AI Exposure vs Actual AI Use: Where 102 Countries Diverge

Two measures that are easy to confuse

AI exposure is our score for how much of a country's workforce does work that current AI tools can perform. It is an employment-weighted average of occupation-group scores from 0 to 10, and the scores are model-estimated, not measured. It describes potential.

AI use is Microsoft's estimate of the share of people aged 15 to 64 who used a generative AI product in the period. It comes from aggregated, anonymised Microsoft telemetry, adjusted for operating-system and device market share, internet penetration and population. It describes realised use. We take the Q2 2026 column of Microsoft's published dataset.

Putting them side by side asks a simple question: where is the workforce structurally exposed, and is anyone actually using the tools yet? The answer is that the two move together, but loosely.

How exposure and use line up

The fit is real but modest. A correlation of 0.66 means exposure and use tend to rise together; 44% of the variation explained leaves the majority unexplained. Both measures also follow how rich and service-heavy a country is, so the relationship should not be read as exposure causing use.

Countries that use AI far more than their exposure predicts

These are the largest gaps above the trend line among countries with their own published figure. The gap is the country's measured use minus the use the trend line predicts for its exposure score.

Source: Microsoft AI Diffusion dataset Q2 2026 (use); site/data/meta.json weighted_ai_exposure (exposure); trend line fitted to 102 countries. Recomputed 2026-10-22.

Countries that use AI far less than their exposure predicts

Source: same as above. A negative gap means measured use is below the trend line.

These countries have workforces our model scores as moderately to highly exposed (4.0 to 4.6) but measured use of 7% to 10%. The data shows the gap, not the cause, and Microsoft's own reports point to a possible measurement effect for three of the six. Its H2 2025 report says the Chinese chatbot DeepSeek surged in Russia, Cuba and Belarus, places where US services face restrictions, and estimates DeepSeek's market share at 43% in Russia, 49% in Cuba and 56% in Belarus (89% in China). Its Q2 2026 report says the current measure leaves out several widely used domestic tools in China, and that counting more tools in the next report should raise measured use in nearly all countries, most of all in China. Neither report says how much use in Russia, Cuba or Belarus is missing from the figures above, so those three gaps may be partly a measurement effect. We have no equivalent evidence for Ukraine, Syria or Armenia. Treat each of these as an open question, not a finding about demand.

For workers, the practical reading is cautious. A high exposure score with low measured use does not mean safety. It means the tools have not yet reached the workforce at scale in this measure, which is a different position from a country where exposure is low.

The G7: exposure and use rank very differently

In our G7 ranking, Germany and Canada have the highest exposure and Italy the lowest. On measured use the order changes completely.

Source: same as above, G7 sorted by measured use. Positive gap means use above the trend line.

France and the United Kingdom use AI more than their exposure alone would predict. Germany has the highest G7 exposure score but ranks 23th overall on use, below the United States. Japan has the lowest measured use in the G7 (24.7%, rank 45). Rank on exposure tells you how much of the workforce is positioned in tasks AI can do. Rank on use tells you how much of that is currently happening.

What we left out: 44 economies with shared values

Microsoft's dataset lists 147 economies. For 44 of the 146 that match our data, the published figure is identical to that of at least two other economies: 10.4% for 13 economies across West Africa and Myanmar, 8.0% for nine East and Central African economies, 9.1% for five, and so on. Identical values to one decimal place across that many countries look like a shared regional estimate rather than separate measurements. Microsoft's Q2 report does not explain the repeats, so we treat them as estimates and exclude them from the correlation, the trend line and every table.

That is nearly a third of the countries we can match, and it matters for who gets a fair reading. Central Asian economies such as Uzbekistan and Turkmenistan sit in these groups, so this analysis cannot say anything about them. The shared flag is in meta.json as ai_use_share_shared.

What this means

Exposure is potential and use is progress, and a country can be ahead on one and behind on the other. If you work in a country with high exposure and low measured use, the data does not say your job is safer. It says the diffusion curve has not caught up with the workforce structure yet. If you work in a country with high use, the effect of AI on your occupation is more likely to be happening now than in a country with the same exposure score and lower use.

Neither measure is a forecast of job losses. Our exposure scores are informed judgements by a language model. Microsoft's use figures are estimates from product telemetry. Where they disagree, treat it as a prompt to look closer, not a conclusion. Microsoft has also said its next report will count more AI tools, which it expects to raise measured use in nearly all economies, so the Q2 figures here will be revised.

Spotted an error or know a better source? Report it and we will check it against the source data.