☀️ AI Morning Minute: Ethan Mollick
The Wharton professor behind the productivity numbers everybody quotes.
Preview text: He ran the study that produced the jagged frontier, then spent years explaining it to people who don’t read working papers.
Almost every claim you’ve heard about AI making knowledge workers more productive traces back, eventually, to one field experiment. Ethan Mollick helped run it.
He’s a professor at the Wharton School who studies what AI does to actual jobs, and he’s the person who named the jagged frontier, which is still the most useful idea anyone has produced about how these tools fail.
Who they are
Mollick teaches management at Wharton, where he holds the Ralph J. Roberts Distinguished Faculty Scholar title and co-directs the school’s Generative AI Labs. He got here sideways. His research was on entrepreneurship, innovation, and teaching business through games, none of which had much to do with machine learning until ChatGPT showed up and rearranged his career.
His newsletter, One Useful Thing, has hundreds of thousands of subscribers. Co-Intelligence, his 2024 book, was a New York Times bestseller and a book of the year at both The Economist and the Financial Times. TIME put him on its list of the most influential people in AI in 2024, and a follow-up, Co-Existence, is due out in October 2026.
Why they matter
The 2023 study he worked on with Harvard Business School and Boston Consulting Group is where the productivity numbers come from. 758 BCG consultants, real tasks, randomly assigned. The ones with GPT-4 finished 12.2 percent more tasks, 25.1 percent faster, with quality ratings about 40 percent higher.
That same study is also where the bad news lives. On a task deliberately built to sit outside what the model could handle, consultants using AI were 19 percentage points less likely to get it right. The failures didn’t arrive gradually. They arrived without warning, which is the whole point of the jagged frontier.
The weakest performers gained the most (43 percent, against 17 percent for the top half). That finding travels a lot further in a newsletter than in a working paper, and he’s one of the few researchers who bothers to move it.
What they’ve said or done
In the introduction to Co-Intelligence, Mollick writes that really getting to know AI costs you “at least three sleepless nights.” No framework attached. Just a claim that you have to use the thing badly for a while before any of it makes sense.
On his newsletter’s about page he mentions that he writes every post himself, and only asks AI for feedback once he has a finished draft. A guy who studies AI productivity for a living, doing it the slow way.