🇺🇸
Country Deep Dive · Feb 2026

United States
AI Adoption Profile

The US accounts for 22% of global Claude.ai traffic — more than the next four countries combined. This page combines GAIA behavioral data, BLS labor statistics, O*NET task content, and Google Trends to map how AI is reshaping American work.

#1
Global AI usage rank
22.2%
Share of global Claude.ai traffic
40.4%
Work-related AI use share
72.2%
Task success rate
0.948
GAIA-R readiness score
923
Occupations with exposure scores

Key Findings

The AI Transition in American Work — 4 Findings

What the data says about where the US stands in the AI transition, which workers are most exposed, and where the labor market signal is visible.

1
The US leads in volume, not work intensity
The US drives 22% of global Claude.ai traffic — but only 40% of that is work-related, compared to 57% in Brazil and 50% in India. Americans use AI more, but proportionally more for personal tasks than peer economies.
2
Highest-exposure occupations are also highest-wage
Computer programmers, mathematicians, and data scientists score 87–100% on AI exposure — and these are also among the highest-paid US occupations. High-wage, high-exposure workers face disruption risk, but also have resources to adapt. Low-wage, high-exposure occupations (clerks, transcriptionists) face the starkest displacement pressure.
3
JOLTS quit rates signal post-ChatGPT labor reallocation
Total nonfarm quit rates dropped sharply after November 2022 — from a 2.6% monthly average to 2.0% post-ChatGPT. This mirrors a "great stay" pattern: workers may be less willing to change jobs as AI uncertainty rises. Information and professional services sectors show the largest divergence.
4
ChatGPT awareness peaked in 2025, not 2022
Google Trends shows US ChatGPT search interest accelerated continuously after the November 2022 shock — peaking in September 2025 at index 100. DC, California, and New Jersey lead state-level awareness. The awareness curve suggests AI adoption is still accelerating, not plateauing.

Section 1 — GAIA Behavioral Data

USA in the Global AI Adoption Index

How US AI usage patterns compare to Brazil, India, Germany, and the global average. Data from Anthropic's Claude.ai behavioral logs, February 2026.

22.2%
Global traffic share
40.4%
Work use
47.0%
Personal use
12.5%
Coursework
0.711
GAIA-A adoption
0.948
GAIA-R readiness

Use Case Breakdown — US vs. Peers

The US tilts more toward personal use than peers like Brazil and India, where AI is used more heavily for work. High volume, but proportionally less of it is professional.

Collaboration Style — US vs. Peers

Shows how Americans work with AI — giving directions, iterating, or learning — compared to other countries. Differences here hint at how mature each market's AI use is.

GAIA Scores — US vs. Peers

Data access by request. Cite as: Aghabarari (2026) DOI: 10.5281/zenodo.20320112

Two scores side by side: GAIA-A (how much a country actually adopts AI) and GAIA-R (how ready/exposed it is). The US scores high on both — near the top globally.

Section 2 — AI Exposure by Occupation

Most and Least AI-Exposed US Occupations

AI exposure scores from Eloundou et al. (Science 2024) — share of occupation tasks performable by GPT-4 augmented with tools (E1+E2 specification). Click any occupation to see all scores.

Top 20 by AI Exposure (E1+E2)

Click an occupation →

Select an occupation from the chart to see its full GAIA profile.

Bottom 20 by AI Exposure (E1+E2)

Click an occupation →

Select an occupation from the chart to see its full GAIA profile.

Data access by request. Cite as: Aghabarari (2026) DOI: 10.5281/zenodo.20320112

→ Full Occupation Explorer

Section 3 — AI Exposure & Wages

AI Exposure vs. Wages: Who Is Most at Risk?

Across all 923 US occupations, higher AI exposure is concentrated in higher-wage jobs — but a pocket of high-exposure, low-wage roles faces the starkest displacement risk. Wages shown are FRED industry-level averages (Apr 2026) mapped to each occupation's sector.

