How AI will change software engineering – with Martin Fowler

22:24Martin Fowler: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 Lisp adage 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.

22:58So 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.

23:43And 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. But Adams had talked 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.

24:30So maybe that's an angle of how we use LLMs. What we have to come up with 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 workbenches. So there's some fascinating stuff around there that will be interesting to see how that plays out.

24:55Martin Fowler:Yeah, and I guess, is this the first time we're seeing a tool that is so wide as software engineering that is non-deterministic? Because we did have neural nets, 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.

25:27Martin Fowler:We know their limits and all those things. And, of course, there are some race conditions and some exotic things. But now we have 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 are the 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 go for the worst case.

26:01We need probably some of that kind of thinking ourselves. What are the tolerances of 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 got skated way too close to the edge in terms of the non-determinism of the tools they're using.

26:25Martin Fowler:Oh, for sure. But before we go into where we could crash, what are some either new workflows or new software engineering approaches that you've kind of observed or aware of that sound kind of exciting that we can now do with LMS, or at least we can try to give them a go that would have been impossible with 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 than 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.

27:03People 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 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 that's really good.

27:41On 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 a year or two ago. And basically the idea is you take the code itself, do 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 as it flows through the program? Incredibly effective.

28:22And 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 it.

28:34Martin Fowler: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 refreshing to hear, by the way. I did not hear this as much, and I guess it helps 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.

29:10Martin Fowler:And they found it to be very effective. And it also has been an ongoing interest for many of us at Fortworks because we have to do it all the time. How do you effectively work with the modernization of legacy systems? Because every big company that is older than a few years has got this problem. And they have it in spades. And then especially just simple things, people leave, right? As simple as that. And having a gen AI that can help you make some progress, it's already better than making no progress. Exactly. So those are two areas that are clearly, right away, I would say, there's great success for using LLMs.

29:51And then there's the areas that we're still figuring out. I mean, I'm certainly seeing some 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 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, was Kent's term for it, or Dusty, the anthropomorphic donkey, which is how Birgitta described it.

30:36I 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 Birgitta 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.

31:14While we know that LLMs can help us understand 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 with james lewis because he's in town as well this morning and he was commenting about he was playing with cursor and he's been it was just building something like this and he said oh i i wanted to change the name of a class um 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 you know 10 of his monthly allocation of tokens and all he's doing is changing the name of a class

31:48Martin Fowler:And we actually, in IDEs, we actually have functionality, which I still remember when it was cutting edge. This was probably 20 years ago when Visual Studio, it wasn't even Visual Studio, it was JetBrains 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 scenes. Somehow it went and changed. You could rename variables. And again, this was a huge deal. In fact, in Xcode, Apple's developer ID, for a while, when Swift came out, you couldn't do these refactors.

32:27Martin Fowler:And it was, you know, people were like, so it's interesting how some things are easy. We've solved it. And LLMs are not very efficient at it. 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 But when working with an existing system and modifying an existing system, that's still really up in the air. And then another area that's really up in the air, both Greenfield and Brownfield, is what happens when you've got a team of people.

33:00Because 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 got some answers, some beginnings of answers, and it's just a fascinating time to watch it all.

33:37You mentioned VibeCoding. What is your understanding and take on VibeCoding? Well, when I use the term VibeCoding, I try to go back to the original term, which is basically you don't look at the output code at all. Maybe 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 no knowledge of programming. It's just spitting out stuff for you. So that's how I define VibeCoding. 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 on Mesh, he just wrote something that we published yesterday.

34:28And 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 pseudo graphs that helps illustrate a point and he asked the uh at llm to create this he described the curves he wanted and produced came up with you and put it up there and i and he committed it to our repo and i was looking at it and thinking yeah that's a good good enough graph i want to tweak it a little bit i want to you know the labels are a bit far away from the lines they're labeling so i'd like to bring them closer so i open up the svg of what the llm has produced and 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 svg 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 gonna produce god knows what and often it really is.

