Im more concerned with an LLM having the ability to be trained to the point where a subset of the graph represents all the nand gates necessary for a cpu and ram, so when you ask it questions it can actually run code to compute them accurately instead of offering a statistical best guess, i.e decompression after lossy compression.
Philosophically, you can start ad hoc-ing functionalities on top of LLMs and expect major progress. Sure, you can make them better, but you will never get to the state where AI is massively useful.
For example, lets say you gather a whole bunch of experts in respective fields, and you give them a task to put together a detailed plan on how to build a flying car. You will have people doing design, doing simulations, researching material sourcing, creating CNC programs for manufacturing parts, sourcing tools and equipment, writing software, e.t.c. And when executing this plan, they would be open to feedback for anything missed, and can advise on how to proceed.
The AI with above capability should be able to go out on the internet, gather respective data, run any soft of algorithms it needs to run, and perhaps after a month of number crunching on a cloud rented TPU rack produce step by step plan with costs on how to do all of that. And it would be better than those experts because it should be able to create a much higher fidelity simulations to account for things like vibration and predict if some connector if going to wobble loose .
Evolution created various neural structures in biological brains (visual cortex, medulla, thalamus, etc) rather ad-hoc, and those resulted in "massively useful" systems. Why should AI be different?
Consider how humans design things. We don't talk through every CPU cycle to convince ourself a design works, we use bespoke tooling. Not all problems are language shaped.
Tool usage is better, because the LLM can access the relevant computing/simulation at the highest fidelity and as fast as they can run on a real or virtual computer, rather than emulated poorly in a giant pyramid of matrix multiplications.
Am I missing the point?
This is why I am very interested in analog again—quantum stuff is statistical already, so why go from statistical (analog) to digital (huge drop off of performance, e.g. just look at basic addition in a ALU) and back to statistical. Very interested. Not sure if it will ever be worth it, but can’t rule it out.
