As the models keep improving, wouldn’t you be able to task a newer AI to “clean up this mess”?
(None of above is theoretical)
Imagine the year is 1995, C exists, but some guy out there is working on essentially what modern Python is. He says to you "check out this language, you can just import stuff, and use it and dynamically modify anything at run time". You can probably come up with hundreds of arguments about things that could go wrong, like memory clean up, threading, e.t.c, but turns out, incrementally, they were all solved and we have the modern Python that basically is good enough to build these large LLM models.
Now imagine modern programming and computing is what C was back in 1995, and AI use is that guy building the Python code.
I think you have some serious misunderstanding here.
The infill will look seamless.
And entirely lack any actual strikes of interest - the outliers are exceptional signal and the entire raison d'etre for building such a database.
Jeez, if AI can just infill where the gold is, why even bother to look in the first place.
The original question was
>"clean up" dropped databases, compromised computers or leaked personal data?
For each of those things, you can right now build an agent that handles all of that. Or use a large frontier model with enough context to build code that ensures all of those edge cases are handled.
Future coding will essentially be like this. The concepts of dynamic vs compiled language will shift towards having frontier edge models put together code versus small runtime edge models dynamically processing data.
Also, Python does not build or run large language models. It orchestrates C code that does that, and it was probably good enough to do that in 1998.
The biggest change that happened was that hardware kept getting better and it became feasible to use garbage-collected languages everywhere including really inefficient implementations like CPython.
That being said, 30 years later Python is still slow as shit even compared to other dynamic languages and runs into all kinds of scaling issues when used for anything serious. And everywhere that performance matters, software continues to be written in typed, compiled languages including C (but also C++, Rust, Go, etc.). Even in ML, Python chiefly acts as a thin wrapper and glue language for high performance CUDA libraries (aka C and C++).
So your historical analogy is mostly anachronistic.
In the future, you won't be dealing with strings, json, or apis. You will be importing agents, and giving them brief instructions, either in plain English or in some intermediate language higher than Python that is more brief. Wanna deal with database reliability ? Import database agent and give it brief instructions on what you want to manage. Just like you mention, right now Python is the wrapper for low level libraries, because everyone who is doing work in ML doesn't want to waste time making sure their C Cuda kernels compile. In the same way, nobody is going to care if they get the API headers right, or if their strings are correctly parsed when you can just invoke a dedicated LLM (which will likely be highly specialized small model able to run on local hardware) to do all that.
You can scream and cry as much as you want how that is bad, how its slow, but nobody is going to care because shit is going to get built faster. Ever notice how despite the massive layoffs across tech, there isn't service degradation in any sector? Good luck trying to sell your Rust skills in the future lol.
It doesn't know what mess you want to clean up. A lot of times AI just starts making up new patterns on top of other patterns and having backwards compatibility between the two. How does it know which one you actually like?
Violets are blue
AI is great
And so are you