· Jan 27, 2026
Ethan Mollick argues in One Useful Thing that management is an AI superpower
Markdown · JSON · Original source
’s essay was published on January 27, 2026. He describes an experimental University of Pennsylvania class in which executive MBA students used AI tools to build startup prototypes and argues that their management and subject-matter expertise helped them direct the work. The prototypes were not yet working startups or fully operational products, but Mollick says the students had moved far along the path toward one and made it easier to explore ideas and change direction. Mollick frames delegation as a trade-off: AI can produce work quickly and cheaply, but people must account for the spent instructing and evaluating it, and for the chance that an output will meet their standards. He argues that clear goals, useful feedback and good evaluation can improve that trade-off. In his view, familiar management practices—defining deliverables, limits and what “done” looks like—can guide as they guide people. The students’ results, he says, came not from being AI experts but from knowing how to scope problems and recognize good work. He concludes that knowing what to ask for is becoming scarce as AI “talent” becomes abundant and cheap. He does not know exactly what work will look like when everyone manages AI agents; he suspects people who can recognize and explain good work will thrive.
Headline and summary by Escape Meridian, prepared by Luna from the attributed source. A source check is not independent confirmation of the source’s claims.
Original source
· Article
Source details
Original source date: Jan 27, 2026 (2026-01-27T16:55:55.000Z).
Date basis: Date supplied by the publisher’s feed.
Added to Escape Meridian: 2026-11-08T11:28:23.076Z. First observed: 2026-09-23T05:39:05.736Z. Retrieved: 2026-10-24T07:57:39.362Z.
Referenced by
Recorded citations and public file attachments. A reference alone does not establish agreement or influence.
No public references recorded here yet.
Limits
Text as retrieved on October 24, 2026; originally published January 27, 2026. Mollick describes an experimental class and his estimate of its results; the essay does not establish how broadly those results apply to other settings. Mollick presents the future of work with as uncertain.
Open question
How well will management skills help people direct when the work requires expertise they do not already have?
Source-stated
Ethan Mollick says students in his experimental class used AI tools to build startup prototypes, and that their management and subject-matter expertise helped them direct the work.
I introduced them to Claude Code and Google Antigravity, which they needed to use to build a working prototype.
Source-stated
Mollick says the student prototypes were not yet working startups or fully operational products, but that the students had moved far along the path toward one.
These were not yet working startups nor were they fully operational products (with a couple exceptions) — but they had shaved months and huge amounts of money and effort from the traditional process.
Source-stated
Mollick argues that whether to delegate to AI depends on the human time required, the time spent requesting and evaluating AI work, and the AI’s probability of producing an acceptable result.
Human Baseline Time: how long the task would take you to do yourself Probability of Success: how likely the AI is to produce an output that meets your bar on a given attempt AI Process Time: how long it takes you to request, wait for, and evaluate an AI output
Source-stated
Mollick argues that management skills, including defining deliverables and evaluating work, can help people work with AI agents; he says knowing what to ask for is becoming scarce.
Now the “talent” is abundant and cheap. What’s scarce is knowing what to ask for.
Source-stated
Mollick says he does not know exactly what work will look like when everyone manages AI agents, and suspects those who can recognize and explain good work will thrive.
I don’t know exactly what work looks like when everyone is a manager with an army of tireless agents. But I suspect the people who thrive will be the ones who know what good looks like — and can explain it clearly enough that even an AI can deliver it.
Source and contributor context
One Useful Thing · Publisher
The publisher is identified here. No individual author has been inferred.
Source media
Media retrieval status: unchecked.
Terms and references
Reference context selected by Escape Meridian; separate from source evidence. Entity mentions do not establish authorship, involvement or endorsement.
AI agent
An AI system that chooses and carries out actions toward a goal, often using software tools.
Ethan Mollick
Ethan Mollick is a Wharton researcher and author exploring how AI changes work, learning and decision-making.
One Useful Thing
One Useful Thing is Ethan Mollick’s publication about AI in work, education and everyday practice.
TIME
TIME is a news magazine publishing reporting on world affairs, business, technology and the workers behind digital services.
Editorial provenance
Version 1. Publication ID: 3958fd09-7acd-7df1-5758-7d71675b7ee6.
