Welcome everyone. It's it's it's so nice

to see all of you. It's so nice to see

lot lot of friendly faces. A lot a lot

of you said hi and also just really good

to to meet Martin and Kent. And I was

joking a little bit beforehand that I

did not expect Martin and Fowler and

Kent Beck to walk into a place where

it's all the kind of the hottest AI

startups and all of them. But here we

are. And we're here for a very, very

good reason. What?

>> What? What the hell is that supposed to

mean?

>> Oh. Oh gosh. Here we go.

So Kent is going to hate me for this cuz

today I already called him old furniture

once.

I don't think that's fair.

Old furniture. Yeah, you're the old guy

in the crowd.

>> I'm at least a year younger than him.

Uh but we're I I I'm psyched that both

of you are are here and a week ago me,

Martin Kent, and a bunch of other people

were in Deer Valley, Utah in the future

of software engine conference that

Martin pulled together some some very

interesting thinkers and we were talking

about like it was nice to reflect that

25 years ago the agile manifesto was

created.

There were 17 people and and two of you

and you were two of you were there.

Since then, you've really helped shape

software engineering as you've helped

influence and and you've had major

contributions. Could I ask both of you

to like recap what the feedback you've

gotten over over these these years,

these decades, what ideas really stuck

with engineers? what what what do you

hear a lot they tell you like thank you

for this I'm using a lot of this

>> that's a really interesting question I

haven't really reflected on that I mean

a lot of people talk about things in

general that we've worked on um whether

it's agile broadly or refactoring

particularly in my case because that was

a book I worked on um but I don't know

anything where any of the pieces of that

necessarily

>> so I Get thank you so much for test

driven development. I also get this test

test driven DEVELOPMENT

ruined my life.

My dog left me,

my house burned down, and it's all your

fault. So, I don't know. Does that

answer your question?

I I think TDD has has been very

divisive. I I've been a I' I've been a

convert at some point and then I I hated

it. But it's interesting because I feel

a lot of a lot of the are like this,

right? They're meant to be provocative.

They're meant to push you.

>> Yeah. I also got uh was actually

chatting with somebody uh yesterday

who's really pushing the AI envelope.

And his comment was, well, thank

goodness for all of your pushing of TDD

for the last 20 years because it's

really important now we've got AI

agents. And uh it it's interesting to

hear that feedback because um I'm always

suspicious of it cuz I want it to be

true. You know, I'm the kind of guy who

when when I hear something I like, I'm

I'm kind of thinking, am I just making

this up? But uh it does make sense to me

that, you know, when we've got a big

powerful genie, you really have to learn

how to verify that it's doing the right

thing for you,

>> which we've been practicing for 25

years.

>> Yeah. Well, I mean, I'm not a big

powerful genie myself, but I still

needed the tests to make sure I was

doing the right thing.

>> So, a lot a lot of folks here, they will

know you from the podcast that we did

together. They might have they probably

read some of your books. Barton, you're

really perfect writer, but can you tell

us what are you up to these days? What

does your day-to-day look like? How do

you stay in touch with technology? I got

a really rude comment that I'm still I

got to fight into someone on LinkedIn

about this. They said like, "Oh, your

your conference like you're having

authors here who are like out of touch

with technology." And I'm like, "Do you

even know who Martin Valer and Kemp Beck

is?" But I just want wanted to to ask

like these days, how what are what are

you up to? Seriously?

>> Well, my I when I finished the second

edition of Refactoring Book, which was,

you know, five or six years ago, I toyed

with the idea of writing another book.

