Forecasting Global Trends in 2026

The COVID-19 pandemic and accompanying policy steps triggered economic disturbance so plain that advanced statistical methods were unnecessary for numerous questions. For instance, unemployment leapt sharply in the early weeks of the pandemic, leaving little space for alternative explanations. The effects of AI, nevertheless, might be less like COVID and more like the web or trade with China.

One typical technique is to compare results in between more or less AI-exposed employees, firms, or markets, in order to isolate the impact of AI from confounding forces. 2 Exposure is usually specified at the job level: AI can grade research but not handle a class, for instance, so teachers are thought about less unwrapped than workers whose whole task can be performed from another location.

3 Our technique combines data from three sources. The O * web database, which identifies jobs associated with around 800 unique occupations in the US.Our own use data (as measured in the Anthropic Economic Index). Task-level direct exposure quotes from Eloundou et al. (2023 ), which determine whether it is in theory possible for an LLM to make a job at least twice as fast.

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4Why might actual use fall short of theoretical capability? Some jobs that are in theory possible may not show up in usage since of design constraints. Others may be sluggish to diffuse due to legal restraints, particular software application requirements, human verification actions, or other obstacles. Eloundou et al. mark "License drug refills and supply prescription information to pharmacies" as totally exposed (=1).

As Figure 1 shows, 97% of the tasks observed across the previous 4 Economic Index reports fall into classifications ranked as in theory possible by Eloundou et al. (=0.5 or =1.0). This figure reveals Claude use dispersed across O * web tasks grouped by their theoretical AI exposure. Tasks rated =1 (fully feasible for an LLM alone) account for 68% of observed Claude usage, while tasks rated =0 (not practical) account for simply 3%.

Our new measure, observed direct exposure, is indicated to quantify: of those tasks that LLMs could theoretically speed up, which are actually seeing automated use in professional settings? Theoretical ability incorporates a much more comprehensive variety of jobs. By tracking how that gap narrows, observed direct exposure offers insight into financial changes as they emerge.

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

Why to Analyze the 2026 Economic Landscape

We then adjust for how the job is being performed: completely automated applications get complete weight, while augmentative usage gets half weight. The task-level coverage measures are balanced to the occupation level weighted by the fraction of time spent on each job. Figure 2 shows observed direct exposure (in red) compared to from Eloundou et al.

We compute this by very first balancing to the profession level weighting by our time fraction procedure, then balancing to the profession classification weighting by overall work. The measure shows scope for LLM penetration in the bulk of jobs in Computer system & Mathematics (94%) and Office & Admin (90%) occupations.

Claude currently covers just 33% of all jobs in the Computer system & Math classification. There is a big uncovered area too; numerous jobs, of course, stay beyond AI's reachfrom physical agricultural work like pruning trees and operating farm equipment to legal jobs like representing clients in court.

In line with other data showing that Claude is extensively utilized for coding, Computer Programmers are at the top, with 75% protection, followed by Client service Representatives, whose main jobs we increasingly see in first-party API traffic. Data Entry Keyers, whose main task of checking out source documents and going into information sees substantial automation, are 67% covered.

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At the bottom end, 30% of employees have zero protection, as their tasks appeared too rarely in our information to fulfill the minimum threshold. This group consists of, for example, Cooks, Bike Mechanics, Lifeguards, Bartenders, Dishwashers, and Dressing Room Attendants. The United States Bureau of Labor Statistics (BLS) publishes regular employment projections, with the most recent set, released in 2025, covering predicted modifications in work for each profession from 2024 to 2034.

A regression at the occupation level weighted by present employment discovers that development forecasts are rather weaker for tasks with more observed exposure. For every single 10 portion point boost in protection, the BLS's development forecast visit 0.6 portion points. This supplies some validation because our procedures track the independently derived estimates from labor market analysts, although the relationship is small.

Each strong dot reveals the typical observed direct exposure and predicted work change for one of the bins. The rushed line shows a simple direct regression fit, weighted by existing employment levels. Figure 5 programs characteristics of employees in the top quartile of exposure and the 30% of employees with no exposure in the three months before ChatGPT was launched, August to October 2022, utilizing data from the Present Population Survey.

The more disclosed group is 16 portion points more likely to be female, 11 portion points most likely to be white, and practically two times as likely to be Asian. They make 47% more, on average, and have greater levels of education. For instance, people with academic degrees are 4.5% of the unexposed group, but 17.4% of the most revealed group, a practically fourfold difference.

Brynjolfsson et al.

( 2022) and Hampole et al. (2025) use job utilize data publishing Burning Glass (now Lightcast) and Revelio, respectively. We focus on joblessness as our priority outcome since it most straight records the capacity for financial harma employee who is jobless desires a job and has not yet discovered one. In this case, task posts and employment do not always indicate the need for policy responses; a decrease in task posts for an extremely exposed function might be neutralized by increased openings in an associated one.