Nous Research CEO：AI 实验室终将围困或取代企业，开源 Agent 才是可控出路

I'm Dylan Rolnick. I'm the CEO of News

Research.

And today we're going to talk about

building on foundations and platforms

that you can actually own.

Whoop, there we go.

All right.

How do you make back a cent a

billion-dollar infrastructure and

research bet?

Labs individually have spent hundreds of

billions of dollars training and

building ever more sophisticated

intelligence system and the industry as

a whole has spent

trillions, perhaps.

though that the models have begun to

commoditize.

You guys in this room who are hopefully

practitioners of AI have known this

intuitively.

You switch among models readily. You

switch between Astra, Fable, Opus,

Kimmy, whatever it may be

without too much regard for who's on the

back end or what it may be. You want to

try the new model.

We've seen this in data, too. There was

some charts going around Twitter this

week showing the average cost of a token

going down while the total spent on

tokens surges and the infrastructure

costs associated with serving those

tokens has stayed flat. Meaning where

there used to be margin in a very, very

performant model, it's now starting to

narrow as we see competition and you

know, a baseline level of intelligence

be available to people.

commoditize and you've spent hundreds of

billions, trillions of dollars on them,

you need to make that back. And you need

to find a different way to do it. And in

my opinion, there are two

pretty obvious ways to do it.

One, you find a different layer of the

stack to compete at. You go down to the

infrastructure, or you go up to the

user.

Or two, you build God, and you build God

faster than anyone else, and you

monetize God.

If you're in front of the user, you have

an opportunity for stickiness.

Stickiness means margin.

shifted to the agent layer lately. We

started talking, I heard somebody

previously talk about 5% of, you know, a

workforce engaging in agents. This is

becoming more and more the zeitgeist of

where the AI battle is taking place.

Consumers, though,

[sighs]

are fickle, and they're terrible

customers.

Businesses, on the other hand, spend

tons of money on tokens, and they have

economically productive use cases for

those tokens.

So, if you going to be competing at

the user layer, and you're going to be

bringing this fight somewhere else,

you're going to be bringing it to

businesses.

in a business, cuz when you're in a

business, it's hard to take you out,

right?

And if you think about your business 2 3

years into the future,

how much of your workforce, as measured

by its output,

is agentic?

70% conservatively, probably more.

And if 70% of your workforce is

organized by somebody else who may or

may not have adverse incentives to you,

they can be organized into a strike.

And so, what you would do if you had to

make back a trillion-dollar investment,

was you would embed, you would use your

asymmetric leverage, and you would

change the rules of the game.

If you succeed at building God,

[sighs]

what you would do is you would point God

at the most lucrative problems in the

world.

The best shot at earning it back is not

to sell it for an ever-decreasing margin

to others to solve these hard and

lucrative problems. The best shot is to

solve them yourselves.

Labs

think that markets are solvable

problems.

And that's exactly how they view the

financial industry as a problem to be

solved.

And a tractable problem at that. Perhaps

not even a very hard one.

they know, at least the smart ones know,

that they don't have enough context,

know-how, or experience in the financial

industry to be able to do that

themselves. So, they will partner. They

hire investment banking analysts. They

partner with some of the biggest firms

on the globe.

And they start to build their foundation

here.

My belief is that they will eventually

go to the source because, like I said,

that 's where the money is, and we're

trying to make back a trillion dollars.

And so, you might say,

"This is nothing new. We have always

been trying to trap people into these

games and change the margin, and once we

have you and you're stuck, we are able

to raise the prices." But, the

difference is that the stakes are much

higher.

We're talking about meaningful

deca-percentages of the Earth's economy.

And you in this room

need AI.

Everybody has seen what they can do,

what these models can do, what agentic

systems can do, and there's no going

back.

People in this room who are

um

making decisions, vice presidents, and

executives, and engineers about what

they're building on, are going to have

to make decisions now about where they

grow roots around these tools and

processes that will affect the future of

these businesses.

And whatever you pick,

you'll start to harden.

You'll gain know-how. It'll stop up the

intelligence of the organiza-

organization itself. You will start to

build processes and systems around it.

I implore you, do not build on quicksand

found- quicksand foundations.

Build on open foundations.

Build on Hermes Agent.

spent a fair amount of time convincing

you, hopefully,

that the highest expected value move in

AI right now is to either

trap you and jack up the prices,

or replace you directly.

So, why am I standing here?

Uh

we view the world

very differently.

And the first thing I hope you guys will

learn about me and about News Research

is that we built this company primarily

for moral and philosophical reasons.

And a lot of times in business, morality

and incentives don't always align, and

when that happens, it's probably easiest

to change your morality.

But, we think the world is changing, and

we think it's different. And for that

reason, we think that the best course of

action is one in which we are both

aligned.

When we built the company originally, we

built it to combat a seemingly

inexorable trend towards centralization

and centralized control of perhaps the

world's most

most important technology and most

important invention.

