Scaling Global Capability Centers for Future Growth

The COVID-19 pandemic and accompanying policy measures caused financial disturbance so stark that advanced statistical approaches were unneeded for many questions. Unemployment leapt dramatically in the early weeks of the pandemic, leaving little room for alternative descriptions. The effects of AI, however, may be less like COVID and more like the web or trade with China.

One typical technique is to compare outcomes in between basically AI-exposed workers, firms, or markets, in order to isolate the result of AI from confounding forces. 2 Exposure is normally defined at the task level: AI can grade research but not manage a class, for example, so teachers are considered less uncovered than employees whose whole task can be performed from another location.

3 Our method integrates data from 3 sources. The O * NET database, which enumerates jobs related to around 800 distinct professions in the US.Our own use information (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 job a minimum of two times as quick.

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Some tasks that are theoretically possible may not show up in use since of design limitations. Eloundou et al. mark "Authorize drug refills and supply prescription details to pharmacies" 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 theoretically practical by Eloundou et al. (=0.5 or =1.0). This figure reveals Claude use distributed throughout O * internet tasks organized by their theoretical AI direct exposure. Jobs ranked =1 (fully possible for an LLM alone) account for 68% of observed Claude usage, while tasks rated =0 (not practical) represent just 3%.

Our new procedure, observed exposure, is meant to quantify: of those jobs that LLMs could theoretically accelerate, which are actually seeing automated use in professional settings? Theoretical ability encompasses a much more comprehensive variety of jobs. By tracking how that space narrows, observed direct exposure offers insight into economic modifications as they emerge.

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

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We then adjust for how the job is being carried out: completely automated executions receive complete weight, while augmentative usage receives half weight. Lastly, the task-level coverage steps are averaged to the occupation level weighted by the fraction of time spent on each task. Figure 2 shows observed exposure (in red) compared to from Eloundou et al.

We determine this by first averaging to the profession level weighting by our time fraction measure, then averaging to the occupation category weighting by total employment. For instance, the measure shows scope for LLM penetration in the majority of tasks in Computer system & Mathematics (94%) and Office & Admin (90%) occupations.

Claude currently covers just 33% of all tasks in the Computer system & Math category. There is a large exposed area too; numerous tasks, of course, remain beyond AI's reachfrom physical farming work like pruning trees and running farm machinery to legal tasks like representing clients in court.

In line with other data revealing that Claude is thoroughly used for coding, Computer system Programmers are at the top, with 75% coverage, followed by Client service Agents, whose main jobs we significantly see in first-party API traffic. Data Entry Keyers, whose main job of reading source files and going into data sees significant automation, are 67% covered.

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

A regression at the occupation level weighted by current employment discovers that development forecasts are rather weaker for jobs with more observed exposure. For each 10 percentage point boost in protection, the BLS's development forecast drops by 0.6 portion points. This supplies some validation in that our steps track the independently derived price quotes from labor market experts, although the relationship is minor.

Each strong dot reveals the typical observed direct exposure and predicted work change for one of the bins. The rushed line reveals an easy linear regression fit, weighted by current employment levels. Figure 5 programs attributes of workers in the top quartile of exposure and the 30% of employees with zero direct exposure in the 3 months before ChatGPT was launched, August to October 2022, utilizing information from the Existing Population Survey.

The more revealed group is 16 portion points more likely to be female, 11 portion points more most likely to be white, and almost twice as most likely to be Asian. They make 47% more, on average, and have greater levels of education. For instance, individuals with academic degrees are 4.5% of the unexposed group, however 17.4% of the most revealed group, an almost fourfold difference.

Scientists have actually taken various approaches. Gimbel et al. (2025) track modifications in the occupational mix using the Current Population Study. Their argument is that any important restructuring of the economy from AI would reveal up as changes in circulation of jobs. (They find that, up until now, modifications have been typical.) Brynjolfsson et al.

Managing In-House Capability Hubs for Future Growth

( 2022) and Hampole et al. (2025) use job posting information from Burning Glass (now Lightcast) and Revelio, respectively. We concentrate on joblessness as our concern result since it most straight records the capacity for financial harma employee who is unemployed desires a task and has not yet discovered one. In this case, job postings and employment do not always signal the requirement for policy responses; a decrease in task postings for an extremely exposed role might be neutralized by increased openings in an associated one.