> To make this more concrete, here are some example ideas that are perfectly true when applied to regular software but become harmfully false when applied to mo

> To make this more concrete, here are some example ideas that are perfectly true when applied to regular software but become harmfully false when applied to modern AIs: ... Hmm, I don't think any of these were true with non-AI software. Commonly held beliefs, sure. If anything, I am glad AI is helping us revisit these assumptions. - Software vulnerabilities are caused by mistakes in the code Setting aside social engineering, mistake implies these were knowable in advance. Was the lack of TLS in the initial HTTP spec a mistake? - Bugs in the code can be found by carefully analysing the code If this was the case, why do people reach for rewriting buggy code they don't understand? - Once a bug is fixed, it won’t come back again Too many counter examples to this one in my lived experience. - Every time you run the code, the same thing happens Setting aside seeding PRNGs, there's the issue of running the code on different hardware. Or failing hardware. - If you give specifications beforehand, you can get software that meets those specifications I have never seen this work without needing to revise the specification during implementation.