[maegul]

Yep! It’s something some aren’t seeing.

The AI coding assistant is now part of the abstraction layers over machine code. Higher level languages, scripting languages, all the happy paths we stick to (in bash, for example), memory management with GCs and borrow checkers, static analysis … now just add GPT. Like mastering memory management and assembly instructions … now you also don’t have to master the fiddly bits of core utils and bash and various other things.

Like memory management, whole swathes of programming are being taken care of by another program now, a Garbage Collector, if you will, for all the crufty stuff that made computing hard and got in between intent and assessment.

[Nullabillity]

The difference is that all of them have theories and principles backing them, and we understand why they work.

LLMs (and "AI" in general) are just bashing data together until you get something that looks correct (as long as you squint hard enough). Even putting them in the same category is incredibly insulting.

[maegul]

There are theories and principles behind what an AI is doing and a growing craft around how to best use AI that may very well form relatively established “best practices” over time.

Yes there’s a significant statistical aspect involved in the workings of an AI, which distinguishes it from something more deterministic like syntactic sugar or a garbage collector. But I think one could argue that that’s the trade off for a more general tool like AI in the same way that giving a task to a junior dev is going to involve some noisiness in need of supervision. But in grand scheme of software development, is devs are in the end tools too, apart of the grand stack, and I think it’s reasonable to consider AI as just another tool in the stack. This is especially so if devs are already using it as a tool.

Dwelling on the principled v statistical distinction, while salient, may very well be a fallacy or irrelevant to the extent that we want to talk about the stack of tools and techniques software development employs. How much does the average developer understand or employ said understanding of a principled component of their stack? How predictable is that component, at least in the hands of the average developer making average but real software? When the end of the pipeline is a human and it’s human organisation of other humans, whether a tool’s principled or statistical may not matter much so long as it’s useful or productive.

[BlueTemplar]

Yes, but this is not something that has been enabled by the new neural networks, but rather by search engines, years ago - culminating in the infamous «copy-paste from Stack Overflow without understanding the code» / libraries randomly pulled from the Web with for instance the leftpad incident.

So what makes it different this time ?