# data[].attributes.body
Users who refuse ads will face rate limits and no image generation or deep research

# data[].attributes.attachment_link.description
Ads are currently only being tested in the US and may change as testing expands.

# data[].attributes.body
Hilarious

# data[].attributes.body
AI bots can rent humans to do tasks in the real world. DO NOT USE!

# data[].attributes.attachment_link.title
RentAHuman.ai - AI Agents Hire Humans for Physical Tasks

# data[].attributes.attachment_link.description
The marketplace where AI agents rent humans. MCP integration, REST API, flexible payments. Book humans for real-world tasks your AI can’t do.

# data[].attributes.body
Something Big is Happening in AI: Job loss expected

# data[].attributes.headline
MPA Demands Seedance 2.0 Halt ‘Infringing Activity’ After Viral AI Video of Tom Cruise-Brad Pitt Fight

# data[].attributes.subject
MPA Demands Seedance 2.0 Halt ‘Infringing Activity’ After Viral AI Video of Tom Cruise-Brad Pitt Fight

# data[].attributes.attachment_link.title
MPA Demands Seedance 2.0 Halt ‘Infringing Activity’ After Viral AI Video of Tom Cruise-Brad Pitt Fight

# data[].attributes.attachment_link.description
The Motion Picture Association slammed ByteDance’s AI video generator, known as Seedance 2.0, accusing the company of “disregarding” copyright laws.

# data[].attributes.body
The "public debate" about AI is confusing for the general public and for policymakers because it is a three-sided debate

# data[].attributes.attachment_link.title
The “public debate” about AI is confusing for the general public and for policymakers because it is a three-sided debate — LessWrong

# data[].attributes.attachment_link.description
Summary of Argument: The public debate among AI experts is confusing because there are, to a first approximation, three sides, not two sides to the d…

# data[].attributes.headline
Supreme Court overrules Aerotel and aligns UK approach to AI and computer-implemented inventions with the EPO

# data[].attributes.subject
Supreme Court overrules Aerotel and aligns UK approach to AI and computer-implemented inventions with the EPO

# data[].attributes.body
https://www.hsfkramer.com/en_US/notes/ip/2026-02/supreme-court-overrules-aerotel-and-aligns-uk-approach-to-ai-and-computer-implemented-inventions-with-the-epo?__cf_chl_tk=TjGLKXZlCmpKV0Ks_k477JFvq4Sq8KGy3PzwAy9Rvwg-1771003761-1.0.1.1-NWNVJ5R3tqk1EoOCTdWgRQD_I9zCKbHSy0.xBoktDcM

# data[].attributes.body
Measuring AI Ability to Complete Long Tasks

# data[].attributes.attachment_link.description
METR is a research nonprofit that evaluates frontier AI models to help companies and wider society understand AI capabilities and what risks they pose.

# data[].attributes.body
AI Governance & Safety Canada is a nonpartisan not-for-profit and community of people across the country, working to ensure that advanced AI is safe. You can join the slack here

# data[].attributes.attachment_link.description
Would you like to see a thriving AI governance & safety community in Canada, helping us contribute to global efforts on AI risk reduction?

# data[].attributes.headline
AI memory is sold out, causing an unprecedented surge in prices

# data[].attributes.subject
AI memory is sold out, causing an unprecedented surge in prices

# data[].attributes.attachment_link.title
AI memory is sold out, causing an unprecedented surge in prices

# data[].attributes.attachment_link.description
Three primary memory vendors — Micron, SK Hynix and Samsung Electronics — make up nearly the entire RAM market, and they’re benefitting from this shortage.

# data[].attributes.headline
Will AI make auditors more efficient or worthless?

# data[].attributes.subject
Will AI make auditors more efficient or worthless?

# data[].attributes.headline
I spent two days gigging at RentAHuman and didn’t make a single cent 

# data[].attributes.subject
I spent two days gigging at RentAHuman and didn’t make a single cent 

# data[].attributes.attachment_link.title
I spent two days gigging at RentAHuman and didn’t make a single cent

# data[].attributes.attachment_link.description
These bots supposedly need a human body to accomplish great things in meatspace.

# data[].attributes.body
Sam Altman really hates Effective Altruism. Blames them for getting him fired. Not actually true of course. Many EA people are doomers (from the 3-sided debate), and are pushing for regulation to pause AI development, which most AI tech bros can not stand

# data[].attributes.attachment_link.title
Sam Altman’s Firing at OpenAI Was Ineffective Effective Altruism

# data[].attributes.attachment_link.description
While the company’s board seemed distracted by fears of artificial intelligence enslaving mankind, Microsoft swooped in for the win.

# data[].attributes.attachment_link.title
How AI is shaping the war in Iran — and what’s next for future conflicts

# data[].attributes.attachment_link.description
Rapid technological development is prompting urgent discussions on regulating the use and procurement of artificial intelligence for military use.

# data[].attributes.headline
Amazon to make senior engineers sign off on AI-assistend changes

# data[].attributes.subject
Amazon to make senior engineers sign off on AI-assistend changes

# data[].attributes.body
Surely this will be a meaningful check on auto-generated failure modes and not an accountability trap for humans

# data[].attributes.attachment_link.readable_text
He asked staff to attend the meeting, which is normally optional.

Junior and mid-level engineers will now require more senior engineers to sign off any AI-assisted changes, Treadwell added.

Amazon said the review of website availability was "part of normal business" and it aims for continual improvement.

"TWiST is our regular weekly operations meeting with a specific group of retail technology leaders and teams where we review operational performance across our store," the company said.

Separately, the company's cloud computing arm-Amazon Web Services-has suffered at least two incidents linked to the use of AI coding assistants, which the company has been actively rolling out to its staff.

AWS suffered a 13-hour interruption to a cost calculator used by customers in mid-December after engineers allowed the group's Kiro AI coding tool to make certain changes, and the AI tool opted to "delete and recreate the environment," the FT previously reported.

Amazon previously said the incident in December was an "extremely limited event" affecting only a single service in parts of mainland China. Amazon added that the second incident did not have an impact on a "customer facing AWS service."

The FT previously reported multiple Amazon engineers said their business units had to deal with a higher number of "Sev2s"-incidents requiring a rapid response to avoid product outages-each day as a result of job cuts.

Amazon has undertaken multiple rounds of lay-offs in recent years, most recently eliminating 16,000 corporate roles in January. The group has disputed the claim that headcount cuts were responsible for an increase in recent outages.
© 2026 The Financial Times Ltd. All rights reserved. Not to be redistributed, copied, or modified in any way.

# data[].attributes.attachment_link.title
After outages, Amazon to make senior engineers sign off on AI-assisted changes

# data[].attributes.attachment_link.description
AWS has suffered at least two incidents linked to the use of AI coding assistants.

# data[].attributes.body
AI impact on job market not clear yet, but predicted to increase

# data[].attributes.attachment_link.readable_text
Key Findings

We introduce a new measure of AI displacement risk, observed exposure, that combines theoretical LLM capability and real-world usage data, weighting automated (rather than augmentative) and work-related uses more heavily

AI is far from reaching its theoretical capability: actual coverage remains a fraction of what's feasible

Occupations with higher observed exposure are projected by the BLS to grow less through 2034

Workers in the most exposed professions are more likely to be older, female, more educated, and higher-paid

We find no systematic increase in unemployment for highly exposed workers since late 2022, though we find suggestive evidence that hiring of younger workers has slowed in exposed occupations

Introduction

The rapid diffusion of AI is generating a wave of research measuring and forecasting its impacts on labor markets. But the track record of past approaches gives reason for humility.

For example, a prominent attempt to measure job offshorability identified roughly a quarter of US jobs as vulnerable, but a decade on, most of those jobs maintained healthy employment growth. The government's own occupational growth forecasts, while directionally correct, have added little predictive value beyond linear extrapolation of past trends. Even in hindsight, the impact of major economic disruptions on the labor market is often unclear. Studies on the employment effects of industrial robots reach opposing conclusions, and the scale of job losses attributed to the China trade shock continues to be debated.

In this paper, we present a new framework for understanding AI's labor market impacts, and test it against early data, finding limited evidence that AI has affected employment to date. Our goal is to establish an approach for measuring how AI is affecting employment, and to revisit these analyses periodically. This approach won't capture every channel through which AI could reshape the labor market, but by laying this groundwork now, before meaningful effects have emerged, we hope future findings will more reliably identify economic disruption than post-hoc analyses.

It is possible that the impacts of AI will be unmistakable. This framework is most useful when the effects are ambiguous-and could help identify the most vulnerable jobs before displacement is visible.

Counterfactuals

Causal inference is easier when the effects are large and sudden. The COVID-19 pandemic and accompanying policy measures caused economic disruption so stark that sophisticated statistical approaches were unnecessary for many questions. For example, unemployment jumped sharply in the early weeks of the pandemic, leaving little room for alternative explanations.

The impacts of AI, however, might be less like COVID and more like the internet or trade with China. The effects may not be immediately clear from aggregate unemployment data; factors like trade policy and the business cycle could cloud interpretations of trend lines.

