How Canada Uses Claude

How Canada uses Claude \ Anthropic Economic Research How Canada uses Claude: Findings from the Anthropic Economic Index Jul 14, 2026

Le français suit. Key findings Based on the latest release of the Anthropic Economic Index, Canada is at the forefront of Claude adoption. Canada represents 2.6% of global Claude.ai traffic and ranks 8th overall by total volume. Usage per capita is more than four times higher than would be expected given the size of its population. Canada’s high adoption rate is generally consistent with its high-income economy, but still stands out within its peer group. Among the top ten countries that collectively represent more than half of all usage, Canada is second only to the United States in usage per capita. Within Canada, adoption is regionally concentrated. Ontario accounts for 43.9% of conversations. Together with Quebec, British Columbia, and Alberta, the four largest provinces account for roughly 94% of national usage. Per capita, British Columbia leads at 1.4x more than expected based on population, followed by Ontario at 1.1x; every other province has below-parity rates of adoption, with Newfoundland and Labrador at 0.2x. In contrast to global, cross-country patterns, provincial income per capita does not appear to explain the gap. Instead, industrial composition appears more important: provinces with large professional, scientific, and technical services sectors use Claude the most. This aligns with other evidence that model capabilities matched to workforce composition determine overall adoption levels within high-income countries. Usage patterns in Canada are largely uncorrelated with adoption rates: Work accounts for 34–40% of conversations in every province, coursework for 13–18%, and personal use for 44–51%. We find evidence that specific use cases coincide with local economic characteristics. Translation requests track public administration employment shares across provinces, likely reflecting Canada's policy of official bilingualism in federal services and communications: New Brunswick, Nova Scotia, and Quebec have both the highest rates of public administration employment and the largest shares of conversations devoted to translation. Document translation is also the most distinctive Canadian use case relative to Anglosphere peers. More generally, Canadian usage tilts toward academic and early-career usage: academic coursework, coding assistance, and resume drafting are all overrepresented, while professional communication and everyday personal tasks are underrepresented.

Canada is at the forefront of Claude adoption Adoption of Claude is high in Canada. Based on a sample of Claude.ai conversations in February 2026, 2.6% of global traffic is in Canada. Adjusting for population, its Anthropic AI Usage Index (AUI) is 4.4, which implies that usage per capita is more than four times higher than would be expected based on its working-age population. Among the top ten countries that lead in terms of overall Claude usage volume, Canada has the second-highest AUI, just behind the United States (Figure 1). Figure 1: Usage share and per capita adoption among top ten countries by global Claude.ai use Bars show each country’s share (left panel) and the Anthropic AI Usage Index (right panel) based on 1M conversations sampled from Claude.ai in February 2026. Canada highlighted in blue. The Anthropic AI Usage Index (AUI) measures whether Claude usage is over- or under-represented in a country relative to its working age population. Canada accounts for 2.6% of global Claude.ai consumer use, ranking eighth globally, and second by AUI. Sources: Anthropic Economic Index, February 2026; World Bank. In part, this reflects the fact that Canada is a high-income country. Among advanced economies, as defined by the IMF, there is a clear link between usage per capita and GDP per working-age capita. But Canada’s adoption exceeds what would be expected based on its income (Figure 2). In this sense, Canada appears further along on its AI adoption curve as compared to peer countries, perhaps reflecting its highly educated workforce and proximity to the US technology frontier. Figure 2: Anthropic AI Usage Index (AUI) and GDP per working-age capita This plot shows the bivariate relationship between each country’s AUI and GDP per working-age person among IMF advanced economies with at least 200 conversations in our sample. The dashed line shows the line of best fit. Canada highlighted in blue. Sources: Anthropic Economic Index, February 2026; IMF; World Bank.

Within Canada, adoption is concentrated and tracks workforce composition Just as global adoption of Claude is disproportionately concentrated among a small number of countries, usage within Canada is unevenly distributed across provinces. Ontario accounts for 43.9% of conversations. Another 50% of usage within Canada comes from Quebec (20.8%), British Columbia (18.9%), and Alberta (10.2%). Adjusting for population, the ordering shifts: British Columbia has 1.4x more Claude usage than expected based on its working age population, followed by Ontario (1.1x). The remaining provinces have below-parity rates of adoption. The province of Newfoundland and Labrador has an AUI of 0.2. Figure 3: Share of Canada’s Claude.ai use by province and territory Left panel: each province's share of Canadian Claude.ai conversations. Right panel: AUI by province, where 1.0 indicates usage proportional to the province's working-age population. Territories are below the reporting threshold. Sources: Anthropic Economic Index, February 2026; Statistics Canada. What explains the pattern of regional adoption? We explore this question in Figure 4. In contrast to the global relationship between income and usage, we find that provincial usage per capita is mostly uncorrelated with income. Instead, we find that the industrial composition of regional economies is a key determinant of usage intensity: regions with larger professional, scientific, and technical service sectors—as measured by the share of employment—have systematically higher usage per capita. Evidently, workforce composition—rather than income per se—is a key determinant of within-Canada patterns of adoption. This evidence is consistent with our earlier findings concerning adoption diffusion within the United States. It appears that AI adoption in high-income countries is primarily shaped by how well matched model capabilities are to the structure of the local...