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AI in District of Columbia

How District of Columbia uses AI · Anthropic Economic Index

District of Columbia accounts for about 0.9% of U.S. Claude.ai (Free and Pro) activity in the Anthropic Economic Index sample , the #26 largest share of 51 states. Of conversations here, 57.5% are people working with AI and 39.2% hand a task to AI.

This is a state's share of national usage, which tracks population — not a per-person adoption rate. Figures are shares of observed Claude.ai conversations, not of jobs or work time, and reflect one assistant's consumer sample.

AI task-exposure of work in District of Columbia

Weighting District of Columbia's job mix (BLS OEWS May 2024 state employment) by each occupation's AI Exposure Index gives a state task-overlap index of 65 (Moderate band) — versus a national 52 , 13 points above the U.S. average. This reflects 684,690 employed workers across 427 occupations.

This is the overlap between the tasks local jobs involve and what today's AI can do — not adoption, not automation, and not a forecast of jobs lost. Every state sits in a narrow national band; see the state choropleth for the relative picture.

Most-exposed occupations here

Least-exposed occupations here

How District of Columbia works with AI

The collaboration patterns Anthropic's classifier assigns to conversations from this state — directive and feedback-loop count as handing work to AI; iteration, learning, and validation count as working with it.

Working with AI vs. handing tasks to AI (share of observed conversations)

Collaboration pattern breakdown

Pattern Share What it means
task iteration 33.3% you and AI go back and forth
directive 31.4% AI does it; you give the instruction
learning 17.0% you ask AI to explain or teach
feedback loop 7.8% AI does it, then adjusts from your feedback
validation 7.3% you do it; AI checks your work

Most common requests in District of Columbia

The specific requests that make up the largest share of this state's local activity.

Request Local share
Draft and refine cover letters for job applications 1.6%
Rewrite and polish existing professional business email drafts 1.3%
Help with comprehensive job search strategy and assistance in hiring tasks 1.2%
Edit and refine diverse written content through iterative revisions 0.9%
Assist with practical political campaign work and electoral information research 0.7%
Create, revise, format, and optimize resumes and CVs 0.7%
Draft and refine general professional business emails and communications 0.7%
Draft and refine operational business emails and customer correspondence 0.7%

How to read this

  • Source: the Anthropic Economic Index (2026-01-15-v4-plus-2025-03-27-v2) country-state aggregates over a sample of Claude.ai Free and Pro conversations.
  • "Share of US usage" is this state's portion of national activity — it tracks population and is not a per-person adoption rate.
  • Autonomy is a model-rated 0–5 estimate of how independently AI acted, averaged over conversations here.
  • Geographic over-indexing compares the local request mix to the national mix, with a sample floor so small states do not produce spurious spikes.

← AI adoption across all states

Sources for this page

Every figure above traces to a named public dataset and the exact release below — not hand-written opinion. See the full methodology for what each measure does and does not mean.

Data compiled June 2, 2026. Figures are estimates, not advice.

Cite this page
Plain

Singulariki. "AI in District of Columbia." Singulariki: a source-backed encyclopedia of work. Built from BLS Occupational Employment and Wage Statistics (OEWS) May 2024; Anthropic Economic Index v4 (2026-01-15) + v2 (2025-03-27); “GPTs are GPTs” (Eloundou et al.) arXiv 2303.10130; AI Occupational Exposure (AIOE) Felten, Raj & Seamans. Accessed June 7, 2026. https://singulariki.com/geography/district-of-columbia

APA

Singulariki. (2026). AI in District of Columbia. Singulariki: a source-backed encyclopedia of work. Retrieved June 7, 2026, from https://singulariki.com/geography/district-of-columbia

BibTeX 
@misc{singulariki-district-of-columbia,
  title  = {AI in District of Columbia},
  author = {{Singulariki}},
  year   = {2026},
  note   = {BLS Occupational Employment and Wage Statistics (OEWS) May 2024; Anthropic Economic Index v4 (2026-01-15) + v2 (2025-03-27); “GPTs are GPTs” (Eloundou et al.) arXiv 2303.10130; AI Occupational Exposure (AIOE) Felten, Raj & Seamans. Accessed June 7, 2026},
  url    = {https://singulariki.com/geography/district-of-columbia}
}

Citations name the underlying public dataset releases — they reflect what this page is built from, not just the URL.