Leveraging AI for Market Analysis

The COVID-19 pandemic and accompanying policy measures caused financial interruption so stark that advanced statistical approaches were unnecessary for many concerns. For example, unemployment leapt dramatically in the early weeks of the pandemic, leaving little room for alternative explanations. The effects of AI, nevertheless, might be less like COVID and more like the internet or trade with China.

One common method is to compare results in between more or less AI-exposed workers, companies, 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 example, so teachers are thought about less exposed than workers whose whole task can be carried out from another location.

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

Harnessing AI to Improve Predictive Analysis

Some tasks that are theoretically possible might not show up in usage because of design restrictions. Eloundou et al. mark "License drug refills and offer prescription info to pharmacies" as fully exposed (=1).

As Figure 1 programs, 97% of the tasks observed throughout the previous four Economic Index reports fall into classifications rated as in theory feasible by Eloundou et al. (=0.5 or =1.0). This figure reveals Claude use dispersed across O * NET jobs grouped by their theoretical AI direct exposure. Tasks rated =1 (totally feasible for an LLM alone) account for 68% of observed Claude use, while tasks rated =0 (not feasible) account for simply 3%.

Our new procedure, observed exposure, is implied to quantify: of those jobs that LLMs could theoretically accelerate, which are actually seeing automated usage in professional settings? Theoretical ability encompasses a much more comprehensive variety of jobs. By tracking how that space narrows, observed exposure provides insight into financial changes as they emerge.

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

Why to Forecast the Global Market Outlook

We then adjust for how the task is being brought out: fully automated implementations receive full weight, while augmentative usage gets half weight. The task-level protection procedures are averaged to the occupation level weighted by the portion of time spent on each job. Figure 2 shows observed exposure (in red) compared to from Eloundou et al.

We calculate this by very first balancing to the profession level weighting by our time portion measure, then averaging to the occupation classification weighting by overall work. For example, the procedure reveals scope for LLM penetration in the bulk of jobs in Computer & Math (94%) and Workplace & Admin (90%) professions.

Claude currently covers simply 33% of all jobs in the Computer & Mathematics category. There is a big exposed location too; many tasks, of course, remain beyond AI's reachfrom physical agricultural work like pruning trees and operating farm machinery to legal tasks like representing clients in court.

In line with other data revealing that Claude is thoroughly utilized for coding, Computer Programmers are at the top, with 75% coverage, followed by Consumer Service Agents, whose primary jobs we increasingly see in first-party API traffic. Data Entry Keyers, whose primary task of reading source files and getting in information sees substantial automation, are 67% covered.

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At the bottom end, 30% of employees have absolutely no coverage, as their tasks appeared too occasionally in our information to meet the minimum limit. This group includes, for example, Cooks, Bike Mechanics, Lifeguards, Bartenders, Dishwashers, and Dressing Space Attendants. The United States Bureau of Labor Data (BLS) publishes regular employment projections, with the latest set, released in 2025, covering anticipated modifications in work for every occupation from 2024 to 2034.

A regression at the profession level weighted by existing employment discovers that development projections are rather weaker for jobs with more observed exposure. For every single 10 percentage point boost in coverage, the BLS's growth forecast drops by 0.6 portion points. This supplies some recognition because our measures track the individually derived estimates from labor market experts, although the relationship is minor.

step alone. Binned scatterplot with 25 equally-sized bins. Each strong dot shows the average observed exposure and predicted work change for among the bins. The dashed line reveals an easy direct regression fit, weighted by current employment levels. The small diamonds mark specific example professions for illustration. Figure 5 shows attributes of workers in the leading quartile of exposure and the 30% of employees with absolutely no direct exposure in the three months before ChatGPT was released, August to October 2022, using information from the Existing Population Study.

The more unveiled group is 16 portion points more likely to be female, 11 percentage points most likely to be white, and practically two times 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 unveiled group, a practically fourfold difference.

Brynjolfsson et al.

( 2022) and Hampole et al. (2025) use job utilize data publishing Information Glass (now Lightcast) and Revelio, respectively. We focus on joblessness as our top priority outcome because it most straight captures the potential for economic harma employee who is out of work wants a task and has not yet found one. In this case, task posts and work do not necessarily indicate the requirement for policy responses; a decline in job postings for an extremely exposed role might be counteracted by increased openings in an associated one.