Today I wanted to have a really [music]

pragmatic and downto-earth conversation

about AI, what is actually happening in

our organizations, what you can expect

to happen um and how agents are changing

the game. And I thought in order to have

this really pragmatic down-to-earth

conversation, I wanted to take us to

space.

I do see a lot of parallels between the

age of exploration and the space race

and the age of AI. So last week I was

talking with a CTO co-founder of a small

startup. I was also talking with a

principal engineering lead at a very big

bank, highly regulated and we sat for a

solid 15 minutes talking about all the

cool stuff that we were building, how

it's brought back joy, the joy of

coding, and we just had all of these

ideas and it seems like we couldn't

build fast enough. There is so much to

learn and so much to build. And it

reminds me of this really lovely quote

from Carl Sean that I love that

somewhere something incredible is

waiting to be known. And I think that

this quote really captures what a lot of

us are feeling about AI and about the

experimentation and just the possibility

that is out there.

This is the same feeling that we had

with the age of space exploration, going

to the moon, going to Mars. But it

didn't come without skepticism.

Why spend all of this money

experimenting and going to the moon when

we had lots of problems to solve here on

Earth?

We had a lot of wonder, but we also had

a lot of skepticism because space

exploration wasn't just about science.

It was also global. It was economic. It

was political.

Space wasn't a silver bullet to solve

all of the problems that we had with

humanity. But we also can't deny that

when a man landed on the moon that it

was a very pivotal defining moment for

all of humankind and had a sense of

wonder and had the world in awe. It was

about redefining what was possible.

And similarly, we have a lot of wonder

and a lot of optimism and a lot of

promise about AI. We can talk about

productivity boosts and all of the hype

around, you know, 100% productivity

boost and all of our code being written

by AI. We have the promise of perhaps

the first singleperson billion-dollar

startup with a person with an idea and

an army of agents. There's a lot of

optimism out there. Similarly, there's a

lot of skepticism. There's a lot of

skepticism in the corporate world about

the real economic impact of AI given how

expensive it is, given the environmental

impact. There's also a lot of skepticism

in a lot of different studies about the

real productivity impact. In certain

circumstances, it can be really um it

can really accelerate and in other

circumstances it can actually slow us

down and get in our way. It's hard to

know what's real.

But as technology changes, it is really

good and fine to have that sense of

wonder of exploring the universe while

also realizing that we have problems

here on Earth to solve. We have to learn

how to balance that sense of wonder and

curiosity with the acknowledgment that

we are living in reality and we need to

keep our our feet firmly planted on this

earth. We need to understand how these

experiments are actually going to apply

to everyday companies. How are we

actually going to improve the world

around us? We need to keep the sense of

wonder while also balancing it with

pragmatism and beating the hype by

looking at data. And so that's what I

want to do right now. I'm going to share

some brand new AI industry benchmarks

with you. This is new data that no one

has ever seen ever before in the world.

Okay, it's coming right now to you. Um,

I just pulled these down. I guess the

the static team has seen them because

they they saw the preview of my slides,

but aside from them, no one has ever

seen it. Um, this is though not really

surprising because a lot of these

numbers have not changed very much from

the last quarter. So, what we're looking

at here is a sample of 121,000

developers at over 450 companies. This

data was pulled from November through

February 1st, 2026. I I really just did

this. Um, we're sitting around 92.6 of

developers are using an AI coding

assistant at least once a month to get

their work done. And about 75% of

developers are using an AI coding

assistant at least once a week. When I

say a AI coding assistant, most

developers define that as cursor, Codex,

Copi, you know, Claude, not ChatgPT

necessarily. Um, but it is a bit

open-ended, so keep that in mind.

When it comes to time savings, time

savings is not the only measure of

productivity impact, but it is an

important signal. It's a good leading

indicator. We're sitting around 4.08

uh self-reported hours saved due to AI

tool usage per week per developer. This

is not all too different from the number

that came in Q2 of 2025. And then the

number for Q4 of 2025 was about 3 uh six

or seven. So this is kind of hovering

around the 4 hour mark. And there's been

a few articles, for example, from Google

in the last year citing about a 10%

productivity increase. And if we look at

it in terms of time savings, we're kind

of hovering around that 10% mark. It

hasn't changed dramatically um over the

last few quarters.

