Ep 147: Scott Wu & Russell Kaplan on the New Era of Software Abundance

you're really only limited to your ideas

and to your imagination,

where you can kind of just turn things into reality.

You were the three-time gold medalist

of the IOI top programming competition

in the world now you're running one of the top AI companies here.

When we launched Devon in March of 24,

it was the first autonomous agent.

One hour of human time spent managing Devon

was worth like six to 12 hours

of that human time doing the work themselves.

Elon had this phrase that he really drilled into us

which is everyone is chief engineer.

Let's talk about this new era of software abundance.

For us at cognition, for example,

our engineers don't type code anymore.

You really can just turn your ideas into reality.

The engineer and the designer and the product manager

all look at each other and say,

I don't need you guys anymore.

The engineer and the designer and the product manager

all look at each other and say,

I don't need you guys anymore.

Scott Wu was a three-time global gold medalist

in programming.

I worked with him in the past.

He's now running one of the fastest growing

AI companies in the world helping to usher

in this era of software abundance.

He and Russell co-founders met up with us.

We played some games, not going to tell you who won.

These are pretty smart guys,

but it's always really interesting to hear

from Scott and Russell about the cutting edge of AI,

how the world's changing,

and what we can create in this new era of abundance.

Scott and Russell built Devon

was a very first AI programming agent two years ago.

They're already launching

in all sorts of other areas.

They're now transforming how governments work as well.

Excited to see where they're headed next.

Welcome to American Optimist.

We have back Scott Wu, the CEO and founder of cognition

and your co-founder Russell.

And through mine people, Scott,

you were the three time gold medalist

of the IOI, a top programming competition in the world.

I think you were the one time world champion at 17.

And we worked together at Adapar after that.

And now you're running one of the top AI companies

here in the world.

Russell, I think you started your career at Tesla

as an ML engineer.

You sold a company to scale.

You guys are both in your 20s, still, right?

No, I'm a shout now, I'm 30.

You're 30, oh.

I just 30 this year, so it's, yeah.

So you're turning 30, okay.

Well, that's okay.

You're getting old like me.

You're still right in the heart of it here.

What's the average age on the team?

Actually, I'm curious.

I think it's probably, so on engineering, it's about 25.

And then obviously on go-to-market, it's a little bit older.

But yeah.

Well, go-to-market is different.

That's fair.

You're running more go-to-market stuff.

Yeah, I think the engineering team, we have 17 year olds,

we have 18 year olds, we have really young folks.

And then, but we'll take anyone at any age

as long as they're ready to grind

and ready to have a big impact.

And you guys have like huge numbers of people

with one gold medal globally in programming.

This is a very advanced technical team here

cutting edge of AI.

Yeah, I mean, some of our favorite people,

I would say are people who are like 17 or 18

and like finishing a high school,

but they had already played around

with a ton of building agents themselves,

like working with AI, training models, and so on.

And it's obviously, I mean, it's-

I actually want to ask you about this really briefly

because so you were gold medals in the world of 15,

world champion of 17.

It's obvious people can be really, really good

at these things at a young age.

Is there something about AI,

where like a young person's brain

that kind of grows up in forms using it

can somehow like be more ahead of anyone

who doesn't or something like that?

How do you think about that?

It's a good question.

Yeah, no, I mean, it's fun enough.

So I went to what's called the USACO, the USACO,

which is like the USA Computing Olympiad.

And from there, that's like the training camps

and all the selection camps that choose the national team

that go represent at the international impact.

And every year, there were about 20 kids,

which was like the 20 kids around the US.

I was from Louisiana.

And most people were from like, you know,

California, or like New York, or like, you know,

around like Massachusetts, like around MIT

and Harvard and stuff like that.

But in my year, actually, there were a ton of others

who all kind of went into AI.

And so obviously, Steven and Andrew,

who started the company, you know,

who started cognition with us, but also a ton of others.

And so Alexander Wang, who started scale, Demi Guil,

who started Pika, the, let's see who else.

Daniel Ziegler, who is one of the co-inventors of RLHF,

Alex Wei, who is like now running a lot of the reasoning

efforts at OpenAI, Johnny Ho, who started perplexity.

So we were all the same year, actually,

out of that like group of 20 people.

And it was kind of an interesting one.

I mean, I think there are a few things there.

I think for one, obviously, I think entrepreneurship

is infectious, you know, and I think, I mean,

Alexander was, I would say, the first to really start

a company and to see real success with the company,

he left freshman year from college to start scale.

And he sold scale, obviously, for like 16,000,000

to face, or whatever, some funny structure.

But yeah, he's successful.

But that and probably inspired other people

who would say, wait, I'm not smart, too.

I can do this, too.

Yeah, so I think that was a big motive.

And then, you know, we all kind of like came up together

and kind of got to go through some of these things together.

And I think that was a big deal.

I think the other thing I would say about AI,

particularly, is I think in AI, what you see is

that really excelling on the technical aspects

just matters much more, I think, in AI

than some of these other fields.

And I think there's been lots and lots of businesses

in the past that have been very, I'll say,

like very intense logistics businesses

or very tough kind of like marketplaces

to get started.

Or for example, businesses where a lot of your edges

just like how you figure out pure distribution

or how you kind of like, you know,

make the right little like,

addicting loop and so on.

And AI has a ton of these, too.

Obviously, and then, you know,

I think all of those same skills are still necessary.

However, I think in AI and a lot of factors

and a lot of these verticals, you know, what you see is that

obviously, pure technical execution,

it's like for every level that you push it,

there's still like another level to go and hit.

And a lot of the best companies that we see

in the valley are the ones that are just able

to roll out like technology pushes or breakthroughs

that others have not.

And to push you guys on this,

there's a 17 year old today who's like the Scott Wu

of today who's a world champion,

who's maybe you're maybe you're hiring.

Do they have some special edge having grown up

in this world where AI is already possible

whether you're using it?

Like is it exciting things further?

Yeah, I mean, everyone starts with the same level

of experience with AI for software engineering, right?

Which is basically none.

I mean, every three months,

you have to throw out your previous experience

and build new experience because the tools

get so much better.

That's probably harder for someone who's my age, I'm 43,

than someone who's like 18 and still learning or not.

Yeah, so because some people say,

oh, you know, it's going to be really hard

for junior engineers now because, you know,

the entry level tasks are being done automatically by AI.

But I think a lot of what we see internally,

it's kind of the opposite in some way,

where if you're coming in with no preconceptions

about how things are supposed to be done

or how things are supposed to work,

then you can just go all in just really embracing

this completely new way of working.

But I think the AI technical depth piece is,

it's actually not just in the sort of modern generative AI era,

you know, when I was at autopilot,

I was a machine learning scientist

working on the sort of the vision neural network.

