Sovereign AI: Owning Your Intelligence
Okay, good morning everyone. Thank you all so much for being here. We have about 80 portfolio company founders and AI leaders 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 sov- sovereign AI refers to companies owning their own intelligence without external dependencies down to the weights📝1. An important nuance here is we are definitely not telling our companies to get off Opus or GPT. That is definitely not the message📝1. 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 Satya speaking up in support of companies owning their own intelligence. Just last week, Jensen led the charge in making sure that open weight 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📝1. 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📝1. 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📝1, 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📝1. Uh especially for low, zero, negative margin companies, sovereign AI isn't a nice-to-have, it's a must-have. Um ironically, the more successful your AI product is, the higher your your AI cogs tend to be📝1. And so it's actually the companies that have been most advanced in the deployment of AI that have been the first to go on this journey of of owning their own models. Reason number two is speed.📝1 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📝1. Num- Reason number three is performance. Um 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📝1. 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 OpenAI, 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📝1. 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 degens. Uh not your keys, not your crypto. And so if somebody was custodying your crypto for you, it fundamentally wasn't yours. I hereby present the AI version of this meme. Not your weights, not your product.📝1 Um I think that for 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📝1. Pat shows this slide at AI Ascent talking about the race for the application layer. Uh the He looks so proud of yourself. Uh the punchline is that both the AGI labs and the application companies are racing to be the user-facing products 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📝1. 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📝1. And so the hottest new 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, Semgrep, Ramp. The list goes on and on, and I think the research is spanning everything from evals and benchmarks to harness engineering to new algorithmic techniques for fine-tuning📝1 and 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 these democratized intelligence world. Like I actually think the application companies are the newest Neo labs📝1. 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?📝1 Step two is team, figuring out how to staff and organize people towards the production of intelligence📝1. Step three is legibility📝1. 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 roadmap. What are the building blocks you need to assemble in order to build your own intelligence?📝1 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.📝1 Um an important part of the strategy is to draw the lines for which intelligence do 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 cost line item zero overall COGS? Uh two is speed and latency. Is it a P0 or not? Um factor three is performance. And this is where it gets very interesting. It used to be that you would choose open weights when you didn't care about performance. Now we're getting to the point where in certain domains you may be able to get better performance by tuning models on your own data. And then finally, proprietary data. Are you in a domain where the data you're giving the model to improve it is super proprietary super proprietary to your business or less so?📝1 Um so here are some examples of how companies have decided to make this trade-off. In coding, you have both agents and you have auto complete. On the agent side, this these are still mostly rented today because you want strong out-of-the-box performance and latency isn't a P0. Um on the other hand, for Tab auto complete models in coding, you really really care about speed and these uh these API calls are so frequent that the cost really rack up. And so most Tab auto complete models now run on sovereign intelligence. I work at a cybersecurity company in stealth. Alon, I think I saw you earlier. Uh that owns its models primarily for speed and performance um and the ability to post-train the model in very bespoke ways📝1. Um bio companies are moving that to their own models because of the value of proprietary data in that space📝1. And so I think this is just a useful framework to think about which AI capabilities do I want to own versus rent? Step two, assemble a team. I've shown two profiles of labs leaders here just as examples. Uh Nico comes more from the research side of the house having done uh research at Apple and then at Google Brain. Alex comes more from the engineering side having held multiple engineering roles at Microsoft and then Ramp. And I I show this just to say there's multiple paths to Nirvana and depending on the flavor of research you're going to be doing at your company, um whether it's going to be more fundamental or applied, there are different profiles uh that work for a labs leader. I also think it's important to think about how to design your organization. Um traditionally AI teams have frequently been organized hub and spoke. So you have a single platform team supporting different uh different AI product uh different application uh product teams. And what I've seen is that a lot of companies are shoehorning this AI platform team into doing the sovereign AI stuff as well. Um I'd encourage folks not to do this. Uh I'm for encourage people to start from scratch here because this fundamentally is not a platform services capability. You want people that are able to think on their feet, think on the frontier, and produce frontier level research📝1. And it's such a different flavor of research and you want them to be playing offense, not just servicing teams. And so we've seen uh small de novo teams get very far here. Harvey, for example, has published a ton of research. They have just a team of seven. And so start small, I'd say start from scratch, um consider making it uh your own lab. Step three, legibility. I think this is totally underestimated in how important it is because my guess is a lot of people in this room are doing wonderful research in-house internally and that not all of it is very externally legible. And Winston Weinberg talked about how the responsibility of a CEO is twofold. One, drive substantive results, but two, control the narrative, control