Most AI Work Can Wait
TLDR ● Covered by 2 sources
Most teams building AI agents choose their models first, but should instead design around routing logic that directs requests to appropriate model tiers based on task classification and complexity. Coinbase reduced AI spending by 50% while increasing token usage through better routing, caching, and defaults, with 70-80% of traffic running on free local models or async batch inference that costs 90% less than real-time inference. By separating skill classification from routing decisions and using synchronous failure detection plus nightly feedback loops, teams can handle most non-coding work on cheaper models while reserving expensive real-time inference for genuinely time-sensitive tasks.
Why it matters
Design systems around routing, not models, for optimal AI implementation.
