The new geography of AI power in a divided world

The global AI picture is no longer a single race with one leaderboard. The United States still sets the pace in frontier model creation and private investment. China is the closest large-scale challenger, with enormous depth in papers, patents, industrial deployment, and consumer uptake. The EU looks weaker if you judge only by blockbuster foundation models, but much stronger if you look at enterprise use, industrial digitisation, and rule-making. India is rising through talent, open-source activity, and state-backed infrastructure building. Africa is moving more slowly at continental scale, yet its policy direction is clearer than many outsiders assume, and its most realistic near-term gains are likely to come from focused, sector-level AI rather than giant frontier systems. Russia remains active, but its trajectory is narrower, more state-led, and harder to compare cleanly with the others.

That distinction matters because “AI level” can mean at least five different things. It can mean who trains the strongest models. It can mean who uses AI in business at scale. It can mean who has the deepest talent pool, the best chips and data centres, the most patents, or the strongest public institutions for deployment. Regions that look dominant on one axis can look ordinary on another. Europe is the clearest example: modest frontier-model output, solid industrial uptake, serious regulatory muscle. India is another: a surging talent base and open-source footprint, still catching up on compute and domestic platform scale. Africa looks weaker in headline rankings, yet the continental policy push is now more coherent than it was two years ago.

A direct answer to the question is this: China and the United States sit at the top of the global AI system, but for different reasons; the EU is stronger in diffusion than in frontier spectacle; India is the fastest-rising large talent base; Africa is still early but no longer strategically absent; Russia is still in the field, though on more constrained terms; and smaller countries such as Singapore, South Korea, the UAE, and a few others are proving that national AI strength is not only a matter of size. Stanford’s AI Index notes that model development is already becoming more global, with notable launches from the Middle East, Latin America, and Southeast Asia.

AI no longer has one scoreboard

The neatest mistake people make is to treat AI like Olympic medal tables. It does not work that way. A country can lead in private investment and still fail to spread AI broadly across its economy. It can flood the patent system and still trail in trusted public adoption. It can produce strong open-source developers and still depend on foreign compute. Stanford’s 2025 AI Index makes the split visible. In 2024, U.S.-based institutions produced 40 notable AI models, China produced 15, and Europe produced three. Yet China still led in AI publications and patents, and business usage of AI rose sharply around the world. The same report says 78% of organizations reported using AI in 2024, up from 55% a year earlier.

There is also a gap between building AI and using AI well. The OECD’s latest work on firms is useful here because it moves away from hype and asks a blunt question: are companies actually putting AI into operations? Across OECD countries where data are available, 20.2% of firms reported using AI in 2025, up from 14.2% in 2024 and 8.7% in 2023. That is real diffusion, but it is still far from universal. The OECD also finds a persistent adoption gap between large firms and SMEs, with smaller firms still blocked by weak digital maturity, limited compute and data access, skill shortages, and financing constraints.

That is why regional comparisons need a layered view. A serious map of global AI has to look at frontier research, patents, chips and compute, developer depth, business adoption, public-sector readiness, and institutional capacity. UNCTAD frames this well. Its 2025 report argues that readiness depends heavily on infrastructure, data, and skills, and it groups countries not by buzz but by their capacity for AI adoption and development. In its framework, some countries are “leaders,” some “creators,” some “practitioners,” and many remain “laggards.” That is a far better lens than the usual headline contest between Washington and Beijing.

A compact snapshot of the big three non-US blocs

This is a condensed reading of current evidence rather than a league table. Europe’s firm uptake is rising fast, China remains unmatched at scale outside the United States, and India’s rise is driven by developers and skills more than by sheer capital intensity.

Europe’s position is stronger than the mood around it

A lot of commentary treats Europe as if it missed AI entirely. That reading is lazy. Europe is not absent from AI. It is weaker at producing the biggest frontier models, but much stronger at turning AI into ordinary business infrastructure than the public argument often admits. Eurostat reported that 20.0% of EU enterprises with at least 10 employees used AI technologies in 2025, up from 13.5% in 2024. The leaders inside the EU were Denmark, Finland, and Sweden. That is not a continent standing still.

The problem is that Europe’s strengths do not always produce glamorous headlines. Stanford’s AI Index says Europe produced only three notable AI models in 2024, against 40 in the United States and 15 in China. On frontier systems, Europe is plainly behind. On the other hand, the EU has something many rivals do not: a dense industrial base full of firms that already know how to absorb digital tools into manufacturing, services, logistics, healthcare, and regulated sectors. That matters because the next stage of AI value will not come only from model labs. It will come from who can embed AI in thousands of boring but profitable workflows.

