If you're using the right models (Claude 3.7 Sonnet, Gemini 2.5 Pro) and are good at prompting it very possibly can write and deploy thousands of lines of code to production without you needing to change a single thing.
Of course, odds are there in fact is something you need to change - maybe a poor design choice or a bug or missing logic. So you of course do need to always thoroughly review it. But reading 1000 lines is faster than coming up with 1000 lines you plan to write and writing them. And also, if you see a missing thing, you can just do a follow-up prompt in the same chat context rather than actually typing a single thing into the text editor.
I know it can feel alien, and I definitely still spend a lot of time manually writing and editing code, but I'm trying to outsource more and more to the model and trying to put myself into a mindset of "first try to see if I can accomplish all this with prompts, and then fallback to 'raw coding' if it fails after a few tries" for everything and I find it's speeding me up a lot.
You should try to give it another shot. Could maybe wait another year first for the editors and models to get even better than they are right now.
>I'm referring to wholly AI generated code with no human input besides a prompt or "vibe coding." You literally can't put enough context into a prompt to have it write the exact code you'd need in every case. Your prompt would end up just being code at that point.
True, but... you can do that! It may or may not be faster than writing the code you want, true, but sometimes I think it will be faster/simpler. Gemini 2.5 now (or soon?) supports a 2 million token context window. You can write a very precise spec in the prompt. Use formal language, or use a little DSL you invent on the spot, or say "it should do X and Y and account for Z and also try to cover other things if you realize there are more", etc. There's a lot you can do.
There absolutely will still be many scenarios where it's faster overall to just write the code or where it really is harder to express what you want to say in English vs. in code, but those scenarios may be less common for you than you currently think or expect.
