Harnessing AI to Improve Market Intelligence

The COVID-19 pandemic and accompanying policy steps triggered economic disturbance so plain that advanced analytical techniques were unneeded for numerous questions. Unemployment jumped greatly in the early weeks of the pandemic, leaving little space for alternative descriptions. The effects of AI, however, may be less like COVID and more like the internet or trade with China.

One common approach is to compare outcomes between basically AI-exposed employees, firms, or markets, in order to isolate the impact of AI from confounding forces. 2 Direct exposure is generally defined at the job level: AI can grade homework however not handle a classroom, for example, so instructors are considered less unwrapped than workers whose entire job can be performed remotely.

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

How to Analyze the 2026 Market Landscape

4Why might actual use fall short of theoretical ability? Some tasks that are in theory possible might disappoint up in use since of design restrictions. Others may be sluggish to diffuse due to legal constraints, specific software requirements, human verification steps, or other hurdles. Eloundou et al. mark "Authorize drug refills and supply prescription information to drug stores" as completely exposed (=1).

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

Our brand-new step, observed exposure, is implied to measure: of those tasks that LLMs could in theory accelerate, which are really seeing automated use in professional settings? Theoretical ability encompasses a much more comprehensive range of jobs. By tracking how that gap narrows, observed exposure provides insight into economic changes as they emerge.

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

Evaluating Offshore Models and Global Units

We then adjust for how the task is being carried out: totally automated executions get full weight, while augmentative usage gets half weight. Finally, the task-level coverage steps are balanced to the occupation level weighted by the portion of time invested in each job. Figure 2 reveals observed direct exposure (in red) compared to from Eloundou et al.

We compute this by very first averaging to the profession level weighting by our time fraction procedure, then averaging to the profession category weighting by overall work. The step reveals scope for LLM penetration in the majority of tasks in Computer & Math (94%) and Office & Admin (90%) professions.

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

In line with other information showing that Claude is thoroughly utilized for coding, Computer system Programmers are at the top, with 75% protection, followed by Customer support Agents, whose primary jobs we significantly see in first-party API traffic. Data Entry Keyers, whose main task of reading source files and entering data sees considerable automation, are 67% covered.

Maximizing Enterprise Performance for AI Systems

At the bottom end, 30% of employees have zero coverage, as their jobs appeared too infrequently in our data to meet the minimum threshold. This group consists of, for example, Cooks, Motorcycle Mechanics, Lifeguards, Bartenders, Dishwashers, and Dressing Room Attendants.

A regression at the occupation level weighted by existing employment finds that growth forecasts are somewhat weaker for jobs with more observed exposure. For every single 10 percentage point boost in protection, the BLS's growth projection come by 0.6 percentage points. This supplies some validation because our measures track the individually derived quotes from labor market experts, although the relationship is small.

Each solid dot reveals the typical observed exposure and predicted work modification for one of the bins. The dashed line shows an easy direct regression fit, weighted by present work levels. Figure 5 shows qualities of workers in the top quartile of exposure and the 30% of workers with no exposure in the three months before ChatGPT was released, August to October 2022, utilizing data from the Current Population Survey.

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

Researchers have taken different techniques. Gimbel et al. (2025) track changes in the occupational mix using the Current Population Survey. Their argument is that any crucial restructuring of the economy from AI would appear as changes in circulation of jobs. (They find that, up until now, changes have been unremarkable.) Brynjolfsson et al.

Charting Future Shifts of Global Commerce

( 2022) and Hampole et al. (2025) use job publishing data from Burning Glass (now Lightcast) and Revelio, respectively. We focus on unemployment as our priority result because it most straight captures the potential for economic harma worker who is jobless desires a job and has not yet discovered one. In this case, job postings and employment do not always signify the requirement for policy actions; a decline in task postings for a highly exposed role might be neutralized by increased openings in an associated one.