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But there is an unanswered question of how far this technology can go based on its fundamentals. Coding is much like driving, you can't do 80% and let the human do the final 20%, because that final 20% requires reasoning about a well understood design that was implemented throughout the first 80%. If your fancy AI coder thingy can't really reason about the end task that the code is solving - and there is little to indicate that it does, or that, any moment now, technology will advance to the point that it will - then the 80% will be crap and there exists no human that can finish the last 20%, not even if they put up 200% of the effort required. We still don't have a working AI solution for driving, a well understood and very limited problem domain, never-mind the infinite domain of all problems that can be explained in natural language and solved with software. What you end up with is a fancier autocomplete, not an AI coder. Boilerplate and coder output might simply increase to take advantage of the new more productive way of generating source code, just like they did for the last decades whenever there was a "revolutionary" new tech, like high level languages, source control, IDEs and debuggers, component distribution etc. etc. |
These are data transformers that can transform raw data without coding at all. At what point does a model itself replace code?
It’s sort of like a CPU, right. You can have hardware that specialized, or general purpose hardware that can do anything once instructed. LLMs have the ability to be general purpose data manipulators without first having to be designed (or coded) to perform a task.