One common approach is to compare outcomes between more or less AI-exposed workers, firms, or industries, in order to isolate the effect of AI from confounding forces. Exposure is typically defined at the task level: AI can grade homework but not manage a classroom, for example, so teachers are considered less exposed than workers whose entire job can be performed remotely.

Our work follows this task-based approach, incorporating measures of theoretical AI capability and real-world usage, before aggregating to occupations.

Measuring exposure

Our approach combines data from three sources.

1. The O*NET database, which enumerates tasks associated with around 800 unique occupations in the US.

2. Our own usage data (as measured in the Anthropic Economic Index).

3. Task-level exposure estimates from Eloundou et al. (2023), which measure whether it is theoretically possible for an LLM to make a task at least twice as fast.

Eloundou et al.'s metric, β, scores tasks on a simple scale: 1 if a task can be doubled in speed by an LLM alone, 0.5 if it requires additional tools or software built on top of the LLM, and 0 otherwise.

Why might actual usage fall short of theoretical capability? Some tasks that are theoretically possible may not show up in usage because of model limitations. Others may be slow to diffuse due to legal constraints, specific software requirements, human verification steps, or other hurdles. For example, Eloundou et al. mark "Authorize drug refills and provide prescription information to pharmacies" as fully exposed (β=1). We have not observed Claude performing this task, although the assessment seems correct in that it could theoretically be sped up by an LLM.

That said, these measures of theoretical capability and actual usage are highly correlated. As Figure 1 shows, 97% of the tasks observed across the previous four Economic Index reports fall into categories rated as theoretically feasible by Eloundou et al. (β=0.5 or β=1.0).

A new measure of occupational exposure

Our new measure, observed exposure, is meant to quantify: of those tasks that LLMs could theoretically speed up, which are actually seeing automated usage in professional settings? Theoretical capability encompasses a much broader range of tasks. By tracking how that gap narrows, observed exposure provides insight into economic changes as they emerge.

Our measure qualitatively captures several aspects of AI usage that we think are predictive of job impacts. A job's exposure is higher if:

Its tasks are theoretically possible with AI

Its tasks see significant usage in the Anthropic Economic Index

Its tasks are performed in work-related contexts

It has a relatively higher share of automated use patterns or API implementation

Its AI-impacted tasks make up a larger share of the overall role

We give mathematical details in the Appendix. We count tasks that are theoretically capable with an LLM as covered if they have seen sufficient work-related usage in Claude traffic. We then adjust for how the task is being carried out: fully automated implementations receive full weight, while augmentative use receives half weight. Finally, the task-level coverage measures are averaged to the occupation level weighted by the fraction of time spent on each task.

Figure 2 shows observed exposure (in red) compared to β from Eloundou et al. (in blue), illustrating the difference between theoretical and actual use on our platform, grouped by broad occupational categories. We calculate this by first averaging to the occupation level weighting by our time fraction measure, then averaging to the occupation category weighting by total employment. For example, the β measure shows scope for LLM penetration in the majority of tasks in Computer & Math (94%) and Office & Admin (90%) occupations.

The red area, depicting LLM use from the Anthropic Economic Index, shows how people are using Claude in professional settings. The coverage shows AI is far from reaching its theoretical capabilities. For instance, Claude currently covers just 33% of all tasks in the Computer & Math category.

As capabilities advance, adoption spreads, and deployment deepens, the red area will grow to cover the blue. There is a large uncovered area too; many tasks, of course, remain beyond AI's reach-from physical agricultural work like pruning trees and operating farm machinery to legal tasks like representing clients in court.

Figure 3 shows the ten occupations most exposed under this measure. In line with other data showing that Claude is extensively used for coding, Computer Programmers are at the top, with 75% coverage, followed by Customer Service Representatives, whose main tasks we increasingly see in first-party API traffic. Finally, Data Entry Keyers, whose primary task of reading source documents and entering data sees significant automation, are 67% covered.

At the bottom end, 30% of workers have zero coverage, as their tasks appeared too infrequently in our data to meet the minimum threshold. This group includes, for example, Cooks, Motorcycle Mechanics, Lifeguards, Bartenders, Dishwashers, and Dressing Room Attendants.

How exposure tracks with projected job growth and worker characteristics

The US Bureau of Labor Statistics (BLS) publishes regular employment projections, with the latest set, published in 2025, covering predicted changes in employment for every occupation from 2024 to 2034. In Figure 4, we compare our job-level coverage measure to their predictions.

A regression at the occupation level weighted by current employment finds that growth projections are somewhat weaker for jobs with more observed exposure. For every 10 percentage point increase in coverage, the BLS's growth projection drops by 0.6 percentage points. This provides some validation in that our measures track the independently derived estimates from labor market analysts, although the relationship is slight. Interestingly, there is no such correlation using the Eloundou et al. measure alone.

Figure 5 shows characteristics of workers in the top quartile of exposure and the 30% of workers with zero exposure in the three months before ChatGPT was released, August to October 2022, using data from the Current Population Survey. The groups are very different. The more exposed group is 16 percentage points more likely to be female, 11 percentage points more likely to be white, and almost twice as likely to be Asian. They earn 47% more, on average, and have higher levels of education. For example, people with graduate degrees are 4.5% of the unexposed group, but 17.4% of the most exposed group, an almost fourfold difference.

Prioritizing outcomes

With these exposure measures in hand, the question is what to look for. Researchers have taken different approaches. For example, Gimbel et al. (2025) track changes in the occupational mix using the Current Population Survey. Their argument is that any important restructuring of the economy from AI would show up as changes in distribution of jobs.¹ (They find that, so far, changes have been unremarkable.) Brynjolfsson et al. (2025) look at employment levels split by age group using data from the payroll processing firm ADP, while Acemoglu et al. (2022) and Hampole et al. (2025) use job posting data from Burning Glass (now Lightcast) and Revelio, respectively.

We focus on unemployment as our priority outcome because it most directly captures the potential for economic harm-a worker who is unemployed wants a job and has not yet found one. In this case, job postings and employment do not necessarily signal the need for policy responses; a decline in job postings for a highly exposed role may be counteracted by increased openings in a related one. Most harmful labor market developments of AI should arguably include a period of increased unemployment, as displaced workers search for alternatives. The Current Population Survey is well suited to tracking this, as unemployed respondents report their previous job and industry.

Initial results

We next study trends in unemployment, matching our occupation-level measures to respondents in the Current Population Survey.

A key question in interpreting our coverage measure is which workers should be considered treated? Should changes in employment be expected from just 10% task coverage? Gans and Goldfarb (2025) show that if an O-ring model best describes jobs, employment effects might be seen only when all tasks have some degree of AI penetration. Hampole et al. (2025) argue that mean exposure decreases labor demand, but concentration of exposure in only certain tasks can counteract this. And Autor and Thompson (2025) highlight the level of expertise required for the remaining tasks.

With an eye toward simplicity, and noting that we are most concerned with large impacts, we center our analysis on the idea that impacts should be felt most in the groups with the highest mean exposure. We compare workers in the top quartile of time-weighted task coverage to those in the bottom. If AI capabilities advance quickly, task coverage might be high for lower percentiles of coverage, which might make an absolute threshold more helpful. But we make the assumption that impacts should affect the most exposed workers first, and present results varying the cutoff we use to define treatment.

The upper panel of Figure 6 shows raw trends in the unemployment rate since 2016 for workers in the top quartile of exposure and the unexposed group. During COVID, the less AI-exposed workers-who are more likely to have in-person jobs-saw a much larger increase in unemployment. Since then, the trends have been largely similar between the two groups. The lower panel measures the size of the gap between the most and least exposed workers in a difference-in-differences framework, mirroring the findings from the raw data. The average change in the gap since the release of ChatGPT is small and insignificant, suggesting that the unemployment rate of the more exposed group has increased slightly but the effect is indistinguishable from zero.

What kind of scenarios can this framework identify? Based on the confidence interval of the pooled estimate, differential increases in unemployment on the order of 1 percentage point would be detectable (this will change as new data comes in, so it is merely a ballpark estimate). If all workers within the top 10% were laid off, it would increase unemployment within the top quartile group from 3% to 43%, and it would increase aggregate unemployment from 4% to 13%.

A smaller but still concerning impact would be a scenario such as a "Great Recession for white-collar workers." During the 2007-2009 Great Recession, unemployment rates doubled from 5% to 10% in the US. Such a doubling in the top quartile of exposure would increase its unemployment rate from 3% to 6%. This should be visible in our analysis as well. Note that our core estimate is based on differential changes in the unemployment rate in the exposed group compared to the less exposed group. If unemployment increased for all workers in parallel, we would not attribute this to AI advancements that still leave many tasks unaffected.

One group of particular concern is young workers. Brynjolfsson et al. report a 6-16% fall in employment in exposed occupations among workers aged 22 to 25. They attribute this decrease primarily to a slowdown in hiring rather than an increase in separations.

We find that the unemployment rate for young workers in the exposed occupations is flat (see Appendix). But slowed hiring may not necessarily manifest as increased unemployment, since many young workers are labor market entrants without a listed occupation in the CPS data and may exit the labor force rather than appear as unemployed. To address hiring directly, we use the panel dimension of the CPS, counting the percent of young (22-25 year old) workers who begin a new job in a more vs. less exposed occupation over time. Figure 7 shows the monthly job finding rate (i.e., when a worker reports a job that they did not have in the previous month) for young workers, split by whether they are entering a high- vs. low-exposure occupation.

