Original version initially published on 12th March 2026 on the WebRTC.ventures blog as Who Watches the Watchmen? AI Code Generation and the Oversight Problem. This is a reviewed and cleaned version.

Some weeks ago I read an article that captured something many experienced developers have been feeling for some time: software development is changing rapidly in the age of generative AI, and not always in ways we fully understand.

One quote from the article especially resonated with me in particular:

An MIT professor called AI “a brand new credit card that lets us accumulate technical debt in ways we were never able to before”. That credit card now writes 41% of the code.

Whether the exact number is accurate or not (Stability AI Ex-CEO Emad Mostaque has been saying so since 2023… and he predicts that it will be 100% by end of the decade), the point is clear: a large and growing portion of modern codebases is now generated with the help of AI tools. This has undeniable benefits: developers can prototype faster, explore ideas quickly, and automate repetitive tasks that previously consumed valuable time.

But this acceleration also introduces a structural challenge: we are producing code faster than we can reasonably understand and validate it. Some years ago, AI systems started to be so much complex that we started to lose track to understand how they think, becoming increasingly opaque. And now that they are starting to write our code, we are in a situation where we start to don’t understand how our code works too, nor how well it does.

In many teams, the question is no longer whether AI-generated code should be used, but how we can maintain quality and reliability when the volume of generated code keeps increasing.

And increasingly, it raises a deeper question: we may start to need AI to review and explain the code generated by AI, but who watches the watchmen?