Tokenmaxxing: Brute-Forcing AGI by Scaling Usage
SMRTR summary
"Tokenmaxxing" is a strategy where everyday AI users dramatically increase token consumption to unlock better results — without training or fine-tuning models themselves. Just as AI labs improve models by scaling compute during training, users can scale inference by running AI in loops, repeatedly refining outputs through self-refinement, agent loops, and spec churning. Research shows iterative refinement delivers roughly 20% better results, while systems like DeepMind's AlphaEvolve — built on standard Gemini models — solved century-old math problems using this approach at massive scale.
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