On this episode of Masters of Data, we dig into what we’re calling the tokenpocalypse, the real cost of running AI at the scale most of us have quietly slid into. We trace the whiplash of the past two years, from “everyone needs the AI hammer” to backlash against obvious AI slop, then get into the harder question of what happens when a mission-critical process runs out of tokens, from chaining Claude to Databricks for a custom dashboard to the eye-watering budget Uber reportedly burned through by April. We also weigh in on cheaper Chinese frontier models, the pull toward localized and specialized AI, and whether the current build-out of AI infrastructure can realistically pay for itself. If you’re the one watching the AI line item on your budget, or building workflows that quietly depend on a model always being available, this conversation is a useful gut check.
0:00:00 – Intro and cohost banter
0:00:35 – Naming the “tokenpocalypse”
0:02:03 – From “use the AI hammer” to backlash against AI slop
0:04:56 – Chaining Claude and Databricks to build a dashboard
0:06:02 – Running out of tokens on mission-critical work, and Uber’s budget blowout
0:08:30 – Why dumping your entire data lake into AI wastes tokens
0:11:13 – Cheaper Chinese models and the pricing fallout
0:13:08 – From ChatGPT to Claude, and the case for localized AI
0:15:21 – Whether the AI infrastructure buildout is built for the wrong future
0:19:52 – Wrap-up