It’s actually just a lot of pretty simple maths from decades ago, but it’s a lot of it. The big changes in those decades have been the feasibility of doing enough of that simple maths to achieve anything useful, and domain-specific network architecture stuff that’s rarely transferable, e.g. LLMs are possible because of the invention of the transformer architecture in 2017, and that’s also turned out to be useful for a few things like image generation and protein folding simulation, but not for all neural network based techniques, and then most of the things that have made successive LLMs better haven’t also been useful for the few other transformer-architecture-based neural networks. Most not-LLM AI isn’t going to be meaningfully easier to create than it would have been had the world got bored after GPT-2 and we’d only focussed on doing image and video generation.