News Research was built by and staffed

by nerds.

Nerds who love science fiction.

And we've all seen what happens when a

centralized entity gets control of

god-like intelligence and uses that for

exploitation.

Skynet was, after all, a closed-source

company.

that there's no longer really a strong

moat in proprietary software.

The marginal cost to reproduce a piece

of software has plummeted.

A few months ago, uh xAI released Grok

bots and we saw that and we thought,

"Wow, that's an intuitive user

interface. That's really nice."

So, we pointed Hermes agent at it and we

reproduced it in a weekend and released

it as a feature within Hermes agent,

open source, of course.

And the game has changed. It used to be

that software was difficult to produce

and it took coordinated teams of

engineers long hours to

pilfer open source and take libraries

and combine them together into

proprietary pieces of software and then

sell that software as SaaS. But, now the

opposite is true.

Anything can be reverse engineered and

there are many, many motivated open

source builders pointing their agents at

these different

softwares.

If the close competitors do manage to

make interesting features, open source

will have it not long afterwards.

The motes of yesterday were in

proprietary IP.

The motes of tomorrow are in network

effects, reputation, and reliability.

the labs have always seemed to

underestimate their own tools or perhaps

they are underestimating the users of

those tools and their genius and their

motivation.

This happened and is happening on the

model side and we'll talk about why

that's important, but

it turns out that models can fairly

easily teach another model to be, you

know,

nearly as smart as the original model.

And the advantage in a model is very

rarely in the architecture or some novel

system that was created in a proprietary

way for that model. In fact, it's

usually in the quality of the data and

the scale.

And

sorry.

The uh

The The model started to The model

company started to realize that the

you know, scale was catching up and that

you could

create these things with supplementary

quality data

by

teaching one model from another. So,

they labeled the teaching process

distillation and they labeled

illicit distillation theft. You cannot

call that copyright infringement because

copyright infringement would imply that

you somehow took every living human

being's written or creative work and

then trained on top of it.

They also labeled the process of They

also trained the models to not assist in

the process of training other models and

they labeled that safety.

about agents and about the new world is

that

agents are like operating systems and

they're like frameworks and they're a

lot more like those than they are like

chatbots.

And so, if you continue to think about

your agent as merely a personal

assistant

you will forever live on the surface of

what it means to engage in agentic

systems. And so, what do I mean by that?

If you guys were here a little bit

earlier, there was a presentation about

a financial firm and how they think

about AI and these different inputs that

go into ultimately what becomes an

investment decision.

Agents are really at their heart a

system that combines determinism and

non-determinism. If you remember from

that talk, there are data sources, there

are routine code, there are people's

opinions and there's judgment. And all

these things come together to create

what is the new application. The new

application is no longer necessary that

it is deterministic and solid and cold

hard code. It can inject intelligence,

reasoning, judgment, all of these things

together to combine to create

the new application. And so, as you at

your company

think about building these new

applications,

you should be building these systems on

foundations that you own. You should be

able to swap in and out the models that

you want. You should be creating ever

more sophisticated towers of

functionality on top of these core

primitives

of

determinism, non-determinism, code, data

sources, document, reasoning, intellect.

you should not be spending your entire

lives being

intelligence companies.

What has made you successful this far?

Most of you are most likely not agent

companies. Most of you are not likely AI

companies.

You should be focused on the things that

you do well. And when the state of the

art changes and it evolves and what it

means to be an agentic system starts to

shift, you should inherit that, not

rebuild it and maintain it.

Remember, these are tools that should

make you better. They are not drop-ins

to replace you.

Do you know how much energy it takes to

create a human?

And the beauty of the Hermes Agent

proposition is that you can run it

however you want, wherever you want,

modify it as you see fit. If you're not

having fun anymore, you can take your

ball and you can go home.

The moat has changed. The moat is no

longer entrapping you.

The moat is in reputation.

And that's what Hermes Agent offers you.

what do I want out of all of this?

Well, I want to build a standard.

Hermes Agent today is the most used and

downloaded open source agent in the

world.

It is order of of magnitude more

downloaded than some of its close

competitors.

And I want everybody to be able to build

on these open foundations. Because as

long as one of us

us

is willing to open source these tools

and these agentic systems, water will

continue to flow downhill.

And I want to be downhill so that I can

work with you to provide support,

safety, scalability to go along with the

world's best open agent.

I've seen where this goes and we've seen

where the motes will lie, network

effects, reliability, and reputation.

People are going to continue to solve

hard problems with agentic systems.

People are going to solve lucrative

problems with agentic systems.

Somebody is going to earn back the

trillion-dollar investments that we've

made.

Make sure that it's you.

Make sure that you are in control of

your own destiny.

You can have your cake and eat it, too.

You can have state-of-the-art features

and reliability without sacrificing your

autonomy.

Build on something you own. Build with

us. Thank you.

[applause]