Every technology choice is a trade-off, yet most teams argue over options that lose on every axis. A century-old idea makes the real decision visible.
A team ran an AI cost-routing layer for four months, then shut it off. The savings had not vanished. They had moved to a budget nobody was measuring.
Tens of billions in AI debt is secured by GPU clusters. But the part that makes a cluster worth the loan is the team running it, and that is the one asset no lender can seize.
AWS just showed customers trillion-dollar bills. The real exposure is the small metering error you can't see, on a consumption cost you can neither audit nor cap.
A cached token costs a tenth of a fresh one, and coding agents hit cache most of the time. Your AI cost curve is an engineering choice, and most teams make it by accident.
Hyperscalers finance AI chips over six years but the hardware is obsolete in two or three. That gap quietly sets the compute price every AI product is planning around.
The AI infrastructure boom is partly lending to itself. That changes the cost curve you're planning around, and most companies building on it haven't noticed.
