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The COVID-19 pandemic and accompanying policy measures triggered economic interruption so stark that advanced analytical techniques were unneeded for lots of concerns. Unemployment leapt dramatically in the early weeks of the pandemic, leaving little space for alternative explanations. The impacts of AI, nevertheless, may be less like COVID and more like the internet or trade with China.

One typical technique is to compare results between basically AI-exposed workers, companies, or markets, in order to isolate the impact of AI from confounding forces. 2 Exposure is normally defined at the task level: AI can grade homework however not handle a class, for instance, so instructors are considered less unwrapped than workers whose entire task can be carried out from another location.

3 Our method integrates information from 3 sources. The O * web database, which enumerates jobs connected with around 800 distinct professions in the US.Our own use data (as measured in the Anthropic Economic Index). Task-level direct exposure estimates from Eloundou et al. (2023 ), which measure whether it is in theory possible for an LLM to make a task at least twice as quick.

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4Why might actual usage fall brief of theoretical capability? Some tasks that are in theory possible may not show up in usage since of model constraints. Others may be slow to diffuse due to legal constraints, particular software requirements, human verification steps, or other hurdles. For example, Eloundou et al. mark "License drug refills and provide prescription info to pharmacies" as fully exposed (=1).

As Figure 1 shows, 97% of the tasks observed throughout the previous 4 Economic Index reports fall under classifications rated as theoretically possible by Eloundou et al. (=0.5 or =1.0). This figure reveals Claude use distributed throughout O * web jobs grouped by their theoretical AI exposure. Tasks ranked =1 (totally feasible for an LLM alone) account for 68% of observed Claude use, while tasks ranked =0 (not feasible) represent just 3%.

Our brand-new measure, observed direct exposure, is indicated to measure: of those jobs that LLMs could in theory accelerate, which are in fact seeing automated use in professional settings? Theoretical ability incorporates a much more comprehensive variety of jobs. By tracking how that gap narrows, observed direct exposure offers insight into economic changes as they emerge.

A task's direct exposure is higher if: Its jobs are theoretically possible with AIIts tasks see significant use in the Anthropic Economic Index5Its jobs are performed in work-related contextsIt has a fairly higher share of automated usage patterns or API implementationIts AI-impacted tasks make up a larger share of the total role6We offer mathematical details in the Appendix.

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The task-level protection steps are averaged to the occupation level weighted by the fraction of time spent on each job. The measure shows scope for LLM penetration in the bulk of tasks in Computer & Math (94%) and Workplace & Admin (90%) professions.

The coverage shows AI is far from reaching its theoretical abilities. For example, Claude presently covers simply 33% of all tasks in the Computer & Mathematics classification. As capabilities advance, adoption spreads, and release deepens, the red location will grow to cover heaven. There is a large exposed area too; lots of tasks, naturally, 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 data showing that Claude is thoroughly used for coding, Computer Programmers are at the top, with 75% protection, followed by Customer Service Agents, whose main jobs we progressively see in first-party API traffic. Data Entry Keyers, whose primary job of reading source documents and entering information sees significant automation, are 67% covered.

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At the bottom end, 30% of employees have zero coverage, as their tasks appeared too occasionally in our data to meet the minimum limit. This group includes, for example, Cooks, Bike Mechanics, Lifeguards, Bartenders, Dishwashers, and Dressing Room Attendants. The US Bureau of Labor Statistics (BLS) publishes regular employment projections, with the current set, published in 2025, covering forecasted modifications in employment for each profession from 2024 to 2034.

A regression at the occupation level weighted by current work finds that growth forecasts are somewhat weaker for jobs with more observed exposure. For each 10 percentage point increase in protection, the BLS's development projection stop by 0.6 portion points. This offers some validation because our procedures track the independently obtained price quotes from labor market analysts, although the relationship is slight.

step alone. Binned scatterplot with 25 equally-sized bins. Each solid dot reveals the average observed direct exposure and projected work change for among the bins. The dashed line shows an easy direct regression fit, weighted by existing employment levels. The small diamonds mark individual example professions for illustration. Figure 5 programs qualities of employees in the top quartile of exposure and the 30% of workers with absolutely no exposure in the 3 months before ChatGPT was released, August to October 2022, utilizing information from the Current Population Survey.

The more unveiled group is 16 percentage points more likely to be female, 11 portion points more likely to be white, and practically twice as most likely to be Asian. They earn 47% more, typically, and have greater levels of education. Individuals with graduate degrees are 4.5% of the unexposed group, but 17.4% of the most unwrapped group, a practically fourfold difference.

Brynjolfsson et al.

( 2022) and Hampole et al. (2025) use job utilize data from Information Glass (now Lightcast) and Revelio, respectively. We focus on unemployment as our concern result because it most directly captures the capacity for economic harma employee who is unemployed desires a job and has not yet found one. In this case, task postings and work do not always signify the need for policy actions; a decline in job postings for an extremely exposed function might be neutralized by increased openings in a related one.