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3 weeks ago
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Bharat Chandar is a postdoctoral fellow at the Stanford Digital Economy Lab. His recent paper with Lab Director Erik Brynjolfsson and Research Scientist Ruyu Chen, “Canaries in the Coal Mine? Six Facts about the Recent Employment Effects of Artificial Intelligence,” provided some of the earliest large-scale evidence consistent with the hypothesis that the AI revolution is beginning to have a significant and disproportionate impact on entry-level workers in the American labor market.
We face enormous uncertainty about how AI will shape labor markets. People have questions about what we have learned, what the future might look like, and what we should do about it. The excellent Jasmine Sun suggested I write a post summarizing the state of knowledge. This is a summary of my sense of what we know and don’t know about AI and labor.1
Some economists believe studying labor effects of AI is too crowded a space, with not much difference from prior technology and not much interesting left to say. I very strongly disagree. The mismatch between supply and demand for research in this topic is unlike anything I have ever seen. Hopefully this article convinces at least some people of that. There is so much we don’t know, and many directions for making meaningful, socially beneficial contributions.
- The overall impact of AI on aggregate employment is likely small right now
- AI may be diminishing hiring for AI-exposed entry-level jobs
- We can probably measure AI exposure across jobs better than most people think
- We are gathering new evidence on which areas we should expect the greatest AI progress in the near term
- We have very little idea about employment changes in other countries
- We could use better data on firm AI adoption
- We do not know how employment trends will progress going forward
- We do not know which jobs will have growing future demand
- We have little evidence on how AI is reshaping the education landscape
- We do not know how personalized AI learning will change job retraining.
- We do not know how workers’ tasks have changed following adoption of AI
- AI has unclear effects on matching between employers and job candidates
- We need more data on how AI will affect incomes and wealth
- We need rigorous modeling of how to deal with economic disruptions, informed by the above data
1. The overall impact of AI on aggregate employment is likely small right now
This is consistent with a range of papers. Chandar (2025), Gimbel et al. (2025), Eckhardt and Goldschlag (2025), and Dominski and Lee (2025) all use the Current Population Survey to show at best small changes in hiring in AI-exposed jobs.2 “Canaries in the Coal Mine” finds quite concentrated employment declines among AI-exposed 22-25 y/o workers, with continued employment growth for most other workers. Humlum and Vestergaard (2025) likewise find small overall effects in Danish data through 2024. Together the evidence suggests overall hiring has not declined meaningfully due to AI.
Path forward: Building trackers using the CPS, ADP, Revelio, and other data sets is a good start. Improving causal estimates will help as well. See point 2.
2. AI may be diminishing hiring for AI-exposed entry-level jobs
This was a key highlight from our “Canaries in the Coal Mine” paper. We found declines in employment concentrated among 22-25 year-old workers in AI-exposed jobs such as software development, customer service, and clerical work.
How strong is this evidence? As with most things, the best way to think about this is as a Bayesian. Our results shifted my own beliefs about the likelihood that AI was responsible for some meaningful share of the slowdown in entry-level hiring. The extent to which your beliefs align with mine depend on how strong your priors are about AI’s labor impact and how credible you think the recent evidence is.
Before our “Canaries” paper, my inclination was that AI was having at most a minimal impact on the labor market. Primarily, this is because I looked at the CPS data and did not find much evidence of aggregate labor impacts, as discussed in point 1. I put added trust in evidence I produce myself because I know what goes into the sausage, but it certainly helped to see corroboration in other studies.
That said, most of the discourse around AI’s impacts on jobs focused on young workers. Evidence for that group was at best weak. The CPS has small sample sizes when filtering to specific age-occupation groups. O’Brien (2025) suggests the level of statistical uncertainty in even the ACS, a much larger data set, is quite large.
Outside of the research community people shared differing views. Numerous news articles claimed disruption to entry-level labor markets using a combination of qualitative interviews and quite speculative analysis of broad labor market data, while others pushed back citing evidence from datasets like the CPS. A report from VC firm SignalFire suggested a startling slowdown in entry-level hiring in the tech sector, with the impact highly concentrated on the youngest workers.
All this made me believe the overall economy-wide impact was small, but with a large amount of uncertainty about young workers in particular. That was my prior when we began looking at the ADP data.
I still believe the overall impact is small, but I have updated my views about the impact on young workers. I now believe that AI may have contributed a meaningful amount to the overall slowdown in hiring for entry-level workers.
This is again a case where I put faith in the work we produced. We did not begin the project with any agenda about which direction the results should go.
We started by showing entry-level employment declines in some case studies such as software development and customer service. Then we found the results held more generally across highly AI-exposed occupations but not in low-exposure occupations. We then found that the same patterns held under an LLM-usage based exposure measure from Anthropic. Strikingly, we then found the same result for occupations with high automative AI usage but not ones with high augmentative usage. We took this as a potential indication that the results might really be driven by AI and not other changes in the economy.
