If you can reduce a problem to a point where it can be solved by simple code you can get the rest of the solution very quickly.
Reducing a problem to a point where it can be solved with simple code takes a lot of skill and experience and is generally still quite a time-consuming process.
Isn't the logical endpoint of this equivalent to printing out a Stackoverflow answer and manually typing it into your computer instead of copy-and-pasting?
Nitpicks aside, I agree that contemporary AIs can be great for quickly getting up to speed with a code base. Both a new library or language you want to be using, and your own organisation's legacy code.
One of the biggest advantages of using established ecosystem was that stack-overflow had a robust repository of already answered questions (and you could also buy books on it). With AI you can immediately cook up your own Stackoverflow community equivalent that provides answers promptly instead of closing your question as off-topic.
And I pick Stackoverflow deliberately: it's a great resources, but not reliable enough to use blindly. I feel we are in a similar situation with AI at the moment. This will change gradually as the models become better. Just like Stackoverflow required less expertise to use than attending a university course. (And a university course requires less expertise than coming up with QuickSort in the first place.)
Isn't the answer on SO the result of a human intelligence writing it in the first place, and then voted by several human intelligencies to top place? If an LLM was merely an automated "equivalent" to that, that's already a good thing!
But in general, the LLM answer you appear to dismiss amounts to a lot more:
Having an close-to-good-human-level programmer
understand your existing codebase
answer questions about your existing codebase
answer questions about changes you want to make
on demand (not confined to copying SO answers)
interactively
and even being able to go in and make the changes
Or, to put it another way, isn't "the logical endpoint" of hiring another programmer and asking them to fix X "equivalent to printing out a Stackoverflow answer and manually typing it into their computer"?
>And I pick Stackoverflow deliberately: it's a great resources, but not reliable enough to use blindly. I feel we are in a similar situation with AI at the moment.
Well, we shouldn't be using either blindly anyway. Not even the input of another human programmer (that's way we do PR reviews).
The word "merely" is doing all of the heavy lifting here. Having human intelligence in the loop providing and evaluating answers is what made it valuable. Without that intelligence you just have a machine that mimics the process yet produces garbage.
That's not some new claim that I made. My answer accepts the premise already asserted by the parent that LLM is "equivalent to printing out a Stackoverflow answer and manually typing it into your computer instead of copy-and-pasting".
My point: if that's true, and LLM is "merely that", then that's already valuable.
>Having human intelligence in the loop providing and evaluating answers is what made it valuable. Without that intelligence you just have a machine that mimics the process yet produces garbage.*
Well, what we actually have is a machine that even without AGI, it does have more practical intelligence than to merely produce garbage.
A machine which programmers than run circles around you and me still use, and find it produces acceptable code, fit for the purpose, and possible to get it to fix any potential initial issues in a first iteration it gives, too.
If it merely produced garbage or was no better than random chance we wouldn't have this discussion.