Tokenmaxxing fails as AI ROI metric; workflow redesign needed for productivity gains
By
Jeremy Kahn
Fresh out the oven, still warm. Top of the tray.
Summary
The article argues that 'tokenmaxxing'—using token usage as a proxy for employee AI innovation—has failed to deliver meaningful ROI for companies. True productivity gains from AI come from fundamentally redesigning workflows rather than simply automating existing tasks. The piece covers broader AI industry news including CNN's lawsuit against Perplexity, IBM and RedHat's $5 billion bug patching project, Snowflake's $6 billion AWS deal, and the White House allocating $9 billion for intelligence agencies to build their own AI chip cluster.
Key quotes
· 3 pulledJust a few weeks ago, it seemed that 'tokenmaxxing' was all the rage inside many companies.
The idea was: if you wanted to find out which employees were being most innovative in deploying AI agents, you should track their token usage.
Token usage is a poor proxy for company-wide productivity gains that only come from redesigning workflows, not just automating them.
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