And Elon had this phrase that he really drilled into us,

which is, you know, everyone is chief engineer, you know,

everyone on the autopilot team has to understand

how the full stack worked.

And this is actually extra important in AI

because what happens is the abstraction boundaries

between different teams start to break down, you know,

the sort of classical way that the self-driving system worked,

which you had a, you know, a perception team,

you had a planning team, you had a controls team,

and they had these like thin interface boundaries between them.

But the nice thing about AI is you can optimize systems end to end.

So if you want to actually optimize systems end to end,

you have to have an accurate mental model

about each of those pieces work.

And so I think more sort of technical breadth

than depth across the entire stack is becoming increasingly relevant.

That is an interesting kind of way Elon does things,

which I've seen a lot of really top people,

not too many, but some people do things.

It's in order to really be the best,

you have to understand everything going on it.

So it's really breath the end depth in a way.

And so you're saying,

I make that a lot easier to do that

because I can give you some of that breadth

you wouldn't have otherwise.

Yeah, I mean, like the way we onboard onto our own code base

for new people, you just asked Devon,

all the questions of what's going on,

why is this done this way?

What's the historical context?

Do you tell them they're also like equivalent

of like the chief engineer where they have to learn everything

or it seems like it's a take a while

to learn, I mean, he's a big code base now.

Yeah, I mean, I think in practice,

it's so much of it is all obviously very connected.

And so I mean, the simple example of this is like,

we bought Windsor, if you know, seven, eight months ago,

at this point, but like we don't have a distinction of like,

oh, this person is an engineer working on Devon

or this person is an engineer working on Windsor.

Like there are a lot of the same people,

should be people who are kind of, you know,

working across both of these.

So catch us up since we last talked.

Like what's the state of cognition?

Where are we now?

You're probably not giving out revenue numbers,

but you've drawn a lot.

Like what can you tell us?

Yeah, no, it's, I mean, it's,

we've had a ton of growth over the last,

I guess it's just under a year since we last talked.

And obviously, you know, back in July,

we bought Windsor, but I think over the last several months,

I think both Devon and Windsor have grown exponentially.

One of the fun stats actually is today's March 9th.

And we actually, at this point,

I've already done more Devon sessions in our customers

in 2026 than we had in 2025.

And so basically over the last like two months in change,

we've already done more Devon usage in total

than we had in all of 2025.

So it's more and more than six, six X or something.

Yeah, yeah.

And obviously, you know, we're working on making sure

that that growth trend continues.

And so we'll see how that goes.

But no, I mean, I think the business has grown a lot.

We've been working with a lot of the biggest companies

in the world, you know, Citibank, Santander, and so on,

on figuring out how we really, you know,

transform their engineering efforts.

Yeah, one of the interesting developments since last year

is, you know, when we launched Devon in March of 24,

it was the first autonomous agent, right?

It was like very early for the form factor.

And it was, I would describe it as kind of like just

at the edge of possible then in terms of doing,

real useful work.

It made a lot more mistakes back then, obviously.

Yeah, yeah, as much less reliable, you know,

our infrastructure around it, the connectivity

with the rest of the codebase was a lot less mature.

I mean, it took us until summer of 2024 for Devon

to become the number one contributor to its own codebase,

like which was like the first real milestone

at then towards the sort of the end of 2024

to really get deployed in production at large scale

at meaningful companies.

But one of the things we learned is that

if you look at sort of where the technology was in 2024,

it was not reliable enough to be used

as the primary source of software engineering for most tasks,

right?

You actually still need a tighter feedback

loop in that year between AI and humans for most things.

And so one of the sort of first niches we found

where this was actually already really useful back then

was in these very large code bases

that just have tons of existing technical data complexity.

And you want to do a large scale refactor

across, you know, 10,000 services.

If you're doing it the sort of normal way as a human engineer,

you might have to define some new architecture

and then manually go implement thousands and thousands of changes.

And these changes would be complex enough

that you couldn't just write a rejects,

but not so complex that AI couldn't help.

And so that's kind of one of the earliest places

I think we got to a real strong product market fit

is inside these very large complex code bases.

And that's one of the reasons now,

if you look at the state of our business,

we're deployed at a lot of the largest

most complex organizations in the world,

like most of the top health insurers, retailers, banks,

government agencies, and these places with large,

complex amounts of code have been actually surprisingly

early adopters of a gentics off.

Well, these guys just have like massive amounts of code

that it's been doing the same thing for 30 years

on a very old architecture.

And you can go in there and pretty easily accurately fix that, I guess.

Totally.

It's like, to your point on, is this a new skill for engineering?

It's, you know, the mindset of it architect

inside one of those organizations has become,

you know, you're almost like a CTO of an agent fleet.

Now, you define the problem space of what you want to go solve,

and then you just spin up your army of devins

to actually go do the implementation work

and then you kind of review the results.

It's like a very different way of programming.

There's all these memes in San Francisco

where like, nerdy guys are on dates,

but they're too distracted watching their fleet of agents

and to talk to the girl.

It's like, it's a problem.

I have gotten in trouble with my wife for that.

Let's talk about this new era of software abundance,

as you call it.

What does software abundance mean?

What's cognitions role there?

Yeah, you know, I think the simple way to put it is,

is, I mean, to Russell's point,

all of these traditional industries, you know,

it's, you, you've, in Silicon Valley,

the way we think about software engineers is, you know,

obviously, these tech startups or these tech companies

that are building, the reality is,

every company in the world has software, right?

I mean, software is, in many ways, I think,

the, the, the premier knowledge work job of this century.

And so, you know, you're, you're talking about like CVS

or you're talking about Walmart,

or you're talking about UHG,

or you're talking about, you know, Goldman Sachs, like,

all of these places have so many, so many software engineers

and they have so much software that they're building.

Because obviously, so much of what we do in the world now

happens, you know, over the web or, yeah,

with computers, right?

And I think what we, what we kind of think of when we think

of software abundance is just getting to a point

where you really can just turn your ideas into reality

and build what you want to build.

And, and, and one way that I like to think about this

is, you know, you can kind of think about software products

on a scale of like, let's say a log scale based on

how much reach they have or how many users they have, right?

And you think about the best products in the world

and the products that everybody uses all the time.

And this is like, you know, YouTube or Instagram

or TikTok or something like that.

And it's, you know, these are incredibly good products.

And it makes sense.

I mean, they have billions of users.

And so they have, you know, 100,000 software engineers

or tens of thousands of software engineers

that are working on them, right?

And you feel it in the product, you know,

it's like the experience is perfect.

Like there's never bugs.

It doesn't go down.

It's streaming you like gigabytes of data.

The algorithm is super addicting, right?

