The Pragmatic Engineer - How AI will change software engineering – with Martin Fowler

Episode Date: November 19, 2025

Brought to You By:•⁠ Statsig ⁠ — ⁠ The unified platform for flags, analytics, experiments, and more. AI-accelerated development isn’t just about shipping faster: it’s about measuring whether, what you ship, actually delivers value. This is where modern experimentation with Statsig comes in. Check it out.•⁠ Linear ⁠ — ⁠ The system for modern product development. I had a jaw-dropping experience when I dropped in for the weekly “Quality Wednesdays” meeting at Linear. Every week, every dev fixes at least one quality isse, large or small. Even if it’s one pixel misalignment, like this one. I’ve yet to see a team obsess this much about quality. Read more about how Linear does Quality Wednesdays – it’s fascinating!—Martin Fowler is one of the most influential people within software architecture, and the broader tech industry. He is the Chief Scientist at Thoughtworks and the author of Refactoring and Patterns of Enterprise Application Architecture, and several other books. He has spent decades shaping how engineers think about design, architecture, and process, and regularly publishes on his blog, MartinFowler.com.In this episode, we discuss how AI is changing software development: the shift from deterministic to non-deterministic coding; where generative models help with legacy code; and the narrow but useful cases for vibe coding. Martin explains why LLM output must be tested rigorously, why refactoring is more important than ever, and how combining AI tools with deterministic techniques may be what engineering teams need.We also revisit the origins of the Agile Manifesto and talk about why, despite rapid changes in tooling and workflows, the skills that make a great engineer remain largely unchanged.—Timestamps(00:00) Intro(01:50) How Martin got into software engineering (07:48) Joining Thoughtworks (10:07) The Thoughtworks Technology Radar(16:45) From Assembly to high-level languages(25:08) Non-determinism (33:38) Vibe coding(39:22) StackOverflow vs. coding with AI(43:25) Importance of testing with LLMs (50:45) LLMs for enterprise software(56:38) Why Martin wrote Refactoring (1:02:15) Why refactoring is so relevant today(1:06:10) Using LLMs with deterministic tools(1:07:36) Patterns of Enterprise Application Architecture(1:18:26) The Agile Manifesto (1:28:35) How Martin learns about AI (1:34:58) Advice for junior engineers (1:37:44) The state of the tech industry today(1:42:40) Rapid fire round—The Pragmatic Engineer deepdives relevant for this episode:• Vibe coding as a software engineer• The AI Engineering stack• AI Engineering in the real world• What changed in 50 years of computing—Production and marketing by ⁠⁠⁠⁠⁠⁠⁠⁠https://penname.co/⁠⁠⁠⁠⁠⁠⁠⁠. For inquiries about sponsoring the podcast, email [e-mail]. Get full access to The Pragmatic Engineer at newsletter.pragmaticengineer.com/subscribe

What similar changes have you seen that could compare to some extent to AI in the technology field? It's the biggest, I think, in my career, I think if we looked back at the history of software development as a whole, the comparable thing would be the shift from assembly language to the very first high-level languages. The biggest part of it is the shift from determinism to non-determinism. And suddenly you're working in a non-with an environment that's non-deterministic, which completely changes FDA. What is your understanding and take on vibe coding? I think it's good for explorations, it's good for throwaways, disposable stuff, but you don't want to be using it for anything that's going to have any long-term capability.

When you're using vibe coding, you're actually removing a very important part of something, which is the learning loop. What are some either new workflows or new software engineering approaches that you've kind of observed? One area that's really interesting is Martin Fowler is a highly influential author and software engineer in domains like agile, software architecture and refactoring. He is one of the authors of the Agile Manifesto in 2001. the author of the popular book Refactoring, and regularly publishes articles on software engineering on his blog.

In today's episode, we discuss how AI is changing software engineering and some interesting and new software engineering approaches at Alums enable. Why refactoring as a practice will probably get more relevant with AI coding tools, why design patterns seem to have gone out of style the last decade, what the impact of AI is on agile practices, and many more. This podcast episode is presented by Statsig, the Unified Platform for Flags, Analytics, Experiments, and more. Check out the show notes to learn more about them and our underseason sponsor.

If you enjoy this show, please subscribe to the podcast on any podcast platform and on YouTube. So, Martin, welcome to the podcast. Well, thank you very much for having them. I didn't expect to be actually doing it face to face with you. That was rather nice. It's all the better this way. I wanted to start with learning a little bit on how you got into software development, which was, what, 40-ish years ago?

Yeah, it was, oof, yeah, it would have been light. 70s, early 80s? Yeah. I mean, like so many things, it was kind of accidental, really. At school, I was clearly no good at writing because I got lousy marks

for anything to do with writing. Really? Yeah, absolutely. But I was quite good at mathematics and that kind of thing in physics. So I was kind of lean towards engineering stuff. And I was interested in electronics and things because the other thing is I'm hopeless with my hands.

I can't do anything requires strength or physical coordination. So all sorts of areas of engineering and building things. I've tried looking after my car and I couldn't get the rusted nuts off or anything. You know, it's hopeless.

So, but electronics is okay because that's all very, you know, it's more than in the brain than, you know, you need to be able to handle a soldering iron, but that was about as much as I needed to do. And then computers, and it's step easier.

I don't even need the soldering iron. So I kind of, drifted into computers in that kind of way. And that was my route into software development. Before I went to university, I had a year at working with the UK Atomic Energy Authority, or ukulele, as we call it. And I did some programming in Fortran 4. And it seemed like a good thing to be able to do.

And then when I finished my degree, which was a mix of electronic engineering and computer science, I looked around and I thought, well, I could go into traditional engineering jobs, which weren't terribly well paid and weren't terribly high status, or I could go into computing where it looked like there was a lot more opportunity. And so I just drifted into computing. And this was before the internet took off. Oh, yeah. What kind of jobs were there back then that you could get into?

And what was your first job? Well, my first job was with a consulting company, Cooper's and Liebrand, or as I referred to them, cheat them and lie to them. And we were doing advice. on information strategy, the particular group I was with. Although that wasn't my job. My job was, I was one of the few people who knew Unix because I'd done Unix at college.

And so I looked after a bunch of workstations that they needed to run this weird software that they were running to help them do their strategy work. And then I got interested in what they were doing with their strategy work and kind of drifted into that. I look at it back now and think, God, that was a lot of snake oil involved. But, hey, it was my route into the,

into the industry, and it got me early into the world of object-oriented thinking. And that was extremely useful to get into objects in the mid-80s. And how did you get into, like, object-oriented was back then, back we're talking, probably the mid-80s. That was a very kind of radical thing. And you said you were working at a consulting company, which didn't seem like the most cutting-edge. So how does a two-plus-two get together?

How did you get to do cutting-edge stuff? Because this little group was into cutting edge stuff, and they had run into this guy who had some interesting ideas, some very good ideas, as well as some slightly crazy ideas. And he packaged it up with the term object orientation, which wasn't really the case, but it was, it kind of, you know, it's part of the snake oil, as it were. I mean, that's a little bit cruel to call it snake oil, because he had some very good ideas as well. But that kind of led me into that direction. And of course, in time, I've found out more about what object orientation was really about. And that events led to my whole career. In the next 10 or 15 years, how did you make your way and eventually end up at ThoughtWorks?

And also, you started to write some books. You started to publish on the side. How did you go from like someone who was brand new to the industry and kind of wide-eyed and just taking it all in, learning things, to starting to slowly become someone who was teaching others? Well, again, bundles of accidents, right? So while I was at that consultant company, I met another guy that they'd brought in to help them work with this kind of area, an American guy, who became really the biggest mentor and influence upon my early career.

His name is Jim O'Dell. And he had been an early adopter of information engineering and had worked in that area. And he saw the good parts of the... these ideas that these folks were doing. And he was an independent consultant and a teacher. And so he spent a lot of his time doing work along those lines. I left Coopers and Libran after about a couple of years

to actually join the crazy company, which is called P-Tech. And I was with them for a couple of years. It was a small company. There was a grand total of four of us in the UK office. And that was the largest office in the company. Wow. Kind of thing. and so I saw a bit of, you know, having seen a big company's craziness,

I then saw a small company's craziness. Did that for a couple of years. And then I was in a position to go independent, and I did. Helped greatly by Jim O'Dell, who was, who fed me a lot of work, basically. And also by some other work I got in the UK. And that was great. I remember leaving Petek and thinking, that's it. Independence life for me, I'm never going to work for a company again.

Famous last words. Exactly. And I carried on, I did well as an independent consultant throughout the 90s. And during that time, I wrote my first books. I moved to the United States in 93. And it was doing very, very happily. And obviously, you got the rise of the internet, lots of stuff going on in the late 90s. It was a good time.

And I ran into this company called Fortworks. And they were just a client. I would just go there and help them out. Yeah, the story gets a more common. I had met Kent and worked with Kent at Chrysler, the famous C-3 project, which was the birth project of extreme programming. So I'd worked on that, seen extreme programming, seen the agile thing. So I'd got the object orientation stuff.

I got the agile stuff. And then I came to Fortworks and they were tackling a big project, a big project for them at the time. still sizable, about 100 people working on the project. So it's a sizable piece of work. And it was clearly going to crash and burn. But I was able to help them both see what was going on and how to avoid crashing and burning. And they figured out how to sort of recover from the problem.

But then invited me to join them. And I thought, hey, you know, join a company again maybe for a couple of years. They're really nice people. they're my favorite client. I always thought of it. Other clients would say, these are really good ideas, but they're really hard to implement.

And while Fort Works would say, these are really good ideas, they're really hard to implement, but we'll give it a try. And they usually pulled it off. And so I thought, hey, we have a client like that. Might as well join him for a little while

and see what we can do. That was 25 years ago. Yeah. And then fast forward today, your title has been for, I think, over a decade chief scientist. Since I joined,

that was my, title I joined. So I have to ask, what does a chief scientist at ThoughtWorks do? Well, it's important to remember, I'm chief of nobody, and I don't do any science. The title was given because that title was used a fair bit around that time for some kind of public-facing ideas kind of person. If I remember correctly, Grady Booch was chief scientist at Rational at the time. Actually, true. And there were other people who had that title. So it was a highfaluting, very pretentious title, but they felt it was necessary. It was weird because one of the things at Fortworks at that time was you could choose your own job title.