We proceeded by listing out the most plausible alternatives we could think of that could be driving the patterns. We found very similar results when excluding the tech sector, computer jobs, and jobs that could be worked remotely. These tests indicate that our findings are not primarily driven by tech overhiring, return from work from home or outsourcing. We then controlled for firm-time effects to control for any economic shocks that impacted overall hiring at the firm, finding the results again looked similar: within firms, entry-level hiring in AI-exposed jobs declined 13% relative to less-exposed jobs. These impacts appeared only after the proliferation of LLMs. Older workers had statistically insignificant impacts. To the extent we believe that interest rate changes or other aggregate economic changes impact hiring of all workers at a firm, not just specific occupations correlated with AI exposure, then this would rule out a variety of alternative explanations.
We then compared college graduates to non-college workers. For people without a college degree, we continued to find a striking divergence between more- and less-exposed jobs, even at higher age groups. This suggested our results could not be explained away by Covid-era education disruptions. We also showed robustness to alternative ways of building the sample, to including part-time and temporary workers, to extending the sample to 2018, and to computing the results separately for men and women.3
So, how strong is the evidence? Ultimately, our paper is observational. We do not have an experiment varying AI adoption. If you believe our various alternative analyses offer compelling evidence of a causal impact of AI, then you should update your beliefs more. If you need more evidence to convince you of a causal impact, then you should update less accordingly.
Since our paper came out, two other articles showed similar results using large-scale data from Revelio Labs. Hosseini and Lichtinger (2025) find employment declines for young workers in US firms that adopt AI technologies, with adoption measured by the content of their job postings. Klein Teeselink (2025) shows that exposed firms and occupations have seen significant contraction in entry-level hiring in the UK.
On the other hand, the newest version of Humlum and Vestergaard (2025), released in late September 2025, finds no difference in entry-level hiring between firms that adopt AI and those that do not in Denmark. They measure firm adoption via worker surveys on AI encouragement at work, which they match to administrative firm data. An interesting question is how to square these results with Hosseini and Lichtinger (2025), who find large differences in hiring between adopting and non-adopting firms after they start using AI but also note some differences even before AI adoption. It would be worthwhile to test if the varying results between these papers stem from 1. Institutional differences between the US and Denmark, 2. Differences in the firm-level exposure measures, or 3. Differences in the specific statistical analyses. We should hope to see more work using high-quality employment and adoption data in the coming months to help sort through the various findings.
Path forward: There are two primary sources of uncertainty.
The first is whether existing studies measure a causal impact. One way to bolster evidence is to collect better data on when individual firms adopt AI (see more in point 4) to track employment changes before and after at the firm level, hopefully improving upon the measures in Humlum and Vestergaard (2025), Hosseini and Lichtinger (2025), and other work. Even better would be to find some kind of experiment in firm-level AI-adoption. An example would be an A/B test at an AI company that randomly offered discounts on subscriptions to different firms. Ideally the experiment would have been run starting in the early days of AI and run for months, if not years.
The second source of uncertainty is how representative the data sets in existing studies are to the broader economy. In my view, this is not as important as the first issue. If we have indeed estimated causal effects, then we’ve still identified a widespread phenomenon affecting tens of thousands of firms and millions of workers. That’s big and important in and of itself. It seems unlikely to me that expanding to the broader economy would meaningfully lead to different conclusions. That said, we should have high-quality public data available for everyone to look at, not just those of us with industry partnerships. That’s why I signed this letter to the Department of Labor.
A third area for further study is how to integrate the large literature measuring productivity improvements in individual workflows with broader employment impacts. The below chart from an Economist article several months back summarizes estimates of how AI affects performance inequality at work.4 Future work should model the implications of these heterogeneous impacts for occupational employment and wage changes across different subpopulations, such as entry-level and experienced workers. Autor and Thompson (2025) makes a seminal contribution on this issue.
3. We can probably measure AI exposure across jobs better than most people think
A common complaint is that existing measures of AI exposure are poor because they do not have validation on real-world economic outcomes. To the extent you believe the recent literature, this is not as true as it used to be. First, Tomlinson et al. (2025) found that AI exposure measures from Eloundou et al. (2024) have very high correlation with Microsoft Copilot usage from 2025. This suggests their predictions were largely borne out, with the caveat that associating chat bot conversations with occupations can be challenging. Using LLMs to predict which job a conversation relates to has become nearly standard practice.
Second, the recent papers finding these AI exposure measures predict employment changes offer further real-world validation. Imagine the exposure measures were completely worthless, essentially randomly assigning different jobs high or low exposure. In that case we should expect the exposure measures wouldn’t predict anything about employment. To the extent they do predict employment changes, they receive some degree of validation, with stronger causal evidence offering greater validation.
Path forward: We should continue to validate the exposure measures we have based on real economic outcomes like employment or LLM usage. It would be great to get actual large-scale data from AI labs on usage by occupation, perhaps via survey rather than relying on predictions based on conversations.5 We should develop and validate new measures of exposure that reflect evolving AI capabilities. Finally, while existing work explores exposure by occupation, an important direction for research is to further measure differences by seniority or demographic characteristics to identify precise subpopulations facing potential job risk. A step in this direction is Manning and Aguirre 2025, which explores exposure by economic vulnerability.
