Leveraging AI for Predictive Intelligence

The COVID-19 pandemic and accompanying policy procedures triggered economic disturbance so stark that advanced statistical approaches were unnecessary for lots of questions. Joblessness leapt sharply in the early weeks of the pandemic, leaving little room for alternative descriptions. The impacts of AI, however, might be less like COVID and more like the web or trade with China.

One typical approach is to compare results in between basically AI-exposed workers, firms, or industries, in order to isolate the effect of AI from confounding forces. 2 Direct exposure is usually defined at the job level: AI can grade research but not manage a class, for instance, so instructors are considered less uncovered than employees whose whole job can be performed remotely.

3 Our approach combines information from three sources. Task-level direct exposure quotes from Eloundou et al. (2023 ), which measure whether it is theoretically possible for an LLM to make a job at least two times as fast.

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4Why might real use fall short of theoretical ability? Some tasks that are in theory possible may disappoint up in use since of design limitations. Others might be slow to diffuse due to legal restrictions, specific software application requirements, human confirmation steps, or other difficulties. Eloundou et al. mark "License drug refills and provide prescription information to pharmacies" as fully exposed (=1).

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

Our new step, observed exposure, is implied to quantify: of those jobs that LLMs could theoretically speed up, which are really seeing automated use in professional settings? Theoretical ability includes a much more comprehensive variety of jobs. By tracking how that space narrows, observed direct exposure supplies insight into economic changes as they emerge.

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

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The task-level protection steps are averaged to the occupation level weighted by the portion of time invested on each job. The measure reveals scope for LLM penetration in the majority of jobs in Computer system & Math (94%) and Workplace & Admin (90%) professions.

Claude currently covers just 33% of all tasks in the Computer system & Math classification. There is a large exposed area too; numerous tasks, 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 revealing that Claude is extensively utilized for coding, Computer system Programmers are at the top, with 75% protection, followed by Customer Service Agents, whose main tasks we progressively see in first-party API traffic. Finally, Data Entry Keyers, whose main 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 employees have no coverage, as their tasks appeared too occasionally in our data to meet 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 present employment finds that development forecasts are rather weaker for tasks with more observed direct exposure. For each 10 portion point increase in coverage, the BLS's growth forecast drops by 0.6 percentage points. This supplies some recognition because our steps track the independently derived estimates from labor market analysts, although the relationship is small.

measure alone. Binned scatterplot with 25 equally-sized bins. Each solid dot shows the average observed exposure and projected employment modification for one of the bins. The dashed line reveals an easy direct regression fit, weighted by current employment levels. The small diamonds mark individual example professions for illustration. Figure 5 shows qualities of workers in the leading quartile of direct exposure and the 30% of employees with zero direct exposure in the three months before ChatGPT was launched, August to October 2022, using information from the Present Population Survey.

The more revealed group is 16 portion points most likely to be female, 11 percentage points most likely to be white, and almost two times as likely to be Asian. They make 47% more, usually, and have greater levels of education. People with graduate degrees are 4.5% of the unexposed group, however 17.4% of the most discovered group, a nearly fourfold distinction.

Researchers have actually taken different techniques. For example, Gimbel et al. (2025) track changes in the occupational mix utilizing the Current Population Study. Their argument is that any crucial restructuring of the economy from AI would show up as modifications in distribution of tasks. (They find that, so far, modifications have been unremarkable.) Brynjolfsson et al.

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( 2022) and Hampole et al. (2025) use task publishing information from Burning Glass (now Lightcast) and Revelio, respectively. We focus on joblessness as our concern outcome because it most straight catches the capacity for financial harma employee who is jobless wants a job and has actually not yet found one. In this case, job posts and work do not always indicate the need for policy reactions; a decline in job posts for a highly exposed role may be combated by increased openings in an associated one.