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October 6, 2025

Article

Uneven Geography: How Location and Income Shape AI Adoption

AI adoption isn’t evenly distributed around the world. New analysis from Anthropic’s Economic Index reveals that AI usage correlates strongly with income and varies widely across countries and even within the U.S.. For small businesses, understanding these patterns highlights where competition is likely to be strongest and why regional dynamics matter.

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Income and adoption go hand in hand

  • The Anthropic AI Usage Index (AUI) measures Claude.ai usage per capita relative to population. High‑income countries have much higher usage. Singapore and Canada outperform expectations by 4.6× and 2.9× respectively.

  • Emerging economies such as Indonesia (0.36×), India (0.27×) and Nigeria (0.2×) have significantly lower usage.

  • Within the U.S., Washington DC (3.82×) and Utah (3.78×) lead per‑capita usage. Regional specializations—IT in California, financial services in Florida, document editing in DC—shape local usage patterns.

Diversity of use vs. specialization

  • High‑adoption countries show more diverse applications of AI across education, science and business. In contrast, lower‑adoption countries primarily use AI for coding.

  • After controlling for task mix, low‑AUI countries are more likely to delegate entire tasks (automation) while high‑adoption areas tend toward collaborative, augmentative use. This suggests that more mature AI markets emphasize human‑AI partnership rather than full task delegation.

Implications for small businesses

  • Competitive intensity: Operating in a high‑adoption region means you’ll face AI‑savvy competitors. Businesses in Singapore or Canada may need to innovate faster and offer advanced automations to stand out.

  • Opportunity in emerging markets: Businesses in regions with lower adoption can differentiate by adopting AI early. Automating sales, marketing and customer support may provide a competitive advantage when local competitors are slower to adopt.

  • Global customers: If you serve clients internationally, be aware that expectations vary. Clients in high‑adoption regions may demand AI‑augmented experiences, while those in emerging markets may need more education.

  • Inequality and ethics: The concentration of AI benefits in already wealthy regions could exacerbate global inequality. Consider how your business can share knowledge or tools to help smaller or emerging markets adopt AI responsibly.

Conclusion

Geographic and economic factors strongly influence AI adoption. By understanding where adoption is highest and why, small businesses can tailor their automation strategies—either to keep up with intense competition or to seize early‑mover advantage in underserved markets. Regardless of location, the opportunity to leverage AI is real; the key is to act strategically and ethically.