US AI Restrictions Push India to Build Sovereign AI Capabilities and Domestic Infrastructure

The next AI arms race will be over certainty, not intelligence

Vinayak Godse The writer is CEO, Data SecurityCouncil of India The recent restrictions on Anthropic's Fable 5 and Mythos 5 models highlight a broader tension emerging as frontier AI systems advance. What stands out is less the initial trigger than the US government's response: comprehensive, immediate and global in scope, with no public disclosure of underlying details. This step could set a precedent for major AI developers. AI has reached an inflection point, enabling capabilities in cybersecurity, biology and scientific research. For the first time, this technology is being developed primarily by the private sector, with capabilities emerging in unpredictable ways. Frontier models demonstrate abilities not always anticipated during development, and the pace of advancement is becoming difficult to predict, even for their developers. Extensive testing, evaluation and red-teaming continue to improve safeguards. Yet, no provider can credibly offer absolute assurance. National security governance has traditionally relied on a deterministic view of the world: threats are classified, risks are modelled, actors identified and intentions inferred, enabling policy instruments grounded in cause and effect. Existing frameworks depend on attribution, accountability, proportionality, deterrence, verification and jurisdiction, and were designed for human actors operating within stable boundaries. Advancing AI introduces new risks. It changes the nature of risk. As agency becomes distributed, decisions are increasingly automated, explanations are system-generated, and consequences cascade beyond immediate visibility, weakening key assumptions of contemporary governance. The challenge isn't only regulation but comprehension. The issue's no longer solely what a model can do. It is what governments fear they cannot predict. Preserving strategic advantage becomes central to decision-making, particularly in environments where uncertainty itself is treated as risk. Any incident of compromise may be treated as a national security concern. The Mythos 5 restriction illustrates this dynamic: uncertainty, rather than scale of harm, becomes the primary driver of response. This emerges as geopolitical competition and tech protectionism reshape the global order. Its implications extend beyond any single company or country. Conditions enabling frontier AI - global talent mobility, open scientific exchange, distributed experimentation, infrastructure access and rapid diffusion - are also those that governments may seek to restrict when faced with capabilities they struggle to interpret and control. India's position in this emerging dynamic is direct. Access to models is critical for leveraging potential from AI for the economy. The natural response is an accelerated push toward sovereign capability: indigenous models, domestic compute infrastructure and greater self-reliance - not primarily for commercial advantage but as insurance against strategic dependence and unilateral denial. Yet, sovereignty pursued defensively introduces tension. The same conditions that support AI progress remain essential for participation in it. India has made significant strides in the global AI ecosystem, with AI data centre capacity potentially reaching 10-15 GW over the next 5 yrs. Pursued in isolation, defensive sovereignty risks undermining these ambitions. India needs to double down on developing foundational models. MeitY's support for 20 model-development initiatives, with 5 released, is welcome. These models must be rapidly strengthened across domains. New approaches such as world models, with potential capability leaps, should be explored intensively. Access to models, and assurances around their safety, security and governance, will have to be central in India's technology diplomacy. Investment in data centre capabilities in India will be a supporting pillar of this strategy. Promise of uninterrupted access to models should be embedded in policy positions. Equally, developing the domestic market will be critical for the success of Indian models and for strengthening India's position in the geopolitics of AI. For companies planning to leverage frontier models and secure their codebases, digital footprints and supply chains, recent restrictions and associated uncertainty have been a shock. Security hygiene, therefore, should be elevated to match the risks posed by increasingly powerful AI systems. A trimodal strategic approach should be adopted, with focus on immediate measures, developments expected in the next 6-12 mths, and structural capabilities required for AI advancement over the next 2-3 yrs. India faces a challenge that extends beyond capability adoption. Strengthening domestic capacity while integrating with global AI ecosystems is one of the most critical policy objectives. The institutional question isn't whether to respond to this condition, but on what terms. The central challenge of AI governance may not be controlling intelligence itself, but governing uncertainty as a persistent structural condition of technological progress. (The writer is CEO, Data SecurityCouncil of India)

