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Notes -
Unfortunately, I think that there are two layers of nonsense compounding on one another in the article.
First, the obvious question is "what kind of code?" Does he mean boilerplate stuff that, before LLMs, was handled mostly by copying previous projects and re-using the basics? Does he man config files and deployment scripts for infrastructure? This is very much still code, but not in the user-facing, self-contained full product sense.
Looking deeper, the next questions are "so what?" and "how much code can AI actually write?" I am reminded of the classic The Mythical Man Month. Writing code isn't a linear function. 1.5x inputs does not yield a corresponding ratio of 1.5x outputs. The actual writing of code is often a pareto or power law function; you spend 80% of your time on 20% (or less!) of the codebase. Much like the hard part of writing is editing, the real slog in coding comes in debugging and, later, refactoring. Shitting out shitty but "hey it works" code is easy.
Every mid-to-senior level developer, data scientist, and ML engineer I've had discussions with more or less comes to the same conclusion space; AI is really handy, right now, for discrete problems. It's a massive time saver. It's actually extra handy for writing tests. In the not so distant future, it will probably be able to do some real system engineering work.
But it can't replace all the devs because, at some point, using more LLMs in your development will actually cause the project to take longer (again, reference The Mythical Man Month). If you look at the "thinking" output of Chain Of Thought models, you can see how it flirts with recursion loops. It tells itself to think about x but also to make sure it considers y too and, oh yeah, definitely make sure z is in there too. And that's for simple chat based prompts. If you have an LLM read a detailed system design plan and then hit the "do it" button, my worry isn't that it would output broken, non-internally consistent code, but that it would never actually output anything functional. Instead, I imagine millions of lions of incomplete functions with a lot of extraneous documentation and the wholesale swapping in and out of design patterns. Spaghetti code, but without even a "fuck it, it works" level of functionality.
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