A growing number of firms are reportedly pulling back on the open-ended AI spending policies they championed, discovering that “encourage everyone to experiment” translates into surprisingly large invoices when multiplied across thousands of employees. The pivot from enthusiasm to austerity has been swift enough to earn the nickname “Tokenpocalypse,” a reference to the per-token pricing models that underpin most large language model APIs.
The cost problem nobody budgeted for
The challenge is compounded by the fact that many companies rolled out AI access without establishing clear ROI frameworks. Teams were told to integrate AI into workflows, but nobody was tracking whether the productivity gains actually justified the spend.
As companies move from lightweight queries to more complex, multi-step AI workflows involving agents and retrieval-augmented generation, the computational overhead per task has increased meaningfully.
Why crypto markets should pay attention
