Home / LinkedIn creators / Ethan Mollick
Associate Professor at The Wharton School. Author of Co-Intelligence
We read 1,276 of their posts. Here is what they actually do.
Average likes per post, by month.
That puts them in the 29th percentile of the 1,374 creators we track in the 100K to 1M follower band. Reach is not the same as resonance, and this is the ratio that separates them.
The opening line of their best-performing posts. This is what stops the scroll.
What they post about, with a line from a real post as evidence.
Citing randomized trials and Stanford papers on LLM reasoning performance gains.
Analyzing spans of control and boundary objects for multi-agent system workflows.
Sharing student vibefounding outcomes and Claude computer use tests on video.
The repeatable moves that show up across their best posts.
Patterns that separate their top posts from the rest.
Every post, grouped by day of week.
Best day is Saturday at 717 average likes.
Text, image, video or carousel, ranked by what actually lands for them.
| Format | Posts | Avg likes | |
|---|---|---|---|
| Carousel | 16 | 1.2K | |
| Text only | 410 | 696 | |
| Image | 677 | 639 | |
| Video | 173 | 366 |
How their posts are built, measured across every post we analysed.
Moves you can apply to your own posts this week.
Ranked by likes, across everything we analysed.
New randomized, controlled trial by the World Bank of students using GPT-4 as a tutor in Nigeria. Six weeks of after-school teacher-led AI tutoring increased test scores by .3 SD, equivalent to 2 years of typical learning gains, outperforming 80% of other educational interventions.
In our new paper we ran an experiment at Procter and Gamble with 776 experienced professionals solving real business problems. We found that individuals randomly assiged to use AI did as well as a team of two without AI. And AI-augmented teams produced more exceptional solutions.
Huh. Looks like Plato was right. A new paper shows all language models converge on the same "universal geometry" of meaning. Researchers can translate between ANY model's embeddings without seeing the original text.
Everyone wants to teach "AI skills" but nobody knows what "AI skills" are as of today, let alone for the future. We can teach a bit about how LLMs work, and give some advice on prompting, but, beyond that, what are people supposed to learn to make them "AI ready"?
This paper is wild - a Stanford team shows the simplest way to make an open LLM into a strong reasoning model. Instead of complex RL or millions of examples, they used just 1,000 carefully curated reasoning samples and a simple trick: when the model tries to stop thinking too early, they just…
Comparable follower counts, analysed the same way.
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Built from public LinkedIn posts, last refreshed 2026-09-26. Follower counts and engagement change over time. Ethan Mollick is not affiliated with 2pr.io. Request removal.