Anybody could use whatever job title they like, but I didn't get to choose mine. I had to take the chief scientist one. They didn't like titles like flagpole or battering ram or loudmouth, which is the one I most prefer. And one thing that ThoughtWorks does every six months, and the latest one just came out, is the Thoughtforce radar. And this latest radar, it just came out, I think, a few days ago. Just today it was launched, I think. Actually, it was today.

So by the time this is in production, it will have been a few weeks. But it's actually really, really fresh. So I just looked at it and things that it lists. I'll just list a few things that I saw there. And the adapting, which is the ones that they recommend using. Pre-Commit hooks, Click House for Database Analytics, VLLM. This is for Lunning LLMs on On-Cloud or on-prem in a really efficient way. for trialling clot code, fast MCP, which is a framework for MTV servers,

and they're also recommending a lot of different things related, for example, to AI and LLMs to assess. Can you share a little bit of how ThoughtWorks comes up with this technology radar or what's the process? And it feels very, very kind of on the pulse every time, like it feels close to the pulse of the industry. And again, I talk with a lot of other people. How do people at ThoughtWorks stay this close to what is happening? happening in the industry. Okay. Well, this will be a bit of a story. Okay, so it started just over 10 years or so ago. Its origin was one of the things that we've really pushed at footworks is to have technical people, practitioners, really involved at various levels of running the business.

And one of the leaders of that was our former CTO, Rebecca Parsons. So Rebecca became CTO and she said, I want an advisory board who will keep me connected with what's going on in projects. So she created this technology advisory board and it had a bunch of people whose job was to brief her as to what was going on would meet two or three times here. She had me on the advisory board not so much for that reason, but because I was very much sort of a public face of a company. She wanted me present and involved in that. And originally that was just our brief.

We would just get together and we'd talk through this stuff. And then one of these meetings, Darrell Smith, who was actually her TA at the time, technical assistant, he said, well, we've got all these projects going on. It would be good to get some picture of what kinds of technologies we're using and how useful they are. And so it's the better exchange ideas. Because like so many companies, we struggle to percolate good ideas around enough. I mean, even then when we're only just a few thousand, it struggled. and we're 10,000 now, so it's hard. So we thought, okay, this is a nice idea.

And he came up with this idea of the radar metaphor and the rings of the radar that we see today. And we had a meeting and we created the radar. And it's a habit that if we do something for internal purposes, we try and just make it public. And that's always been a strong part of the footworks ethos. It's part of why I'm there, of course, is, you know, we talk about everything that we do

and we share everything. We give away our secret source all the time. So we did that. And people were very interested. And so we continue doing it. Now, the process has changed a bit over time. At that original meeting, many of the people that were in the room were actually hands-on on projects advising clients all the time. Now, as we've grown an order of magnitude, it's much harder to do that.

And we've also created more of a process where people can submit blips, nominate them. A blip is something. A blip being a point on the radar and entry. And they will go to somebody that, either connected through geographically or through the line of business or technology or whatever

and say, hey, we think this technology is interesting. They'll brief us a little bit about it. And then they briefed the members of the what's now called the Doppler group because we make a radar. Yeah, I mean, we can be a bit loose of our metaphors at times.

And then at the meeting, we'll decide which of these blips to put on the radar and not. And obviously you get some cross-pollination because somebody will say, oh yeah, I talk to somebody about this as well. And so it's very much this bottom-up exercise. And that's how it's created now. So we will have these, we will do blip-gathering sessions about a month or two before the radar meeting and gradually shake them up. And then in the meeting itself, we go through them one by one. And for me, it's a bit weird because I'm so detached from the day to day these

days that it's just this lineup of technologies and things. I have no idea what most of them are. but interesting to hear about. And sometimes I latch on to certain themes or something like that. And that was an important part of microservices about 10 years ago because that came up in through that radar process. And we got together with James Lewis and we ended up writing a good bit further about that. But that's really what happens is we go through this process of spotting this stuff. Yeah.

And the radar analogy, I know some companies also take the idea. Which, by the way, ThoughtWorks encourage us say, make your own radar, take it in your own company. You can, I think they even like have tools around it. I really like how ThoughtWorks never said, like, this is the thing for the industry. They said, this is a thing for us. This is what we see. This is what we recommend our team, our team members, or maybe our clients to consider.

Or there's also, I like that there's a hold. Maybe just beware. We're not seeing great results with this. And here's the reasons for it. And yeah, I guess the reason it feels fresh is probably a lot of work that ThoughtWorks does is it feels cutting edge because it's all about half of it or a third of it feels that it is around the hottest topic right now, AI, LLMs, and all the techniques that people are trying to see if they work or the things that we are seeing that actually starts to work.

Yeah, I mean, Fortworks has basically got several thousand technologists all over the world, doing projects of various kinds, all sorts of different organizations. And the radar is a mechanism that we've discovered is a way of getting some of that information out of their heads and spreading it around both internally and to the industry as a whole. And you're right, it is a recommended thing for clients to do is to try and do their own radars. It's slightly different when it's a client radar thing because sometimes there it can be more of a, this is what we think you should be doing with a bit more of a forcefulness to it than we would give. And also they can be a bit more choosy in the sense of they can say, yeah, we're just not interested in doing certain technologies.

Well, for us, it's a case that if our clients are doing it, then we're going to find out about it, right? We have to use it. Of course, the radar is full with a lot of AI and LMU. Things because this is a huge change in my professional career, it feels by far the biggest technology innovation change that's coming in. Looking back on your career, what similar changes have you seen that could compare to some extent to AI in the technology field? It's the biggest, I think, for my career. I think if we looked back at the history of software development as a whole, the comparable thing would be the shift from assembly language to the very first high-level languages, which is before my time. Right, when first started coming up with Colbill and Fortran and the like, I would imagine that would be a similar level of shift.

So you started to work with Fortran, and you probably knew people who were still doing assembly or at least knew some people from that generation. There was a bit of assemble around when I was working still. From what you picked up around that time, what was that shift like in terms of mindset or, you know, like, because it was a big change, right? You really needed to know the internals of the hardware and the instructions and the different. I did very little assembly at university, but it's been very useful because I never want to do it again. Very wise. But what did you pick up in terms of what needed to change and how it changed the industry, just moving from mostly assembly to mostly higher level languages? Well, I mean, for a start, as you said, things were very specific to individual chips.

The instructions were different on every chip, you know, as well as things like registers, where you access memory. You had these very convoluted ways of doing even the simplest thing because your only instruction was for something like move this value from the memory location to this register. And so you've always got to be thinking in these very, very low-level forms. And even the very relatively poor high-level language like Fortran, at least I can. can write things like conditional statements and loops, else is in my conditional statements in Fortran 4. But I can, I used to go if, and I can get one statement. I can't do a block of statements. I have to use go-toes. But, you know, it's better than what you can do in assembly, right? And so there's a definite shift of moving away from the hardware to thinking in terms of

something a bit more abstract. And I think that is a very, very big shift. And then, of course, once I'm using Fortran, I can be insulated to some degree away from the hardware I'm running on. I'm now, am I running this on a mainframe? Am I running this on a minicumputer? I mean, there's issues because the language is always varied a little bit from place to place, but you've got a degree of decoupling there that was really quite significant, I think. I mean, I only did it on small microprocessor-like units because, again, it was the electronic engineering part, right? So we were fairly close to the metal anyway for some of that. But you definitely had that mind shift.

And I think it's with LLMs, it's a similar degree of mind shift. Although, as I've written about it, the interesting thing is the shift is not so much of an increase of a level of abstraction, although there is a bit of that. The biggest part of it is the shift from determinism to non-determinism. And suddenly you're working in a non-with an environment that's non-deterministic, which completely changes you have to think about it. Martin just talked about how AI is the most disruptive change since the move from assembly to high-level languages.

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because one very naive or naive way of looking at is saying, like, well, we've had the three levels, right? We have assembly where you have commands for the hardware. You need to be intimately aware of the hardware. We have high-level programming languages starting with C, later Java, later JavaScript, and where you don't need to be aware of the hardware. You're aware of the logic. And what you might say is, well, we have a new abstraction,

is you have the English language, which will generate this code. you're saying you don't think it's an abstraction jump. Why do you think this is? I think there's a bit of an abstraction jump. I think the abstraction jump difference is smaller than the determinism, non-determinism jump. And it's worth remembering one of the key things about high-level languages, which I didn't mention

as I was talking about earlier on, is the ability to create your own abstractions in that language. That is particularly important as you get to things like object orientation, towards more expressive functional languages like LISP, which didn't really have so much. in Fortran and Calvo, you could do that to some extent

because at least with Fortran you can create subroutines and build abstractions out of that but you've got so many more tools for building abstractions when you've got the abilities of more modern languages. And that ability to build abstractions is crucial. So you can build a building block inside of the language that sets you.

And of course here we have like domain driven development later enables these things and so on. Exactly. I mean an old list badge is really what you want to do is to create your own language in LISP and then solve your problem using the language that you've created. And I think that way of thinking is a good way of thinking in any programming language. You're both solving the problem and creating a language to describe the kinds of problems you're trying to solve in.

And if you can balance those two nicely, that is what leads to very maintainable and flexible code. So the building of abstractions, that's, I think to me, a key element of high-level languages. And AI helps us a little bit in that. because we can build abstractions a bit more easily, a bit more fluidly, but we have this problem and now we're talking about non-deterministic implementations of those abstractions, which is an issue. And we've got to sort of learn a whole new set of balancing tricks to get around that.

My colleague, Unmesh Joshi, has written a couple of things that I've really been really enjoying about his thinking about how, because he's really pushing this, using the LLM to co-build. an abstraction and then using the abstraction to talk more effectively to the LLM. And that I'm finding really, really interesting way of thinking about how he's working with that because he's really pushing that direction. There's a thing I read in, and I can't remember the book off the top of my head, we'll have to dig it out later, about how apparently if you can describe to an LLM a whole load of chess matches and describe it just in plain English.

and the LLM, when you do that, the LLM can't really understand how to play chess. But if you take those same chess matches and describe the LLM to those chess matches in chess notation, then it can. And I thought that was really interesting that obviously you're shrinking down the token size

but you're also using a rigorous, a much more rigorous notation to describe the problem. So maybe that's an angle of how we use LLMs. What we have to come up is a rigorous way of speaking and we can get more traction that way. And of course, that has great parallels in with the ideas of domain-driven design and ubiquitous languages

and also some of the stuff that I was working on a decade or so ago around domain-specific languages and language workbenchers. So there's some fascinating stuff around there that would be interesting to see how that plays out. Yeah, and I guess, is this the first time we're seeing a tool

that is so wide a software engineering that is non-deterministic? Because we did have neural. And that's, for example, in the past, they were not, but they were more, I feel the application of those was a lot more kind of niche and not everywhere. Now, every single developer is, I mean, if you're using code generation, you are using non-deterministic things. Of course, we're integrating them left and right, trying out where it works. Is it fair to say that this is probably the first time we're facing this challenge of deterministic computers, which we know very well.

