From Science Fiction to Standard Operating Procedure: Wharton’s Third AI and the Future of Work Conference

Building Public Goods in a Day 

Closing the loop between research and practice, Daniel Rock, assistant professor of Operations, Information and Decisions at Wharton, presented results from an experiment run in partnership with the Gates Foundation: the Wharton-Gates Public Goods Build-a-thon. The premise was simple — gather some 30 people of varied expertise from academia, industry, and nonprofits, hand them the conference’s own animating question of how AI is reshaping jobs, and ask them to build something useful in a single day. 

Five teams formed, drawing participants from Arizona State, Columbia University, CUNY, MIT, Microsoft, Jobs for the Future, and Opportunity@Work, among others. The projects ranged from JobShock, which compares job postings against the work actually being done, to a faculty tool for updating syllabi toward greater AI relevance, to a scenario-planning tool built on jobsdata.ai that pairs AI exposure with demand elasticities to model which roles are most at risk.  

The standout came from a team led by Xinlan Emily Hu, a computational social scientist at MIT, with Andreas Haupt, David Holtz, and Prashant Raganathan. Their system, SimulaCrew, builds digital twins of team members to simulate a collaboration before it happens, predicting outcomes, optimizing who should be in the room, and surfacing people who are missing. They field-tested it on the Build-a-thon itself, predicting what each team would produce from nothing more than participant profiles and a seed document of ideas. In one unsettling case, the simulation predicted Emily’s own team would abandon its starting idea and pivot to an “AI hiring audit” tool — closely mirroring a project she was separately, and privately, already working on. “How did it know this?” she asked. “I didn’t even put anything about hiring into the seed information.” 

Rock framed the broader payoff in human terms: pair people who know engineering and “vibe coding” with people who deeply understand a problem domain, give them half a day, and they learn an enormous amount from one another. The most common reaction afterward, he said, was simply: “I didn’t know this was possible in such a short amount of time.” 

Or, as he put it after SimulaCrew’s demo: we’ve all felt that a meeting could have been an email. “Now we can see a meeting could have been a fake meeting.”