Prepared by openai/gpt-6-luna; grounding review by anthropic/claude-opus-5.5.
Source check: passed by anthropic/claude-opus-5.5 on 2026-11-08T11:23:45.712Z. This checks fidelity to the source, not whether its claims are independently true.
Editorial selection: accept; reasons: bridge_admitted_by_model, second_opinion_categorical.
Observation: c76005c554e5352406fbd9e0eab24f6285ac794b3a5764faea4ee7fd563ceeac. Source text hash: eaf3a9b37e1dd41bc7a6cc9516125699d3412e3b0864ae66e30989e48ff90962.
Revision and source-check history
Version 1 · 2026-11-08T11:28:23.076Z
Ethan Mollick argues in One Useful Thing that management is an AI superpower
’s essay was published on January 27, 2026. He describes an experimental University of Pennsylvania class in which executive MBA students used AI tools to build startup prototypes and argues that their management and subject-matter expertise helped them direct the work. The prototypes were not yet working startups or fully operational products, but Mollick says the students had moved far along the path toward one and made it easier to explore ideas and change direction. Mollick frames delegation as a trade-off: AI can produce work quickly and cheaply, but people must account for the spent instructing and evaluating it, and for the chance that an output will meet their standards. He argues that clear goals, useful feedback and good evaluation can improve that trade-off. In his view, familiar management practices—defining deliverables, limits and what “done” looks like—can guide as they guide people. The students’ results, he says, came not from being AI experts but from knowing how to scope problems and recognize good work. He concludes that knowing what to ask for is becoming scarce as AI “talent” becomes abundant and cheap. He does not know exactly what work will look like when everyone manages AI agents; he suspects people who can recognize and explain good work will thrive.
Original source
· Article
Source details
Original source date: Jan 27, 2026 (2026-01-27T16:55:55.000Z).
Date basis: Date supplied by the publisher’s feed.
Added to Escape Meridian: 2026-11-08T11:28:23.076Z. First observed: 2026-09-23T05:39:05.736Z. Retrieved: 2026-10-24T07:57:39.362Z.
Limits
Text as retrieved on October 24, 2026; originally published January 27, 2026. Mollick describes an experimental class and his estimate of its results; the essay does not establish how broadly those results apply to other settings. Mollick presents the future of work with as uncertain.
Open question
How well will management skills help people direct when the work requires expertise they do not already have?
Source-stated
Ethan Mollick says students in his experimental class used AI tools to build startup prototypes, and that their management and subject-matter expertise helped them direct the work.
I introduced them to Claude Code and Google Antigravity, which they needed to use to build a working prototype.
Source-stated
Mollick says the student prototypes were not yet working startups or fully operational products, but that the students had moved far along the path toward one.
These were not yet working startups nor were they fully operational products (with a couple exceptions) — but they had shaved months and huge amounts of money and effort from the traditional process.
Source-stated
Mollick argues that whether to delegate to AI depends on the human time required, the time spent requesting and evaluating AI work, and the AI’s probability of producing an acceptable result.
Human Baseline Time: how long the task would take you to do yourself Probability of Success: how likely the AI is to produce an output that meets your bar on a given attempt AI Process Time: how long it takes you to request, wait for, and evaluate an AI output
Source-stated
Mollick argues that management skills, including defining deliverables and evaluating work, can help people work with AI agents; he says knowing what to ask for is becoming scarce.
Now the “talent” is abundant and cheap. What’s scarce is knowing what to ask for.
Source-stated
Mollick says he does not know exactly what work will look like when everyone manages AI agents, and suspects those who can recognize and explain good work will thrive.
I don’t know exactly what work looks like when everyone is a manager with an army of tireless agents. But I suspect the people who thrive will be the ones who know what good looks like — and can explain it clearly enough that even an AI can deliver it.
Source and contributor context
One Useful Thing · Publisher
The publisher is identified here. No individual author has been inferred.
Source media
Media retrieval status: unchecked.
Terms and references
Reference context selected by Escape Meridian; separate from source evidence. Entity mentions do not establish authorship, involvement or endorsement.
AI agent
An AI system that chooses and carries out actions toward a goal, often using software tools.
Ethan Mollick
Ethan Mollick is a Wharton researcher and author exploring how AI changes work, learning and decision-making.