I've got several half-written books out

there to work on, and I decided I should

not do that. Instead, what I should do

was work with people who are actually

still doing real work on real projects,

writing real code as it were. Um, and

get them to get their ideas out and what

they learn out. So, that's been my main

project ever since, primarily focused on

the martinfaller.com website because,

hey, I control that. There's no big

corporation that's going to sweep in and

and clobber it. At least not without my

um me selling out and getting lots of

money out of it. Um

and um as the AI thing has come, I've

been very keen to capture that. And what

my big focus is on is trying to re

understand details of people's workflow

and what exactly they are doing. what

are the kind of conversations they're

having with the genie as as Kent um

calls it and you know what if you're

reviewing things what are you looking

for if and particularly what decisions

are you are we the humans still making

and how is that decision flow changing

um so that's my interest is it's not

what I'm doing it's what my colleagues

are doing and trying to spread that

around and that's that's my focus

>> and so I've been

uh my personal mission in life is to

help geeks feel safe in the world. And

uh our people do not feel safe right now

for some good reasons and some not good

reasons. And one of the things I noticed

is for 25 years we've kind of had the

answers. Somebody comes to us and says

we have too many bugs. Or like all

right, well here's how you write tests.

Oh, I can't write tests. Well, here's

how you design so you can write tests

and just kind of press play

on the recorder. And the thing that's

changed is at this moment nobody knows

the answers to anything.

And so what I've been trying to do is

both for my own geeky curiosity

satisfaction,

let me go back into explore mode and

find out as well as I can

uh what you can do to be effective with

these new tools and then demonstrate

that to the next generation of people

who've been used to getting answers. Oh,

I'm having some trouble. let me look up

in the book what the solution is that

that worked for the last 20 years and it

doesn't work for the last year and won't

work for the an extended period. So, as

seniors, I figure it be behooves us to

demonstrate not just how to use these

tools effectively, but how to figure out

how to use these tools effectively

because that's a whole different set of

skills and you say all right we don't

know what what will come how no one has

to figure it out. I wanted to take you

back into your professional journey. One

thing that you share here you have seen

a lot more than a lot of us myself

included a lot of people in in the room

as well. Do you remember a time where

there was a technology change which

looked similarly kind of unpredictable

or scary like AI does right now? What

was the thing that comes the closest in

your career?

>> Well, nothing has hit with the magnitude

of AI. That's I mean this is a a whole

size difference from anything that we've

faced before. um on a smaller scale um I

would say and we were very much involved

in the the growth of object-oriented

languages I mean and that scared a lot

of people it didn't scare us so much

because we were part of it um I would

say that the impact of the internet um

had a huge impact upon us all um and of

course obviously we were spreading the

the the challenge of agile software

development and that had a very big

impact on a lot of organizations because

you could tell by how hard they resisted

it. Um, but the thing about AI is that

you know all of these things we were

talking about how important they were

and how valuable were and trying to

persuade people of the importance of

them. Yes, even the internet that may

sound surprising but there were people

who would weren't thinking that was

important. Um, but AI there's kind of no

argument about how important it is.

People can't I mean you cannot put

blinkers on to deny the importance of

this thing.

So the the other analogy that I have is

to the introduction of the

microprocessor.

Before that, computers were a big box.

You couldn't move them around. If you

wanted another one, you'd mortgage your

house again. It was a it was a big it

was a big deal. And I was a kid in

Silicon Valley with my dad as a

programmer when the Intel 404 hit and we

went, "Wait a minute, that's a

computer." Oh my goodness, the the

possibilities

suddenly expanded. If you can figure out

how to write software, if you can figure

out how to design hardware around this

thing, you can suddenly do things we

can't even imagine. And so, uh, I think

part of AI is this expansion expansion

of imagination. So, I'm writing projects

that are ridiculously ambitious. I'm uh

working on a persistent small talk. I

I'm writing library quality code for

Rust. I'm just, you know, anything I can

imagine to trying to do, I'm going to

try and do it and see. Now, a bunch of

those fail and that's fine. That's part

of this process. But um it it's not like

this is the first time the heavens have

opened and and we've been brought tons

of new opportunities

>> and back either with with

object-oriented spreading or with

microprocessors. Do you remember what

the the feeling was in the industry and

what was the difference between

experienced professionals who you know

just got

thrived in this new world and ones who

were just honestly left behind?

