India isn't out of the AI race as private equity investors see promise

In its 19 October report, investment bank Goldman Sachs estimated that prospective initial public offerings (IPOs) over the next 2-5 years could add $50-$100 billion in incremental market capitalization to India's listed AI complex. Does the growing interest of private equity investors in Indian AI companies suggest that equity market investors are underestimating India's role in the global AI value chain?

“It’s not about ‘underestimating’, but availability of paper,” said Somnath Mukherjee, managing partner and chief investment officer, ASK Wealth Advisors. There aren't many AI companies listed in India today, leaving equity market investors with few options to invest in the theme. The stocks that offer exposure to AI are also few in number, and most are too small to support investments at scale, he explained.

However, the pipeline of AI companies that could eventually go public appears to be growing, as private investments help them scale up. PE investment in Indian AI startups tripled in 2025 to approximately $5 billion, from $1.6 billion in 2024, according to the Goldman Sachs report. Investment has gathered pace in 2026, with Indian AI startups already raising around $5 billion in the first eight to nine months, putting the sector on track to surpass last year's record for the full year.

The share of total PE funding for Indian AI startups has also quadrupled, rising from roughly 5% in 2024 to around 20% in 2026, the report highlighted. According to Goldman, the scale of funding challenges the perception among investors that India is a marginal player in the global AI buildout. “According to Stanford University’s 2026 AI Index Report, India ranked as the sixth largest destination for private AI funding globally in 2025, ahead of most major economies outside the US, China, UK, France and Canada, and recorded the fourth largest number of newly-funded AI companies worldwide, trailing only the US, UK and China.”

The investment bank estimates that upstream AI enablers—including power generation and transmission, semiconductors, data centres, cloud services and data management—account for the largest share of the private AI contenders’ combined estimated value, at $35 billion. Companies in this category include renewable energy platforms such as Greenko Group and Fourth Partner Energy, data centre operators such as CtrlS Data Centers and Nxtra Data, and AI computing infrastructure provider Neysa.

Downstream AI adopters, valued at an estimated $18 billion, include businesses applying AI across healthcare, enterprise services, conversational AI and computer vision, such as Gupshup, Kore.ai and NetraDyne. Foundational AI model developers and training companies, including Sarvam AI and Deccan AI, form a smaller segment, valued at $2 billion. Indian companies are building app layers, small language models, AI models with specific use cases, data centres etc.

Some of these businesses present interesting opportunities, said Pushkar Jauhari, chief executive officer (CEO)-private equity and venture capital, Kotak Alternate Asset Managers. “Public equity investors are responding to what’s available in Indian listed markets today.” AI investment opportunities are largely in private markets today. Indian public equity fund managers have missed out on the AI trade because, unlike the US, India lacks listed companies focused on computing and large language models (LLMs), he said.

However, “This will change in the coming years as some of the private companies become larger and get listed," Jauhari added. Ashish Kumar, co-founder of Fundamentum Partnership, which has launched a new frontier-tech investment platform called Fundamentum Frontier Advisors (F2A), said India’s AI opportunity needs to be assessed over a longer time horizon. F2A, the Nandan Nilekani-anchored AI and deeptech investment firm, is increasing the target size of its maiden fund by ₹500 crore to ₹3,500 crore after stronger-than-expected demand from investors.

“The US AI story is also only three to four years old. We are now beginning to see similar developments in India,” Kumar said. He said that India’s opportunity extends beyond frontier models to enterprise AI adoption, applications and specialised production capabilities. “India will create frontier-model companies and highly sophisticated chips, but that journey may take longer,” he said, adding that these businesses require specialised talent that remains relatively scarce in India.

Kumar believes that the narrative that Indian markets have lost out on AI will begin to reverse over the next six to 12 months. “Investors will start recognising revenue growth and anticipating future listings before these businesses are ready to enter the public markets,” he added. While private equity and venture capital investment in Indian AI companies is growing, India remains a relatively small player compared with the US, where funding is concentrated in AI models, applications and the infrastructure supporting them.

According to the Q4 2025 PitchBook-NVCA Venture Monitor, published in January 2026, AI and machine learning deals accounted for 65.4% of all US venture capital deal value in 2025, totalling $222 billion of the $339 billion deployed. More recently, KPMG’s Venture Pulse Q3 2026, published on 14 October, showed that countries like US and China are recording multiple large billion dollar deals in AI firms like USA’s Databricks, and AI infrastructure firm Crusoe, China’s Moonshot AI and Kling AI and France’s Mistral AI, as capital flows into AI models, software, data centres, computing capacity, semiconductors and energy infrastructure, alike.

The opportunity for India is likely to be in applications, enterprise adoption and services, pointed out Aakash Agrawal, associate director, Anand Rathi Investment Banking. According to him, public markets may be underestimating that opportunity, but it still needs to translate into meaningful revenue and profits. Will rising PE investment eventually translate into earnings growth and stock-market returns, or is the disconnect between private and public markets justified?

Agrawal feels that there is no automatic link between PE investment and listed-market returns. PE investors can take a longer view, invest in unlisted businesses and back companies whose business models are still evolving. On the other hand, public markets need greater visibility on earnings, margins and valuations. “The real test is whether AI helps companies grow faster, improve productivity or build entirely new revenue streams.

Until that shows up in financial performance, the gap between private-market enthusiasm and public-market valuations is understandable.” Fundamentum's Kumar expects infrastructure companies, including data-centre businesses, to potentially enter public markets over the next couple of years. Enterprise AI companies could follow in around three to three-and-a-half years, while vertical infrastructure and production-capability businesses, including application-specific semiconductor and model companies, could take around five years to generate revenue.