Tokenomics and tokenflation

Plain English. The unit economics of paying for AI by the token. Tokenflation is the trap inside it: the price per token keeps falling while bills keep rising, because harder tasks — and reasoning models especially — consume vastly more tokens per job.

Why it moves money. Per-token prices for a fixed capability level fell roughly 85-fold in about fifteen months on Epoch's data (reported), yet business AI spending rose over the same period; enterprises have hit token budget caps mid-quarter, and Accenture's leaked framing was that non-engineers, not engineers, drive consumption. The metric that survives this is cost per task, not price per token — and it is already legible: frontier-quality task completion has been measured at nine US cents (see $/M tokens). Vendors quote the falling number; bills follow the rising one.

What to watch. Cost-per-task disclosures replacing per-token price cards; token meters and caps appearing in enterprise products; whether reasoning-token consumption per task keeps climbing as models think longer.

From the signals. Token prices fall, AI bills rise: the measurement gap. Cost per task falls to nine cents. The tokenpocalypse gets a name and a playbook.