Yeah, there with all of those there was

that sense of the mix between the the

the people chasing the hype and the the

people who were saying no this is

nothing special. Um I think it you've

always got to have that balance

of skepticism and curiosity

um in order to be able to do that and

you are selective about it. I mean I

have been completely skeptical about

some big um changes. I mean um

blockchain for instance I was extremely

skeptical about that um and but as I

like to say you know about about my

skepticism about technologies which is

well rooted because I've seen so much

snake oil projected out by the industry

um over the years my skepticism has to

be absolute and total which means I have

to be skeptical about my skepticism and

that requires that curiosity and I think

that's where the thing is you've got to

be curious enough to say may this looks

like but maybe it isn't. How do I probe

in order to detect that there's signs of

of something coming out there?

>> Yeah. What what's the smallest

experiment I can run to verify to my own

satisfaction and everybody's level of

satisfaction is going to be different

whether or not this claim is true.

That's that's the skill that has

suddenly in the last year

become a thousand times more valuable is

that skill of saying what's the least I

can do to validate for my to my own

satisfaction whether this claim is true

or not.

>> But there's also another step in there

that you also got to be aware that your

early interactions may not actually be a

true signal. I mean, when I started

playing around with AI, I guess it was

the co-piloty like stuff about a year,

year and a half ago, I was pretty

unimpressed, right? I mean, I I set up

I'm an Emacs guy. I set up Emacs.

The one true editor. Um, I set it I set

up Emacs so that I could just have it

prompt and complete automatically

because, you know, Emacs is capable of

doing that. And I used it for maybe

three or four days before I just got

because sometimes it would give you

something wonderful but most of the time

it gave you such garbage that you would

just control K right away. And if that

had been my impression of AI and I have

said that's what I think of AI, I would

have just immediately flip the bozo

switch on it just like I did with

blockchain. But on the other hand, I'm

also probing out there. So my my

most valuable discovery in all of this

is in the room next door Simon Willis's

blog which I read and one of the things

that I took from that was to use this

tool well you have to learn how to use

it well which was also something very

true of object orientation people would

say oh objects you know and I you look

at what they were doing you're not using

objects very well in fact we kind of

mckent I were kind of at yeah they were

using C++ and Java they didn't actually

do the real stuff. Um, but the the point

was you have to be also listening to the

folks out there and being able to read

with a critical eye and getting a sense

of okay, if you do run across a Simon

Willis, is he hyping everything

wonderful or does he seem to be

recognizing real problems at the same

time and giving me straight stuff? And

that I found was a really I mean when

people give you that balance of good and

bad and also most importantly are

prepared to say I don't know then that

that involves something to listen to. So

him and also some of my colleagues in

Fort Works like Mike Mason and Bit

Berkel they really kind of showed me

that oh I've shouldn't be relying too

much on my initial reactions.

>> Yeah and it can change week to week. I

I'll I'll I'll I'll try something with

Gemini one week fails miserably.

This Gemini thing cloud code then that

works pretty well and then it's doesn't

work well and then I tried Gemini for

the same thing and it works this week

and it didn't work la last week. That's

a you know people want the answer and

the answer is changing. So you can't

possibly in this environment have the

answer. Now, that's the bad news. The

good news is nobody else has the answer

either. So, you're just as smart as

everybody else because you're just as

ignorant as everybody else.

>> Is that reassuring?

>> Is that reassuring? By a show of hands.

One thing that struck me as like a bit

of a similarity is back in 2001 when the

almost exactly 25 years ago when the

agile manifesto came out with that

website with all the 17 names listed and

Kent Beck being the first one. Why were

we the first one?

>> Alphabetical

strictly alphabetical but it is a source

of unending joy.

You can very much take of that.

