Ethan Mollick2024Wharton professor, author of One Useful Thing
Living and Working with AI
AI is brilliant at some of your work and quietly bad at the task next to it. The only map of that edge is the one you draw yourself, by using it on real work and staying the one who decides.
The core idea
Ethan Mollick is a professor at the Wharton School of the University of Pennsylvania who studies innovation and entrepreneurship and writes the One Useful Thing newsletter. He argues that AI is best treated as a strange new kind of co-worker: very good at some tasks, surprisingly bad at others that look similar, and only understood by using it. He calls that uneven edge the jagged frontier, and offers four rules for working with it: always invite AI to the table, be the human in the loop, treat AI like a person but tell it what kind of person it is, and assume this is the worst AI you will ever use. The middle of the book looks at AI as a person, a creative, a coworker, a tutor and a coach. It draws on his own classroom experiments and on research with working professionals, including a study of consultants at Boston Consulting Group, and ends with four scenarios for where AI could go next.
The book's central picture is the jagged frontier. Some tasks fall inside it, where AI does well, and some fall outside, where it fails. From the outside the two can look alike. The study with Boston Consulting Group showed the cost of guessing wrong: the consultants using GPT-4 did better on work inside the frontier, and worse on a task that sat just beyond it.
So Mollick does not hand you a list of what AI can do. He hands you a way of working: try it on everything, keep your judgement switched on, decide which parts of each job stay yours, and expect the edge to move as the models improve.
You cannot predict AI's skill at one task from its skill at a similar one. You learn it task by task.
Talk to it, give it a role and context, push back on it. Never forget it is software that can be confidently wrong.
You check what it says, decide what goes out, and keep your own skill sharp enough to spot its mistakes.
Interactive · the frontier mapper
List the tasks in your week. Sort each into the book's three piles, choose centaur or cyborg, and rate how well you think AI does it today. The map shows where your edge runs, and the panel picks one task to bring AI into this week, with a prompt to start from.
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These six are examples. Rename, rerate or delete them, and add your own.
This week's experiment
HourLife's tool, built on the book's piles, team-ups and rules. The prompt is our template, not the author's. Your tasks stay in this browser. Check your workplace's AI policy before pasting in real work.
Concept anatomy
The rule names are the book's. The explanations and the things to try are HourLife's.
Try it on the work you actually do, within your legal and ethical limits. You only find the frontier by walking up to it.
Try: Bring it into one task this week you would never have thought to ask about.
It can be confidently wrong. You check, you judge, and the work that goes out is yours.
Try: Before you use an answer, list the facts in it that you have not checked.
Give it a role, an audience and some context. A named persona turns a generic answer into a specific one.
Try: Start your next prompt with one line: who it is, and who the work is for.
The tools keep improving, so a verdict you reach today may be out of date within a year.
Try: Keep a list of the tasks it failed, and test them again when a new model arrives.
Where each task in your work belongs today.
Rule 4 in practice: as the models improve, expect some tasks to move right.
Two ways to share a single task with AI.
A clean line down the middle. You do your half, the AI does its half, and the seam is clear.
Woven together. You hand over a sentence, take it back, rewrite it and ask again, many times inside one job.
AI stops improving much. Even so, today's tools are enough to change a lot of work.
Steady, gradual improvement that people and organisations can adapt to.
Capability compounds quickly, and the changes arrive faster than habits can.
Machines that outthink people. The most extreme of the four.
Community insights
Entry 01HourLife takeaway
The line between what AI does well and what it does badly is uneven, and tasks that feel equally hard to us can land on opposite sides of it. Mollick describes a study with Boston Consulting Group consultants: on tasks inside the frontier, those using GPT-4 did better work, and on a task chosen to sit just outside it, they did worse than colleagues working without it.
Entry 02HourLife takeaway
Nobody has a manual for this, and the people who build these systems are often surprised by what they can do. That is the reason for the first rule, always invite AI to the table. Try it on the tasks you actually do, within your legal and ethical limits, and notice where it helps and where it fails.