When frontier AI can be switched off: India's sovereignty challenge

India must not depend on foreign AI models, as US export controls demonstrate potential access denial. Past reliance on external tech, like nuclear programs, led to self-sufficiency. For AI, India needs a continuity doctrine, fostering multiple research labs and institutions, not just symbolic wins. Learning from Singapore's biotech success, building foundational capabilities now is crucial to avoid perpetual import reliance and secure future agency. The world can't base its AI strategy on the assumption that the world's most advanced models will remain globally available, commercially accessible and continuously usable. This is the signal sent by the US government's restrictions on Anthropic's Fable 5 and Mythos 5, which make clear that any country can be denied access to frontier intelligence at any given moment. The US uses export controls to slow the diffusion of strategic technologies, such as encryption source code, advanced chips, chip design software and semiconductor equipment. The Anthropic restrictions offer an early glimpse of a potential future in which the next layer, controlled by export directives, could become frontier intelligence. So, India and others cannot rely on partnerships with foundational AI tech companies alone, since they are not sovereign players. India had earlier requested Anthropic for access to Mythos under Project Glasswing, to understand capabilities of frontier AI models for cybersecurity across several sectors like banking and telecom. That access has now been disrupted. US export control directives are not unpredictable moves. In fact, other countries should have seen this coming. Historically, technology restrictions have shaped global information flows. Anthropic operates inside the US state. So, if White House decides a frontier model must be restricted, even the largest AI lab will have to comply. It doesn't matter that India is Anthropic's second-largest consumer base, since being a customer is not the same as having control. History shows India having faced such denial before. But it led to a pivot in its own pathway, not a change in its destination. India's nuclear programme was built over decades, and its strategic capabilities developed in a world where external access could not be assumed. But it can't be compared with AI. Nuclear capabilities were anchored in physical capabilities and reached strategic thresholds, while frontier AI development is evolving. But governing AI is like tackling nuclear technology. AI is politically sensitive, strategically consequential and difficult to govern through certainty alone. With AI models improving rapidly, chips evolving, and inference costs and applications changing across the economy, frontier intelligence will proliferate with or without India's quest for AI sovereignty. Some argue that India can respond by diagnosing it as a competitiveness problem. The issue is not only that it has a compute dependency and hasn't developed foundational models, labs or research ecosystems. The deeper problem is that India lacks an AI continuity doctrine to preserve its agency and continuity in the world, as frontier intelligence is becoming a more gated and controlled strategic layer. That's why India cannot answer this challenge through a 'checklist for AI sovereignty' - buying GPUs, building data centres, funding a few startups and announcing some foundational models are being built. It should not expect legacy IT to build frontier intelligence labs. These require research depth, not thin R&D spending, a willingness to fail, and commitment to building for India and the world. Indian IT services model has never operated on these aspects, having instead built its legacy on labour arbitrage. IndiaAI Mission's compute capacity and foundational model push are important beginnings. Sarvam's progress in sovereign AI, compute models and deployment is encouraging. But a country of India's scale needs many competing labs, not 1-2 symbolic winners. Anthropic emerged from OpenAI. India needs conditions for 10 Sarvams. It needs to start building institutions and conditions that can support an AI continuity doctrine now. One answer lies in Singapore's biomedical journey. The entrepot economy was transformed into a global biotech hub over several decades. It did for biotech what India needs to do for frontier AI: build institutions before the ecosystem is ready. Through Agency for Science, Technology and Research (A*STAR) and Biopolis, Singapore not only recruited global talent, built research autonomy and gave institutional backing to MIT's Jackie Ying to lead its Institute of Bioengineering and Nanotechnology, but also saw its success compound over the years. The US may ease controls on frontier models in the future to protect interests of its AI ecosystem again. But can we afford to wait for that, given that we cannot stop relying on current frontier intelligence, while those institutions can't wait in perpetuity to be developed if India doesn't want to be the one billed for importing frontier intelligence? (Kapoor is chair, and Zutshi is research manager, Institute for Competitiveness)

