I would not call it tokenpocalypse and do nothing. Token price shocks hurt when your product treats long context like free air. Many teams burned margin themselves.
That pain is often self-inflicted.
Audit prompt bloat first
Are you shipping entire documents when a summary would work?
Are you looping models for tasks a smaller one handles?
Are retries unbounded?
Fix engineering waste before you tweet about apocalypse.
Model mix is margin strategy
Use cheaper models for classification, routing, and drafts, then reserve expensive models for the steps that truly need them, because paying frontier prices for every label or routing decision is how a profitable feature turns into a charity project.
Log cost per user per feature weekly.
Pass costs through honestly
If a feature is inference-heavy, price it, cap it, or gate it.
Subsidizing power users with unlimited tokens is a fast way to die when prices jump.
Customers care about outcomes, not tokens
Talk about limits in human terms: summaries per month, pages analyzed, seats.
Token drama is insider vocabulary.
Token shocks are real. They are also a forcing function to run AI products like software with gross margin, not like magic with infinite budget.
