Associate professor of management at Wharton
Ethan Mollick
Ethan Mollick is a Wharton professor who studies AI's effects on work and education, co-directs its Generative AI Labs, and wrote Co-Intelligence.
About Ethan Mollick
Ethan Mollick is an associate professor of management at the Wharton School of the University of Pennsylvania. His research examines how artificial intelligence affects work, entrepreneurship, and education. He also co-directs the Generative AI Labs at Wharton, which conduct research and build prototypes.
He writes about practical uses of AI for a general audience and is the author of Co-Intelligence: Living and Working with AI. His teaching and research also explore educational simulations. He earned his PhD and MBA at MIT's Sloan School of Management.
Mentioned in our coverage
46 articles name Ethan Mollick, excluding author credits.
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- Google AI Scientist at ICML 2026: ScientistOne, Chain-of-Evidence, and the End-to-End Research Pipeline →
On July 7, 2026, Google Research invited ICML attendees to meet Jinsung Yoon and Rui Meng and experience "AI Scientist" — a multi-agent pipeline that maps to ScientistOne's published Chain-of-Evidence architecture. explainx.ai breaks down the three stages, CoE Integrity Audit numbers, how failed branches are handled, and how this differs from Co-Scientist and MARS.
- The Map Is Not the Territory: Finding Your Unknowns with Claude Fable 5 →
A new post from Claude Code engineer Thariq argues that working with Claude Fable 5 keeps re-teaching an old lesson: your prompt, skills, and context are a map of the work, not the work itself. Here's what that means for PRs, skills, and finding the unknown unknowns before your agent does.
- Context vs Prompt vs Loop vs Harness Engineering: The Four-Layer Agent Stack →
Most teams conflate prompt writing with context design, loop orchestration, and harness code. They are four layers of the same stack. Here is how they nest, what breaks when you skip one, and which layer to fix when agents fail.
- Human-in-the-Loop AI: When to Let the Agent Run and When to Stop It (2026) →
Most AI agent failures aren't model failures — they're gate failures. Someone gave an agent write access, delete access, or send access without deciding upfront which of those actions required a human checkpoint. This guide gives you the framework to fix that.
- Asian AI fills the Mythos gap: Sakana Fugu, 360 Tulongfeng, and the export-ban vacuum →