35:29And 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, that we published yesterday, is when you're using VibeCoding 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 approach.

36:11And Unmesh's 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, oh, Vibe Coding, as a consulting company, so many problems to fix.

36:38Martin Fowler:For sure. But you are right on 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 and 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.

37:17Martin Fowler: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 am very much paying attention to what Unmesh 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.

37:58And I do feel that is a promising and very much a more promising line attack. and 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, 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. Godot, yeah. Yeah, Godot.

38:33And 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 LLM, oh, well, how do I do so-and-so in R that I've done 20 times, but I still can't remember how to do it? And exploring, and O'Mersha makes a point, again, setting up initial environments. Give me a sample starting skeleton project and 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.

39:19Martin Fowler:I wonder if this is not all that new in the sense that I remember 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 Googled for questions, you bumped into this site called Experts Exchange 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 them. 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.

39:51Martin Fowler: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 like 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 engineers 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 turns out that a good part of software and developers just used that one.

40:35Martin Fowler:I feel we've kind of been around this already. Yeah, it's a similar kind of thing. Maybe at a smaller scale. But even more boosted in on steroids and with the question of 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.

41:10I mean, I have no problem with taking something from the LLM and 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

41:36Martin Fowler: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 quality software, I need to mention our season sponsor, Linear. I recently sat on one of Linear's internal weekly meetings called Quality Wednesdays, and I was completely blown away. This was a 30-minute meeting that happens weekly. In this session, the team went through 17 different quality improvements in half an hour. 17! It's a fast and super efficient meeting. Boom, boom, boom. Every developer shows a quality improvement or performance fix that they made that week.

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43:18Martin Fowler:And now, let's get back to the importance of testing when working with LLMs. 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, you know, Birgitta 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 LLMs struggle because you tell them to do the tests and I'm only hearing problems or experiencing them myself.

44:00Like when the LLM tells me, oh, and I ran all the tests, everything's fine. You got NPM test, five failures. Hmm.

44:05Martin Fowler: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 saw 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 blob. And I put the date of when I added it just in the comments saying added on October 2nd, added on November 1st.

44:43Martin Fowler: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. Let me correct that for you. And it put yesterday's date. and and i i feel you need to get this experience to see that it can gaslight you for a simple thing of today's date which uh you know you know you could call a function whatnot but it's 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 etc etc etc so like in the end even for the simplest things you are, when you're a professional working on important stuff, you should not trust it.

45:30Yeah, absolutely. Never, yeah. You've got to, don't trust, but do verify.

45:36Martin Fowler:Verify, yes. Speaking with developers at ThoughtWorks and the people you're chatting with, what are areas that they are successfully using LLMs day-to-day though? Like we did mention just right now testing. We also mentioned things like prototyping, but do 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 another LLM. 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 know, you trust it significantly less than you would trust Wikipedia 10 years ago.

46:22Those are the things that I'm hearing so far. Yeah.

46:25Martin Fowler:One interesting area that Birgitta is exploring is speculative development. There's this idea of what, well, you know, LLMs have their own limitations, but what if we define pretty well what we want it to do and give it this like really good specification and, you know, it can run with it, it can run long, it has 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 waterfall is where people try and say, let's create a large amount of spec and not pay much attention to the code.

47:07And here, I mean, whether you're talking about, again, this is what you mean by spec different development. 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.

47:46But to me, what matters is the tight loops, the thin slices, that kind of thing. And I know Biggita definitely agrees on that point. I mean, and you have to be the human in the loop verifying every time. That's clearly crucial. Where the spec different 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 I mentioned what the wooden measure 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.

48:30But 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.

49:00Martin Fowler:It's interesting, it's 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 of 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.

49:37And 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. But that kind of thinking, trying to make an internal DSL of a programming language, or maybe building your own external DSL.

49:58Martin Fowler:And 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 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 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.