GAIA-E Score (Eloundou 2024) vs. Industry Wage — all 923 occupations

Data access by request. Cite as: Aghabarari (2026) DOI: 10.5281/zenodo.20320112

Each dot is one occupation. Wages are FRED sector averages (Apr 2026) assigned by occupational group — a proxy for within-occupation wage variation. Hover for details.

▲ Upper-right quadrant (high exposure, high wage): Computer & Math, Management, Business & Financial. Lower-right (high exposure, low wage): Office & Admin, Sales, Food Preparation — displacement pressure greatest here. Wages = FRED industry averages (Apr 2026), annualized at 2,080 hrs/yr.

Exposure vs. median wage — the four quadrants

x = E1+E2 (%, Eloundou 2024) · y = median annual wage (OEWS) · quadrant lines at the medians

Source: E1+E2 = dv_rating_beta (Eloundou et al. 2024, Science); median annual wage & employment = BLS OEWS (a_median, tot_emp) via gaia_occupations.csv.

US employment by AI-exposure quartile

Share of total US employment across E1+E2 quartiles, weighted by OEWS employment.

Source: E1+E2 = dv_rating_beta (Eloundou 2024); employment weight = BLS OEWS tot_emp. Quartiles are on the published E1+E2 distribution.

Section 5 — FRED · Labor Market by Industry

Wages, Job Openings & Unemployment — Before and After ChatGPT

Three labor market dimensions by industry from the St. Louis Fed FRED database, 2020–2026. The vertical line marks November 2022. High-AI-exposure industries (Information, Prof/Business) show distinct earnings and openings trajectories.

Industry labor-market charts earnings · openings · unemployment, 2020–2026 — click to expand

Average Hourly Earnings by Industry ($/hr, monthly 2020–2026)

▲ Nov 2022 = ChatGPT launch. Information sector earns $54.83/hr (Apr 2026) — 47% above the all-industry average. Note the wage growth acceleration post-2022 in high-AI sectors.

Job Openings by Industry (thousands, JOLTS monthly 2020–2026)

▲ Nov 2022 = ChatGPT launch. Professional & Business Services and Education & Health dominate openings. Note the broad decline in openings post-2022 as hiring slowed across sectors.

Unemployment Rate by Industry (%, monthly 2020–2026)

▲ Nov 2022 = ChatGPT launch. Construction and Manufacturing show higher structural unemployment; Wholesale & Retail elevated post-2022 as e-commerce AI adoption accelerated.

Pre vs. Post ChatGPT — Average Hourly Earnings by Industry

Section 6 — BLS JOLTS · Labor Market Trends

Hiring, Quits & Layoffs — Before and After ChatGPT

Monthly Job Openings and Labor Turnover Survey data from the BLS API, 2020–2026. The vertical line marks November 2022 — the ChatGPT public release. The key question: did AI uncertainty change worker and firm behavior?

3.4%
Hires rate Mar 2026
2.0%
Quit rate (post-ChatGPT avg)
2.6%
Quit rate (pre-ChatGPT avg)
−23%
Quit rate change post-Nov 2022
Hiring, quits & layoffs charts JOLTS monthly series, 2020–2026 — click to expand

Total Nonfarm — Hires, Quits & Layoffs (Monthly Rate, 2020–2026)

Data access by request. Cite as: Aghabarari (2026) DOI: 10.5281/zenodo.20320112

▲ Vertical line = November 2022 (ChatGPT launch). Post-launch quit rates fell and have remained below pre-2022 levels.

Industry Comparison — Average Quit Rate Pre vs. Post ChatGPT

Section 7 — O*NET Task Content

What Do High-Exposure Workers Actually Do?

O*NET task statements for high and low AI-exposure occupations. Understanding the task content helps explain why some occupations score high. Data: O*NET 29.0, merged with GAIA-E and GAIA-B scores.

Select an occupation to see its tasks:

← Select an occupation

Data access by request. Cite as: Aghabarari (2026) DOI: 10.5281/zenodo.20320112