Building Enterprise Capability Hubs for Future Growth

The COVID-19 pandemic and accompanying policy measures triggered economic interruption so plain that sophisticated statistical methods were unnecessary for lots of concerns. For example, joblessness jumped greatly in the early weeks of the pandemic, leaving little room for alternative explanations. The impacts of AI, however, may be less like COVID and more like the web or trade with China.

One typical technique is to compare outcomes between basically AI-exposed employees, firms, or industries, in order to separate the effect of AI from confounding forces. 2 Exposure is normally defined at the job level: AI can grade research but not handle a classroom, for example, so instructors are considered less reviewed than workers whose entire job can be carried out from another location.

3 Our approach combines data from 3 sources. 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 short of theoretical capability? Some tasks that are theoretically possible may not show up in use because of model constraints. Others might be slow to diffuse due to legal constraints, specific software requirements, human confirmation steps, or other obstacles. Eloundou et al. mark "License drug refills and offer prescription details to pharmacies" as fully exposed (=1).

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

Our new procedure, observed exposure, is indicated to measure: of those tasks that LLMs could theoretically accelerate, which are in fact seeing automated usage in professional settings? Theoretical ability encompasses a much wider range of tasks. By tracking how that space narrows, observed direct exposure offers insight into economic modifications as they emerge.

A task's exposure is greater if: Its tasks are theoretically possible with AIIts tasks see substantial usage in the Anthropic Economic Index5Its tasks are performed in job-related contextsIt has a reasonably higher share of automated use patterns or API implementationIts AI-impacted jobs make up a larger share of the general role6We offer mathematical details in the Appendix.

Mapping Economic Shifts of Global Commerce

We then change for how the job is being brought out: totally automated executions get complete weight, while augmentative usage receives half weight. Finally, the task-level protection steps are averaged to the profession level weighted by the portion of time invested in each task. Figure 2 reveals 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 portion procedure, then averaging to the profession category weighting by overall employment. The measure reveals scope for LLM penetration in the majority of jobs in Computer & Mathematics (94%) and Workplace & Admin (90%) occupations.

The protection shows AI is far from reaching its theoretical capabilities. Claude currently covers just 33% of all jobs in the Computer & Math category. As capabilities advance, adoption spreads, and implementation deepens, the red location will grow to cover heaven. There is a big exposed location too; many jobs, of course, stay beyond AI's reachfrom physical agricultural work like pruning trees and operating farm equipment to legal jobs like representing clients in court.

In line with other data showing that Claude is extensively used for coding, Computer Programmers are at the top, with 75% coverage, followed by Customer support Agents, whose primary jobs we significantly see in first-party API traffic. Data Entry Keyers, whose primary task of checking out source files and going into data sees considerable automation, are 67% covered.

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

A regression at the profession level weighted by existing work finds that growth projections are rather weaker for tasks with more observed direct exposure. For each 10 percentage point boost in protection, the BLS's development projection visit 0.6 percentage points. This offers some recognition in that our steps track the separately obtained quotes from labor market analysts, although the relationship is slight.

step alone. Binned scatterplot with 25 equally-sized bins. Each strong dot reveals the typical observed direct exposure and forecasted work change for among the bins. The rushed line reveals a basic linear regression fit, weighted by existing work levels. The small diamonds mark private example occupations for illustration. Figure 5 shows qualities of employees in the top quartile of exposure and the 30% of workers with no direct exposure in the 3 months before ChatGPT was launched, August to October 2022, using data from the Present Population Study.

The more discovered group is 16 portion points more likely to be female, 11 portion points most likely to be white, and nearly twice as likely to be Asian. They make 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 reviewed group, an almost fourfold difference.

Scientists have taken various approaches. For example, Gimbel et al. (2025) track modifications in the occupational mix using the Present Population Study. Their argument is that any crucial restructuring of the economy from AI would show up as changes in distribution of tasks. (They find that, up until now, changes have been typical.) Brynjolfsson et al.

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( 2022) and Hampole et al. (2025) use job posting information from Burning Glass (now Lightcast) and Revelio, respectively. We concentrate on joblessness as our priority result due to the fact that it most straight catches the potential for economic harma worker who is unemployed wants a job and has not yet discovered one. In this case, job posts and work do not always signify the need for policy reactions; a decrease in task posts for an extremely exposed function may be counteracted by increased openings in an associated one.