Erik Brynjolfsson and the Stanford Digital Economy Lab have rolled out a public set of dashboards that pull together employment trends, macroeconomic signals, and adoption rates to give a clearer picture of AI's real economic footprint, with monthly refreshes on two of the three tools and open access for anyone tracking labor shifts or growth patterns.
rishi#236
AI Economic Indicators is live! This new platform from @DigEconLab tracks the economic impact of AI. I joined @erikbryn and the wonderful team at DEL earlier this year to work on this project, and I'm thrilled to have it out in the wild.
The Canaries Dashboard draws on ADP data to flag slower employment growth or outright declines in roles with high AI exposure, especially among the youngest workers, while distinguishing automation-heavy uses from those that augment tasks.
The Takeoff Tracker scans twelve indicators including productivity, capital share, and energy use, yet current readings show mostly neutral results with no decisive evidence of explosive AI-driven expansion.
Many users praised Stanford's new AI Economic Indicators Platform for delivering real-time data to track job and productivity shifts, enabling evidence-based discussion over speculation.
Today, the Stanford @DigEconLab launches the AI Economic Indicators, a new platform for tracking how AI is reshaping work, productivity, adoption, and the economy.
1/6
We need more real time data on how AI may be impacting the economy - this is a really useful addition.
Today, the Stanford @DigEconLab launches the AI Economic Indicators, a new platform for tracking how AI is reshaping work, productivity, adoption, and the economy.
1/6
Each month, we will update it with new data, and periodically, we'll add new metrics and dashboards.
Check out the website: https://digitaleconomy.stanford.edu/project/indicators/
And let me know what you think!
6/6
It's been a thrill to turn this idea into a public resource with colleagues across the Lab. Special thanks to @connacher_ , who leads the project, along with @therealcko, Susan Young, and Matty Smith — and to the many researchers behind it, including @NelaRichardson, @BharatKChandar, @RuyuChen, @Andrew Wang, @pawtrammell, and @I_Am_NickBloom.
5/6
Three key components:
1. The Canaries Dashboard (jointly with ADP Research) — labor market outcomes across occupations and worker groups with different levels of AI exposure.
This is the same data that we used for our "Canaries in the Coal Mine?" paper, updated monthly.
2/6
Today, the Stanford @DigEconLab launches the AI Economic Indicators, a new platform for tracking how AI is reshaping work, productivity, adoption, and the economy.
1/6
The site (https://indicators.stanford.edu/) has three components at launch: (1) the Canaries Dashboard, (2) the Takeoff Tracker, and (3) the Adoption Monitor. Each will be updated regularly---(1) and (2) monthly.
AI Economic Indicators is live! This new platform from @DigEconLab tracks the economic impact of AI. I joined @erikbryn and the wonderful team at DEL earlier this year to work on this project, and I'm thrilled to have it out in the wild.
2. The Takeoff Tracker — macroeconomic indicators to give us an early warning of when and where better AI capabilities lead to an economic take-off.
3/6
Three key components:
1. The Canaries Dashboard (jointly with ADP Research) — labor market outcomes across occupations and worker groups with different levels of AI exposure.
This is the same data that we used for our "Canaries in the Coal Mine?" paper, updated monthly.
2/6
3. The Adoption Monitor — AI adoption by consumers, workers and firms across multiple datasets.
4/6
2. The Takeoff Tracker — macroeconomic indicators to give us an early warning of when and where better AI capabilities lead to an economic take-off.
3/6
It's been a thrill to turn this idea into a public resource with colleagues across the Lab. Special thanks to @connacher_ , who leads the project, along with @therealcko, Susan Young, and Matty Smith — and to the many researchers behind it, including @NelaRichardson, @BharatKChandar, @RuyuChen, @Andrew Wang, @pawtrammell, and @I_Am_NickBloom.
5/6
3. The Adoption Monitor — AI adoption by consumers, workers and firms across multiple datasets.
4/6
(3) summarizes adoption survey work from many great researchers — all credit to them! Includes @I_Am_NickBloom, @iyotzov, @ProfDavidDeming, @Jon_Hartley_, @FilipJole, @BrendanDMoore, and many researchers I couldn't find on Twitter.
First, individuals.
In (2), @pawtrammell, along with @erikbryn and me, updates and extends Nordhaus (2021), "Are We Approaching an Economic Singularity?" His answer: "no." Our answer: "still no." But we will be updating the results monthly. https://www.aeaweb.org/articles?id=10.1257/mac.20170105
May data coming soon!
We're also now integrating the job transitions framework data from @Alex_M_Richmond, @RonnieChatterji, and co.
The original paper details robustness checks.
I plan to write more on how Canaries fits into the portfolio of AI x labor market measurement
We have plans to expand each of these three products, including new cuts of Canaries (e.g., by gender) as well as next-generation exposure measures, new indicators of takeoff (e.g., unmeasured consumer surplus in GDP), and frontier model usage data.
@BasilHalperin I've interest-rate pilled (now realizing I should've credited you, Chow, and Mazlish in the tweet below)
And we hope to add more pages on changing skill demands, inference market structure, and model capabilities. Lastly, we look forward to expanding our geographic scope beyond the U.S.
Please get in touch with questions, comments, or ideas for extensions!
@connacher_ @DigEconLab @erikbryn been hoping for something just like this forever
If you're more interested in recent trends, we also summarize year-over-year changes on the site. With our monthly updates, we'll be able to track these results through changing economic conditions unrelated to AI.
(1) Extends the "Canaries in the Coal Mine" work of @erikbryn, @BharatKChandar, @RuyuChen, made possible by a collaboration with @AdpResearch. With updated data through April 2026, we largely match the findings of the original paper...
@erikbryn @DigEconLab Looks like 100 should be 2023?
Early-career (22-25) employment correlates w/ Eloundou et al. exposure. As we move up in age, this pattern becomes less stark, then disappears. The 22-25 group is ~7% of our sample, so this divergence isn't enough to generate large-scale differences across workers of all ages.
When we use Anthropic Economic Index data (h/t @PeterMcCrory and team), we see that this early-career divergence replicates only with automation related usage, and not augmentation. The type of usage matters.
Erik Brynjolfsson and the Stanford Digital Economy Lab have rolled out a public set of dashboards that pull together employment trends, macroeconomic signals, and adoption rates to give a clearer picture of AI's real economic footprint, with monthly refreshes on two of the three tools and open access for anyone tracking labor shifts or growth patterns.
rishi#236AI Economic Indicators is live! This new platform from @DigEconLab tracks the economic impact of AI. I joined @erikbryn and the wonderful team at DEL earlier this year to work on this project, and I'm thrilled to have it out in the wild.