We know their limits and all those things. And of course, there's some race conditions and some exotic things. But now we have this problem. This problem to solve for. It's a whole new way of thinking. It's got some interesting parallels to the forms of engineering. The other forms of engineering, you think in terms of tolerances. My wife's a structural engineer, right?

She always thinks in terms of what other tolerances, how much extra stuff do I have to do beyond what the math tells me, because I need it for tolerances, because, yeah, I mean, I mostly know what the properties of wood or concrete or steel are, but I've got to, you know, go for the worst case. We need probably some of that kind of thinking ourselves. whatever tolerance is the non-determinism that we have to deal with and realizing that we can't skate too close to the edge

because otherwise we're going to have some bridges collapsing. I suspect we're going to do that, particularly on the security side. We're going to have some noticeable crashes, I fear, because people have skated way too close to the edge in terms of the non-determinism of the tools they're using. Oh, for sure. But before we go into where we could crash, what are some either new workflows or new suffering-engineering approaches

is that you've kind of observed or aware of that, that sound kind of exciting that we can now do with Elms or at least we can try to give them a goal that would have been impossible with, you know, our old deterministic toolkit. Right. One area is, one that has got lots of attention already, is the being able to knock up a prototype in a matter of days

that's just way more that you could have done previously. So this is the vibe coding thing. But it's more than just that, because it's also an ability to try, explorations, people can go, hey, I'm not really quite sure what to do with this, but I can spend a couple of days exploring the idea much, much more rapidly than I could have before. And so for throwaway explorations, for disposable little tools and things of that kind, and including stuff by people who don't think of themselves as software developers, I think there's a whole area.

And, you know, we can, with good reason, be very suspicious of taking that too far, because there's a danger there. But we also realize that as long as you treat that within its right bounds, that's a very valuable area. And I think, well, that's really good. On a completely opposite end of scale, one area that's really interesting is helping to understand existing legacy systems. So my colleagues have put a good bit of work in this year or two ago.

And basically the idea is you take the code itself do the essentially the semantic analysis on it, populate a graph database essentially with that kind of information, and then use that graph database as kind of in a rag-like style, and you can begin to interrogate and say, well, what happens to this piece of data? Which bits of code touch this data is it flows through the program? Incredibly effective.

And in fact, if I remember correctly, we put actually understanding of legacy systems into the Adopt-Ring, because we said, yeah, If you're doing any work with legacy systems, you should be using LLMs in some way to help you understand. So in this ring, in the ThoughtWorks radar, the fewest things are in the Adopt. Adopt says we strongly suggest that you look at this, or at least ThoughtWorks themselves, look at it. There's only four items, and one of them is, yes, to use Gen AI to understand legacy code, which to me tells that you have seen great success, which is, it's refreshing to hear, by the way.

I did not hear this as much, and I guess it helped. at ThoughtWorks, I'm sure you have to work with a lot of. Well, I mean, it came from the fact that some of the folks who had done some really interesting work on Legacy Code stuff, happened to bump into and look at this and say, hey, let's try this out. And they found it to be very effective. And it also has been an ongoing interest for many of us at ThoughtWorks, because we have to do it all the time.

And how do you effectively work with the modernization of legacy systems? because every big company that, you know, is older than a few years has got this problem. Yep. And they have it in spades. And then especially, just simple things, people leave, right? As simple as that. And having Gen AI that can help you make some progress is it's already better than making no progress. Exactly.

So those are two areas that are clearly right away, I would say, those are great success for using LLMs. And then there's the areas that we're still figuring. at. I mean, I'm certainly seeing some interests, more and more interesting stuff as people try to figure out how to work with an LLM on a one-to-one basis to build decent quality software. We're seeing some definite signs of how you've got to work with very thin, rapid slices, small slices. You've got to treat every slice as a PR from a rather dodgy collaborator who's very productive in the lines of code sense of productivity. But, you know, you can't trust a thing that they're doing.

So you've got to review everything very carefully. When you play with the genie like that, the genie is, of course, Kent's term for it. Yep. Or Dusty the sort of the anthropomorphic donkey, which is how Birgita described it. I love her take. Yeah, but using it well,

you can actually definitely get some speed up in your process. It's not the kind of speed up that the advocates are talking about, but it is non-trivial. It's certainly worth learning how to make some use of this. And it's folks like Begita or Kent or Steve Yegg. Those are the folks, I think, who are pushing this. We're still, I think, learning how to do this. Everyone is learning it, absolutely.

And it's still the question. And most of the experience we're gaining is building in a greenfield environment. So that leaves big questions in terms of A, the brownfield environment. Well, we know that LLMs can help us understand the legacy code. Can they help us modify legacy code in a safe way? It's still a question. I mean, I was just chatting with James Lewis because he's in town as well this morning

and he was commenting about he was playing with Cursor and he was just building something like this and he said, oh, I wanted to change the name of a class in a not too big program and he sets it off to do that and comes back an hour and a half later and has used 10% of his monthly allocation of tokens and all he's doing is changing the name of a class.

And we actually, in IDs, we actually have which I still remember when it was cutting edge. This was probably 20 years ago when Visual Studio, it wasn't even Visual Studio, was JetBra who came out with an extension called Resharper, which helped refactor code. And people paid serious money. This was like $200 per year or something to get this plugin.

And now you could right click and say rename class. And it went and it built that graph behind the scene. Somehow it went and changed. You could rename variables. And again, this was a huge deal. In fact, an X code. Apple's developer ID for a while. When SWIP came out, you couldn't do these refactors.

And it was, you know, people were like, so it's interesting how some things are easy. We've solved it. And LMs are not very very efficient at and not very good at it. Yep. Yes. And then, I mean, he did that just to see what it was going to be like, right? Because he knows you can just, I mean, we've had this for a long, technology for a long time. So it's kind of amusing.

I mean, but it's also to the point that when working with an existing system and modifying the existing system, that's still really up in the air. And another area that's really up in the air, both Greenfield and Brownfield, is what happens when you've got a team of people. Because most software has been built by teams and will continue to be built with teams. Because even if, and I don't think it will, AI makes us order of magnitude more productive, we still need a team of 10 people to build what a team of 100 people needed to build. And we'll always want this stuff.

There's no sign of demand dropping for software. So we will always want teams. And then the question is, of course, how do we best operate with AI in the team environment? And we're still trying to figure that one out as well. So there's lots of questions. We've got some answers, some beginnings of answers. And it's just a fascinating time to watch it all. You mentioned vibe coding.

What is your understanding and take on vibe coding? Well, when I use the term vibe coding, I try to go back to the original term, which is basically you don't look at the output code at all. maybe, you know, take a glance at it, out of curiosity, but you really don't care. And maybe you don't know what you're doing because you've got knowledge of programming. It's just spitting out stuff for you. So that's how I define vibe coding. And my take on it is kind of as I've indicated.

I think it's good for explorations. It's good for throwaways, disposable stuff. But you don't want to be using it for anything that's going to have any long-term capability because it's, I mean, again, this is a silly anecdote, but I was working, my colleague, Ganesh, he just wrote something that we published yesterday. And as part of doing this, we create this little pseudograph of capability over time kind of thing,

which is, you know, one of those silly little pseudographs that helps illustrate a point. And he asked an LLM to create this. He described the curves he wanted and came out with him, and put it up there. And he committed it to our repo, and I was looking at it and thinking, yeah, that's a good enough graph. I want to tweak it a little bit.

You know, the labels are a bit far away from the lines of their labelling, so I like to bring them closer. So I open up the SVG of what the LLM has produced. And, oh, oh, oh, oh, oh. I mean, it was astonishingly how complicated and convoluted it was. For something that I had written the previous one myself, and I knew it was, you know, a dozen lines of SVG. And SLEG is not exactly a compact language, right?

Because it's XML. But this thing was gobsmackingly weird. And I mean, that's the thing. When you vibe code stuff, it's going to produce God knows what. And often it really is. And you cannot then tweak it a little bit. You have to basically throw it away and hope that you can generate whatever it is you're trying to tweak. And the other thing, of course, it's a difference.

And this is the heart of the article that Unmesh wrote, of it we published yesterday is when you're using vibe coding in this kind of way, you're actually removing a very important part of something, which is the learning loop. If you're not looking at the output, you're not learning. And the thing is that

so much of what we do is we come up with ideas, we try them out on the computer, with this constant back and forth between what the computer does with what we're thinking, we're constantly going through that learning loop program approach. And an unmatchezious point, which I think is absolutely true,

is you cannot shortcut that process. And what LLMs do, they just kind of skim over all of that and you're not learning. And when you're not learning, that means that when you produce something, you don't know how to tweak it and modify it and evolve it and grow it. All you can do is nuke it from orbit and start again.

The other thing I've done occasionally with vibe coding is that, oh, vibe coding, as a consulting company, so many problems to fix. For sure. But you are right on the learning, the learning side, both on vibe coding and AI. One thing that I'm noticing on myself is it is so easy to give a prompt, you get a bunch of output,

and you know you should be reviewing a lot of this code either yourself or in a code review. But what I'm seeing on myself is I'm at some point I start to get a bit tired than I just let it go. And this is also what I'm hearing when talking with software engineers is the ones who are working in companies which are adopting these tools, which is pretty much every company. There's a lot more code going out there, a lot more code to review. And they're asking, how can I be vigorous at code reviews when there's just more and more of them than before? Have you seen approaches that help people, both less experienced people and also more experienced engineers keep learning with these tools, just approaches that seem promising? Not a huge amount.

I do, I am very much paying attention to, well, on mesh, is. doing with this because his approach very much is that notion of let's try and build a language to talk to the LLM, work with the LLM to produce a language to communicate to the LLM more precisely and carefully what it is that we're looking for. And I do feel that is a promising and very much a more promising line attack. I make sure to create our own specialized language for working with whatever problem that we're working on. And I think that actually brings another when we're talking about things we know LLMs are useful for. Another thing, and this is, again, something Unmesh has highlighted,

is understanding an unfamiliar environment. Again, I was chatting with James. He was working with, he's working on a Mac with C Sharp, which is not a language he's terribly familiar with, using this game engine called Godot. Godo, yeah. Yeah, go ahead. And he doesn't know anything about this, right?