4. We are gathering new evidence on which areas we should expect the greatest AI progress in the near term
In which dimensions should we expect AI to improve most rapidly? In which dimensions will progress be slower?
Nascent work has been making strides in answering these questions. Ongoing work from Erik Brynjolfsson, Basil Halperin, and Arjun Ramani, along with Rishi Bommasani and his coauthor team, track LLM improvement along different dimensions of economically-relevant intelligence. GDPval from OpenAI is also an excellent recent development, along with Apex from Mercor. Developing economically-relevant evals is a quite exciting and active research area. Making predictions about AI progress along different dimensions of intelligence will help with creating better measures of future occupational exposure, among other things.
Another perspective is that while existing measures are probably better than most people think (see point 3), they may also have lots of room for improvement. Developing economically-relevant evaluations, and directly assessing LLM capabilities for these measures, can lead to even sharper estimates of present occupational exposure than the existing ones.
Path forward: Continued research in this space, identifying areas of rapid and slow progress. A next step would be to associate these rates of improvement with occupational task information. Which occupations face greater AI risk as the models improve? GDPval is a quality step towards understanding this. Another good idea is to solicit predictions, perhaps via markets, about future disruption by occupation.6
5. We have very little idea about employment changes in other countries
The two studies that speak to this are Klein Teeselink (2025) in the UK and Humlum and Vestergaard (2025) in Denmark. I am aware of no evidence from any other part of the world.
The most relevant data we have are recent estimates from Anthropic and OpenAI about usage in different countries. Older work develops coarse country-specific occupational exposure measures (Gmyrek 2025).
Path forward: More research should be done on other labor markets. Three promising avenues are to use Revelio or ADP in other countries, if feasible; use other private payroll data from other countries; or use government administrative data to track employment changes. Some infrastructure likely needs to be built out to measure AI exposure for local occupations.
A particular area of focus should be countries with high levels of employment in exposed jobs such as call center operations. Further modeling can also help with predicting how impacts may vary across different institutional contexts.
6. We could use better data on firm AI adoption
The issue is both conceptual and empirical. First, what does it mean for a firm or worker to “adopt” AI? They use it once? Every day? 1% of the company uses it? 10%? It’s used for “production of goods and services?” Or back office tasks? They use the free version, or the subscription version? They use the older models or the newer models? Chatterji et al. (2025) find that a large share of consumer ChatGPT use seems work-related, which makes this even more complicated.
The main challenge empirically is that we have little data on adoption, even setting aside these conceptual questions. Bonney et al. (2024) from the US Census finds an AI adoption number close to 10%, but they have only a 16% response rate and ask a potentially narrow question: “Between MMM DD – MMM DD, did this business use Artificial Intelligence (AI) in producing goods or services? (Examples of AI: machine learning, natural language processing, virtual agents, voice recognition, etc.).” Why only ask about usage for “production of goods and services” as opposed to back office tasks or internal processes? Would Walmart, for example, respond yes to this question? See also Hyman et al. (2025) and this review article by Crane et al. (2025) from February.
Another source is the Ramp AI Index. They find an adoption number closer to 44% based on business subscriptions to AI services. While Ramp firms may have a higher proclivity to adopt technology, the company also sees a very low share of Gemini usage and misses the consumer app usage for work that Chatterji et al. (2025) document. Chatterji et al. (2025), Hartley (2025), and Bick et al. (2025) show consumer-level adoption but do not have as much to say about firms. Hampole et al. (2025) and Hosseini and Lichtinger (2025) measure adoption by whether AI appears in job postings and descriptions, but they likely miss a meaningful share of firm usage. Humlum and Vestergaard (2025) use worker survey data in Denmark that they match to firms.
Path forward: Ideally we would have some sort of continuous index of AI adoption, with differences in “how much” firms or workers have adopted AI. Business spend data seems especially promising here. Another option is the number of unique users or the number of conversations. We should encourage AI companies to share data on this to the extent feasible. Business surveys should also explore alternative questions and test how sensitive reported adoption rates are to the specific wording.
7. We do not know how employment trends will progress going forward
Prior technologies decreased labor demand in some occupations. Simultaneously they increased labor demand in other occupations and created new forms of work (Acemoglu and Autor 2011, Autor et al. 2024). As a result employment has remained fairly stable for decades, with real wages rising along with productivity. These forces help explain why John Maynard Keynes was wrong in his prediction that by now we would be working 15 hours a week.
Will AI likewise lead to rising real wages without compromising employment? Or will AI capabilities advance far and fast enough that even new work is better performed by machines instead of humans? We do not yet know if this time will be different.
There is an extraordinary amount of disagreement on this issue. A sizable share of employees at the AI labs believe with a high degree of conviction that AI will within several years replace a large amount of work. On the other hand, some economists believe AI’s labor market impacts will be similar to prior technologies.
The computer and Internet revolutions likely account for the predominant share of rising inequality over the past several decades due to skill-biased technical change. Even if AI is like prior technologies it may have profound effects on society.
Path forward: We should build trackers to continue to follow employment trends by AI exposure and demographic variables like age. We should also develop credible predictions about future labor impacts; see point 4.
