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Ok? You still have to read the code.


You're missing the point. The point is that reading the code is more time consuming than writing it, and has always been thus. Having a machine that can generate code 100x faster, but which you have to read carefully to make sure it hasn't gone off the rails, is not an asset. It is a liability.


Tell that to Mitchell Hashimoto.


> The point is that reading the code is more time consuming than writing it, and has always been thus.

Huh?

First, that is definitely not true. If it were, dev teams would spend the majority of their time on code review, but they don't.

And second, even if it were true, you have to read it for code review even if it was written by a person anyways, if we're talking about the context of a team.


That's just not what has been happening in large enterprise projects, internal or external, since long before AI.

Famous example - but by no means do I want to single out that company and product: https://news.ycombinator.com/item?id=18442941

From my own experience, I kept this post bookmarked because I too worked on that project in the late 1990s, you cannot review those changes anyway. It is handled as described, you keep tweaking stuff until the tests pass. There is fundamentally no way to understand the code. Maybe its different in some very core parts, but most of it is just far too messy. I tried merely disentangling a few types ones, because there were a lot of duplicate types for the most simple things, such as 32 bit integers, and it is like trying to pick one noodle out of a huge bowl of spaghetti, and everything is glued and knotted together, so you always end up lifting out the entire bowl's contents. No AI necessary, that is just how such projects like after many generations of temporary programmers (because all sane people will leave as soon as they can, e.g. once they switched from an H1B to a Green Card) under ticket-closing pressure.

I don't know why since the beginning of these discussions some commenters seem to work off wrong assumptions that thus far our actual methods lead to great code. Very often they don't, they lead to a huge mess over time that just gets bigger.

And that is not because people are stupid, its because top management has rationally determined that the best balance for overall profits does not require perfect code. If the project gets too messy to do much the customers will already have been hooked and can't change easily, and when they do, some new product will have already replaced the two decades old mature one. Those customers still on the old one will pay premium for future bug fixes, and the rest will jumpt to the new trend. I don't think AI can make what's described above any, or much worse.


If your team members hand off unreviewable blobs of code and you can't keep up, your problem is team management, not technology.




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