Reading up on large-language-models

The Waymo effect shows frictionless AI tools replace valuable human interaction in research, eroding serendipity and depth unless institutions actively fund collaborative friction.

The paper proposes a roadmap for recursive self‑improvement in AI, using HCI to diagnose LLM limits and outlining autonomy stages toward genuine meta‑improvement.

Antirez argues LLMs are not stochastic parrots, that reinforcement learning will drive further scaling, and that chain-of-thought improves output by enabling internal search and learned reasoning steps.