But with the LLM, he can learn a bit about it because he can try things out. And if you take it with that exploring sense, and I mean, I can't remember. I've certainly got to the point where I'm typing in to the L.A. Oh, well, how do I do so-and-so in R? But I've done 20 times, but I still can't remember how to do it. And exploring, and immersion makes a point, again, setting up initial environments. You know, give me a sample starting skeleton project, so just get moving.

And so that kind of exploratory stuff and helping in an unfamiliar. environment and just learning your way around an unfamiliar set of APIs and coding ideas and the like, it can be quite handy for. I wonder if this is not all that new in the sense that I remember, you know, one of the last kind of big productivity boosts in the industry about 10 or 15 years ago was Stack Overflow appearing. So before Stack Overflow, when you Google for questions, you bumped into the site called Experts of Change and there was the question and you had to pay money to see the answer or you had to pay money to get an expert to answer, but usually there was nothing behind it, even if you paid, and most of us, I was a college student, I just didn't pay.

So you just couldn't find the answer and you were all frustrated, but then Stack Overflow came along and suddenly you had code snippets that you could copy. And of course, what a lot of young people or less experienced developers, even like myself did, is you just take the code, put it in there and see if it works. As you got to more experienced engineers or developers, you started to tell the junior engineer and you're like, you need to understand that first. Like, or even if it works, you need to understand why it works. You need to, you should read the code. And I feel we've been, there was a few years where we were going back and forth of people mindlessly copying, pasting, snippets. There were problems with, I think there was a question about email validation and a top-voted

answer was not entirely correct. And it turns out that a good part of software and developers just use that one. I feel we kind of been around this already. Yeah, yeah. It's a, similar kind of thing, but... Maybe at a smaller scale. Yeah, but even more boosted and non-steroids and with the question of, you know, how are things going to populate in the future? Because who's going to be writing Stack Overflow answers anymore? Yeah, so I wonder if what we're getting to is, like, you need to care about the craft. You need to understand what the LLM's output is and is there to help you. And if you're not doing it, I mean, like, you should. But if you're not, you'll eventually be no better than someone just

prompting it mindlessly. Exactly. Yeah. I mean, I mean, I have no problem with taking something from the LLM and stick, putting it in to see if it works. But then once you've done that, understand why it works, as you say. And also look at it and say, is this really structured the way I'd like it to be? Don't be afraid to refactor it.

Don't be afraid to put it in. And then, of course, the testing combo. Anything you put in that works, you need to have a test for. And if you constantly are doing that back and forth with the testing process. Martin Fowler was just talking about the importance of testing when working with LLMs and in general when building quality software. Speaking of the quality software, I need to mention our season sponsor, Linear. I recently sat on one of the linear's internal weekly meetings called Quality Wednesdays, and I was completely blown away. This was a 30-minute meeting that happens weekly.

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to the importance of testing when working with the alarms. I mean, one of the people I particularly focus on in this space is Simon Willison. And something he stresses constantly is the importance of tests. But testing is a huge deal to him and being able to make these things work. And of course, Begita is from ThoughtWorks. We're very much an extreme programming company, so she's steeped in testing as well. So she will say the same thing.

You've got to really focus a lot on making sure that the tests work together. And of course, this is where Yellow Lem struggle, because you tell them to do the tests. And I'm only hearing problems. Or experiencing them myself, like when the LLM tells me, oh, and I ran all the tests, everything's fine. You got NPM test, five failures. Hmm.

Yeah, I see some improvements there, by the way, with clock code also like other agents. But yes, it's the non-deterministic angle. Sometimes they can lie to you, which is weird. I'm still not. They do lie to you all the time. In fact, if they were truly a junior developer, which you so sometimes people like to say they should be characterized, I would be having some words with HR. Yeah, like the other day I just had this really weird experience, which is the simplest thing.

I have a configuration file where I add just new items, a new JSON, you know, blob, and I put the date of when I added it, just in the comment saying added on, you know, October 2nd, added on November 1st. It's always a current date. And I told the LM, can you please add this configuration thing and add the current date? And it added it, and it added, just copied the last date. And I said, that is not today's date. I said, oh, I'm so sorry, you know, let me correct that for you.

And I put yesterday's date. And I feel you need to get this experience to see that it can gaslight you for a simple thing of today's date, which, you know, you could call a function and whatnot, but it's down to which who knows which model I was using, how that model works, whether the company creating it is optimizing for token usage or not, et cetera, et cetera, et cetera. So, like, in the end, even for the simplest things, you are, as a, when you're a professional, working on important stuff, you should not trust it. Yeah, absolutely. Never, yeah, it's got to, you've got to, don't trust, but do verify. Verify, yes. Speaking with developers at ThoughtWorks and the people you're chatting with,

what are areas that they are successfully using LLM's day-to-day, though? Like, we did mention just right now testing. We also mentioned things like prototyping, but you see some other things where it's starting to become a bit of a routine. Like if I'm doing this thing, let me reach for a number. it can probably help me. That, yeah, I mean, I've mentioned many of that, right? The prototyping, the legacy code understanding.

Oh, yes. The fact that you can use it to explore new technology areas, potentially even new domains, as long as you, you trust it significantly less than you would trust Wikipedia 10 years ago. Those are the things that I'm hearing so far. Yeah. One interesting area that Birgitta is,

boring, expect of development. There's this idea of what, well, you know, LMs have their own limitations, but what if we define pretty well what we wanted to do and give it this like really good specification and, you know, it can run with it, it can run long, it had iterations

and so on. What is your take on this? And do you have a bit of a deja vu because we've heard this once, right? We have indeed. Your career started around this thing called waterfall development. So how are you seeing it somewhere but also different this time? Well, The similar to a waterfall is where people try and say, let's create a large amount of spec and not pay much attention to the code.

And here, I mean, whether you talk about, again, this is what you mean by spec, divin and drivenment. Is it so much focusing on that or is it doing small bits of spec, do the tight loop? I mean, to me, the key thing is you want to avoid the waterfall problem of trying to build the whole spec first. it's got to be the smallest amount of spec you can possibly get to make some forward progress cycle with that build it get it tested get it in production if possible and then cycle with these thin slices

what role a spec may play to drive in either case could be argued to be a form of spec driven development but to me what matters is the tight loops the thin slices that kind of thing and I know Biggitta definitely agrees on that point I mean because she's and you have to be the human in the loop very

verifying every time. That's clearly crucial. Where the spectre and development then ties in interesting, again, it comes back to this thinking of building domain languages and domain specific languages and things of that kind. Can we craft some kind of more rigorous spec to talk about? And that's, you know, I mentioned what the one match was doing there, using it to build an abstraction. Because eventually what we're saying is that it gives us the ability to build and express abstractions in a slightly more fluid form than we would be able to do if we were building them purely

within the code base itself. But we still don't want them to deviate too much from the code base. We still want the ubiquitous language notion that it's the same language in our head as is in the code. And we're seeing the same names and they're doing the same kinds of things.

The structure is clearly parallel. But obviously the way we think is a bit more flexible than the way the code can be. And then, you know, can we blur that boundary a bit by using the LLM as a tool in that area. So that's the area that I think is interesting in that direction.

It's interesting as new, because I feel we've never been able to use language as close to representing code ever or like business logic. And this is very new. Yeah, although, again, people, I mean, there are plenty of people who take that kind of DSL-like thinking into their programming. And I know people who would say, yeah,

I would get to the point where I could write certain parts the business logic in a programming language like say Ruby and show it to a domain expert and they could understand it. They wouldn't feel the ability to be able to write it themselves, but they could understand it enough to point out what was wrong or what was right in there. And this is just programming code, but that requires a certain degree of the way you go about projecting the language in order to be able to get that kind of fluidity. And so it's, but that kind of thinking, trying to make an internal DSL of the programming language,

or maybe building your own external DSL based on that. DSL meaning domain specific language. Like if you're working with accountants, you're going to have the terms that they use, the way they use it and so on. Yeah. And what you're trying to do, of course, is create that communication route

where a non-programmer can at least read what's going on and understand it enough to, to be able to find what's wrong about it and to suggest changes, which may not be syntactically correct, but you can easily fix them because you're as a programmer, you can see how to do that.

And that's the kind of goal, and some people have reached that goal in some places. So the interesting thing is whether LLMs will enable us to make more progress in that direction and see that happening more widely. And I guess this must be, I'm just assuming, correct me if I'm wrong, this must be especially important to enterpices,

these very large companies where software developers are not the majority of people, let's say they're 10 or 20% of staff and there is going to be accounting, marketing, special business divisions who all want software written for them and they know what they want. And historically, there's been layers of people translating this. May that be the project manager, the technical product, etc. So you're saying that there could be a pretty interesting opportunity or just an experiment with LMs that maybe we can make this a bit easier for both sides. That is the world I'm most familiar with, right?

It's that world. I mean, my sense is you're very familiar with the big tech company and the startup worlds. But this corporate enterprise world, of course, is a whole different kettle of fish. Because exactly the reason that you said, suddenly the software developers are a small part of the picture. And there's very complex business things going on that we've got to somehow interface in. And of course, also is usually a much worse legacy system problem as well. And there's going to be regulation, there's going to be a history, there's going to be exceptions because of all the knowledge. I think we can all just think of banks of all the things because there's a perfect storm, right?

They have regulation that changes all the time. They have incidents that they want to avoid going to future. They'll have special VIP, I don't know, accounts or whatever that they'll want to do. And of course, they have all these business units that all know their own rules and frameworks. And they've been around since before technology. Some of the banks have been around for, you know, 100 plus years. Yeah. And remember, the banks tend to be more technological advanced than most other corporations in software. You're looking at the good bit when you're talking about banks.

You have work with some of the less advanced folks as well. I mean, you know, retailers, airlines, government agencies, things of that kind. I mean, it was interesting. I was chatting with some folks working in the Federal Reserve in Boston. and, you know, they have to be extremely cautious. They are not allowed to touch LLMs at the moment because, you know, the consequences of error when you're dealing with, you know, a major government banking organization are pretty damn serious. So you've got to be really, really careful about that kind of stuff.

