Concentric and incremental advances: How AI leadership is built
29 Jan 2026 22:27:30
Nil Konsam
At global platforms such as the World Economic Forum in Davos, credibility is earned through demonstrated capacity rather than asserted ambition. Statements are weighed against institutions, systems and outcomes. It is in this context that recent claims placing India in the top tier of Artificial Intelligence powers, alongside the United States and China, appear misaligned with observable realities.
Artificial Intelligence is an intricately complex and capital-intensive domain. Progress does not emerge from isolated successes or individual talent alone, but from institutionalised investments, system design and long term process discipline. Countries that lead in AI have done so by building foundations over decades, through education, sustained research funding, reliable energy, semiconductor capability and control over foundational software layers.
The Investment Background: Where the System Begins
AI outcomes are a lagging indicator of education and research investment made fifteen to twenty five years earlier. On this measure, the global landscape is relatively clear.
In absolute terms, the United States and China each invest close to one trillion dollars annually in education. Their combined public and private research and development spending exceeds $700 billion a year. Both allocate between 2.5 and 3.5 percent of GDP to R&D, supporting dense ecosystems of universities, National laboratories, industrial research centres and defence linked innovations.
India’s investment profile differs sharply. While education spending is substantial in nominal terms, it remains far below that of the leading economies. R&D expenditure is approximately $70 billion annually, less than one tenth of that of the frontrunners, and has remained below 0.7 percent of GDP for decades. This gap is not marginal; it establishes the upper bound of achievable technological outcomes.
Expecting comparable AI performance under these conditions reflects not ambition, but a misunderstanding of how complex technological systems evolve.
Infrastructure Before Intelligence
No country has achieved frontier AI capability without robust physical infrastructure. Where uninterrupted electricity cannot be reliably assumed, advanced AI systems cannot be sustained. Large-scale AI requires stable power grids, hyper scale data centres, continuous compute availability and extensive cooling capacity. In practical terms, training and operating frontier scale models requires near perfect uptime over extended periods, a condition that only a handful of National grids currently support. AI ultimately runs on electricity.
Semiconductor access is equally foundational. The United States maintains dominance through its control of chip design and privileged access to advanced fabrication. China, despite external constraints, has invested consistently and extensively in building domestic semiconductor capacity. India, by contrast, has yet to produce advanced logic chips at scale and remains dependent on external supply chains even for mid range fabrication. Without progress in this area, AI autonomy remains limited.
These constraints are not theoretical. Frontier AI training clusters operate at power densities and reliability thresholds where even brief interruptions impose prohibitive costs. Countries that lead in AI therefore treat grid reliability, data centre zoning and energy pricing as strategic inputs rather than auxiliary concerns. Where these prerequisites remain uneven, AI ambition predictably shifts from creation to consumption.
Foundational Software and System Depth
Technological power ultimately resides at the lower layers of the stack: operating systems, kernels, compilers and cloud orchestration. India has developed globally competitive service layer software firms, but it does not control a widely adopted operating system or the foundational AI frameworks shaping the field. Leadership at the application layer does not confer control over platforms.
Human capital alone cannot compensate for institutional gaps. India produces capable engineers in large numbers, yet many reach peak effectiveness only after entering foreign ecosystems that provide compute access, research funding and mature institutional support. This reflects not a shortcoming of individuals, but the decisive role of systems in enabling excellence.
This assessment does not discount India’s genuine strengths. The country has demonstrated notable capability in building large scale digital public infrastructure at low cost and population scale, particularly in identity, payments and platform governance. Its IT services sector remains globally competitive, and Indian engineers occupy influential roles across leading technology firms and AI research institutions worldwide. India is also a rapid adopter of applied AI in finance, governance, healthcare and enterprise services. These achievements are significant, but they represent proficiency in deployment and integration rather than leadership in foundational AI development. Maintaining this distinction is essential for accurate assessment.
It is sometimes argued that India need not pursue full stack leadership, that dominance in applied AI and large scale deployment is sufficient. This view underestimates how quickly strategic leverage migrates downward in the technology stack. As models, architectures and training paradigms evolve, those who control foundational layers set standards, capture disproportionate value and define the boundaries of permissible innovation. Applied excellence without foundational influence may deliver near term returns, but it leaves long term direction in the hands of others.
Overstatement, Insecurity and Global Perception
Seen against this backdrop, the defensive over assertion at Davos was unnecessary and counterproductive. The remarks it responded to were measured and diagnostic, identifying constraints alongside opportunities for cooperation. They did not question National standing.
Global forums operate on norms distinct from domestic debate. International audiences tend to value candour about limitations and sustained investment to address them. Overstatement risks shifting the discussion from collaboration to scepticism, with consequences for credibility in precisely the settings where it matters most.
Conclusion: Systems, Not Overstatement
The strategic choice facing India is therefore straightforward. Rather than pursuing symbolic parity, it must focus on structural depth : assured 24×7 power for compute infrastructure; a sustained increase in public R&D spending toward at least 2 percent of GDP; targeted and realistic semiconductor capability; and long horizon investment in foundational software, systems research and national research institutions.
AI leadership is not an event but a compound capability. It is achieved through long term strategic investment in incremental yet concentric advances across complex and difficult domains, steadily building intangible competencies that reinforce one another over time. It is not the product of simultaneous initiatives or declaratory ambition, but of disciplined execution sustained across decades. In advanced technology, credibility is not asserted at summits; it is accumulated quietly, in laboratories, power stations and balance sheets, long before it is recognised on global stages.