>> That that that kicked off some really

interesting things in the indust

industry because what what my

interpretation was like well use this

agile here's these four pretty simple

easy to understand and easy to identify

with things to build better software

faster cheaper higher quality you name

it. Now, when I think of why so many

companies are adopting AI, they're kind

of expecting the same thing, better,

faster, cheaper, and so on. And so, I

wanted to can you reflect on how agile

actually went? Speaking of snake oil,

>> well, it it turns out that people don't

want faster, cheaper, better.

>> Tell me more.

>> Inside a company, the incentives are so

misaligned with actually achieving that.

And so so as as geeks trying to achieve

that and say, "Well, it's 40% better and

it's 12% cheaper and it's less

fattening."

Uh people will punish you for that if

that doesn't in align with their

incentives inside of organizations.

Yeah. In the ideal organization,

everybody would care about the same

things. And that's just not the way it

works.

But I

>> so and and we haven't touched that

problem. So if AI is coming along to

promise the same things, we're going to

see exactly the same reaction.

>> And and this is what I I wanted to ask

like looking back from what you've seen

for agile now 25 years and it played out

at a lot lot slow slower pace. What

similarities do you see right now with

AI? How do you think the curve could

fit? And also what is very different

about that agile movement and and that

took the industry by like a very slow

storm and now with AI.

>> Well, what's obviously very different is

the sheer magnitude and speed that h

that is hitting with AI. So that is

definitely different. I suspect one

thing I think there will still be some

similarities. Um one of them I think is

there will be a big difference between

people who use it well and people who

use it badly. And the trick is figuring

out how to use it well and putting the

effort in to learn to use it well. Um I

think there will be a big distinction

between those two groups. Um I think

another similarity is I mean the core

notions uh behind agile and extreme

programming are solid and good but a

huge snake industry appeared around it.

The agile industrial complex as I like

to refer to it. Um, and that will

happen. That is happening with AI right

now. And it's often hard to see the

difference between where is the snake

oil and where is the real stuff. And so

that's another thing that you got to be

constantly probing and be aware of and

be wary of as as you're looking at it.

>> Yeah. A AI is an amplifier.

And if uh if you're young and learning

quickly, AI is going to amplify that or

can amplify that. So I I personally

think this is this is the golden age of

the junior programmer. I get people

coming to me all the time, oh my son

started his second year in CS and he

wants to go into something more

commercial like art history.

And I I'd say this is like if you're a

carpenter and they just introduced the

the circular saw and you think ah well

carpentry is over. anybody can build a

house now.

Well, no, you have more powerful tools.

You you have less time that you have to

do, you know, kind of the crummy work.

So, I think that that's a the young

people who are learning fast are going

to learn faster. The experienced people

who are working effectively are going to

work more quickly and more effectively.

And my concern, and this is something I

learned last week, that middle, if we

look back at the.com crash, there was a

there was um also a middle of people

who'd gotten into programming because it

was a way to make money. And those

people went into real estate, more or

less.

And I don't know where the middle's

going to go now, because that middle is

much bigger now than it was 25 years

ago. But that middle has also been

flushed out to some degree by the

retrenchment in the software industry,

the end of the zero interest rate

period. Um, so that's an interesting

difference because we've had these two

things occurring at once, the AI boom

and the economic headwinds that we've

had in the last two or three years. Um,

so which is an interesting kind of mix

of things that wasn't the case back in

the '9s with the dotcom boom because

that was pretty much all solid boom.

>> Yeah. So, another interesting uh

confluence of factors is we have these

periodic we get to get rid of all the

programmers. Woohoo.

>> Cobalt programming

>> starting with Cobalt, right? When the

business analysts were going to be able

to write the programs and we didn't have

to have programmers anymore. And so that

comes back repeatedly. Agile was

definitely not that. We wanted

programmers to be more effective in

their jobs. And since we started it and

were programmers, we were able to push

that agenda pretty effectively. But now,

now we have this repeating

this repeating, hey, we get to get rid

of all the programmers, which it

behooves us as programmers to think

about why they keep wanting to get rid

of us.

