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The COVID-19 pandemic and accompanying policy procedures caused economic interruption so plain that sophisticated analytical techniques were unneeded for many concerns. Unemployment jumped dramatically in the early weeks of the pandemic, leaving little room for alternative descriptions. The impacts of AI, nevertheless, may be less like COVID and more like the web or trade with China.

One typical technique is to compare outcomes in between more or less AI-exposed employees, firms, or industries, in order to isolate the impact of AI from confounding forces. 2 Direct exposure is typically specified at the job level: AI can grade homework but not manage a classroom, for example, so instructors are thought about less exposed than employees whose entire task can be performed remotely.

3 Our technique integrates information from 3 sources. The O * NET database, which mentions jobs connected with around 800 distinct professions in the US.Our own usage data (as measured in the Anthropic Economic Index). Task-level exposure price quotes from Eloundou et al. (2023 ), which measure whether it is theoretically possible for an LLM to make a job at least two times as fast.

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Some jobs that are theoretically possible might not show up in usage due to the fact that of model constraints. Eloundou et al. mark "License drug refills and provide prescription information to drug stores" as fully exposed (=1).

As Figure 1 shows, 97% of the tasks observed across the previous 4 Economic Index reports fall into categories ranked as theoretically possible by Eloundou et al. (=0.5 or =1.0). This figure shows Claude use distributed across O * NET tasks grouped by their theoretical AI direct exposure. Jobs rated =1 (completely possible for an LLM alone) represent 68% of observed Claude usage, while jobs ranked =0 (not possible) account for just 3%.

Our brand-new step, observed exposure, is suggested to quantify: of those tasks that LLMs could theoretically accelerate, which are in fact seeing automated use in expert settings? Theoretical capability encompasses a much broader variety of jobs. By tracking how that space narrows, observed direct exposure provides insight into financial changes as they emerge.

A task's exposure is greater if: Its jobs are in theory possible with AIIts jobs see substantial use in the Anthropic Economic Index5Its jobs are carried out in job-related contextsIt has a reasonably higher share of automated use patterns or API implementationIts AI-impacted jobs make up a larger share of the general role6We provide mathematical details in the Appendix.

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The task-level coverage measures are balanced to the profession level weighted by the portion of time invested on each job. The step reveals scope for LLM penetration in the majority of jobs in Computer & Math (94%) and Workplace & Admin (90%) occupations.

Claude currently covers just 33% of all jobs in the Computer & Mathematics category. There is a large exposed location too; lots of jobs, of course, stay beyond AI's reachfrom physical agricultural work like pruning trees and running farm equipment to legal jobs like representing clients in court.

In line with other information showing that Claude is thoroughly utilized for coding, Computer system Programmers are at the top, with 75% coverage, followed by Customer care Agents, whose primary jobs we progressively see in first-party API traffic. Data Entry Keyers, whose main job of reading source files and entering information sees considerable automation, are 67% covered.

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At the bottom end, 30% of employees have zero coverage, as their jobs appeared too rarely in our data to fulfill the minimum limit. This group consists of, for example, Cooks, Motorbike Mechanics, Lifeguards, Bartenders, Dishwashers, and Dressing Space Attendants. The United States Bureau of Labor Data (BLS) publishes routine employment projections, with the newest set, published in 2025, covering anticipated changes in employment for every occupation from 2024 to 2034.

A regression at the profession level weighted by present work finds that growth projections are somewhat weaker for tasks with more observed exposure. For every 10 portion point increase in protection, the BLS's development forecast drops by 0.6 percentage points. This supplies some validation because our measures track the independently derived price quotes from labor market experts, although the relationship is minor.

measure alone. Binned scatterplot with 25 equally-sized bins. Each solid dot shows the typical observed direct exposure and predicted employment modification for one of the bins. The rushed line shows a simple linear regression fit, weighted by current work levels. The small diamonds mark private example occupations for illustration. Figure 5 shows attributes of workers in the top quartile of exposure and the 30% of employees with zero direct exposure in the 3 months before ChatGPT was released, August to October 2022, using data from the Current Population Survey.

The more unwrapped group is 16 percentage points more most likely to be female, 11 portion points most likely to be white, and almost twice as likely to be Asian. They make 47% more, usually, and have higher levels of education. Individuals with graduate degrees are 4.5% of the unexposed group, however 17.4% of the most revealed group, a practically fourfold distinction.

Brynjolfsson et al.

( 2022) and Hampole et al. (2025) use job utilize data from Burning Glass (now Lightcast) and Revelio, respectively. We focus on unemployment as our priority result because it most straight captures the capacity for economic harma employee who is unemployed desires a job and has not yet discovered one. In this case, job postings and employment do not necessarily indicate the requirement for policy responses; a decrease in job posts for a highly exposed function might be combated by increased openings in a related one.