At global platforms such as the World Economic Forum in Davos, credibility is earned through demonstrated capacity rather than asserted ambition. Statements are weighed against institutions, systems and outcomes. It is in this context that recent claims placing India in the top tier of Artificial Intelligence powers, alongside the United States and China, appear misaligned with observable realities.
Artificial Intelligence is an intricately complex and capital-intensive domain. Progress does not emerge from isolated successes or individual talent alone, but from institutionalised investments, system design and long term process discipline. Countries that lead in AI have done so by building foundations over decades, through education, sustained research funding, reliable energy, semiconductor capability and control over foundational software layers.
The Investment Background: Where the System Begins
AI outcomes are a lagging indicator of education and research investment made fifteen to twenty five years earlier. On this measure, the global landscape is relatively clear.
In absolute terms, the United States and China each invest close to one trillion dollars annually in education. Their combined public and private research and development spending exceeds $700 billion a year. Both allocate between 2.5 and 3.5 percent of GDP to R&D, supporting dense ecosystems of universities, National laboratories, industrial research centres and defence linked innovations.
India’s investment profile differs sharply. While education spending is substantial in nominal terms, it remains far below that of the leading economies. R&D expenditure is approximately $70 billion annually, less than one tenth of that of the frontrunners, and has remained below 0.7 percent of GDP for decades. This gap is not marginal; it establishes the upper bound of achievable technological outcomes.
Expecting comparable AI performance under these conditions reflects not ambition, but a misunderstanding of how complex technological systems evolve.
Infrastructure Before Intelligence
No country has achieved frontier AI capability without robust physical infrastructure. Where uninterrupted electricity cannot be reliably assumed, advanced AI systems cannot be sustained. Large-scale AI requires stable power grids, hyper scale data centres, continuous compute availability and extensive cooling capacity. In practical terms, training and operating frontier scale models requires near perfect uptime over extended periods, a condition that only a handful of National grids currently support. AI ultimately runs on electricity.
Semiconductor access is equally foundational. The United States maintains dominance through its control of chip design and privileged access to advanced fabrication. China, despite external constraints, has invested consistently and extensively in building domestic semiconductor capacity. India, by contrast, has yet to produce advanced logic chips at scale and remains dependent on external supply chains even for mid range fabrication. Without progress in this area, AI autonomy remains limited.
These constraints are not theoretical. Frontier AI training clusters operate at power densities and reliability thresholds where even brief interruptions impose prohibitive costs. Countries that lead in AI therefore treat grid reliability, data centre zoning and energy pricing as strategic inputs rather than auxiliary concerns. Where these prerequisites remain uneven, AI ambition predictably shifts from creation to consumption.
Foundational Software and System Depth
Technological power ultimately resides at the lower layers of the stack: operating systems, kernels, compilers and cloud orchestration. India has developed globally competitive service layer software firms, but it does not control a widely adopted operating system or the foundational AI frameworks shaping the field. Leadership at the application layer does not confer control over platforms.
Human capital alone cannot compensate for institutional gaps. India produces capable engineers in large numbers, yet many reach peak effectiveness only after entering foreign ecosystems that provide compute access, research funding and mature institutional support. This reflects not a shortcoming of individuals, but the decisive role of systems in enabling excellence.
This assessment does not discount India’s genuine strengths. The country has demonstrated notable capability in building large scale digital public infrastructure at low cost and population scale, particularly in identity, payments and platform governance. Its IT services sector remains globally competitive, and Indian engineers occupy influential roles across leading technology firms and AI research institutions worldwide. India is also a rapid adopter of applied AI in finance, governance, healthcare and enterprise services. These achievements are significant, but they represent proficiency in deployment and integration rather than leadership in foundational AI development. Maintaining this distinction is essential for accurate assessment.
It is sometimes argued that India need not pursue full stack leadership, that dominance in applied AI and large scale deployment is sufficient. This view underestimates how quickly strategic leverage migrates downward in the technology stack. As models, architectures and training paradigms evolve, those who control foundational layers set standards, capture disproportionate value and define the boundaries of permissible innovation. Applied excellence without foundational influence may deliver near term returns, but it leaves long term direction in the hands of others.
Overstatement, Insecurity and Global Perception
Seen against this backdrop, the defensive over assertion at Davos was unnecessary and counterproductive. The remarks it responded to were measured and diagnostic, identifying constraints alongside opportunities for cooperation. They did not question National standing.
Global forums operate on norms distinct from domestic debate. International audiences tend to value candour about limitations and sustained investment to address them. Overstatement risks shifting the discussion from collaboration to scepticism, with consequences for credibility in precisely the settings where it matters most.
Conclusion: Systems, Not Overstatement
The strategic choice facing India is therefore straightforward. Rather than pursuing symbolic parity, it must focus on structural depth : assured 24×7 power for compute infrastructure; a sustained increase in public R&D spending toward at least 2 percent of GDP; targeted and realistic semiconductor capability; and long horizon investment in foundational software, systems research and national research institutions.
AI leadership is not an event but a compound capability. It is achieved through long term strategic investment in incremental yet concentric advances across complex and difficult domains, steadily building intangible competencies that reinforce one another over time. It is not the product of simultaneous initiatives or declaratory ambition, but of disciplined execution sustained across decades. In advanced technology, credibility is not asserted at summits; it is accumulated quietly, in laboratories, power stations and balance sheets, long before it is recognised on global stages.