Will Predictive Data Transform Global Strategy?

The COVID-19 pandemic and accompanying policy steps triggered economic disruption so stark that sophisticated statistical methods were unnecessary for many concerns. Unemployment jumped greatly in the early weeks of the pandemic, leaving little space for alternative explanations. The impacts of AI, nevertheless, may be less like COVID and more like the web or trade with China.

One common technique is to compare results in between basically AI-exposed employees, firms, or markets, in order to separate the effect of AI from confounding forces. 2 Direct exposure is usually defined at the job level: AI can grade homework but not handle a classroom, for example, so teachers are considered less revealed than workers whose whole task can be carried out from another location.

3 Our method combines information from three sources. Task-level exposure estimates from Eloundou et al. (2023 ), which determine whether it is theoretically possible for an LLM to make a task at least two times as quick.

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4Why might actual use fall brief of theoretical capability? Some jobs that are theoretically possible may disappoint up in usage due to the fact that of model constraints. Others may be sluggish to diffuse due to legal restraints, specific software requirements, human verification steps, or other hurdles. For example, Eloundou et al. mark "License drug refills and provide prescription info to drug stores" as fully exposed (=1).

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

Our new step, observed exposure, is implied to measure: of those jobs that LLMs could theoretically speed up, which are actually seeing automated usage in professional settings? Theoretical ability incorporates a much more comprehensive variety of tasks. By tracking how that space narrows, observed direct exposure offers insight into financial modifications as they emerge.

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

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The task-level protection measures are balanced to the occupation level weighted by the portion of time spent on each task. The procedure reveals scope for LLM penetration in the bulk of jobs in Computer & Math (94%) and Office & Admin (90%) professions.

The coverage shows AI is far from reaching its theoretical abilities. Claude presently covers just 33% of all tasks in the Computer & Math classification. As capabilities advance, adoption spreads, and release deepens, the red location will grow to cover heaven. There is a big exposed location too; numerous tasks, of course, remain beyond AI's reachfrom physical agricultural work like pruning trees and operating farm equipment to legal tasks like representing customers in court.

In line with other data showing that Claude is thoroughly used for coding, Computer Programmers are at the top, with 75% protection, followed by Consumer Service Representatives, whose primary tasks we progressively see in first-party API traffic. Data Entry Keyers, whose primary job of checking out source documents and getting in information sees considerable automation, are 67% covered.

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At the bottom end, 30% of workers have zero protection, as their jobs appeared too rarely in our data to meet the minimum limit. This group includes, for example, Cooks, Bike Mechanics, Lifeguards, Bartenders, Dishwashers, and Dressing Room Attendants.

A regression at the profession level weighted by existing work discovers that growth projections are rather weaker for tasks with more observed direct exposure. For every 10 percentage point increase in protection, the BLS's development forecast drops by 0.6 percentage points. This provides some recognition because our measures track the independently derived price quotes from labor market experts, although the relationship is small.

Each strong dot reveals the typical observed direct exposure and forecasted employment change for one of the bins. The dashed line reveals a simple linear regression fit, weighted by current employment levels. Figure 5 shows attributes of workers in the top quartile of exposure and the 30% of employees with no direct exposure in the 3 months before ChatGPT was launched, August to October 2022, using information from the Current Population Survey.

The more revealed group is 16 percentage points more likely to be female, 11 percentage points more most likely to be white, and practically twice as likely to be Asian. They earn 47% more, on average, and have higher levels of education. For instance, people with graduate degrees are 4.5% of the unexposed group, but 17.4% of the most discovered group, a nearly fourfold distinction.

Brynjolfsson et al.

( 2022) and Hampole et al. (2025) use job posting data from Burning Glass (now Lightcast) and Revelio, respectively. We focus on unemployment as our top priority result since it most directly records the potential for financial harma employee who is jobless wants a job and has not yet found one. In this case, job posts and work do not always indicate the requirement for policy reactions; a decline in task postings for a highly exposed function may be counteracted by increased openings in a related one.