Like they've, they've, they've really like perfected

the software.

I know myself used two to three you mentioned.

They're too addicting.

Yeah, they're too addicting.

And then you go to the next tier of like, okay,

instead of billions of users,

let's talk about the products that have hundreds

of millions of users, right?

And you're thinking about like, you know, banks

or you're thinking about like, you know, apps like Uber

or DoorDash or you're thinking about like, you know,

a lot of these various other kind of services

and products that we all use, right?

And it's similarly, it's like, you know,

you feel the software and it's obviously built quite well.

But it's already, you know, I think you notice

a different level of like how much execution there is.

And then there's next level and next level

and next level and it goes all the way down to, you know,

you're like, you know, your kid's website,

a school website, which is from like 2001 or something

and it's like an elementary school

and it has like a picture and it's like super outdated

and has no other information about that.

Right.

And so like maybe one way to put this is, you know,

I think software abundance means making it much easier

for everyone with every idea or every product

or use case that they want to serve

to be able to climb that ladder and build products

as well as the best products in the world

are built right now.

And for someone for a lot of listeners

or not as in the AI world, they might be CEOs

of a banker or something or running other businesses.

Like what is actually changing

about how engineers work, right?

Like what does a great engineer's workflow look like now?

Yeah, you know, it's a great question.

And the simple way that I put this is that

for us at cognition, for example,

our engineers don't type code anymore.

Like that's just reality.

And this is out of the last several months.

And this is within the last, yeah,

three to six months, honestly,

when the ships has really happened

and there are other steps to be clear obviously.

But I think at this point,

you know, you used to maybe one way to put it

is like, I mean, you used to work with punch cards,

for example, and you used to work.

And now in many ways, the medium has shifted away

from code and a lot more of it has become basically English,

right?

And so, you know, we obviously use a ton of Devon internally.

We use, you know, WinServe and then the agents

inside WinServe internally as well.

But at this point, either way, whatever tools you're using,

you know, it's not really you typing out the lines

of code yourself.

It's you looking, understanding what it is

that you want to do, thinking about,

okay, how do I want to handle this case

or this behavior?

And you just tell the agent what you want it to do

in English, right?

And it goes and builds it all.

Yeah.

And in terms of then, like what the impact of that is,

especially, you know, if it's like a CEO level objective,

what we're seeing across the board,

across the customer base is people are just getting

more ambitious, you know, like it used to be the case that

you have this entire software development lifecycle.

And every step of that cycle is oriented around

not wasting the super precious time

of engineers writing code.

And now you have suddenly this overflowing abundance

of the ability to generate code from ideas, from prompts.

So you can iterate faster in ideas to use try stuff more.

You can try stuff a lot faster.

You know, you don't necessarily need to spend

three weeks on design before you then hand it off

to engineering because engineering so fast

that the turn, you know, the cycle time is much tighter.

And so, you know, increasingly, especially at the executive

level, it's like, oh, do we have to choose between A or B?

Let's just try both.

I mean, could the product people themselves

create some things now or how does that work?

I mean, we see it every, there's like a joke

where it's sort of, you know, the engineer

and the designer and the product manager all look at each

other and say, I don't need you guys anymore.

Because they're all just doing it all themselves, right?

It's like every person is empowered to do the other aspects

of the product development lifecycle.

And I think it's really rewarding people who are, you know,

actually personally highly agentic.

And thinking about, okay, what's the impact I can go have?

And be sort of like self-reline in that way.

And I know, you know, with Devon, a lot of product managers,

one of the very first things they started using Devon for

was actually to not bother the engineers with questions,

with silly questions, you know, how often have, you know,

you're a new employee to a company,

you don't understand what's going on somewhere

and you're a little nervous to say, hey, can you explain this to me?

You know, Devon is very non-judgmental,

you know, you'll ask a question, you get the answer.

And I actually ask it a lot of dumb questions, too,

it's like, I'm the boss, I'm supposed to know,

something like crap, what is that acronym you get again?

But you know, in judges, you do it right away.

Totally, yeah, you know, and you'll cite the,

it will sort of actually cite the source code alongside it, too.

And, you know, give, it kind of puts everyone actually

more on the same playing field in a way,

wherever it has the same content?

In, by the way, agency something I'm thinking a lot about

for society recently, it seems like highly agentic people,

this is like a big advantage for everyone, right?

But along those lines, like, what are the habits or instincts

of someone who's going to be particularly good

at leveraging Devon, like are there certain things you're seeing?

Yeah, yeah, for sure.

No, I mean, one way to put it is, you know,

I think software engineering for people

who've grown up doing programming and so on

have been through all these previous errors.

You know, usually what software engineering has looked like

is like, why do people who love coding, love coding, right?

And I think the answer is like 10% of that job is basically

this really fun part of just pure problem solving,

thinking about what you want to build,

being creative, like understanding the different solutions,

deciding, okay, what architecture makes sense here?

How exactly am I going to, you know, achieve my goals here?

What do I want to build, right?

And then 90% of the job is, once you've figured out all

of those parts, just doing all the dirty work

of the implementation to go make that happen, right?

And there's like a million bugs

that your customers have reported for you,

and you have to deal with this messy migration

or this upgrade to make your stuff still work,

or like, you have to go implement all the little cases

and all the little details and like write all that,

you know, the front-end code that serves this thing

that you just built, right?

And I think what we're seeing is that the best engineers

are just doing 10 times more of the first part

because you don't have to do that 90% right?

You have an agent, you have to have

and that's gonna go and do that for you.

And I think to your point, that obviously means that,

having high agency is really, really important.

We think about this ourselves internally.

It's one of the jokes is like,

we're a reasoning lab building agents,

and so the things that we really value are agency and reason.

And I think to your point, a lot of the skills

that really matter are you the kind of person

that's gonna think about, okay, this should be this way

instead of that way, or like,

what is the right way to solve this problem?

Or what do we wanna do here?

And also just like internally embracing

the abundance mindset, too, where,

a lot of times you might be in your head,

oh, should we do it this way or that way?

I think a lot of our best engineers,

they just rip it all of the way at the same time.

And then you get a bunch of devins come back

and then you can sort of analyze the results

and try and parallel.

It reminds me again, it's kind of like the mindset

of actually being a good machine learning researcher

is now relevant for every type of software engineering.

If you look back a few years ago,

what we're machine learning researchers doing.

So at Tesla, we had one mantra,

which was, never go to sleep while the GPUs are idling.

If you let your cluster idle overnight,

that's just a huge waste of resources.

Just kick off some experiments.

Before you go to bed, so you wake up,

you get some more data.

And it's the same for all of software now.

Why would you go to sleep while the devins are idling, right?

You could just be ripping a ton.

You could be ripping a ton of these.

