Coinbase cut its AI bill in half while token usage went up. Costs went down, consumption went up, and that combination is not luck. It is engineering.
On June 28, Brian Armstrong posted the exact playbook his team used. Five moves. Most teams are running none of them, which is why most teams watch their AI spend climb while their output barely moves.
I am an applied AI architect at Microsoft, and I have put production-grade agents inside Fortune 500 environments. So when I say Coinbase's playbook is smart, I mean it: it is exactly the kind of move the recent progress in open-weight models now makes possible.
The reflex answer most teams reach for is the wrong one. They renegotiate the contract, throttle usage, or wait for prices to drop. Coinbase did something different. They left the models alone and re-engineered the system around them. The bill fell by half and usage went up, because lower cost per task meant teams could afford to point AI at more tasks
This week’s edition breaks down …
