A specialized 2026 report by Inc42 and Google (the Bharat AI Startup Report) has highlighted a critical structural dependency in India’s technology sector: approximately 75% of Indian AI startups currently rely on Western-developed foundation models and APIs to power their products.
While India is scaling rapidly in the “application layer,” this reliance on foreign infrastructure—primarily from US-based giants like OpenAI, Anthropic, and Google—is being viewed as both a catalyst for speed and a potential risk for “sovereign AI” autonomy.
1. The “Application-Heavy” Skew
The report identifies a massive tilt in where Indian startups are focusing their innovation.
- Layer Concentration: Nearly 86% of all AI funding in India since 2020 has flowed into the application layer (software built on top of existing models).
- The “Wrapper” Dilemma: Only a small fraction of capital is being directed toward the “infrastructure” and “foundation model” layers. This has created a vibrant ecosystem of AI tools that are highly effective but fundamentally dependent on the pricing, policies, and availability of Western APIs.
- Execution Moat: India’s advantage is shifting toward “speed-to-deployment.” By using ready-made Western APIs, Indian startups are shipping products in months rather than years, but they lack the deep intellectual property (IP) of the underlying models.
2. Why Startups Prefer Western APIs
The choice to rely on Western stacks is driven by pragmatic economic and technical factors:
- Infrastructure Deficit: Access to high-end GPUs remains the single largest bottleneck for domestic training. Most Indian developers find it cheaper and faster to call an OpenAI or Anthropic API than to rent and manage their own compute clusters.
- Superior Coding & Logic: A 2026 Anthropic study found that coding tasks account for over 50% of all Claude usage in India. Startups use these models because they offer “production-ready” quality that domestic models are still striving to match.
- Global Interoperability: Using standard Western APIs makes it easier for Indian startups to sell their software to global enterprises in the US and Europe.
3. The Risk: “Geopolitical Supply Shocks”
Government officials and industry leaders have warned that this 75% reliance creates a strategic vulnerability.
- API Tightening: If global tech giants were to restrict API access or significantly hike prices, a vast majority of India’s AI ecosystem would be “blinded” overnight.
- Data Sovereignty: Concerns remain about sensitive Indian data being processed on foreign servers during the model-training and inference stages.
- The Talent Trap: While India has the highest global growth in AI talent concentration (252% since 2016), much of this talent is currently being “trained” to build on Western platforms rather than creating indigenous breakthroughs.
4. India’s 2026 “Sovereignty” Response
To reduce this dependency, the Indian government and private sector have launched several counter-measures:
- IndiaAI Compute Pillar: The government is subsidizing access to 38,000 GPUs at rates as low as ₹65 per hour, encouraging startups to move away from foreign APIs and train their own models.
- Sovereign Models: Initiatives like BharatGen and the deployment of indigenous models like Sarvam-1 are designed to provide “culturally and linguistically relevant” alternatives that keep data within Indian borders.
- The “Deep-Tech” Stack: Under Semicon 2.0, India is incentivizing “fabless” design startups to create custom AI chips, aiming to plug the hardware gap that currently forces startups to look West.
Conclusion: From Consumption to Creation
The 75% reliance figure is being treated as a “wake-up call” for 2026. While using Western APIs has allowed India to become a global leader in AI adoption, the next four years (2026–2030) are viewed as the “defining years” for achieving escape velocity—where India must transition from being a consumer of AI breakthroughs to a creator of them.
