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The COVID-19 pandemic and accompanying policy procedures triggered economic disturbance so plain that advanced analytical approaches were unneeded for many concerns. For example, unemployment jumped dramatically in the early weeks of the pandemic, leaving little room for alternative explanations. The effects of AI, however, 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 workers, companies, or industries, in order to separate the impact of AI from confounding forces. 2 Direct exposure is usually specified at the job level: AI can grade homework however not handle a class, for instance, so instructors are thought about less unveiled than employees whose whole job can be performed remotely.

3 Our approach integrates information from three sources. The O * web database, which mentions jobs associated with around 800 unique 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 determine whether it is in theory possible for an LLM to make a task at least two times as fast.

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4Why might actual usage fall brief of theoretical capability? Some tasks that are theoretically possible may not show up in usage due to the fact that of design restrictions. Others may be slow to diffuse due to legal constraints, specific software application requirements, human confirmation steps, or other difficulties. For instance, Eloundou et al. mark "License drug refills and supply prescription info to drug stores" as totally exposed (=1).

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

Our new step, observed direct exposure, is implied to quantify: of those tasks that LLMs could theoretically accelerate, which are really seeing automated usage in professional settings? Theoretical capability includes a much broader range of tasks. By tracking how that space narrows, observed exposure offers insight into economic changes as they emerge.

A task's exposure is higher if: Its jobs are in theory possible with AIIts tasks see significant use in the Anthropic Economic Index5Its jobs are carried out in job-related contextsIt has a relatively greater share of automated usage patterns or API implementationIts AI-impacted jobs comprise a bigger share of the general role6We give mathematical information in the Appendix.

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We then adjust for how the task is being performed: completely automated implementations get complete weight, while augmentative use receives half weight. Lastly, the task-level protection steps are averaged to the profession level weighted by the fraction of time invested in each task. Figure 2 shows observed direct exposure (in red) compared to from Eloundou et al.

We calculate this by first averaging to the profession level weighting by our time fraction procedure, then averaging to the occupation classification weighting by overall employment. The measure shows scope for LLM penetration in the bulk of tasks in Computer system & Math (94%) and Workplace & Admin (90%) occupations.

The protection shows AI is far from reaching its theoretical capabilities. Claude presently covers simply 33% of all tasks in the Computer & Math category. As abilities advance, adoption spreads, and implementation deepens, the red location will grow to cover the blue. There is a large exposed location too; numerous tasks, obviously, remain beyond AI's reachfrom physical farming work like pruning trees and running farm equipment to legal tasks like representing clients in court.

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

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At the bottom end, 30% of workers have absolutely no protection, as their tasks appeared too rarely in our data to satisfy the minimum threshold. This group consists of, for example, Cooks, Motorbike Mechanics, Lifeguards, Bartenders, Dishwashers, and Dressing Space Attendants.

A regression at the occupation level weighted by present employment finds that growth projections are somewhat weaker for tasks with more observed direct exposure. For every 10 percentage point increase in protection, the BLS's growth forecast drops by 0.6 percentage points. This provides some validation in that our steps track the separately derived estimates from labor market analysts, although the relationship is slight.

measure alone. Binned scatterplot with 25 equally-sized bins. Each strong dot shows the average observed direct exposure and projected work modification for one of the bins. The rushed line reveals a simple direct regression fit, weighted by present work levels. The small diamonds mark specific example professions for illustration. Figure 5 programs characteristics of workers in the top quartile of direct exposure and the 30% of workers with no direct exposure in the 3 months before ChatGPT was launched, August to October 2022, utilizing data from the Current Population Survey.

The more bare group is 16 percentage points more most likely to be female, 11 portion points more likely to be white, and nearly two times as most likely to be Asian. They earn 47% more, typically, and have higher levels of education. Individuals with graduate degrees are 4.5% of the unexposed group, however 17.4% of the most unwrapped group, an almost fourfold distinction.

Brynjolfsson et al.

( 2022) and Hampole et al. (2025) use job utilize data from Information Glass (now Lightcast) and Revelio, respectively. We focus on joblessness as our priority outcome since it most directly records the potential for financial harma worker who is out of work wants a job and has not yet discovered one. In this case, task postings and work do not always signify the need for policy reactions; a decrease in task postings for an extremely exposed function may be counteracted by increased openings in a related one.