| Hello, author here. Thanks for your comment. I agree with your first point, maybe AI will close some of those gaps with future advances, but I think a large part of the damage will have been done by then. Regarding the memory of reasoning from LLMs, I think the issue is that even if you can solve it in the future, you already have code for which you've lost the artifacts associated with the original generation. Overall I find there's a lot of talks (especially in the mainstream media) about AI "always learning" when they don't actually learn new anything until a new model is released. > Why does it require 100% accuracy 100% of the time? Humans are not 100% accurate 100% of the time and we seem to trust them with our code. Correct, but humans writing code don't lead to a Bus Factor of 0, so it's easier to go back, understand what is wrong and address it. If the other gaps mentioned above are addressed, then I agree that this also partially goes away. |
But this already exists! At work, our code is full of code where the original reasoning for the code is lost. Sometimes someone has forgotten, sometimes the person who wrote it is no longer at the company any more, and so on.
> Correct, but humans writing code don't lead to a Bus Factor of 0, so it's easier to go back, understand what is wrong and address it.
But there are plenty of instances where I work with code that has a bus factor of 0.
The conclusion of your article is that vibe coding is "fundamentally flawed". But every aspect you've described about vibe coding has an analog in normal software engineering, and I don't think you would claim that is "fundamentally flawed".