[jeff_vader]

Reminds this current slightly comedic (IMO) situation in my office: a few months ago developers were given access to GitHub's "Copilot Enterprise". Then, a month or so later, organisation also adopted another "AI" product checking pull requests for risks associated with "use of generative AI". And needless to say it does occasionally fail code written without any "generative AI"..

[nerdponx]

The flipside is that the AI-written code I've seen at work is usually painfully obvious upon human code review. If you need a tool to detect it, either it's good AI-written code, or you have particularly inept code reviewers.

[boredpudding]

Be careful here about confirmation bias. If you only spot 10% of the AI-written code, you'll still think you see all of it, because a 100% of the ones you spot are indeed AI-written. And the 10% you see, will indeed be painfully obvious.

The ones you don't notice aren't obvious.

[nerdponx]

That's fair.

It depends on why you care about AI-written code.

At the code review stage, we care mostly that the code is good (correct, readable, etc). So if the AI-written code passes muster there, then there's nothing wrong with it being "AI-written" in our eyes.

If you care about AI-written for the sake of preventing AI usage by your developers, then I think it's already impossible to detect and prevent.

[HPsquared]

Deeply ironic.

[mwigdahl]

It's the best of both worlds! A new product to improve productivity, and then a whole new layer of process and analytics (powered by yet another product) to mitigate the risk and soak up the surplus. Everybody wins -- particularly the 3rd party consultants and product vendors!

[HPsquared]

The purpose of a system is what it does, I suppose.