Global Market Trends for Emerging Economies

The COVID-19 pandemic and accompanying policy steps caused financial disturbance so plain that sophisticated statistical approaches were unneeded for numerous questions. Joblessness jumped sharply in the early weeks of the pandemic, leaving little space for alternative explanations. The effects of AI, however, might be less like COVID and more like the internet or trade with China.

One common technique 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 generally defined at the job level: AI can grade homework but not manage a class, for instance, so teachers are thought about less reviewed than workers whose entire job can be performed remotely.

3 Our method combines information from 3 sources. Task-level exposure quotes from Eloundou et al. (2023 ), which determine whether it is in theory possible for an LLM to make a task at least two times as fast.

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

As Figure 1 shows, 97% of the jobs observed across 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 shows Claude use distributed throughout O * NET jobs grouped by their theoretical AI direct exposure. Tasks ranked =1 (completely possible for an LLM alone) represent 68% of observed Claude use, while tasks ranked =0 (not practical) account for simply 3%.

Our new measure, observed direct exposure, is meant to quantify: of those tasks that LLMs could theoretically accelerate, which are in fact seeing automated use in professional settings? Theoretical capability encompasses a much broader variety of jobs. By tracking how that gap narrows, observed direct 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 substantial use in the Anthropic Economic Index5Its tasks are performed in work-related contextsIt has a fairly greater share of automated use patterns or API implementationIts AI-impacted jobs make up a bigger share of the general role6We give 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 job. The procedure shows scope for LLM penetration in the bulk of jobs in Computer & Mathematics (94%) and Office & Admin (90%) occupations.

Claude presently covers simply 33% of all jobs in the Computer & Mathematics classification. There is a big uncovered area too; lots of tasks, of course, stay beyond AI's reachfrom physical farming work like pruning trees and running farm equipment 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 Client service Agents, whose main tasks we progressively see in first-party API traffic. Data Entry Keyers, whose primary task of reading source files and entering information sees substantial automation, are 67% covered.

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At the bottom end, 30% of employees have absolutely no protection, as their jobs appeared too infrequently in our data to satisfy the minimum threshold. This group consists of, for example, Cooks, Bike Mechanics, Lifeguards, Bartenders, Dishwashers, and Dressing Room Attendants. The US Bureau of Labor Data (BLS) publishes regular work forecasts, with the most recent set, released in 2025, covering predicted modifications in work for every profession from 2024 to 2034.

A regression at the profession level weighted by present employment finds that growth projections are rather weaker for jobs with more observed exposure. For every single 10 percentage point boost in protection, the BLS's growth forecast come by 0.6 percentage points. This offers some validation because our steps track the independently obtained quotes from labor market analysts, although the relationship is slight.

procedure alone. Binned scatterplot with 25 equally-sized bins. Each solid dot shows the typical observed exposure and predicted employment modification for one of the bins. The dashed line reveals a basic linear regression fit, weighted by present employment levels. The little diamonds mark individual example occupations for illustration. Figure 5 shows qualities of workers in the top quartile of direct exposure and the 30% of workers with absolutely no direct exposure in the 3 months before ChatGPT was launched, August to October 2022, using data from the Existing Population Survey.

The more bare group is 16 portion points most likely to be female, 11 portion points most likely to be white, and almost two times as most likely to be Asian. They earn 47% more, on average, and have higher levels of education. Individuals with graduate degrees are 4.5% of the unexposed group, but 17.4% of the most revealed group, a practically fourfold difference.

Scientists have taken various approaches. Gimbel et al. (2025) track changes in the occupational mix utilizing the Existing Population Study. Their argument is that any crucial restructuring of the economy from AI would appear as changes in circulation of tasks. (They discover that, so far, changes have been typical.) Brynjolfsson et al.

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( 2022) and Hampole et al. (2025) use job publishing information from Burning Glass (now Lightcast) and Revelio, respectively. We focus on unemployment as our top priority outcome due to the fact that it most straight captures the capacity for economic harma employee who is unemployed desires a job and has not yet discovered one. In this case, job postings and employment do not necessarily indicate the requirement for policy responses; a decrease in job posts for a highly exposed role might be combated by increased openings in a related one.