Token Efficiency Is the New Performance Optimization

A post titled 'I burned all my tokens researching how to save tokens' generated a real thread about prompt engineering economics. The core finding: starting in the right part of the search space matters more than any compression trick, and using cheap models to narrow the problem before escalating to expensive ones is the biggest lever. This is basically the same insight as query planning in databases.

The thread also surfaced something pointed: American LLMs are trained on English-dominant data, which creates invisible quality degradation for non-English use cases. Token efficiency isn't just about cost, it's about the model's actual knowledge density varying by language and domain.

Separately, the LoRA Speedrun leaderboard post is pushing toward efficiency from a different angle, optimizing fine-tuning wall-clock time rather than inference cost. Both threads are circling the same pressure: compute costs are real, and the 'just throw more at it' approach has diminishing returns.