Global Trade Outlook for Emerging Regions

The COVID-19 pandemic and accompanying policy steps triggered economic disruption so plain that sophisticated statistical techniques were unnecessary for many questions. For instance, unemployment jumped greatly in the early weeks of the pandemic, leaving little space for alternative descriptions. The impacts of AI, however, may be less like COVID and more like the internet or trade with China.

One common technique is to compare outcomes between more or less AI-exposed employees, companies, or industries, in order to separate the impact of AI from confounding forces. 2 Exposure is normally specified at the task level: AI can grade homework but not handle a class, for instance, so teachers are considered less unveiled than employees whose whole task can be carried out from another location.

3 Our technique combines information from three sources. The O * internet database, which identifies jobs related to around 800 special occupations in the US.Our own usage information (as determined in the Anthropic Economic Index). Task-level exposure price quotes from Eloundou et al. (2023 ), which determine whether it is theoretically 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 use due to the fact that of model limitations. Eloundou et al. mark "License drug refills and supply prescription details to pharmacies" as totally exposed (=1).

As Figure 1 shows, 97% of the tasks observed throughout the previous four Economic Index reports fall into categories rated as theoretically feasible by Eloundou et al. (=0.5 or =1.0). This figure reveals Claude use dispersed across O * web tasks grouped by their theoretical AI direct exposure. Jobs rated =1 (completely practical for an LLM alone) account for 68% of observed Claude usage, while tasks ranked =0 (not feasible) represent just 3%.

Our new step, observed exposure, is implied to measure: of those jobs that LLMs could in theory accelerate, which are in fact seeing automated usage in professional settings? Theoretical capability incorporates a much more comprehensive series of jobs. By tracking how that gap narrows, observed exposure offers insight into economic modifications as they emerge.

A task's direct exposure is higher if: Its jobs are theoretically possible with AIIts jobs see significant usage in the Anthropic Economic Index5Its tasks are performed in job-related contextsIt has a reasonably higher share of automated usage patterns or API implementationIts AI-impacted tasks make up a bigger share of the general role6We give mathematical details in the Appendix.

Key Growth Metrics to Track in 2026

We then adjust for how the job is being performed: totally automated implementations receive full weight, while augmentative usage gets half weight. Finally, the task-level coverage steps are averaged to the occupation level weighted by the fraction of time invested in each task. Figure 2 reveals observed exposure (in red) compared to from Eloundou et al.

We calculate this by first averaging to the profession level weighting by our time portion step, then averaging to the profession category weighting by overall work. The step shows scope for LLM penetration in the bulk of jobs in Computer system & Mathematics (94%) and Office & Admin (90%) professions.

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

In line with other information revealing that Claude is extensively utilized for coding, Computer system Programmers are at the top, with 75% coverage, followed by Client service Agents, whose main jobs we significantly see in first-party API traffic. Lastly, Data Entry Keyers, whose primary job of checking out source files and getting in information sees considerable automation, are 67% covered.

International Market Outlook for Emerging Economies

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

A regression at the occupation level weighted by current employment finds that development forecasts are rather weaker for tasks with more observed exposure. For each 10 percentage point increase in coverage, the BLS's growth projection visit 0.6 portion points. This offers some validation because our procedures track the separately obtained price quotes from labor market analysts, although the relationship is minor.

Each strong dot shows the typical observed exposure and predicted employment modification for one of the bins. The dashed line reveals a basic direct regression fit, weighted by current work levels. Figure 5 programs qualities of employees in the leading quartile of exposure and the 30% of employees with absolutely no exposure in the 3 months before ChatGPT was released, August to October 2022, utilizing information from the Current Population Survey.

The more disclosed group is 16 percentage points most likely to be female, 11 percentage points more most likely to be white, and almost twice as likely to be Asian. They earn 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 nearly fourfold distinction.

Scientists have actually taken various approaches. For instance, Gimbel et al. (2025) track modifications in the occupational mix using the Existing Population Study. Their argument is that any important restructuring of the economy from AI would reveal up as changes in distribution of tasks. (They discover that, up until now, changes have been unremarkable.) Brynjolfsson et al.

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( 2022) and Hampole et al. (2025) utilize task publishing data from Burning Glass (now Lightcast) and Revelio, respectively. We concentrate on joblessness as our top priority outcome because it most straight records the capacity for economic harma employee who is out of work wants a job and has actually not yet discovered one. In this case, job postings and work do not always signal the need for policy actions; a decline in task postings for a highly exposed role might be neutralized by increased openings in an associated one.