Key Growth Metrics to Watch in 2026

The COVID-19 pandemic and accompanying policy steps triggered financial disturbance so plain that sophisticated statistical techniques were unneeded for lots of concerns. For instance, unemployment leapt dramatically in the early weeks of the pandemic, leaving little space for alternative descriptions. The impacts of AI, however, might be less like COVID and more like the web or trade with China.

One typical method is to compare results between more or less AI-exposed employees, companies, or markets, in order to separate the effect of AI from confounding forces. 2 Direct exposure is typically defined at the job level: AI can grade research but not handle a class, for example, so instructors are thought about less exposed than employees whose entire job can be performed remotely.

3 Our approach integrates data from 3 sources. The O * web database, which enumerates tasks associated with around 800 special professions in the US.Our own usage information (as determined in the Anthropic Economic Index). Task-level exposure quotes from Eloundou et al. (2023 ), which measure whether it is in theory possible for an LLM to make a task a minimum of two times as quick.

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Some tasks that are in theory possible might not reveal up in use since of model limitations. Eloundou et al. mark "Authorize drug refills and provide prescription information to pharmacies" as completely exposed (=1).

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

Our brand-new step, observed direct exposure, is meant to quantify: of those tasks that LLMs could in theory speed up, which are really seeing automated use in expert settings? Theoretical ability includes a much more comprehensive variety of jobs. By tracking how that gap narrows, observed direct exposure supplies insight into economic changes as they emerge.

A task's exposure is greater if: Its jobs are theoretically possible with AIIts tasks see substantial use in the Anthropic Economic Index5Its jobs are performed in work-related contextsIt has a relatively higher share of automated usage patterns or API implementationIts AI-impacted tasks make up a larger share of the total role6We give mathematical details in the Appendix.

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We then adjust for how the task is being brought out: totally automated executions receive complete weight, while augmentative use gets half weight. The task-level protection steps are averaged to the profession level weighted by the fraction of time spent on each task. Figure 2 reveals observed direct exposure (in red) compared to from Eloundou et al.

We calculate this by first balancing to the profession level weighting by our time portion procedure, then balancing to the profession classification weighting by overall employment. For example, the step shows scope for LLM penetration in the bulk of tasks in Computer system & Mathematics (94%) and Office & Admin (90%) professions.

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

In line with other data revealing that Claude is thoroughly used for coding, Computer Programmers are at the top, with 75% protection, followed by Customer care Agents, whose primary jobs we increasingly see in first-party API traffic. Data Entry Keyers, whose main task of reading source documents and going into information sees substantial automation, are 67% covered.

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At the bottom end, 30% of workers have zero coverage, as their tasks appeared too infrequently in our information to fulfill the minimum threshold. This group consists of, for example, Cooks, Motorbike Mechanics, Lifeguards, Bartenders, Dishwashers, and Dressing Space Attendants.

A regression at the occupation level weighted by present work finds that growth projections are somewhat weaker for jobs with more observed exposure. For each 10 portion point increase in protection, the BLS's growth projection visit 0.6 portion points. This offers some recognition in that our procedures track the individually obtained price quotes from labor market analysts, although the relationship is small.

procedure alone. Binned scatterplot with 25 equally-sized bins. Each solid dot reveals the average observed direct exposure and predicted employment change for among the bins. The rushed line shows a simple direct regression fit, weighted by present employment levels. The small diamonds mark individual 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 three months before ChatGPT was launched, August to October 2022, using information from the Current Population Survey.

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

Brynjolfsson et al.

( 2022) and Hampole et al. (2025) use job posting data publishing Burning Glass (now Lightcast) and Revelio, respectively. We focus on joblessness as our priority result since it most directly captures the potential for economic harma employee who is unemployed desires a job and has actually not yet discovered one. In this case, job posts and work do not necessarily indicate the requirement for policy actions; a decline in task postings for a highly exposed role may be combated by increased openings in a related one.