A useful counterweight to leaderboard culture: measure work units and outcomes, not just tokens consumed.
It gives the site an important enterprise counterargument: leaders want evidence of work completed, not a vanity race over who consumed the most model capacity.
This is the anti-scoreboard read. Token volume may show activity, but it becomes useful only when tied to accepted outputs and business workflows.
Salesforce's work-unit framing is a good phrase to keep next to any token leaderboard or agent-spend chart.
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.
Canadian workplace experts argue token dashboards can show AI adoption, but they are weak measures of output quality or business value.
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.