And therefore, according to major-citation-needed pie chart, editing code is only 5% of the job, thus AI will always and forever only be able to save us 5% or less of our time.
Seems pretty wooly.
One reason I think ChatGPT is so impressive is because it's remixing and suggesting code, from already well structured code bases, functions etc, calling on well designed libraries etc. My gut feeling is that MS have allowed all of Github to be "sampled", which is why the code ChatGPT is serving up seems to "familiar".
Unless these models start to actually improve the code, ie suggest better, less complicated paths and architectures, there is a chance software will go backwards to the point where just getting LLMs to pattern match a change into a 500,000 long line codebase of auto-generated, randomized solutions stops working, then of course, we'll need a "bigger" model to grok it all, explain what the code is doing etc.
We could call it "drift" or similar, but I think the fact we write code that is easy to understand for others, including LLMs is actually a feature and not a bug. Anything "cognitive" would have to agree at some stage, unless we have unlimited cognition, for every code change available, which is honestly a fantasy at this stage.
I might be wrong, and I'm happy to be wrong, but this is probably the best time in history to be using auto-completion for coding. There's plenty of well thought out, nice training data available, I don't think that will hold water if we just get copy-pasting, neglect using nice libraries etc.
One interesting example I saw recently was someone showed me how they were using ChatGPT-4 to check their code for vulnerabilities, it actually did quite well, albeit it was very basic / best practices stuff everyone should know that needed fixing. Then i watched someone else in a different context, use it to generate code, that was vulnerable and susceptible to the same problems.
We could automate the vulnerability checking (many people already do), but then it becomes an interesting back and fourth between systems writing and fixing code, at some stage it probably becomes inefficient to do this in it's own right. Brute forcing.
Or is real-world code an impossibly complex algorithm soup ?
Some other people see code as a way to communicate about systems specifications. This is the foundation of collaboration on complex software products. In this arena, lowering entry barriers and the cost of writing code will reduce quality and introduce complex, hard to debug failure modes.