Potential listings by private artificial intelligence companies could expand India’s publicly traded technology sector over the next two to five years, with infrastructure providers expected to drive most of the opportunity

India’s artificial intelligence ecosystem could add between $50 billion and $100 billion to the country’s listed market capitalisation over the next two to five years as a new generation of AI-focused companies considers entering the public markets, according to an assessment by Goldman Sachs.

The investment bank estimates that approximately 25% to 50% of nearly 70 mid-to-late-stage private AI companies could launch initial public offerings (IPOs) during this period. These potential listings are projected to achieve a weighted average valuation of around four times their current estimated value, although actual outcomes will depend on market conditions, business performance and investor demand.

The projected expansion could broaden India’s listed technology universe beyond traditional software services and introduce investors to businesses involved in data centres, cloud infrastructure, semiconductor supply chains, enterprise AI and industry-specific applications.

Private AI Funding Gains Momentum in India

India’s AI ecosystem has attracted increasing attention from private investors as businesses explore applications ranging from automation and predictive analytics to conversational AI and advanced computing.

Goldman Sachs estimates that private equity funding for Indian AI startups has tripled since 2024. AI’s share of total private equity allocations has also quadrupled to approximately one-fifth of private capital deployed in India.

The country is now among the leading global destinations for private AI funding and newly funded AI companies, according to the report’s assessment referencing Stanford University’s 2026 AI Index Report.

This expansion reflects growing interest in businesses that can develop AI infrastructure, provide specialised models or integrate AI into commercial operations. However, increased funding does not automatically guarantee commercial success. Companies will need to demonstrate sustainable revenue, sound unit economics and a clear path towards profitability to attract public-market investors.

Goldman Sachs Identifies 84 Private AI Contenders

Goldman Sachs has identified 84 private companies as potential AI-market contenders, with a combined estimated current value of approximately $55 billion. These businesses account for nearly 40% of AI-related private capital raised in India since 2025, according to the report.

The companies operate across different layers of the AI value chain, ranging from infrastructure providers that support computing and data processing to developers of AI technologies and businesses using AI to improve products and services.

The bank’s broader list of 84 contenders differs from the nearly 70 mid-to-late-stage companies used for its potential IPO estimate. Not every company identified is expected to go public, and the projected listings remain conditional rather than confirmed.

The potential market-capitalisation increase will therefore depend on which businesses reach the public markets, their valuations at listing and the extent of investor demand.

Upstream AI Infrastructure Could Drive Most Listings

The upstream segment is the largest by estimated value within the identified group. It includes businesses involved in power generation, power equipment, electricity transmission, semiconductor supply chains, chip design, quantum computing, data centres, cloud services and data management.

Goldman Sachs estimates the current value of this segment at approximately $35 billion. It could account for around 66% of potential AI-related listings by value, making infrastructure the biggest expected contributor to the next phase of India’s AI IPO market.

AI systems require significant computing capacity, reliable electricity, specialised hardware and extensive data infrastructure. As adoption increases, demand for these supporting services could create opportunities for companies operating beyond the traditional software sector.

For investors, this means the AI investment theme may extend to infrastructure and industrial businesses, rather than being limited to companies developing AI applications.

However, infrastructure projects often require substantial capital expenditure. Their profitability will depend on utilisation rates, financing costs, customer commitments, technological changes and the ability to generate adequate returns on investment.

AI Adoption Companies Form the Largest Group

Downstream AI adopters represent the largest category by number, with 42 companies and an estimated combined value of $18 billion.

These businesses apply AI across areas such as computer vision, conversational systems, robotics, edge computing, healthcare, financial services, enterprise software, defence, cybersecurity and digital commerce.

Their business models may differ significantly. Some companies use AI to improve existing services, while others develop specialised products for particular industries. The commercial opportunity will depend on whether these solutions deliver measurable benefits, such as lower operating costs, faster processing, better customer service or improved decision-making.

Goldman Sachs estimates that downstream adopters could represent approximately 31% of potential AI-related listings by value.

As these businesses approach public markets, investors are likely to assess the proportion of revenue directly attributable to AI, customer retention, recurring income, competitive differentiation and the cost of developing and maintaining their technology.

Core AI Developers Remain a Smaller Segment

The midstream segment consists of core AI developers engaged in foundational research, model training and AI operations. Goldman Sachs identifies seven companies in this category, with a combined estimated value of approximately $2 billion.

These businesses operate closer to the underlying technology that powers AI applications. Their work can involve developing models, improving training methods and creating tools that enable other companies to deploy AI systems.

Although the segment is smaller by estimated value, its strategic importance may be considerable. Successful model developers can influence the broader AI ecosystem, but they may also face high research expenses, significant computing costs and competition from well-funded international players.

The bank estimates that core AI developers could account for approximately 3% of potential AI-related listings by value.

Bengaluru, Delhi-NCR and Mumbai Lead the AI Ecosystem

India’s AI companies are concentrated in established technology and business centres, including Bengaluru, Delhi-NCR, Mumbai and Hyderabad. Ahmedabad, Pune and Chennai are also emerging as additional centres of activity.

These locations offer varying combinations of technology talent, engineering capabilities, enterprise customers, research institutions and access to capital.

A broader wave of AI listings could increase opportunities for domestic investors to participate in businesses emerging from these ecosystems. It could also encourage companies to strengthen corporate governance, improve financial disclosures and establish more transparent performance metrics as they prepare for public-market scrutiny.

The distribution of future listings, however, will depend on company readiness, funding requirements, regulatory considerations and prevailing market conditions.

What the Potential AI IPO Wave Means for Investors

The projected $50 billion to $100 billion addition to listed market capitalisation represents a potential expansion of India’s public equity universe, not a guaranteed increase in existing investors’ wealth or a forecast of equivalent new capital inflows.

Market capitalisation depends on the valuation assigned to listed shares. A company’s IPO can increase the range of publicly traded businesses without necessarily producing sustained share-price appreciation after listing.

Investors will need to distinguish between companies with genuine technological advantages and those using AI primarily as a marketing label. Important considerations include revenue quality, customer concentration, operating cash flow, research spending, capital requirements, intellectual-property protection and the ability to defend market share.

Valuation discipline will be particularly important because investor enthusiasm for AI can lead to expectations that are difficult for businesses to meet. Even companies with strong growth potential can deliver disappointing investment returns if their IPO valuations leave little room for execution risks.

Market Outlook

India’s emerging AI IPO pipeline could become an important source of diversification for domestic equity markets over the next two to five years. Goldman Sachs’ estimate of $50 billion to $100 billion in potential additional listed market capitalisation highlights the opportunity to bring more AI infrastructure providers, application companies and technology developers into the public markets.

Upstream infrastructure businesses are expected to account for the largest share of potential listings, while downstream adopters could broaden exposure to AI applications across healthcare, finance, enterprise software and other industries.

The key indicators to monitor will be the number of companies that formally announce IPO plans, their financial performance, valuation expectations and the ability of AI-related businesses to convert investment into sustainable earnings. For investors, the opportunity is significant, but selectivity and valuation discipline will remain essential as India’s listed AI ecosystem develops.