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Ethan Mollick: The chatbot era is ending, and work is shifting to managing agents

Ethan Mollick argues that, as the length of tasks AI can complete autonomously keeps lengthening, the mainstream usage is shifting from collaborating with chatbots to handing work over to agents. Citing benchmarks such as METR, the UK AI Safety Institute, and GDPval, he says capabilities are improving faster than exponential growth; Epoch found that Opus 4.7 worked autonomously for 14 hours to complete a software package that would originally have required 2-17 weeks of human engineering, at a cost of $251 in tokens. He also mentions that joint research by OpenAI and academics shows that within OpenAI, non-technical roles such as legal and HR are adopting agents at a pace close to that of programmers, with a quarter of employees running at least four agents simultaneously each week.

GLM-5.2 Is a Turning Point for Open Source Agents

The author of Interconnects believes GLM-5.2 is the first open-weight model that is truly usable in coding agent frameworks, and community benchmarks show it is the only open source model able to compete head-to-head with the latest models from OpenAI and Anthropic. The author tested calling the model in Claude Code via the Fireworks API; overall capability immediately felt usable, but they encountered an issue where the framework sending images to the model caused the API session to break, requiring manual clearing of the context. The author calculates that 204 days separate the release of Claude Opus 4.5 from the release of GLM-5.2, falling within the commonly cited 6-to-9-month performance gap between open source and closed source, and believes this will intensify pricing pressure on heavy token consumers, benefiting the open source model inference and fine-tuning ecosystem.

Claude Dispatch and the Interface Debate: Why the Chat Window Is Not the Right Way to Get Work Done with AI

Ethan Mollick argues that AI's capabilities have long outstripped how most people use it, and that the bottleneck is the chat window interface itself. He cites a study noting that when finance staff used GPT-4o for valuation tasks, they saw productivity gains, but these were offset by the cognitive burden of long blocks of text and meandering topics, with less experienced employees affected the most. Using Anthropic's Claude Cowork and Dispatch as examples, he shows that messaging an agent on your desktop from your phone and having it read your calendar and email and edit PowerPoint charts is closer to collaborating with an assistant than a chat window is; he also mentions that OpenClaw became the fastest-growing open-source project by relying on familiar entry points like WhatsApp, but it is hard to use and carries high security risks. He further notes that Claude can already generate interactive visualizations within a conversation, and argues that the future is not a unified interface but AI generating interfaces on demand for the task at hand.

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.