And, you know, their constraints are very different. And it brought to mind, there's an adage of it says that to understand how the software development organization works. You have to look at the core business of the organization and see what they do. Interesting, I was at this Agile Conference for the Federal Reserve in Boston, and they took me a tour of the Federal Reserve, but where they handled the money. And so I saw the places where they bring in the notes that have been brought in from the banks and they kind of clean them and count them and all the rest of it and send out the stuff again. And you look at the degree of care and control that they go through. And it's a way.

you can imagine. I mean, when you're bringing in huge wadges of cash and it has to be sorted and counted and all the rest of it, the controls have to be really, really strenuous. And you look at that and you look at the care with which they do all of this and you say, yep, I can see why in the software development side, that mindset percolates because they are used to the fact that they really have to be careful about every little thing here. A lot of corporations, of course, have that similar notion. you're involved in an airline. You are really concerned about safety.

You're really concerned about getting people to... That affects your whole way of thinking or ought to, and it does. And I guess this is a reason we are clearly seeing... We always see a divide in technology usage because you have the startups, which is a group of people. They just raise some funding or they have no funding. They have nothing to lose. They have zero customers.

They have everything to gain. They need to jump on the latest bandwagon. They want to try out the latest technologies. oftentimes build on top of them or sell tools to use the latest technology, and they're here to break the rules. And midway, when you start to have a few customers in a business, you're starting to be a bit more careful. And of course, you know, 50 or 70 years down the road,

when the founders have gone and now it's a large enterprise, you will just have different first tolerance, right? Exactly. Yeah. But what I find fascinating talking about is that I'm unsure if there has been any new technology that has, has been so rapidly adopted everywhere. You mentioned that, let's say the Federal Reserve or some other government organizations might say,

let's not touch this yet, but they are also evaluating, it sounds like it. So if they're, you know, they're one of the most, I guess, behind in the technology curve for a very good reason, they're already aware of it or using it. It just probably means that it's everywhere now. Oh, it is. I mean, it is. I mean, we see it all over the place. But again, with that big, with more caution in the enterprise world where they're saying, yeah, we also see the danger.

here. And then you're seeing kind of more nimble companies that you work with and the more enterprise focus. What would you say that is the biggest difference between their relationship of AI, their approach? Is it this caution or are there other characteristics that the big, more traditional, less, more risk-averse companies approach it differently? The important thing to remember with any of these big enterprises is they are not monolithic. So it'll be small portions of these companies can be very adventurous and other portions can be extremely not so. And so what you'll see is small, I mean, like, you know, when I started at Cheatham and Lightwem,

right, and I was in this little bit that was being very, very aggressively doing really wacky things, right? I mean, you'll find that in any big organization, you'll find some small bits doing some stuff. And so it's really the variation within an enterprise often is bigger than the variation between enterprises. You've got to give that a mind. So speaking about refactoring, ELMs are very good.

that refactoring and you've written the book back in 1999 called refactoring. This is now the second edition, which 20 years later it's been refreshed. And it's actually a really detailed book going through different coat smells that could show that where the code is techniques of refactoring it. On the first page already, it has, I really like this. It has a list of refactoring's on, I don't know how the publisher printed this because it's so unusual, but it's right here on the table of contents. Why did you decide to write this book back? in 1999. Can you bring us back on what the environment was like and what was the impact of the first edition of this book? Okay, so I first came across refactoring at Chrysler when I was working with

Kemp Beck. Right early on in the project, I remember in my hotel room, the courtyard or whatever in Detroit, him showing me how he would refactor some small talk code. And what, I mean, I was always someone who liked going back to something I'd already written and make it more understandable. I've always cared a lot about something being comprehensible. That's true in my prose writing and in my software writing. And so that I knew, but what he was doing was taking these tiny little steps. And I was just astonished at how small each step was, but how, because they were small, they didn't go wrong and they would compose beautifully and you could do a huge amount with this sequence of little steps. And that really blew me.

blew my mind away. I thought, wow, this is a big, big deal. But Kent was at the time, his energy was to write the first extreme programming book, the white book. He didn't have the energy to write a refactoring book. So I thought, well, I'm going to do it then. And I started by, you know, whenever I was refactoring something, I would write careful notes. And partly it's because I needed it for myself. How do I extract a, extract a method so as I don't screw it up? And so we would write careful notes on each one. And then each of those turned the mechanics in the refactoring book would be that step. And then I'd make an example for each one.

And that was the first edition in a book. And I did it in Java, not in Smalltalk, because Small Talk was dying sadly. And Java was the language of the future, the only programming language would ever need in the future in the late 90s. And so that's what led to the first book. and the impact, well, I mean, and also refactoring, I should also stress, it wasn't invented by Kent. I mean, it was very much developed by Ralph Johnson's crew at University of Illinois, the Bonner-Champaign. They built the first refactoring browser in Smalltalk, which is the first tool that did the automatic refactoring, so we talk about now. That was the original, the refactoring browser built by, I'm blanking on John Brandt and Don Roberts, did that.

and then when the book came out, that got more interest. There was already some interest from the IBM Visual Age folks because they came out of Small Talk, the original versions of Visual Age were in fact built-in Small Talk. And so they were already aware of what was going on to some degree, but it was the JetBrains folks that really caught the imagination because they put it into the early versions of IntelliJ idea and really ran with it.

And then you ran into it with ReSharper, of course. And they really made the automated refactoring become something that people could rely on. But it's still good to know how to do them yourself because often you're in a language where you haven't got those refacturums available to you. So it's nice to be able to pull out that stuff and some of them are obviously in there.

And yeah, so the impact it's had is refactoring became a word. And of course, like all of these words, got horribly misused and people use refactoring to mean any kind of change to a program, which, of course, it isn't because refactoring is very strictly these very small semantics behavior presaving changes that you make.

tiny, tiny steps. I always like to say, each step is so small that it's not worth doing. But you string them together, and you can really do amazing things with them. I think we've all had that story. At least I've had the story where one of my colleagues, or, you know, it could have been me, but oftentimes one of my colleagues would say,

like, oh, and it's stand up saying, like, oh, I'm just going to do a refactoring. And then next day, oh, I'm still doing the refactoring. Next day, oh, I'm still doing the refactoring. And, you know, that, that missed a part of the small changes for sure. What made you do a second edition for the book 20 years later in 2019, which was fairly recent? Well, it was a sense of wanting to refresh some of the things that were in it.

There were some new things that I had. I was also concerned that, I mean, when you've got a book that's written in late 1990s Java, it shows its age a bit. And although the core ideas I felt were sound and people could still use it, I felt you coming, giving it a more, doing it in a more modern. an environment. And then the question was, which, you know, would I stay with Java or did I switch to another language? And in the end, I decided to switch to JavaScript. I thought it would reach a broader audience that way. And also allow a less object-oriented, centered way of describing things. So instead of extract method, it's extract function because, of course, it's the same process for functions. And also some things that you don't, wouldn't necessarily think of doing in an object-oriented

language. But it was mainly just to get that refresh, to redo the examples, to really hopefully give it another 20 years of life, because it's got to keep me going until I croak, you know. Yeah. So you published this book 25 years ago or 26 years ago. In the industry, based on your interactions with developers, how has the perception of refactoring change? Because in the book, you specifically wrote that you see refactoring is a key element in a software development life cycle and you've also talked about how when you refactor the overall cost of changing code over time can be a lot cheaper. Was there a time where there was a lot more uptake on this? Or is there still? Or do you feel it's kind of like a little bit like being, maybe refructuring

went a little bit out of style as some of those really innovative tools at the time like JetBrains and others, they're maybe not as kind of referenced, even though they're everywhere? It's hard to say for me because, I mean, again, most of the interaction I have is with folks at Fortworks, they tend to be more clued up with this kind of stuff than the average developer. Certainly I read plenty of things on the internet that make me just shake my head at how even refactoring is being described, let alone the lack of doing it. And certainly in the kind of structured way, controlled way, but I like to do it because I like doing it quickly and effectively. and it's one of those things where the disciplined approach actually is faster, even though it may seem strange to describe it that way. But, I mean, I have to, at least been part of our language now.

People talk about doing it. It's in these tools, and they do it very effectively, the refactoring's that they do. I mean, it's wonderful to work in an environment where you can actually automatically do so many of these things. And so I feel we've definitely made some progress, maybe not as much as I'd have hoped for, but, you know, that's often the way with these things.

Looking ahead with AI tools, they generate a lot more code a lot faster, so we're just going to have a lot more code. We already have a lot more code. How do you think the value of refactoring, thinking about your intended meaning of those small ongoing changes, is going to be important? And are you already seeing some of this being important? I wouldn't say I'm already seeing it. But I can certainly expect it to be increasingly important. because again, if you're going to produce a lot of code of questionable quality, but it works,

then refactoring is a way to get it into a better state while keeping it working. These tools at the moment can't definitely kind of refactor on their own, although we've combined with other things. Adam Tornhill does some interesting stuff with combining LLMs with other tools to be able to get a much more effective route. And I think that kind of approach, combining could be a good way to do it. But definitely the refactoring mindset and thinking,

how do I make changes by basically boiling them down to really small steps that compose easily? That's really the trick of it, the smallness and the composability. Combine those two, and you can make a lot of progress. That's interesting because right now, if you want to refactor, you need to have your IDE open for sure. And I mean, the fast way is just using the built-in tools or you moving things around. What I found as well is describing it when I have a command line open with like clot code or something similar,

it's tough. I spend more time explaining it than doing that small change. And I do wonder if we will see more integrations in this end as well so that LMs can actually do it or some of them might do it automatically because as you say, it doesn't work out of the box, but I think for any quality software that,

I mean, we all learn the hard way that if you just kind of leave it there and don't go back and don't change it up when your functions get just a simple thing, right? When your function gets too long, when your class gets too long, you break it up. Otherwise, you're not going to understand it later. Yeah, it'll be interesting as well to see if it provides a way for us to control the tool. I mean, one of the things that interests me is where people are using LLMs to describe queries against relational databases that turn into SQL.

you don't know how to get the sequel right, but if you type the thing at the LLM, it will give you back the sequel, and you can then look at it and say, oh, this is right or not right, and tweak it. And it gets you started, right? And so similarly, with refactoring, it may allow you to get started and say,

oh, these are the kinds of changes I'm looking at and be able to make some progress in that. I mean, particularly where you're talking about these automated changes across large code basis. There was an example of this, Was it a year ago or so? One of these big companies talked about this massive change and made to change APIs and clean up the encoder.

