Sensitivity and network position
- Rank
- 170of 250
- Sensitivity
- 4.2rank 216
- Breadth
- 0of 20 scenarios High or above
- PageRank
- 32.5rank 136
- Eigenvector
- 1.5
- Betweenness
- 15.3
- Links
- 12trends at Moderate similarity or above
- Composite
- 16.1
Attention over three years
How much this trend is read about on Wikipedia and searched for on Google, week by week, set against the typical trend in the list so that what moves every trend at once is taken out. 100 is this trend's own three-year average. The score measures public attention, which is not the same as the strength of the force: a trend can deepen unnoticed, and a news event can lift attention for a fortnight.
- Share of attention
- 0.08%rank 197 of 250 · a year earlier 0.06%
- Now
- 184
- Over a year
- +54%Surging
- Over three years
- +307%
- Peak
- 25528 Jun 2026
- Sources
- Bothagreement 0.77
- Coverage
- 2languages · 53 nations
Trailing 4-week average. 100 is this trend’s three-year average.
By language
Wikipedia reading in each language edition where the trend’s pages are read enough to measure (2 of 28). Share is of all the readers counted.
| Language | Share | Now | Over a year | Three years |
|---|---|---|---|---|
| English | 96% | 217 | +90% | |
| Japanese | 4% | 121 | +42% |
By country
Google search interest over the three years, as a share of each country’s searching. 100 is the nation where interest is highest. The search terms are English, which favours countries that search in English.
100
100
80
31
31
27
21
20
19
18
17
16
The 12 highest of 53 nations with a reading. All countries (CSV).
By region and G20 member
Attention to this trend inside each place, from Google searches made in its countries and Wikipedia reading in the languages mostly read there, averaged where both exist. Each line compares the trend with its own past in that place; the lines do not compare places.
Regions
| Place | Read from | Now | 90 days | 1 year | 3 years | Three years |
|---|---|---|---|---|---|---|
| North America | Google in 2 countries + Wikipedia in English | 184 | −17% | +48% | +454% | |
| Europe | Google in 1 country | 209 | −30% | +209% | +1442% | |
| Indo-Pacific | Google in 2 countries + Wikipedia in Japanese | 126 | −59% | +104% | +251% | |
| South Asia | Google in 1 country | 138 | −51% | +69% | +196% |
G20 members
| Place | Read from | Now | 90 days | 1 year | 3 years | Three years |
|---|---|---|---|---|---|---|
| Canada | 176 | −53% | +150% | +1392% | ||
| India | 138 | −51% | +69% | +196% | ||
| Japan | Google + Wikipedia in Japanese | 121 | −63% | +108% | +244% | |
| South Korea | 146 | −67% | +199% | +3878% | ||
| United Kingdom | 209 | −30% | +209% | +1442% | ||
| United States | Google + Wikipedia in English | 180 | −13% | +33% | +424% |
Share of attention by region
GINC Share of Attention: this trend's share of all the public attention the trends in the list draw, worldwide and in each region, week by week (a 4-week average). Each place is read from the Wikipedia languages mostly read there and Google searching in its countries; where a place's language is shared with others, as English is, Google stands alone.
| Place | Share, last 13 weeks | Rank there | A year earlier | Read from |
|---|---|---|---|---|
| World | 0.08% | 197 of 250 | 0.06% | Wikipedia reading in all 28 languages, and Google searching worldwide |
| Asia Pacific | 0.04% | 233 of 250 | 0.03% | Wikipedia reading in the region's own languages, not English; Google searching in 15 countries, not China |
| Europe | 0.03% | 239 of 250 | 0.03% | German, French, Italian, Polish, Dutch and Swedish reading; Google searching in 10 countries, the UK among them |
| Africa | 0.02% | 209 of 250 | 0.02% | Google searching in 7 countries; Afrikaans and Swahili reading reaches too few trends to count |
| South America | 0.02% | 238 of 250 | 0.02% | Spanish and Portuguese reading, Spanish also read in Spain; Google searching in 5 countries |
| North America | 0.04% | 192 of 249 | 0.04% | Google searching in the United States, Canada and Mexico; English reading cannot be split by country |
| Oceania | 0.04% | 191 of 249 | 0.03% | Google searching in Australia and New Zealand; English reading cannot be split by country |
| United States | 0.06% | 169 of 249 | 0.05% | Google searching; English reading cannot be split by country |
| United Kingdom | 0.04% | 177 of 249 | 0.03% | Google searching; English reading cannot be split by country |
| India | 0.05% | 151 of 249 | 0.02% | Google searching; Hindi, Bengali and Tamil reading reaches too few trends to count, and English cannot be split by country |
| China | · | · | · | Wikipedia and Google are blocked in China; no open source counts reading there |
| Russia | · | · | · | Not read here. Russian reading, which is also done across the former Soviet states; Google's figures for Russia cannot be bought |
| Japan | 0.08% | 198 of 250 | 0.08% | Japanese reading and Google searching |
| Germany | 0.03% | 229 of 250 | 0.04% | German reading and Google searching |
| France | 0.02% | 243 of 250 | 0.02% | French reading, also done in Africa and Canada, and Google searching |
| Italy | 0.02% | 235 of 250 | 0.03% | Italian reading and Google searching |
| South Korea | 0.04% | 204 of 250 | 0.05% | Korean reading and Google searching |
| Indonesia | 0.02% | 209 of 250 | 0.01% | Google searching; Indonesian reading reaches too few trends to count |
| Brazil | 0.02% | 224 of 250 | 0.02% | Portuguese reading and Google searching |
| Türkiye | 0.00% | 211 of 250 | 0.00% | Google searching; Turkish reading reaches too few trends to count |
Relation to each scenario
A trend relates to a scenario when it changes how likely the scenario is, how hard it lands, or is itself sharply changed by it. Each score is the median of three scores given separately by the members of the model panel.
Related trends
Similarity = 0.3 × shared tags (Jaccard) + 0.3 × likeness of scenario profiles (cosine) + 0.3 × likeness of names and descriptions (TF-IDF cosine) + 0.1 if in the same capability domain, cut into seven levels at 0.12, 0.22, 0.33, 0.45, 0.58, 0.72. Shown at Moderate and above.
| Similarity | Trend | Theme | Rank |
|---|---|---|---|
| 5 · Very high | Compute scaling & AI infrastructure | AI, compute & quantum | 133 |
| 4 · High | Automated AI research | AI, compute & quantum | 83 |
| 4 · High | General-purpose AI capability | AI, compute & quantum | 95 |
| 4 · High | Open-weight AI diffusion | AI, compute & quantum | 149 |
| 4 · High | Frontier model concentration | AI, compute & quantum | 168 |
| 3 · Moderate | AI & knowledge-work automation | People, health & work | 53 |
| 3 · Moderate | Capital concentration in AI | AI, compute & quantum | 67 |
| 3 · Moderate | AI safety & governance | State capacity & governance | 69 |
| 3 · Moderate | AI chips & accelerator supply | AI, compute & quantum | 111 |
| 3 · Moderate | Data-centre power & water demand | Energy, networks & climate resilience | 144 |
| 3 · Moderate | Advanced semiconductors | AI, compute & quantum | 165 |
| 3 · Moderate | General-purpose & humanoid robotics | AI, compute & quantum | 225 |