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Stanford Launches AI Economic Indicators Platform to Track Jobs, Adoption, and Productivity
In a bold move to keep pace with the pace of AI, Stanford’s Digital Economy Lab has rolled out a new public platform—AI Economic Indicators—that stitches together data on how artificial intelligence is reshaping employment, firm behavior, and macroeconomic outcomes.
The launch, announced in early June 2026, brings three dashboards to the fore: the Canaries Dashboard, the Takeoff Tracker, and the Adoption Monitor. Each offers regularly refreshed metrics aimed at policymakers, business leaders, researchers, and workers who need up‑to‑date signals about AI’s economic footprint.
The platform’s core ambition is to cut the lag that has traditionally separated rapid technological change from the arrival of conventional economic statistics. In a LinkedIn post, Susan Young, Director of Strategic Initiatives at the lab, explained that “one challenge with major technological change is that it can take years for traditional data sources to fully capture what is changing. Part of the motivation behind the Indicators is to shorten that gap.”
Canaries Dashboard The Canaries Dashboard focuses on employment across occupations and worker groups that differ in their exposure to AI. Workers are assigned an AI exposure score that estimates how likely their job tasks are to be automated or augmented. Early findings show modest differences among the five exposure groups, with the most exposed occupations experiencing the lowest employment growth. A separate view on early‑career workers—ages 22‑25—reveals that the two most exposed groups have seen noticeable employment declines since ChatGPT’s debut, while the other three groups have grown. This early‑career cohort represents about 7.0 % of employment in the Canaries sample at baseline. Patterns become less pronounced and eventually fade for older workers.
Takeoff Tracker The Takeoff Tracker monitors 12 macroeconomic indicators that could signal a broader shift driven by AI. According to the lab, the current set of indicators shows no decisive evidence of a takeoff at present.
Adoption Monitor The Adoption Monitor tracks generative‑AI use by individuals and firms using available surveys and datasets. Self‑reported adoption rates differ across studies: Hartley et al. report a decrease, whereas Gallup and the work of Bick, Blandin, and Deming report continued increases toward roughly 50 % adoption. The platform also tracks current and expected firm‑level AI adoption. U.S. firms lead in current adoption, but the gap between current and expected use is small. In contrast, firms in the United Kingdom, Germany, and Australia anticipate higher future adoption.
The indicators are organized around five guiding questions: 1. How does AI affect employment and wages? 2. Do broader measures such as productivity and GDP reflect AI effects? 3. Do traditional metrics capture consumer benefits? 4. How are worker skill requirements changing? 5. Is AI being used to complement or replace human labor?
The AI Economic Indicators project is backed by Schmidt Sciences, the Siegel Family Endowment, and other individual donors. Young underscored that the platform is a “living project” that will add new data sources, dashboards, and measurement efforts over time.
This launch builds on years of work by the lab on AI’s economic impact, including the Canaries study and a collaboration with ADP Research. By making the data publicly available, Stanford seeks to provide a reliable, up‑to‑date resource that can inform policy decisions, corporate strategy, and academic research on the evolving role of AI in the economy.
The dashboards can be explored at indicators.stanford.edu, where users can examine employment trends by AI exposure, monitor macroeconomic signals, and track generative‑AI adoption across sectors and regions. As AI technologies evolve, the lab plans to expand the indicators to capture new dimensions of AI impact.
In short, Stanford’s AI Economic Indicators offer a timely, data‑driven snapshot of how AI is reshaping labor markets, firm behavior, and macroeconomic outcomes. Early findings highlight modest employment declines in highly AI‑exposed early‑career occupations, a lack of clear macroeconomic takeoff signals, and mixed self‑reported adoption rates among workers and firms. The lab’s ongoing updates and expansions aim to keep the indicators current as AI adoption accelerates.
The launch, announced in early June 2026, brings three dashboards to the fore: the Canaries Dashboard, the Takeoff Tracker, and the Adoption Monitor. Each offers regularly refreshed metrics aimed at policymakers, business leaders, researchers, and workers who need up‑to‑date signals about AI’s economic footprint.
The platform’s core ambition is to cut the lag that has traditionally separated rapid technological change from the arrival of conventional economic statistics. In a LinkedIn post, Susan Young, Director of Strategic Initiatives at the lab, explained that “one challenge with major technological change is that it can take years for traditional data sources to fully capture what is changing. Part of the motivation behind the Indicators is to shorten that gap.”
Canaries Dashboard The Canaries Dashboard focuses on employment across occupations and worker groups that differ in their exposure to AI. Workers are assigned an AI exposure score that estimates how likely their job tasks are to be automated or augmented. Early findings show modest differences among the five exposure groups, with the most exposed occupations experiencing the lowest employment growth. A separate view on early‑career workers—ages 22‑25—reveals that the two most exposed groups have seen noticeable employment declines since ChatGPT’s debut, while the other three groups have grown. This early‑career cohort represents about 7.0 % of employment in the Canaries sample at baseline. Patterns become less pronounced and eventually fade for older workers.
Takeoff Tracker The Takeoff Tracker monitors 12 macroeconomic indicators that could signal a broader shift driven by AI. According to the lab, the current set of indicators shows no decisive evidence of a takeoff at present.
Adoption Monitor The Adoption Monitor tracks generative‑AI use by individuals and firms using available surveys and datasets. Self‑reported adoption rates differ across studies: Hartley et al. report a decrease, whereas Gallup and the work of Bick, Blandin, and Deming report continued increases toward roughly 50 % adoption. The platform also tracks current and expected firm‑level AI adoption. U.S. firms lead in current adoption, but the gap between current and expected use is small. In contrast, firms in the United Kingdom, Germany, and Australia anticipate higher future adoption.
The indicators are organized around five guiding questions: 1. How does AI affect employment and wages? 2. Do broader measures such as productivity and GDP reflect AI effects? 3. Do traditional metrics capture consumer benefits? 4. How are worker skill requirements changing? 5. Is AI being used to complement or replace human labor?
The AI Economic Indicators project is backed by Schmidt Sciences, the Siegel Family Endowment, and other individual donors. Young underscored that the platform is a “living project” that will add new data sources, dashboards, and measurement efforts over time.
This launch builds on years of work by the lab on AI’s economic impact, including the Canaries study and a collaboration with ADP Research. By making the data publicly available, Stanford seeks to provide a reliable, up‑to‑date resource that can inform policy decisions, corporate strategy, and academic research on the evolving role of AI in the economy.
The dashboards can be explored at indicators.stanford.edu, where users can examine employment trends by AI exposure, monitor macroeconomic signals, and track generative‑AI adoption across sectors and regions. As AI technologies evolve, the lab plans to expand the indicators to capture new dimensions of AI impact.
In short, Stanford’s AI Economic Indicators offer a timely, data‑driven snapshot of how AI is reshaping labor markets, firm behavior, and macroeconomic outcomes. Early findings highlight modest employment declines in highly AI‑exposed early‑career occupations, a lack of clear macroeconomic takeoff signals, and mixed self‑reported adoption rates among workers and firms. The lab’s ongoing updates and expansions aim to keep the indicators current as AI adoption accelerates.
