Will Real-Time Analytics Transform Global Growth?

The COVID-19 pandemic and accompanying policy procedures triggered financial interruption so plain that advanced statistical techniques were unnecessary for lots of questions. For example, joblessness leapt dramatically in the early weeks of the pandemic, leaving little room for alternative descriptions. The effects of AI, however, may be less like COVID and more like the web or trade with China.

One common method is to compare outcomes 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 generally specified at the task level: AI can grade homework however not manage a class, for instance, so teachers are considered less uncovered than employees whose entire task can be carried out from another location.

3 Our approach integrates data from 3 sources. Task-level direct exposure price 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 quick.

Predicting Global Trends in 2026

4Why might real use fall short of theoretical capability? Some tasks that are theoretically possible may not show up in use because of design restrictions. Others might be slow to diffuse due to legal constraints, particular software requirements, human confirmation steps, or other obstacles. For instance, Eloundou et al. mark "License drug refills and supply prescription details to pharmacies" as totally exposed (=1).

As Figure 1 programs, 97% of the jobs observed throughout the previous 4 Economic Index reports fall under classifications rated as theoretically feasible by Eloundou et al. (=0.5 or =1.0). This figure shows Claude usage dispersed across O * NET tasks grouped by their theoretical AI exposure. Jobs rated =1 (totally feasible for an LLM alone) represent 68% of observed Claude use, while jobs ranked =0 (not practical) account for simply 3%.

Our brand-new measure, observed exposure, is implied to measure: of those jobs that LLMs could theoretically accelerate, which are really seeing automated usage in expert settings? Theoretical ability encompasses a much broader variety of tasks. By tracking how that space narrows, observed direct exposure provides insight into economic changes as they emerge.

A job's exposure is higher if: Its jobs are theoretically possible with AIIts tasks see considerable usage 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 bigger share of the total role6We provide mathematical details in the Appendix.

Key Expansion Statistics to Track in 2026

We then adjust for how the job is being performed: completely automated executions get full weight, while augmentative use 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 direct exposure (in red) compared to from Eloundou et al.

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

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

In line with other data revealing that Claude is extensively used for coding, Computer system Programmers are at the top, with 75% coverage, followed by Customer support Agents, whose primary tasks we increasingly see in first-party API traffic. Lastly, Data Entry Keyers, whose primary task of reading source documents and going into information sees significant automation, are 67% covered.

Will Real-Time Data Transform Global Strategy?

At the bottom end, 30% of employees have zero coverage, as their jobs appeared too rarely 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 employment discovers that growth forecasts are somewhat weaker for jobs with more observed direct exposure. For every 10 portion point boost in protection, the BLS's growth projection stop by 0.6 portion points. This supplies some validation because our procedures track the separately derived quotes from labor market experts, although the relationship is minor.

Each solid dot shows the average observed direct exposure and projected employment modification for one of the bins. The rushed line shows a basic linear regression fit, weighted by current work levels. Figure 5 shows attributes of employees in the top quartile of direct exposure and the 30% of workers with absolutely no exposure in the 3 months before ChatGPT was launched, August to October 2022, utilizing data from the Current Population Study.

The more bare group is 16 percentage points most likely to be female, 11 percentage points more likely to be white, and nearly twice as most likely to be Asian. They make 47% more, typically, and have higher levels of education. For instance, individuals with academic degrees are 4.5% of the unexposed group, but 17.4% of the most unwrapped group, an almost fourfold distinction.

Scientists have actually taken different techniques. Gimbel et al. (2025) track changes in the occupational mix using the Existing Population Study. Their argument is that any important restructuring of the economy from AI would reveal up as changes in distribution of jobs. (They discover that, so far, changes have actually been average.) Brynjolfsson et al.

How Advanced BI Reports Drive Corporate Growth

( 2022) and Hampole et al. (2025) use task posting information from Burning Glass (now Lightcast) and Revelio, respectively. We focus on unemployment as our priority outcome due to the fact that it most straight captures the potential for economic harma worker who is unemployed wants a job and has not yet discovered one. In this case, job posts and employment do not necessarily signal the need for policy reactions; a decrease in job postings for a highly exposed role may be neutralized by increased openings in a related one.