Harnessing AI to Improve Market Analysis

The COVID-19 pandemic and accompanying policy measures caused financial interruption so stark that advanced statistical techniques were unneeded for numerous concerns. For example, unemployment leapt greatly in the early weeks of the pandemic, leaving little space for alternative descriptions. The effects of AI, nevertheless, may be less like COVID and more like the web or trade with China.

One common method is to compare results between basically AI-exposed employees, companies, or markets, in order to isolate the effect of AI from confounding forces. 2 Exposure is normally defined at the task level: AI can grade homework however not handle a classroom, for instance, so instructors are considered less discovered than workers whose whole task can be carried out remotely.

3 Our approach combines data from 3 sources. The O * internet database, which enumerates tasks related to around 800 distinct occupations in the US.Our own use information (as determined in the Anthropic Economic Index). Task-level exposure estimates from Eloundou et al. (2023 ), which measure whether it is in theory possible for an LLM to make a job a minimum of twice as quick.

Predicting Economic Shifts in 2026

Some jobs that are in theory possible might not reveal up in usage because of design limitations. Eloundou et al. mark "Authorize drug refills and offer prescription information to pharmacies" as totally exposed (=1).

As Figure 1 shows, 97% of the tasks observed throughout the previous 4 Economic Index reports fall into categories rated as in theory feasible 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. Jobs rated =1 (completely practical for an LLM alone) account for 68% of observed Claude usage, while jobs ranked =0 (not practical) account for simply 3%.

Our new procedure, observed direct exposure, is suggested to measure: of those jobs that LLMs could in theory speed up, which are really seeing automated usage in professional settings? Theoretical capability includes a much wider variety of jobs. By tracking how that space narrows, observed direct exposure offers insight into economic changes as they emerge.

A task's exposure is higher if: Its jobs are theoretically possible with AIIts jobs see considerable use in the Anthropic Economic Index5Its tasks are performed in job-related contextsIt has a relatively higher share of automated use patterns or API implementationIts AI-impacted jobs make up a larger share of the overall role6We give mathematical details in the Appendix.

Evaluating Traditional Models and Global Hubs

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

We determine this by first averaging to the occupation level weighting by our time portion step, then balancing to the occupation classification weighting by overall work. The procedure shows scope for LLM penetration in the bulk of tasks in Computer system & Mathematics (94%) and Office & Admin (90%) professions.

Claude presently covers just 33% of all tasks in the Computer & Math category. There is a big uncovered location too; many jobs, of course, stay 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 information revealing that Claude is thoroughly utilized for coding, Computer system Programmers are at the top, with 75% coverage, followed by Consumer Service Agents, whose primary jobs we significantly see in first-party API traffic. Finally, Data Entry Keyers, whose primary task of reading source documents and going into information sees substantial automation, are 67% covered.

How to Analyze the Global Economic Landscape

At the bottom end, 30% of workers have absolutely no coverage, as their jobs appeared too infrequently in our information to satisfy the minimum threshold. This group consists of, for example, Cooks, Motorcycle Mechanics, Lifeguards, Bartenders, Dishwashers, and Dressing Space Attendants. The United States Bureau of Labor Statistics (BLS) releases regular employment forecasts, with the current set, published in 2025, covering anticipated changes in employment for every profession from 2024 to 2034.

A regression at the profession level weighted by present work finds that development forecasts are rather weaker for jobs with more observed exposure. For every single 10 portion point increase in coverage, the BLS's growth projection drops by 0.6 percentage points. This offers some validation because our measures track the independently derived estimates from labor market analysts, although the relationship is small.

Each solid dot reveals the average observed direct exposure and forecasted work modification for one of the bins. The rushed line reveals an easy linear regression fit, weighted by existing employment levels. Figure 5 shows qualities of employees in the leading quartile of exposure and the 30% of workers with no direct exposure in the 3 months before ChatGPT was released, August to October 2022, using data from the Existing Population Study.

The more unveiled group is 16 percentage points most likely to be female, 11 portion points more most likely to be white, and nearly two times as most likely to be Asian. They earn 47% more, usually, and have higher levels of education. For instance, people with graduate degrees are 4.5% of the unexposed group, however 17.4% of the most unveiled group, an almost fourfold distinction.

Brynjolfsson et al.

( 2022) and Hampole et al. (2025) use job posting task publishing Information Glass (now Lightcast) and Revelio, respectively. We focus on joblessness as our priority result since it most straight catches the capacity for economic harma employee who is unemployed wants a job and has not yet discovered one. In this case, task posts and employment do not always signify the need for policy actions; a decline in job postings for a highly exposed role might be counteracted by increased openings in a related one.