50:41Martin Fowler:And I guess this must be, I'm just assuming, correct me if I'm wrong, this must be especially important in enterprises, 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's 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, et cetera. So you're saying that there could be a pretty interesting opportunity or just an experiment with LLMs that maybe we can make this a bit easier for both sides.

51:17That 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, there's usually a much worse legacy system problem as well.

51:47Martin Fowler: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 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.

52:16Martin Fowler:Yeah. And remember, the banks tend to be more technologically advanced than most other corporations in software. you're looking at the good bit when you're talking about banks you you have worked with some with some of the less advanced folks as well i mean yeah 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 uh you know there 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.

53:01So 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 that 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. Interestingly, 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 handle 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.

53:38And you look at the degree of care and control that they go through, and as 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.

54:12You are really concerned about safety. You're really concerned about getting people to adapt. That affects your whole way of thinking or ought to. And it does.

54:21Martin Fowler: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 and a business, you're starting to be a bit more careful.

54:52Martin Fowler:And of course, 50 or 70 years down the road, when the founders have gone and now it's a large enterprise, you will just have different retailers, right? Yeah, exactly. But what I find fascinating talking about this is that I'm unsure if there has been any new technology that has been so rapidly adopted everywhere. You mentioned that, let's say, the Federal Reserve or some other government organizations might say, well, let's not touch this yet, but they are also evaluating it, sounds like it. So if they're one of the most, I guess, behind in the technology curve for very good reason, they're already aware of it or using it.

55:28Martin Fowler: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 more caution in the enterprise world where they're saying, yeah, we also see the dangers 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.

56:03So 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 Cheetahman Lightroom, 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 any variation between enterprises. You've got to keep that in mind.

56:36Martin Fowler:So speaking about refactoring, LMs are very good at 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 code 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 refactorings 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?

57:10Martin Fowler:Can you bring us back on what the environment was like and what was the impact of the first edition of this book? I first came across Refactoring at Chrysler when I was working with Ken Beck. Right early on in the project, I remember uh 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 my, 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.

57:51And 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 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 the 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 because I needed it for myself how do I extract a extract a method so as I don't screw it up and so I would write careful notes on each one and then each of those turn 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 now and I did it in Java not in small talk because small talk was dying sadly and Java was the language of the future, the only programming language we'd ever need in the future in the late 90s.

59:00And 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 the University of Illinois, the Bonne-Champagne. 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 Smalltalk.

59:44The original versions of Visual Age were in fact built in Smalltalk. 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 refactorings 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 refactorings available to you. So it's nice to be able to pull out that stuff.

1:00:13And 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 preserving challenges 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.

1:00:41Martin Fowler:I think we've all had that story. At least I 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, let'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 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.

1:01:16I 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 doing it in a more modern environment. And then the question was, would I stay with Java or did I switch to another language? And in the end, I decided to switch to JavaScript. I felt 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.

1:01:53And also some things that you 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.

1:02:10Martin Fowler: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 changed? Because in the book, you specifically wrote that you see refactoring is a key element in the software development lifecycle. 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 refactoring 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.

1:02:55It's hard to say for me because, I mean, again, most of the interaction I have is with folks at ThoughtWorks. 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, you know, it's one of those things where the disciplined approach actually is faster, even though it may seem strange to describe it that way.

1:03:32But, I mean, I have to, it's 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 refactorings 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.

1:03:53Martin Fowler: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.

1:04:33These tools at the moment can't definitely 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.

1:05:10Martin Fowler:It's interesting because right now, if you want to refactor, you need to have your IDE open for sure. end. 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 cloud code or something similar. It's tough where 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 LLMs 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 the simple things, right?

1:05:53Martin Fowler: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 SQL right, but if you type the thing at the LLM, it will give you back the SQL 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.