AI Sovereignty: India Seeks to Scale up AI Infrastructure

Recent developments around the US efforts to monopolise their AI seems to have pushed India into this path President Trump's decision to yank off Anthropic's latest AI models off the shelf has once again veered conversations around artificial intelligence to sovereignty issues. Prime Minister Narendra Modi told G7 leaders that technology leads to progress only when democratised. Now, it appears that the Niti Aayog is following through to supervise a domestic AI mission. According to officials in the body that took over from the Planning Commission, a strategy is currently being prepared to develop sovereign AI infrastructure to bolster the use of AI in governance and create a vast talent pool through targeted fiscal and policy initiatives. Of course, all of this would run in parallel to government's efforts to build India as a preferred destination for global datacentres and capability centres in the future. Towards this end, the Niti Aayog has been tasked to identify gaps in the current AI ecosystem and suggest measures to strengthen India's core capability to develop and deploy AI using its infra and workforce. Those in the know suggest that the Prime Minister Modi and his team wants India to be "Atmanirbhar" on AI right from the get go. Therefore, the recommendations from the Niti Aayog could be incorporated into the already existing India AI Mission. Maybe, it could just be India AI Mission 2.0, the officials said. Incidentally, the India AI Mission was originally created by the Niti Aayog and rolled out by the central ministry for electronics and information technology (MeitY) back in 2024. The recent developments around how the US has sought to link its national security to new AI models and keep it away from reaching other countries, is causing concern among policy planners. The original mission, which had an outlay of over Rs.10,300 crore when it was set up, could see more funds getting pumped in once the gaps are identified. In fact, several Indian companies such as Zoho and Sarvam AI have voiced their concerns about the US companies holding back their technology and suggested that government push its own AI agenda. An immediate focus on the Niti Aayog could be on pushing for more localised datacentres and graphic processing unit (GPU) clusters while side-stepping the need to rely heavily on cloud service providers such as AWS, Azure and Google Cloud. Of course, these companies too can join the fray if they ensure that entire processing remains within India's geographical limits. Another challenge that the country faces is higher adoption of AI, which currently is said to be well below 50% via the digital public infrastructure. Some reports suggest that India's AI market could expand to around $32 billion over the next five years from less than $8 billion currently. Unlike the global markets, India's AI growth could come from the growing complexity around public infrastructure itself. Imagine the ever-growing population in major cities, the pressure on public transport and the real concerns around monitoring systems. Video surveillance is everywhere but cannot remain limited to recording and storage alone - there must be a way of initiating positive action in case the agencies find something askance. The massive volumes of data being generated by such surveillance and monitoring systems require massive AI infrastructure and compute to be able to analyse, interpret and respond in real time. We are not talking about the future anymore. These are problems of the present and the India AI Mission needs to focus on solutions.

For Sarvams to scale, tap global capital

India's generative AI landscape is thriving, yet it seeks greater global financial backing. While local startups are making strides in fundraising, their valuations still pale in comparison to those in Silicon Valley. To harness the potential of AI, India must unlock international investments. India presents a contradiction in AI. It's home to the world's 2nd-largest hub for generative AI startups. Yet, it attracts limited global capital because of constrained ambition. Companies developing foundation models are securing domestic venture funding. But even Sarvam, which became a unicorn after raising $234 mn this week, remains modestly valued compared with Silicon Valley peers. Without pairing local innovation with global capital at a larger scale, India risks missing the AI bus. 'Bharat Innovates 2026' at Nice, France, is currently showcasing technical talent in the EU, another market vulnerable to overreliance on offshore AI. A stronger international marketing push may be needed for Indian AI innovation to gain global attention. The EU is a good starting point. Its consumer-centric approach limits access to data critical for building foundation AI models. India has the technical talent to build AI models to train on enormous data it generates. There is an obvious synergy between these two markets, which are equally at risk of being denied access to frontier AI models developed in the US or China. They also share concerns over data protection and adverse economics of becoming AI-consuming regions. These issues resonate in other parts of the world and can be shaped into coherent policy to widen the field in AI. Sovereign AI matters in upholding cultural diversity, jurisdiction and security. It requires independent ability to develop, deploy and govern AI using local infra, data and models. This is critical to retaining the productivity gains from AI within the economy. Strategic wake-up calls tilt the debate over sovereign AI towards economics, where countries can negotiate acceptable terms. A hybrid model of global and local AI should emerge where India can play a significant role in tech development. This will be an open-source, culturally-sensitive environment that prioritises public-funded infra. Such a model of AI development will have many takers. But India will have to provide proof of execution.

