Why this warning matters
I write this as someone who has long argued that data is not merely a by‑product of modern life but a strategic national asset. Recently, a high‑profile speech by the Adani Group chairman — delivered at the inauguration of a new Centre of Excellence in AI in Baramati — crystallised a clear warning: without a domestic AI stack, India risks letting foreign algorithms shape our data, markets and even decisions (Economic Times).
I want to unpack that claim, explain what was actually said, and lay out the practical choices India faces if it wants to preserve economic and strategic sovereignty in the AI era.
What was said — the core argument
The central thesis of the speech was straightforward: AI is not just a productivity tool; it is infrastructure and power. The argument ran along these lines:
- AI amplifies decision‑making at scale; foundational models, data centres and semiconductors are strategic assets.
- If India relies primarily on foreign models and infrastructure, foreign entities will train on Indian behaviour and monetise the insights — in effect extracting value from our people and markets.
- That extraction risks not only economic loss but also dependency over choices and cultural narratives.
Those are strong statements, and they echo debates we have had for years about data localisation, platform dominance and who benefits when population‑scale data is aggregated and modelled.
How this links to data sovereignty
Data sovereignty means three things in practice:
- Physical/technical control of data flows and storage (where data lives and who can access it).
- The ability to build and govern models that interpret that data (the AI stack: models, compute, datasets, and tooling).
- Institutional and legal frameworks that ensure the public interest guides data use.
If a country lacks the second piece — its own AI models and compute ecosystems — then even if data is stored locally, influence can still flow outward through APIs, licensing, or inference services owned by foreign providers. The speech’s warning is that ceding modelling and inference to external actors hands them a lever over choices and economic value.
Economic and strategic risks
The risks are real but not inevitable. Main areas of concern:
- Economic extraction: foreign firms that control models can monetise insights, capture market share in services built on those models, and repatriate profits.
- Market influence: recommender systems or pricing algorithms can shape consumption patterns, advertising markets and small business opportunities in ways that favour external platforms.
- Strategic vulnerability: dependence on foreign compute or chips exposes critical infrastructure (finance, health, defence) to geopolitical shocks or supply restrictions.
- Cultural impact: language models trained without local context can misrepresent or flatten nuanced cultural content.
All of these add up to a loss of agency — not in the dramatic sense of tanks on borders, but in the day‑to‑day choices that shape economies and societies.
Balanced perspective: opportunities and limits
It’s important to be empirical. Building a sovereign stack is costly and complex. Global platforms also bring benefits: developer tools, scale, rapid innovation and commercial services that Indian companies currently use to build faster. A wholesale retreat from global ecosystems would be neither feasible nor desirable.
So the policy challenge is to preserve openness and access to global innovation while ensuring India retains the ability to govern, build and deploy critical AI capabilities aligned with national priorities.
Practical policy responses — a toolkit for decision makers
Below I outline practical, implementable steps that balance ambition with realism.
- Build a layered national AI stack
- Publicly funded foundational models for Indian languages and domains (health, agriculture, governance) with open licences for public interest use.
- National compute hubs and certified data centres (green, resilient) that reduce single‑vendor lock‑in.
- Open datasets and evaluation benchmarks focused on local languages and contexts.
- Smart regulation and governance
- Fast‑track clear rules on cross‑border data flows, model transparency and algorithmic accountability.
- Create a lightweight certification regime for models used in critical sectors (finance, health, elections).
- Reinforce consent and privacy frameworks so individuals share in the value of their data.
- Public-private partnerships (PPPs)
- Use targeted grants and matched investments to accelerate domestic model development by startups and incumbents.
- Encourage consortiums (industry, academia, public research labs) to co‑build reusable infrastructure and data trusts.
- Talent and skills pipeline
- Scale graduate and vocational programs in ML engineering, data engineering and AI governance.
- Incentivise diaspora engagement and reverse brain drain through fellowships, sabbaticals and industry‑academia chairs.
- Infrastructure and supply chain resilience
- Invest in semiconductor partnerships, local chip design labs and strategic stockpiling of critical components.
- Prioritise energy‑efficient data centre builds coupled with renewables — both for resilience and climate alignment.
Expert views and where I stand
Observers who emphasise strategic risk tend to call for state action and public foundational models; market‑oriented voices warn against over‑insulation that hampers innovation. I believe the right middle path is to treat AI as both an economic opportunity and a public good: enable private innovation but ensure the state and public interest institutions own or govern certain foundational layers.
This is not new to my own thinking. I have written previously about why personal data and national capabilities matter and how India’s technology architecture must be designed so citizens — not external platforms — capture the dividend from data (Why Personal Data Needs to Be Stored in India?).
Final thoughts — choices, not inevitabilities
The essence of the warning is a choice: accept gradual erosion of agency by outsourcing the intelligence layer, or invest now in an ecosystem that preserves sovereignty while remaining connected to global innovation. The latter requires careful strategy, funding, regulation and a long view — but it’s feasible. India can build trusted, affordable AI infrastructure and open models that align with local needs; doing so will shape whether AI becomes a tool of empowerment or a vector of dependency.
Regards,
Hemen Parekh
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