What is changing and what is moving up

very quickly is the amount of code

getting merged upstream or in a

customer-f facing environment that was

written by AI that was merged without

significant human intervention. We call

that AI authored code. And in a sample

of around 42,600

developers from that same time frame,

uh, November 1st to February 1st, 2026,

we're at about 26.9% industrywide for

all of these developers. That's how much

code is hitting production that was AI

authored.

This is moving up from 22% in the last

quarter, which is actually a pretty

significant change quarter over quarter.

And we can see that daily users of AI

have crested over that 30% mark. So

almost a third of their code is being

written by AI that is actually being

merged, passing through code review and

getting into a customer-f facing

environment.

One of my favorite use cases for

applying AI is to onboarding. And I had

a bit of a hunch that AI was going to be

a great tool for onboarding, helping

connect people with information earlier

and sooner. And I have all of this data

and I thought, let me look at this

quarter over quarter. And in fact, if we

look at Q1 of 2024 all the way over here

on the left side and fast forward to Q4

of 2025, we have about a half uh we have

a half reduction in onboarding time.

This is looking at the time to 10th PR.

So by the time a developer hits their

10th PR, that's a pretty important

onboarding milestone that the industry

has mostly aligned on in terms of

onboarding and that has been cut in half

now. And when we correlate that with the

uptick of AI usage, it makes a really

pretty graph. Uh AI is fantastic for

onboarding. And this is not just brand

new hires to your company. We've also

seen plenty of evidence that this is for

engineers who are moving projects or

even non-engineers coming onboarding

into projects. What's really important

about this number is that there was a

separate study done um by Brian Hulcat

at Microsoft. He's the co-author of the

space framework of developer

productivity and they found in

Microsoft's context that the time to

10th PR actually that performance sticks

with an engineer for their first two

years of tenure. So if you onboard

faster that productivity gain isn't just

onboarding it actually sticks with them

for at least two years after they have

started at the company. So this is a

very important and significant uh trend

that we're seeing here with using AI to

connect developers, reduce cognitive

load, and get them onboarded more

quickly into their code bases.

One thing that's really important for me

to call out, although I have just shared

with you industry benchmarks, averages

are just math. And as the polls move

further away from each other, the

average stays the same. Average does not

mean typical. It does not mean what is

going to happen to you. And it doesn't

mean what a common experience is. One

thing that is absolutely true, one thing

that is common is that there is no

typical experience with AI.

There is no typical experience with AI.

It is it is extremely different in every

single company because every company has

their own problems and their own

culture.

This uneven impact can take us back to

space for just a minute.

So we can go back to the origins of the

universe. We had the big bang and there

was this massive release of energy and

as this energy released the time the the

space and time in between objects grows

bigger right things are moving apart and

for a lot of us the emergence of AI and

AI coming into our organizations and in

the industry has felt a lot like this

big bang we've had this explosive

release of energy in the center of our

world and things keep moving apart

organizational performance is

multi-dimensional and these

organizations are just going off into

different extremes teams based on what

they were doing before. AI is an

accelerator. It's a multiplier and it is

moving organizations off in different

directions. The best example I can share

with you of this is quality. Okay, so in

this case, this is not every

organization, but some organizations are

facing twice as many customer-f facing

incidents and this is from a sample of

over 67,000 developers from Q1. So that

same time frame of uh November to

February. So just looking in that time

frame organizations are experiencing

twice as many customerf facing incidents

at the same time at the same time

companies are also experiencing 50%

fewer incidents. So some companies have

used AI they have a really healthy

system it has amplified that system they

are seeing fewer incidents they're

moving faster they are accelerating with

higher quality higher code

maintainability higher change

confidence. On the other side though,

blasting off into the other part of the

universe, we have organizations who were

dysfunctional already. No, they're more

dysfunctional. They're dysfunctional and

dysfunctional faster.

Okay.

Similarly to this uneven impact,

organizations are seeing really uneven

results like economically from using AI.

there are a lot of steep drop off drop

offs when it comes to using AI in a

pilot context to production and then

actually trying to tie it to profit.

