Applied Methods
~SignalsAgents Over Bubbles

External signal·Stratechery·Mar 16, 2026·Ben Thompson·10 min read

Agents Over Bubbles

NeutralMid-Term (3-5 yrs)
agents actively verify the results without humans needing to be in the loop

Summary

Ben Thompson distinguishes "answer inference" (the model hands a human an answer) from "agentic inference" (the model does a task autonomously, with agents verifying their own work without a human in the loop). Tracing three LLM inflection points — ChatGPT for token prediction, o1 for reasoning, and Opus 4.5 / Claude Code for the first usable agents — he argues the surge in autonomous, task-completing agents is what drives soaring compute demand and undercuts the AI-bubble thesis, with Anthropic and OpenAI becoming the integration points in the enterprise value chain.

Predictions for the future of work

Predicts agentic inference — software that completes whole tasks rather than answering questions — becomes the dominant mode and the main driver of compute demand, making Anthropic and OpenAI the layer enterprises build on. The future-of-work implication is indirect but pointed: the trajectory is toward work performed autonomously, without a human in the loop.

agentic inferenceenterprise AItoken demandproductivityBen Thompson

Originally published by Stratechery · Mar 16, 2026

Read the original at Stratechery