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The COVID-19 pandemic and accompanying policy measures caused economic interruption so stark that advanced statistical approaches were unnecessary for many questions. For example, joblessness leapt sharply in the early weeks of the pandemic, leaving little space for alternative explanations. The effects of AI, nevertheless, might be less like COVID and more like the internet or trade with China.

One typical technique is to compare results between more or less AI-exposed employees, firms, or markets, in order to separate the result of AI from confounding forces. 2 Exposure is normally specified at the job level: AI can grade homework however not handle a class, for instance, so instructors are thought about less revealed than employees whose whole task can be carried out from another location.

3 Our technique combines information from three sources. The O * web database, which identifies jobs connected with around 800 unique professions in the US.Our own usage information (as measured in the Anthropic Economic Index). Task-level exposure price quotes from Eloundou et al. (2023 ), which determine whether it is in theory possible for an LLM to make a job at least twice as quick.

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4Why might real usage fall brief of theoretical capability? Some jobs that are theoretically possible may disappoint up in usage because of design limitations. Others might be slow to diffuse due to legal constraints, specific software application requirements, human confirmation actions, or other hurdles. Eloundou et al. mark "License drug refills and supply prescription info to drug stores" as completely exposed (=1).

As Figure 1 programs, 97% of the jobs observed throughout the previous four Economic Index reports fall into categories ranked as in theory possible by Eloundou et al. (=0.5 or =1.0). This figure reveals Claude use dispersed throughout O * web jobs grouped by their theoretical AI exposure. Tasks rated =1 (totally feasible for an LLM alone) represent 68% of observed Claude usage, while jobs ranked =0 (not possible) account for simply 3%.

Our brand-new procedure, observed direct exposure, is implied to quantify: of those jobs that LLMs could theoretically speed up, which are actually seeing automated usage in expert settings? Theoretical capability includes a much more comprehensive variety of jobs. By tracking how that space narrows, observed exposure offers insight into economic modifications as they emerge.

A task's direct exposure is higher if: Its jobs are in theory possible with AIIts tasks see substantial usage in the Anthropic Economic Index5Its jobs are performed in job-related contextsIt has a reasonably greater share of automated usage patterns or API implementationIts AI-impacted tasks comprise a larger share of the total role6We provide mathematical details in the Appendix.

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We then adjust for how the task is being carried out: fully automated executions get full weight, while augmentative usage receives half weight. The task-level protection procedures are averaged to the occupation level weighted by the fraction of time spent on each job. Figure 2 reveals observed direct exposure (in red) compared to from Eloundou et al.

We calculate this by first balancing to the profession level weighting by our time fraction measure, then averaging to the profession category weighting by total employment. For example, the procedure reveals scope for LLM penetration in the majority of jobs in Computer & Math (94%) and Office & Admin (90%) professions.

Claude currently covers just 33% of all tasks in the Computer & Mathematics category. There is a big uncovered location too; many tasks, of course, remain beyond AI's reachfrom physical farming work like pruning trees and operating farm equipment to legal tasks like representing customers in court.

In line with other information showing that Claude is thoroughly utilized for coding, Computer Programmers are at the top, with 75% coverage, followed by Customer Service Agents, whose main tasks we increasingly see in first-party API traffic. Data Entry Keyers, whose main task of reading source documents and entering data sees significant automation, are 67% covered.

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At the bottom end, 30% of employees have zero protection, as their tasks appeared too rarely in our data to meet the minimum limit. This group includes, for example, Cooks, Motorcycle Mechanics, Lifeguards, Bartenders, Dishwashers, and Dressing Space Attendants. The United States Bureau of Labor Statistics (BLS) publishes regular work projections, with the most recent set, published in 2025, covering forecasted changes in work for every occupation from 2024 to 2034.

A regression at the profession level weighted by present work finds that development forecasts are somewhat weaker for tasks with more observed direct exposure. For every 10 portion point boost in protection, the BLS's development projection come by 0.6 percentage points. This provides some recognition because our measures track the individually obtained quotes from labor market experts, although the relationship is small.

procedure alone. Binned scatterplot with 25 equally-sized bins. Each strong dot shows the typical observed exposure and forecasted employment modification for one of the bins. The dashed line reveals a simple linear regression fit, weighted by existing employment levels. The small diamonds mark specific example professions for illustration. Figure 5 programs qualities of workers in the leading quartile of exposure and the 30% of workers with zero direct exposure in the three months before ChatGPT was launched, August to October 2022, utilizing data from the Existing Population Survey.

The more bare group is 16 portion points most likely to be female, 11 portion points more likely to be white, and almost twice as most likely to be Asian. They earn 47% more, usually, and have greater levels of education. People with graduate degrees are 4.5% of the unexposed group, however 17.4% of the most discovered group, an almost fourfold difference.

Researchers have taken different approaches. Gimbel et al. (2025) track changes in the occupational mix utilizing the Current Population Survey. Their argument is that any essential restructuring of the economy from AI would show up as changes in circulation of tasks. (They find that, so far, changes have actually been unremarkable.) Brynjolfsson et al.

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( 2022) and Hampole et al. (2025) use job publishing information from Burning Glass (now Lightcast) and Revelio, respectively. We concentrate on unemployment as our concern outcome due to the fact that it most directly records the capacity for economic harma employee who is unemployed wants a task and has not yet found one. In this case, task posts and work do not necessarily signify the requirement for policy actions; a decrease in task postings for a highly exposed function might be counteracted by increased openings in an associated one.