This is from uh an MIT study that was

published in July of 2025 called the Gen

AI divide. And what the study concluded

they did a survey of 152 organizations

was that right now where we are in the

industry is that we have really high

adoption, right? That 92.6 number. Um

DORA also does its own research. We're

hovering around that 90% adoption

number. high adoption but actually low

transformation because as it turns out

transformation is really uncomfortable

and organizations that were ready to

give up on the cloud transformation on

the agile transformation are also giving

up on their AI transformations. It is

really really difficult to look at your

whole organization and look at the

problems and think we got to change

something about this and that is what

organizations need to do in order to

actually see change to their bottom

line.

All of this to say back to my previous

point we have 92.6 adop uh percent

adoption among developers in our

industry but adoption doesn't mean

impact. Using the tool doesn't mean that

it's going to actually advance your

organization or do anything. It is an

organizational problem that needs

organizational change management. But

that's not really what we were promised

with all of the hype was like, hey,

experiment with AI and then something

happens and then we profit.

What happens though is that these tools

were primarily deployed into individual

coding tasks. And what this MIT study

found in this high adoption low

transformation is that when we apply it

only to the surface area of a developer

sitting at their desk there is a very

very low ceiling of productivity gain.

This is an organizational problem. If we

want organizational results we have to

think about it on an organizational

level not on a coding task level.

Fortunately, our universe is expanding

right now

and that expanding is coming through the

use of agents in agentic workflows.

Our universe is getting bigger and so

are all of the promises and all of the

hype, but so is the possibility.

So, let's go back to the moon landing,

right? Like the ultimate hype was that

we're all going to be living on the moon

by now in flying cars like jetson style.

Um, similarly here we have a little bit

of like crazy ideas. Um, Gas Town, if

any of you have used it, um, there

there's just like there's so much crazy

stuff to do right now. Um, Gas Town is

infinitely interesting to me. There are

so many interesting things. Um,

disclaimer, don't use Gas Town. It is

unhinged. Um, we've got OpenClaw,

Maltbot, Clawbot, whatever it's called.

We've got Ralph loops. We've got all the

stuff, right? There is so much

experimentation and so much fun. It's

just really fun to build. Um, but me

building my nail polish matching like

color scheme app while I'm sitting at

the nail salon is not the same as a

multinational bank being able to change

their revenue because of AI. Those are

really different things. Um, and I was

at this retreat with Martin Fowler and

Kent who are I think back there. Hello.

We'll talk about that a bit more later.

We spent a lot of time trying to connect

AI and the use of AI to bottom line, to

profit, to P&L. And interestingly, kind

of where we landed at the end was this

question of like what is the value of

innovation? Was it still valuable to go

to the moon even though I'm not really

located on the moon right now? And I

would argue that yes, it is valuable to

innovate. And that can get into some

murky area because this is a business,

right? This isn't just society and and

doing things for the good of humankind.

we have to do them in an economic

context and that can get a little bit

tricky.

So when we think about this quote

something uh somewhere something

incredible is waiting to be known. There

is a sense of wonder and AI and space

are both the age of exploration and it

is so exciting.

But the point of going to the moon

wasn't that we all need to live on the

moon. In fact, the point of going to the

moon and the point of exploring and

doing all this crazy stuff was to

improve life on Earth. It was to use the

space exploration and all of this wonder

to apply it to the systems level

problems that we had back on Earth. Not

everyone wants to live on the moon. Um,

but we have sunglasses, we have space

blankets, we have barcodes, we have

quartz watches. We have so much

technology and so many improvements back

on Earth because of this crazy age of

exploration where we all went to space.

Even though we're not living on the

moon, we've still used the lessons and

applied it to our systems back here on

Earth.

And so thinking about agentic workflows,

agents expand the possibilities of what

we can build, how we can build it, and

who we can build it for. Not everyone

goes to the moon, and it's okay not to

go to the moon. Not everyone is going to

be building crazy stuff with Gas Town

every day in in your enterprise context.

And that's also okay because the

experimentation helps push the boundary

of what's possible and helps us think

about solving problems in new ways.