1:06:37I mean, particularly where you're talking about these automated changes across large code bases. There was an example of this, was it a year ago or so, one of these big companies talked about this massive change that made to change APIs and clean up the code. And they mentioned it as an LLM thing, but it wasn't an LLM. It was that different tool, and I'm completely blanking on what the names of all of these things were. Oh, to 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.

1:07:11But that was, 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.

1:07:30Martin Fowler:Speaking about going on from refactoring to software architecture, you were very busy writing books around the early 2000s. 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 an 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.

1:08:08Martin Fowler:Software architecture was talked about, my sense was in a lot of places or a lot more. then something happened something starting from 2010s i 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 why was it important to to talk about it and put it into the industry and how have you seen this change of where we stopped talking more on on patterns and you think it happened? Yeah, I mean, I've always found it. I mean, what you're doing with patents is you're trying to create a vocabulary to talk more effectively about this kind of these kinds of situations.

1:08:53I 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. Yes. And with patents, 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.

1:09:30I mean, patterns are only useful in certain contexts, So you very much got to understand the context of when to use them. And yeah, it's kind of a shame that some of the wind has gone out of the sails 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 recently with Unmesh on his book on patterns in distributed systems. And I felt that was a very good way of coming up with, again, 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.

1:10:12So 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.

1:10:43Martin Fowler:I wonder if, because I've seen, I've worked at places where we use these things and then places where we just like 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 their 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.

1:11:22Martin Fowler: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 college students. And then when my parents went on there, it was super uncool to use Facebook.

1:11:57Martin Fowler:Or my grandparents came on there. Like 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, a 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.

1:12:34You 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 and then you get the the mismatches that occur as you only we only really

1:13:14Martin Fowler:notice that as you cross these different environments grady butch had a 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 around the 2010s. One interesting thing that happened around that time is cloud. The cloud started to get bigger, AWS, Google Cloud, and a lot of companies started to build similar things.

1:13:50Martin Fowler:They started to build either initially on-premise backend services where you had most of your business logic, later it 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 story. 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 have these building blocks and now you're talking about using this database on top of this system.

1:14:26Martin Fowler: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 that I do see is every company names their system. Some have wacky names, some have logical names. But when you talk about architecture, you typically talk about like, you know, like at Uber, we had the bank emoji service, which called, which was being migrated to Gulfstream, which was, you know, these all sound like doesn't make too much sense if you're from the outside.

1:15:13Martin Fowler:Sometimes they have like 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 PP2. anyway but inside any 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 lingo 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 of these systems and how they interconnect.

1:15:52Martin Fowler:Well, one of the fascinating conversations 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 on how to socialize ideas and get things out. And I asked how long have we been working on this? It has been three years. And I was like, okay, so are you done? He's like, no, no, this is just a planning. we're close to finishing the planning and to me it didn't compute because like in three years for planning but again once you I started to understand the scale of the business how much money how many legacy systems they have half of half of what he did was talk 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 10 or later, you still don't see this, but it might come in 10 years.

1:16:49Oh yeah, it certainly will. I was interesting. I remember chatting, I was chatting with somebody who had joined a bank, an established bank, and they joined from a startup and one of their jobs was to modernize the way the bank stuff was learning. 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.

1:17:31And 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 organization. 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 situation. And yeah, I mean, Uber's lucky that 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

1:18:18Martin Fowler:the changes the the layers of processes and so on which is kind of like it's necessary so as you grow speaking of change and iteration uh on an agile so you were part of the 17 people who created the agile manifesto and i previously asked ken 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.

1:19:01And 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.

1:19:37And 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 Anguilla in the Caribbean. And for whatever reason, we ended up in Utah and skiing. and so we and 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 was involved i got involved in he mentioned some lunch in chicago which is very likely because i was going to chicago all the time before works at the time so i probably did but i don't remember and of the meeting itself.

