Building Global Capability Hubs for Future Growth

The COVID-19 pandemic and accompanying policy steps caused financial interruption so stark that sophisticated statistical techniques were unnecessary for numerous questions. For instance, joblessness jumped greatly in the early weeks of the pandemic, leaving little space for alternative explanations. The impacts of AI, however, might be less like COVID and more like the web or trade with China.

One common technique is to compare outcomes between basically AI-exposed employees, companies, or industries, in order to isolate 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 exposed than employees whose whole task can be performed from another location.

3 Our method integrates information from 3 sources. The O * NET database, which enumerates jobs connected with around 800 unique occupations in the US.Our own use data (as measured in the Anthropic Economic Index). Task-level direct exposure quotes from Eloundou et al. (2023 ), which determine whether it is theoretically possible for an LLM to make a job at least twice as fast.

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4Why might actual usage fall short of theoretical ability? Some jobs that are in theory possible may not show up in usage since of design constraints. Others may be sluggish to diffuse due to legal restrictions, specific software application requirements, human verification steps, or other difficulties. Eloundou et al. mark "Authorize drug refills and offer prescription details to drug stores" as completely exposed (=1).

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

Our brand-new procedure, observed direct exposure, is meant to measure: of those jobs that LLMs could theoretically accelerate, which are in fact seeing automated usage in professional settings? Theoretical capability includes a much broader range of tasks. By tracking how that gap narrows, observed direct exposure offers insight into economic changes as they emerge.

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

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We then adjust for how the job is being performed: totally automated applications receive complete weight, while augmentative use receives half weight. Lastly, the task-level protection steps are averaged to the occupation level weighted by the fraction of time spent on each task. Figure 2 reveals observed exposure (in red) compared to from Eloundou et al.

We calculate this by first balancing to the profession level weighting by our time fraction procedure, then balancing to the profession category weighting by overall work. The procedure reveals scope for LLM penetration in the bulk of jobs in Computer system & Mathematics (94%) and Office & Admin (90%) professions.

The protection reveals AI is far from reaching its theoretical capabilities. For instance, Claude currently covers simply 33% of all tasks in the Computer system & Mathematics category. As abilities advance, adoption spreads, and release deepens, the red area will grow to cover the blue. There is a big exposed area too; numerous jobs, of course, remain beyond AI's reachfrom physical agricultural work like pruning trees and operating farm machinery to legal tasks like representing clients in court.

In line with other information revealing that Claude is thoroughly utilized for coding, Computer system Programmers are at the top, with 75% protection, followed by Client service Representatives, whose primary jobs we significantly see in first-party API traffic. Data Entry Keyers, whose primary job of checking out source documents and going into information sees substantial 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 satisfy the minimum threshold. This group consists of, for example, Cooks, Bike Mechanics, Lifeguards, Bartenders, Dishwashers, and Dressing Space Attendants.

A regression at the occupation level weighted by current employment discovers that growth projections are somewhat weaker for tasks with more observed exposure. For every 10 percentage point increase in protection, the BLS's development projection visit 0.6 portion points. This provides some validation in that our steps track the individually derived quotes from labor market experts, although the relationship is slight.

Each solid dot shows the average observed direct exposure and projected work modification for one of the bins. The dashed line reveals an easy direct regression fit, weighted by existing employment levels. Figure 5 shows qualities of employees in the leading quartile of direct exposure and the 30% of workers with zero direct exposure in the three months before ChatGPT was released, August to October 2022, using information from the Existing Population Study.

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

Brynjolfsson et al.

( 2022) and Hampole et al. (2025) use job posting task publishing Information Glass (now Lightcast) and Revelio, respectively. We focus on joblessness as our priority outcome since it most straight catches the potential for economic harma employee who is jobless wants a job and has not yet discovered one. In this case, job posts and work do not necessarily indicate the need for policy actions; a decline in task postings for a highly exposed role might be counteracted by increased openings in a related one.