So, let's talk a little bit about how

agents are being used in the industry

right now. Again, this is new data that

I'm sharing for the first time here. Um,

agentic use is on the rise. There's not

a lot of companies, honestly, that are

so far ahead of the curve that they're

already uh instrumenting their agentic

use cases with really good telemetry.

This sample is a little bit smaller.

It's around 3,000 developers at six

companies. Keep in mind, these companies

are ahead of the curve. They're already

instrumenting their agentic workflows

with telemetry. Um, we have about 80% of

developers using these agentic workflows

at least once a week with over 50% using

agentic workflows every single day to

get their work done.

We talked about codecs I think in the

previous panel. So on February 2nd, the

Codeex desktop app was released and

since then there's been a million over a

million downloads by now. I got this

data yesterday. I'm sure it's quite

different by now. There's been a 60%

growth in users just in the last week.

Um they also launched uh GPT 5.3 codecs

uh last Thursday. They're processing

trillions of tokens per week. Internally

at OpenAI, 95% of developers are using

codecs to ship stuff. And of the

developers who are using codecs versus

other AI tools, the developers who use

codecs are shipping about 60% more PRs

per week, which is very interesting. a

data point, not the only data point, but

it just speaks to the very high ceiling,

the high possibility, the sense of

wonder that we have with building all of

the stuff with cool new tools like

Agentic Workflows.

I want to bring it back to a nonAI

startup though. Um, I want to highlight

Haven Headache and Migraine Center. So,

this is a company that's based here in

San Francisco, actually just a few

blocks away. Haven set out to answer the

question, can we solve headaches with

Zoom? And it turns out you can. Um, so

if you're a headache sufferer, this

might be useful for you to to learn

about. In healthcare, it's really really

uh crucial for Haven and their

development team to distinguish between

using agents for durable code or

disposable code. One of the things that

they're doing that's very cool since

they are a disruptor, they are a small

startup is using um using agentic

workflows to rapidly prototype new

custom uh like new patient workflows. So

they're working on a patient portal

building with Ralph loops taking uh

linear and Figma artifacts changing it

into a PRD uh you know spitting that out

in JSON and then just having Ralph loops

run. What they're getting though isn't

garbage disposable AI slop. What they're

getting is really high quality

prototypes with really excellent

documentation, excellent tests, much

higher quality at a way faster rate than

they would have um if they would have

built it by hand the oldfashioned way.

The other thing that they're doing that

I really admire is improving the

standard of care for their patients by

training a HIPACO compliant model on

hundreds of thousands of symptom logs.

So Haven meets you where you're at. You

get a text message, you can log your

symptoms and then they can um instrument

your care, figure out what needs to

happen from there. So they're training a

hypocmplant model on hundreds of

thousands of these messages so that

those messages can be routed to, you

know, medication refill or schedule

follow-up appointment just meets you

where you are. And the result of this is

that they have 3x the industry average

in customer satisfaction for a

healthcare tool like this, but also real

real meaningful clinical outcomes. So

their patients have fewer headache days

per month and also the severity of their

headaches is much uh much less severe.

So good job Haven. In the enterprise,

there are lots of examples of big

enterprise companies experimenting with

agent workflows. So there's an

enterprise manufacturing company that's

using it for solely internal developer

purposes. They used C-pilot and Claude

to build out a dev portal to accelerate

uh developer onboarding. At Cisco,

there's 18,000 engineers using codecs

daily. They're using the uh codecs for

complex migrations and also code review

leading to a 50% reduction in the amount

of time it takes to do code review.

There's a really cool paper as well by

JP Morgan Chase's multi-agent framework

for annotation, MAFA. If you Google

that, you can find the source paper.