Apart from some large swings in 2020-2021, these series visually diverge in 2024, with young workers relatively less likely to be hired into exposed occupations. Job finding rates at the less exposed occupations remain stable at 2% per month, while entry into the most exposed jobs decreases by about half a percentage point. The averaged estimate in the post-ChatGPT era is a 14% drop in the job finding rate compared to that in 2022 in the exposed occupations, although this is just barely statistically significant. (There is no such decrease for workers older than 25.)

This may provide some signal of the early effects of AI on employment, and echoes the findings from Brynjolfsson et al. But there are several alternative interpretations. The young workers who are not hired may be remaining at their existing jobs, taking different jobs, or returning to school. A further data-related caveat is that job transitions may be more vulnerable to mismeasurement in surveys.

Discussion

This report introduces a new measure for understanding the labor market effects of AI and studies impacts on unemployment and hiring. Jobs are more exposed to AI to the extent that their tasks are theoretically feasible with LLMs and observed on our platforms in automated, work-related use cases. We find that computer programmers, customer service representatives, and financial analysts are among the most exposed. Using survey data from the US, we find no impact on unemployment rates for workers in the most exposed occupations, although there's tentative evidence that hiring into those professions has slowed slightly for workers aged 22-25.

Our work is a first step toward cataloging the impact of AI on the labor market. We hope that the analytical steps taken in this report, especially around coverage and counterfactuals, will be easy to update as new data on employment and AI usage emerge. An established approach may help future observers separate signal from noise.

There are several improvements to be made to the present work. Our usage data will be incorporated in future updates, forming an evolving picture of task and job coverage in the economy. The Eloundou et al. metric could also be updated, to the extent that it is linked to LLM capabilities as of early 2023. And, given the suggestive results around young workers and labor market entrants, a key next step might be to look at how recent graduates with educational credentials in exposed areas are navigating the labor market.

Appendix

Available here.

Acknowledgements

Written by Maxim Massenkoff and Peter McCrory.

With acknowledgements to: Ruth Appel, Tim Belonax, Keir Bradwell, Andy Braden, Dexter Callender III, Miriam Chaum, Madison Clark, Jake Eaton, Deep Ganguli, Kunal Handa, Ryan Heller, Lara Karadogan, Jennifer Martinez, Jared Mueller, Sarah Pollack, David Saunders, Carl De Torres, Kim Withee, and Jack Clark.

We additionally thank Martha Gimbel, Anders Humlum, Evan Rose, and Nathan Wilmers for feedback on earlier versions of this report.

Citation

@online{massenkoffmccrory2026labor, author = {Maxim Massenkoff and Peter McCrory}, title = {Labor market impacts of AI: A new measure and early evidence}, date = {2026-03-05}, year = {2026}, url = {https://www.anthropic.com/research/labor-market-impacts}, }

References

Acemoglu, Daron and Pascual Restrepo, "Robots and Jobs: Evidence from US Labor Markets," Journal of Political Economy, 2020, 128 (6), 2188-2244.

Acemoglu, Daron, David Autor, Jonathon Hazell, and Pascual Restrepo, "Artificial intelligence and jobs: Evidence from online vacancies," Journal of Labor Economics, 2022, 40 (S1), S293-S340.

Appel, Ruth, Maxim Massenkoff, Peter McCrory, Miles McCain, Ryan Heller, Tyler Neylon, and Alex Tamkin, "Anthropic Economic Index report: economic primitives," 2026.

Autor, David H, David Dorn, and Gordon H Hanson, "The China syndrome: Local labor market effects of import competition in the United States," American Economic Review, 2013, 103 (6), 2121-2168.

Autor, David H, & Thompson, N. (2025). Expertise. NBER Working Paper, (w33941).

Blinder, Alan S et al., "How many US jobs might be offshorable?," World Economics, 2009, 10 (2), 41.

Borusyak, Kirill, Peter Hull, and Xavier Jaravel, "Quasi-experimental shift-share research designs," The Review of Economic Studies, 2022, 89 (1), 181-213.

Brynjolfsson, Erik, Bharat Chandar, and Ruyu Chen, "Canaries in the coal mine? six facts about the recent employment effects of artificial intelligence," Digital Economy, 2025.

Eckhardt, Sarah and Nathan Goldschlag, "AI and Jobs: The Final Word (Until the Next One)," Economic Innovation Group (EIG), August 2025. Available at: https://eig.org/ai-and-jobs-the-final-word/

Eloundou, Tyna, Sam Manning, Pamela Mishkin, and Daniel Rock, "Gpts are gpts: An early look at the labor market impact potential of large language models," arXiv preprint arXiv:2303.10130, 2023, 10.

Fujita, S., Moscarini, G., & Postel-Vinay, F. (2024). Measuring employer-to-employer reallocation. American Economic Journal: Macroeconomics, 16(3), 1-51.

Gans, Joshua S. and Goldfarb, Avi, "O-Ring Automation," NBER Working Paper No. 34639, December 2025. Available at SSRN: https://ssrn.com/abstract=5962594

Gimbel, Martha, Molly Kinder, Joshua Kendall, and Maddie Lee, "Evaluating the Impact of AI on the Labor Market: Current State of Affairs," Research Report, The Budget Lab at Yale, New Haven, CT October 2025. Available at: https://budgetlab.yale.edu.

Graetz, Georg and Guy Michaels, "Robots at Work," Review of Economics and Statistics, 2018, 100 (5), 753-768.

Hampole, Menaka, Dimitris Papanikolaou, Lawrence DW Schmidt, and Bryan Seegmiller, "Artificial intelligence and the labor market," Technical Report, National Bureau of Economic Research 2025.

Handa, Kunal, Alex Tamkin, Miles McCain, Saffron Huang, Esin Durmus, Sarah Heck, Jared Mueller, Jerry Hong, Stuart Ritchie, Tim Belonax, Kevin K. Troy, Dario Amodei, Jared Kaplan, Jack Clark, and Deep Ganguli, "Which Economic Tasks are Performed with AI? Evidence from Millions of Claude Conversations," 2025.

Hui, Xiang, Oren Reshef, and Luofeng Zhou, "The short-term effects of generative artificial intelligence on employment: Evidence from an online labor market," Organization Science, 2024, 35 (6), 1977-1989.

Johnston, Andrew and Christos Makridis, "The labor market effects of generative AI: A difference-in-differences analysis of AI exposure," Available at SSRN 5375017, 2025.

Massenkoff, Maxim, "How predictable is job destruction? Evidence from the Occupational Outlook," 2025. Working Paper.

Ozimek, Adam, "Overboard on Offshore Fears," 2019. https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3777307

Tamkin, Alex and Peter McCrory, "Estimating AI productivity gains from Claude conversations," 2025.

Tomlinson, K., Jaffe, S., Wang, W., Counts, S., & Suri, S. (2025). Working with AI: measuring the applicability of generative AI to occupations. arXiv preprint arXiv:2507.07935.

Footnotes

1. Job offshorability: Blinder et al. (2009) and Ozimek (2019); Government growth forecasts: Massenkoff (2025); Robots: Graetz and Michaels (2018) and Acemoglu and Restrepo (2020); China shock: Autor et al. (2013) and Borusyak et al. (2022).

2. Brynjolfsson et al. (2025) compare employment trends for workers in more versus less AI-exposed occupations, using the task exposure measures from Eloundou et al. (2023) and payroll data from ADP. Johnston and Makridis (2025) do a similar task-based analysis using US administrative data, but they aggregate treatment to the industry level. Hui et al. (2024) study how freelance jobs on Upwork responded to the release of ChatGPT and advanced image generation tools, comparing workers in directly affected categories to those in unaffected categories before and after each tool's release date. Hampole et al. (2025) instrument for firm-level AI adoption using historical university hiring networks: firms that historically recruited from universities whose graduates later entered AI-related roles faced lower adoption costs.

3. Our task- and occupation-level exposure measures can readily incorporate other usage data, and be extended to different countries. We intend to apply this methodology to new settings over time.

4. In their framework, "Directly exposed'" tasks were those that could be completed in half the time with an LLM (with a 2,000-word input limit and no access to recent facts). Tasks that were "exposed with tools" were those subject to the same speedup with an LLM that had access to software for, e.g., information retrieval and image processing. Tasks that were not exposed could not have their duration reduced by 50% or more using an LLM.

5. We use the previous two Anthropic Economic Index datasets, covering usage from August and November 2025. For ONET tasks that are highly semantically similar, we split the counts across them.

6. There are judgment calls involved at every step. Should the Eloundou et al. (2023) measure enter as {0, 0.5, 1} or something else? What determines "significant" use? How do we handle tasks which seem very similar to those with high usage, but are too rare to have been picked up specifically in the sampling for the Economic Index? How much more should automation workflows count compared to augmentation? A reassuring finding which we expand on in the Appendix is that the Spearman (rank-rank) correlation of job exposure across many resolutions to these questions is exceedingly high.

