Why nations rise and decline has been a question of scholarly analysis for long, and various academics have tried to examine and even offer prescriptive guidelines from time to time. However, as is the nature of history, it’s difficult to proffer any fixed reason or explanation as different factors have led to the rise and fall of nations over centuries.
In the current era, artificial intelligence (AI) has emerged as the newest benchmark for national power. Once, it was naval superiority, then industrial prowess and nuclear capabilities. Today, AI is increasingly viewed as the key enabler of economic growth, military innovation, and societal transformation. The US and China have for long positioned themselves at the heart of this fierce technological competition. However, India, too, has entered the race recently and is intent on carving its own path in the global race.
To reduce dependency on foreign AI systems and assert digital sovereignty, the government some time back selected Sarvam AI as the lead firm to build India’s own foundational large language model. Recently, three additional firms, Soket AI, Gan AI, and Gnani AI, have been chosen to join this effort. The thinking is that control over core technologies is equivalent to control over economic and strategic autonomy.
In his seminal work The Rise and Fall of the Great Powers, Paul Kennedy argues that the fortunes of great nations have historically depended on the delicate balance between economic strength and military ambition. According to Kennedy, states rise when they possess the economic capacity to support expansive goals, and they fall when they overreach, burdened by military commitments their economic base can no longer sustain, a condition he termed imperial overstretch. His analysis, spanning from 1500 to the late 20th century, shows how power has consistently gravitated toward nations that managed this equilibrium.
Contrasting Kennedy’s geopolitical-economic perspective, Daron Acemoglu and James A Robinson offer a fundamentally institutional argument in their Nobel Prize-winning work, Why Nations Fail. Their thesis puts forward the idea that political and economic institutions shape the trajectory of national development. Inclusive institutions, those that distribute power widely, protect property rights, and encourage innovation, lead to prosperity. In contrast, extractive institutions, those that concentrate power and suppress dynamism, lead to stagnation and eventual decline. For Acemoglu and Robinson, it is not merely the accumulation of resources or power that matters, but how those are governed and distributed within society. They caution that even resource-rich or technologically advanced nations can decline if their institutions stifle inclusive growth.
The latest to enter this dialogue is Jeffrey Ding, whose recent book, Technology and the Rise of Great Powers, presents a highly relevant dimension in the context of the AI revolution. Ding examines how AI technologies diffuse across nations and how this diffusion, not just invention, determines national power. His key insight is that it is not necessary to be the inventor of AI to benefit from it; what’s more important is how effectively a country can adopt, adapt, and apply AI technologies across sectors.
To support this, Ding draws on historical examples, such as Britain during the second industrial revolution which was overtaken in certain sectors by Germany and the US — not because it lacked invention, but because those nations were quicker to scale up and integrate technologies like chemical and electrical engineering into their industries. Similarly, post-war Japan became a tech powerhouse not by inventing new technologies, but by mastering diffusion — adopting global innovations and applying them efficiently to boost productivity and economic growth.
This idea is particularly relevant for countries like India, which may still not be at the cutting edge of foundational AI research but possess the scale, talent, and policy momentum to implement AI meaningfully within their unique socioeconomic contexts. The government, industry, and all stakeholders should look closely at Ding’s emphasis on diffusion. With its vast population, expanding digital infrastructure, and a burgeoning ecosystem of start-ups and developers, India is well positioned to localise and deploy AI at scale, even if it has not invented much so far.
The interplay of theories shows that no single explanation is sufficient to capture the complexity of national rise and decline. Kennedy’s caution about overstretch remains relevant as nations invest heavily in AI without fully considering long-term economic sustainability or regulatory oversight. Acemoglu and Robinson remind us that AI in the hands of extractive institutions could lead to inequality and authoritarianism. Ding’s thesis offers a powerful lens to view the technological race, but it, too, must be situated within the broader political and institutional framework of each country.
AI is not merely a tool; it is a system-shaping technology that can redefine economic production, labour markets, and political governance. Therefore, for India, the challenge is not only to develop AI capabilities but also to ensure its institutions support their equitable diffusion and ethical use.
True, AI has become both the symbol and substance of national ambition, and no doubt invention is important in this race. But as Ding rightly observes, it is diffusion — the capacity to absorb and apply — which will yield the right results. And for this to happen, sectoral policies will not work, every policy step would need to examine how AI can be diffused. While it’s nobody’s case that invention and the drive towards making India atechnology product nation should be abandoned, it’s equally important to remember that diffusion trumps invention.