The European Commission’s current approach shows that Brussels understands this. The 2025 AI Continent Action Plan aims to make Europe a global AI leader, and the later Apply AI Strategy is explicitly about raising adoption across industrial and public sectors, especially among SMEs. That is a revealing shift. Europe is no longer speaking only in the language of safety and law. It is speaking more openly about deployment, competitiveness, and sector use. The old caricature of Europe as a place that regulates first and builds later is now too crude. Europe still regulates hard, but it is also trying to turn that regulatory base into a deployment advantage.

Even so, Europe’s constraints are real. Venture financing remains smaller than in the United States, and OECD figures show that U.S. investors historically dominated global AI VC activity, with Chinese investors second and EU27 investors well behind. Europe also faces fragmentation across languages, markets, procurement systems, and defense-industrial policy. A company that wants to deploy at continental scale still faces more friction than it would in the United States or China.

The deeper truth is that Europe’s AI story is not about winning the frontier-model beauty contest. It is about whether it can become the world’s most dependable region for trusted industrial AI. If that happens, Europe may end up with a less noisy but more durable position than many critics expect. If it fails, the risk is not that Europe will have no AI at all. The risk is that it will become a rich deployment market for systems built somewhere else.

China pairs research scale with mass deployment

China’s position is easier to summarize and harder to measure. Easier, because the scale is obvious. Harder, because outside observers do not see the whole system clearly. Oxford Insights notes that the global leadership picture is increasingly bipolar, with the United States and China as the two dominant forces, while also warning that China’s position in external rankings is likely understated because of limited transparency and the fact that much of its domestic ecosystem operates outside Western platforms and standards.

What can be measured is already impressive. Stanford says China produced 15 notable AI models in 2024 and closed much of the quality gap with U.S. models on major benchmarks. China also continues to lead in AI publications and patents. Stanford’s research chapter says China accounted for 69.7% of all AI patent grants as of 2023. WIPO’s generative AI patent work adds another striking point: its patent landscape report documented 54,000 GenAI inventions through 2023, and Chinese inventors were filing by far the most. China is not just adopting AI. It is industrialising AI.

The consumer side is just as important. CNNIC reported 249 million generative AI users in China by December 2024. By June 2025, an official government summary citing CNNIC said that number had reached 515 million, with user penetration at 36.5%. Those numbers tell you something deeper than market size. They show that China’s digital platforms, payment systems, and online service habits create unusually fertile conditions for rapid AI diffusion once products are approved and scaled.

This is where China differs from Europe most clearly. Europe often has a stronger language of trust and governance. China has scale, speed, and a tighter link between industrial policy and deployment. If the state wants AI pushed into manufacturing, logistics, health, education, or public administration, it has far more direct leverage than Brussels does. That does not guarantee better outcomes. It does guarantee a different tempo.

China still has weak points. Access to the most advanced chips remains contested, and benchmarking Chinese model performance against Western peers is not always straightforward. International visibility into deployment quality, safety practices, and public-sector use is partial. Yet those caveats should not obscure the basic point. China is already one of the two central AI powers in the world, and it is probably the strongest large-scale deployment machine outside the United States. Anyone who still talks about AI as if China is mostly “catching up” has not updated their map.

India is turning talent into national leverage

India’s AI position looks different again. It is less capital-heavy than the U.S. path, less vertically integrated than China’s, and less regulation-led than Europe’s. Its strongest asset is people. India’s rise is first a talent story, then a software and infrastructure story, and only after that a frontier-model story. Stanford’s 2025 AI Index says India had one of the highest AI skill penetration rates in the world, at 2.5 relative to the global average. The same report shows India as the second-largest contributor to AI projects on GitHub in 2024, with 19.9% of the total, just behind the United States and slightly ahead of Europe. UNCTAD also notes that India, China, and Brazil have produced large pools of AI developers.

That matters because AI strength does not emerge only from a handful of superstar labs. It also comes from how many engineers, founders, integrators, data specialists, and domain teams can actually build and adapt systems. India’s scale in software services, English-language reach, startup culture, and technical education gives it a base that few countries can match. Large countries can absorb mediocre per-capita indicators if their absolute talent pool is huge. UNCTAD says exactly that in effect: the United States has the biggest developer base, followed by India and China, and country size changes strategic options.

The state has started trying to close the next gap, which is infrastructure. The IndiaAI Mission says its aim is to democratise computing access, improve data quality, expand skill development, and support innovation. The Principal Scientific Adviser’s office describes the IndiaAI Dataset Platform as a unified repository of high-quality, anonymised, non-personal datasets, and it says India has established AI Centres of Excellence in healthcare, agriculture, and sustainable cities, with a fourth one for AI in education announced in Budget 2025. This is a recognizable national strategy: use public platforms, public data, and public infrastructure to widen the base before trying to dominate the top end.