It's really fascinating. Um, what

they're doing is building out like a

whole business of agents. So like a true

multi- aent workflow similar to Gastown

where each agent has a special job to

do. What they're also doing in this

model is introducing consensus among the

agents. So they're taking all of these

interactions and then they're annotating

them. this was you know the intent what

was it an FAQ what what were all these

interactions the agents are annotating

them and then there's another set of

agents who are responsible for reranking

and calibrating and validating the

output and then of course we have to

introduce consensus algorithms to the

party because now we have multiple

agents with maybe multiple different

opinions about things um this is really

fascinating and I believe consensus

among agents is going to be a huge

problem to solve in 2026

I spoke about this retreat. I was lucky

enough to be invited by Martin Fowler

and thoughtworks to the future of

software development retreat celebrating

the 25th anniversary of the agile

manifesto of Gerge joined me. A few

other folks who are here also joined me.

We spent a day and a half up in the

mountains talking about agents. That's

really all we talked about about using

agents responsibly, ethically,

sustainably, how we can use them for

organizations. And our conclusion even

though there was so much interesting

stuff, Steve Yaggi was there, we were

working on Gas Town things, like there

was a lot of experimentation happening,

but the conclusion that we came to was

that AI does not solve organizational

systems problems. It only can do that

when you apply AI to the system problem,

which means you need to acknowledge that

the system problem exists in the first

place. AI is not a magic silver bullet.

Even though things like Gastown exist,

even though there is so much sense of

curiosity and wonder in the universe,

um we kind of had a sort of off-the cuff

conversation. Uh Kent Beck, Steve and I

were just catching up um outside uh of

one of the sessions in between

conversations. And here's sort of where

we summarized our thoughts.

Organizations are constrained by human

and systems level problems. We remain

skeptical of the promise of any

technology to improve organizational

performance without first addressing

those human and systems level

constraints. We remain skeptical and we

also remain human because the risk is if

we don't address the systems level

problems, we will just take them to

space with us. We will just take them to

space with us. We're not actually going

to solve the human factors that are the

driving force behind all of the

constraints that organizations have

right now. We can apply AI to those

problems, but we still need to solve

them. We can't just go to the moon and

expect that pollution and garbage and

traffic aren't going to be a problem

anymore.

And so the question is not how to

colonize Mars, but the question is how

to get real organizational impact with

agents and AI. At this retreat, we also

talked a lot about common factors that

we see. What do we see organizations

doing? What are the common patterns that

is kind of like the secret to to

winning? What do they have in common?

The first one is that organizations who

win with AI and are winning with AI have

goals and they measure their progress

against those goals. Spray and prey does

not work. Spray and prey, what I mean by

that is just giving all of your

developers licenses and hoping for the

best. It does not work. I can say that

very very clearly. I have a lot of

evidence that does not work. If you can

point AI innovation and that

experimentation to a problem, have a

concrete goal and then measure if you're

reaching that goal, that is what winning

organizations are doing right now.

Because as Spock has told us,

insufficient facts always invite danger.

We need to measure things. We need to

have data. And I know this is something

that's really difficult for a lot of

organizations right now because

developer productivity and engineering

excellence are also really hard

problems. And this is happening all at

the intersection. So I have something

that can help you if that is in uh a

problem that you're facing in your

organization. This is the AI measurement

framework. This is a framework that I

co-authored with Abby notto who's the

CEO of DX.

This complements our core four framework

which some of you might have heard

otherwise it's in the impact column

here. What we're looking to do is track

not just usage and adoption and

utilization of AI but then also

translate that into real organizational

impact. Is this changing your speed,

your developer experience, your quality,

your innovation ratio? Those are really

important questions to connect the

adoption to impact. Finally, we have to

look at the cost. Are we getting a good

deal?

Maybe some of us are for now. Um, and we

need to understand as the cost of these

tools keeps going up and up, is the

investment the right one?

The second thing that is helping

organizations win is that developer

experience matters now more than ever.

Here is a piece of very unconventional

advice that I'll give you is just

anything that you were going to talk

about with your leadership team about

developer experience. Just call it agent

experience and you'll get money for it.

