One Useful Thing · Sep 18, 2026
Ethan Mollick argues people can get more from AI by bringing their own strengths
Markdown · JSON · Original source
Ethan Mollick published this essay on September 18, 2026. He argues that capable AI models are not being used or understood to their full potential, and that people can get more from them by applying their own strengths. Mollick calls the gap between what models can do and what most people use them for a “capability overhang.” He illustrates the gap with projects in which AI made a playable 3D version of Zork, reconstructed parts of Umberto Eco’s library, and produced book trailers using Blender and a video generator. He says these projects still required his direction and judgment. Mollick identifies four advantages for working with AI: deep knowledge, wide knowledge, taste, and agency. Deep knowledge helps people recognize errors and guide work in a field; wide knowledge helps them draw on ideas across fields. Taste helps select or refine outputs, while agency means testing what AI can do rather than waiting for others to find out. He argues that these strengths could help people use AI to do work neither could do alone. He also calls for shared ways of working with AI that enhance, rather than only replace, human labor. The essay does not establish how widely its examples generalize. What evidence would show whether people who apply these four advantages get better results from AI across different kinds of work?
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
One Useful Thing · Article
Source details
Original source date: Sep 18, 2026 (2026-09-18T17:54:32.000Z).
Date basis: Date supplied by the publisher’s feed.
Added to Escape Meridian: 2026-10-29T16:53:39.463Z. First observed: 2026-09-23T05:24:06.504Z. Retrieved: 2026-10-23T20:59:41.135Z.
Referenced by
Recorded citations and public file attachments. A reference alone does not establish agreement or influence.
- Ethan Mollick: use existing AI capabilities with knowledge, taste, and agency · Reading
Cites the original source. Recorded by Escape Meridian. Relationship described in that record: supports.
Cites the original source. Recorded by Escape Meridian. Relationship described in that record: qualifies.
Cites the original source. Recorded by Escape Meridian.
Limits
Text as retrieved on October 23, 2026; originally published September 18, 2026. The examples are Mollick’s demonstrations; the essay does not establish how broadly their results generalize or whether others would obtain similar results. The essay offers an argument for the value of four human advantages, but does not provide comparative evidence measuring their effects across kinds of work.
Open question
What evidence would show whether people who apply these four advantages get better results from AI across different kinds of work?
Source-stated
Mollick argues that a gap exists between AI models’ capabilities and how most people use them.
The capability overhang, the gap between what these models can do and what almost anyone is doing with them
Source-stated
Mollick says his AI projects still depended on his choices and judgment.
Still, none of these projects happened on their own. I chose them, I knew enough about Zork and Eco and my own book to see where the AI went wrong
Source-stated
Mollick identifies deep knowledge, wide knowledge, taste, and agency as personal advantages for working with AI.
In my book, I outline four particular personal advantages that matter a lot if you want to use AI in unique and enhancing ways: deep knowledge, wide knowledge, taste, and agency.
Source-stated
Mollick argues that people should use AI to enhance human labor rather than only replace it.
develop and share models of AI-human work that enhance, rather than only replace, human labor.
Source-stated
The source dates the essay September 18, 2026.
Sep 18, 2026
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.
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.
Editorial provenance
Version 1. Publication ID: 65a64c03-f399-b011-b1af-c886ba91c938.
Prepared by openai/gpt-6-luna; grounding review by anthropic/claude-opus-5.5.
Source check: passed by anthropic/claude-opus-5.5 on 2026-10-29T16:52:54.283Z. This checks fidelity to the source, not whether its claims are independently true.
Editorial selection: accept; reasons: bridge_admitted_by_model.
Observation: 09c0a139473960566c32fec596aa37af8ead66746c41493c612dcc9aa1ae8c01. Source text hash: e1538217420d2fb7af45dd1464022eecba45721a52735b38f8ba1d3893ec0360.