Entry 03HourLife takeaway
Mollick knows AI is software, not a person, but finds it works better when you give it a role and some context: an editor, a sceptical critic, a teacher for a particular audience. A persona moves the answer from generic to specific. He also warns against forgetting what it really is.
Entry 04HourLife takeaway
AI can state invented facts with total confidence, and it cannot reliably tell you when it is wrong. Being the human in the loop means checking, judging and owning what goes out. It also means keeping your own skill sharp, because you can only catch a mistake you know how to spot.
Entry 05HourLife takeaway
A centaur divides a job: you choose the argument, the AI drafts the chart. A cyborg works back and forth inside the task, handing over a sentence and taking it back rewritten. Mollick treats both as good ways to work. What matters is choosing the split on purpose.
Entry 06HourLife takeaway
Just Me tasks stay with you, because AI cannot do them well or because you want them to be yours. Delegated tasks go to AI and come back to you to check. Automated tasks can run on their own. As the tools improve, tasks move from one pile to the next.
Entry 07HourLife takeaway
The fourth rule is a warning against settling your opinion early. Something AI fails at this year may be easy for it next year. Write down the tasks you have ruled out, and test them again when a new model arrives.
Entry 08HourLife takeaway
In the chapters on AI as a tutor and a coach, Mollick sees real promise in explanation and feedback pitched at your level. The effort of learning still has to happen in your own head, though. Using AI to skip the struggle means skipping the skill, and the skill is what lets you judge the AI.
Entry 09HourLife takeaway
The last chapter lays out four scenarios: AI stays about as good as it is, it keeps improving slowly, it improves exponentially, or it reaches something like a machine god. Mollick's point is that people and organisations still choose how AI is used at work, whichever future arrives.
Action steps
Write down eight to twelve things you do in a normal week. Sort each one as Just Me, Delegated or Automated, and rate how well you think AI handles it today.
Before you paste work into an AI tool, find your employer's or school's policy on it. Note which kinds of data, such as client names or health details, must never go in.
Write three short role briefs you can paste at the top of a prompt, such as a blunt editor, a sceptical analyst and a patient tutor. Keep them in one note.
For each Delegated task, write down what you check before anything leaves your hands: names, numbers, dates, quotes and sources. Keep the list next to where you work.
Pick one task where AI let you down before. Try it again with the current model, a clear persona and more context, and write down what changed.
Each Monday, pick one task you have never tried with AI and try it once. Afterwards, decide whether it belongs in Just Me, Delegated or Automated.
When you need ideas, ask for twenty rather than one, and ask for some odd ones. Your job is to pick and combine the best, not to accept the first.
After each real use, write one line: the task, what worked and what failed. Read the log once a month to see where your frontier has moved.
Before handing a task to AI, write your own rough answer in a few lines. It keeps your skill in use and gives you something to judge the AI's answer against.
Last entry
Find the edge by working on it: bring AI in, keep the judgement yours, and test again when the tools change.HourLife note after Ethan Mollick
Pick setup actions and daily habits from Co-Intelligence: Living and Working with AI, choose a start date, and download a printable checklist with real calendar pages.
Ethan Mollick is a professor at the Wharton School of the University of Pennsylvania who studies innovation and entrepreneurship and writes the One Useful Thing newsletter. He argues that AI is best treated as a strange new kind of co-worker: very good at some tasks, surprisingly bad at others that look similar, and only understood by using it. He calls that uneven edge the jagged frontier, and offers four rules...
HourLife’s key takeaways include: AI's abilities form a jagged frontier, so how it does on one task tells you little about the next. The only way to learn where the frontier runs through your work is to use AI on your work. Talk to AI like a person, and tell it which person to be.
It's a strong pick for readers exploring Digital Literacy, High Performance, and Learning & Mastery. HourLife distills its core idea into key insights and practical actions worth trying.
Map the tasks in your week — Write down eight to twelve things you do in a normal week. Sort each one as Just Me, Delegated or Automated, and rate how well you think AI handles it today.
About five minutes. The HourLife summary distills Co-Intelligence: Living and Working with AI into its core idea, 9 community insights, and 9 practical actions you can apply right away.
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