Global Commerce Trends for Future Economies

The COVID-19 pandemic and accompanying policy steps triggered economic interruption so plain that advanced statistical techniques were unneeded for numerous concerns. Unemployment jumped dramatically in the early weeks of the pandemic, leaving little space for alternative explanations. The effects of AI, however, may be less like COVID and more like the web or trade with China.

One common approach is to compare outcomes in between more or less AI-exposed employees, companies, or industries, in order to separate the impact of AI from confounding forces. 2 Direct exposure is normally defined at the job level: AI can grade research but not manage a class, for instance, so teachers are considered less discovered than employees whose whole task can be carried out remotely.

3 Our method combines information from 3 sources. The O * NET database, which enumerates tasks related to around 800 unique occupations in the US.Our own use information (as measured in the Anthropic Economic Index). Task-level direct exposure estimates from Eloundou et al. (2023 ), which measure whether it is in theory possible for an LLM to make a task at least twice as quick.

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Some tasks that are in theory possible may not show up in use since of design limitations. Eloundou et al. mark "Authorize drug refills and offer prescription information to pharmacies" as fully exposed (=1).

As Figure 1 programs, 97% of the jobs observed across the previous 4 Economic Index reports fall into categories ranked as in theory possible by Eloundou et al. (=0.5 or =1.0). This figure shows Claude usage dispersed across O * NET jobs grouped by their theoretical AI direct exposure. Tasks rated =1 (completely possible for an LLM alone) represent 68% of observed Claude use, while jobs ranked =0 (not possible) account for just 3%.

Our new measure, observed direct exposure, is suggested to measure: of those jobs that LLMs could theoretically accelerate, which are in fact seeing automated usage in professional settings? Theoretical capability includes a much wider variety of tasks. By tracking how that gap narrows, observed exposure offers insight into economic changes as they emerge.

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

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The task-level coverage procedures are averaged to the occupation level weighted by the portion of time spent on each task. The step reveals scope for LLM penetration in the majority of jobs in Computer system & Math (94%) and Workplace & Admin (90%) occupations.

Claude presently covers just 33% of all tasks in the Computer & Mathematics category. There is a big uncovered location too; numerous tasks, of course, remain beyond AI's reachfrom physical farming work like pruning trees and running farm equipment to legal jobs like representing customers in court.

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

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At the bottom end, 30% of workers have no protection, as their tasks appeared too infrequently in our data to satisfy the minimum threshold. This group includes, for example, Cooks, Motorcycle Mechanics, Lifeguards, Bartenders, Dishwashers, and Dressing Space Attendants.

A regression at the occupation level weighted by present work finds that growth forecasts are somewhat weaker for tasks with more observed direct exposure. For every single 10 portion point increase in protection, the BLS's growth forecast stop by 0.6 portion points. This offers some recognition in that our measures track the separately obtained price quotes from labor market analysts, although the relationship is slight.

procedure alone. Binned scatterplot with 25 equally-sized bins. Each strong dot reveals the typical observed exposure and projected employment modification for among the bins. The dashed line shows a simple linear regression fit, weighted by current employment levels. The little diamonds mark private example professions for illustration. Figure 5 programs characteristics of workers in the top quartile of exposure and the 30% of employees with zero direct exposure in the 3 months before ChatGPT was released, August to October 2022, utilizing information from the Current Population Survey.

The more unwrapped group is 16 percentage points most likely to be female, 11 percentage points most likely to be white, and almost two times as likely to be Asian. They make 47% more, usually, and have greater levels of education. Individuals with graduate degrees are 4.5% of the unexposed group, but 17.4% of the most uncovered group, an almost fourfold distinction.

Brynjolfsson et al.

( 2022) and Hampole et al. (2025) use job utilize task publishing Burning Glass (now Lightcast) and Revelio, respectively. We focus on joblessness as our top priority outcome since it most straight captures the potential for financial harma worker who is out of work desires a task and has not yet discovered one. In this case, job postings and work do not necessarily signify the need for policy actions; a decline in task postings for a highly exposed role may be neutralized by increased openings in an associated one.