Key Growth Metrics to Watch in 2026

The COVID-19 pandemic and accompanying policy procedures triggered economic disruption so plain that sophisticated statistical methods were unneeded for many concerns. For instance, unemployment leapt dramatically in the early weeks of the pandemic, leaving little room for alternative explanations. The impacts of AI, nevertheless, might be less like COVID and more like the web or trade with China.

One common method is to compare outcomes between basically AI-exposed employees, firms, or markets, in order to isolate the result of AI from confounding forces. 2 Direct exposure is usually defined at the task level: AI can grade research however not manage a class, for example, so teachers are considered less unwrapped than workers whose whole task can be performed from another location.

3 Our method combines data from three sources. The O * NET database, which identifies jobs associated with around 800 special occupations in the US.Our own usage information (as determined in the Anthropic Economic Index). Task-level direct exposure quotes from Eloundou et al. (2023 ), which measure whether it is theoretically possible for an LLM to make a task at least two times as fast.

How to Analyze the Global Economic Landscape

Some tasks that are in theory possible may not show up in usage since of model restrictions. Eloundou et al. mark "Authorize drug refills and supply prescription information to pharmacies" as completely exposed (=1).

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

Our new measure, observed exposure, is indicated to measure: of those jobs that LLMs could theoretically accelerate, which are actually seeing automated usage in professional settings? Theoretical capability includes a much broader series of tasks. By tracking how that gap narrows, observed exposure offers insight into financial modifications as they emerge.

A task's exposure is greater if: Its jobs are in theory possible with AIIts jobs see substantial usage in the Anthropic Economic Index5Its tasks are carried out in work-related contextsIt has a reasonably higher share of automated use patterns or API implementationIts AI-impacted jobs make up a larger share of the general role6We provide mathematical information in the Appendix.

Evaluating Offshore Models and In-House Units

The task-level protection measures are averaged to the occupation level weighted by the fraction of time spent on each task. The procedure shows scope for LLM penetration in the majority of tasks in Computer & Math (94%) and Workplace & Admin (90%) occupations.

The protection reveals AI is far from reaching its theoretical abilities. For example, Claude currently covers simply 33% of all tasks in the Computer & Math category. As capabilities advance, adoption spreads, and release deepens, the red location will grow to cover the blue. There is a big uncovered area too; numerous jobs, obviously, remain beyond AI's reachfrom physical farming work like pruning trees and operating farm machinery to legal tasks like representing clients in court.

In line with other data showing that Claude is thoroughly used for coding, Computer system Programmers are at the top, with 75% coverage, followed by Customer care Representatives, whose main tasks we increasingly see in first-party API traffic. Lastly, Data Entry Keyers, whose primary task of reading source documents and entering data sees substantial automation, are 67% covered.

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At the bottom end, 30% of employees have no coverage, as their tasks appeared too occasionally in our information to satisfy the minimum threshold. This group consists of, for example, Cooks, Motorcycle Mechanics, Lifeguards, Bartenders, Dishwashers, and Dressing Room Attendants.

A regression at the profession level weighted by current employment finds that development projections are somewhat weaker for jobs with more observed exposure. For each 10 portion point boost in coverage, the BLS's development projection stop by 0.6 portion points. This offers some recognition in that our measures track the individually obtained quotes from labor market analysts, although the relationship is slight.

Each solid dot shows the typical observed exposure and predicted employment modification for one of the bins. The dashed line shows a basic linear regression fit, weighted by present work levels. Figure 5 programs characteristics of workers in the leading quartile of exposure and the 30% of employees with no direct exposure in the three months before ChatGPT was launched, August to October 2022, using data from the Present Population Study.

The more uncovered group is 16 portion points most likely to be female, 11 portion points most likely to be white, and nearly two times as likely to be Asian. They make 47% more, usually, and have higher levels of education. For instance, people with graduate degrees are 4.5% of the unexposed group, but 17.4% of the most revealed group, an almost fourfold distinction.

Brynjolfsson et al.

( 2022) and Hampole et al. (2025) use job posting task from Burning Glass (now Lightcast) and Revelio, respectively. We focus on unemployment as our concern outcome since it most directly catches the capacity for economic harma worker who is jobless desires a job and has not yet found one. In this case, job postings and work do not always signify the need for policy actions; a decrease in task posts for an extremely exposed role may be neutralized by increased openings in a related one.