Some of that's about us and some of it's

not, but some of it is. So we should

think about that but also that amps up

amps up the fear factor that everybody

is experiencing

>> and also one of the interesting things

is when people are say oh we're getting

rid of code I mean the you hear people

saying sessions oh no one's going to

write code in six months time I go to

myself well yeah but what do you mean by

code

because that kind of implies nobody's

writing anything well we're at least

doing some prompting we're having some

interaction with the genie

What's that going to be if it's not some

form of code in some way? I think the

nature of what code is is going to be

quite possibly very radically different,

but I think there is still a need to

produce it and be able to interact with

it in some way.

One thing I really like and respect

about both of you is you are two feet on

the ground. We've heard from OpenAI who

are a leading lab and of course they're

building amazing technology but they

also have to talk their book. We've

heard about Laura who talks with so many

people and so has so many good insights.

In the end, you know, she will have a

small bias to to help sell some of the

tools that that help do this. Martin,

you're talking with so many companies

especially like large skeptical

companies as well as well as startups as

through thought works uh consulting

advising. So do you Kent, what do you

see on the ground? What is interest and

and what is surprising you about how

these smaller and larger companies often

the the more enterprisey ones the more

traditional ones what are they doing

with the technology and how are they

thinking about it

>> um at the moment large scale confusion

and panic is pretty much the order of

the day right across the board

>> so so if that is your strategy you're

right in the middle

>> I mean enterprise I mean large

enterprises have this thing where they

just have enormous amounts of code in

complex systems that fit together in

difficult to together ways. Um where

someone says you know can can these

tools handle a million lines of code

because that that's a smaller code base

as far as many of these systems are

concerned and it's a very different

picture to the startup world because no

you do not want to take a risk that's

cause going to cause your airline to go

offline for a day or two. That's not an

acceptable um thing to consider. Um, and

also, um, there are other risks

involved. I mean, I I've now run into

several different groups, including some

at surprisingly large companies, that

are talking about, let's have the LLM

have complete control over my email. It

can read all my emails, and it can reply

to most of the emails. And I'm going,

NO,

>> WHAT?

>> NO.

>> I mean, the security risk of that is,

you know, mindboggling. Um but you know

it's a very it's I I am very much

concerned we're going to have some

really bad security incidents um over

this year because people are just not

paying attention and um those are the

kinds of things that are out there as

well. So there's a kind of blind rush to

uh say let's let's let's grab these

nicel lookinging things at the same time

as some real concerns that are coming

across it as well.

So I see a a a big trend is the

resoloing of programming

where

a big part of extreme programming is

creating a safe social environment for

basically antisocial people.

Not just asocial, antisocial.

And the when I think about the degree of

interaction on an XP team, people are

talking to each other hours a day and

happy to be doing so because it's set up

for that to be a positive experience.

What I see now is, well, I'm a

programmer and I've got six agents, so

really I'm managing a team. No, you're

not. You're using six tools at once,

which is fine, but it's that's very

different than having a conversation

with somebody who believes things that

are a little different than what you

believe or somebody who's got a

different energy level today than you

have. And but I I see the the the trend

is Oh, good. We used to have

programmers, you remember individual

offices? We we'd have offices and doors

and you shut the door and you slide the

pizza under.

>> It was a thing

>> and and that was that was easy to manage

and easy to control. And then along came

this messy, social, complicated, chaotic

process that just happened to produce

really good results.

And oh, that was uncomfortable. Oh,

good.

I can, you know, instead of having 50

people on my team, I have five people on

my team. They don't have to talk to each

other and they can each have 10 agents

and that's the same. It's not the same.