AI Sovereignty: Sarvam AI Gets HCL Tech Funding; Zoho Launches AI-ready Servers

India's tryst with AI sovereignty must start now and maybe the likes of Zoho and Sarvam AI can lead this journey The AI sovereignty plug made by Indian technologists appears to be fructifying, albeit slowly. Barely a couple of days after Sarvam AI CEO called out the White House for blocking access to Claude Mythos and Fable5 AI models, the company received a $234 million in a fundraise led by the HCL Group to push itself into a unicorn category. The latest round of fund infusion by HCLTech, Bessemer Venture Partners, Khosla Ventures and Peak XV Partners takes the company's valuation to $1.5 billion in this Series B round, wherein Sarvam had targeted a total raise of $300 million. More than the actual fundraise, it is the sentiment attached to Indian tech companies that makes this round important, especially given that another Indian company HCLTech is acquiring a 10.5% stake in the domestic AI startup and paying $150.7 million in cash. Incidentally, Sarvam had raised $41 million two years ago as seed and Series A funding for launching open source AI models. Sarvam did the fundraise barely three days after its cofounder and CEO Pratyush Kumar had shared his views around the Fable 5 withdrawal. He felt countries cannot afford to mistake access to cutting edge AI systems for true technological ownership. "Fable 5 ban is a good instigation for more people to engage in recognising the need for sovereignty," Kumar had said. Kumar's commentary came in response to Zoho founder Sridhar Vembu's comment that when it came to AI, globalisation was dead. Having created software that takes on the likes of Microsoft and Google, the company has now launched Nathu La, its first in-house server platform that aims to cut down AI infrastructure costs via datacentre indigenisation. Nathu La was launched last week and Vembu believes the move will cut down infrastructure costs, improve energy efficiencies and provide greater control over a tech stack that supports all Zoho products. The server uses Intel Xeon 6 processors. Zoho had said that the platform delivers performances that match global standard but cuts down on power consumption by between 12% to 18% while also lowering total cost of ownership (TCO) by up to 30%. "Hardware is one area where we have traditionally relied on global OEMs. But infrastructure has become foundational and if compute becomes foundational, we should own it," Ramprakash Ramamoorthy, Director of AI Research at Zoho Corp, had said. When Zoho launched Nathu La, they said it would not be available commercially and the platform would only be for internal use. "We launched a server platform primarily for internal use. We are dogfooding it as we speak. Zoho runs on Zoho," Ramamoorthy said. It remains to be seen whether Sridhar Vembu puts money where the mouth is and expands this effort to make Nathu La available to all Indian companies in the short-term. One way would be for some AI companies like Sarvam AI to get into circular deals the same way that several companies have done so in the US, leading to what many are describing as an AI bubble that could burst. Of course, both Sarvam and Zoho could stay away from "Musking" as our contributing author Alok Gurtu had described the phenomenon that bolsters value without actually showcasing or delivering it. Sarvam's first step in this direction could make other companies also seek funding from Indian companies, given that Indian tech industry quickly needs to deliver a broader push towards developing sovereign infrastructure. Especially in the wake of what we saw play out between Anthropic and Trump's White Houselate last week. Sarvam has long claimed that its AI models are designed for Indian languages and use cases while its products are for deployment across sectors like banking, insurance, and government services. The latest fundraise makes this AI startup a strategic partner with deep pockets whereby HCLTech might open its rolodex for building new enterprise-level relationships. Maybe, it is a good thing that the Fable5 fiasco happened when it did. Maybe it would be a good idea for Prime Minister Modi to stay away from asking for President Trump's help. The last time India was faced with such a scenario post its nuclear tests in 1998, its space program took flight despite foreign governments starving India of know-how and materials. Maybe it is time India's scientific minds come together and create tech stacks that are locally placed and AI models that aren't the generic ones but designed to perform task at an enterprise level. Maybe, India can lead the way in bidding adieu to LLMs and welcoming SLMs.