Token-Maxing
/ˈtoʊkən ˈmæksɪŋ/
Also known as: token maxxing, full-strength agents
businessbeginner
What is Token-Maxing?
Token-maxing is the practice of giving an AI agent as much context and compute as the task can use, instead of rationing tokens to save cost. Garry Tan describes it as loading hundreds of thousands of tokens into each request through a self-managed harness such as OpenClaw or Hermes Agent, because hosted chat products limit how much compute they give each user.
Why It Matters
The argument is economic. Tan estimates full-strength use at $50,000-$100,000 a year, which is small next to a founder's or CEO's time. The output is work done at frontier quality on every request, plus reusable skills that capture it.
Related Reading
- Agent Skills - Where token-heavy work gets captured for reuse
- Agent Harness - The layer that decides what goes into context