Harnessing AI for Predictive Analysis

The COVID-19 pandemic and accompanying policy steps triggered financial interruption so plain that sophisticated analytical methods were unneeded for numerous concerns. For instance, unemployment jumped sharply in the early weeks of the pandemic, leaving little room for alternative explanations. The effects of AI, however, might be less like COVID and more like the web or trade with China.

One typical approach is to compare outcomes between more or less AI-exposed employees, firms, or markets, in order to isolate the effect of AI from confounding forces. 2 Exposure is typically specified at the task level: AI can grade research but not handle a classroom, for example, so instructors are thought about less unveiled than employees whose whole job can be carried out remotely.

3 Our approach integrates data from three sources. The O * internet database, which identifies jobs related to around 800 unique professions in the US.Our own usage data (as determined in the Anthropic Economic Index). Task-level direct exposure estimates from Eloundou et al. (2023 ), which measure whether it is theoretically possible for an LLM to make a job a minimum of two times as quick.

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4Why might actual usage fall brief of theoretical capability? Some tasks that are theoretically possible might disappoint up in use due to the fact that of design limitations. Others may be slow to diffuse due to legal constraints, specific software requirements, human verification actions, or other hurdles. Eloundou et al. mark "License drug refills and supply prescription information to pharmacies" as fully exposed (=1).

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

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

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

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The task-level coverage procedures are balanced to the profession level weighted by the fraction of time spent on each task. The measure shows scope for LLM penetration in the majority of tasks in Computer system & Math (94%) and Workplace & Admin (90%) occupations.

Claude currently covers just 33% of all jobs in the Computer system & Mathematics category. There is a large exposed area too; lots of jobs, 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 data revealing that Claude is extensively utilized for coding, Computer Programmers are at the top, with 75% protection, followed by Consumer Service Agents, whose primary tasks we significantly 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 employees have zero protection, as their jobs appeared too infrequently in our information to meet the minimum threshold. This group consists of, for example, Cooks, Motorbike Mechanics, Lifeguards, Bartenders, Dishwashers, and Dressing Room Attendants. The United States Bureau of Labor Statistics (BLS) publishes regular work forecasts, with the current set, released in 2025, covering anticipated changes in employment for every single occupation from 2024 to 2034.

A regression at the occupation level weighted by current work finds that development forecasts are somewhat weaker for tasks with more observed exposure. For each 10 percentage point increase in protection, the BLS's development forecast drops by 0.6 portion points. This supplies some validation in that our measures track the independently obtained quotes from labor market experts, although the relationship is minor.

measure alone. Binned scatterplot with 25 equally-sized bins. Each strong dot reveals the average observed exposure and forecasted employment change for one of the bins. The rushed line reveals an easy linear regression fit, weighted by current employment levels. The small diamonds mark private example occupations for illustration. Figure 5 programs qualities of workers in the top quartile of exposure and the 30% of employees with no direct exposure in the three months before ChatGPT was released, August to October 2022, using information from the Current Population Survey.

The more revealed group is 16 percentage points most likely to be female, 11 portion points more most likely to be white, and almost twice as most 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 practically fourfold difference.

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 outcome due to the fact that it most directly catches the potential for financial harma employee who is jobless wants a job and has actually not yet discovered one. In this case, task posts and employment do not always signal the need for policy actions; a decrease in task postings for an extremely exposed role might be counteracted by increased openings in a related one.