Stanford Digital Economy Lab

Bringing together the world's best minds, whether human or machine, to study how digital technologies can transform the economy @StanfordHAI
Director: @ErikBryn

[Stanford Digital Economy Lab @DigEconLab]

A new revised version of "Canaries in the Coal Mine?" is now available from @erikbryn, @BharatKChandar, and @RuyuChen.

"No Widespread Displacement, but the AI Employment Gap for Young Workers Has Widened to 19%"

Read the authors' summary here: https://t.co/1ZiDXoWUbx

[Molly Kinder @Mollykinder]

This is so cool. I have lived and breathed O*NET tasks and occupations over the last few years while I researched AI's job impacts during my time at Brookings. It is the best data we have...

....but oy, was it ready for a refresh. I love this TaskNet project and I hope it can be scaled, especially for the occupations that are likely to be impacted by AI first. Kudos to @mercor @onet_center @DigEconLab @andreas_h0wpt @robinsonaisaac @erikbryn @jennjwang @Diyi_Yang @stanfordnlp
@StanfordAILab!

One thing I would love to see emerge from this work is a clearer delineation of task bundles by seniority. One of the big limitations of O*NET is it treats occupations as a monolith: there is just one "lawyer" occupation, for ex. But obviously the daily tasks of a newly minted junior lawyer look almost nothing like those of a law firm partner.

I think this is why the risks to early career roles were originally missed, until the Stanford "Canaries" paper showed it in the ADP data last year. Early studies at the time suggested that AI might be a great "leveler," boosting the inexperienced the most.

After ChatGPT was launched, I was hearing something different when I interviewed workers, from Hollywood writers to insurance underwriters and illustrators. They all told me AI put junior roles most at risk. But it was hard to systematically analyze this experience-adjusted exposure risk from O*NET as only a tiny number of occupations split junior and senior roles into distinct jobs. (e.g., market research analyst vs marketing manager.)

I ended up using my subjective judgement *within* occupations to rank tasks that required more or less experience. For instance, in this essay I wrote for Bloomberg two years ago on "How AI Could Break the Career Ladder," I used the example of O*NET's lawyer tasks and broke out what I thought were the junior vs senior tasks to illustrate the AI exposure gap: https://t.co/iQ3TlzLMHv. Without better data, it was hard to do much more than that.

The TaskNet approach of using AI to interview workers is a great way to fill in these gaps systematically and enable more granular insights. I am excited to see where this goes.

[Jennifer Wang @jennjwang]

Jobs are modeled as bundles of tasks. But as jobs change, how do we keep those task lists up to date?

Workers often experience new responsibilities and tools before they reach their job descriptions.

We start by listening to them. 🧵

[Stanford Digital Economy Lab @DigEconLab]

Read

[Stanford Digital Economy Lab @DigEconLab]

TaskNet is live!

By accurately mapping the tasks workers perform at their jobs, the project will help us gain a better understanding of AI's impact on work.

Explore Tasknet here: https://t.co/GCwaaN0lES

[Stanford Digital Economy Lab @DigEconLab]

@mercor's Isaac Robinson introduces TaskNet here: https://t.co/uPxIKaTzrk

[Bharat Chandar @BharatKChandar]

Happy to share I am now Research Director for AI Economics and Policy at @DigEconLab. As part of new role I will be leading the AI Economic Indicators.

I am also now a Research Fellow at @GovAIOrg. I'm excited to work with them on most important questions in econ of AI.

[Stanford Digital Economy Lab @DigEconLab]

Transformative AI's potential impact demands new approaches to research on work, growth, inequality, and more.

@erikbryn, @professor_ajay, and @akorinek outline nine "grand challenges" in a new piece for @CACMmag.

Read their full agenda here: https://t.co/1iZPF6kEEc

[Stanford Digital Economy Lab @DigEconLab]

The AI Economic Indicators have received their October update! Check out new data shared on the Canaries Dashboard and Transformation Tracker.

https://t.co/Lfwxkb6zIw
https://t.co/b8wg1l3CRK

[Stanford Digital Economy Lab @DigEconLab]

This Monday @CarinaLHong, CEO of @axiommathai, stops by the Lab for "AI That Can Prove It: From Mathematical Discovery to Verified Software."

Register for free on Zoom: https://t.co/IO1VsWn1ku

[Stanford Digital Economy Lab @DigEconLab]

While automation dominates much of the AI conversation, what other research directions should we prioritize?

@random_walker and @_gabrielunger argue for a broader research agenda in "Steering AI Beyond Automation."

Read the essay here: https://t.co/ofoiKSXUfG

[Gregory Daco @GregDaco]

Kicking off Day 3 at #NABE2026 with @NelaRichardson discussing the shifting AI and talent paradigm focusing on how AI is shaping the demand for « tasks » — great collaboration between @ADP and @DigEconLab

[Stanford Digital Economy Lab @DigEconLab]

This Monday’s seminar features @ProfNeilT‘s “Forecasting AI’s Impact on Human Expertise and the Future of Work.”

Visit here for registration and abstract: https://t.co/ri0ENNDCQo

In addition to the Zoom, members of the Stanford community are invited to attend in-person.

[Stanford Digital Economy Lab @DigEconLab]

The DEL Seminar Series is back! Bright minds from around the world visit the Lab to discuss various subjects surrounding economics and technology, presented on Zoom for free.

This fall our guests include @RichardSocher, @CarinaLHong, and @danielrock.

[Stanford Digital Economy Lab @DigEconLab]

The next panel for World Development Report 2026 begins at 11am PT! This all-day event looks at how AI offers developing economies a rare path to greater prosperity.

Watch the livestream for “How Will AI Impact Jobs and Productivity?” here: https://t.co/zRiNjdpDJK

[Bharat Chandar @BharatKChandar]

New paper with @BKleinTeeselink!

Measuring AI adoption and employment changes across 41 countries. This is some of the first evidence on global changes in labor markets from AI.

Paper and Substack explainer + lit review at bottom of 🧵

[Stanford Digital Economy Lab @DigEconLab]

Explore these updated dashboards and more at https://t.co/cV5aKnzgsT

[Stanford Digital Economy Lab @DigEconLab]

The Indicators have updated with new data on AI's impact on employment and the economy.

First, the Canaries dashboard saw that, after modest recovery in July, early career employment in the most exposed quintile continued to fall in August, down 1.4% month-over-month.

[Stanford Digital Economy Lab @DigEconLab]

The Transformation Tracker sees ten-year real interest rates remaining elevated, and electricity generation returning to the "mild" evidence category after briefly reverting to neutral.

The tracker in aggregate still sees no strong indication of transformation.