[doyougnu]

I've interfaced with some AI generated code and after several examples of finding subtle and yet very wrong bugs I now find that I digest code that I suspect coming from AI (or an AI loving coworker) with much much more scrutiny than I used to. I've frankly lost trust in any kind of care for quality or due diligence from some coworkers.

I see how the use of AI is useful, but I feel that the practitioners of AI-as-coding-agent are running away from the real work. How can you tell me about the system you say you have created if you don't have the patience to make it or think about it deeply in the first place?

[teej]

Your coworkers were probably writing subtle bugs before AI too.

[munificent]

Would you rather consume a bowl of soup with a fly in it, or a 50 gallon drum with 1,000 flies in it? In which scenario are you more likely to fish out all the flies before you eat one?

[solid_fuel]

I think the dynamic is different - before, they were writing and testing the functions and features as they went. Now, (some of) my coworkers just push a PR for the first or second thing copilot suggested. They generate code, test it once, it works that time, and then they ship it. So when I am looking through the PR it's effectively the _first_ time a human has actually looked over the suggested code.

Anecdote: In the 2 months after my org pushed copilot down to everyone the number of warnings in the codebase of our main project went from 2 to 65. I eventually cleaned those up and created a github action that rejects any PR if it emits new warnings, but it created a lot of pushback initially.

[andrei_says_]

I wonder if there’s a way to measure the cost of such code and associate it with the individuals incurring it. Unless this shows on reports, managers will continue believing LLMs are magic time saving machines writing perfect code.