To be honest, I feel like what you describe in the second part (the monkey analogy) is more of a genetic algorithm than a machine learning one, but I get your point.

Quick side note, I wasn't at all including a discussion about energy consumption and in that case ML based algorithms, whatever form they take, will mostly consume more energy (assuming not completely inefficient "classical" algorithms). I do admit, I am not sure how much more (especially after training), but at least the LLMs with their large vector/matrix based approaches eat a lot (I mean that in the case for cross-checking tokens in different vectors or such). Non LLM, ML, may be much more power efficient.

My main point, however, was that people only remember AI from ~2022 and forgot about things from before (e.g. non LLM, ML algorithms) that were actively used in code completion. Obviously, there are things like ruff, clang-tidy (as you rightfully mentioned) and more that can work without and machine learning. Although, I didn't check if there literally is none, though I assume it.

On the point of game "AI", as in AI opponents, I wasn't talking of that at all (though since deep mind, they did tend to be a bit more ML based also, and better at games, see Starcraft 2, instead of cheating only to get an advantage)

How so? A Large Language Model is usually a transformer based approach nowadays, right (correct me if outdated)?

AI is artificial intelligence, which has been used and abused for many different things, none of which are intelligent right now (among others used for machine learning).

Machine learning is based on linear algebra like linear regression or other methods depending what you want to do.

An algorithm is by definition anything that follows a recipe so to say.

All of these things, bare transformers and newer in development approaches like spiked neural networks or liquid neural networks are fairly basic, no?

EDIT: typos

I am not talking about what it does, I am talking about what it is.

And all tools do tend to replace human labor. For example, tractors replaced many farmhands.

The thing we face nowadays, and this is by no means limited to things like AI, is that less jobs are created by new tools than old destroyed (in my earlier simile, a tractor needs mechanics and such).

The definition of something is entirely disconnected from its usage (mainly).

And just because everyone calls LLMs now AI, there are plenty of scientific literature and things that have been called AI before. As of now, as it boils down all of these are algorithms.

The thing with machine learning is just that it is an algorithm that fine tunes itself (which is often blackbox-ish btw). And strictly speaking LLMs, commonly refered to as AI, are a subclass of ML with new technology.

I make and did not make any statement of the values of that technology or my stance on it

Sure just like Republicans were against big tech a few years ago.