Scaling Global Capability Hubs for Future Growth

The COVID-19 pandemic and accompanying policy procedures triggered economic disruption so stark that sophisticated analytical techniques were unnecessary for numerous concerns. Unemployment jumped greatly in the early weeks of the pandemic, leaving little space for alternative descriptions. The impacts of AI, nevertheless, might be less like COVID and more like the web or trade with China.

One common approach is to compare outcomes between basically AI-exposed employees, companies, or industries, in order to separate the effect of AI from confounding forces. 2 Direct exposure is generally specified at the task level: AI can grade research however not manage a classroom, for example, so instructors are thought about less unveiled than workers whose entire task can be carried out remotely.

3 Our technique integrates information from three sources. The O * web database, which mentions tasks connected with around 800 special occupations in the US.Our own usage information (as determined in the Anthropic Economic Index). Task-level direct exposure estimates from Eloundou et al. (2023 ), which determine whether it is theoretically possible for an LLM to make a job a minimum of twice as fast.

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Some tasks that are theoretically possible may not reveal up in usage because of model restrictions. Eloundou et al. mark "Authorize drug refills and offer prescription info 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 classifications rated as theoretically possible by Eloundou et al. (=0.5 or =1.0). This figure reveals Claude usage dispersed throughout O * internet tasks organized by their theoretical AI exposure. Jobs rated =1 (fully feasible for an LLM alone) account for 68% of observed Claude usage, while jobs rated =0 (not practical) represent just 3%.

Our new step, observed direct exposure, is suggested to measure: of those tasks that LLMs could theoretically accelerate, which are really seeing automated usage in professional settings? Theoretical ability includes a much wider series of tasks. By tracking how that space narrows, observed exposure supplies insight into financial changes as they emerge.

A job's direct exposure is higher if: Its tasks are theoretically possible with AIIts jobs see considerable use in the Anthropic Economic Index5Its jobs are carried out in work-related contextsIt has a fairly higher share of automated use patterns or API implementationIts AI-impacted jobs comprise a bigger share of the total role6We offer mathematical details in the Appendix.

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

We calculate this by first averaging to the occupation level weighting by our time fraction step, then balancing to the profession classification weighting by total work. For example, the measure shows scope for LLM penetration in the bulk of tasks in Computer system & Math (94%) and Workplace & Admin (90%) professions.

Claude presently covers just 33% of all jobs in the Computer system & Math classification. There is a large uncovered location too; numerous jobs, of course, remain beyond AI's reachfrom physical agricultural work like pruning trees and running farm machinery to legal jobs like representing clients in court.

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

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At the bottom end, 30% of workers have zero coverage, as their tasks appeared too infrequently in our data to fulfill the minimum limit. This group consists of, for example, Cooks, Motorbike Mechanics, Lifeguards, Bartenders, Dishwashers, and Dressing Space Attendants.

A regression at the profession level weighted by existing work discovers that growth projections are somewhat weaker for jobs with more observed exposure. For each 10 portion point increase in protection, the BLS's growth forecast drops by 0.6 portion points. This offers some recognition in that our measures track the independently derived price quotes from labor market analysts, although the relationship is small.

step alone. Binned scatterplot with 25 equally-sized bins. Each solid dot shows the average observed exposure and predicted employment modification for one of the bins. The dashed line shows a basic linear regression fit, weighted by current work levels. The little diamonds mark specific example occupations for illustration. Figure 5 programs qualities of employees in the leading quartile of direct exposure and the 30% of workers with zero exposure in the 3 months before ChatGPT was launched, August to October 2022, using data from the Existing Population Survey.

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

Researchers have actually taken different methods. Gimbel et al. (2025) track changes in the occupational mix using the Existing Population Survey. Their argument is that any essential restructuring of the economy from AI would show up as modifications in circulation of tasks. (They find that, so far, modifications have actually been unremarkable.) Brynjolfsson et al.

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( 2022) and Hampole et al. (2025) use task posting information from Burning Glass (now Lightcast) and Revelio, respectively. We focus on unemployment as our priority outcome since it most straight catches the capacity for financial harma employee who is jobless wants a task and has not yet found one. In this case, task posts and employment do not always indicate the requirement for policy reactions; a decrease in job postings for a highly exposed function may be counteracted by increased openings in an associated one.