Vital Growth Metrics to Watch in 2026

The COVID-19 pandemic and accompanying policy steps caused economic interruption so plain that advanced statistical techniques were unnecessary for many concerns. Unemployment jumped greatly in the early weeks of the pandemic, leaving little space for alternative explanations. The impacts of AI, nevertheless, 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, firms, or industries, in order to isolate the result of AI from confounding forces. 2 Exposure is typically defined at the task level: AI can grade research but not manage a class, for example, so instructors are thought about less unwrapped than workers whose entire job can be carried out remotely.

3 Our technique combines data from three sources. Task-level direct exposure quotes from Eloundou et al. (2023 ), which determine whether it is theoretically possible for an LLM to make a task at least two times as fast.

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4Why might actual usage fall brief of theoretical capability? Some tasks that are theoretically possible may disappoint up in usage due to the fact that of design constraints. Others might be sluggish to diffuse due to legal restrictions, specific software application requirements, human confirmation actions, or other obstacles. Eloundou et al. mark "Authorize drug refills and offer prescription information to drug stores" as fully exposed (=1).

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

Our brand-new step, observed direct exposure, is suggested to measure: of those tasks that LLMs could theoretically speed up, which are actually seeing automated use in professional settings? Theoretical capability incorporates a much more comprehensive range of jobs. By tracking how that space narrows, observed direct exposure supplies insight into financial modifications as they emerge.

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

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We then change for how the task is being performed: fully automated applications get full weight, while augmentative use receives half weight. The task-level protection steps are balanced to the occupation level weighted by the fraction of time invested on each job. Figure 2 reveals observed exposure (in red) compared to from Eloundou et al.

We determine this by first balancing to the profession level weighting by our time fraction step, then balancing to the profession category weighting by total work. The procedure reveals scope for LLM penetration in the majority of tasks in Computer & Mathematics (94%) and Office & Admin (90%) professions.

Claude presently covers just 33% of all tasks in the Computer & Mathematics classification. There is a large exposed location too; numerous jobs, of course, stay beyond AI's reachfrom physical farming work like pruning trees and running farm equipment to legal jobs like representing customers in court.

In line with other data showing that Claude is extensively used for coding, Computer system Programmers are at the top, with 75% protection, followed by Customer support Agents, whose main jobs we significantly see in first-party API traffic. Data Entry Keyers, whose primary job of checking out source files and going into data sees significant automation, are 67% covered.

How to Analyze the 2026 Economic Landscape

At the bottom end, 30% of workers have zero coverage, as their jobs appeared too infrequently in our information to satisfy the minimum threshold. This group includes, for example, Cooks, Motorcycle Mechanics, Lifeguards, Bartenders, Dishwashers, and Dressing Space Attendants. The United States Bureau of Labor Data (BLS) releases routine work forecasts, with the most current set, published in 2025, covering forecasted modifications in work for each occupation from 2024 to 2034.

A regression at the occupation level weighted by existing employment discovers that development projections are rather weaker for tasks with more observed exposure. For each 10 portion point boost in protection, the BLS's growth projection come by 0.6 percentage points. This provides some validation in that our measures track the individually derived estimates from labor market analysts, although the relationship is small.

Each strong dot shows the typical observed direct exposure and projected employment change for one of the bins. The rushed line shows a simple linear regression fit, weighted by existing work levels. Figure 5 shows qualities of workers in the top quartile of exposure and the 30% of employees with zero direct exposure in the 3 months before ChatGPT was launched, August to October 2022, using information from the Existing Population Survey.

The more uncovered group is 16 percentage points most likely to be female, 11 portion points more likely to be white, and nearly two times as most likely to be Asian. They make 47% more, typically, and have greater levels of education. For example, people with graduate degrees are 4.5% of the unexposed group, but 17.4% of the most reviewed group, a nearly fourfold difference.

Brynjolfsson et al.

( 2022) and Hampole et al. (2025) use job utilize data from Burning Glass (now Lightcast) and Revelio, respectively. We focus on joblessness as our priority outcome since it most straight catches the capacity for financial harma worker who is unemployed wants a task and has not yet discovered one. In this case, job posts and work do not necessarily signal the need for policy actions; a decline in job posts for a highly exposed function may be counteracted by increased openings in a related one.