The era of “move fast and break things” in AI didn’t just slow down—it was handed a 90-minute eviction notice by Washington. When the US government retroactively pulled Anthropic’s most powerful consumer AI models, Mythos 5 and Fable 5, from the global market via an export control law, it sent a shockwave that India cannot afford to ignore.
Citing national security, the mandate was so sweeping that it even barred Anthropic’s own US-based foreign employees from accessing the systems, forcing an immediate, total withdrawal to ensure compliance.
A handful of Indian institutions only had access to these advanced models, which can find security vulnerabilities in banks or telecom networks faster than any human team, for barely a week before it was shut off.
Though tense negotiations later led Commerce Secretary Howard Lutnick to lift the blanket ban, Anthropic emerged on a tight digital leash, bound to co-develop safety protocols directly with the state. The chilling effect was instantaneous: OpenAI quietly restricted its unreleased GPT 5.6 Sol model to a government-approved client list.
For global enterprises, the wake-up call was absolute. The disruption did more than just frustrate users; it reignited the debate over AI sovereignty. The defining question fundamentally shifted from “Can you afford the model?” to “Are you legally permitted to use it?”
Access vs. Ownership: The “Tariff Moment”
For tech ecosystems outside the United States, particularly in India, this sudden disruption was a watershed “tariff moment”, mirroring the fallout when tariffs squeezed Indian exporters and again when overnight hikes to H1B fees sent techies into a tizzy, but carried far deeper structural consequences.
Plumbing of startups and enterprises integrating Anthropic’s cutting-edge models was turned off within a narrow hour-and-a-half window. Indian AI firm Sarvam’s founder Pratyush Kumar aptly observed during the fallout: “We should not confuse access with ownership.”
Wholly relying on foreign tech providers leaves a nation’s foundational commercial and developmental infrastructure at the mercy of a geopolitical kill switch. The vulnerability isn’t hypothetical; just last year, tech giants like SAP and Microsoft immediately terminated services to an Indian refinery simply because it purchased Russian oil, bringing domestic operations to an abrupt standstill.
When the world’s leading frontier labs—OpenAI, Anthropic, Google, and xAI—are concentrated under a single jurisdiction, global technology strategies cease to be assets. Instead, they become inherently rented. However, these heavy restrictions create powerful new incentives for other countries, enterprises, and investors to fund domestic AI alternatives to ensure stability.
The Capital Scale Discrepancy
The Trump administration’s protectionist policies have inadvertently accelerated the growth of non-American ecosystems like DeepSeek, Mistral, Coherent, and India’s Sarvam AI. Under the government’s AI Mission, roughly 700 small open-source models are accessible to the public completely free of cost, allowing domestic companies and public infrastructure to extract immediate productivity gains.
Homegrown players such as Sarvam AI are making notable progress, having recently released two fully indigenous models trained entirely on local infrastructure. However, a massive scale gap persists. Sarvam’s largest model was trained on roughly 1,000 GPUs, while building competitive frontier models typically demands over 10,000 advanced GPUs running continuously for months. This infrastructure deficit is stark when compared to the United States, where private and public sectors together channel close to $1 trillion annually into hyper-cloud and AI infrastructure.
Despite promising strides by Indian startups, bridging this compute and investment chasm remains a formidable challenge. The pioneering partnership between HCLTech and Sarvam AI, however, serves as a powerful blueprint for the future. Since developing homegrown AI models requires immense capital, collaborative industry investments are essential to accelerating technological sovereignty and achieving true self-reliance.
The Anatomy of India’s AI Vulnerability
If AI is becoming as essential as electricity, telecommunications, or semiconductors, sovereignty is entirely unavoidable.
China’s DeepSeek recently raised over USD 7 billion while safeguarding founder control, a strategic move to cultivate robust domestic AI. This massive investment in sovereign technology appears increasingly prudent given the real risk of sudden disconnection from western models. The exact same conversation is playing out loud in India, which is highly vulnerable because the fundamental building blocks of its technology, databases, and communication software are foreign-owned (primarily American, like Microsoft, Oracle, and Google). Relying on external hyperscalers for data storage and AI models leaves India open to sudden geopolitical lockouts.
To build a true fallback against foreign tech curbs, industry experts emphasise that India must address vulnerabilities across the five core layers of the AI Ecosystem:
- The Power Layer: While India generates substantial renewable and solar energy, its real vulnerability rests in shaky transmission infrastructure, risking peak-period disruptions for high-intensity data centres. There is a need to invest in transmission urgently during the next one year.
- The Chip Layer: India cannot instantly manufacture cutting-edge 2nm–5nm chips. The fundamental hardware remains tethered to companies like Nvidia, which are bound by the exact same US export control laws that triggered the Anthropic shutdown. However, India boasts of the world’s largest talent pool of chip designers and testers. Capitalising on this strength requires aggressive funding for domestic chip design firms alongside mandated local procurement policies to guarantee a thriving domestic market.
- The Hyper-Cloud Layer: India’s heavy reliance on US tech giants – AWS, Microsoft Azure, and Google Cloud – to store its operational data leaves the country vulnerable to unexpected geopolitical disruptions. Introducing a ₹2 lakh crore ECLG scheme to fund domestic startups like Yotta and CtrlS is a vital step toward securing our data independence and keeping our digital assets at home.
- Large Language Models (LLMs): Blindly spending billions to compete with American hyper-scalers on proprietary brute-force models is a losing financial battle, given the inevitable commoditization of the underlying technology. Instead, a pragmatic, short-term strategy should involve combining open-source models, localised ‘Made in India’ solutions, and a diversified selection of international frontier models to mitigate dependency. Crucially, this must be paired with robust incentives to drive adoption across both the public and private sectors.
- The Application Layer: India possesses an exceptional developer ecosystem building specialised tools for fintech and healthcare, but these startups face a domestic market that aggressively beats down software pricing, causing a severe local capital crunch. The government needs to transition from micro-grants to a massive high-risk innovation fund of 50,000 crores annually managed by an entity like SBI to bypass typical bureaucratic delays, as rightly put by T V Mohandas Pai, former CFO and Board Member of Infosys Ltd.
The call of the hour is massive, scalable execution.
The New Geopolitical Paradigm
True technological sovereignty means possessing enough internal control over digital, manufacturing, and AI systems that a nation cannot be blackmailed or disconnected by external forces.
For India, building an independent fallback is no longer a forward-looking economic strategy—it is a core requirement for national security. Until the underlying hardware chips and model capabilities are fully domestic, tech sovereignty remains entirely on credit.
The coming years will test whether execution matches ambition in this high-stakes domain. Patience and persistence will be essential, but the strategic pivot is already underway.