I'm sure it's working on your sleeping.

Exactly, exactly.

And so I think there's actually a lot of parallels in my head

where it's not just about that.

It's also some comfort with non-determinism.

A lot of engineering, it's an incredibly precise craft.

And it's sort of naturally uncomfortable, unnatural,

to not have full control over everything that's going on.

But if you get a little bit more willing to embrace

the non-determinism and, okay, exactly,

how should this be implemented

as long as we can validate system performance end to end

and I can actually understand the results.

I think machine learning went through that lesson

years ago and now we're going through it

for the rest of the software.

So I mean, everyone's in all long while

someone would probably check the machine learning to machine.

You know, sorry, I was so much like the machine assembly code

actually on something.

If they're really trying to optimize something,

do people, there's still people checking

the actual regular code on things

if they really have to these days?

How do you think about that?

Yeah, for sure.

And for what it's worth, I think we are still in the midst

of a lot of this change.

And so I think while people are producing code

with just English, often you do come back

and you're still reviewing that code,

you're making sure that it looks right

or you're looking at the code as it is

to understand what's going on.

And I think to your point, it is kind of like,

in the right cases, when you want to peel back

the layer of abstraction.

And I think what we'll see over the next 12, 18 months

is that we will continue to get further and further

to this point where you don't have to use English

for almost everything.

You don't have to do any more.

And what does this mean?

So obviously, a lot's happening a lot faster.

We just covered this.

And this is money movement.

This is military, we're just off.

This is healthcare, it's flights, it's maintenance,

it's scheduling, building new things,

and permitting, everything in the world,

a lot more people realize is software.

And we're going now, what, five times faster,

10 times faster, soon on software,

there's almost like we're getting multiple years done

in one year, right?

So what does that mean for society?

How do you think about that?

Yeah, I think about this a lot.

Have you ever seen the graph of inflation

by different sectors?

And you can see, you know, the sort of highly

regular constraint sectors at the top,

you know, like tuition, healthcare spending.

The cost of these sectors are getting everything else.

Right, and then the plasma screen TVs are just way down.

Jeff, please taught us what to do.

Exactly.

Yeah, I kind of think that that chart is going to happen

before all of society, basically.

And in particular, if you think about where is software,

we're going to enter this hyper-deflationary cycle

of software, where it's so easy to build, it's so abundant.

That doesn't mean that people are going to stop producing,

software, they're actually going to produce thousands

of times more software, right?

And so you're going to have this total software abundance,

but basically any problem that's kind of solvable

in the digital realm should just get solved

in the digital realm.

And so I think what you're left behind with is,

you know, now how do we direct a lot of this new energy

to going improving the physical world,

going improving everything else?

You actually have to do things with real operations,

the real people in the real world.

Now I guess robots could change that too.

But for now anyway, with the robots aside,

the returns to actually doing things in the real world

should go up by comparison because it's harder

to make those cheaper, right?

Yeah, we think about that a lot at cognition of,

how do we not just improve things,

improve software for software's sake,

but how did those improvements basically translate

into real world benefits for everyone?

Ultimately, these things are tools and service

of our own lives, our companies, our businesses,

and our livelihood.

The real world's messy, and you have to interact with them.

It's super messy, it's super messy.

And I think a lot of engineers fall into the trap of,

oh, I want to solve this very pure problem, you know?

And there's nothing wrong.

It's actually really fun to solve, pretty fun.

You know, a lot of our team,

they spend their entire programming careers

just optimizing their ability to solve,

you know, the purest algorithmic programming problems

in the world.

But it's an interesting contrast to, you know,

one of the things that really differentiates cognition

in the market right now, what we keep hearing from customers

is, you know, we have this forward to plan

engineering team that will go partner really deeply

with large organizations and say, hey,

it's not just about the tools we're delivering,

but how do we get into the weeds together

to actually go drive like big structural changes?

Tell me a bit more about that.

You guys have obviously deployed

with a lot of the biggest companies in the world.

What are some of the more impressive results

you guys have seen in the wild?

Yeah, so the earliest results that we,

where we saw, we were like, oh, there's something here

where they started with basically like modernization programs.

So people that had large legacy existing things,

they needed to transform.

And if you sort of did the math to scope out how long it would take,

maybe it would be like a two year project.

And you know, relatively quickly by like late 2024,

we were measuring, you know,

somewhere between a six to 12 X productivity gain

for those types of projects,

meaning that, you know, one hour of human time

spent managing Devon was worth like six to 12 hours

of that human time doing the work themselves.

And so that was a big, that was like a big early result.

We started doing lots of, lots of engagements

where our customers would use Devon

to just refactor my great modernization large systems.

Now the interesting trend that we're seeing is a shift

from really reactive to proactive engineering work.

So if you think of the early days of the internet,

most of the packets that were sent on the internet,

it was like a human clicking a button

or visiting a link or initiating some requests.

And then at some point it totally flipped

and now most of the packets are initiated

by machines talking to other machines.

And I think we're now seeing the sort of the flippening

there for software itself,

which is the process of deciding what code to write.

It used to be entirely a human scope thing.

Now we have people, you know, we have people

wiring up Devon to all sorts of events and alerts

inside their organizations to say,

hey, let's just start the engineering work

right away when something happens.

I'll give you one example.

One of the largest regulated firms in the world,

they do very thorough sort of security vulnerability scanning

on their code.

And they make sure to understand,

oh, is this potentially insecure here or there?

And there's great existing tools for scanning that, you know,

so in our cube, Vera code sneak, there's this whole,

there's this whole market of tools.

And what they did was they hooked up all of those alerts

they were getting from those tools

and they just started piping them to Devon.

Saying, Devon, can you take the first pass triage?

Because if you're a human engineer at a company,

you're drowning in these alerts, it's not,

it's honestly not a really fun part of your job.

And they're remediating 70% of these automatically now

in production with Devon.

And so it's really flipping the, you know, kind of flipping

that in.

I've been thinking a little bit about the pyramids

with regard to this lately,

whereas like, it's awesome possible to see how people

with like no tools at all built the pyramids.

It does seem amazing if you look at the software

that we have built up until like a couple of years ago

that all this exists, all by people.

When you, and then, I feel like that would be like

something you'd look at 20 years for now

and everyone just uses it.

I don't know if you could build all of that

by hand.

How do you imagine that?

It seems like totally impossible.

Yeah, yeah, no, I think to your point too,

I mean, I think there's sometimes a question

about, you know, we're talking about, okay,

this is gonna get way easier to build.

It's gonna be way cheaper to build these things

and so then what happens?

And I think the reality is like, we have so much more,

I mean, I've just think about this even like today,

you know, it's like you wake up, you're like,

you know, you're like, let's say you're like logging in

to check your medical records and it's like,

not a very good example.