7. To match O*NET-SOC codes to occ1990 codes in the CPS, we use the crosswalk provided by Eckhart and Goldschlag (2025).

8. We explore this further in three ways in the Appendix. First, we ask whether the percentile cutoff that we use to define treatment matters, varying it from the median to the 95th percentile. In all cases, the impact is flat or negative (meaning that unemployment decreases for the exposed group). Next, we focus on young workers in particular, those aged 22 to 25 as in Brynjolfsson et al. (2025). Finally, we use data on unemployment insurance claimants from the Department of Labor to measure the unemployment, rather than CPS survey responses. In no extension do we find clear impacts on exposed jobs.

9. This range is wide because the authors provide estimates against multiple counterfactuals. The 6 percentage point drop compares to a counterfactual of flat employment growth. The 16 percentage point estimate comes from a design comparing similar workers in the same firm with different occupations.

10. See Fujita, et al. (2024).

Key Findings

We introduce a new measure of AI displacement risk, observed exposure, that combines theoretical LLM capability and real-world usage data, weighting automated (rather than augmentative) and work-related uses more heavily

AI is far from reaching its theoretical capability: actual coverage remains a fraction of what's feasible

Occupations with higher observed exposure are projected by the BLS to grow less through 2034

Workers in the most exposed professions are more likely to be older, female, more educated, and higher-paid

We find no systematic increase in unemployment for highly exposed workers since late 2022, though we find suggestive evidence that hiring of younger workers has slowed in exposed occupations

Introduction

The rapid diffusion of AI is generating a wave of research measuring and forecasting its impacts on labor markets. But the track record of past approaches gives reason for humility.

For example, a prominent attempt to measure job offshorability identified roughly a quarter of US jobs as vulnerable, but a decade on, most of those jobs maintained healthy employment growth. The government's own occupational growth forecasts, while directionally correct, have added little predictive value beyond linear extrapolation of past trends. Even in hindsight, the impact of major economic disruptions on the labor market is often unclear. Studies on the employment effects of industrial robots reach opposing conclusions, and the scale of job losses attributed to the China trade shock continues to be debated.

In this paper, we present a new framework for understanding AI's labor market impacts, and test it against early data, finding limited evidence that AI has affected employment to date. Our goal is to establish an approach for measuring how AI is affecting employment, and to revisit these analyses periodically. This approach won't capture every channel through which AI could reshape the labor market, but by laying this groundwork now, before meaningful effects have emerged, we hope future findings will more reliably identify economic disruption than post-hoc analyses.

It is possible that the impacts of AI will be unmistakable. This framework is most useful when the effects are ambiguous-and could help identify the most vulnerable jobs before displacement is visible.

Counterfactuals

Causal inference is easier when the effects are large and sudden. The COVID-19 pandemic and accompanying policy measures caused economic disruption so stark that sophisticated statistical approaches were unnecessary for many questions. For example, unemployment jumped sharply in the early weeks of the pandemic, leaving little room for alternative explanations.

The impacts of AI, however, might be less like COVID and more like the internet or trade with China. The effects may not be immediately clear from aggregate unemployment data; factors like trade policy and the business cycle could cloud interpretations of trend lines.

One common approach is to compare outcomes between more or less AI-exposed workers, firms, or industries, in order to isolate the effect of AI from confounding forces. Exposure is typically defined at the task level: AI can grade homework but not manage a classroom, for example, so teachers are considered less exposed than workers whose entire job can be performed remotely.

Our work follows this task-based approach, incorporating measures of theoretical AI capability and real-world usage, before aggregating to occupations.

Measuring exposure

Our approach combines data from three sources.

1. The O*NET database, which enumerates tasks associated with around 800 unique occupations in the US.

2. Our own usage data (as measured in the Anthropic Economic Index).

3. Task-level exposure estimates from Eloundou et al. (2023), which measure whether it is theoretically possible for an LLM to make a task at least twice as fast.

Eloundou et al.'s metric, β, scores tasks on a simple scale: 1 if a task can be doubled in speed by an LLM alone, 0.5 if it requires additional tools or software built on top of the LLM, and 0 otherwise.

Why might actual usage fall short of theoretical capability? Some tasks that are theoretically possible may not show up in usage because of model limitations. Others may be slow to diffuse due to legal constraints, specific software requirements, human verification steps, or other hurdles. For example, Eloundou et al. mark "Authorize drug refills and provide prescription information to pharmacies" as fully exposed (β=1). We have not observed Claude performing this task, although the assessment seems correct in that it could theoretically be sped up by an LLM.

That said, these measures of theoretical capability and actual usage are highly correlated. As Figure 1 shows, 97% of the tasks observed across the previous four Economic Index reports fall into categories rated as theoretically feasible by Eloundou et al. (β=0.5 or β=1.0).

A new measure of occupational exposure

Our new measure, observed exposure, is meant to quantify: of those tasks that LLMs could theoretically speed up, which are actually seeing automated usage in professional settings? Theoretical capability encompasses a much broader range of tasks. By tracking how that gap narrows, observed exposure provides insight into economic changes as they emerge.

Our measure qualitatively captures several aspects of AI usage that we think are predictive of job impacts. A job's exposure is higher if:

Its tasks are theoretically possible with AI

Its tasks see significant usage in the Anthropic Economic Index

Its tasks are performed in work-related contexts

It has a relatively higher share of automated use patterns or API implementation

Its AI-impacted tasks make up a larger share of the overall role

We give mathematical details in the Appendix. We count tasks that are theoretically capable with an LLM as covered if they have seen sufficient work-related usage in Claude traffic. We then adjust for how the task is being carried out: fully automated implementations receive full weight, while augmentative use receives half weight. Finally, the task-level coverage measures are averaged to the occupation level weighted by the fraction of time spent on each task.

Figure 2 shows observed exposure (in red) compared to β from Eloundou et al. (in blue), illustrating the difference between theoretical and actual use on our platform, grouped by broad occupational categories. We calculate this by first averaging to the occupation level weighting by our time fraction measure, then averaging to the occupation category weighting by total employment. For example, the β measure shows scope for LLM penetration in the majority of tasks in Computer & Math (94%) and Office & Admin (90%) occupations.

The red area, depicting LLM use from the Anthropic Economic Index, shows how people are using Claude in professional settings. The coverage shows AI is far from reaching its theoretical capabilities. For instance, Claude currently covers just 33% of all tasks in the Computer & Math category.

As capabilities advance, adoption spreads, and deployment deepens, the red area will grow to cover the blue. There is a large uncovered area too; many tasks, of course, remain beyond AI's reach-from physical agricultural work like pruning trees and operating farm machinery to legal tasks like representing clients in court.

Figure 3 shows the ten occupations most exposed under this measure. In line with other data showing that Claude is extensively used for coding, Computer Programmers are at the top, with 75% coverage, followed by Customer Service Representatives, whose main tasks we increasingly see in first-party API traffic. Finally, Data Entry Keyers, whose primary task of reading source documents and entering data sees significant automation, are 67% covered.

At the bottom end, 30% of workers have zero coverage, as their tasks appeared too infrequently in our data to meet the minimum threshold. This group includes, for example, Cooks, Motorcycle Mechanics, Lifeguards, Bartenders, Dishwashers, and Dressing Room Attendants.

How exposure tracks with projected job growth and worker characteristics

The US Bureau of Labor Statistics (BLS) publishes regular employment projections, with the latest set, published in 2025, covering predicted changes in employment for every occupation from 2024 to 2034. In Figure 4, we compare our job-level coverage measure to their predictions.

A regression at the occupation level weighted by current employment finds that growth projections are somewhat weaker for jobs with more observed exposure. For every 10 percentage point increase in coverage, the BLS's growth projection drops by 0.6 percentage points. This provides some validation in that our measures track the independently derived estimates from labor market analysts, although the relationship is slight. Interestingly, there is no such correlation using the Eloundou et al. measure alone.

Figure 5 shows characteristics of workers in the top quartile of exposure and the 30% of workers with zero exposure in the three months before ChatGPT was released, August to October 2022, using data from the Current Population Survey. The groups are very different. The more exposed group is 16 percentage points more likely to be female, 11 percentage points more likely to be white, and almost twice as likely to be Asian. They earn 47% more, on average, and have higher levels of education. For example, people with graduate degrees are 4.5% of the unexposed group, but 17.4% of the most exposed group, an almost fourfold difference.

Prioritizing outcomes

With these exposure measures in hand, the question is what to look for. Researchers have taken different approaches. For example, Gimbel et al. (2025) track changes in the occupational mix using the Current Population Survey. Their argument is that any important restructuring of the economy from AI would show up as changes in distribution of jobs.¹ (They find that, so far, changes have been unremarkable.) Brynjolfsson et al. (2025) look at employment levels split by age group using data from the payroll processing firm ADP, while Acemoglu et al. (2022) and Hampole et al. (2025) use job posting data from Burning Glass (now Lightcast) and Revelio, respectively.

We focus on unemployment as our priority outcome because it most directly captures the potential for economic harm-a worker who is unemployed wants a job and has not yet found one. In this case, job postings and employment do not necessarily signal the need for policy responses; a decline in job postings for a highly exposed role may be counteracted by increased openings in a related one. Most harmful labor market developments of AI should arguably include a period of increased unemployment, as displaced workers search for alternatives. The Current Population Survey is well suited to tracking this, as unemployed respondents report their previous job and industry.