And they'd mention it as an LM thing, but it wasn't an LLLM. It was that different tool. And I'm completely blanking on what the names of all of these things were. Oh, I'd have a 60-year-old brain and can't be able to remember anything anymore. It'll come to me at some point. But actually, it was a combination of maybe 10% LLM and 90% of this other tool. but that was it again provided that extra leverage

that allowed them to make the progress I think those kinds of things are really quite interesting using the LLM as a starting point to drive a deterministic tool and then you're able to see what the deterministic tool is doing that's I think where there's some interesting interplay speaking about going on from refactoring to software architecture you were very busy writing books

around the early 2000 you wrote the book patterns of enterprise application architecture in 2002. And this was a collection of more than 40 patterns, things like lazy load, identity map, template view, and many others. And I remember around this time, there was your book about enterprise architecture patterns. There was also the Gang of Four book. There was a lot of talk. When I was interviewing around that time on interviews, they were asking me questions about

how to do a factory pattern and Singleton and all of these things. software architecture was talked about, my sense was in a lot of places or a lot more. Then something happened. Some things, starting from 2010s, I no longer hear most technologists talk about patterns or architecture patterns. How have you observed this period of when the book came out, what was the impact of it and why was it important to talk about it and put it into the industry? and how have you seen this change of where we stopped talking more on patterns and why do you think it happened?

Yeah, I mean, I've always found it I mean, what you're doing with patterns is you're trying to create a vocabulary to talk more effectively about these kinds of situations. I mean, it's just like in the medical world, they come up with this jargon in Greek and Latin to more precisely talk about things that are quite involved and complex.

And with patterns, what we're trying to do is trying to evolve that same kind of language, except we're not doing it in Greek and Latin. I certainly feel that they do help communication flow more effectively. You know, once people are familiar with that terminology, I mean, you don't look at them as some kind of, you know, how many of them can you cram into the system you're building. It's more a sense of how can you use it to describe your alternatives and the options that you have.

And also think about more about when to apply things or not apply them. I mean, patterns are only useful in certain. context. So you've very much got to understand the context of when to use them. And yeah, it's kind of a shame that some of the windows gone out of the sales of that, perhaps because people were overusing them in terms of trying to use them as a sort of like pinning medals on a chest. But it can still be very, I mean, I worked very recent with Unmesh on his book on Distribans and Distributed Systems. And I felt that was a very good way of coming up with, again, in a language to describe how we think about the core elements and better gain and understanding

of how distributed systems work, which is an important aspect of how to deal with life these days because we're all building these kinds of distributed systems. So I still feel that they can be a very good way of expressing that. It's hard for me to get a sense of why they kind of became less fashionable. Maybe they'll become more fashionable again. Who knows? But I'm always looking for ways to try to spread knowledge around and make things more understandable. And I do feel that this idea of trying to identify these, create these nouns that we can talk about things more precisely is a good way of part of doing that.

I wonder if, because I've seen, I've worked at places where we use these things and then places where we just threw them out the window and no one was using it. And a difference was honestly just kind of the age and the attitude of the company because there was a sense at some point that the patterns there were for legacy companies. So startups would just start from a blank sheet of paper, you know, a whiteboard. You know, UML was a perfect example where UML had pretty strict rules on how to do the arrows. And if you do that right, you could even generate code and do all these things. And at startups, software architecture still exists, but you just put it on the whiteboard and you just drew a box or a circle. and you didn't care about the arrows. And it was just, I guess,

we're not going to lock ourselves into existing ways of doing things. And it's a bit of an education as well. Like, you do need to onboard to these things. You all need to have a shared understanding. And maybe it's just a combination of these two things. And I guess it's a generational thing as well. You know, every few years a new generation comes out. And the same way where at some point I was one of the first people in college

where it was super cool to use Facebook. and it was just all scholar students. And then when my parents went on there, it was super uncool to use Facebook. Or my grandparents came on there. I kind of like stopped using it when they started using it. So I wonder if there's like these waves going back and forth. Because inside of these startups,

there is a language like, you know, lingo about how they talk about the architecture. And it starts to form over time. You start to see it, whether it's longer tenured people. you get more and more of the jargon except it's not in a book that anyone can read but you have to go in there

or go to a similar company where they take the jargon with them. Exactly. And people will create these jargons and it's an inevitable part of communication. You need to, you need to, it can't explain everything from first principles requiring five paragraphs every single time. If you're using the term all the time, you just make a word out of it

and then everybody creates their own words and all you're doing when you're coming up with a book like the patterns of distributed systems is you're trying to say, okay, here's a set of words, with a lot of definition and explanation of them, and let's hope we can kind of converge on that so that we can communicate a bit more widely. But it's also quite natural for people to say, you know, within our little environment, we create our own little jargon,

so we don't take a notice of that, and then you get the mismatches that occur as you only really notice that as you cross these different environments. Grady Butch had an interesting take on this, by the way. So I asked him about the same thing because he's been so much into software. He still is into a software architecture and he's progressed the field a lot. And he said that what he thinks happened is that starting in like 2000, because the patterns died out from mainstream industry. I'll say again, it's still in some pockets, but are under 2010s.

One interesting thing that happened around that time is the cloud started to get bigger, AWS, Google Cloud. and a lot of companies started to build similar things. They started to build either initially on-premise backend services where you had most of your business logic, later moved to the cloud. And Grady said that these hyperscalers, the cloud providers, AWS, for example, they build all these services that are really well architected. So you can kind of use one after the other, and it's well done. You don't need to worry too much about your data store.

You just use, let's say, DynamoDB or a managed Postgres service. So suddenly, architecture is not all that important because these blocks take it care of you. You have these building blocks and now you're talking about using this database on top of this system. His observation was maybe architecture was solved with a well-architected building block that you could use and you didn't have to reinvent the wheel. Yeah, but I suspect there's still patterns of using these things. And that's something I haven't delved into because I just haven't had the opportunity to focus on that. Or more precisely, I haven't had enough of my colleagues banging me on the door with draft articles to be able to publish on it. Well, one pattern I do see as every company names their system, some have wacky names, some have logical names.

But when you talk about architecture, you typically talk about like at Uber, we had the bank emoji service, which called, which was being migrated to Gulf Stream, which was, you know, these all sound like doesn't make too much sense. if you're from the outside. Sometimes they have proper names. They try with that, the payment profile service, but then there's a new version, and that's now the payment, that's PPP2. Anyway, but inside every company, like you will talk about these specific names,

and you will talk about how they work, how small they are, how large they are, and that's kind of, I feel that's oftentimes a lingo. Yeah, it is. It becomes, that's again, again, part of the lingo of larger organizations. And again, you take a company that's been around for much longer than Uber and of course

that Lillinger is baked into the organization can take you several years just to figure out what the hell's going on because it just takes you that long to learn all these systems and how they interconnect. Well, one of the fascinating conversation that I had many years ago was someone very high up in American Express and

we were talking about how he was responsible for re-architecting their system to the next generation and he was just getting ideas and how to socialize ideas and get things out and ask how long have been working on this. There's been three years. And I was like, okay, so where, like, where are you like done? It's like, no, no, this is just a planning. Like, we're close to finishing the planning. And to me,

it didn't compute because like, three years for planning. But again, once you, I start to understand the, the scale of the business, how much money, how many legacy systems, they have, half of, half of what he did was taught with business stakeholders to convince them or get buy-in. I guess this eventually happens with like most companies, except when we are at the younger company or digital first or tech first companies, meaning founded in 2010 or later, you still don't see this,

but it might come in 10 years. Oh yeah, it certainly will. It's interesting. I remember chatting. I was chatting with somebody who had joined a bank, an established bank, and they joined from a startup,

and what of their jobs was to modernize the way the bank stuff was And the comment was, now we've been here three years. Now I think I can understand the problem. I've got some idea of what I can do, what can be done. But it just takes you that long to just really understand where you are in this new landscape because it's big and it's been around a long time and it's complicated and it's not logical because it's built by humans, not by computers and it's not a logical system.

And there's all sorts of history in there because all sorts of things happen because so-and-so, met so and so and had it around with so and so and all of these things kind of percolate over time and this vendor came in here and was popular over here and then the person who liked this vendor got moved to a different part of the organisation somebody else came in who wanted a different vendor and all of this stuff builds up over time to a complicated mess and any big company is going to have that kind of complicated mess because it's very hard to not get that that situation And yeah, I mean, Uber's lucky, but it's only, you know, relatively young company. But it will be, you know, assuming it survives in 50 years time, it'll be like American Expresses, right?

Yep. You can already see the changes, the layers of processes and so on, which is kind of necessary. So as you grow. Speaking of change and iteration, on an agile, so you were part of the 17 people who created, the Agile Manifesto. And I previously asked Kent Beck about this, who was another person involved. Can you tell me from your perspective, what was the story there on how you all came together, how this pretty chaotic, I think, day played out?

And what was the reception, as you recall back then? This was 2001. Right. So, I mean, the origin of it, I always feel, was actually a meeting we had that Kent ran about a year before we did the Agile Manifesto. It was a gathering of extreme programming folks who were working with extreme programming. And we had it at this place near where Kent was living at the time in middle of nowhere, Oregon. And he also invited some people who weren't directly part of the extreme programming group, folks like Jim Highsmith along as well.

And part of the discussion we had was should extreme programming be the relatively narrow thing that Kent was describing in the white book? Or should it be something more broad that. had many of the similar kind of principles in mind. And Kent decided he wanted something more concrete and narrow. And then the question is, well, what do we do with this broader thing and how it overlaps with things like what the scrum people were doing and all that kind of stuff? That's what led to the idea of getting together people from these different groups.

And we had the argument about whether we were going to hold it in Utah, because Alistair wanted it in Utah. I mean, Dave Thomas wanted to have it in Anguila in the Caribbean. And for whatever reason, we ended up in Utah and the skiing. and so we gathered together the people that we did and of course

it was a case of who actually came along because obviously lots of people were invited who didn't come and I wasn't terribly involved with that although Bob Martin does insist

that I got involved in, he mentioned some lunch in Chicago which is very likely because I was going to Chicago all the time at the Fulworks at the time so I probably did

but I don't remember and of the meeting itself I actually don't remember very much of it which is a shame I you know curse myself for not writing a detailed journal of those few days. I'd love to know, you know, how did we come up with that this over that structure for

the values, for instance, which I think was really wonderful, but I have no idea how that got put together. So unfortunately, I get very vague about the actual doing of it. I do remember, have a fairly clear memory, although we should be wary about that. I'll come to that perhaps later, about why, of Bob Martin being the one who was really insistent on I want to make a manifesto and me thinking, oh well, yeah, we can do that. The manifesto itself will be a complete, useless and ignored, of course, but the exercise of writing it will be interesting it.