You're like logging into your bank,

it's like not a very good example.

So much to fix, so much to fix.

So many of these things and to your point,

I think the reality is there's so much more

that we can do with software.

I mean, I think some of the things

that people started talking more about,

which is I think what we're gonna get to

over the next little bit are things like,

even like generative UIs or like single U software, right?

If you get to a point where, you know,

so much of the work that you want to do is,

it can be done in code.

It just doesn't make sense to like write code

for a single, you know, for something

that you're only gonna do one time

or even something that you're only gonna do

like 10 or 20 times, right?

I love the AI can actually write code

for this instance coming up and give you the right UI

for what's going on.

It gives you exactly the right thing and builds it.

Yeah, and so this is, I mean,

people talk about Jeven's paradox and I think,

I mean, in software it is perhaps,

I think there's nowhere that it is more true

than in software, right?

As we have produced more and more

as it's gotten easier,

the reality is that actually demand has only gone up.

That's actually a really fun idea.

Like, right now luxury is like, it's like Vakunya,

it's really nice, it's stitching is right,

but what if luxury was like, it's a perfect UI for you,

just for this instance in your life

where you happen to need it, right?

It's got more funny idea.

No, either that or actually luxury might flip the other side

because artisanal handcrafted software

is gonna be so rare.

It's like, you know, in the early days

of the Industrial Revolution was like,

oh great, mechanized labor for goods.

Like, that was the higher status good

because of course the machines were more precise.

The bags were more, you know, we're more perfectly made.

And now it's totally flipped if something's handmade

that's obviously much higher status

because it's so rare and like,

how could you expend the resources for that?

I think site was handmade.

No, it can tell because of all the little bugs.

I think we're actually gonna,

I think we're gonna see that in software, yeah.

It's like handcrafted artisanal code.

It's like the homage, except that there's any software.

Yeah, yeah, my hands, it's like a 2020 society frozen in time.

People making you handmade because that's very silly.

So speaking of like things that are broken

and that there's like infinite need to fix

like government to me is like one of those areas

where I think if you like,

what a graph everything in terms of getting better

or worse and efficient or less efficient

is this like probably unfortunately comes off

is like really, really messed up.

And I'm always starting to hear some pretty interesting things

in government.

My friend Jared Kushner, who's obviously,

you know, been involved in these administrations,

he worked with Qatar with Elad.

And they built something where the permits

will only take 120 minutes there.

So if you want a permit to build something,

like it backed you into ours, it's pretty cool, right?

So there's like, there's always ways government

could do things better with software.

You guys just launched cognition for government.

Like what's the goal with that?

What's going on?

Yeah, I mean, I think in a high level,

I mean, we talk about places where there are

so much more software to build, so many things to fix.

And I mean, I mean, government is an obvious example

of that in all of these departments.

Similarly, you know, things that we used to do by hand,

not even that long ago, honestly.

I mean, 20, 30 years ago, like lots of these things

were done by hand at the Treasury.

Now, so much of that is software,

and yet so much of that software, obviously,

still has such a, such a long way to go.

And I think from our perspective, you know,

when we think about how do we make sure

that the US stays in pace with what it needs to be?

How do we make sure that, you know,

that the breakthroughs that are coming through an AI

in the private sector are also coming through

in the public sector?

It's something that's a really important problem for us to learn.

I also think there's something poetic about it,

because I think people don't appreciate this,

but the government is really responsible

for a lot of modern technical innovation,

you know, going back decades and centuries,

Scott drew the analogy earlier to, you know, punch cards.

And I think people don't appreciate this,

but really the first wide-scale production use

of punch cards was the 1890 census, okay?

In the 1880s, they did the census by hand,

they tallied it, it took like seven years,

and they were doing the math,

and they realized that 1890,

if they did the census the same way,

it was gonna be longer than 10 years.

And the census every 10 years, so they were screwed.

And so the government basically put out this call

for call for technology help.

Can we solve this problem with technology?

And there was a guy by the name of Hollerith,

who he invented, but later became the Hollerith machine

to use punch cards for actually running the 1890 census,

and it was on time, it was under budget,

and it kind of kickstarted actually a lot of modern,

somewhere, you know, where I studied at Stanford,

a lot of that, the Silicon Valley ecosystem

was actually really invested in by the government

back decades ago, partially for defense.

And now we're in this, we're sort of this flipping point,

even by the self-driving.

I mean, the DARPA doesn't get enough credit,

I think, for really kickstarting the self-driving revolution

with the DARPA grand challenge.

And now we're kind of in this state of the world

where the government spends $100 billion a year

on just IT modernization.

We have huge amounts of governments still written in cobalt

with people who have left or no longer understand

how the code works, and it's really holding us back.

And I mean, you feel it as a citizen day to day.

Think about your interaction with the DMV

or trying to pay your taxes.

Or waiting for a permit to come for some time.

Or waiting for a permit?

Yeah, I mean, these things have real world consequences.

And that's one of the things I'm personally really excited for

is you can bring technology like this

across the private and public sector.

It's like a great equalizer for entering work.

Can you deal with the cobalt stuff?

Is that something, David?

Cobalt is actually a massive use case for us.

Yeah, one of the early bets and investments we made

was in what we call code-based intelligence.

So if you think about a modern language model,

there's a limited context window, right?

But a lot of the larger organizations in the world,

they have very, very complex code bases

that do not fit inside a single context window.

We've done a lot of work both on the model training

and RL side, but also on the sort of harness engineering

around that and the indexing around that

to figure out how do you work with really messy custom

languages.

And so cobalt, it's actually not even the hardest one for us.

One of the examples I like is Goldman Sachs

has invented some of their own programming languages.

People don't know this, but Goldman has a pretty insane

internal engineering team.

And they've literally written their own programming languages.

And they've been able to sort of customize and tune

dev into work on those internal languages too.

So cobalt was actually easier in some sense than that.

So in the government, I mean, they spend 100 billion

a year on IT.

It's ironic because you're right.

Sometimes government in the past, especially when

innovation was really expensive, they

pushed some new things that otherwise wouldn't have happened.

Today, most $100 billion is spent on special interest.

Don't seem to be using the money well.

So it's a giant mess.

What are the types of projects you're working on?

Totally.

I mean, the incentives are obviously super screwed up

for a bunch of reasons that your listeners are probably

familiar to.

One of the less known ones that I think we actually

might be able to just sidestep is the government

is a really unique buyer of software for a bunch of reasons.

But one of them is that a lot of times they

want to own the IP of the software they're using.

And this is a really big implications

for most SaaS businesses.

If you make scheduling software and your businesses

SaaS business, you don't want the government to own your IP.

You want them to have a license to it to use it for scheduling.

