It's tough to generalize about "AI code", there's a huge difference between "please make a web frontend to this database that displays table X with some ability to filter it" and "please refactor this file so that instead of using plain strings, it uses the i18n api in this other file".
trivial doesn't mean the AI will get it right. A trivial request can be to move an elephant into a fridge. Simple concept right?
Except AI will probably destroy both the elephant and the fridge and order 20 more fridge of all sizes and elephants for testing in the mean time (if you're on MCP). Before asking you that if you mean an cold storage facility, or if it is actually a good idea in the first place
They’re inherently very different activities. Refactoring a file assumes you’ve made a ton of choices already and are just following a pattern (something LLMs are actually great at). Building a front-end from nothing requires a lot of thought, and rather than ask questions the LLM will just give you some naive version of what you asked for, disregarding all of those tough choices.
Disagree. There is almost no decision making in converting to use i18n APIs that already have example use cases elsewhere. Building a frontend involves many decisions, such as picking a language, build system, dependencies, etc. I’m sure the LLM would finish the task, but it could make many suboptimal decisions along the way. In my experience it also does make very different decisions from what I would have made.
The AI will pick the most common technologies used for this purpose, which is both "good enough" and also what people generally do at scale (for this exact reason).
This was the promise of no-code. ”All apps are crud anyway”, ”just build for common use cases” etc. This didn’t turn out to be true as often as predicted. If averaging people’s previous decisions was truly a fruitful way, we’d have seen much stronger results of that before AI.
Building even a small a web frontend involves a huge number of design decisions, and doing it well requires a detailed understanding of the user and their use-cases, while internationalisation is a relatively mechanical task.
And that’s kind of decision making is what’s important. More often than not, you ask someone to explain their decision making in building a feature, and what you get is “I don’t know really”. And the truth is that they have externalized their thinking.
