Vital Growth Metrics to Watch in 2026

The COVID-19 pandemic and accompanying policy procedures caused financial disturbance so plain that sophisticated analytical methods were unneeded for lots of questions. Unemployment jumped dramatically in the early weeks of the pandemic, leaving little space for alternative descriptions. The effects of AI, nevertheless, might be less like COVID and more like the internet or trade with China.

One typical method is to compare outcomes between more or less AI-exposed employees, companies, or industries, in order to separate the effect of AI from confounding forces. 2 Exposure is typically specified at the task level: AI can grade homework however not handle a classroom, for instance, so instructors are considered less discovered than employees whose whole task can be performed remotely.

3 Our method combines data from three sources. The O * web database, which enumerates jobs connected with around 800 unique occupations in the US.Our own use information (as measured in the Anthropic Economic Index). Task-level direct exposure price quotes from Eloundou et al. (2023 ), which determine whether it is theoretically possible for an LLM to make a task a minimum of twice as quick.

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

As Figure 1 programs, 97% of the tasks observed throughout the previous 4 Economic Index reports fall under classifications ranked as theoretically feasible by Eloundou et al. (=0.5 or =1.0). This figure reveals Claude usage distributed across O * web tasks organized by their theoretical AI direct exposure. Tasks rated =1 (totally possible for an LLM alone) represent 68% of observed Claude use, while jobs ranked =0 (not feasible) represent just 3%.

Our brand-new measure, observed direct exposure, is indicated to quantify: of those tasks that LLMs could theoretically accelerate, which are actually seeing automated use in professional settings? Theoretical ability encompasses a much wider range of tasks. By tracking how that space narrows, observed exposure supplies insight into economic changes as they emerge.

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

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

Claude currently covers just 33% of all tasks in the Computer & Mathematics category. There is a big exposed location too; many tasks, of course, stay beyond AI's reachfrom physical farming work like pruning trees and running farm machinery to legal jobs like representing clients in court.

In line with other data revealing that Claude is extensively utilized for coding, Computer Programmers are at the top, with 75% coverage, followed by Client service Agents, whose main tasks we progressively see in first-party API traffic. Data Entry Keyers, whose primary job of reading source documents and entering data sees substantial automation, are 67% covered.

Vital Growth Metrics to Watch in 2026

At the bottom end, 30% of employees have no protection, as their jobs appeared too occasionally in our data to fulfill the minimum limit. This group consists of, for example, Cooks, Motorbike Mechanics, Lifeguards, Bartenders, Dishwashers, and Dressing Room Attendants. The United States Bureau of Labor Data (BLS) publishes regular work forecasts, with the latest set, published in 2025, covering forecasted changes in employment for each profession from 2024 to 2034.

A regression at the occupation level weighted by existing employment discovers that growth forecasts are somewhat weaker for tasks with more observed direct exposure. For each 10 percentage point boost in coverage, the BLS's development projection stop by 0.6 portion points. This offers some recognition because our procedures track the separately obtained quotes from labor market experts, although the relationship is minor.

Each solid dot reveals the average observed direct exposure and projected employment change for one of the bins. The rushed line shows a basic linear regression fit, weighted by current employment levels. Figure 5 shows attributes of workers in the leading quartile of exposure and the 30% of employees with no direct exposure in the 3 months before ChatGPT was released, August to October 2022, using data from the Current Population Survey.

The more unveiled group is 16 percentage points more most likely to be female, 11 percentage points most likely to be white, and almost twice as likely to be Asian. They make 47% more, usually, and have greater levels of education. For example, people with academic degrees are 4.5% of the unexposed group, but 17.4% of the most unwrapped group, a nearly fourfold difference.

Brynjolfsson et al.

( 2022) and Hampole et al. (2025) use job posting task from Information Glass (now Lightcast) and Revelio, respectively. We focus on unemployment as our concern result because it most straight captures the potential for financial harma worker who is jobless desires a job and has actually not yet discovered one. In this case, job posts and work do not always signify the need for policy responses; a decline in job posts for a highly exposed function may be neutralized by increased openings in an associated one.