And actually, that desire is literally incompatible

with how a lot of government contracting has worked

and happened historically.

So you end up in a situation where the government

says, oh, this scheduling provider is a real example.

This scheduling provider that has great SaaS that

can do scheduling, I can't use it because I wouldn't own the IP.

So I have to go work with the systems integrator

and completely custom build my own, right?

Insane.

Now, what we could, could we lobby and go try

to convince the government to change their policies?

But actually, I think easier for us to just sidestep the problem

and say, look, Devon can just write this thing for you.

You know, we're actually because of AI agents,

you're in the regime where actually now everyone

can own their own IP more easily than before.

So I think that's one of the ways

we're sort of trying to sidestep some of this.

Can you be your own government contractor?

Are you going to have your own government

contract doing this then?

Are you just going to power others?

How are you thinking about that?

Yeah, I mean, right now, we have dozens

of kind of FedRAMS deployments of cognition, both with agencies

and with Primes, you know, we work with US Army, US Navy,

the Treasury, we work with folks like Palantir,

with Andrew, and other Primes.

And so we just want to build sort of the most capable,

most useful, agentex software engineering platform

and then work like heck to kind of get in the hands

of people to make it useful.

And then Coddish for government,

this is a fast growing business for you then right now?

I would say government is one of those things

that it really takes some time to kind of build

and be compliant and work in the way that people want to work.

And then once you're there, once you're there,

it's much easier to be helpful.

And so we've over the past year,

we've really done a lot of the live work to, you know,

how do you get your FedRAMS certification?

How do you understand the needs of these agencies,

which are actually pretty different in a lot of ways.

Again, just in the civilian sector,

these, they're operating in a completely different trade-offs,

right?

A lot of the software they use is actually

by statute not allowed for them to write themselves.

Talk about regulatory capture and entree.

You know, there are literally laws saying,

you government agencies are not allowed

to maintain your own website.

You have to bid this out to a contractor.

And so it kind of sounds insane,

but you know, that's the way the system works.

And I guess my experience working on AI with technology

is that a lot of times it's actually easier

to solve like a frontier science or engineering problem

than it is to sort of change the molasses

of the existing world, right?

When we were working on self-driving,

a Tesla, a lot of folks would ask us,

hey, like, why don't you make the cars talk to each other?

Wouldn't that be way easier for self-driving

if all the cars could talk to themselves?

And the answer is, yeah, it totally would.

If you got every car talking to itself,

but until then, you're gonna have to deal

with human-driven cars.

And so then you have to actually solve the much harder

science problem of predicting the motion

of all those human cars.

I think it's the same for our worth of government.

So yeah, I mean, right now,

we have dozens of these deployments,

you know, I think folks are giving us really good feedback

on how much it's accelerating their work.

And some of the missions are really exciting,

you know, I think of an organization like NASA JPL.

You know, who doesn't want to help us get back

to space faster?

I love it.

Yeah, we just actually interviewed

Jared Isaacman is running NASA.

So it's a great guy to partner with.

It is true in government.

A lot of times people say, well,

this solution would work.

We just put this thing in the middle

and made everything talk to it.

And I'm like, sure,

everyone always tries to do in government.

But these guys are never gonna all work together.

They're all gonna debate.

And so you actually have to design it

knowing it's distributed.

It's very interesting.

Like the real world's messy

and you deal with it as it is.

Not necessarily.

Yeah, it's like the XKCD comic.

Like we have a dozen standards.

Like this is a mess.

We need one more.

Now, you know, you know, you have 13 more, yeah.

Exactly.

So it's very honorable to go work in government.

America needs that you're fixing it.

Obviously, you're growing even faster

in the enterprise in general.

So these are both big businesses.

This is obviously a very competitive time right now.

There's a lot of the smartest people in the world.

You have a lot of them here.

There's some of them in other places as well.

I think very famously,

some of the big labs like Anthropic have been,

I think they've doubled into the tens of billions

in the last few months.

It's obviously the highest growth thing right now

in the general area.

You guys are obviously, you know,

don't say you're exactly revenue,

but you're likely to get into the billions soon

if you're not already there.

What's the competition?

Like do people use that with you?

Is it help you when they do well?

Like, how do you think about these things?

Yeah, yeah, for sure.

So no, I mean, it's an exciting one obviously.

And software engineering and code is just so big

that I think there's a ton to do.

I mean, we've seen a ton of growth as well.

I mean, our usage, for example, of Devon

and our customers has, I think,

roughly tripled already since the start of this year.

So just the customer inside the customers alone

and the customer is alone, that's right.

But what I would say,

I think a couple of thoughts here.

One, again, there's so much different work

to go do in code.

And so you'll see a lot of others who are, for example,

building products that will help you make a quick

little website or something like that

or build something like fun, which is,

you can use Devon for that,

but I think that's not necessarily what we specialize in.

On the other hand, a lot of what we really, really focus on

to Russell's point is working with enterprises,

governments, regulated industries,

working with massive, massive code bases

and trying to make sense of that

and work in all of those systems, right?

And so a lot of the problems that you have to go deal with

and work on are, how do you absorb

all of the messy context and the knowledge of this code base?

How do you work with a massive,

something that has hundreds of thousands of different files

and work across that?

How do you test and iterate against your existing unit,

testing framework or how do you click around

and use these products to yourself

and make sure that the edits that you made were good?

And that's a lot of what we've always focused on

with cognition.

And so, basically all the guys that you mentioned here

in terms of the foundation labs and so on,

we actually partner with them

and we work very closely with them.

I think what we tend to find is that for obviously,

they're kind of like base research

and the work that they do with models,

there's a ton of interesting work for us to do together.

Whereas I think for a lot of this work

with really enterprise transformation,

we want to be like on the ground working very deeply

with folks and figuring out with them

how we use software to help them achieve their goals.

So Palantir originally we created this thing

called for-to-played engineers,

which didn't really make sense to people 10 or 15 years ago.

And it turns out there is like certain types of workflows

where you could build a product and do a lot

but then the Ford Flight Engineer would actually have to

understand the business value and kind of connect the dots

and then take things that they created

that oftentimes go back to the course,

the core gets better, so the core could do it next time.

Like do you have something similar to this?

Do you have a lot of these?

It's a big part of the value you're providing.

Yeah, so it's interesting.

There's some similarities and some differences for us

with the sort of the Palantir model.

So one of the interesting differences

is that a lot of times our customers,

they just use our product on their own,

even without us talking to them or without us discovering them.

People bring in our tools and they just start using them immediately.

But once Devon is inside an organization,

the ceiling of what you can accomplish

if you're a world class agent manager

versus a new engineer who's just sort of learned the tools,

it's an enormous delta.

