Then use batteries as an example. Going on 200+ years now most of the major changes have been an easy swap: chemistry. Lead acid --> nickel cadium --> nickel metal hydride --> lithium ion. Improvements in batteries are hard and the surrounding ecosystem has gotten better. Better BMS and better motors have also made gains to increase the efficiency of the battery. Compounding gains within the ecosystem doesn't truly improve the battery directly, but improves them for implementation and use cases.

All the remaining problems with batteries are a hard solve because it's tradeoff after tradeoff. Lithium sulfur, for example, could be amazing but die after a couple hundred cycles at most [0]. So... If LLMs are so novel, as many continue to claim, why hasn't this been solved? Probably because LLMs are an interpretation of our current understanding of everything. An LLM currently only does what any human can do, albeit in a compressed timeframe, mostly.

I think it follows. LLMs have made some considerable improvements, but use "reasoning" (really just a mechanical, autoregressive, loop: generate tokens --> append tokens to context --> feed the expanded context back into the model --> generate the next token --> repeat) as an example. You can do this by hand as well in a traditional chat volley, but it's slower. So we improved LLMs by putting them in an automatic loop (oversimplification - but at the end of the day holds water).

Until LLMs showcase true external breakthroughs that aren't driven by human guidance (not happening anytime soon) we are in this loop of hacks being used to improve the 80% (the LLM itself). Notable jumps? "Reasoning" and with Mythos-like models we now have "Advanced Reasoning" (by leveraging a more capable looping framework such as an agentic harness).

[0] https://advanced.onlinelibrary.wiley.com/doi/10.1002/adfm.20...