Leveraging AI to Improve Market Intelligence

The COVID-19 pandemic and accompanying policy measures triggered financial disruption so stark that sophisticated statistical techniques were unneeded for numerous concerns. For example, joblessness jumped dramatically in the early weeks of the pandemic, leaving little space for alternative descriptions. The effects 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 more or less AI-exposed employees, companies, or markets, in order to separate the result of AI from confounding forces. 2 Exposure is generally defined at the task level: AI can grade homework however not handle a classroom, for example, so instructors are thought about less unwrapped than workers whose entire task can be performed remotely.

3 Our approach integrates data from three sources. Task-level direct exposure estimates from Eloundou et al. (2023 ), which determine whether it is in theory possible for an LLM to make a job at least two times as quick.

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Some jobs that are in theory possible might not show up in usage because of model constraints. Eloundou et al. mark "Authorize drug refills and supply prescription information to pharmacies" as totally exposed (=1).

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

Our brand-new step, observed exposure, is meant to quantify: of those jobs that LLMs could theoretically accelerate, which are actually seeing automated use in professional settings? Theoretical capability incorporates a much broader variety of jobs. By tracking how that gap narrows, observed direct exposure provides insight into economic modifications as they emerge.

A job's exposure is greater if: Its tasks are theoretically possible with AIIts jobs see substantial usage in the Anthropic Economic Index5Its jobs are carried out in job-related contextsIt has a relatively greater share of automated use patterns or API implementationIts AI-impacted tasks comprise a bigger share of the total role6We give mathematical details in the Appendix.

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We then change for how the task is being carried out: totally automated executions get complete weight, while augmentative usage receives half weight. The task-level protection steps are averaged to the profession level weighted by the portion of time spent on each job. Figure 2 reveals observed exposure (in red) compared to from Eloundou et al.

We determine this by first balancing to the occupation level weighting by our time portion procedure, then averaging to the occupation category weighting by overall work. The procedure shows scope for LLM penetration in the majority of tasks in Computer system & Math (94%) and Office & Admin (90%) occupations.

Claude presently covers simply 33% of all tasks in the Computer system & Mathematics category. There is a large exposed area too; numerous tasks, of course, stay beyond AI's reachfrom physical farming work like pruning trees and running farm machinery to legal jobs like representing customers in court.

In line with other information revealing that Claude is thoroughly used for coding, Computer system Programmers are at the top, with 75% protection, followed by Client service Representatives, whose primary jobs we significantly see in first-party API traffic. Lastly, Data Entry Keyers, whose primary task of reading source documents and going into data sees significant 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 information to satisfy the minimum limit. This group consists of, for instance, Cooks, Bike Mechanics, Lifeguards, Bartenders, Dishwashers, and Dressing Space Attendants. The US Bureau of Labor Stats (BLS) releases routine work forecasts, with the most recent set, released in 2025, covering anticipated modifications in employment for each profession from 2024 to 2034.

A regression at the profession level weighted by current work discovers that growth projections are rather weaker for jobs with more observed direct exposure. For every 10 portion point increase in coverage, the BLS's growth forecast come by 0.6 portion points. This provides some recognition because our steps track the independently obtained estimates from labor market experts, although the relationship is small.

procedure alone. Binned scatterplot with 25 equally-sized bins. Each strong dot shows the average observed exposure and forecasted work modification for among the bins. The rushed line reveals an easy direct regression fit, weighted by current work levels. The small diamonds mark private example occupations for illustration. Figure 5 shows attributes of employees in the top quartile of direct exposure and the 30% of employees with zero exposure in the three months before ChatGPT was launched, August to October 2022, utilizing information from the Present Population Survey.

The more reviewed group is 16 portion points more most likely to be female, 11 percentage points more likely to be white, and almost twice as most likely to be Asian. They make 47% more, usually, and have higher levels of education. Individuals with graduate degrees are 4.5% of the unexposed group, but 17.4% of the most disclosed group, an almost fourfold difference.

Brynjolfsson et al.

( 2022) and Hampole et al. (2025) use job posting data publishing Information Glass (now Lightcast) and Revelio, respectively. We focus on joblessness as our top priority result since it most straight records the capacity for economic harma employee who is unemployed desires a job and has not yet discovered one. In this case, job postings and work do not always signify the need for policy reactions; a decline in task posts for an extremely exposed function might be neutralized by increased openings in a related one.