1:20:27I actually don't remember very much of it, which is a shame. I curse myself for not writing a detailed journal of those few days. I'd love to know, how did we come up with 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 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 it'll the manifesto itself will be a complete useless and ignored of course but the exercise of writing it will be interesting it um and that was my reaction to it and that's how i felt about the manifesto if i felt uh nobody will take any notice of this oh wow but um hey we're having fun writing it and we're understanding each other better and that will be the value, right?

1:21:26Martin Fowler:We'll understand each other better. And then 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 that lovely quote from Alastair Coburn that 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 that it manifests to us four different lines and so people just pick and choose which one they want to point to. And don't forget the 12 principles. principles. And the fact that it says at the beginning we are uncovering that this is a continuous process and the manifesto is just this is what we've got so far.

1:22:04So it's a snapshot of a point in time of where we were in 2000, 2001. Yeah, all sorts of subtleties

1:22:10Martin Fowler:to the manifesto. But I think it had an impact in the sense that my feelings were there's a certain way that we wanted to write software at ThoughtWorks 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 and 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?

1:22:53I mean, that was the mentality

1:22:55Martin Fowler: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 organizations and operate 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 a much greater extent than we were able to do back in 2000. And so that is the success. I just wanted the world to be safe for those people that wanted to work that way to be able to work that way.

1:23:35Yeah, 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.

1:23:43Martin Fowler: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 this is just common everywhere, right? Can we say that or at least? I would say we've made significant progress, but compared to how we'd like it to be and who 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.

1:24:25We 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 were always in that situation where, you know, we're kind of nudging our way forwards at a much slower rate than we'd like to be.

1:24:41Martin Fowler:Yeah. Now, of course, AI is coming and it is now, it is everywhere and it will be everywhere. And one thing's 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 up front. Do you think that Agile will work just as well with AI with even shorter increments?

1:25:19Martin Fowler:Or do you think we might start to think about some different way to work 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 thing 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 human sort of humans reviewing it is still the way to bet. What AI 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 we need to, I'd rather get smaller, more frequent slices than more stuff in each slice.

1:26:06Improving the frequency is usually what we, 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. And I still get a sense of that when talking to people, it's still saying, 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, just look at what you're doing. Look for the cues in your flow and figure out how to cut those cues down.

1:26:46If you were able to get some ideas from idea to running code in two weeks, how do 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.

1:27:02Martin Fowler: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 Antrofic, 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 at. 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 had videos, he just recorded these as he went, the exact prompt that he used, the output, and these were interactive prototypes.

1:27:42Martin Fowler:So they were not just on the paper, but they were inside. And to me, this was like, wow, 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 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 the limit 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?

1:28:18And then 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 we have to be learning about what it is we're trying to do.

1:28:29Martin Fowler: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 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.

1:29:05The 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.

1:29:39And 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 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 Birgitta, who has been writing with me. So that's good. Um, Simon, was excellent.

1:30:05Martin Fowler:Yeah. Spaghetti stuff is superb. Um, Simon Willison, I keep an eye on what he's doing all the time. Um, I wish I had his energy work rate for getting stuff out. Actually, I wish I had your energy for you, the man, the stuff you get out these days. And so I look for sources like that. I'm always interested in what folks like Kent are up to, because let's face it. So much of my career has been leeching 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.

1:30:41So a lot of it is in that kind of direction. I might even watch a video occasionally, although I really hate watching videos.

1:30:48Martin Fowler: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. 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, how do you identify what a good source is of information? And this is more general. This is due to 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.

1:31:29And 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.

1:32:08There was a lot being written in the Java world. This is back around the late 90s. Lots of 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. It was very tentative all the time, very much clear of this was how he was currently feeling. But you 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.

1:32:50And I think that's important. Also, someone who's keen to explore nuances of saying, well, this works in these circumstances. is not. If somebody tells me, oh, you should always use microservices, or somebody says that you should never use microservices, I mean, both of those arguments can be completely discounted. It's when you say, ah, 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, ah, 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.

1:33:22You'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 railroad 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?