We next study trends in unemployment, matching our occupation-level measures to respondents in the Current Population Survey.

A key question in interpreting our coverage measure is which workers should be considered treated? Should changes in employment be expected from just 10% task coverage? Gans and Goldfarb (2025) show that if an O-ring model best describes jobs, employment effects might be seen only when all tasks have some degree of AI penetration. Hampole et al. (2025) argue that mean exposure decreases labor demand, but concentration of exposure in only certain tasks can counteract this. And Autor and Thompson (2025) highlight the level of expertise required for the remaining tasks.

With an eye toward simplicity, and noting that we are most concerned with large impacts, we center our analysis on the idea that impacts should be felt most in the groups with the highest mean exposure. We compare workers in the top quartile of time-weighted task coverage to those in the bottom. If AI capabilities advance quickly, task coverage might be high for lower percentiles of coverage, which might make an absolute threshold more helpful. But we make the assumption that impacts should affect the most exposed workers first, and present results varying the cutoff we use to define treatment.

The upper panel of Figure 6 shows raw trends in the unemployment rate since 2016 for workers in the top quartile of exposure and the unexposed group. During COVID, the less AI-exposed workers-who are more likely to have in-person jobs-saw a much larger increase in unemployment. Since then, the trends have been largely similar between the two groups. The lower panel measures the size of the gap between the most and least exposed workers in a difference-in-differences framework, mirroring the findings from the raw data. The average change in the gap since the release of ChatGPT is small and insignificant, suggesting that the unemployment rate of the more exposed group has increased slightly but the effect is indistinguishable from zero.

What kind of scenarios can this framework identify? Based on the confidence interval of the pooled estimate, differential increases in unemployment on the order of 1 percentage point would be detectable (this will change as new data comes in, so it is merely a ballpark estimate). If all workers within the top 10% were laid off, it would increase unemployment within the top quartile group from 3% to 43%, and it would increase aggregate unemployment from 4% to 13%.

A smaller but still concerning impact would be a scenario such as a "Great Recession for white-collar workers." During the 2007-2009 Great Recession, unemployment rates doubled from 5% to 10% in the US. Such a doubling in the top quartile of exposure would increase its unemployment rate from 3% to 6%. This should be visible in our analysis as well. Note that our core estimate is based on differential changes in the unemployment rate in the exposed group compared to the less exposed group. If unemployment increased for all workers in parallel, we would not attribute this to AI advancements that still leave many tasks unaffected.

One group of particular concern is young workers. Brynjolfsson et al. report a 6-16% fall in employment in exposed occupations among workers aged 22 to 25. They attribute this decrease primarily to a slowdown in hiring rather than an increase in separations.

We find that the unemployment rate for young workers in the exposed occupations is flat (see Appendix). But slowed hiring may not necessarily manifest as increased unemployment, since many young workers are labor market entrants without a listed occupation in the CPS data and may exit the labor force rather than appear as unemployed. To address hiring directly, we use the panel dimension of the CPS, counting the percent of young (22-25 year old) workers who begin a new job in a more vs. less exposed occupation over time. Figure 7 shows the monthly job finding rate (i.e., when a worker reports a job that they did not have in the previous month) for young workers, split by whether they are entering a high- vs. low-exposure occupation.

Apart from some large swings in 2020-2021, these series visually diverge in 2024, with young workers relatively less likely to be hired into exposed occupations. Job finding rates at the less exposed occupations remain stable at 2% per month, while entry into the most exposed jobs decreases by about half a percentage point. The averaged estimate in the post-ChatGPT era is a 14% drop in the job finding rate compared to that in 2022 in the exposed occupations, although this is just barely statistically significant. (There is no such decrease for workers older than 25.)

This may provide some signal of the early effects of AI on employment, and echoes the findings from Brynjolfsson et al. But there are several alternative interpretations. The young workers who are not hired may be remaining at their existing jobs, taking different jobs, or returning to school. A further data-related caveat is that job transitions may be more vulnerable to mismeasurement in surveys.

Discussion

This report introduces a new measure for understanding the labor market effects of AI and studies impacts on unemployment and hiring. Jobs are more exposed to AI to the extent that their tasks are theoretically feasible with LLMs and observed on our platforms in automated, work-related use cases. We find that computer programmers, customer service representatives, and financial analysts are among the most exposed. Using survey data from the US, we find no impact on unemployment rates for workers in the most exposed occupations, although there's tentative evidence that hiring into those professions has slowed slightly for workers aged 22-25.

Our work is a first step toward cataloging the impact of AI on the labor market. We hope that the analytical steps taken in this report, especially around coverage and counterfactuals, will be easy to update as new data on employment and AI usage emerge. An established approach may help future observers separate signal from noise.

There are several improvements to be made to the present work. Our usage data will be incorporated in future updates, forming an evolving picture of task and job coverage in the economy. The Eloundou et al. metric could also be updated, to the extent that it is linked to LLM capabilities as of early 2023. And, given the suggestive results around young workers and labor market entrants, a key next step might be to look at how recent graduates with educational credentials in exposed areas are navigating the labor market.

Appendix

Acknowledgements

Written by Maxim Massenkoff and Peter McCrory.

With acknowledgements to: Ruth Appel, Tim Belonax, Keir Bradwell, Andy Braden, Dexter Callender III, Miriam Chaum, Madison Clark, Jake Eaton, Deep Ganguli, Kunal Handa, Ryan Heller, Lara Karadogan, Jennifer Martinez, Jared Mueller, Sarah Pollack, David Saunders, Carl De Torres, Kim Withee, and Jack Clark.

We additionally thank Martha Gimbel, Anders Humlum, Evan Rose, and Nathan Wilmers for feedback on earlier versions of this report.

@online{massenkoffmccrory2026labor, author = {Maxim Massenkoff and Peter McCrory}, title = {Labor market impacts of AI: A new measure and early evidence}, date = {2026-03-05}, year = {2026}, url = {https://www.anthropic.com/research/labor-market-impacts}, }

References

Acemoglu, Daron and Pascual Restrepo, "Robots and Jobs: Evidence from US Labor Markets," Journal of Political Economy, 2020, 128 (6), 2188-2244.

Acemoglu, Daron, David Autor, Jonathon Hazell, and Pascual Restrepo, "Artificial intelligence and jobs: Evidence from online vacancies," Journal of Labor Economics, 2022, 40 (S1), S293-S340.

Appel, Ruth, Maxim Massenkoff, Peter McCrory, Miles McCain, Ryan Heller, Tyler Neylon, and Alex Tamkin, "Anthropic Economic Index report: economic primitives," 2026.

Autor, David H, David Dorn, and Gordon H Hanson, "The China syndrome: Local labor market effects of import competition in the United States," American Economic Review, 2013, 103 (6), 2121-2168.

Autor, David H, & Thompson, N. (2025). Expertise. NBER Working Paper, (w33941).

Blinder, Alan S et al., "How many US jobs might be offshorable?," World Economics, 2009, 10 (2), 41.

Borusyak, Kirill, Peter Hull, and Xavier Jaravel, "Quasi-experimental shift-share research designs," The Review of Economic Studies, 2022, 89 (1), 181-213.

Brynjolfsson, Erik, Bharat Chandar, and Ruyu Chen, "Canaries in the coal mine? six facts about the recent employment effects of artificial intelligence," Digital Economy, 2025.

Eckhardt, Sarah and Nathan Goldschlag, "AI and Jobs: The Final Word (Until the Next One)," Economic Innovation Group (EIG), August 2025. Available at: https://eig.org/ai-and-jobs-the-final-word/

Eloundou, Tyna, Sam Manning, Pamela Mishkin, and Daniel Rock, "Gpts are gpts: An early look at the labor market impact potential of large language models," arXiv preprint arXiv:2303.10130, 2023, 10.

Fujita, S., Moscarini, G., & Postel-Vinay, F. (2024). Measuring employer-to-employer reallocation. American Economic Journal: Macroeconomics, 16(3), 1-51.

Gans, Joshua S. and Goldfarb, Avi, "O-Ring Automation," NBER Working Paper No. 34639, December 2025. Available at SSRN: https://ssrn.com/abstract=5962594

Gimbel, Martha, Molly Kinder, Joshua Kendall, and Maddie Lee, "Evaluating the Impact of AI on the Labor Market: Current State of Affairs," Research Report, The Budget Lab at Yale, New Haven, CT October 2025. Available at: https://budgetlab.yale.edu.

Graetz, Georg and Guy Michaels, "Robots at Work," Review of Economics and Statistics, 2018, 100 (5), 753-768.

Hampole, Menaka, Dimitris Papanikolaou, Lawrence DW Schmidt, and Bryan Seegmiller, "Artificial intelligence and the labor market," Technical Report, National Bureau of Economic Research 2025.

Handa, Kunal, Alex Tamkin, Miles McCain, Saffron Huang, Esin Durmus, Sarah Heck, Jared Mueller, Jerry Hong, Stuart Ritchie, Tim Belonax, Kevin K. Troy, Dario Amodei, Jared Kaplan, Jack Clark, and Deep Ganguli, "Which Economic Tasks are Performed with AI? Evidence from Millions of Claude Conversations," 2025.