And so a lot of the, there's kind of some parallels

with our business that are more like a,

kind of a data breaks or snowflake type thing

where you just kind of get in

and then people start using your products more

and consuming more.

On the other hand, if you actually want to go drive major outcomes,

like real business change that results

in like structural, you know,

oh, I can launch this entire product line

that I wouldn't have had the capacity to, you know, otherwise.

Like that type of outcomes, you know,

R4 deployed engineers are among the best in the world

at managing agents at really high scale,

because that's like exactly what they,

they sort of focus on and do every day.

So at Palantir, we had these frameworks of like,

like building an ontology of all the data

making it talk to each other, ontology, the processes.

And we had all sorts of different frameworks over time

that our conceptual framings would use when you go in.

Do you guys have like your own conceptual frameworks

for business value and do you have something called ontologies?

Like, like, and not to get the secret sauce.

Oh, yeah.

But other things like this, you could, you could tell us.

We really look at it from the perspective

of the software development lifecycle.

So we go inside an organization,

they have a way of doing things, right?

Of developing software, starting from planning

and deciding what they even want to write,

understanding all of their existing code

and process to then maybe scoping it out

and maybe writing some code, testing it, fixing it

when it's wrong, iterating on it,

deploying it in production, monitoring it.

You know, there's a pretty standardized

software development lifecycle at this point.

And what's happening is agents are just eating more

and more of the cycle.

And it kind of started with the writing of the code.

And now we're like well past that, right?

And so in fact, one of the more recent products we shipped

is called Devin Review.

It's because we observed that there's this totally new bottleneck

in the software development,

in the software development lifecycle

that wasn't the case previously,

which is there's this abundance of code being written by AI now.

How can humans even keep up with it all

to understand what's going on?

Again, we work with a lot of like regulated, large,

complex organizations that are,

they're running mission-critical systems

and you can't just sort of vibe code,

you know, your way and like, yolo-mers.

To the translation, be vibe-coded.

No, that, that, that, that, that, it's actually really important.

And it starts with, eventually, you know,

are we gonna like have English as a source of truth

and people are just gonna be collaborating on specs?

Yes.

I think, you know, March, 2026.

No, you still need to understand the code that you're merging.

And so we, you know, a big thing we care about a cognition

is we're building tools that are like future-looking,

but still meet people where they are, right?

We still wanna meet people where they are.

And so Devin Review, it's not just having like an AI

auto-comment on every PR and say,

oh, this was good or not good.

It's actually tools for humans to really deeply understand

huge quantities of AI generated code.

These are some sense that at some point,

one of the AI models, if it gets a little too tricky,

could like sneak something in to the things

that are being built to like do something

we didn't want it to do.

Yeah, so it's a great question.

Obviously, I think with a lot of these things,

that the simple answer in software is,

you wanna be working with the same review process

and the same QA processes that we all have, right?

And so any big enough engineering organization,

frankly, already has to think about this

with their humans, you know?

And it doesn't have to be on purpose, obviously.

Maybe you accidentally introduced a security risk

or so on, right?

And that's why you have review and that's why you have QA

and that's why you have user testing

and that's why you have release cycles

and all of these other things.

And I think, you know, I do think this is one of the,

one of the things that often comes up,

which is like, how do you make sure that your agent

behaves correctly, which of course is like a very important

problem.

The reality is that I think in software,

we have actually a lot easier or maybe at least

a lot more grounded of a path to do that

because we have the same thing already

with all of our humans, right?

And so when you work with Devon, for example,

Devon is making commands, is submitting, you know,

code diffs and so on.

Devon is not allowed to go and deploy your code

to production by itself or anything like that, right?

It works in all those same systems

and has the same guard rails.

And so let's just fast forward for fun,

a few years in the future.

I talked to some of the people running a top labs,

they're pretty convinced things are going to keep getting

better at a pretty high pace for at least two or three years.

It's like, I like with Moore's,

like you can't really see out what's going to get there

five years, but it feels like things are going to change

a lot.

So to tell us about 2028, 2029,

like there's certain things that just look very different

or certain things that are like,

we have to do now, we don't have to do it all there.

Like what are the unsolved problems for Devon

to just be like doing a massive project

on itself in three years?

Yeah, I think a couple shifts.

I mean, one of the obvious ones which I'll just call out

is just much more widespread usage of all of this

and just good knowledge on how to use these things.

I think right now, you know, you have this core group

of we'll call it like agent forward engineers, right?

Or agent forward companies that understand how to use this

and they are seeing these, you know, 5X, 10X,

productivity games as a result.

I mean, obviously, you know, all of these big organizations

or these companies or governments or things like that

are seeing the same results and re-icing.

Wait, we can't just sit here and be five times slower.

And you know, we have to go learn how to do this right now.

And so that's really happening.

I mean, even this year, I would say,

in terms of the continued capabilities gains

that we're going to see.

Yeah, I mean, I think the models are going to get better

and better.

You know, one of the stats that people talk about a lot

is this METR report, which basically says

for each different model that comes out roughly

how much human work can it do in an automated fashion

before you have to go interrupt it and say,

oh, that was wrong.

Let's go do this, right?

And so it's like, you know, just two or three years ago,

the answer was like 10 seconds or something.

You know, you would have it right one line.

And then it's like, all right, the next line is already wrong.

Okay, let's stop here, right?

And at this point, it's already gotten to the point

where it's in the scale of, you know, 10, 20 hours

is what the latest one has been.

I think Opus 4.6, for example, I think was around 18 hours

as well.

This is always very weird to me

because it's applying the model's work in human time though,

right?

Which is so weird.

So how did I see?

Yeah, so the answer, they do typically do the tasks

in less time than a human would.

And then the 18 hours is basically,

this is how long it would take a human to do that amount

of work in between each of the interruption points, right?

And the AI might be doing that in one hour or two hours

or something like that, right?

And the thing that's really crazy about the stat

is you just see it very consistently double.

And I think for the last few years,

it's doubled about four or five times every year,

you know, which is insane.

Which means, you know, you wait two or three months

and it's already doing twice as much work.

So the whole world is changing over two or three months

in terms of what's possible.

Yeah.

And this is what we've kind of seen as well.

And I think we're going to see even more of that.

And to some of Russell's previous points,

the thing that's kind of interesting for us is

that the form factor of what you want to deliver

or how you want to work with the AI changes a lot

as you're going through that, right?

And so when we're saying, okay,

you do 10 seconds of work, obviously the answer is,

you as a human need to be staring at your file of code

and like, shepherding it and hand holding it

with every single step, right?

If you're talking about,

it's doing days of work or weeks of work,

now you're actually giving it whole, you know,

output level tasks of like, hey, you know,

we really need to go make this app much faster.

