Token cost optimization becomes its own research problem

A thread titled 'I burned all my tokens researching how to save tokens' attracted comments about practical strategies for reducing LLM context size, including getting models to communicate in shorter formats and managing what goes into context windows. This is a small thread but it points to something real: as AI agents become more complex and multi-step, token costs compound fast.

The LoopGain thread ran adjacent to this, proposing applying control theory to stop agent loops instead of just setting max_iterations. Both threads are symptoms of the same problem: agentic AI workflows are expensive and unpredictable, and the current best practices are crude.

The pattern: the first generation of AI products was about getting the model to do the thing. The second generation is about making it do the thing cheaply and reliably enough to survive in production. That transition is happening now.