Hui, Xiang, Oren Reshef, and Luofeng Zhou, "The short-term effects of generative artificial intelligence on employment: Evidence from an online labor market," Organization Science, 2024, 35 (6), 1977-1989.

Johnston, Andrew and Christos Makridis, "The labor market effects of generative AI: A difference-in-differences analysis of AI exposure," Available at SSRN 5375017, 2025.

Massenkoff, Maxim, "How predictable is job destruction? Evidence from the Occupational Outlook," 2025. Working Paper.

Ozimek, Adam, "Overboard on Offshore Fears," 2019. https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3777307

Tamkin, Alex and Peter McCrory, "Estimating AI productivity gains from Claude conversations," 2025.

Tomlinson, K., Jaffe, S., Wang, W., Counts, S., & Suri, S. (2025). Working with AI: measuring the applicability of generative AI to occupations. arXiv preprint arXiv:2507.07935.

1. Job offshorability: Blinder et al. (2009) and Ozimek (2019); Government growth forecasts: Massenkoff (2025); Robots: Graetz and Michaels (2018) and Acemoglu and Restrepo (2020); China shock: Autor et al. (2013) and Borusyak et al. (2022).

2. Brynjolfsson et al. (2025) compare employment trends for workers in more versus less AI-exposed occupations, using the task exposure measures from Eloundou et al. (2023) and payroll data from ADP. Johnston and Makridis (2025) do a similar task-based analysis using US administrative data, but they aggregate treatment to the industry level. Hui et al. (2024) study how freelance jobs on Upwork responded to the release of ChatGPT and advanced image generation tools, comparing workers in directly affected categories to those in unaffected categories before and after each tool's release date. Hampole et al. (2025) instrument for firm-level AI adoption using historical university hiring networks: firms that historically recruited from universities whose graduates later entered AI-related roles faced lower adoption costs.

3. Our task- and occupation-level exposure measures can readily incorporate other usage data, and be extended to different countries. We intend to apply this methodology to new settings over time.

4. In their framework, "Directly exposed'" tasks were those that could be completed in half the time with an LLM (with a 2,000-word input limit and no access to recent facts). Tasks that were "exposed with tools" were those subject to the same speedup with an LLM that had access to software for, e.g., information retrieval and image processing. Tasks that were not exposed could not have their duration reduced by 50% or more using an LLM.

5. We use the previous two Anthropic Economic Index datasets, covering usage from August and November 2025. For ONET tasks that are highly semantically similar, we split the counts across them.

6. There are judgment calls involved at every step. Should the Eloundou et al. (2023) measure enter as {0, 0.5, 1} or something else? What determines "significant" use? How do we handle tasks which seem very similar to those with high usage, but are too rare to have been picked up specifically in the sampling for the Economic Index? How much more should automation workflows count compared to augmentation? A reassuring finding which we expand on in the Appendix is that the Spearman (rank-rank) correlation of job exposure across many resolutions to these questions is exceedingly high.

7. To match O*NET-SOC codes to occ1990 codes in the CPS, we use the crosswalk provided by Eckhart and Goldschlag (2025).

8. We explore this further in three ways in the Appendix. First, we ask whether the percentile cutoff that we use to define treatment matters, varying it from the median to the 95th percentile. In all cases, the impact is flat or negative (meaning that unemployment decreases for the exposed group). Next, we focus on young workers in particular, those aged 22 to 25 as in Brynjolfsson et al. (2025). Finally, we use data on unemployment insurance claimants from the Department of Labor to measure the unemployment, rather than CPS survey responses. In no extension do we find clear impacts on exposed jobs.

9. This range is wide because the authors provide estimates against multiple counterfactuals. The 6 percentage point drop compares to a counterfactual of flat employment growth. The 16 percentage point estimate comes from a design comparing similar workers in the same firm with different occupations.

10. See Fujita, et al. (2024).

# data[].attributes.attachment_link.title
Labor market impacts of AI: A new measure and early evidence

# data[].attributes.attachment_link.description
Anthropic is an AI safety and research company that's working to build reliable, interpretable, and steerable AI systems.

# data[].attributes.body
Kevin Roose @kevinroose

"i am begging academics to study AI capabilities using frontier models."

# data[].attributes.wish_content.attachment_props.text
i am begging academics to study AI capabilities using frontier models. 

the models used in this study (which is going to be cited for years as proof that "AI is bad at health advice") are GPT-4o, Llama 3, and Command R+, two obsolete models and one i've never heard of. https://t.co/ekq502dhPe

# data[].attributes.wish_content.attachment_props.original_text
i am begging academics to study AI capabilities using frontier models. 

the models used in this study (which is going to be cited for years as proof that "AI is bad at health advice") are GPT-4o, Llama 3, and Command R+, two obsolete models and one i've never heard of. https://t.co/ekq502dhPe

# data[].attributes.attachment_link.embed_code
i am begging academics to study AI capabilities using frontier models.

the models used in this study (which is going to be cited for years as proof that "AI is bad at health advice") are GPT-4o, Llama 3, and Command R+, two obsolete models and one i've never heard of. pic.twitter.com/ekq502dhPe
— Kevin Roose (@kevinroose) February 9, 2026

# data[].attributes.attachment_link.metadata.attachment_props_source.data.text
i am begging academics to study AI capabilities using frontier models. 

the models used in this study (which is going to be cited for years as proof that "AI is bad at health advice") are GPT-4o, Llama 3, and Command R+, two obsolete models and one i've never heard of. https://t.co/ekq502dhPe

# data[].attributes.attachment_link.metadata.attachment_props.original_text
i am begging academics to study AI capabilities using frontier models. 

the models used in this study (which is going to be cited for years as proof that "AI is bad at health advice") are GPT-4o, Llama 3, and Command R+, two obsolete models and one i've never heard of. https://t.co/ekq502dhPe

# data[].attributes.attachment_link.metadata.attachment_props.text
i am begging academics to study AI capabilities using frontier models. 

the models used in this study (which is going to be cited for years as proof that "AI is bad at health advice") are GPT-4o, Llama 3, and Command R+, two obsolete models and one i've never heard of. https://t.co/ekq502dhPe

# data[].attributes.headline
Open letter from Amazon employees and allies

# data[].attributes.subject
Open letter from Amazon employees and allies

# data[].attributes.body
https://www.amazonclimatejustice.org/open-letter?ms=nai

# data[].attributes.attachment_link.description
Sign our open letter below to tell Amazon leadership that we need a more responsible rollout of AI. Every single signature makes our message stronger.

# data[].attributes.headline
Lopatto diagnoses Andreessen as having outsourced his agency to LLMs

# data[].attributes.subject
Lopatto diagnoses Andreessen as having outsourced his agency to LLMs

# data[].attributes.body
https://www.theverge.com/tldr/897566/marc-andreessen-is-a-philosophical-zombie

# data[].attributes.attachment_link.title
Marc Andreessen is a philosophical zombie

# data[].attributes.headline
Interesting somewhat nuanced ethical argument from hedonism against "vibes" / AI coding

# data[].attributes.subject
Interesting somewhat nuanced ethical argument from hedonism against "vibes" / AI coding

# data[].attributes.body
https://www.williamjbowman.com/blog/2026/03/13/against-vibes-part-2-ought-you-use-a-generative-model/

# data[].attributes.attachment_link.readable_text
Since the wide-spread availability and forced deployment of generative models, people have argued about the ethics of using them. Many arguments have been presented to argue that they're bad: they use too much electricity, boil the oceans, massively infringe on copyright, put people out of work, and generate slop. And therefore, you are ethically obliged to not use them.

I sympathize with the fundamental conclusion: that there is something ethically bad about the current situation. But I can't really take these arguments seriously, for two reasons: (1) if you look into any one of the arguments, the details are (shocking) a little more complicated (2) it's hard to judge the validity of an ethical argument when no one is willing to make explicit their system of ethics.

Ought you use a generative model? I don't mean is it good for a specific task; that's an is question, about whether it is useful. I mean ought you use a model: is there an ethical argument for or against you using generative models, categorically. I'm not going to spoil the ending, because the argument is more important than my conclusion.

An Ethical Framework

It is slightly infuriating to me that people will argue about ethics without interrogating their own ethical axioms, or those of their interlocutor. It makes so many ethical arguments seem like a series of randomly generated sentences.

The dominant ethics pretty much everywhere is Utilitarianism. Utilitarianism makes ethical decisions by not supposing apriori what is good and bad, but instead that everyone seeks some generalized notion of utility. Maybe you get utility from eating delicious baked goods, but you lose utility from having to work a soul-crushing 9-5 job. An ethical action, under this framework, is the one that maximizes utility, or in negative utilitarianism, minimizes negative utility.

This is a stupid ethical framework. It falls prey to various paradoxes, like stupid trolley problems and the utility monster who derives more utility from harming you than you lose by being harmed. It presupposes that "utility" is quantifiable, and ignores standard problems that arise in optimization problems, like that to optimize one variable you must both ignore and therefore minimize other variables. As a consequentialist framework, it falls prey to many of the standard problems of trying to predict the consequences of one's actions, but as a framework that pretends to be very mathematical, it carries these problems out to infinity. This leads to bonkers philosophies like Effective Altruism and Longtermism, which support absurd beliefs like that to maximize utility we must rush headlong to develop a general purpose artificial intelligence that is the utility monster and maximize its utility.

