Decision-maxxing

Optimizing AI use around whether it helps people make better decisions, measured by accuracy and judgment quality rather than speed alone.

A hiring manager using AI to screen resumes might process candidates in half the time. If the AI's ranking biases toward keyword-stuffed resumes, though, the decisions get worse even as they get faster. Decision-maxxing names this gap: did the AI help you choose more wisely, or just more quickly? The term is newer and less established than tokenmaxxing or outcome-maxxing, yet it captures something teams routinely overlook when they celebrate efficiency gains.

Builder example

Any product that claims to improve judgment (copilots, dashboards, recommendation engines) needs a way to measure whether decisions actually improved. Without that measurement, a polished AI explanation can make people trust a flawed recommendation more confidently than they would have trusted their own instincts.

A founder asks whether to buy a CRM or build a custom one. The AI says 'build' because custom software sounds strategic.

Show maintenance cost, integration risk, vendor lock-in, data ownership, and the next reversible test. Help the human decide, not just agree.

Common confusion: Speed and quality are different metrics. Cutting decision time in half is valuable only when the decisions remain at least as accurate. Faster bad choices are still bad choices.