Can Deep Data Reshape Industry Strategy?

The COVID-19 pandemic and accompanying policy steps caused economic disruption so plain that advanced statistical techniques were unnecessary for many questions. For example, joblessness jumped greatly in the early weeks of the pandemic, leaving little space for alternative descriptions. The effects of AI, however, may be less like COVID and more like the internet or trade with China.

One typical method is to compare results between basically AI-exposed workers, firms, or industries, in order to isolate the effect of AI from confounding forces. 2 Exposure is usually defined at the task level: AI can grade homework but not manage a classroom, for instance, so teachers are considered less unwrapped than workers whose entire task can be performed remotely.

3 Our technique integrates data from three sources. The O * NET database, which identifies tasks related to around 800 unique professions in the US.Our own usage information (as determined in the Anthropic Economic Index). Task-level exposure estimates from Eloundou et al. (2023 ), which measure whether it is in theory possible for an LLM to make a task a minimum of twice as quick.

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4Why might actual use fall short of theoretical capability? Some jobs that are in theory possible may disappoint up in use because of design constraints. Others may be sluggish to diffuse due to legal restrictions, specific software requirements, human confirmation actions, or other difficulties. Eloundou et al. mark "Authorize drug refills and offer prescription information to drug stores" as fully exposed (=1).

As Figure 1 shows, 97% of the tasks observed across the previous four Economic Index reports fall under categories ranked as theoretically possible by Eloundou et al. (=0.5 or =1.0). This figure reveals Claude use dispersed across O * web tasks grouped by their theoretical AI direct exposure. Tasks ranked =1 (fully possible for an LLM alone) represent 68% of observed Claude use, while jobs ranked =0 (not practical) represent just 3%.

Our new measure, observed exposure, is suggested to measure: of those tasks that LLMs could in theory accelerate, which are in fact seeing automated use in professional settings? Theoretical capability includes a much wider variety of tasks. By tracking how that space narrows, observed exposure supplies insight into financial modifications as they emerge.

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

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We then change for how the task is being brought out: totally automated implementations get full weight, while augmentative usage receives half weight. The task-level protection procedures are balanced to the profession level weighted by the portion of time invested on each task. Figure 2 shows observed direct exposure (in red) compared to from Eloundou et al.

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

Claude currently covers just 33% of all tasks in the Computer system & Mathematics category. There is a large exposed area too; numerous 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 system Programmers are at the top, with 75% protection, followed by Consumer Service Agents, whose primary jobs we increasingly see in first-party API traffic. Finally, Data Entry Keyers, whose primary job of checking out source files and entering data sees substantial automation, are 67% covered.

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At the bottom end, 30% of workers have absolutely no protection, as their tasks appeared too infrequently in our data to meet the minimum limit. This group includes, for example, Cooks, Motorcycle Mechanics, Lifeguards, Bartenders, Dishwashers, and Dressing Room Attendants.

A regression at the profession level weighted by present employment discovers that development projections are somewhat weaker for jobs with more observed exposure. For each 10 portion point boost in protection, the BLS's growth projection stop by 0.6 portion points. This supplies some validation because our steps track the separately obtained quotes from labor market experts, although the relationship is minor.

step alone. Binned scatterplot with 25 equally-sized bins. Each solid dot shows the average observed exposure and forecasted employment modification for among the bins. The dashed line reveals an easy linear regression fit, weighted by present work levels. The small diamonds mark specific example professions for illustration. Figure 5 programs attributes of employees in the top quartile of direct exposure and the 30% of employees with zero exposure in the 3 months before ChatGPT was released, August to October 2022, using data from the Present Population Study.

The more revealed group is 16 percentage points more likely to be female, 11 portion points more most likely to be white, and almost two times as likely to be Asian. They earn 47% more, usually, and have greater levels of education. For example, people with academic degrees are 4.5% of the unexposed group, but 17.4% of the most uncovered group, an almost fourfold distinction.

Brynjolfsson et al.

( 2022) and Hampole et al. (2025) use job utilize data publishing Burning Glass (now Lightcast) and Revelio, respectively. We focus on joblessness as our priority outcome due to the fact that it most straight catches the potential for economic harma worker who is unemployed desires a task and has not yet discovered one. In this case, job postings and work do not always indicate the need for policy actions; a decline in job posts for an extremely exposed role might be neutralized by increased openings in a related one.