India is far behind in the artificial intelligence race, according to a recent report by Business-Today. While US AI startups Anthropic and OpenAI head toward public listings worth billions of dollars, India is barely scratching the surface. For the first time since at least 2000, local companies are reportedly out of the top 10 constituents of the MSCI Emerging Markets Index, with TSMC alone accounting for 42% of Taiwan’s benchmark index—thanks to its advanced chips powering the world’s top AI models.
The funding gap: billions versus millions
Indian AI startups raised nearly $1.5 billion in the March quarter, about 38% of all startup funding in the country, according to Ashish Bhatia, founder & CEO of India Accelerator. But this pales in comparison to leading markets. Anthropic alone raised $65 billion in recent funding at a valuation of $965 billion. The Business-Today report notes that India lacks an AI-first company generating $40-50 million in annual revenue—a milestone that triggers large late-stage capital.
| Metric | India | US (Anthropic) |
|---|---|---|
| AI startup funding (Q1 2026) | $1.5B | $65B (single company) |
| Revenue milestone for late-stage funding | None at $40-50M | Multiple companies |
Pankaj Mitra, partner at Bessemer Venture Partners India, said: “The depth of the market in India today is not yet enough to support massive billion dollar funding rounds and trillion dollar valuations like in the US.” He added that India needs deeper AI talent in fundamental research.
Sovereign compute and infrastructure gaps
Big tech giants Meta and Google are building AI data centers in India, but the report argues this is not enough. Poorvi Vijay, principal at Elevation Capital, stated: “We cannot build serious AI without GPU access at scale.” India needs sovereign compute and a real say over intelligence built on Indian data, not just where the data sits, said an investor quoted in the report. The recent move by the US government to restrict foreign access to Anthropic’s Fable 5 and Mythos 5 models only emphasizes the need for India to step up its own AI capabilities, investors said. The policy focus so far has only been data localization, which is insufficient.
Nikunj Doshi, managing partner at Bay Capital, noted: “Money worldwide is flowing into global AI giants. The market perception is that India is not doing enough in terms of building LLMs or in areas like semiconductor manufacturing.”
Innovation across the AI stack
Despite these challenges, Indian founders are building across the AI stack, investors say. Most activity is at the middleware layer—the plumbing between foundation models and enterprise deployment. Anup Jain, founding partner at BlueGreen Ventures, highlighted initiatives like Sarvam AI’s Sarvam-30B and Sarvam-105B as important progress toward sovereign AI capabilities, but noted that India still relies largely on models developed and controlled by foreign companies. Jain added: “India doesn’t need to win the foundation model race….that ship has sailed. What India needs to win is the deployment race: taking AI into agriculture, healthcare, climate and financial services at scale that no country can match.”
The road ahead for enterprise leaders
For enterprise technology leaders in India, the current landscape means limited access to cutting-edge AI models and compute resources, potentially slowing digital transformation. The lack of a domestic $40-50 million AI revenue champion indicates immature ecosystems for enterprise-grade AI solutions. To compete globally, India must prioritize sovereign compute infrastructure, deepen AI talent in fundamental research, and shift policy from data localization to enabling AI deployment at scale. The deployment race—embedding AI into core sectors—may be India’s best path forward, as Jain suggests, turning constraints into opportunities for large-scale impact.
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