Vital Growth Statistics to Track in 2026

The COVID-19 pandemic and accompanying policy measures caused economic disruption so stark that sophisticated statistical techniques were unneeded for lots of questions. For instance, joblessness leapt dramatically in the early weeks of the pandemic, leaving little room for alternative descriptions. The impacts of AI, nevertheless, might be less like COVID and more like the internet or trade with China.

One common approach is to compare outcomes in between basically AI-exposed employees, companies, or markets, in order to separate the effect of AI from confounding forces. 2 Exposure is usually defined at the job level: AI can grade homework but not handle a class, for example, so teachers are considered less revealed than employees whose entire job can be carried out from another location.

3 Our method integrates data from three sources. Task-level direct exposure price 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.

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Some tasks that are in theory possible may not reveal up in use because of model restrictions. Eloundou et al. mark "Authorize drug refills and offer prescription info to pharmacies" as completely exposed (=1).

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

Our new procedure, observed direct exposure, is implied to quantify: of those jobs that LLMs could in theory accelerate, which are actually seeing automated usage in expert settings? Theoretical ability encompasses a much broader range of tasks. By tracking how that gap narrows, observed exposure supplies insight into financial changes as they emerge.

A task's exposure is higher if: Its jobs are in theory possible with AIIts jobs see significant usage in the Anthropic Economic Index5Its jobs are carried out in work-related contextsIt has a relatively higher share of automated use patterns or API implementationIts AI-impacted jobs make up a larger share of the general role6We provide mathematical information in the Appendix.

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We then adjust for how the task is being performed: fully automated executions receive complete weight, while augmentative use gets half weight. Lastly, the task-level coverage procedures are averaged to the profession level weighted by the portion of time invested in 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 profession level weighting by our time portion procedure, then balancing to the occupation category weighting by total employment. For example, the measure shows scope for LLM penetration in the majority of tasks in Computer & Mathematics (94%) and Office & Admin (90%) occupations.

The protection shows AI is far from reaching its theoretical abilities. Claude currently covers simply 33% of all jobs in the Computer system & Mathematics category. As capabilities advance, adoption spreads, and release deepens, the red area will grow to cover the blue. There is a big exposed location too; many tasks, of course, stay beyond AI's reachfrom physical agricultural work like pruning trees and running farm machinery to legal tasks like representing customers in court.

In line with other information revealing that Claude is thoroughly used for coding, Computer Programmers are at the top, with 75% protection, followed by Customer care Agents, whose main jobs we increasingly see in first-party API traffic. Data Entry Keyers, whose main job of checking out source documents and going into information sees considerable automation, are 67% covered.

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At the bottom end, 30% of workers have absolutely no protection, as their tasks appeared too rarely in our data to satisfy the minimum threshold. This group includes, for example, Cooks, Bike Mechanics, Lifeguards, Bartenders, Dishwashers, and Dressing Space Attendants.

A regression at the occupation level weighted by current employment discovers that development forecasts are rather weaker for jobs with more observed direct exposure. For every 10 portion point boost in protection, the BLS's growth forecast come by 0.6 percentage points. This provides some recognition because our measures track the separately derived estimates from labor market analysts, although the relationship is slight.

Each strong dot reveals the typical observed direct exposure and projected employment change for one of the bins. The dashed line reveals a simple direct regression fit, weighted by present employment levels. Figure 5 shows qualities of workers in the leading quartile of direct exposure and the 30% of employees with no direct exposure in the 3 months before ChatGPT was launched, August to October 2022, using information from the Current Population Survey.

The more discovered group is 16 percentage points more likely to be female, 11 portion points more likely to be white, and practically two times as most likely to be Asian. They earn 47% more, usually, and have greater levels of education. People with graduate degrees are 4.5% of the unexposed group, but 17.4% of the most unveiled group, an almost fourfold difference.

Scientists have taken different methods. Gimbel et al. (2025) track changes in the occupational mix utilizing the Existing Population Survey. Their argument is that any essential restructuring of the economy from AI would appear as modifications in distribution of jobs. (They discover that, up until now, changes have actually been average.) Brynjolfsson et al.

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( 2022) and Hampole et al. (2025) use job posting information from Burning Glass (now Lightcast) and Revelio, respectively. We concentrate on joblessness as our top priority result since it most straight captures the potential for economic harma employee who is jobless wants a job and has not yet discovered one. In this case, job postings and employment do not always indicate the requirement for policy actions; a decrease in job postings for an extremely exposed role might be neutralized by increased openings in an associated one.