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.

An important nuance here is we are

definitely not telling our companies to

get off Opus or GPT. 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 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.

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 must-have.

Um ironically, the more successful your

AI product is, the higher your your AI

cogs tend to be. 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. 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. 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.

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.

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.

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.

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.

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 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 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.

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 roadmap. 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

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?

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.

Um bio companies are moving that to

their own models because of the value of

proprietary data in that space. 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. 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. 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. 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.

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. 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. 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. 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. 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.

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.

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, 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. Um so I'd encourage

anyone that wants to chat about memory

to go find Dan during one of the breaks.

Um and then alongside the production

stack, the development stack becomes way

more important when you own your own AI.

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.

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.

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]