Forecasting Market Trends in 2026

The COVID-19 pandemic and accompanying policy steps caused financial disturbance so plain that advanced statistical techniques were unnecessary for lots of questions. Joblessness jumped dramatically in the early weeks of the pandemic, leaving little space for alternative explanations. The impacts of AI, nevertheless, might be less like COVID and more like the web or trade with China.

One common technique is to compare outcomes between more or less AI-exposed workers, companies, or markets, in order to separate the result of AI from confounding forces. 2 Exposure is usually specified at the job level: AI can grade homework but not manage a classroom, for instance, so teachers are considered less unveiled than employees whose entire job can be performed from another location.

3 Our method integrates data from 3 sources. The O * NET database, which mentions jobs connected with around 800 distinct occupations in the US.Our own use information (as determined in the Anthropic Economic Index). Task-level direct exposure estimates 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 jobs that are theoretically possible may not show up in usage because of model restrictions. Eloundou et al. mark "License drug refills and offer prescription info to pharmacies" as fully exposed (=1).

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

Our new measure, observed exposure, is suggested to quantify: of those jobs that LLMs could theoretically accelerate, which are actually seeing automated use in professional settings? Theoretical ability incorporates a much wider series of jobs. By tracking how that gap narrows, observed direct exposure supplies insight into financial modifications as they emerge.

A job's direct exposure is higher if: Its jobs are in theory possible with AIIts jobs see substantial use 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 tasks comprise a larger share of the total role6We give mathematical information in the Appendix.

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The task-level protection steps are balanced to the occupation level weighted by the fraction of time invested on each task. The measure shows scope for LLM penetration in the majority of tasks in Computer system & Math (94%) and Workplace & Admin (90%) professions.

The protection reveals AI is far from reaching its theoretical capabilities. Claude presently covers simply 33% of all tasks in the Computer & Math classification. As capabilities advance, adoption spreads, and implementation deepens, the red area will grow to cover heaven. There is a big exposed area too; lots of jobs, naturally, remain beyond AI's reachfrom physical agricultural work like pruning trees and running farm machinery to legal tasks like representing clients in court.

In line with other data revealing that Claude is thoroughly utilized for coding, Computer Programmers are at the top, with 75% protection, followed by Customer Service Representatives, whose main tasks we significantly see in first-party API traffic. Lastly, Data Entry Keyers, whose main job of checking out source documents and getting in data sees significant automation, are 67% covered.

Analyzing Economic Trends in 2026

At the bottom end, 30% of employees have zero protection, as their jobs appeared too infrequently in our information to satisfy the minimum limit. 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 employment finds that development projections are somewhat weaker for jobs with more observed exposure. For every 10 percentage point increase in protection, the BLS's development projection come by 0.6 percentage points. This supplies some validation in that our procedures track the individually derived estimates from labor market analysts, although the relationship is minor.

procedure alone. Binned scatterplot with 25 equally-sized bins. Each solid dot reveals the average observed exposure and forecasted employment modification for among the bins. The dashed line reveals a basic linear regression fit, weighted by current work levels. The small diamonds mark private example professions for illustration. Figure 5 shows attributes of employees in the top quartile of direct exposure and the 30% of employees with zero direct exposure in the three months before ChatGPT was released, August to October 2022, utilizing data from the Present Population Survey.

The more unwrapped group is 16 percentage points more most likely to be female, 11 percentage points more most likely to be white, and nearly twice as likely to be Asian. They make 47% more, usually, and have greater levels of education. For instance, individuals with graduate degrees are 4.5% of the unexposed group, but 17.4% of the most discovered group, a practically fourfold distinction.

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 concern result since it most directly records the capacity for financial harma worker who is jobless wants a task and has actually not yet discovered one. In this case, task posts and employment do not necessarily indicate the requirement for policy reactions; a decrease in task posts for an extremely exposed function might be neutralized by increased openings in a related one.