Vital Expansion Metrics to Track in 2026

The COVID-19 pandemic and accompanying policy measures triggered economic interruption so stark that advanced statistical methods were unnecessary for lots of questions. For instance, unemployment leapt greatly in the early weeks of the pandemic, leaving little space for alternative explanations. The effects of AI, nevertheless, may be less like COVID and more like the internet or trade with China.

One typical approach is to compare outcomes between more or less AI-exposed employees, companies, or industries, in order to isolate the result of AI from confounding forces. 2 Direct exposure is typically defined at the task level: AI can grade homework but not handle a class, for example, so instructors are thought about less bare than workers whose entire job can be carried out from another location.

3 Our technique combines information from 3 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 two times as fast.

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Some jobs that are theoretically possible may not show up in usage since of design constraints. Eloundou et al. mark "License drug refills and provide prescription information to drug stores" as fully exposed (=1).

As Figure 1 shows, 97% of the tasks observed throughout the previous 4 Economic Index reports fall into categories rated as theoretically possible by Eloundou et al. (=0.5 or =1.0). This figure shows Claude usage distributed throughout O * internet jobs organized by their theoretical AI direct exposure. Tasks rated =1 (completely practical for an LLM alone) represent 68% of observed Claude usage, while jobs rated =0 (not feasible) represent just 3%.

Our brand-new measure, observed direct exposure, is implied to quantify: of those tasks that LLMs could in theory accelerate, which are really seeing automated usage in expert settings? Theoretical capability encompasses a much more comprehensive range of jobs. By tracking how that space narrows, observed exposure provides insight into economic changes as they emerge.

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

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The task-level protection steps are balanced to the occupation level weighted by the portion of time spent on each task. The procedure reveals scope for LLM penetration in the majority of jobs in Computer & Mathematics (94%) and Office & Admin (90%) professions.

The coverage shows AI is far from reaching its theoretical abilities. Claude presently covers simply 33% of all jobs in the Computer system & Math category. As capabilities advance, adoption spreads, and implementation deepens, the red location will grow to cover heaven. There is a large exposed area too; numerous tasks, obviously, 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 data revealing that Claude is thoroughly used for coding, Computer system Programmers are at the top, with 75% coverage, followed by Client service Representatives, whose main jobs we significantly see in first-party API traffic. Finally, Data Entry Keyers, whose main task of reading source documents and getting in information sees significant automation, are 67% covered.

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At the bottom end, 30% of workers have zero coverage, as their jobs appeared too infrequently in our information to meet the minimum threshold. This group includes, for instance, Cooks, Bike Mechanics, Lifeguards, Bartenders, Dishwashers, and Dressing Space Attendants. The United States Bureau of Labor Statistics (BLS) releases regular employment projections, with the latest set, published in 2025, covering predicted modifications in employment for each occupation from 2024 to 2034.

A regression at the occupation level weighted by current employment discovers that growth projections are somewhat weaker for tasks with more observed exposure. For each 10 percentage point increase in coverage, the BLS's growth projection visit 0.6 percentage points. This offers some recognition because our measures track the separately obtained price quotes from labor market analysts, although the relationship is slight.

measure alone. Binned scatterplot with 25 equally-sized bins. Each strong dot reveals the typical observed direct exposure and projected work modification for one of the bins. The rushed line shows a basic direct regression fit, weighted by current work levels. The small diamonds mark private example professions for illustration. Figure 5 shows qualities of employees in the top quartile of direct exposure and the 30% of workers with no direct exposure in the three months before ChatGPT was released, August to October 2022, utilizing information from the Existing Population Survey.

The more unveiled group is 16 portion points most likely to be female, 11 portion points most likely to be white, and almost twice as most likely to be Asian. They earn 47% more, on average, and have higher levels of education. For instance, people with academic degrees are 4.5% of the unexposed group, however 17.4% of the most discovered group, a nearly fourfold distinction.

Scientists have taken various methods. For instance, Gimbel et al. (2025) track changes in the occupational mix using the Existing Population Study. Their argument is that any essential restructuring of the economy from AI would reveal up as modifications in circulation of jobs. (They find that, so far, changes have been typical.) Brynjolfsson et al.

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( 2022) and Hampole et al. (2025) utilize task publishing information from Burning Glass (now Lightcast) and Revelio, respectively. We concentrate on joblessness as our top priority result because it most straight captures the capacity for economic harma employee who is unemployed wants a job and has actually not yet discovered one. In this case, task posts and employment do not always signify the requirement for policy reactions; a decrease in task postings for an extremely exposed function may be neutralized by increased openings in a related one.