How to Forecast the 2026 Market Landscape

The COVID-19 pandemic and accompanying policy measures caused economic disruption so stark that advanced statistical techniques were unneeded for numerous questions. For example, joblessness jumped greatly 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 common approach is to compare outcomes in between basically AI-exposed employees, firms, or industries, in order to isolate the effect of AI from confounding forces. 2 Direct exposure is typically defined at the job level: AI can grade research however not handle a classroom, for example, so teachers are considered less reviewed than workers whose whole task can be carried out remotely.

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

How to Forecast the Global Economic Landscape

4Why might real use fall brief of theoretical capability? Some tasks that are theoretically possible might not reveal up in usage since of design restrictions. Others may be slow to diffuse due to legal restrictions, specific software application requirements, human confirmation steps, or other difficulties. Eloundou et al. mark "License 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 4 Economic Index reports fall under classifications ranked as theoretically practical by Eloundou et al. (=0.5 or =1.0). This figure reveals Claude usage dispersed across O * NET tasks organized by their theoretical AI exposure. Tasks ranked =1 (totally practical for an LLM alone) represent 68% of observed Claude usage, while tasks ranked =0 (not practical) account for just 3%.

Our new measure, observed exposure, is meant to measure: of those jobs that LLMs could theoretically speed up, which are actually seeing automated usage in professional settings? Theoretical ability encompasses a much wider series of tasks. By tracking how that gap narrows, observed direct exposure supplies insight into economic changes as they emerge.

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

Key Growth Metrics to Track in 2026

We then adjust for how the task is being brought out: fully automated implementations receive complete weight, while augmentative use receives half weight. The task-level protection measures are averaged to the occupation level weighted by the fraction of time invested on each job. Figure 2 reveals observed direct exposure (in red) compared to from Eloundou et al.

We determine this by very first balancing to the profession level weighting by our time portion step, then averaging to the profession classification weighting by overall work. The step reveals scope for LLM penetration in the majority of tasks in Computer system & Math (94%) and Workplace & Admin (90%) occupations.

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

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

Evaluating Offshore Models and Global Units

At the bottom end, 30% of employees have absolutely no protection, as their jobs appeared too occasionally in our data to fulfill the minimum limit. This group consists of, for example, Cooks, Bike Mechanics, Lifeguards, Bartenders, Dishwashers, and Dressing Room Attendants.

A regression at the occupation level weighted by current work discovers that development forecasts are somewhat weaker for jobs with more observed exposure. For every single 10 portion point increase in coverage, the BLS's development forecast visit 0.6 percentage points. This provides some validation because our measures track the independently 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 average observed direct exposure and predicted employment change for among the bins. The dashed line reveals an easy direct regression fit, weighted by current employment levels. The small diamonds mark private example professions for illustration. Figure 5 shows attributes of workers in the top quartile of exposure and the 30% of workers with absolutely no exposure in the 3 months before ChatGPT was launched, August to October 2022, utilizing information from the Present Population Survey.

The more bare group is 16 portion points most likely to be female, 11 percentage points most likely to be white, and almost two times as most likely to be Asian. They earn 47% more, on average, and have greater levels of education. People with graduate degrees are 4.5% of the unexposed group, however 17.4% of the most bare group, an almost fourfold distinction.

Brynjolfsson et al.

( 2022) and Hampole et al. (2025) use job posting data publishing Burning Glass (now Lightcast) and Revelio, respectively. We focus on unemployment as our concern result due to the fact that it most directly captures the capacity for economic harma employee who is unemployed wants a task and has actually not yet found one. In this case, job posts and employment do not necessarily signify the need for policy actions; a decline in job posts for a highly exposed role might be neutralized by increased openings in an associated one.