OpenAI's Jalapeño Chip and the 15x Agent Token Problem

Nvidia's own framing for its Vera Rubin extensions puts agentic workloads at roughly fifteen times the token consumption of a single chat request, because an agent chains queries, tool calls and sub-agents. In the same cycle, OpenAI announced its first custom inference chip — with benchmarks reportedly run independently by SemiAnalysis showing it ahead of currently available Nvidia hardware — Nvidia claimed up to thirty times more work per watt, Google halved the introductory price of its fast Gemini Flash tier, and OpenAI cut GPT-5.6 pricing with a guarantee through at least November. Four layers of the stack, one arithmetic problem: agent products only work if the cost of a completed task falls faster than agents' token appetite rises. Alongside that, five announcements filled  ...