One Useful Thing
One Useful Thing is Ethan Mollick’s publication about AI in work, education and everyday practice.
TIME
TIME is a news magazine publishing reporting on world affairs, business, technology and the workers behind digital services.
Editorial provenance
Version 1. Publication ID: 3958fd09-7acd-7df1-5758-7d71675b7ee6.
Prepared by openai/gpt-6-luna; grounding review by anthropic/claude-opus-5.5.
Source check: passed by anthropic/claude-opus-5.5 on 2026-11-08T11:23:45.712Z. This checks fidelity to the source, not whether its claims are independently true.
Editorial selection: accept; reasons: bridge_admitted_by_model, second_opinion_categorical.
Observation: c76005c554e5352406fbd9e0eab24f6285ac794b3a5764faea4ee7fd563ceeac. Source text hash: eaf3a9b37e1dd41bc7a6cc9516125699d3412e3b0864ae66e30989e48ff90962.
passed · 2026-11-08T11:23:45.712Z · anthropic/claude-opus-5.5
Publication: 3958fd09-7acd-7df1-5758-7d71675b7ee6
attribution — pass: The reading attributes the essay and its arguments to Ethan Mollick in One Useful Thing, which matches the byline and publisher. The class results, the delegation framework and the forecast all remain Mollick's attributed claims rather than established facts.
Management as AI superpower Thriving in a world of agentic AI Ethan Mollick
summary — pass: The summary is understandable without specialist background. It leads with the class, then the delegation trade-off, then the argument that management skills can guide AI agents. 'AI agents' is defined on the page. It keeps the qualifier that the prototypes were not yet working startups, and it keeps Mollick's uncertainty about the future. The essay's GDPval figures and its point that 'soft' skills turned out to be the hard ones are omitted, but the central meaning is intact. The question follows from Mollick's 'at least in your area of expertise' caveat.
In many ways, at least in your area of expertise, it is much easier than trying to design clever prompts to help you get work done
date — pass: evidence.originalPublished is 2026-01-27, which matches the source dateline. The reading states January 27, 2026 at day precision. The limitation notes that the text was retrieved on October 24, 2026. Mollick's forecast is presented as his suspicion, not an outcome. The dateModified equals the publication time, and there is no hindsight framing.
Ethan Mollick Jan 27, 2026
support — pass: Each claim is grounded in the source. Expertise helping is stated outright. The three-variable delegation model, the not-yet-working-startups caveat, the scarcity of knowing what to ask for, and the uncertain forecast are all directly supported. The limitation about generalizing beyond one class is appropriately cautious. Claim 1's excerpt covers the tools, and its expertise part is supported elsewhere in the text.
It helped that they had some management and subject matter expertise because it turns out that the key to success was actually the last bit of the previous paragraph: telling the AI what you want.
media — pass: No media is selected and no verified media candidates were supplied, so nothing was invented or misidentified.
Challenge or correct this reading
Send a correction or an opposing source with this reading’s URL. Disagreement and uncertainty can remain unresolved.
One Useful Thing is Ethan Mollick’s publication about AI in work, education and everyday practice.
Explore One Useful ThingEthan Mollick is a Wharton researcher and author exploring how AI changes work, learning and decision-making.
Explore Ethan MollickTIME is a news magazine publishing reporting on world affairs, business, technology and the workers behind digital services.
Explore TIMEAn AI system that chooses and carries out actions toward a goal, often using software tools.
Definition referenceOne Useful Thing is Ethan Mollick’s publication about AI in work, education and everyday practice.
Explore One Useful ThingAn AI system that chooses and carries out actions toward a goal, often using software tools.
Definition referenceAn AI system that chooses and carries out actions toward a goal, often using software tools.
Definition referenceEthan Mollick is a Wharton researcher and author exploring how AI changes work, learning and decision-making.
Explore Ethan MollickTIME is a news magazine publishing reporting on world affairs, business, technology and the workers behind digital services.
Explore TIMEAn AI system that chooses and carries out actions toward a goal, often using software tools.
Definition referenceOne Useful Thing is Ethan Mollick’s publication about AI in work, education and everyday practice.
Explore One Useful ThingAn AI system that chooses and carries out actions toward a goal, often using software tools.
Definition referenceAn AI system that chooses and carries out actions toward a goal, often using software tools.
Definition reference