Managing In-House Capability Hubs for Future Growth

The COVID-19 pandemic and accompanying policy measures caused financial disruption so stark that sophisticated analytical methods were unneeded for many questions. Unemployment leapt sharply in the early weeks of the pandemic, leaving little room for alternative explanations. The effects of AI, however, may be less like COVID and more like the internet or trade with China.

One common technique is to compare results in between more or less AI-exposed employees, companies, or markets, in order to isolate the impact of AI from confounding forces. 2 Exposure is typically specified at the task level: AI can grade research however not manage a classroom, for example, so teachers are considered less reviewed than employees whose entire job can be performed remotely.

3 Our method combines information from 3 sources. Task-level exposure estimates 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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Some jobs that are in theory possible might not reveal up in use because of model restrictions. Eloundou et al. mark "License drug refills and provide prescription details to drug stores" as fully exposed (=1).

As Figure 1 shows, 97% of the tasks observed throughout the previous 4 Economic Index reports fall under categories ranked as in theory practical by Eloundou et al. (=0.5 or =1.0). This figure reveals Claude use dispersed throughout O * NET tasks organized by their theoretical AI exposure. Tasks ranked =1 (fully feasible for an LLM alone) account for 68% of observed Claude use, while jobs rated =0 (not possible) account for just 3%.

Our brand-new procedure, observed exposure, is meant to quantify: of those jobs that LLMs could theoretically speed up, which are actually seeing automated usage in professional settings? Theoretical ability includes a much broader variety of tasks. By tracking how that gap narrows, observed exposure offers insight into economic modifications as they emerge.

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

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We then adjust for how the job is being brought out: fully automated applications get full weight, while augmentative usage gets half weight. Lastly, the task-level coverage measures are balanced to the occupation level weighted by the portion of time invested in each task. Figure 2 shows observed exposure (in red) compared to from Eloundou et al.

We compute this by very first averaging to the profession level weighting by our time fraction measure, then averaging to the profession classification weighting by overall work. The measure shows scope for LLM penetration in the bulk of tasks in Computer & Mathematics (94%) and Workplace & Admin (90%) professions.

Claude presently covers simply 33% of all tasks in the Computer & Mathematics classification. There is a big uncovered location 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 customers in court.

In line with other information revealing that Claude is extensively used for coding, Computer system Programmers are at the top, with 75% coverage, followed by Consumer Service Agents, whose primary tasks we significantly see in first-party API traffic. Lastly, Data Entry Keyers, whose main task of reading source files and going into data sees substantial automation, are 67% covered.

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At the bottom end, 30% of workers have absolutely no coverage, as their jobs appeared too rarely in our data to fulfill the minimum limit. This group includes, for example, Cooks, Bike Mechanics, Lifeguards, Bartenders, Dishwashers, and Dressing Room Attendants. The United States Bureau of Labor Data (BLS) publishes regular work forecasts, with the most recent set, published in 2025, covering predicted changes in work for each occupation from 2024 to 2034.

A regression at the occupation level weighted by existing work finds that development forecasts are somewhat weaker for tasks with more observed direct exposure. For every single 10 portion point increase in protection, the BLS's development forecast visit 0.6 percentage points. This offers some validation in that our procedures track the individually derived quotes from labor market experts, although the relationship is small.

Each strong dot reveals the typical observed exposure and predicted employment modification for one of the bins. The dashed line reveals a simple linear regression fit, weighted by present employment levels. Figure 5 shows qualities of employees in the leading quartile of direct exposure and the 30% of employees with zero direct exposure in the three months before ChatGPT was released, August to October 2022, utilizing data from the Existing Population Study.

The more reviewed group is 16 percentage points more likely to be female, 11 percentage points more likely to be white, and almost two times as likely to be Asian. They earn 47% more, typically, and have greater levels of education. For example, individuals with graduate degrees are 4.5% of the unexposed group, however 17.4% of the most disclosed group, a practically fourfold distinction.

Brynjolfsson et al.

( 2022) and Hampole et al. (2025) use job posting data from Information Glass (now Lightcast) and Revelio, respectively. We focus on unemployment as our top priority result since it most straight catches the potential for financial harma worker who is jobless wants a task and has not yet discovered one. In this case, job postings and employment do not necessarily signify the requirement for policy actions; a decline in task postings for a highly exposed function may be counteracted by increased openings in an associated one.