LLMs learned JavaScript from a web that never deletes anything. Now they generate 2013’s best practices at scale, publish them back onto the web, and the next model learns from that. The graveyard has a feedback loop.
Context isn’t what you paste into the prompt. It’s what the model sees, prioritizes, and is constrained by. That’s not prompting — that’s system design.
LLMs don’t ship software. They ship text. You ship software by constraining the problem until text reliably becomes change sets you can trust.
The bottleneck was never intelligence. It was instrumentation. Once we learn to define what systems should do, the execution becomes trivial.
Large Language Models are best described as GAC — Glorified Auto-Complete. If Google Search Suggest was the ape, LLMs are the upright-walking hominid.