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News4 min read·2026-10-21DisconfirmAI Editorial

'Co-Intelligence' Author Ethan Mollick Proposes the 'Conjurer' Workflow: Summon AI Agents, Evaluate Results

On the release of his new book Co-Existence, Ethan Mollick draws on his own experience producing data charts with Opus 5.5 to propose a third mode of human-AI collaboration: the 'conjurer'—summoning AI agents, assigning tasks, and evaluating results without step-by-step guidance. He considers this the most powerful way to use AI today, but it means losing control over what the AI is actually doing.

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Original publication time 2026-10-20 23:57 GMT+8

Ethan Mollick recently produced a set of data charts for his new book Co-Existence. The tool he used was Opus 5.5 inside Claude Projects—an 'orchestrator' agent that, upon receiving instructions, splits tasks on its own, spins off sub-agents, assigns work, and relays results back to him. He gave a couple of sentences of suggestions, and the AI completed what would have taken him hours: all the research, all the graphing, all the layouts, all the images, all the citations. He even told the AI to 'make it better'—a vague reaction that would drive a human team mad—and the AI complied. More surprisingly, it caught a math error he had made.

This experience itself became the starting point for his argument. In Co-Intelligence, published in early 2024, Mollick described two modes of human-AI collaboration: 'centaur' work (divided between AI and humans, each doing their own parts) and 'cyborg' work (blended between AI and humans). Both keep you in the loop. But now that we have real agents, he added a third: the 'conjurer.' Conjurers summon agents when needed, assign them work, and evaluate what comes back. The way he made these charts was conjurer-style: summon agents, give some general guidance, and say 'make it better' repeatedly.

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Why has this shift happened? Mollick uses the METR Long Horizon Benchmark to illustrate. The benchmark measures the amount of human work an AI can do with at least a 50% chance of success from a single prompt. From the release of GPT-4 to the end of the METR measure, that number doubled eight times. In 2024, an AI could do a four-minute task; by April 2026, when he turned in his draft, an AI could do 17 hours of human work. Then the benchmark itself ran out—METR said they could no longer accurately measure the amount of work an AI could do using their current methods. Other studies suggest that number might be 17 weeks of work, or more.

Costs have dropped in parallel. The Google Proof Question and Answer Exam he mentions—giving AI problems that regularly stump graduate students—used to cost 30 cents in AI usage to get a single correct answer; now it costs four hundredths of a cent. The model landscape has shifted too: GPT-4's period of undisputed dominance lasted roughly a year, after which twenty different models have held the lead at one time or another, all from Anthropic, Google, or OpenAI. A total of 180 AI models are now better than GPT-4 was at its release, many of them open-weights models that anyone is free to use or modify. Nineteen can run on a modern smartphone.

But Mollick emphasizes that the frontier of AI ability is still jagged. Just as it was 30 months ago, some areas have stayed stubbornly weak—AI is still quite a bad long-form writer—while others, like solving math problems, went from terrible to superhuman in the same period. And even where AI is excellent, small gaps create large needs for humans—at least for now.

The risk of the conjurer mode lies precisely here. It is the most powerful way to use AI, but we lose control over what, exactly, the AI is doing to get us those results. You do not know what the agent did in the middle, what it skipped, or where it might have gone wrong. Mollick himself says he told the AI 'make it better' many times—a vague reaction that would drive a human team mad, but the AI complied.

His new book Co-Existence does not try to keep up with the news or the latest model releases. Rather, it focuses on trends: AI is improving exponentially but unevenly, and the way we use it is shifting from chatbots to agents that do real work. The book's core question is what that means for the humans who have to co-exist with these systems: where we still have an advantage. His answer comes down to four things—deep knowledge, wide knowledge, taste, and agency. If you understand the trends, the present and near future become much less surprising.

Sources

  • 原始出处oneusefulthing.org↗
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