A look at India's efforts to catch up in the AI race, as the government reviews 67 bids from startups and research labs seeking funding for domestic AI models
Krishn Kaushik / Financial Times :
Context & Ripple Effects
The review operationalizes India’s earlier approved AI investment program, which included compute, model development, and AI education rather than a single-model bet.
It also answers pressure to use public research support for foundational technology after calls for state-backed AI research argued that private-sector risk appetite was insufficient.
First-order effects
- The 67 startup and research-lab applicants enter a government selection process for support to build domestic AI models; the state becomes a near-term gatekeeper for this pool of model-development funding.
- India’s AI push moves from broad program approval toward evaluating specific domestic-model proposals.
Second-order effects
- Applicants will have an incentive to tailor technical roadmaps, compute needs, and deployment cases to public funding criteria, while non-selected teams may need to seek private capital or narrower product paths.
- The review makes the quality of India’s funding-selection process consequential: it determines which domestic model efforts gain resources first, rather than leaving that allocation solely to private markets.
Third-order effects
- If repeated, competitive public funding rounds could make state-mediated selection a durable part of India’s AI ecosystem, alongside private investment and research institutions.
- The effort is part of a broader sovereign-AI contest in which countries seek domestic model capacity, though funding alone does not resolve the underlying challenge of building competitive foundational models.
The trend: India is shifting from AI-policy commitments toward state-directed funding mechanisms intended to seed domestic foundational-model capacity.