Predicting Market Trends in 2026

The COVID-19 pandemic and accompanying policy procedures caused economic interruption so stark that advanced statistical approaches were unneeded for many questions. For example, joblessness jumped sharply in the early weeks of the pandemic, leaving little space for alternative explanations. The effects of AI, however, may be less like COVID and more like the internet or trade with China.

One typical approach is to compare results between more or less AI-exposed workers, firms, or markets, in order to separate the impact 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 instructors are considered less uncovered than workers whose whole task can be performed from another location.

3 Our approach combines information from 3 sources. Task-level exposure estimates from Eloundou et al. (2023 ), which determine whether it is in theory possible for an LLM to make a task at least twice as fast.

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

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

Our new procedure, observed exposure, is meant to quantify: of those jobs that LLMs could theoretically speed up, which are in fact seeing automated usage in expert settings? Theoretical ability includes a much more comprehensive variety of jobs. By tracking how that space narrows, observed direct exposure provides insight into financial modifications as they emerge.

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

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

We compute this by first balancing to the occupation level weighting by our time portion step, then balancing to the profession category weighting by total employment. For instance, the measure shows scope for LLM penetration in the bulk of tasks in Computer & Math (94%) and Workplace & Admin (90%) professions.

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

In line with other information showing 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 progressively see in first-party API traffic. Data Entry Keyers, whose primary job of checking out source files and entering data sees significant automation, are 67% covered.

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At the bottom end, 30% of workers have absolutely no coverage, as their tasks appeared too infrequently in our data to meet the minimum limit. This group includes, for example, Cooks, Motorcycle Mechanics, Lifeguards, Bartenders, Dishwashers, and Dressing Space Attendants. The United States Bureau of Labor Data (BLS) publishes regular employment forecasts, with the newest set, released in 2025, covering forecasted modifications in employment for every single profession from 2024 to 2034.

A regression at the profession level weighted by current employment discovers that growth forecasts are somewhat weaker for jobs with more observed direct exposure. For each 10 portion point increase in protection, the BLS's development forecast come by 0.6 percentage points. This provides some recognition because our procedures track the separately obtained estimates from labor market experts, although the relationship is slight.

measure alone. Binned scatterplot with 25 equally-sized bins. Each solid dot reveals the typical observed direct exposure and projected employment change for one of the bins. The rushed line reveals a simple linear regression fit, weighted by present work levels. The small diamonds mark individual example professions for illustration. Figure 5 shows qualities of employees in the top quartile of direct exposure and the 30% of employees with no direct exposure in the three months before ChatGPT was launched, August to October 2022, using information from the Existing Population Study.

The more disclosed group is 16 portion points more likely to be female, 11 percentage points most likely to be white, and almost twice as most likely to be Asian. They earn 47% more, on average, and have higher levels of education. For instance, people with academic degrees are 4.5% of the unexposed group, however 17.4% of the most discovered group, a nearly fourfold distinction.

Brynjolfsson et al.

( 2022) and Hampole et al. (2025) use job posting data from Burning Glass (now Lightcast) and Revelio, respectively. We focus on joblessness as our priority result since it most straight captures the capacity for economic harma employee who is unemployed desires a job and has actually not yet found one. In this case, task postings and work do not necessarily signal the need for policy actions; a decline in task posts for an extremely exposed function might be combated by increased openings in an associated one.