I don’t think tokenmaxxing is a sign of AI’s demise—it’s the result of very real growing pains. Spend time around an AI-native product development team, and you’ll see significant productivity gains. But the prevailing approach to capturing those gains has been almost caveman-like: more AI good, with little discipline around cost.
Wasted token usage is a highly solvable problem. The catch is that the fixes go against the grain of AI “best practice.” Taken together, cost savings of 5x–10x are very achievable.
What this essay explores
- Choose models by task instead of treating frontier models as the safe default.
- Use reusable skills selectively, balancing predictable outputs against the context they add.
- Reserve AI for work that needs it rather than replacing deterministic rules.
From fixed rules to learned behavior: the shift from the computation era to the learning era.
Key takeaway
Three fixes to reduce token burn and improve ROI.
