Token Efficiency: Building AI That Spends Intelligence Wisely
CIO, Thursday, October 15th, 2026
Rebuilding a platform with AI showed token waste comes mostly from architecture, not model choice.
The author describes rebuilding an enterprise AI platform using AI and discovering that token consumption depends as much on architecture as on the model.
Waste came from recursive agents spawning subagents, frontier reasoning models doing routine classification, retry loops without a clear success definition, and context carried long after it stopped mattering.
The fixes included matching reasoning effort to the task, moving deterministic steps into code and tools, and capturing reasoning as durable artifacts such as specifications or compiled skills.
Small inefficiencies multiplied across millions of executions outweighed any model upgrade.