[blablablerg]

Or worse, subtly inaccurate. The problem I have with generative AI right now, its product looks like it makes sense and sometimes it does, but there is always the risk of total nonsense hidden somewhere in the middle. So you still need someone capable to check and correct for most professional work, and sometimes that is harder or more time consuming than making the product itself.

The same sort of problem with self driving cars, they are often correct but not often enough, and staying alert to correct the AI is worse than driving yourself which is more work, paradoxically enough.

AI might manage to push through these barriers, but I remain skeptical with the technology in the current state: statistical machines that are good in the common cases but sketchy at the edges.

[xenonite]

Which is similar to correcting subpar human results:

Rather than meticulously correcting the works of a subpar programmer it is much more efficient to let a proficient developer produce the code. Or even better, engage a 10x developer.

[oceanplexian]

Most problems don’t require efficient code.

If an inefficient programmer can solve a real world business problem in a fraction of the time, but it requires slightly more computing resources, I’d still pay for it. Efficiency can be measured in a lot of dimensions and often times spending time and money writing optimized code is inefficient as well.

[zabzonk]

they do require correct code

[riskable]

> there is always the risk of total nonsense hidden somewhere in the middle

This is true of human-generated code as well. Trust me: Reviewing other people's code is my day job.

It's exceptionally rare that a malicious actor is trying to sneak something into the code. The common scenario is the developer who's new to the project not fully understanding how everything works so they copy & paste something they think is necessary but ultimately isn't and could in fact be very wrong. Just like how ChatGPT works.