AI coding tools make technical debt worse because it's trivially easy for them to add code, but they resist removing it even when explicitly asked, so the codebase just keeps growing.
Wes points out that people who haven't maintained long-running codebases underestimate how real tech debt is, and that AI's tendency to add code freely while refusing to delete it will come back to haunt teams. ✦ AI generated
Wes Bos · Syntax · 2026-07-08 · original ↗
starts at this moment · 10:36
“On my team, a common workflow is an engineer asks an agent to implement a feature or fix a bug, open a PR, address automated review comments if necessary, waits for CI to pass, and merges. In many cases, there is little to no meaningful human review of the actual code.”
That tech debt is really real and and tech debt is is such a major problem with AI now because it's so easy to add code and like AI hates to remove code even when you beg it to remove code. So like yeah, I just feel like the amount of code that we be adding sometimes is going to come back to haunt teams.
verbatim transcript · starts at 10:36
10:36never maintained old systems or long-running systems or these types of things. That tech debt is really real and and tech debt is is such a major problem with AI now because it's so easy to add code and like AI hates to remove code even when you beg it to remove code. So like yeah, I just feel like the amount of code that we be adding sometimes is going to come back to haunt teams.
11:02And sure, you know, Fable is going to come out again and the the clouds will part and you'll say hey make it better and they'll make it better. [laughter] Um and and maybe that's the case but yeah, in the meantime I think we're going to be seeing a lot of software and apps like barely held together cuz that's that's wild to me. Next question here from Sarah Chen. In episode 1009 you
11:26mentioned that nobody knows what a local model is. Can you do an explainer on what local models are? Well, local models are just uh models that you're running on your local machine. I think some people really disconnect that and think, "Ooh, I'm running the software locally, therefore it's local." But it like when reality they're going off and doing API calls. That was the whole thing >> Yeah, all these people are like, "Just
11:52use a local thing." It runs a local use open code and deep seek for local. Like, that's not local. It's going to China. >> Yeah, so it's it's literally running on your machine. And I will point you to the CJ did a really incredible video called your guide to local AI. And so instead of just recreating that here, cuz you know we could I could just recreate CJ's awesome video on the fly
12:15here, right? I'm just going to point you to that cuz it's really super good and he even uses a piece of bread to show you how big his computer is. So that's fun. >> [laughter] >> Yeah, the the local model stuff is really interesting. We'll we'll keep coming back to it because there's like this like idealistic thing of yes, I would love to run local models. It's it's private and whatever
- ·Tech debt is a real, major problem with AI coding
- ·AI makes it trivially easy to add new code
- ·AI resists removing code, even when asked directly
- ·Even begging AI to remove code often fails
- ·Code volume keeps expanding unchecked
- ·This growing debt will come back to haunt teams