How to Build a Self-Improving Company with AI

This is based a little bit off a talk Diana gave.

There's a video up over the weekend, which is super cool.

Um Jack Dorsey was tweeting some stuff like two

or three weeks ago

that I thought was super cool.

And I've kind of um stolen a bunch of those ideas

and shoved them in here.

This talk is like pretty conceptual

and high-level about thinking about how to build companies.

So, the Roman legions were designed to project power over two continents

or something from Rome at the center to like these people on Hadrian's Wall

up in Scotland.

And the idea was um this nested hierarchies with consistent spans of control.

And you had like named individuals with spans of control to pass orders down

and send information back up the hierarchy.

And if you think about most companies today,

they are organized like a Roman legion,

where human beings are the conduit for information flowing up and down.

And so, Jack Dorsey's tweet

that I thought was great was this like this underlying assumption

that hierarchically organized companies are the are the way

that we should be organizing like our economic units of value.

And I think AI basically breaks that.

If you talked to people a year ago about how AI was useful,

they talked about productivity.

Like co-pilots making engineers 20% more productive, adding co-pilots to workflows,

shipping more software.

But I think that is actually a broken way of thinking about AI.

That's like P had a great blog post where basically just like taking the

old way of working

and adding like a more powerful engine onto it.

And instead of that,

I think you can reimagine like what a company is and how it acts.

And so, as Gary's talking,

like he I genuinely believe can produce more code than an entire engineering team.

The thing that's really stuck with me is this idea of like extracting the

domain knowledge from your company

and defining it as a

as like context or a set of skills

or whatever you want to call it.

But like this idea

that there's domain knowledge

or business knowledge or like some know-how that's inside the heads of people

and in Slack messages

and in emails and in Notion,

all of this like information together defines how your company works.

And if you can make that legible,

you suddenly can can move from this hierarchical organization to a sort of intelligent

AI-powered organization with AI-native software.

AI isn't the something It's not something you bolt onto the side of a

company.

It's not like a tool you give to engineers to make them more productive.

But I think you can reimagine what a company is

as a set of recursive self-improving AI loops.

I think this is really, really, really important because when it gets there,

I think the company starts to self-improve even when you're sleeping.

So let me give you an example.

Diane's talk talks about this as well.

This AI loop, you start with like a sensor layer

which is like That's a fancy word,

but really it might be like emails from your customers.

Might be support tickets, code changes, people canceling their subscription, uh product telemetry.

It's like sensor data to get information from the outside world.

And then a a policy layer, decision layer,

like rules about what you can do,

what it has to ask a human permission for, what it must log.

A tool layer that's kind of Gary's skills and code,

like the tool layer is Gary's code.

It's basically deterministic APIs, things like query my database or look at my calendar.

Um a set of tools that the the AI can call.

A quality gate, like that might be eval's deterministic checks, safety filters,

human review for high-risk stuff.

And then a learning mechanism.

It's like your system interacts with the real world,

picks up where it doesn't work and loops back into the top again.

And if you can run every single step of

that without human intervention without with minimal human intervention,

your system gets better

and better and better

while you're are And I can give you actual examples of this

that are live right now.

We started with an agent

that you can ask

and if it has deterministic tools to query our database.

Pretty simple, like when did I last have office hours with this company?

Then it got a little bit smarter, which was like,

for this company I'm doing office hours with right now,

they need introductions for anyone in petrochemicals or something.

And it could query the database in different ways

and use rag and all sorts of stuff to like come up with five

relevant founders for you to meet.

But again, this is like this is a sidekick, right?

This is an agent.

This is like the old This is last year's version of how a how

AI is making me better

as a group partner.

It's making me 20 or 30% more effective.

The aha moment for me came

when we put a monitoring agent on top of

that,

which looked at every single query every single YC employee was doing

and saw when it worked

and when it did not work.

And when it did not work, it's like, oh, why not?

What would have made this query work?

Do we need different deterministic tools?

Do we need to update the skills file?

