What happens to a job when AI touches it? The answer depends on something most frameworks aren't designed to measure.

In this episode, Ben sits down with Daniel Rock, assistant professor at Penn and co-founder of Work Helix, to dig into AI exposure.

Topics covered:

• What "AI exposure" measures
• Why tasks are "assemblages," not atoms, and what that means for how we think about job change
• The task chaining paper: why the sequence in which tasks are automated matters as much as which tasks get automated
• Why the handoff costs of breaking work into steps also have handoff benefits and when human checkpoints create value rather than friction
• Jobs as equilibrium objects: why there may never be a complete theory of how tasks get bundled into jobs, and what we can learn from the attempt
• What academics can learn from entrepreneurs and vice versa
• Why the green shoots of AI's impact on science and medicine point toward something much bigger than productivity gains at work

About Daniel Rock: Daniel Rock is an assistant professor at the Wharton School of the University of Pennsylvania and co-founder of Work Helix. His research sits at the intersection of economics, organizational behavior, and artificial intelligence, with a focus on how technology changes work and how firms can measure and manage that change.

Follow Daniel on LinkedIn

Papers discussed:

Weak Bundle, Strong Bundle

Task Chaining

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