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.
TechCrunch reports Accenture is reining in employees who spend premium AI tokens on trivial jobs — like converting PDFs into slide decks — after agentic AI lead Justice Kwak flagged spend turning unpredictable and material to costs.
CFO Brew reports vendors including Pegasystems and Intercom are shifting from token-metered pricing toward outcome-based fees as buyers question whether uncapped AI spend ever paid for itself.
Fortune India charts the move from tokenmaxxing to ROI: Uber spent its ~$3.4B-equivalent annual AI budget in four months and capped engineers at $1,500/mo, while only 21% of firms have mature agentic-AI governance, per Deloitte.