But if I'm honest, I think my fundamental problem with it is that it exists to justify harm-to say, "yes, actually doing harm is the ethically correct choice". That feels wrong.

So let me start with my ethical axioms, my framework for making ethical decisions. There are many like it, but this one is mine.

I'm some kind of hedonist: I believe that which is enjoyable is ethically good, and that which is harmful is ethically bad. I believe one should take actions that do not cause harm, and ideally take actions that are also enjoyable.

I believe one is ethically obligated not to cause harm, but has no positive obligation to cause pleasure; it is actually a kind of negative hedonism. But if you can cause pleasure, that is good.

My hedonism is stratified: base (Type 0) good things (e.g., deriving pleasure from eating a pastry) are good; base bad things (e.g., intentionally causing harm to an innocent being) are bad. Deriving pleasure from a (Type 0) harm, sadism, is a (Type 1) pleasure that is bad. This is ethically distinct from deriving a (Type 0) pleasure merely at the expense of something else, like eating a cruelly harmed animal, which is also bad but for a different reason. I have notes on further levels somewhere, but I think these suffice for the purposes of my arguments.

I reject that "good" and "bad" are quantifiable. I cannot decide, ethically, whether one harm outweighs another. Harm always outweighs pleasure; there is no "quantity". "Is it more ethical to kill one man to save two men?" what a stupid question.

I also subscribe to some kind of bounded rationality. It is entirely pointless to try to consider all consequences of your actions out to infinity, taking into account all possible information. I am only obligated to act on information I can reasonably be expected to have, and with respect to consequences that are reasonably foreseeable. It also entirely pointless to try to consider all possible actions one could take; you aren't actually infinitely capable. As things get more and more complex, and less and less certain, I say: who knows, try your best.

So there's my framework: a stratified anti-quantitative bounded negative hedonism. Now that we have a framework, lets start looking at ethical arguments about generative models.

The Resource Argument

One argument against generative models is that these models "use too many resources", typically electricity or water. So what are the ethics of this argument?

Using electricity, in itself, does not cause harm. There are many examples of using electricity bringing joy and happiness to people; those uses are good. There are certainly uses to which electricity can be put that are unethical: I could electrocute you, causing you harm (unless you're into that). But that's not the electricity causing harm; that would be me causing harm.

The argument about electricity is probably about the effect on climate change, since increased electricity use probably means increased carbon emissions, which cause harm. So using a generative model, and therefore increasing electricity use, and therefore maybe increasing emissions, may cause harm.

This is not a strong argument. The source of electricity matters, for one. If all or most electricity came from renewables, there would be no harm. Increasingly, renewables are becoming a dominant mode of electricity generation.

Given the uncertainty involved, I don't think this on its own creates an ethical obligation on individuals to use or not use generative models.

Worse, the argument is contingent on the technical capabilities of generative models. If they become more efficient, do they become ethical?

There's one other form of this argument, which shifts the conversation from the mere resource cost to the cost of data centres or the industry as a whole. This is also not really a resource argument: I'll call it "the power argument", and address it separately. Again, a data centre using electricity doesn't necessarily cause harm, and even if it did, it doesn't tell us whether an individual ought to use a generative model, since their action has no effect on the deployment of new data centres and therefore the increased resource usage.

So in short, I don't think there is an ethical imperative to not use generative models because using them increases resource consumption.

The Intellectual Property Argument

One argument I find super weird is the intellectual property argument: training generative models has "stolen" tons of work, or reproduces copyrighted work. This is particularly weird when it comes from people who normally don't give a shit about intellectual property, consuming much of their media via pirate sites or torrents.

Even if it were infringement, while infringement might be illegal, that doesn't make it unethical. Who is harmed if I pirate all of The Fast and Furious movies, or if I train a model that reproduces GPL source code?

Ignoring whether or not it even is infringement, intellectual property itself isn't necessarily good. Why is it ethically good to give a person a complete monopoly on the production of something? And for copyright, that monopoly is crazy. 70 years after the death of the original author? What kind of creativity does that inspire? Why should we give such power to one individual, to legally go after anyone who wants to compete at the production of a good idea whose author is long dead? There's nothing ethical about this, and in fact, I firmly believe intellectual property as it is currently implemented causes significant harm.

So, no, I don't think you have an ethical imperative to avoid generative models because they might infringe copyright.

The Slop Argument

A very common complaint, and sort of argument, is that generative models produce slop. That is, they produce output that is not of high quality; it doesn't meet the requirements or goals of the output. In technical work, it doesn't meet engineering standards or design requirements. In creative work, it fails to express anything of artist interest or intent.

This is not really an ethical argument, but a description of technical capability. If the models improve, do they become ethical to use? The mere slop argument would suggest they do!

It could be an ethical argument, if you form it as a utilitarian argument: they produce more harm than the produce utility. This would require us to admit they produce harm, and start to justify the harm. But I've already rejected utilitarianism.

If not careful, the slop argument also attributes the cause of the harm to the generative model. But a model does not, despite the fevered branding of the industry, have agency. It cannot produce something that causes harm by itself. If I go stopper all your sinks and leave the water running in your house, it wasn't the water that caused you harm.

This argument might be a good argument for not allowing generative models to be used in some settings. If the balance of probability is that the output will cause harm compared to an alternative, best not to use a generative model. This quickly becomes a technical argument: whether or not the quality of the output can be assured. Perhaps it can't, so a conservative approach is needed. But this isn't an ethical argument categorically forbidding the use of generative models.

So I don't think there is an ethical imperative to not use generative models because some of their output is not of high quality.

The Employment Argument

One argument is see from time to time is: generative models are putting people out of jobs, and therefore you shouldn't use them. As statement, this is a confused argument in many ways.

For one, an individual's use of a generative model probably doesn't put anyone out of a job.

For two, it's not clear to me that this is necessarily harmful. "Not having a job" is not necessarily harmful, and so this debate quickly devolves into questions about unemployment, about whether new jobs created in place of old jobs, etc. This has nothing to do with an individual's use of a generative model, and much more to do with the economic systems we're embedded in, and all the particulars matter.

For three, it's not obviously true that generative models are putting people out of work. Many CEO's are claiming that's why they're firing people, but there's plenty of evidence to suggest that's either a lie to make layoffs more acceptable, or a lie to convince shareholders that the trillions in investments are paying off.

Finally, the generative model didn't decide to lay anyone off and therefore make their continued existence in a society in which having a job is a necessary precondition to continued survival. Some profit seeking business boy did.

So I don't really think there's an ethical imperative to not use generative models based on layoffs.

The Power Argument

There's only one argument I buy at all, and I don't see it very often.

Even phrasing this argument requires care: we have to separate the individual use of a generative model from the "AI" industry.

Here, I use "AI", which I've been loath to use until now, very explicitly. "AI" is a brand. It is not a technology. It is a marketing term deployed to convince people that the underlying technology (most recently, generative models) is more capable than it is-that the technology is "intelligent".

The power argument goes like this: the "AI" industry is accumulating power at the expenses of others, possibly doing harm in the process, possibly using that power to do harm. Therefore one should not use generative models.

This argument... doesn't follow. Not as is, anyway.

The "AI" industry is concentrating power in the hands of capital, and removing it from labour. The claim of the industry is that tasks previously only able to be done by highly skilled people can-sort of, in some cases-be done by generative models. This would allow someone with a lot of GPUs and a ton data to exercise power over people they previously couldn't. And the industry definitely wants to use that power, and obviously doesn't care who they harm in the process.

In fact, all the previous arguments can be augmented into a power argument, and they start to make more sense.

The problem with resource usage isn't about generative models, but about the "AI" industry. The "AI" industry is spinning up data centre after data centre, without regard for resource use. They sometimes deploy carbon-intensive generators to meet capacity, or consume already scarce water because they have the power to take it from others who need it. These are direct harms that the "AI" industry is engaging in.

The problem with slop is not that one can produce poor quality work. That is not even a new problem. The enshittification process was started long before generative models reached their current capabilities. Long before large language models, others forms of text generation were used to mass produce garbage webpages for small amounts of profit. The problem is that the "AI" industry is wielding an immense amount of power to force "AI" use that lowers the quality of work into new areas. They're replacing web search with "AI". They're replacing customer service with "AI" (chatbots were already common, but this makes them next to trivial to deploy). They're forcing engineers to use "AI" to generate software. They're deploying "AI" to make decisions about who to investigate for crimes, who to arrest, who to kill.

The problem with employment isn't that generative models can take jobs, which I doubt, but that the "AI" industry can replace the output of skilled workers with poor quality output created by generative models. The "AI" industry can replace labour with capital. They're not even particularly coy about this; some of them have gone on record about this goal. The goal is to gain power over labour, and exercise that power for profit.

Interestingly, sometimes I see the opposite of this argument: people arguing that somehow generative models democratize skilled labour. This is madness. That could, possibly, maybe, be true if these models were tiny and could run on lower powered devices that everyone had access to. Even then, I'm skeptical, as so far I don't believe they can be used unless you already have the domain skills required to do the work in the first place.