Can you go and run this whole thing

and then, you know, do a smoke test

and make sure all the changes look right.

And it's going to go off and do that entire project, right?

Versus, or even bigger initiatives of like,

yeah, can you auto-response to all of the upgrades

or the potential vulnerabilities

that are coming in with our reporting

and just make sure all of that, right?

So you're going to see a lot more, I think, proactive work.

You're going to see a lot more, basically,

like event-driven kickoff work

where it doesn't have to be human.

That's moderating it every step of the work.

And then in terms of like, what does that mean

for society or where does it go?

I actually think one thing I'll go on the record

is I think there's going to be an explosion

in small businesses.

I think AI is actually like an extremely small business

enabling technology in particular

where you think about what's hard

about about building a starting small business.

It's, you know, you don't have the resources

of a large company that's specialization of labor

in each part of the, in each part of the process.

And I think AI, it's extraordinarily enabling, right?

Think about, you know, the quality

of quick legal gut check you can get from, you know,

from a chatbot, from a frontier lab.

The quality of, you know, analysis of your financials,

the quality of software that you build, right?

It's like, it's all coming together

to empower each individual person again

if you exercise that agency

to just do so much more on their own.

I actually, I love this.

I actually have a small thing on the side

where I'm trying to help create

10,000 to small business owners.

So I'm totally aligned with this.

This is a really good theme right now

for us for us to do.

What one last thing I want to ask you guys

about the business that I'm just so impressed by.

So I hired a lot of first 300 people

of Palantir.

It's been a lot of time on talent.

Obviously even higher, it's got at one point

a long time ago with one of my companies with,

with Vlad's help.

How did he do it?

How did he do it in his interest?

He's very, very, very impressive.

I did not, I did not see him at the time

as someone who was like a CEO person.

So it's, he really grew a lot, which is good.

I mean, you know, he's, he's like,

he's learning and growing as he goes

and it definitely seemed as a CEO and founder now.

But one thing you guys have done is like a significant

percent of the cognition hires are actually former founders.

So not only you hiring the best people in the world,

you're hiring a ton of former founders.

Like, like, like why are you doing that?

How are you doing that?

Tell us a little bit about the talent stuff.

Yeah, yeah, for sure.

No, I mean, I, I think the,

I think the reality is we just have such a massive problem

that we're going after, we're just, you know,

solving all of code.

And I think the way we even started this company is,

you know, Brussels has founded before this.

I was a founder before this.

All of us were, you know, I think of our,

of our kind of like initial crew.

And, and the idea for us, I think was,

let's make this one the big one.

You know, we're going to go for it all.

We're going to go for the most ambitious,

I don't even, I mean, I've, the, the, the play

of solving software engineering feels like a big enough one,

you know, that, that we can all do that together.

And, and I think that's a lot of what it comes down to,

honestly, is just like, yeah,

are we working on something and doing something

that's, that's, that's exciting for folks who,

to your point, are, are, you know,

I think a ton of the folks at cognition

are, could, could very, very easily go off

and start their own companies and get funded

and build their teams and so on.

And, and the question, I think for us,

always has been about how, how do we make this the place

that, that makes more sense for them to do that?

And it's, it's honestly easier to do that now

than ever before.

As a, as a company, like I'm thinking,

you know, we have one team of the company,

they're, they're called special projects engineers.

And basically every person on that team

is a former founder, like every single one.

And, and they, they do, they do, you know,

a really interesting mix of engineering work,

of product work, of talking to customers,

of driving commercial outcomes, you know,

the problem space to, to your point is so big

that actually if you're, again, a high agency person,

you're going to take initiative and you're working

at a small fast growing company with, with a,

you know, with the problem space so big

that, you know, you're only constrained

like your own ambition.

Well, it does seem like there's just a renaissance,

a revolution going on in a world where the capabilities

are doubling every two or three months.

I'm like, this is a place you can come and be around

some of the other smartest people in the world

who are part of growing something with that,

which I guess is pretty fun for people.

We have a good time.

What, one thing about the interview process

or the selection process to your point,

which I think is kind of interesting to call out is,

I think, you know, one or two years ago,

I think a lot of people had this mentality

in terms of how you interview of like,

okay, there's all these AI tools.

How do we interview people in a way that,

you know, how do we make sure that people

aren't using AI while we're interviewing them

considering, and I think that has totally flipped.

Honestly, I think that was wrong, right?

And I think if you're asking the question of like,

how do we evaluate people on exactly the thing

that AI can already do that kind of the wrong?

And so, you know, I think for us,

and this has always been the case for us,

you know, our interview process has always been,

you can use as much AI as you want to use, you know,

it's just like, we're going to give you a few hours,

just build your whole own product surface, right?

And build a, build your own, a lot of these are projects

that frankly is like, if you were trying to do this by hand,

you would not be able to get done in a few hours.

So you kind of have to use AI for this, right?

But in reality, what we actually want to test

is in addition to it to kind of how familiar

you are with these tools, what we actually want to test

is, yeah, what do you think is the right thing to build

or how do you make these product decisions?

How do you make these trade-offs?

How do you decide or collect information

about what you should be doing?

And so we found that that's helped us a lot.

I love it.

Well, we started the American Optimist podcast

to try to push back on cynicism

and pessimism in our country.

What's the best case for an optimistic AI future?

Wouldn't one inspire you about what you're seeing?

Yeah, no, it's a great question.

So funnily enough, I think recently,

we had this whole Satrini piece come out,

which I thought was frankly ridiculous.

I mean, I think it's, I think it gets a lot

of the basic economics wrong is maybe a simple way

to put it and it's, I think it goes off

of some of the same things that we are calling out,

which is it's going to be easier to build things,

it's going to be cheaper and so on.

And then somehow concludes that the outcome

is going to be much worse for all of us.

And I think maybe from an economics perspective,

there's a distinction between what is real versus nominal

deflation, which I think is maybe one thing to call out here,

which is of course prices are going to get cheaper.

But why should that mean that we're all worse off, right?

But I think the simple thing that I'd call out about AI

is I think we right now are at a point

where so many of the things that we want to build

and so many of the things that we want to do

are just hard bottlenecks by pure execution, right?

And I think we're pretty quickly getting to a point

where that's not the case.

And my favorite line on this is our co-founder,

Walden says this, which is for so long,

we've all been living in Minecraft survival mode

and now we're going to be in creative mode.

And I think that's really, I think that's what we're going

to see over the next, honestly, the next five or 10 years

is getting to a point where you're really only limited

to your ideas and to your imagination

where you can kind of just turn things into reality.

And I think that's going to be a great future.

Awesome.

Well, it's inspiring now to leave it on Thanksgiving.

Cool. Thanks for having us.

Thanks for having us.

Yeah.