How do we prepare workers for disruption if we cannot see how work is changing?

Artificial intelligence is already changing how employers operate, organize work, and value different capabilities. Yet much of the workforce ecosystem still relies on job postings to understand employer demand. Those postings can be useful, but they are advertisements. They do not reliably tell us which opportunities are real, who was hired, what work that person performs, what the job pays, or whether it lasts.

Economist Julia Lane has spent decades working on this problem. She is a professor at New York University, and author of Democratizing Our Data.

Julia explains why technological change reaches workers through employer decisions. Companies choose whether to adopt AI, how to redesign their operations, and which workers and capabilities they need. Understanding the future of work therefore requires better information about what employers actually do - not only what they advertise.

This is a conversation about data, but its consequences are deeply human. Bad information wastes employer resources, directs training dollars toward uncertain outcomes, and leaves workers making consequential choices without a clear view of opportunity. Better information could change the narrative from inevitable disruption to informed adaptation.

EPISODE CHAPTERS

00:00 - The Planes That Never Returned: How selection bias distorts what we think we know.

01:10 - Why We Cannot Prepare for Change We Cannot See: The limits of job-posting data.

05:00 - Learning at Work: Why on-the-job training matters across a working life.

06:40 - Labor Supply, Employer Demand, and Technology: Where economic disruption enters the workforce.

08:20 - The Missing Longitudinal View: Why surveys struggle to capture labor-market churn.

10:35 - The Employment Data We Already Collect: What unemployment-insurance wage records reveal.

13:10 - Data Built for Yesterday's Questions: Moving from surveys to administrative records.

14:15 - Industries of Ideas: Tracking how technology shocks move through local economies.

16:10 - What Early AI Adoption May Be Showing: Initial findings from Ohio employers.

17:10 - What JEDx Could Add: Better work locations and faster reporting.

18:25 - What Better Data Means for Learners: Local jobs, earnings growth, and durable pathways.

20:00 - Why Job Postings Are Not Enough: Representation, documentation, and validation.

21:35 - Building the Missing Feedback Loop: Connecting training to actual worker outcomes.

23:05 - A More Optimistic Future of Work: How better signals could help employers and workers adapt.

Learn more about Julia Lane and Industries of Ideas: https://industriesofideas.ai/ Connect with Julia: https://www.linkedin.com/in/julia-lane/ Explore Signal Mapping: https://signollabs.com/blog-signal-mapping/ Learn more about Signol Labs: https://signollabs.com/ Connect with Ian Davidson: https://www.linkedin.com/in/ianwdavidson/ Recommend a future guest: connect@signollabs.com