India still has real constraints. It does not yet have the same compute depth, semiconductor position, or foundation-model prestige as the U.S. or China. Much of its advantage still sits in talent supply and open development rather than in owning the most powerful proprietary stacks. That is not a minor issue. AI economics increasingly reward those who control compute, distribution, and data, not just those who write code.

Still, India may be the clearest example of a country that is not waiting to become rich before becoming consequential in AI. UNCTAD’s readiness work shows India performing above what its income level would predict in frontier-technology readiness. That does not mean India has already arrived as a full-spectrum AI superpower. It does mean that anyone assessing global AI through venture capital alone will underrate it badly.

Africa’s AI future depends on infrastructure more than slogans

Africa is where the global AI debate often becomes shallow. People swing between two bad extremes: either the continent is dismissed as too early to matter, or it is talked about in broad moral language with almost no hard institutional detail. Both miss the point. Africa is not a single AI market, not a single readiness profile, and not a passive spectator. What it has lacked is not strategic awareness. It has lacked enough power infrastructure, data infrastructure, compute access, financing depth, and specialized skills to scale fast across many countries at once.

The policy movement is clearer than outsiders often realise. The African Union adopted its Continental AI Strategy in July 2024. The AU describes AI as a strategic asset for Agenda 2063 and says the strategy is Africa-centric, development-focused, and built around ethical, responsible, and equitable practice. That is not a trivial milestone. It gives the continent a shared political frame, even if national execution will vary widely. The CIPIT State of AI in Africa report says the AU strategy has become the key continental instrument shaping national efforts, rights-based governance debates, and thinking about infrastructure, startup ecosystems, priority sectors, and cross-border data flows.

The harder part is the base layer. The World Bank’s 2025 AI foundations report says low- and middle-income countries face steep challenges in adapting or deploying AI effectively and at scale, though it also points to the emergence of “Small AI” solutions. UNCTAD is even blunter: many developing countries remain in the early stages of AI adoption and development and often lack dedicated strategies or instruments for AI-specific needs. In UNCTAD’s framework, readiness turns on infrastructure, data, and skills. That sounds dry, but it is the whole game. Without reliable electricity, affordable connectivity, strong data systems, and enough technical workers, AI remains a pilot project rather than an economic layer.

That is why Africa’s near-term path will probably look different from China’s or even India’s. The most productive uses are likely to be narrower and closer to sector problems: agriculture support, local-language tools, health triage, public-service automation, logistics, education support, and financial inclusion. The World Bank’s emphasis on “Small AI” points in that direction. So does the AU strategy’s stress on development outcomes rather than frontier prestige. Africa does not need to train the next giant general model to get major value from AI. It needs systems that work under local constraints and solve real bottlenecks. That is an inference from the current readiness gap, but it is a practical one.

There is also a geopolitical risk in delay. UNCTAD warns that 118 countries, primarily in the global South, are not parties to sampled international AI initiatives or instruments. That leaves many countries outside the room where norms, standards, and supply chains are being shaped. Africa’s continental strategy is partly a response to that danger. It is a move to avoid becoming only a downstream consumer of AI systems designed elsewhere, under value assumptions set elsewhere.

So where is Africa’s AI level right now? Politically, the continent is more organized than before. Institutionally, it is still uneven. Commercially, there are real pockets of activity. Structurally, the ceiling remains low until infrastructure catches up. That is a more honest answer than either triumphalism or dismissal.

Russia is moving on a narrower, state-centered track

Russia is the hardest region in this comparison to place cleanly because the public data are less open, the domestic tech stack is more insulated, and wartime pressures change incentives. Even so, a few points are clear. Russia is not out of AI. It is still investing politically, institutionally, and in parts of the enterprise sector. But it is pursuing a narrower path than China, India, or the EU. The Kremlin has kept AI high on the strategic agenda through the National Strategy for the Development of Artificial Intelligence through 2030, and official 2025 discussions around AI and high-performance computing show that the state still treats compute capacity as a strategic matter.

On enterprise use, the available Russian data are mixed but not negligible. HSE University reported in late 2025 that 29% of enterprises in Russia had implemented AI, placing Russia above the EU in that survey and below the United States, India, and Singapore. Another HSE summary said that in 2023 nearly one-third of domestic companies reported using AI. Those are meaningful numbers, though they should be handled carefully because survey definitions and cross-country comparability vary.

