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Ethan Mollick: In the Age of AI Agents, We Can Already See the Outline of "Thing"

Ethan Mollick argues that since late 2025, AI agents such as Claude Code, OpenAI Codex, and OpenClaw have shifted humans from collaborating with AI to managing AI, entering a new stage. He cites benchmarks including the METR long-task curve, GPQA (the strongest AI scores 94%), and GDPval (the latest AI meets or exceeds top humans in 82% of scenarios), showing that AI capabilities are still improving exponentially. He takes the Software Factory built by a three-person team at StrongDM as an example: this model stipulates that code must not be written or reviewed by humans, and each engineer spends at least $1,000 per day on AI tokens. He also mentions that on February 22, a fictional report by Citrini Research triggered market volatility; on February 26, Block announced 40% layoffs; and on February 27, the Pentagon and Anthropic clashed over rules for Claude usage. He believes this week foreshadowed a future in which AI capabilities, markets, employment, and policy are intertwined. Dario Amodei of Anthropic, and Demis Hassabis of OpenAI and Google DeepMind, have all mentioned that recursive self-improvement (RSI) is advancing.

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Claude Code and What Happens Next

Ethan Mollick had Claude Code autonomously develop a startup project earning $1,000 per month; after the AI asked three multiple-choice questions, it worked independently for 1 hour and 14 minutes, generated hundreds of code files, and delivered a runnable, deployable website. He believes the leap in this kind of coding tool comes from two things: the latest models can autonomously complete more work in programming tasks and self-correct, and they are given an agentic harness of tools and methods. Using Opus 4.5-powered Claude Code as an example, the article explains how it breaks through LLM limitations through mechanisms such as context compaction, Skills, and subagents, and notes that OpenAI Codex with GPT-5.2 and Google Antigravity with Gemini 3 show similar performance.

The Shape of AI Capabilities: Jagged Frontiers, Bottlenecks, and Reverse Protrusions

In an article, Ethan Mollick points out that AI capabilities remain distributed in a jagged pattern, that weak spots such as memory have long shown no marked improvement, and that the system as a whole is constrained by its worst link. Taking Google's Nano Banana Pro as an example, he explains that image generation quality was once a bottleneck, and that after it was broken through, NotebookLM could directly generate full-page slides from source materials; he argues that judging where AI is heading should focus on bottlenecks rather than benchmark scores.

Ethan Mollick: AI agents can now complete real work

In an article in One Useful Thing, Ethan Mollick argues that AI agents have crossed the threshold of being able to complete real work with economic value. He had Claude Sonnet 4.5, using only the paper text and replication data, read the paper on its own, organize files, convert STATA statistical code to Python, and check each item one by one; it successfully reproduced multiple research results, which he then double-verified with GPT-5 Pro. He believes the improvement in agent capability stems from the fact that a small gain in accuracy is enough to complete more steps, and from new models' ability to self-correct; at the same time, he warns that using them without judgment may also produce 17 PowerPoints that nobody needs.