Tech Explained: Sovereignty gaps exposed in India AI Impact Summit 2026 in Simple Terms

Despite attempts at grand staging by Indian Prime Minister Narendra Modi during the recently concluded India AI Impact Summit 2026 that was held in New Delhi, the underlying reality it exposed was far less triumphant.

Even as India projected itself as a future leader of artificial intelligence, the technologies showcased, demonstrated and discussed were entirely developed abroad. From advanced chatbots and enterprise automation systems to the cloud infrastructure underpinning them, the backbone of India’s AI ecosystem rests largely on foreign platforms.

India is using artificial intelligence built elsewhere. From chatbots and enterprise tools to cloud infrastructure and decision-making systems, the core technologies powering India’s AI adoption originate largely in the United States or China. In effect, India today is one of the world’s biggest AI markets but not one of its principal creators.

Even as India projected itself as a future leader of artificial intelligence the technologies showcased, demonstrated and discussed were entirely developed abroad

The summit itself illustrated this dependence. Executives from global firms such as OpenAI, Google, Microsoft and Amazon dominated discussions, product launches and investment announcements. Indian companies largely presented solutions built on top of these platforms  customer-service bots, analytics engines, language interfaces rather than foundational models developed independently within the country.

This pattern reflects a long-standing structural trait of India’s technology sector. The country’s IT industry rose to global prominence through services, outsourcing and system integration, not through the creation of proprietary platforms. Building frontier AI systems requires enormous financial investments, specialised hardware, exclusive datasets and research ecosystems sustained over many years. The cost of training a cutting-edge large language model can run into billions of dollars, a scale of investment few Indian corporations have historically undertaken.

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Meanwhile, India’s policy response has focussed heavily on infrastructure. Under the national AI mission, thousands of advanced Graphics Processing Units (GPUs) are being procured and large data-centre projects are underway. While these developments are essential prerequisites, hardware alone does not produce sovereign intelligence. Without indigenous models, algorithms and research pipelines, these facilities will primarily host software developed abroad.

Ironically, India generates vast quantities of data that could power world-class AI. With more than 850 million internet users and billions of digital transactions daily, the country is by far the largest data producer in the world. However, this information is dispersed across public and private institutions, often poorly structured for machine learning and subject to complex privacy regulations. India’s linguistic diversity  spanning dozens of major languages and hundreds of dialects further complicates model training at scale.

Indian engineers and scientists play pivotal roles in global AI breakthroughs, particularly in Silicon Valley. Yet much of this expertise benefits multinational corporations rather than domestic research institutions. Frontier innovation ecosystems where academia, venture capital and industry collaborate intensively remain comparatively underdeveloped within India.

Major domestic IT companies such as Tata Consultancy Services, Infosys and Wipro have integrated AI into their service offerings, but typically through partnerships with foreign providers. Even ambitious announcements at the summit involved collaborations with global firms like IBM rather than purely homegrown systems. The result is a form of technological dependence wrapped in the language of digital transformation.

From a national-security perspective, this dependence carries profound implications. Artificial intelligence increasingly underpins critical infrastructure, defence systems, financial networks and public administration. Control over algorithms can influence economic stability, information flows and even political processes. Countries that dominate AI development will shape global power structures in the coming decades.

Cybersecurity experts warn that reliance on foreign AI platforms creates vulnerabilities that extend beyond economics. Systems trained and maintained abroad may embed opaque decision-making processes, unknown security risks or external dependencies that are difficult to audit or control domestically.

Rohan Mehta, a cybersecurity researcher describes the situation as a strategic blind spot.

“From a cybersecurity standpoint, dependence on external AI systems is not merely a technological gap it is a sovereignty issue. When core algorithms, training pipelines and cloud infrastructure are owned by foreign entities, you are effectively outsourcing cognition itself. These systems process sensitive data, shape decisions in finance, healthcare and governance, and increasingly influence public discourse. If geopolitical tensions escalate or access is restricted, critical sectors could be disrupted overnight. Moreover, opaque models trained on unknown datasets may contain hidden vulnerabilities, biases or backdoors that domestic regulators cannot fully audit. India today is consuming intelligence produced elsewhere. We are building applications, not architectures; interfaces, not engines. Without sovereign capabilities  our own models, our own chips, our own secure data ecosystems  we remain structurally dependent. In cyber-strategic terms, that means we are participants in the AI era, but not its controllers,” Mehta tells Media India Group.

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Despite official optimism, the hard numbers tell a far less encouraging story about India’s indigenous AI push. Public funding under the national AI mission  roughly INR 100 billion, or just over USD 1.1 billion, spread over several years pales in comparison with USD 2.5 trillion that the world is expected to invest in AI in 2026, with an overwhelming share of about 90 pc coming only from the United States and China. Microsoft alone has committed around USD 80 billion for AI infrastructure in a single fiscal cycle, while Amazon and Google are each spending comparable sums on data centres, chips and model development. Even mid-tier American startups are raising funding rounds larger than the entire AI research budgets of many countries. Against this backdrop, India’s initiatives appear not merely modest but structurally inadequate to compete at the frontier.

The imbalance is equally stark in computing power the lifeblood of modern AI. India’s planned deployment of roughly 50,000 to 100,000 GPUs sounds ambitious domestically, yet single hyperscale projects in the United States now deploy clusters of several hundred thousand advanced chips. Moreover, nearly all of India’s AI hardware is imported, primarily from Nvidia, American technology company  leaving the country dependent on foreign supply chains vulnerable to export controls or geopolitical tensions. Without domestic semiconductor manufacturing at scale, India lacks control over the foundational layer of AI development.

Research output reflects a similar gap. While India produces a large number of engineering graduates each year, its share of highly cited AI research papers remains far behind that of the United States and China. Much of the cutting-edge work involving Indian-origin scientists is conducted abroad, funded by foreign institutions and commercial labs. In effect, India exports talent and imports technology a model that reinforces dependency rather than sovereignty.

Venture capital patterns further expose the weakness. Indian startups receive substantial funding for applications fintech, e-commerce, logistics, customer support but relatively little for deep-tech research that may take years to yield commercial returns. Frontier model development is extraordinarily expensive and risky, requiring patient capital that is scarce in India’s investment ecosystem. As a result, domestic companies overwhelmingly build products on top of foreign AI platforms instead of competing with them.

Against this reality, claims of “Swadeshi AI” appear aspirational at best. Most indigenous initiatives focus on language tools, sector-specific solutions or pilot projects rather than foundational systems capable of competing globally. Even these projects frequently rely on foreign cloud infrastructure and imported chips, meaning the underlying dependency remains intact. Far from closing the gap, such efforts often deepen it by locking Indian users into external ecosystems.

Ultimately, the summit underscored India’s role not as a technological superpower but as a strategic marketplace valuable for its scale, data and workforce rather than its innovation leadership. The country demonstrated its ability to convene governments and corporations to discuss AI governance, yet conspicuously lacked a flagship domestic platform commanding global attention. In practical terms, India is shaping conversations about AI without shaping the technology itself.

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