Do we need a different database for you?

Do we need a new index?

And this happened This literally happens overnight now.

Let's write the code, put in a merge request to the YC code base,

have an agent review it, and merge it, and deploy it.

So, when a human comes the next day to ask the same query,

it will now succeed.

For me, that was like the holy

But that's not just AI making you 20

or 30% more valuable,

it is the AI going through this loop to figure out how to self-improve.

And I think basically,

if you can identify parts of your company

that work like this

and eliminate as much of have the human in kind of a monitoring

or supervisory capacity,

you can just throw tokens at this problem and your company will get better.

And so, other examples might be, if you have product analytics,

having an agent go through your product analytics to to figure out what part

of your sales funnel is presenting the highest amount of friction,

researching best practices, putting in place an AB test, running it for a week,

picking the best version, and deploying it.

Then doing that again and again and again for your product.

So you have a self-optimizing like product loop.

Or you do it with customer service queries.

You have customer suggestions coming in and in and in.

You triage it with a kind of You have to have an agent

which is like your chief product officer

and your chief technology officer who make kind of judgment calls about Okay,

this is a suggestion which we just don't want to do.

We'll discard it.

But no, this is a suggestion

which is now in line with our road map.

Um we can do it overnight.

Let's write the code.

Let's deploy it.

Let's ship it to the customer without a human being involved.

So I think if you can think about each part of your company

as a self-improving like recursive AI loop,

it becomes very very different to this like hierarchically organized Roman legion of a

company.

So what?

So like if you want to do this, what are the implications?

One is like burn tokens, not head count.

We are seeing companies get to demo day with about 5x more revenue per

employee than they did 18 months ago.

And I think that's going to continue to series A and series B.

And so I think you're going to be constrained on token usage,

not on head count, really really soon.

The blunt measure now is just like measuring everyone's token usage,

which is obviously like dumb and gameable at the extreme,

but directionally I think is correct.

We're in the phase of like what is possible right now.

And so everyone should be experimenting to the max to figure out what we

can even do with this crazy new intelligence we have.

As soon as you turn it into a leaderboard

and people get promoted

or fired based on it,

obviously it gets gamed.

Obviously that's dumb.

But I think directionally figuring out who in the organization is token maxing,

who is not, is like a good way to think about

which employees you should be spending your time with.

I think middle management is done.

I just don't think you need middle management for this coordination problem.

I think AI should be doing it.

And for me, there are two roles.

Jack Dorsey has three.

I actually don't like the third one, so I deleted it.

But there are two roles that really, really matter for me.

I think everyone just has to be an IC now, a builder, an operator.

And I think crucially having directly responsible individuals to get anything done,

I think you need a named human.

Not a committee, not a group of people, just a single person.

And I think you can build companies based on ICs effectively.

I I think just middle management is is over.

So, building the self-improving company, that's the dream.

And by the way,

I think like people are at the bleeding edge of this right now.

I'd be interested to see where you all are,

but it feels like people are like exploring the boundaries here.

I'm not sure anyone has a truly self-improving company in every function.

I might be wrong.

You might prove me wrong.

What would I do?

First of all, this is really, really important.

I would make the entire organization legible to AI.

What does that mean?

It means you've got to record everything.

Um simplistically, all of our um partner emails,

now if you email a YC partner, that email is in the YC database.

Every Slack message, every DM,

every office hour we've started recording for the last three or four months.

Every single thing that happens, if it is recorded, it happened to the AI.

If it did not get recorded,

it is it did not happen to your intelligence.

You know what I mean?

And so, I was talking with some founders over here um just now,

and we're having like really good conversations about their company.

But I every conversation I had, I was like, "Fuck,

I need to be recording this conversation."

Because some guy wanted an introduction to I can't even remember who the introduction

was now.

Uh who was that?

I was talking to someone about and I promised you an introduction.

I said, "Yes."

And I said, "Email me afterwards cuz I would I I'm going to forget

this.

I'm going to talk to 20 people."

