A variation of that argument props up most common AI skepticism. I don’t think there’s anything out right now that would convince you, but from what I know, everything you pointed out will be solved within the next few years.
- Create a new programming language like Go or Rust?
- Create new infrastructure patterns like containers and kubernetes?
- Build tools like MapReduce/Arrow/Airflow or TF/JAX? Blockchain?
- Understand a new tool or framework it was not trained on?
- Decide when and how to refactor a code base because the mapping to the underlying problem has reached its limits?
- Write or modify compilers for emerging platforms like RiscV or WASM?
- Help to resolve a 1:1000 50x returned by my server?
Do today's ML models come close to the "why" these problems have?
Of course humans aren't exactly great at that part either. But I do think I'd bet against, within the next 4 years, an AI tool being able to take tickets in the form
1. Expected behavior
2. Observed behavior
3. Steps to reproduce
and produce a changelist that legibly fixes the problem, and does not break anything else, at a level better than a typical junior software developer. I think the ability to do that is probably AGI complete.
The steps you elucidated are all expressible in natural language, and we see models like Codex Edit making headway there. One of the most fascinating parts of this is that once access to the known baselines are provided to high-level engineers, they then go on to do much more than what the models alone can do.
The main hinderance to enterprise was compliance but the move toward Azure, etc, will dissolve those barriers this year.
Let's get real, nothing except simple crud apps are getting replaced, if any, anytime soon.