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

[Stanford Digital Economy Lab @DigEconLab]

More information on the event here: https://t.co/HjDsD3aGjh

[Stanford Digital Economy Lab @DigEconLab]

Streaming this afternoon: Phil Lewis (National Center for O*NET Development) will discuss data infrastructure the US Department of Labor provides and how AI is used to update it in a special seminar and fireside chat!

Register for the free Zoom here: https://t.co/vNVskbEae1

[Stanford HAI @StanfordHAI]

Official labor statistics often confirm change only after it has happened. Stanford HAI’s @DigEconLab tracks AI's effect on jobs, wages, and adoption month by month to catch shifts as they unfold. Here's what the AI Economic Indicators show so far:
https://t.co/yeK9GC7Bhz

[Stanford Digital Economy Lab @DigEconLab]

ICYMI, @CarinaLHong's visit to the DEL Seminar Series is now available on YouTube!

Watch "Math, Niche Math, & the Quest of Trusted Code, on Demand, at Scale" here: https://t.co/tqIl0dt1Je

[Stanford Digital Economy Lab @DigEconLab]

Watch the authors discuss the project here: https://t.co/p7llGutdLt

[Stanford Digital Economy Lab @DigEconLab]

Congratulations to @Diyi_Yang, @tatsu_hashimoto, @chrispiech, and @erikbryn!

"Collaborative Intelligence for the Future of Work," aimed at preparing people for AI's disruption of the workforce, has been awarded a grant through @LaudeInstitute's Moonshots // ONE.

[Laude Institute @LaudeInstitute]

What could the future look like if AI moves all of humanity forward?

Last year, we asked this of research teams across the US and Canada and invited them to shoot for the moon. We assembled a program committee of research luminaries, rising stars, and interdisciplinary experts to select eight seed grantees across four categories: Workforce Reskilling, Civic Discourse, Advancing Healthcare, and Accelerating Science.

Each team spent six months developing proposals for a multi-year, $10 million lab. Last week, they made their pitches at Demo Day in San Francisco. And today, we're announcing the four winners of Moonshots // ONE.

Researchers have power — to change how we work, commune, live, and discover. Especially when done in the open, their work is the fuel for human progress.

Join us in congratulating the first four Laude Moonshot Labs!

Read the announcement: https://t.co/y1hOtBzIeK

Meet the winners👇

[Stanford Digital Economy Lab @DigEconLab]

An 11-minute version available to everyone here:

Spotify: https://t.co/fEnqjo872u

Apple: https://t.co/LStwxN6S3J

[Stanford Digital Economy Lab @DigEconLab]

"Will AI take my job? Ask an economist!"

@erikbryn and @DAcemogluMIT answered questions from listeners for a new segment on @planetmoney's The Indicator.

The full hourlong conversation available to NPR+ members: https://t.co/QQSajqYQQm

[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