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Human-AI collaboration

Human-AI collaboration in professional, scientific, and technical services

Weighted by the 10.8 million jobs BLS counts in professional, scientific, and technical services, AI use with a classified collaboration pattern (excluding unclassified conversations) on this industry's occupations is 49.1% augmentation and 50.9% automation (Anthropic Economic Index, May 2026).

Data compiled 2026-10-09 · BLS 2025–35 · O*NET 31.0 · AEI May 2026

Augmentation share, employment-weighted

49.1%

Global Claude.ai conversations with a classified collaboration pattern (excluding unclassified conversations): 51.4% augmentation.

Anthropic Economic Index (May 2026)

At a glance

AI use and exposure across the industry's occupations

49.1%

Augmentation share of AI use

Shares exclude conversations with no classified collaboration pattern. Automation share: 50.9%. Occupations with usage data cover 94.0% of the industry's jobs.

Anthropic Economic Index (May 2026)

47%

Average LLM exposure (β)

Employment-weighted across occupations covering 94.9% of jobs. U.S. average: 30%.

Eloundou et al., Science 2024

46.8%

Jobs in high-exposure occupations

Share of covered 2025 jobs in occupations with β of 0.5 or more.

Eloundou et al., Science 2024 · BLS National Employment Matrix 2025–35

10.8 million

Jobs in 2025

BLS projects +8.6% to 11.7 million by 2035 (Professional, scientific, and technical services, NAICS 540000).

BLS National Employment Matrix 2025–35

Collaboration patterns

Employment-weighted mix of how people work with AI on this industry's tasks

Directive · Automation31.9%

The person hands over the whole task with minimal back-and-forth.

Feedback loop · Automation18.0%

AI completes the task, guided by feedback such as error messages relayed by the person.

Task iteration · Augmentation32.1%

The person and AI refine the work together over several turns.

Learning · Augmentation13.1%

The person uses AI to understand a topic or build a skill.

Validation · Augmentation2.9%

The person asks AI to check or improve work they did.

No clear pattern · Unclassified2.1%

Conversations that fit none of the patterns above.

Anthropic Economic Index (May 2026) · BLS National Employment Matrix 2025–35

Largest occupations

By 2025 employment in the industry

Jobs and projected change: BLS National Employment Matrix 2025–35. Exposure: Eloundou et al., Science 2024. Automation share: Anthropic Economic Index (May 2026).
OccupationJobs in industry, 2025Change to 2035Exposure (β)Automation share
Software Developers688,700+13.6%45%60.8%
Accountants and Auditors484,400+6.7%52%45.0%
Lawyers470,200+5.9%48%31.5%
General and Operations Managers437,200+11.4%39%44.4%
Management Analysts359,700+13.6%50%45.5%
Paralegals and Legal Assistants309,600+0.4%45%37.5%
Project Management Specialists303,800+11.0%40%51.5%
Sales Representatives of Services291,600+8.6%57%45.7%

Sources & methodology

Every figure on this page comes from the public datasets below. We do not edit the source values; derived figures are explained here.

  1. Collaboration shares come from Anthropic Economic Index data for Claude.ai conversations in May 2026, classified to the O*NET tasks they match. "Automation" groups directive and feedback-loop conversations; "augmentation" groups task iteration, learning, and validation. Both buckets are shares of conversations with a classified collaboration pattern, excluding the unclassified "none" pattern; the six individual pattern shares include unclassified conversations. They describe how people use Claude on this occupation's tasks — not what share of workers in the occupation use AI.
  2. Industry figures weight each occupation's AEI shares by its 2025 employment in the industry from the BLS National Employment Matrix. Coverage shows the share of the industry's detailed-occupation employment with AEI data.
  3. Average exposure is the employment-weighted Eloundou et al. β; "high-exposure jobs" are occupations with β of 0.5 or more.
  4. BLS occupations are matched to O*NET-SOC codes with the BLS O*NET-to-NEM crosswalk; where a BLS occupation spans several O*NET occupations, we average their values.

  • Anthropic Economic Index (May 2026)

    Massenkoff, M., Lyubich, E., Sacher, S., Hitzig, Z., Zhang, S., Heller, R., & McCrory, P. (2026). "Anthropic Economic Index report: Cadences." Anthropic, June 26, 2026. Claude.ai usage metrics by occupation, global, May 2026.

    Vintage: Release of June 26, 2026; Claude.ai conversations from May 2026. License: CC BY (data). Retrieved 2026-10-09.

  • BLS National Employment Matrix 2025–35

    U.S. Bureau of Labor Statistics, Employment Projections program. 2025–35 National Employment Matrix, industry-occupation employment.

    Vintage: 2025 base year, 2035 projection, released August 27, 2026. License: Public domain (U.S. federal government work). Retrieved 2026-10-09.

  • Eloundou et al., Science 2024

    Eloundou, T., Manning, S., Mishkin, P., & Rock, D. (2024). "GPTs are GPTs: Labor market impact potential of LLMs." Science, 384(6702), 1306–1308. doi:10.1126/science.adj0998. Occupation- and task-level exposure data from the authors' public repository (openai/GPTs-are-GPTs).

    Vintage: Published June 21, 2024; human annotations of O*NET tasks collected 2023. License: Data repository: MIT License. Retrieved 2026-10-09.

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