What Russia does not appear to have is a broad, openly visible AI ecosystem comparable to the U.S. or China in frontier research, capital markets, platform scale, or international developer influence. Its strength sits more in state direction, domestic champions, security-sensitive applications, and sector-specific deployment. That can still produce real capability. It also makes Russia more inward-looking. The country may continue to build useful AI in defense, public administration, industrial settings, and domestic consumer ecosystems without shaping the global mainstream in the same way as the top two powers.

That narrower path could still matter. Large states do not need to win the whole global market to get strategic value from AI. They need enough domestic competence in compute, models, cybersecurity, language systems, automation, and decision support to reduce dependence on rivals. Russia’s official language around sovereign capability points in that direction. The question is not whether Russia has AI. It plainly does. The question is whether its AI system will remain broad enough, open enough, and commercially dynamic enough to stay competitive outside its own protected space.

A compact snapshot of the harder comparisons

These cases matter because they show that AI strength does not spread evenly with GDP alone. Some regions are held back by infrastructure, some by capital access, some by market size, and some by fragmented institutions. Others outperform because they align policy, skills, and deployment more tightly than larger economies do.

Smaller powers are rewriting the middle of the map

The usual AI conversation obsesses over the U.S.-China rivalry, with Europe and India treated as supporting actors. That misses the middle of the map, where some of the most interesting movement is happening. Smaller states are proving that AI relevance does not always require continental scale. Stanford’s Global AI Vibrancy Tool paper placed the United States first, China second, and the United Kingdom third for 2023, while also highlighting the rise of smaller nations such as Singapore, especially on a per-capita basis. The tool was built precisely to show that national AI standing is multidimensional and not reducible to raw size.

Oxford Insights reaches a similar conclusion from the government side. Its 2025 Government AI Readiness Index assesses 195 governments and describes a multipolar landscape even while acknowledging the dominant weight of the U.S. and China. The value of that index is not that it hands out medals. It shows where governments are actually capable of enabling AI for public benefit, not just talking about it. Countries with cohesive administration, strong digital public services, good data systems, and relatively fast policy coordination can often move faster than bigger but more fragmented economies.

UNCTAD’s work adds another angle. It notes that countries such as Brazil, China, India, and the Philippines outperform what their income levels would predict in frontier-technology readiness. That matters for “elsewhere” because the next meaningful AI nodes are not all going to be in North America, Western Europe, or East Asia. Some will emerge from countries that combine large domestic demand with improving digital infrastructure. Others will come from compact, high-capacity states that use policy and procurement intelligently.

The broad implication is straightforward: the next wave of AI geography will be more distributed than the first wave of headline attention. Model-building at the absolute frontier may remain concentrated. Useful AI capability will not. Middle powers and smaller states can still gain a lot if they focus on public datasets, cloud access, language tools, education, SME adoption, and sector pilots that graduate into routine systems. In that sense, the most interesting contest after the U.S. and China may be less about who trains the biggest model and more about who builds the most workable national ecosystem.

Adoption capacity is the real dividing line

After you strip away the noise, one dividing line matters more than the rest: can a region connect compute, data, skills, institutions, and demand well enough for AI to move from demos into daily use? OECD work on SMEs lists the practical enablers clearly: connectivity; data, algorithms and compute; skills; and finance. UNCTAD boils the readiness side down to infrastructure, data, and skills. The World Bank’s AI foundations report says much the same in development language. Different organizations, same diagnosis.

That diagnosis explains the current regional picture better than ideology does. The EU already has enough firms and systems to absorb AI if it can reduce fragmentation and speed deployment. China has the scale and policy machinery to push adoption quickly. India has the talent and an emerging public infrastructure strategy, but needs deeper compute and product ecosystems. Africa has clear use cases and growing policy coordination, but the base layer is still too thin in many countries. Russia has state focus and some enterprise use, but a narrower ecosystem and weaker international visibility. Smaller high-capacity states can outperform because they align the pieces tightly.

That is also why debates about who is “winning” often land in the wrong place. If you only count frontier models, you underrate Europe. If you only count regulation, you underrate China. If you only count venture funding, you underrate India. If you only count today’s data-centre density, you underrate Africa’s policy shift. If you only count public visibility, you misread Russia’s internal state push. AI power is becoming a layered stack, not a single ranking.

The next few years will probably make the differences sharper. More countries will buy or lease compute instead of owning everything outright. More firms will use smaller and domain-specific systems rather than frontier models directly. Governments will matter more, not less, because procurement, data policy, energy, training, and standards are becoming central to deployment. Stanford’s AI Index already shows governments getting much more active on regulation and policy, while OECD and World Bank work show the deployment gap moving into the heart of economic policy.

