The AI Bill Is Too Damn High: Tokenomics 101 — Eugene Cheah, Featherless AI

If your token spend keeps going up and productivity isn't keeping pace, start here. Eugene Cheah lays out four steps for cutting AI inference costs at scale: measure, right-size, buy capacity well and check your processes. Along the way he covers splitting API keys per team, agentic vs. process workloads, open models that match frontier models on specific tasks, classifiers, fine-tuning, the speed-vs-cost tradeoff and "slop grenades." Recorded live at AI Engineer NYC 2026. 🎟️ Join us at the next one: AIE CODE Summit → https://aie.codes Speaker Eugene Cheah — X: https://x.com/picocreator Featherless AI — https://featherless.ai Timestamps 0:00 Intro: Eugene Cheah, Featherless AI 1:48 What "scale" looks like: 2T tokens/day 2:13 Disclaimers: who this is for 3:13 The 4 moves 3:23 Move 1: Measure (stop sharing one API key) 5:12 Agentic vs. process workloads 9:27 Move 2: Right-size your models 12:42 Classifiers: a new class of cheap models 14:37 Plan with the smart model, build with the cheap one 15:47 Nobody switches models to save money 19:27 Give teams model labels, not model names 25:27 Bootstrapping your own fine-tuned model 27:42 Shadow traffic to keep improving 28:57 Move 3: Buying GPU capacity 30:07 2x the speed = 3x the cost 32:22 Bursty traffic & dedicated capacity 34:32 Move 4: Check your processes 37:07 Tokens are not work done 42:07 Don't throw slop grenades 44:27 Q&A: Fine-tuning & data sets 48:02 Q&A: Golden traces 50:04 Q&A: Escaping harness lock-in 52:37 Q&A: Caching & auto-routing