legibility around what you're building. And I totally agree. Um when it comes to owning your AI stack, legibility really matters because every single buyer right now is choosing their AI champion📝1. And so they're trying to they're they're getting the same pitch over and over again. They're trying to discern which vendor is sophisticated enough to take me to the promised land. Um and you know, they want to they want to choose people that know what they're doing. Increasingly, that means putting out your own research. And so, being legible here means doing excellent technical marketing. Maybe having your own separate branded labs or research group. Publishing research with high taste. All of this matters a lot. I think it goes overlooked. And so, um for the all the Sequoia companies in this room, I would really really push on us to think about this. And then finally, step four, setting your technical roadmap. Um at a high level, the uh the rough journey that I see companies take and every company goes on a different journey, but first you set your strategy. Um second, defining evals. This is so important. Um it is unglamorous work. It is not fun work, but the more that you do up front, the better positioned you are for everything after📝1. And so, I think this is a really really crucial to get right at the beginning. Um next, we see companies starting to play with model routers, with harnesses📝1. Um some companies find they can get good performance with out-of-the-box models. Um others are finding strong performance gains from post-training, in some rarer cases needing to move into mid-training, pre-training. And then finally, setting that machine up so that live customer data is actually creating a feedback loop where your model, your intelligence, is improving with every customer interaction📝1. And so, this is the rough journey that I see people go on. Um again, every company is very very different. And I'd encourage everyone in the in the audience today is just compare notes with with people around you. Everyone is everyone's somewhere on this journey. The beauty of owning your stack is that you can actually drive frontier-level performance now. And so, this is somewhat new. And in large part, this is thanks to the newest open weight models, uh especially Kimmy K3 and GLM 52 being extremely good. Um because the weights are available, they're actually much more malleable than working with the closed APIs📝1. And so you start with a baseline that's already close to frontier, and then within with a good enough technical roadmap, so with strong post-training, prompt harness engineering, online learning, you can actually reach better than frontier performance by owning your stack. And so this is new for 2026📝1. I think this is very, very important. This is a big part of why people are starting to think about owning their intelligence. Um I like diagrams, and so in an attempt to orient us all, here is how I think about the stack from an infrastructure perspective. On the left-hand side, this is production. This is your user-facing intelligence. This is the stack that drives um every every uh token that your user ends up seeing. And fundamentally, I think of the production stack as a harness on top of a model. Alongside that, you have a development stack📝1. These are the tools and vendors that you use to get your intelligence good. In the closed model ecosystem, this entire stack is very simple. You have foundation models like Opus, GPT. Um and you have the harnesses that come out of the box with each. And you can get quite far with the stack, including building your own harnesses, prompts, feeding contexts into the models, doing your own evals. Um but you're not really having to collect a ton of data. You're not really having to train your own models. And so it's much simpler stack. And so I'd say this is a higher floor, but it's a lower ceiling because you don't actually have the ability to take your own data, to take online production data, and then improve your own intelligence. The minute that you start to think about owning your own intelligence, it is like opening a Pandora's Box because that beautiful, clean API call is now um having to train your own models. And so instead of having a single model API and some uh good performance out of the box, you have to choose a open-source base, do a lot of post-training on top📝1. For your harness, you've got a choice of several open harnesses, and then it's up to you to configure the harness, the logic, the tools, the context uh versus taking an agent that just works out of the box. Um I just on the side here, context is really, really important to driving performance📝1. Um and there's several different flavors of context that that that are driving these these models. Um a vector database like a Turbo puffer, an enterprise knowledge graph like a Glean, um open source connectors obviously via MCP📝1, um and then even novel approaches to context. Uh Dan Biederman from N Gram is here. Uh they're doing novel research around encoding context in in the weights themselves📝1. Um so I'd encourage anyone that wants to chat about memory to go find Dan during one of the breaks📝1. Um and then alongside the production stack, the development stack becomes way more important when you own your own AI📝1. You have to carefully monitor evals to know how your intelligence measures up um and watch how the model performance is drifting in production. You need a lot of high-quality data to post train the models for your domain. Sometimes this is expert trajectories. Uh sometimes it's synthetic data. Sometimes it's RL environments📝1. And then finally you need to think about how to set up online learning for your system so that your intelligence gets better and better with every single user interaction. And so the way that we've set up today is we've picked a series of technical workshops to give you deep dives into everything you need to build your own AI other than pre-training your own models. Uh Lynn from Fireworks is going to lead a workshop on post training. Harrison from LangChain is going to lead a workshop on harnesses and evals. Brendan from Mercor is going to lead a workshop on RL environments, synthetic data, and more. And then finally Trajectory is going to lead a workshop on online learning📝1. And then to bring it all together, Harvey's going to lead a workshop on how they approach building their entire AI stack and strategy. And just yesterday they announced Harvey research. You'll see that um Um, uh, of their technical partners are actually speaking today. And so, we've really gone all out to get the best possible lineup of speakers today, both inside and outside the portfolio. >> [applause]