And that was my reaction to it. And that's how I felt about the manifesto. Nobody will take any notice of this. Oh, wow. But, hey, we're having fun writing it and we're understanding each other better, and that will be the value. We'll understand each other better. And of course, the fact that it made a bit of an impact was.

kind of a shock. And then, of course, it gets misused by most of the time because there's this lovely quote from Alastair Coburn, but your brilliant idea will either be ignored or misinterpreted, and you don't get to choose which of the two it is. Well, it also helps the manifest to us four different lines, and so people just pick and choose which one they want to. And don't forget, 12 principles. Oh, and the 12 principles, which, yes. And the fact that it says, and says at the beginning we are uncovering, that it is a continuous process, and what the manual, Festo is just this is what we've got, how we've got so far. So it's a snapshot of a point in time of where we were in 2001.

Yeah, all sorts of subtleties to the manifesto. But I think it had an impact in the sense that my feelings were, was a certain way that we wanted to write software at Fort Works for our clients in 2000. And it was a real struggle because they didn't want to work the way we wanted to. We said we want to put all this effort into writing tests. We want to have an automated build process. And we want to do these kinds of things. We want to be able to progress in small increments.

All of these kinds of things which were anathema. No, we've got to have a big plan over five years and we'll spend two years doing a design and we'll produce a design. And then it'll get implemented over the next year or so. And then we'll start testing. Right. I mean, that was the mentality of how things ought to be done.

Yeah, that was just the commonly understood wisdom, right? Yeah, and our notion of, no, we'd like to do that entire process for a subset of requirements in one month, please. Only a month? And of course, we really wanted to do it in a week, but, you know, baby steps. And so to me, the great thing about Agile is that we can actually go into organisations and operate it much closer to the way that we'd like to be able to do. Our clients will let us work the way we want to to much greater extent than we were able to do back in 2000. And so that is the success. just wanted the world to be safe for those people that wanted to work that way to be able to work

that way. Yeah, there's all sorts of other bad things that have happened as a result of all of this, but on the whole, I think we are a bit better off. And do you see like the way you look, especially when you look at the enterprise clients that you have a lot more visibility to, you see the definite change from like 25 years ago to like the concept of agile are way more accepted, like working with the customer, having a lot more incremental delivery, forgetting about these, like, very long pieces of work. Like, it's just common everywhere, right? Can we say that or at least though?

I would say we've made significant progress, but compared to how we'd like it to be and where our vision is, it is still a pale shadow of what we wanted. I mean, I suspect most of the 17 that are still with us would agree with that. we still feel we can go much, much better than we've been, but we have actually made material progress. And the thing is that we're always in that situation where we're kind of nudging our way forwards at a much slower rate than we'd like to be.

Now, of course, AI is coming, and it is now everywhere and it will be everywhere. And one thing is with AI, so the core idea behind Agile was that you make incremental improvements and the shorter the better. Now, with, and you could then build software that incrementally start to improve.

But today with AI, especially with AI, there's going to be more software everywhere, there already is, and there's a sense that customers don't necessarily want to wait for incremental improvements, they want to see quality upfront. Do you think that Agile will work just as well with AI, with even shorter increments?

Or do you think we might start to think about some different way to work with, with AI putting on the quality lens up front as well. And getting back to a little bit of the spec-driven development, like getting a version of the software that is just great to start with. I don't know how the AI is going to play out because we're still in the early days. I still feel that building things in terms of small slices

with the humans reviewing it is still the way to bet. What I hopefully will allow us to do is to be able to do those slices faster. and maybe do a bit more in each slice, but I'd rather get smaller, more frequent slices than more stuff in each slice. Improving the frequency is usually what I think we need to do and just cycled out those steps more rapidly. That's where I felt we've had our biggest gains

is through that more rapid cycle rather than trying to do more stuff in the same cycle, as it were. I still get a sense of that when talking to people that's still saying, you know, can you look at all of the things that you do in software development and increase the frequency, do half as much, but in half the time, and speed up that cycle. Look for ways to speed that through. And also, you know, just look at where, what you're doing. Look for the cues in your flow and figure out how to cut those cues down.

If you were able to get some ideas from idea to running code in two weeks, how do you you get it down to a week. Just try to constantly improve that cycle time. And I still feel that that's our best form of leverage at the moment is improving cycle time. Yeah. And I'm talking with some of the leading AI labs on how they use it. Because of course, they're going to be on the bleeding edge. They will use it. They are also in their own interest to use their own tools. At Antrophic, the cloud code team, one of the creators of Cloud Code, Boris, he shared how he did 20 prototypes of a feature of how the progress bar, when you do a task, how it lists. out different steps and how it shows you where it's up.

And he built 20 different prototypes that he all tried out and got feedback on and decided which one to go in two days. And he showed me. So actually he has, he had videos. He just recorded these as he went the exact prompt that he used the output. And these were interactive prototypes. So they were not just, you know, like on the paper, but they were inside.

And to me, this was like, wow, like if you would have told me I built 20 prototypes and you asked me how long it took it, I would have said two weeks. maybe a week if there were small like paper prototypes, but you can still speed it up and it is still manageable. Some of them he threw it away. Some of them he shared with a small group, bigger group.

So I feel you're right on how we have not reached a limit of of how quickly can we look at things. Yeah, it comes back to feedback loops. I mean, so much of it is trying, how do we introduce feedback loops into the process? I mean, how do we tighten those feedback loops so we get the feedback faster?

so that we're able to learn. Because in the end, again, it comes back to, you know, we have to be learning about what it is we're trying to do. Speaking about learning and keeping up to date, how do you learn about AI, how do you keep up to date with what's happening, what approaches work for you? And what are approaches you see your colleagues follow who are also staying up with, you know, what's going on? Well, the main way I learn these days is by working with people who are writing articles that are going on onto my site because my primary effort these days is getting good articles onto my site and my view is that I'm not the best person to write this stuff because I'm not doing the day-to-day production work. I haven't been doing for a long time. The only production

code I write is ironically the code that runs the website. I still write code. I still generate stack traces but it's only within this very very esoteric little area. So as a result, it's better for me to work with people who actually are doing this kind of work and help them get their ideas and what their lessons and express them to as many people as possible. So I'm learning through the process of working with people to write their ideas down,

which is a very interesting way of learning because, of course, you're very deeply involved in the editing process for a lot of that material. And that's my primary form. I do do some experimentation when I get the chance, not as much as I'd like, but I do see that as a second priority to working with people.

So, you know, it's necessity only in the off time that I get to do that. And of course, reading from where I feel are some of the better sources. I mean, fortunately, one of those better sources is Begita, who has been writing with me, so that's good. He's excellent.

Yeah. Begitta stuff is superb. Simon Willison, I keep an eye on what he's doing all the time. I wish I had his energy work rate for getting stuff out. Actually, I wish I had your energy you, the bad stuff you get out these days.

And so I look for sources like that. I'm always interesting what folks like Kent are up to because, let's face it, so much of my career has been leaching off Kent's ideas. And there's no reason to stop doing that if it's still working, right? And so those are the kinds of sources. I mean, then sometimes some books that come out that come through and work through those. So a lot of it is in that kind of direction. I might even watch a video occasionally, although I really hate watching videos.

So it sounds like find the sources of the people you trust, the sources you trust. Again, your blog I can very much recommend it because you have several people writing on it. So you actually have a pretty good frequency of in-depth articles about interesting. Like I rarely see topics that have been discussed in depth. And so I enjoy checking out because of it. One of the questions that I've been pondering on is when asked of how do you identify what a good source is, of information. And this is more general. This is due to through our profession, but of course due to the world generally as we seem to be in an epistemological crisis of trying to understand

what's going on in the world. And at some point I'm going to sit down and write this down and I'll get a more coherent answer from it. But part of what I'm always looking for is a lack of certainty is, I think, a good thing. When people tell me, oh, I know the answer to this, I'm usually a good bit more suspicious. And I'm much more conscious of when people say, this is what I understand at the moment, but it's fairly unclear. I remember one of my favorite early books when I was writing on the software architecture. I remember desperately looking for something in the Microsoft world, as opposed to something in the Java world. There was a lot being written in Java world. This is back around the late 90s. Lots of being stuff was being written in Java land,

not much in Microsoft land. And when I discovered, this Swedish guy, Jimmy Nilsson. And his book was full of stuff that says, well, this is how I'm feeling about, this is the way to approach this stuff. He was very tentative all the time, very much clear of this was how he was currently

feeling, but he understood that things might change. I've since got to know Jimmy really well, and he's a fantastic guy. But what impressed me so much and what influenced me so much is I felt very much the degree to which, oh, this is somebody I can trust because they're not trying to give me this full sense of certainty and confidence.

And I think that's important. Also, someone who's keen to explore nuances of saying, well, this works in these circumstances, if somebody tells me, oh, you should always use microservices, or somebody says you should never use microservices, I mean, both of our arguments can be completely discounted. It's when you say, these are the factors that you should be considering about whether to go in this direction or that direction. Whenever someone is stepping back and saying,

oh, it's a trade-off, there's various things involved, here's the factors you should go. And it's not going to be a simple answer. You've got to dig into the nuances. Then again, that increases my confidence because again, I'm feeling this is someone who's thinking these things through and not just coming on a sort of simple railrod and going down it. And I guess with these sources, you can also trust that everything we do in software engineering,

it's going to be trade-off, right? the most common answer of like, how long will it take is it depends. It depends on are we doing a prototype. It depends on, on do I know technology, et cetera. So if you're reading sources or if you're accessing sources where they tell you, okay, in my situation, you actually learn about their situation and you can figure out like, okay, in this specific case for them, this worked or it didn't work. And later, you can probably apply it a bit better because, again, it's very different if you're going to be working as a software, and you're inside a highly regulated retailer,

that's 70 years old versus you've just started a brand new startup where go and knock yourself out, zero customers. Contacts make a huge difference. Yeah. And then that's I mean, and again, and you see it, I mean, we see it with, frankly, we see it with clients. A lot of clients say,

give us the answer, give us the cookbook straightforward answer that I just need to apply. Yeah, if you're looking for that kind of cookbook answer, you're going to get in trouble because anybody who will tell you there's a cookbook answer, they either don't understand it or they're deliberately covering it up for you because there's always tons of nuance involved. We keep going back to this, like now more than 50-year-old,

although the no silver bullets, right? One question I got from online, I asked what people would like to ask from you is what would your advice be today for junior software engineers? We're starting out, there's all this AI stuff going on. We know with learning, I think you also mentioned, or it might have been Umash who mentioned with junior engineers. It could be a bit iffy of if you're relying too much on AI, will that hinder your learning because learning is important.

