International Market Outlook for Future Regions

The COVID-19 pandemic and accompanying policy measures triggered economic disturbance so plain that sophisticated analytical approaches were unnecessary for numerous concerns. For instance, unemployment leapt greatly in the early weeks of the pandemic, leaving little room for alternative explanations. The effects of AI, however, may be less like COVID and more like the web or trade with China.

One typical method is to compare outcomes in between more or less AI-exposed employees, firms, or markets, in order to separate the effect of AI from confounding forces. 2 Exposure is typically defined at the task level: AI can grade homework however not manage a class, for instance, so teachers are thought about less disclosed than workers whose whole job can be performed from another location.

3 Our technique integrates information from three sources. The O * internet database, which enumerates tasks connected with around 800 special occupations in the US.Our own use information (as measured in the Anthropic Economic Index). Task-level direct exposure quotes from Eloundou et al. (2023 ), which measure whether it is in theory possible for an LLM to make a job at least two times as fast.

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Some jobs that are in theory possible might not show up in usage since of model limitations. Eloundou et al. mark "Authorize drug refills and supply prescription details to pharmacies" as totally exposed (=1).

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

Our brand-new measure, observed exposure, is suggested to quantify: of those tasks that LLMs could theoretically accelerate, which are actually seeing automated usage in expert settings? Theoretical capability incorporates a much broader variety of tasks. By tracking how that space narrows, observed direct exposure supplies 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 considerable use in the Anthropic Economic Index5Its jobs are performed in work-related contextsIt has a relatively higher share of automated usage patterns or API implementationIts AI-impacted jobs make up a bigger share of the overall role6We provide mathematical details in the Appendix.

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The task-level protection measures are balanced to the profession level weighted by the fraction of time spent on each task. The measure reveals scope for LLM penetration in the majority of tasks in Computer system & Mathematics (94%) and Workplace & Admin (90%) professions.

Claude currently covers just 33% of all jobs in the Computer system & Mathematics classification. There is a big exposed location too; numerous jobs, of course, remain beyond AI's reachfrom physical farming work like pruning trees and operating farm machinery to legal jobs like representing customers in court.

In line with other data showing that Claude is thoroughly used for coding, Computer system Programmers are at the top, with 75% protection, followed by Client service Representatives, whose primary jobs we progressively see in first-party API traffic. Lastly, Data Entry Keyers, whose primary task of checking out source documents and entering information sees significant automation, are 67% covered.

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At the bottom end, 30% of employees have no protection, as their tasks appeared too occasionally in our information 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 Data (BLS) releases regular employment projections, with the most recent set, released in 2025, covering predicted changes in work for every occupation from 2024 to 2034.

A regression at the profession level weighted by existing employment discovers that development forecasts are somewhat weaker for tasks with more observed exposure. For each 10 percentage point boost in protection, the BLS's growth projection come by 0.6 portion points. This provides some validation in that our steps track the individually obtained estimates from labor market experts, although the relationship is small.

Each strong dot shows the typical observed exposure and projected work modification for one of the bins. The dashed line reveals a simple direct regression fit, weighted by current employment levels. Figure 5 programs qualities of workers in the leading quartile of exposure and the 30% of employees with no direct exposure in the 3 months before ChatGPT was released, August to October 2022, using information from the Present Population Survey.

The more bare group is 16 percentage points more likely to be female, 11 percentage points most likely to be white, and almost two times as likely to be Asian. They make 47% more, usually, and have higher levels of education. For example, people with academic degrees are 4.5% of the unexposed group, however 17.4% of the most disclosed group, an almost fourfold difference.

Brynjolfsson et al.

( 2022) and Hampole et al. (2025) use job posting data publishing Information Glass (now Lightcast) and Revelio, respectively. We focus on unemployment as our concern outcome since it most straight captures the capacity for financial harma employee who is out of work desires a job and has actually not yet found one. In this case, task postings and employment do not always signify the requirement for policy reactions; a decrease in job posts for a highly exposed function may be counteracted by increased openings in a related one.