The next phase will reward builders, not loud talkers

The cleanest way to end the comparison is to separate prestige AI from working AI. Prestige AI is the visible contest over flagship models, giant rounds, and spectacular benchmarks. Working AI is the slower spread of systems into offices, factories, logistics chains, classrooms, clinics, farms, and government workflows. Both matter. Only one of them touches most economies.

On prestige AI, the map is still led by the United States, with China closest behind. Europe is not absent, but it is not central. India is rising, though not yet at the top frontier tier. Russia is present on narrower terms. Africa is mostly outside that layer for now. On working AI, the story changes. Europe is more competitive than the frontier narrative suggests. China is formidable because deployment can move so fast. India has the human base to become far more important than its current capital stock implies. Africa has room to make highly practical gains if infrastructure improves. Smaller high-capacity states may keep surprising larger rivals.

So what is the level of AI and AI use in the EU, China, Russia, India, Africa and elsewhere? The EU is strong in adoption and governance, China is strong across scale and deployment, India is rising through talent and public infrastructure, Africa is strategically awake but structurally constrained, Russia remains active but narrower, and the rest of the world is more competitive than the old center-periphery story suggests. That is the real map. It is uneven, messy, and much more interesting than a single leaderboard.

FAQ

Author:
Jan Bielik
CEO & Founder of Webiano Digital & Marketing Agency

This article is an original analysis supported by the sources cited below

The 2025 AI Index Report
Stanford’s main 2025 synthesis of global AI models, investment, regulation, public sentiment, and organizational adoption.

Research and Development | The 2025 AI Index Report
Stanford’s detailed section on AI publications, patents, notable models, and hardware trends.

CHAPTER 4: Economy
Stanford’s economy chapter with data on AI skill penetration, labor-market indicators, and talent distribution.

Which countries are leading in AI?
Stanford’s interactive cross-country tool for comparing national AI vibrancy across multiple pillars.

The Global AI Vibrancy Tool November 2024
Methodology paper behind Stanford’s vibrancy rankings, useful for cross-country comparison and per-capita interpretation.

The Adoption of Artificial Intelligence in Firms
OECD’s broad evidence base on how firms are adopting AI and what enables or slows diffusion.

Artificial intelligence
OECD overview page with current figures on firm adoption and investment patterns across OECD countries.

AI adoption by small and medium-sized enterprises
OECD paper on the persistent adoption gap between SMEs and larger firms, and the practical barriers behind it.

20% of EU enterprises use AI technologies
Eurostat’s latest headline figures on AI use among EU enterprises in 2025.

Usage of AI technologies increasing in EU enterprises
Eurostat’s 2024 benchmark for EU business adoption, useful for showing the pace of growth.

European approach to artificial intelligence
European Commission overview of the AI Act, AI Continent Action Plan, and Apply AI Strategy.

The 55th Statistical Report on China’s Internet Development
Official CNNIC report with consumer and platform-level figures, including China’s generative AI user base.

China’s generative AI users double to 515 mln: report
Official English-language government summary of updated CNNIC usage data for China.

China-Based Inventors Filing Most GenAI Patents, WIPO Data Shows
WIPO summary of its generative AI patent landscape, highlighting China’s lead in filings.

INDIAai | Pillars
Official IndiaAI Mission page outlining India’s national push on compute, datasets, innovation, and skills.

Artificial Intelligence | Principal Scientific Adviser
Indian government overview of AI mission initiatives, dataset platforms, and Centres of Excellence.

Continental Artificial Intelligence Strategy
African Union’s official continental framework for Africa-centric, development-focused AI policy.

Digital Progress and Trends Report 2025: AI Foundations
World Bank report on the foundational conditions countries need to adopt and scale AI.

Technology and Innovation Report 2025: Inclusive artificial intelligence for development
UNCTAD’s main 2025 report on inclusive AI, global divides, and national readiness.

Technology and Innovation Report 2025 (Overview)
UNCTAD overview with the adoption-and-development readiness framework and country groupings.

Government AI Readiness Index 2025
Oxford Insights’ global index focused on how ready governments are to enable and govern AI.

Artificial Intelligence Transforms Employment in Russian Companies
HSE University report with survey-based evidence on AI implementation in Russian enterprises.

HSE’S Achievements in AI Presented at AIJ
HSE summary highlighting Russian enterprise AI adoption and domestic research signals.

AI Journey international conference
Official Kremlin page showing the continued strategic importance of AI in Russia’s state agenda.

News – The Russian Government
Official Russian government item on high-performance computing capacity and AI-related infrastructure priorities.