If one of these engineers ask you like, hey, I'm a junior engineer, I'd like to eventually become a more experienced engineer. What tactics would you advise me, especially with AI tools? Should I rely on them? Should I not? Is there something that might work better than other things? Well, I mean, certainly we have to be using AI. tools and exploring their use. The hard part with you're more junior is you don't have this sense

of is to what extent is the output I'm getting good. And in many ways, the answer is what it's always been. Find some good senior engineers who will mentor you because that's the best way that you're going to learn this stuff. And a good experienced mentor is worth their weight in gold. And in fact, in many ways, it's worth prioritizing that above many other things that you, when it comes to your career is getting that meant. I mean, again, me finding Jim O'Dell early on in my career was enormously valuable. The best thing that could have possibly happened to me is just blind luck. But seek out somebody like that who can be your mentor. I mean, although we're peers in some ways, I often think of Kent Beck as a mentor.

because we may be at the same age or whatever, but he's thinking he's always leaping forwards. And so watching what he's doing has been very valid. So again, find somebody like that. The AI can be handy, but always remember it's gullible and it's likely to lie to you. So be probing on asking it, okay, why are you giving me this advice?

What are your sources? What's leading you to say this? I mean, I remember this is generally a good thing, is whenever people are giving you something, is to say, what is leading you to say that? What is the background? What is the context you're coming from? What are the things that are leading you to this point of view? And by probing that, you can get a better understanding of where they're coming from.

And I think you have to do the same thing with the AI. Because in the end, the AI is, it's just regurgitating something it saw on the internet. So the question is, did it see good stuff on the internet or did it see most of the crap that's on the internet? But if you can find your way to the good stuff, then that can be much more useful. And looking at all this change, that's happening right now with AILMs, how do you feel about the tech industry in general? I mean, in the broad sense, I'm positive because I still feel there's so many huge things

that can be done with technology and software. and we are on, we're still in a situation where demand is way more than we could imagine. But that's a long-term view. I mean, at the moment we're in this very, I'm going to say very straight, life has always been a strange phase. I mean, strange in different ways. The current strangeness is we're basically in a huge, certainly in the developed world, depression. I mean, we've seen a huge amount of jab layoffs. I mean, I've heard numbers banded around of quarter million, half a million jobs lost.

I mean, it's that kind of magnitude. I mean, we're seeing it. I mean, at Fort Works. We used to be growing at 20% a year all the time until about 2021. I mean, we've hit a wall. And we see our clients are just not spending the money on this stuff. I mean, AI is doing its own separate thing, but it's almost like a separate thing going on. It's clearly bubbly, but the thing with bubbles is you never know how big they're going to grow.

You don't know how long it's going to take before they pop. And you don't know what's going to be after the pop. I mean, all this stuff is unpredictable. I do think there's value in AI in a way that's said it wasn't with blockchain and crypto. There's definitely stuff in AI, but exactly how it's going to pan out. Who knows? I mean, I went through this cycle we did dot com stuff in the 90s and 2000. So it's a repeat of that only, probably an order of magnitude more stuff.

scale. So all of that's going on, but really what's happening, the most important thing that's hit us is not AI. It's the end of zero interest rates. That's the big thing that really hit us. And that's what the job losses started before AI because of that kicking in. And we don't know how that's going to change because this is a much more macroeconomic thing. We have a lunion driving the bus in the United States. We have all sorts of other pressures going on internationally. great uncertainty at the moment, and that's affecting us because it means that businesses aren't investing. And while businesses aren't investing, it's hard to make much progress in the software world. And so we have this weird mix of no investment, pretty much depression in the software industry,

with an AI bubble going on, and they're both happening at the same time. And one of those masks in the other end. It depends on where you are. I was in Silicon Valley, and if you're an AI company, it's all inside, it looks all great. If you're outside, again, you can benefit from it, but it's a lot more careful. And if you're outside of this bubble, let's say you're at a startup or a company that is not an AI, it's just tough. So you have these worlds happening. I mean, this is still, I think, an industry with plenty of potential in the future.

I think it's a good one to get into. It's not a, you know, the timing is not as great as it would be getting into this industry in, say, 2005. But, you know, I still feel there's a good profession here. I don't think AI is going to wipe out software development. I think it'll change it in a really manifest way, like the change from Assembly to high-level languages did, but the core skills are still there. And the core skills are being a good software developer, in my view,

are still, it's not so much about writing code. That's part of the skill. A lot of the skill is understanding what to write, which is communication, and particularly communication with a use. users of software and crossing that divide, which has always been the most critical communication path. And you've also mentioned the expert general is becoming a lot more important, which all of that,

when I looked into the details, we'll link it in the show notes, the article that I think it was again... Unmesh is on fire. He's on fire. He's on fire. But all the traits seem to do nothing to do with AI. It's about curiosity. It's about going deep.

It's about going broad. It sounds like I'm hearing more and more people who are thinking longer of what it means to be a standout software engineer. The basics don't seem to change. Right. Yeah. And I do think that. And it is always been communication and being able to collaborate effectively with people has always been, to my mind, the outstanding quality of what really makes the very best developers come through. Certainly in the enterprise commercial world, which is the one I'm most familiar with.

because all the software we're writing for is for people who are doing something very different to what we do. I remember when I was working in health service. I mean, I always said, here I am doing this conceptual modeling of healthcare. I understand a huge amount about the process of healthcare. You are not going to want me to treat whatever your medical problems are because I'm never going to have that skill because I'm not a doctor.

And so therefore the doctors have to be involved in the process. So as closing, I just wanted to do some rapid questions where I'll fire in and then you come what comes to mind. What is your favorite programming language and why? I would say at the moment my favorite programming language is Ruby because it's become, I'm so familiar with it. I've been using it for so long. But the one that is my love is Smalltalk, without it out.

Small talk, there was nothing as much fun as programming in Smalltalk when I was able to do it in the 90s. There was such a fantastic environment. You and Ken Beck and Ken Beck is writing his small talk, a server. It's his baby. I think he's making progress. And I mean, there is still stuff going on. There is the Farrow project in Small Talk.

And I keep thinking, you know, if I could just take off some weeks and stop everything else I was doing, maybe investigate to see what's going on in the Small Talk world again. Because it was, I mean, and it has still so much power in that language. What are one or two books you would recommend? And why? So a book I do particularly like to recommend is Thinking Fast and Slow by Daniel Kainman. I like it because he does a really good job of trying to give you an intuition about numbers and spotting some of the many mistakes and fallacies we make when we're thinking in terms of probability and statistics.

And this is important in software development, and because I mean a lot of what we do is greatly enhanced by the fact if we can understand the statistical effects of what we see, but also in life in general because I think our world would be a half, a hell of a lot better if way more people understood a bit more about probability and statistics than they do. I mean, I like most kids probably when they did maths at school, it was heavily calculus-based. I really do feel that it would have been a lot better if, you know, it was much more

statistics-based because that, the knowledge of being able to use that well. I mean, one of the things that has helped me more with probability is and probabilistic reasoning has been the fact that I'm heavily into tabletop gaming, where you have, to constantly think in terms of probabilistics. And I just honestly feel that knowing that is important. And this book is, I think, a great way to get into that. And so it's one of the best reads I've had in the last few years. Another book that I'd mention that it's completely separate

and is challenging in a completely different way that I've been totally obsessed with is a book called The Power Broker. So this is a book about a guy called Robert Moses, who most people have never heard of, but was the most powerful official in New York City for about 40 years from about 1924 to 1960. He was never elected to any office. He controlled more money than the mayor or the governor of New York during that time. And this book is about how he rose to power, how power works in a democratic society, often not in plain sight.

And it's a fascinating book for that. It's also a fascinating book because it is so well written. There have been moments when I would just, I would have been reading a several page passage of something and I would just have to stop to just appreciate how brilliant what I just read was. And that's valuable because to be a better writer and I think we all gain by being a better writer,

it's really important to read really good writing. and his writing is magnificent. The downside is it's 1,200 pages. It's a really long book, but I was enjoying it so much that I didn't mind. And then once you go on from that, you move on to his second biography, because he's only written two biographies,

and that's his currently five-volume biography of Lyndon Baines-Johnson, LBJ, which is equally brilliant, and I've been reading it, but it's a lot more to ask because it's four volumes so far and he still hasn't finished the fifth. But again, there are a moment.

when I was just gobsmacked by how brilliant the writing was, and gobsmacked by the way, again, power works in a democratic society. And I think to understand how our world works, these kinds of books are really, really valuable. And finally, can you give us a board game recommendation? You are very heavily into board games. Your website has a list of them as well. Yeah, it's a tricky one because it's kind of like saying, I'm really interested in getting into watching movies, which would be the movie you would recommend, right? Because I get it. So many different tastes and things. If I'm going to pick something that's, I think, not too complicated for someone to get into that I think is still got quite a lot of richness,

at the moment, I think the game I'd pick out would be something called Concordia. It's fairly abstract in its nature, but it's easy to get into, and it's got quite a good bit of decision making in the process. Well, Martin, thank you so much. It was great that we could make it happen in person as well? Yes, I think that worked out really well. It just happened to be in Amsterdam for something else. And I know somebody in Amsterdam, so I thought I'd get in touch. And we finally get the chance to meet face to face.

It was amazing. Thank you. Thank you. Thanks very much to Martin for this interesting conversation. One of the things that really stuck with me is how the single biggest change with AI is about how we're going from deterministic systems to non-deterministic ones. This means that our existing software engineering approaches that were based on assuming a fully deterministic system, like testing, refactoring, and so on, this probably won't work that well, and we might need new ones,

unless we can make elements more deterministic, that is. I also liked how Martin mentioned to us that the problem with vibe coding is that when you stop paying attention to the code generated, you stop learning, and then you stop understanding. And you mind it up with software that you have no understanding of, so be mindful in the cases when you are happy with this trade-off. For more reading on AI engineering best practices, and an overview of how the software engineering field changed in the past 50 years,

check out related deep dives into Pragmatic Engineer, which are linked in the show notes below. If you've enjoyed this podcast, please do subscribe on your favorite podcast platform and on YouTube. This helps more people discover the podcast, and a special thank you if you leave a rating as well. Thanks, and see you in the next one.