1:33:42Martin Fowler: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 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 engineer 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.

1:34:21Martin Fowler:zero customers. Contexts make a huge difference. And then that's, I mean, and again, 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 article, the no silver bullets, right?

1:34:53Martin Fowler: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 who are starting out? There's all this AI stuff going on. We know with learning, I think you also mentioned, or it might've been Umesh 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 asked 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?

1:35:29Martin Fowler: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 your more junior is you don't have this sense of, 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, many ways, it's worth prioritizing that above many other things that you, when it comes to your career is getting that.

1:36:11I 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, you know, we may be at the same age or whatever, but his thinking is 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?

1:36:53What 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 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?

1:37:32And if you can find your way to the good stuff, then that can be much more useful.

1:37:36Martin Fowler:And looking at all this change that's happening right now with AI elements, 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 strange, Life has always been a strange phase. I mean, it's strange in different ways. The current strangeness is we're basically in a huge, certainly in the developed world, depression.

1:38:18I mean, we've seen a huge amount of job 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.

1:38:29Martin Fowler:I mean, we're seeing it. I mean, at ThoughtWorks, 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 and 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, say, there wasn't with blockchain and crypto.

1:39:08There's definitely stuff in AI, but exactly how it's going to pan out, who knows? I mean, I went through this cycle with the dot-com stuff in the 90s and 2000s. So it's a repeat of that, only probably an order of magnitude more 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 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 loony on driving the bus in the United States.

1:39:45We 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.

1:40:11Martin Fowler:And one of those masks are the other, and yeah, it depends on where you are. Like, I was in Silicon Valley, and if you're an AI company, 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 in 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, you know, the timing is not as great as it would be getting into this industry in, say, 2005.

1:40:46But, 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 of 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 the users of software and crossing that divide, which has always been the most critical communication path.

1:41:22Martin Fowler: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 has been an absolute pair. Unmesh is 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 has 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.

1:42:09Certainly 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, you know, 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 am never going to have that skill because I'm not a doctor. And so therefore, the doctors have to be involved in the process.

1:42:39Martin Fowler:So as closing, I just wanted to do some rapid questions where I'll fire 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 I'm so familiar with it. I've been using it for so long. But the one that is my love is Smalltalk, without a doubt. There was nothing as much fun as programming in Smalltalk when I was able to do it in the 90s. That was such a fantastic environment. You and Kenbeck. And Kenbeck is writing his Smalltalk server. It's his baby. I think he's making progress. And I mean, there is still stuff going on.

1:43:17There is the Faro project in Smalltalk. And I keep thinking, you know, if I could just take off some weeks and stop everything else I was doing, maybe investigate, see what's going on in the Smalltalk world again. Because it was, I mean, and still so much power in that language.

1:43:32Martin Fowler: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 Kahneman. 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 because, I mean, a lot of what we do is greatly enhanced by the fact that we can understand the statistical effects of what we see, but also in life in general. because I think our world would be a hell of a lot better if way more people understood a bit more about probability and statistics than they do.

1:44:18I mean, 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 it was much more statistics-based because the knowledge of being able to use that well. I mean, one of the things that has helped me more with probability 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 was one of the best reads I've had in the last few years.

1:44:59Another book that I'd mentioned that is 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 1923 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.

1:45:43It's also a fascinating book because it is so well written. There have been moments when I would just, you know, I've been reading a several page passage of something and I would just have to stop to just appreciate how brilliant what was 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.

1:46:36But again, there are moments 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.

1:46:52Martin Fowler: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, 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 a decision making in the process.

1:47:31Martin Fowler:Well, Martin, thank you so much. It was great that we could make it happen in person as well. Yes. I think that, that worked out really well. I just happened to be in Amsterdam for something else. And, uh, 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.

1:48:11Martin Fowler: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 might end 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 in the Pragmatic Engineer, which are linked in the show notes below.

1:48:42Martin Fowler: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.