About this episode

This episode explores the paradox of AI agent economics, highlighting how cheaper compute does not necessarily lead to lower AI costs.

What if building more highways made your commute *slower*? That's the paradox at the heart of AI agent economics: even as per-token inference costs have plummeted dramatically over the past two years, total LLM spending keeps climbing. Drawing on a surprising lesson from Robert Moses's mid-century New York infrastructure projects, this episode unpacks why cheaper compute doesn't necessarily mean cheaper AI — and what's really driving the economics of running agents at scale.

People in this episode

Host: Katie Malone

Topics covered

• AI economics
• LLM spending
• infrastructure projects
• compute costs
• agent scaling

Keywords

• AI agents
• inference costs
• Robert Moses
• New York infrastructure
• economics of AI

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