The Wharton School

I am a professor and Co-Director of the Generative AI Lab at Wharton, where I study and teach about innovation, AI, and entrepreneurship. I also build tools, games, and AI applications to democratize education.

Associate Professor

PhD Candidate in Management of Technology, Innovation, and Entrepreneurship

eMeta Corporation

VP Business Development/Co-founder

Mysterious and Exciting Past

Previous positions included co-founding a successful software company, inventing the transistor, squaring the circle, and winning the gold in the 1976 Olympics in gymnastics at the age of one.  I was also the original Lord of the Dance.

Various and Sundry

I research the factors that lead to success in entrepreneurship and innovation, and how to teach people to become more effective leaders and innovators. Most recently, I have focused on AI, and especially how it affects education and work. I co-founded Wharton Interactive, an effort to democratize education using games and simulations, while applying rigorous approaches to pedagogy at scale.

I have worked with a lot of different organizations (from startups to government agencies), co-founded a technology company, and have a mysterious past.

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. The teams using AI were happier as well.

Even more interesting: AI broke down professional silos. R&D people with AI produced more commercial work and commercial people with AI had more technical solutions.

The standard model of "AI as productivity tool" may be too limiting. Today’s AI can function as a kind of teammate, offering better performance, expertise sharing, and even positive emotional experiences.

This was a massive team effort with work led by Fabrizio Dell'Acqua, Charles Ayoubi, and Karim Lakhani along with Hila Lifshitz, Raffaella Sadun, Lilach M., me and our partners at P&G: Yi Han, Jeff Goldman, Hari Nair and Stewart Taub 

Subatack about the work here: https://lnkd.in/ehJr8CxM

Paper: https://lnkd.in/e-ZGZmW9

Ethan Mollick

Associate Professor at The Wharton School. Author of Co-Intelligence

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Big firms like Amazon and Microsoft shipping LLMs need to recognize that IT is not always the center of AI use in companies - the key to productivity is often workers and subject matter experts using chatbots who experiment & share what they learn. This is where we saw the gains in our experiments at Procter and Gamble and at BCG. 

Making key features and products only accessible to IT functions and expecting them to build centralized solutions means that the tools are not in the hands of the people who will figure out the best use cases. There is a role for IT, obviously, but it is not always the traditional one in plays in technology adoption.

What is now trivial with multimodal image generation (these are the exact prompts): "ChatGPT show me a photorealistic drone shot of a fantasy city, the walls are white alabaster streaked with gold, while massive brass trellises built into the towers allow vines of maroon to climb them. At the center is a tower with a balcony and a hooded figure on it"

"in the next shot we move closer to the balcony, keeping the rest of the details, we cannot yet see a face in the cloak's darkness, but see a gleaming clasp to the cloak, worked with red stones, we also can se the figure holds a map case in one hand" (note how the buildings keep consistency)

"the scene is the same in all of its details, and remains photorealistic, but the cloak is pulled back to reveal a middle aged man, his eyes set with determination, his hair is woven with golden threads, he holds the open case in one hand and, in the other, a short wand of obsidian." (really good detail consistency overall, though you can see some changes to the towers and vines... and the belt)

"we zoom in on his face, which fills the frame, one side is illuminated by a sickly green light from the wand, which is covered with a network of fine glowing lines." (really impressive character consistency, including the outfit)

A new paper shows that AI agents are improving rapidly at long tasks (doubling length of the tasks they can do every 7 months!) - but they aren't FULLY reliable yet.

That being said this feels significant: "more than 80% of successful runs cost less than 10% of what it would cost for a human [L4 software engineer] to perform the same task."

If you wanted to see how little attention folks are paying to the possibility of AGI (however defined) no matter how much the labs publicly warn about the direction things are heading, here is an official course from Google Deepmind whose first session is "we are on a path to superhuman capabilities."

It has less than 1,000 views.

You certainly don't have to believe that super-intelligent AI are possible, by the way, because nobody knows the future, but I find it interesting how little people seem to be even considering it as a possibility when the AI developers seem to be convinced it is coming soon.

o3 is far more agentic than people realize. It is worth playing with a more than a typical new model to see what it can do. You can get remarkably complex work out of a single prompt.

It just does things. (Of course, that makes checking its work even harder, especially for non-experts.)

Now that everyone has access to at least two free multimodal image generators, there is no longer a need to use the ubiquitous default DALL-E glowing blue brains or shiny white robots to represent AI. 

You can invent your own representation and just have Gemini or GPT-4o make it for you.

I wrote about what makes multimodal image generation so different than previous AI image generation techniques, its many uses beyond the controversial and viral application of copying the style of an artist, & a bit about what it might mean for the future.

Open article: No elephants: Breakthroughs in image generation by open.substack.com

No elephants: Breakthroughs in image generation

There seems to be a real bifurcation growing between the IT-facing API side of OpenAI & the user-facing ChatGPT side.

You can increasingly only access the high power, high cost models (GPT-4.5, Deep Research) through ChatGPT while the API supports cheap fast models (the new GPT-4.1).

Some tension there for any organizational use, as IT departments and companies making wrappers will not be able to provide the same capabilities that users can access on their own. Giving employees direct access to frontier models is going to remain a big consideration.

Our research at Procter and Gamble found very large gains to real-world work quality & productivity from AI. It was conducted using GPT-4 last summer.

Since then we have seen Gen3 models, reasoners, large context windows, full multimodal, deep research, web search…

Open article: The Cybernetic Teammate by oneusefulthing.org

Interestingly, Perplexity’s version Deep Research prioritizes verbosity in its replies. Is it to make people feel good (long reports make it seem like they have a thorough answer)? Is it something having to do with actual result quality?

Either way, it is why it is worth knowing the system prompts companies are using

graphical user interface, text, application, email

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