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AI can 10x developers...in creating tech debt

Ryan sits down with Michael Parker, VP of Engineering at TurinTech to discuss the newest kind of tech debt—AI-generated tech debt. They dive into the uneven productivity results of AI tools, how tech teams are evolving their roles and work in response to these massive technological shifts, and what the nervous developer can do to maintain joy in their work.

January 23, 2026

AI can 10x developers...in creating tech debt

Founded by computer and data scientists from the University College London, TurinTech builds Artemis, an AI engineering platform designed to help teams safely evolve, improve, and maintain existing codebases over time. Preview their new Artemis coding agent for free.

Connect with Michael on LinkedIn.

User Adam Franco won a Stellar Answer badge—and this week’s shoutout—for their answer to How can I delete a remote tag?.

TRANSCRIPT

[Intro Music]

Ryan Donovan: Hello everyone, and welcome to the Stack Overflow Podcast, a place to talk all things software and technology. I am your host, Ryan Donovan, and today we're talking about a new category of AI-generated tech debt. AI has created a lot of new things for us, and some of the things it's caused are problems. So, we're gonna be talking about why we're not getting the productivity and how we can get better productivity from AI. And my guest today is Michael Parker, VP of Engineering at TurinTech. So, welcome to the show, Michael.

Michael Parker: Thanks, Ryan. Great to be here.

Ryan Donovan: So, before we get into the meat of the topic, we like to get to know our guests a little bit, so tell us a little bit about how you got into software and technology.

Michael Parker: My dad was a programmer growing up and we had a lot of computers kicking around, so I started programming when I was about 11. We had a ZX Spectrum that's coding some basic. I come from quite a big family, so I made a few games with my brothers and sisters and you know, 'what's your name? I like you. I don't like you.' That kind of fun stuff. And so, I used it growing up as a way to connect to people, making things for my friends and family. Yeah. And then I did computer science at uni. I went into games. I was really into computer games for a while, and then I pivoted back to hands-on keyboard developing. So, I was a game designer for a while, and then I went into programming, and then I eventually shifted into team leadership and management. And I spent just over six years at Docker, most recently, building Docker Hub and Docker Desktop for millions of developers, which was great. And then I got bitten by the AI bug like everybody else, I guess there are some bulls and some bears out there, and I'm one of those bulls, and I was so impressed with the fundamentals of what AI could do and the models that I thought I really need to jump in with both feet. So, I joined TurinTech [in] February, and I've been there ever since, building what I hope to be the next generation of developer tools. I'm on a mission to make development more fun, bring the joy back to development, get rid of all this tech debt, and give us some hope again.

Ryan Donovan: Everybody's trying to get that developer joy back into the game. Everybody saw the promise of AI three years ago or whatever it was, but as we get into it, we're not seeing the results. I think there's a stat you all shared that experienced developers are 19% slower when using AI tools. Can you dig into that and your understanding of that?

Michael Parker: Yeah, I think it's actually really uneven across the industry. Software development is so large now. There's so many different types of organizations, and developers, and code bases that I'm starting to see. There's not just one developer that you can build for with one set of problems. There's such a broad range, and there are certainly some cutting-edge small teams that are getting insane productivity with AI, especially when they work on code bases that are in modern technologies. If they're writing everything in Node and Python, and using React as a front end, and you've got two or three developers, and it's a greenfield code base, let's go, right? It's maximum speed. I'm a thousand percent going, but unfortunately, that's not the whole world, right? There's a lot of legacy code. There's a lot of enterprise developers, and LLMs just aren't trained on your internal libraries, and all these ancient versions of things that you might be using, and you can't just get rid of them overnight. We can't just rewrite the world's code into Python and React overnight, right? So, we have to be able to deal with this. This is where I'm seeing the change in the expectations, depending on which edge of the spectrum you're on. So, this 19% is a misleading figure, right? 'cause for some developers, AI is just completely useless, and for other developers, it's like the savior. It's the Messiah that's arrived to save us all and make us 100 times more productive. So, I think averaging is probably not the right thing to do. You know, enterprises that are really struggling, they're really getting alienated by some of the people at the other end of the spectrum who are claiming AI is gonna save them.

Ryan Donovan: With those enterprise code bases, you have so much context that you have to have for any change, right? There's so much code, and then the AI tools – one of the challenges has been giving it that context. Have you found anybody is being more successful? Any techniques to give AI context?

Michael Parker: Yeah, absolutely. There's a few different classes of developers that are emerging here, I think. There's the cutting-edge developer coach. I dunno what this role is called. It's not quite a developer, it's not like a manager; it's like something in between. And they tend to spend all of their time tweaking the factory rather than tweaking the code. And so, when their AI writes bad code, they don't fix it; they fix their prompt, or they fix their rules file, or they build a subagent. And there's all these teams that are hacking together all of these pieces because the system that they need does not exist in the marketplace. We have all these IDs that have chat boxes, and is a chat box the perfect way to interact with a multi-agent development system? I'm not sure, you know, maybe, but maybe there's more to come here. And so, it's interesting to see this role that's emerging that's not development, but I don't know what to call it, but it's very interesting. And how do we build tools for those people, and how do we build tools for developers that stay in development? There's kind of two different problems.

Ryan Donovan: And I've heard of people building style guides for AI Agent coding tools. They're building specs for stuff they're trying to build, getting incredibly detailed in the prompts, where what you're doing is essentially writing code without writing code, right? Saying things like a conversation I had the other day, 'iterate over this four loop to produce this.' That's basically code.

[...]

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