Yeah, that's the uh I mean it's part of

the question that that uh and again I

think something that was discussed last

week, you know, are we seeing that you

know two pizza teams are going to become

one pizza teams because agents don't eat

pizza or do we see two pizza teams

staying but just being able to much be

much more effective and capable and my

>> or or do you create a genie that can't

eat pizza? That's the one.

>> My bet is on the more effective two

pizza teams. Um, and it it's also some

interesting, you know, feedback we're

beginning to get in terms of pair

programming. I mean, with pair

programming, do you say pair programming

is the human and the genie? Or is it two

humans and n genies? Because if it's two

of us, we can control the genies perhaps

a little bit better. And we also have

that same interaction. And I'm I'm going

to find it very interesting to hear

reports of people trying that kind of

route where they're saying yes, we have

those pairs controlling genies um and

possibly beyond pairs. I mean we we

there's also the whole mob programming

thing and whether that will go a route

with that com combined with the genies

that combination I but I don't

necessarily think one person many genies

is necessarily the right answer.

>> It's the simplest thing to to

understand. It's the simplest framing,

>> but my my experience pairing with a two

humans and a genie or multiple genies

has been very positive. And the fact

that they're kind of slow is really

nice. So, every time the models come out

and they're faster, I'm like, "Oh,

there's less time to talk."

You give a prompt and it's like, "Oh,

well, blah blah blah." And then it's

gone for 3 minutes and we can talk about

our philosophy of naming or you know how

do we express conditionals or what other

you know what should we be doing next

and if it pops back in 15 seconds you

don't have time to have that

conversation.

So, a lot of things are are changing as

as a closing question before we we head

over to Q&A for software engineers who

really care about the craft and engine

leaders who care about the craft and you

know they they've learned to love this

industry. They're seeing a lot of

things. You're shifting for example,

you're not writing the code, you're

losing a lot of control. What advice

would you give to them to, you know,

stay stay afloat and and hopefully come

out thriving from this this change in

abstraction? Basically,

>> I I like to think of a comment again

that came up last week and I can't

remember who to who I shouldn't

attribute it to. Um, which is that the

uh the ven diagram of developer

experience and agent experience is a

circle.

And the point here is that you know what

we do that that's good for the agents is

good for the humans and vice versa. I'm

hearing a lot of feedback saying, "Yeah,

actually if you have well modularized

code that actually makes it easy for the

agents to work with and we've we're

already getting, you know, lots of

reinforcement saying actually focusing

on tests, good tests helps the agents as

well as helps us." So I think there's

there's a good bit of potential overlap

here. And again, this could be me again

just wishful thinking because I want it

to be true. Um, but uh I'm going to run

with it for a bit at least. And I think

so focus on those craft things and focus

on um using that and teaching the agent

as it were and working with the agent to

find find out how best to express that.

One of the things that I found really

fascinating was talking with another

colleague of mine, Unashi, about how he

was working in domains and he says the

way he finds working often with an agent

is to try and develop a language, a

precise language to communicate about

the domain with the agent, which is

basically the kind of model building,

language building, domain driven design

stuff that we're used to doing, but it

makes him more efficient to talk to the

agent. So those kinds of things give me

a sense of there's there's definitely a

huge overlap between what is good about

our practices and what will be good

continuing to drive with AI.

So I think for me I I take a kind of uh

OCD enjoyment in the in the craft and I

need to let go of that because that that

satisfaction of getting this one

function just right

just doesn't make a difference anymore.

Uh getting an overall understanding of

what's going on. And and I say this with

sadness because I really enjoyed getting

in the zone and you got some file and

it's a big mess and you make tiny little

safe steps and you don't know quite

where it's going and then you start to

get a glimmering and then it's there and

then oh pop it just pops into focus and

it oh that feels so good and I can't do

that anymore.

But I can still develop an overall

understanding of what I'm doing. And I

need to shift my focus to enjoying

understanding the domain and its

connection to my program in a way that I

used to be focused on the program as the

domain and I could make that better and

better. It just doesn't have leverage

anymore.