It's funny but it works. Um, it works

because developer experience, feedback

loops, um, you know, clearly defined

services, great documentation, fast CI,

these are all things that we have been

screaming about for decades, literally,

and we've been begging for pennies from

our organizations to please let us

invest, please let us invest in

developer experience. And we've been

told no over and over again. Come to

find out, in fact, these are the things

that make AI really successful. We need

to have really solid testing and quality

practices. We need to have great

documentation. These are critical for

agentic workflows. It is disheartening

that we didn't want to spend the money

when it came to human engineers, but

when it comes to robot engineers, we're

okay with it. But that is the world that

we live in and let's capitalize on our

opportunity. So Devex matters more than

ever. In fact, when we look at the data

right now, remember we're hovering

around that 4hour mark for time savings.

when we look at all of the other factors

of developer experience like AI time

savings is not going to make up for bad

meeting like bad meeting culture and

lots of interruptions and um you know

developers who are constantly being

pulled out of their work unplanned work

interruptions outages those kinds of

things AI will not make up for that we

can use AI to help solve that problem

but AI in and of itself is not going to

make up for it then when we look kind of

in the bottom half build and test wait

time toil and dev environment we put all

that together We realize that just the

time savings from coding task speed up

isn't going to get us very far. But what

will get us far is when we can take AI

and point it at those problems. Can we

use AI to help reduce meeting frequency?

Can we use AI to improve CI weight time?

Can we use AI to reduce dev environment

toil? That is what winning organizations

are doing right now. They are putting

DevX at the center of their universe and

seeing AI as a tool to fix systems level

problems.

They're doing it also on an

organizational level. If you want

organizational outcomes like revenue,

P&L, time to market, you have to think

about AI on an as an organizational

problem, not as an individual problem

that your developer needs to solve at

their desk. It has to apply to workflows

that span entire value streams.

back to that MIT study when we looked at

the barriers to organizational adoption

uh or the organizational barriers to AI

adoption, they weren't technical. This

wasn't about the models necessarily. It

wasn't even about the tools that wrap

the models. It was about things like

change management or lack of executive

sponsorship when you have an executive

team saying go with AI, but they

themselves have never cracked their

laptop open and fired up windsurf or

cloud code or codeex. um poor user

experience, just very unclear

expectations about AI. Those are the

things that get in the way.

If this sounds familiar to you and

perhaps your organization could do a

better job, there's two things that I

want to point you to. Um the first one

is the Dora AI capabilities model. These

are models that kind of communicate and

help you get ready for AI. So think

about this as an AI readiness model or

an AI capabilities model. This has in a

crazy amount of data from organizations

that Dora studies. They do a lot more

than just the four key door metrics. Um,

finding correlations between practices

that organizations have and good

outcomes with AI. So, if you use AI and

have a good clear and communicated AI

stance, you are going to do better

organizationally than a company that

does not have one. You can find this at

dora.dev. It's the Dora AI capabilities

model. There was just a new paper that

came out last month. Last month, uh,

Nathan is here who leads Dora over at

Google Cloud. If you want to talk to him

about this, he's probably the the guy.

Um the other one is the thoughtworks

forest framework. This is similar to the

AI capabilities model kind of a

different flavor on it. Um if you go to

thoughtworks.com and look in their white

papers you can read through this but

these are both really solid

wellressearched industrybacked AI

readiness models to help convince your

leadership team if you need that um or

just help you do an internal audit of

are we doing the right things to make

ourselves ready to you know reap the

benefits of all this experimentation.

The last thing is that organizations who

are doing really well with AI right now

are experimenting by solving real

customer problems. Again, space

exploration and going to Mars is great,

but that is not sustainable for your

whole entire organization to be

experimenting with going to Mars. It

just costs too much money. It distracts

too much from the core business problem.

It does not serve your customers. So,

keep experimentation going. Other

experimentation can be really laser

focused on real customer problems that

you have. And that is how you're going

to see the organizational results.

Somewhere something incredible is

waiting to be known.

There is so much possibility of how we

can build what we can build, who we can

build it for right now with AI and

agents are just accelerating this. They

are expanding our universe.

We are definitely in an age of

exploration. The urge uh the thing I

want to urge all of you to take with you

into the rest of the sessions today is

to find that balance between a sense of

wonder and a sense of awe and aiming for

Mars and aiming for your moon colony but

also understanding that we need to solve

the problems here on Earth and we have

to live in this reality. So please stay

grounded, stay skeptical, stay human,

most of all stay pragmat stay pragmatic.

Thank you all. [music]

[applause]