, but companies are increasingly finding that they want their own set of independent legs to stand on as well. Um, does anybody here come from the crypto days or remember the crypto days? Okay. Um, do you guys remember this meme? Okay. Uh, in the crypto of the days, there was this meme for the DeFi DJs.

Uh, not your keys, not your crypto. And so, if somebody was cussing your crypto for you, it fundamentally wasn't yours. I hereby present the AI version of this meme. Not your weights, not your product. Um, I think that for a product to be truly yours, I think it's reasonable to think that you need to be able to control control and custody your own weights. Pat shows this slide at AI ascent talking about the race for the application layer.

Uh, the you look so proud of yourself. Uh, the punch line is that both the AGI labs and the application companies are racing to be the userfacing product from different ends. the foundation model labs from the model side and the application companies from the user back. I think we're seeing a new dynamic emerge now which is actually that battleground is increasingly becoming not just the race for the application layer but the race for the intelligence layer.

And so this this battleground is no longer just about who gets to control the product, the UI, the go to markets, the wrapping. It's actually about who can own the intelligence itself and shape better intelligence in the product. So the product is the intelligence and the newest battleground is for not just the product surface but for the intelligence layer itself. And so the hottest nail labs in my opinion are actually uh the applied research that we see coming out of companies right now like Harvey, like factory, glean, open evidence, sam grep the list goes on and on and I think the research is spanning everything from eels and benchmarks to harness engineering to new algorithmic techniques for spine tuning and a a lot lot more and we started this uh we started this morning talking about centralized versus decentralized intelligence.

I think it's really wonderful to see the amount of innovation that is happening in the democratized intelligence world. Like I actually think the application companies are the newest Neolabs. Okay. So I assume that everyone here today is pretty bought into this journey. Let's assume that you want to build your own lab, build your own models. How do you go from zero to one to 100? We're going to do something a little bit different today here.

Um, I'm going to lay out an opinionated framework and technical roadmap. And so, take that with a giant grain of salt. Uh, every company is different and and I'm not technical and so take this with a giant giant grain of salt, but I hope it provides a useful starting point for how to think about building your own intelligence. Uh, step one um to owning your intelligence is strategy. What parts of your AI do you want to own? what parts do you want to rent?

Step two is team. Figuring out how to staff and organize people towards the production of intelligence. Step three is legibility. I really think this gets glossed over um and is incredibly important. So more on this later. And then finally, step four, we're going to talk about a technical road map. What are the building blocks you need to assemble in order to build your own intelligence? So let's dig in. Um step one, defining which capabilities you want to own versus rent.

Sovereign AI isn't binary. Uh you're not 0% or 100% sovereign. Um an important part of the strategy is to draw the lines for which intelligence you want to own and which you're comfortable outsourcing. And so here's a useful framework to think about um what parts you want to own versus rent. I think there are four important factors that go into this. Uh one is cost. Like how how important is this