AI has let people develop systems that cannot be managed or reasoned about without the assistance of AI, creating a lock-in effect and a dependency on the models.
Being able to utilize a complex system can be a competitive advantage, but it can also put you in a position where it is difficult to extend the system and it cannot even be used without spending money.
Though others disagree, imo AI has made tech debt more manageable, not less. Now the type of debt an org seems to take on is knowledge debt - the fact that no one deeply understands the system.
As long as the tech debt being created isn't pure slop, AI can actually patch up a half-baked system much faster than before. Nowadays it is much less painful to make a second pass on a rough piece of software.
Yes, you still want to be deliberate about the tech debt you take on. But most kinds of tech debt feel cheaper to pay down than they used to.
But the larger type of debt being issued nowadays is knowledge debt.
Knowledge debt is working software that no one fully understands anymore. The code runs, the product works, but the team no longer has a clear mental model of how it all fits together.
It’s insane how much code is being shipped now. It used to be a struggle to keep an entire system of microservices in your head, but now a single service can be a challenge.
Haven't reviewed PRs in a week? Well here are 10k lines of code for ya. Now you are at the mercy of LLM code summaries not hallucinating.
There is a lock-in effect where now you are dependent on LLMs for you work for even the most basic changes.
Once again though, this is debt. You can pay it down. Take a week and build the mermaid charts, HTML visuals, integration tests, and whatever else helps the team rebuild a mental model of how the system works at an engineering level, not just a product level.