Diginomica warns that enterprise AI programs can drift into tokenmaxxing consumption goals, creating spend without clear business results and amplifying security risk.
Without visibility into where tokens go and what they produce, AI budgets expand while leaders still can't explain ROI or risk exposure.
Build FinOps-style governance: budgets, chargeback, and "good prompts" playbooks that prioritize narrower context and verifiable outputs.
Don't mandate "AI first" by token counts - mandate measurable outcomes, and treat token burn like cloud waste to prune.
langfuse/langfuse
Open-source LLM engineering platform for observability, traces, metrics, evals, prompt management, datasets, and playground workflows.
promptfoo/promptfoo
A CLI and CI workflow for testing prompts, agents, and RAG systems across models, with evals and red-team style checks.
stanfordnlp/dspy
A framework for programming and optimizing language-model pipelines rather than hand-tuning one prompt at a time.
Fortune reports that internal AI leaderboards can encourage "tokenmaxxing" - running trivial tasks to inflate usage - turning adoption into a status game instead of value delivery.
InfoWorld argues tokenmaxxing repeats the old mistake of treating a countable activity metric as developer productivity.
The Conversation pushes tokenmaxxing out of productivity talk and into a philosophical question about what work is for.