Leveraging AI for Market Intelligence

The COVID-19 pandemic and accompanying policy procedures caused financial disturbance so stark that sophisticated statistical methods were unneeded for many concerns. 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 internet or trade with China.

One typical approach is to compare results in between more or less AI-exposed employees, companies, or markets, in order to separate the effect of AI from confounding forces. 2 Exposure is typically specified at the task level: AI can grade homework however not handle a class, for example, so instructors are thought about less exposed than employees whose whole job can be carried out from another location.

3 Our method integrates data from 3 sources. Task-level exposure price quotes from Eloundou et al. (2023 ), which measure whether it is theoretically possible for an LLM to make a task at least two times as quick.

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Some tasks that are in theory possible may not reveal up in use because of model restrictions. Eloundou et al. mark "Authorize drug refills and provide prescription information to pharmacies" as totally exposed (=1).

As Figure 1 programs, 97% of the tasks observed across the previous 4 Economic Index reports fall under classifications rated as theoretically possible by Eloundou et al. (=0.5 or =1.0). This figure reveals Claude usage distributed across O * web tasks grouped by their theoretical AI exposure. Tasks rated =1 (completely practical for an LLM alone) account for 68% of observed Claude use, while jobs ranked =0 (not practical) account for simply 3%.

Our new procedure, observed direct exposure, is implied to measure: of those jobs that LLMs could in theory accelerate, which are really seeing automated use in professional settings? Theoretical ability includes a much more comprehensive series of jobs. By tracking how that space narrows, observed direct exposure offers insight into economic modifications as they emerge.

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

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The task-level protection measures are balanced to the profession level weighted by the fraction of time spent on each task. The measure reveals scope for LLM penetration in the bulk of jobs in Computer & Mathematics (94%) and Office & Admin (90%) occupations.

The coverage reveals AI is far from reaching its theoretical capabilities. Claude presently covers simply 33% of all tasks in the Computer & Math category. As abilities advance, adoption spreads, and implementation deepens, the red location will grow to cover the blue. There is a large uncovered area too; numerous jobs, of course, remain beyond AI's reachfrom physical agricultural work like pruning trees and running farm machinery to legal tasks like representing customers in court.

In line with other information revealing that Claude is extensively used for coding, Computer system Programmers are at the top, with 75% protection, followed by Client service Agents, whose main tasks we significantly see in first-party API traffic. Finally, Data Entry Keyers, whose primary task of reading source documents and getting in data sees considerable automation, are 67% covered.

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At the bottom end, 30% of workers have no protection, as their jobs appeared too infrequently in our data to meet the minimum threshold. This group includes, for example, Cooks, Bike Mechanics, Lifeguards, Bartenders, Dishwashers, and Dressing Space Attendants. The United States Bureau of Labor Data (BLS) releases routine work forecasts, with the most recent set, published in 2025, covering anticipated modifications in work for every occupation from 2024 to 2034.

A regression at the occupation level weighted by present employment finds that development forecasts are somewhat weaker for tasks with more observed exposure. For every single 10 percentage point increase in coverage, the BLS's growth projection come by 0.6 percentage points. This supplies some validation because our procedures track the individually derived price quotes from labor market experts, although the relationship is small.

Each strong dot shows the typical observed direct exposure and forecasted work change for one of the bins. The dashed line shows an easy direct regression fit, weighted by existing work levels. Figure 5 programs characteristics of employees in the top quartile of exposure and the 30% of employees with zero exposure in the three months before ChatGPT was released, August to October 2022, utilizing data from the Present Population Study.

The more unveiled group is 16 portion points more most likely to be female, 11 portion points more most likely to be white, and nearly two times as likely to be Asian. They make 47% more, usually, and have higher levels of education. For example, individuals with graduate degrees are 4.5% of the unexposed group, but 17.4% of the most unwrapped group, a nearly fourfold distinction.

Researchers have taken different approaches. Gimbel et al. (2025) track changes in the occupational mix using the Present Population Study. Their argument is that any crucial restructuring of the economy from AI would appear as modifications in distribution of tasks. (They discover that, up until now, changes have been plain.) Brynjolfsson et al.

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( 2022) and Hampole et al. (2025) use task posting information from Burning Glass (now Lightcast) and Revelio, respectively. We concentrate on joblessness as our top priority outcome due to the fact that it most straight captures the potential for financial harma employee who is out of work desires a job and has not yet discovered 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.