Yeah, so it needs to be on my phone

or a or or smart glasses.

Or we deck out every room with like microphones.

But basically, everything needs to be recorded

so that it can be legible to the AI.

And then as Garry talked about like diarization,

you cannot pump in 100,000 hours worth of recordings into context window.

So, you have to diarize it.

You have to basically aggregate it down, synthesize it into the important parts,

and then give the AI breadcrumbs.

So, like okay, so here's an example.

Who's read the user manual, the YC user manual?

Hopefully, everyone in this room has at least opened the user manual at one

point in time,

right?

Like it's fine.

It was written 5 to 10 years ago, most of it.

It's kind of out of date.

So, Harj thought uh last weekend,

since now we've got about 2,000 hours of recorded office hours from the last

3 months,

why don't we regenerate the user manual?

And so, you can click like you give it a set of instructions,

you basically diarize it down, synthesis like categorize it into certain areas like fundraising,

hiring, co-founder disputes, whatever, and then write me a new user manual.

And by the end of the weekend, he had a 150 page user manual,

which is dramatically better than the existing user manual.

And now we can also update it every single month.

So, our user manual becomes self-improving.

Every new piece of advice we give,

it's compared with the existing user manual and either incorporated or thrown away.

So, the user manual becomes this up-to-date living brain of the advice we give

to founders.

And obviously, it doesn't stop as a user manual,

you then pump it in as context to an AI agent,

and suddenly you can ask a superintelligent AI

and get the combined wisdom of 16 YC partners in one.

But only if it's legible.

So, you have to record everything.

The second point is kind of the same, right?

Like if it creates an artifact that can self-improve, it's legible.

If it doesn't, you throw it away.

The third point then is

that every function can generate This used to say dashboards.

It's not just dashboards, it's on-demand software.

Codex 55 is now good enough you can one-shot most simple like most internal

software dashboards you can one-shot to a pretty high level of quality.

I tried it over the weekend on a bunch of our stuff.

It's just unreal.

So, all of your internal operations teams should be sitting on this layer of

like kind of intelligence understanding,

and then creating their own dashboards and their own workflows.

And I would see that those as entirely disposable.

I would very preciously store all the data.

So, as Garry said, he puts it all all of his emails in markdown.

Never throw anything away, but then treat these the software as ephemeral.

You can you can generate it.

You can regenerate it.

The valuable part is like the comprehension inside people's heads of like this is

how the function works.

This is how we run a YC event, whatever.

The software to actually run the event you can generate for the event.

You can throw it away.

The the models get smarter in a month or two.

Throw the software away.

Give it your original set of instructions and regenerate the software.

So I think the business context and and skills are the valuable part.

I think the software on top of it is ephemeral.

So what what are humans for in this world?

I think basically we're talking about a company brain.

And I know a bunch of people in this room are building this.

But the bit in the middle, like all of your data,

all of your emails, your DMs, the skills, the know-how,

that is like the company brain.

And I think the humans sit around the edge of this interfacing with the

real world.

So it's where this intelligence makes contact with reality.

Human beings reach into places the models can't go yet.

That might be like a conference.

It might be a I'm trying to think of examples.

I I would say a phone call,

but I think the AI can reach into phone calls pretty easily now.

Um I think it's like novel situations, ethical considerations, high-stakes moments.

You know, it's like it's where the founder comes to us

and is like thinking about breaking up with their co-founder.

Right?

It's like those real high-stakes,

high-emotion moments where you really want a human being.

I think that's where the human fits.

For all of you, like sales conversations.

I think that's a human being in the room for the next 20 years.

So the humans live I think around the edge.

And I'm over time and Kulveer should bullhorn me.

I will leave you this one question.

If you were building your company today, would you start it in this shape?

For most of you, you're small enough to build it right.

And so I don't think you have any excuse.

And I know there are a few of you who are in the process

of ripping up and rebuilding your company.

So with that I will stop and will hand over to Pete.

Thank you for listening.