What the big new AI trend Tokkenmaxxing is & why its a big problem

Most AI spending right now is measured in tokens consumed. Jellyfish tracked 12,000 developers across 200 companies and found the heaviest users produced twice the output at 600 times the cost. Uber's internal numbers are even worse: 70% of submitted code was AI-generated, but only 11% of the code running in production was AI-written. So almost all of that AI code never made it into their app. There's a name for what's going on: tokenmaxxing.This episode goes past the leaderboard stories. The four forces driving token bills up faster than productivity can justify are a pricing model most teams don't fully understand, a workplace culture that turned consumption into a status signal, a quality gap that doesn't show up on dashboards, and something called the orientation tax, which is probably the biggest driver nobody has named yet.The second half covers what the companies getting real ROI from AI are doing differently, including why Salesforce built a new metric called Agentic Work Units to replace token counts, and what the right unit of measurement looks like for engineering, sales, legal, support, and marketing teams.⏭️ Episode highlights(01:00) – Uber's CTO: the budget was gone by April(03:30) – Where "tokenmaxxing" actually comes from(06:00) – Meta's Claudeonomics leaderboard: 60 trillion tokens in 30 days(08:30) – Jellyfish data: twice the output, 600 times the cost(11:00) – Goodhart's Law and the Soviet chandelier factory(13:30) – The orientation tax: why agents burn tokens before doing anything useful(17:00) – Salesforce's Agentic Work Units and why they matter(19:30) – How to define your own unit of work that actually held