HBR ranking reveals that the race for AI leadership is not just about technology but also about regulations, investments, talent, diversity, and digital foundations
The grip artificial intelligence has gained over humanity in 2023 – orat least the increase in conversations about whether it will be a force for revolutionary good or apocalyptic destruction – hasled AI to be given the title of “word of the year” by the makers of Collins Dictionary. The European Union has come out with a regulatory framework to ensure safe AI development, and holding humans as ultimately responsible for AI decisions, with clear accountability mechanisms. This positions EU as a global leader in AI governance. And as the year comes to a close in a just a few weeks, Harvard Business Review has come up with a ranking of countries based on their AI prowess. It shows US as a clear leader with a score of 90.7 distantly followed by China with 68.5. India ties up with Belgium for the 14th position with a score of 46.7.
An emerging geography
AI has been the talk of the town for quite some time now. It has become ubiquitous, from businesses and schools to Hollywood and election campaigns. Even as some investors complain of AI fatigue, there is no escaping its enormous potential. Generative AI alone could affect 300 million jobs and create as much as $4.4 trillion annually in new economic value worldwide, according to some analysts. It is not just the tech powers in competition to capture the value; there is a global race among nations for AI leadership – an emerging geography of AI.
This race will determine which applications get priority, where innovative capacity and investments can be focused, what AI regulations emerge, what risks might arise, which biases and data deficiencies get heightened or mitigated, and whether competitive innovation gets prioritised over safety and public oversight. The geography of AI is key to the technology’s future.
The US and China are both vying to be the world’s top AI economy, locked in a “digital cold war,” but the larger cast of nations is evolving. The EU had led the effort to regulate AI in democratic societies, and now the US is playing catch-up, while Canada is the first country with a national AI strategy. As some countries tighten AI regulation, others might lure cutting-edge companies by promising unfettered “pro-innovation” environments. Alternatively, others might attract those that prefer safety or openness. Populous developing countries, such as India, are aiming to be the leading data-rich nations, with fast-growing pools of data. Despite operating under sanctions, Iran has declared its aspirations to be among the world’s top 10 in AI. This is likely to ratchet up worries about the national security risks of AI and put pressure on other actors in the region to aim for a similar goal.
US-China lead, butothers catching up fast
Given the high stakes of this race, which countries are in the lead? Which are gaining on the leaders? How might this hierarchy shape the future of AI? Identifying AI-leading countries is not straightforward, as data, knowledge, algorithms, and models can, in principle, cross borders. Even the US-China rivalry is complicated by the fact that AI researchers from the two countries cooperate – and more so than researchers from any other pair of countries. Open-source models are out there for everyone to use, with licensing accessible even for cutting-edge models. Nonetheless, AI development benefits from scale economies and, as a result, is geographically clustered as many significant inputs are concentrated and don’t cross borders that easily.
Using data from over 20 different institutional sources – including public databases such as the ITU and the World Bank and proprietary data partnerships such as SeekOut and George Washington University’s Data Governance Hub – as well as its own databases and models from Digital Planet, HBR has mapped that emerging geography of AI leadership, identifying where – and how – the forces that drive AI development are lining up.
The race for AI leadership is not just about technology but also about regulations, investments, talent, diversity, and digital foundations. The index introduced here takes into consideration these factors to compare AI across countries:
- Data: The volume and complexity of the core resource used to train and improve algorithms.
- Rules: How data can be accessed.
- Capital: The human, financial, diversity, and digital foundations for building AI.
The US leads in data and capital while China leads in rules. However, other countries are catching up fast. Canada has made significant strides in capital and rules while India has emerged as a leading country in data.
Understanding the emerging geography of AI leadership is essential for businesses and governments alike. As the race for AI leadership intensifies, it will shape which AI applications are prioritised, which societies and sectors of the economy get the most benefits, what data are used to train algorithms, and which biases get included and which get mitigated. It is time for countries to take a strategic approach towards AI development and regulation to ensure that they stay ahead in this race.