Tom’s Hardware reports Uber leaders are questioning whether rising AI token spend is translating into shipped features and measurable productivity.
As agentic tools multiply calls behind the scenes, usage-based billing can surge faster than impact. Execs are starting to demand ROI signals instead of “use more AI” KPIs.
Treat tokens like cloud spend: instrument cost per workflow, set per-team budgets, and tie “AI usage” to outcome metrics (deploys, tickets, revenue touches) so loops don’t run unchecked.
Uber’s COO says the link between more token usage and more useful product isn’t clear yet.
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.
Amazon nixed an employee-created AI leaderboard called "KiroRank" after concerns it encouraged excessive AI spending.
Axios is useful this week because it treats enterprise AI spending as a proof problem, not just an adoption milestone.
Business Insider adds a market-skeptic angle: tokenmaxxing can be read as demand strength, but skeptics are asking how durable that demand really is.