Revision and source-check history
Version 1 · 2026-10-29T16:53:39.463Z
Ethan Mollick argues people can get more from AI by bringing their own strengths
Ethan Mollick published this essay on September 18, 2026. He argues that capable AI models are not being used or understood to their full potential, and that people can get more from them by applying their own strengths. Mollick calls the gap between what models can do and what most people use them for a “capability overhang.” He illustrates the gap with projects in which AI made a playable 3D version of Zork, reconstructed parts of Umberto Eco’s library, and produced book trailers using Blender and a video generator. He says these projects still required his direction and judgment. Mollick identifies four advantages for working with AI: deep knowledge, wide knowledge, taste, and agency. Deep knowledge helps people recognize errors and guide work in a field; wide knowledge helps them draw on ideas across fields. Taste helps select or refine outputs, while agency means testing what AI can do rather than waiting for others to find out. He argues that these strengths could help people use AI to do work neither could do alone. He also calls for shared ways of working with AI that enhance, rather than only replace, human labor. The essay does not establish how widely its examples generalize. What evidence would show whether people who apply these four advantages get better results from AI across different kinds of work?
Original source
One Useful Thing · Article
Source details
Original source date: Sep 18, 2026 (2026-09-18T17:54:32.000Z).
Date basis: Date supplied by the publisher’s feed.
Added to Escape Meridian: 2026-10-29T16:53:39.463Z. First observed: 2026-09-23T05:24:06.504Z. Retrieved: 2026-10-23T20:59:41.135Z.
Limits
Text as retrieved on October 23, 2026; originally published September 18, 2026. The examples are Mollick’s demonstrations; the essay does not establish how broadly their results generalize or whether others would obtain similar results. The essay offers an argument for the value of four human advantages, but does not provide comparative evidence measuring their effects across kinds of work.
Open question
What evidence would show whether people who apply these four advantages get better results from AI across different kinds of work?
Source-stated
Mollick argues that a gap exists between AI models’ capabilities and how most people use them.
The capability overhang, the gap between what these models can do and what almost anyone is doing with them
Source-stated
Mollick says his AI projects still depended on his choices and judgment.
Still, none of these projects happened on their own. I chose them, I knew enough about Zork and Eco and my own book to see where the AI went wrong
Source-stated
Mollick identifies deep knowledge, wide knowledge, taste, and agency as personal advantages for working with AI.
In my book, I outline four particular personal advantages that matter a lot if you want to use AI in unique and enhancing ways: deep knowledge, wide knowledge, taste, and agency.
Source-stated
Mollick argues that people should use AI to enhance human labor rather than only replace it.
develop and share models of AI-human work that enhance, rather than only replace, human labor.
Source-stated
The source dates the essay September 18, 2026.
Sep 18, 2026
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.
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.
Editorial provenance
Version 1. Publication ID: 65a64c03-f399-b011-b1af-c886ba91c938.
Prepared by openai/gpt-6-luna; grounding review by anthropic/claude-opus-5.5.
Source check: passed by anthropic/claude-opus-5.5 on 2026-10-29T16:52:54.283Z. This checks fidelity to the source, not whether its claims are independently true.
Editorial selection: accept; reasons: bridge_admitted_by_model.
Observation: 09c0a139473960566c32fec596aa37af8ead66746c41493c612dcc9aa1ae8c01. Source text hash: e1538217420d2fb7af45dd1464022eecba45721a52735b38f8ba1d3893ec0360.
passed · 2026-10-29T16:52:54.283Z · anthropic/claude-opus-5.5
Publication: 65a64c03-f399-b011-b1af-c886ba91c938
attribution — pass: The essay is correctly attributed to Ethan Mollick, the byline on the One Useful Thing post. The reading frames its claims as his arguments.
Ethan Mollick Sep 18, 2026
summary — pass: The title, summary and question match the essay. They cover the capability overhang, which is defined in plain language, along with the illustrative projects and the four advantages, each briefly explained. Zork and Blender are named without much context, but the summary still makes clear what was produced, so a general reader can follow. The question is open and not leading.
The capability overhang, the gap between what these models can do and what almost anyone is doing with them, is an opportunity
date — pass: September 18, 2026 matches the source dateline and the UTC day of originalPublished. The evidence kind is publisher-page, so no archive disclosure is needed. modifiedAt equals the publication time. The book's planned October 20 release is not presented as an outcome, and there is no hindsight framing.
Sep 18, 2026
support — pass: Each claim is source-stated with an accurate excerpt. The statement that these strengths could help people do work neither they nor the AI could do alone reflects Mollick's own wording. The limitations correctly note that the examples are Mollick's own demonstrations and that the essay offers no comparative evidence. No unsupported benefits or costs are implied.
you want to use your human advantages as basis of working with AI to do things that neither of you could do alone
media — pass: No media was selected and no verified media candidates were supplied, so nothing is misidentified.
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