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.
The text is the source's own description of its publication. The content belongs to AI Engineer.
More from AI Engineer
Inside Arca's Long-Horizon Agents That Run the Business - Aman Singh, Arca
AI Engineer 23 Oct
Real AI Agent Attacks and What Would Have Stopped Them — Andrey Rybka, Bloomberg
AI Engineer 23 Oct
Outside of tech there has been no financial payoff to US companies for using AI, now viewed as a margin-hurting cost center, as VC-backed firms burn more tokens
Techmeme 4 h ago
Source: Replit recently moved most AI spend from Anthropic to OpenAI, which gave 30%+ off in a deal worth $100M+/year, after Anthropic discontinued its discount
Techmeme 6 h ago