Comment by ecshafer
8 days ago
>Corporate leaders are starting to question whether soaring AI spending is delivering meaningful returns.
This isn't surprising. Ive recently run into quite a few rabbit holes where AI is bad enough that its much more efficient to do it myself. I wanted to refactor some code, gave it a design pattern to go towards, some specific classes and methods, etc. making it a well described problem. AI just couldn't do it satisfactorily. The code was ugly, overly verbose, and after multiple tries with multiple prompts saying to keep things simple. They still would introduce new classes, useless fields, etc.
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"AI" is "bad" and "just couldn't do it"? Specifics w model and harness would lend more credence.
This was GPT 5.5 and codex. The specific model and harness isn't that important here. AI could do it. But the issue seems to be that there are some tasks where AI kind of falls over and provides poor results. It was easier, better, and faster for me to just do it myself. I have found a lot of cases where AI is great. If you have a UML diagram already, or translating code from language x to y, fixing unit test failures, generating boiler plate. But I can definitely see if people are using large amounts of AI for writing code, analyzing code, etc. that they are not actually seeing returns.