Why Does India Lack AI-First Companies with $100M Revenue Compared to US's $121B Funding?

India’s AI startup ecosystem has grown steadily, but it still lacks AI-first companies crossing the $100 million revenue mark. This gap stands out sharply against the US, which saw US AI funding 2025: $121B across 765 rounds (141% jump!) according to TechCrunch. India’s AI funding, by contrast, is only about $600 million, roughly 2% of the US level. This blog explores why India trails behind in building large AI-first companies despite its growing interest in AI and technology.

The Scale of AI Funding in India vs US

The numbers tell a clear story. The US attracted $121 billion in AI funding in 2025 alone, spread over 765 investment rounds. This massive capital influx has fueled the rise of multiple AI unicorns and companies with revenues exceeding $100 million. India, with just $600 million in AI funding, faces a significant funding gap. This difference is not just about money but reflects deeper challenges in the AI ecosystem.

Search trends highlight this interest and concern:

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These searches show that founders, investors, and policymakers are actively seeking answers.

Foundational Models Gap Holds India Back

One major reason for the lack of $100 million revenue AI-first companies is the foundational models gap. India currently lacks the depth in large language models (LLMs) and core AI research that powers many US startups. Foundational models require significant investment in research and infrastructure, often backed by government or large corporate labs.

Accel partner Prayank Swaroop, quoted in TechCrunch on December 27, pointed out this gap: “No $100M revenue AI-first company” exists in India partly because the country has not yet developed foundational AI models comparable to those in the US.

Without these base models, Indian startups often build on existing US or global AI technologies rather than creating proprietary AI engines. This limits their ability to scale and differentiate.

Talent Drain Limits India’s AI Growth

India produces a large number of engineering graduates, but the top AI researchers often move abroad to the US or UK. These countries offer better research facilities, funding, and career opportunities. This talent drain reduces India’s ability to innovate at the foundational AI level.

Many Indian AI experts join global tech giants or startups in the US, contributing to the US AI funding ecosystem indirectly. This brain drain slows the growth of deep AI research hubs in India, which are essential for building AI-first companies with significant revenue.

Patient Capital Shortage Slows AI R&D

AI research and product development require patient capital. Building AI-first companies often takes years before reaching substantial revenue. Indian venture capitalists typically look for faster returns, often within three years. This mismatch creates a patient capital shortage for AI startups.

Accel partner Prayank Swaroop highlighted this challenge: Indian VCs want 3-year exits, but AI R&D needs longer timelines. This pressure forces startups to focus on short-term applications rather than foundational AI innovation.

In contrast, US investors are more willing to fund long-term AI projects, supporting startups through multiple funding rounds until they reach scale.

Opportunity in Application Layer for India-Specific Problems

While foundational AI models lag, India has a strong opportunity in the application layer. Startups can build practical AI solutions tailored to India’s unique challenges, such as agriculture, healthcare, education, and financial inclusion.

These applications can leverage existing AI technologies and adapt them for local languages, data, and user needs. This approach can create valuable businesses even without owning foundational models.

For example, AI startups focusing on crop disease detection using smartphone images or AI-powered vernacular education platforms address real problems and have growth potential.

What Can Help India Close the AI Funding and Revenue Gap?

To build AI-first companies with $100 million revenue, India needs to address several key areas:

• Invest in foundational AI research: Increase government and corporate funding for AI labs focused on LLMs and core AI technologies.

• Retain and attract AI talent: Create incentives for top researchers to stay or return to India, including better research infrastructure and career paths.

• Encourage patient capital: Develop funding mechanisms that support long-term AI R&D beyond typical VC timelines.

• Focus on India-specific AI applications: Support startups solving local problems with AI to build scalable businesses.

• Promote collaboration: Foster partnerships between academia, industry, and government to build a strong AI ecosystem.

  
  

Final Thoughts

India’s AI ecosystem shows promise but faces a clear gap compared to the US’s massive AI funding and revenue achievements. The lack of $100 million revenue AI-first companies reflects deeper issues like foundational model development, talent retention, and patient capital availability.

By focusing on these challenges and leveraging India’s unique market needs, the country can build a stronger AI startup landscape. For AI founders, investors, and policymakers, the next step is to support long-term AI innovation and practical applications that can scale.