But ignoring my arguments about utility, it's certainly not true now that generative models give labour power. The only useful ones burn billions of dollars merely to operate, and required almost trillions to reach that state. The only useful models are owned and operated by the "AI" industry. If you want to use them, you have to go to the "AI" industry. That's not democratization of anything; that's the "AI" industry having power over you.

So the "AI" industry definitely has power, and they use that power to cause harm. So is there an ethical imperative to not use generative models, based on the power of the "AI" industry?

Well, these harms are not caused by generative models. They're caused by the "AI" industry, not the underlying technology. They do not go away if you stop using generative models.

But your action to use a generative model may give power to this industry, and thus, cause harm. So let's consider individual actions.

The Individual Action Problem

The main source of the "AI" industry's power is economic power. They promise to be able to reduce labour costs, and they need a lot of money to make that a reality (or so their pitch goes).

I'm skeptical that mere use supports the "AI" industry in a monetary sense. The "AI" industry is currently supported by an ungodly amount of investor money and debt, not by the small amount of revenue it brings in. You using a generative model, and even paying a subscription, does not provide support for the power of this industry.

I want to be clear that I'm not rejecting individual action. I think individual action is important. If your action causes harm, no matter how small, I consider that unethical.

The money you give the "AI" industry is not the only way to give the "AI" industry power, and taking these other actions causes harm. Using generative models uncritically gives the "AI" industry power, supporting the claims they make and the transfer of power to them. Reporting on generative models and the "AI" industry uncritically gives their claims credibility, giving the "AI" industry power. Enabling their wide-spread deployment, in addition to any harm caused by slop, gives the "AI" industry power over the context in which they were deployed.

In addition to being a hedonist, or perhaps because of it, I'm also an anarchist. I am very against power and hierarchy. I think the main thing they do is cause harm, and we should seek to reduce power and hierarchy as much as possible.

I think there is plenty of evidence that this technology, in the hands of this industry, is causing harm. I don't think certain individual uses of generative models are unethical, but any action that empowers the "AI" industry is certainly unethical.

So what should you do?

I'm normally pretty reluctant to give direct advice on actions you should take. My standard disclaimer is: all advice is one person's opinion.

... But we're several thousand words in and I've made my ethical arguments as clear and precise as I can, so strap in buddy while I tell you what to think and how to act.

There is no ethical imperative to not use a generative model; that depends on whether the particular use will cause harm or not.

There is an ethical imperative to deny "AI" (the industry) power.

So how does one deny "AI" power? Well if I knew that, I assure you we wouldn't be in this mess. But I can tell you some actions I'm taking.

First is education.

I've given talks on "prompt engineering" and how it's not engineering, written these blog posts, and remain engaged in the "AI" discourse despite hating it.

I want people to think clearly about this technology, this industry, and ethics. I want people to understand the technology, which is not magic. I think this is necessary to refute the lies this industry is telling. I think it's necessary to demonstrate exactly what the technology is and is not capable of. I want people to be able to separate out different concerns and arguments so they can refute lies and nonsense arguments others are telling.

Will this work? I don't know; maybe.

Second is policy work.

At UBC, I've tried to inform policy around the use of generative model. I'm trying to restrict the use of generative models around the university. Some of that is public, some is not. Some has been successful, some has not.

In my own lab, I am in a position of power (ugh), and in that role I try to create sensible policies. I don't outright forbid the use of generative models; I'm against my own power, and not sure using a generative model is necessarily problematic. I do make clear that the user is responsible for the use of the generative model. Every line of every artifact, if the user cannot stand by it, cannot justify a design decision, cannot explain something, then they, not the "AI", have failed.

Third is boycotting.

As I said already, I'm not sure that mere use is unethical. It's certainly not if you have no choice. Even if you have a choice, mere use may not empower the industry.

Still, I, largely, refuse to pay money for this technology. (I spent $10 on various experiments.) I don't think this has a direct effect, but I think it's an important line to draw to do what I can to deny "AI" power. I try to avoid technologies and companies and products that adopt "AI" and support this industry.

I think boycotts have indirect effects as well as direct effects. Refusing to engage can cause conversations, it can change minds. That will probably have more effect than denying these companies a few dollars a month.

I have no qualms about using local models. The ones I've used are little more than toys. We have some running in the lab that I haven't tried; maybe I'll build a machine with a proper GPU and give those a shot.

I do use some industry models; as a faculty member, I can access some pro models for free. I don't use them very enthusiastically, but I also don't worry too much about using them. I think using them is necessary to understand them and to educate others. And it marginally costs the industry money when I use them, which is a small bonus.

Fourth is sabotage.

I have deployed lots of "AI" countermeasures, both for training and inference. I don't know how effective these are, but I deploy them anyway. I should probably go read some activism books, such as How to Blow Up a Pipeline: Learning to Fight in a World on Fire, to find more effective strategies.

I'm not sure any of this will be effective, but I'm ethically obligated to do something.

Addendum

There are many things I wish I'd said differently, and other things I could have considered in this blog post. I'm going to leave it mostly as is, but I will add a few addenda following discussions:

1. I didn't discuss one argument that I didn't see phrased as an ethical argument until after publishing this: the brain rot argument. Mere use of a generative model can harm the user, by limiting stunting their learning. There is scientific evidence that supports this in the context of education. I hadn't seen it posed as harm, but instead about the effectiveness of the tools for education. However, I find the harm argument compelling, and don't see why harm against oneself shouldn't be considered unethical.

2. One criticism of my "mere use" arguments is that they suppose a "spherical [generative model] user in a vacuum". This is a valid criticism. In practice, a generative model user may be unable to act as independently of all systems and all harms as I've presented, or be unable to correctly judge the quality and therefore harm of their actions. Therefore an ethical user may only exist in theory. That's very possibly true, and I think there's plenty of evidence to support it. I think it's still useful to isolate the pure theory of harm for the purposes of discussion.

3. In the power argument, mere use may be unethical because mere use may provide e.g. usage analytics that support continued investment and therefore the concentration of power. This is a pretty compelling argument, and is probably related to why I instinctively boycott these things.

There are certainly other arguments I haven't considered, but as I said in the beginning, my conclusion is not the important part of this post.

# data[].attributes.attachment_link.keywords[]
since the wide-spread availability and forced deployment of generative models

# data[].attributes.attachment_link.keywords[]
people have argued about the ethics of using them. many arguments have been presented to argue that they're _bad_: they use too much electricity

# data[].attributes.attachment_link.title
Against Vibes Part 2: Ought You Use a Generative Model

# data[].attributes.attachment_link.description
Since the wide-spread availability and forced deployment of generative models, people have argued about the ethics of using them. Many arguments have been presented to argue that they’re _bad_: they use too much electricity, boil the oceans, massively inf…

# data[].attributes.headline
Canada rejected her permanent residence application. Her job duties were made up — by Immigration’s AI reviewer

# data[].attributes.subject
Canada rejected her permanent residence application. Her job duties were made up — by Immigration’s AI reviewer

# data[].attributes.attachment_link.title
Canada rejected her permanent residence application. Her job duties were made up — by Immigration’s AI reviewer

# data[].attributes.attachment_link.description
Immigration Department decision, which openly said it used Generative AI, cites job duties of applicant that bear no relation to her actual tasks.

# data[].attributes.headline
Atlassian’s new data collection policy protects rich customers while AI eats the rest

# data[].attributes.subject
Atlassian’s new data collection policy protects rich customers while AI eats the rest

# data[].attributes.attachment_link.title
Atlassian to train AI on user data unless law or cash say no

# data[].attributes.attachment_link.description
: From August 17, the outfit will collect customer metadata by default unless you pay for the top tier

# data[].attributes.headline
Pentagon strikes deals with top firms to expand AI use within the military

# data[].attributes.subject
Pentagon strikes deals with top firms to expand AI use within the military

# data[].attributes.attachment_link.title
Pentagon strikes deals with top firms to expand AI use within the military

# data[].attributes.attachment_link.description
Group of companies excludes Anthropic, which the U.S. Defense Department has labelled a supply-chain risk

# data[].attributes.headline
MS Copilot stereotypes different nationalities when given the same data set for each

# data[].attributes.subject
MS Copilot stereotypes different nationalities when given the same data set for each

# data[].attributes.headline
US law enforcement warns of “anti-tech extremism” as AI hatred grows

# data[].attributes.subject
US law enforcement warns of “anti-tech extremism” as AI hatred grows

# data[].attributes.attachment_link.title
US law enforcement warns of “anti-tech extremism” as AI hatred grows

# data[].attributes.attachment_link.description
The feds are raising the alarm about a new category of threat.

# data[].attributes.headline
Papal encyclical encourages pursuit of relig. exemptions to AI mandates

# data[].attributes.subject
Papal encyclical encourages pursuit of relig. exemptions to AI mandates

# data[].attributes.attachment_link.title
She won an exemption from using AI at her tech job. The Pope’s remarks could fuel similar appeals.

# data[].attributes.attachment_link.description
Legal experts say employers must take AI-related religious objections seriously, as a 2023 ruling raised the bar for denying such accommodations.