What the Economic Survey Signals for India’s Startup-Led AI Push

India’s technology ecosystem is entering a decisive phase, marked not by expansion alone, but by a shift towards capability-driven, higher-value innovation. This transition is most visible in the IT-ITeS sector, where deep tech, intellectual property creation, and artificial intelligence are increasingly defining growth.

According to Nasscom, deep tech companies recorded a 78% rise in funding in CY2024, signalling renewed investor confidence in IP-led businesses. This momentum is unfolding within India’s technology startup ecosystem, the world’s third largest, which today comprises roughly 32,000-35,000 startups. Over 2,000 were added in CY2025 alone, including more than 900 funded ventures, highlighting the sector’s resilience despite global capital constraints.

Nowhere is this shift clearer than in generative AI. India’s generative AI startup ecosystem has expanded more than threefold in just over a year, from around 240 in H1 CY2024 to over 890 by H1 CY2025.

This surge is driven by lower experimentation costs and rising demand for AI solutions, particularly in key hubs like Karnataka, Maharashtra, Delhi, Telangana, Haryana, Tamil Nadu, and Uttar Pradesh.

Innovation Clusters and IP-Led Growth

India’s innovation output is also becoming more geographically and institutionally embedded. Bengaluru, Delhi and Mumbai now feature among the world’s top 50 innovation-intensive clusters, reinforcing India’s integration into global knowledge networks. At a national level, this has translated into a sharp rise in intellectual property creation.

In 2024, India ranked fourth globally in trademark filings, sixth in patents, and seventh in industrial designs. Between FY20 and FY25, patent applications nearly doubled, trademark registrations grew 1.5 times, and design registrations rose 2.5 times.

The simultaneous rise in design and patent filings points to a structural change, one where differentiation, branding, and process innovation are becoming central to value creation. As the Economic Survey 2026 notes, this marks an economy steadily moving up the value chain.

Crucially, innovation is no longer driven primarily by the public sector. While government-led R&D once dominated, a vibrant startup ecosystem now plays a central role. India ranks 12th globally on entrepreneurship policies and culture, according to the World Intellectual Property Organization, reflecting a maturing institutional environment.

Since the launch of the Startup India initiative in 2016, the number of DPIIT-recognised startups has grown from around 500 to over two lakh by 2025. Flagship financing instruments, including the Fund of Funds for Startups, the Startup India Seed Fund Scheme, and the Credit Guarantee Scheme, have lowered early-stage risk and enabled experimentation, particularly in deep tech.

India’s AI Advantage, and Its Limits

India enters the AI era with significant strengths. It ranks among the top global contributors to AI research output and has one of the world’s largest pools of AI talent. As of 2024, India’s AI-literate workforce is second only to the US. The country also holds a unique comparative advantage in its vast and diverse domestic data, spanning health, agriculture, finance, education, and public administration.

Yet, constraints remain. Access to cutting-edge compute infrastructure is limited, large-scale model training is capital-intensive, and private participation in foundational AI research remains muted compared to global leaders. These realities make it difficult to sustain a frontier-model-first strategy, the survey said.

Sagar Vishnoi, director and co-founder of Future Shift Labs, echoes the call for deeper investment in research and institutional capacity. While initiatives such as the ANRF are strengthening India’s research ecosystem, he argues they must be scaled further to catalyse deep tech innovation.

He also stresses the importance of sustained funding for AI startups, expanded AI literacy missions, and strategic investment in domestic compute and indigenous datasets to ensure long-term technological autonomy.

Moreover, the survey also advocates a bottom-up AI approach, prioritising application-specific, smaller models tailored to defined sectoral needs. Such models are computationally efficient, easier to fine-tune, and capable of running on locally available hardware. Notably, they democratise innovation by allowing startups, research institutions, public agencies, and domain-specific firms to participate without prohibitive entry barriers.

As the Union Budget 2026-27 approaches, industry leaders argue that policy must now shift from signalling intent to building durable infrastructure. Raj K Gopalakrishnan, co-founder and CEO of KOGO Tech Labs, frames the moment succinctly: if last year’s Budget was an “adrenaline shot” for the AI ecosystem, the next must provide the “structural steel” to sustain it.

For AI startups, he argues, the primary bottleneck is compute, not capital. Expanding the IndiaAI Mission to democratise access to high-performance GPUs is essential for building sovereign AI capabilities.

He also points to the need for incentives, such as tax breaks or adoption grants, that would move AI from experimental pilots to economy-wide productivity tools. Regulatory certainty, through mechanisms such as an AI innovation sandbox, would further enable startups to scale responsibly while solving real-world problems.

Data Governance

The globalised digital services landscape, coupled with the concentration of AI development among a few multinational firms, has created imbalances in how data value is generated and captured, the survey revealed. This requires a reevaluation of data governance frameworks, not to restrict data flows but to ensure accountability and fair practices.

India’s data governance has intentionally avoided strict data localisation to capitalise on the benefits of cross-border data flows and foster a favourable investment climate. However, to retain the value from domestic data, certain aspects of this approach must be expanded. As AI adoption accelerates, data governance is emerging as a defining constraint.

Ibrahim Khatri, CEO and co-Founder of privEzi, notes that AI data sourcing, once treated as a low-cost engineering shortcut, has become a governance and financial decision under the Digital Personal Data Protection Act.

While legal obligations sit with data fiduciaries, Khatri points out that penalties, remediation costs, and reputational damage directly impact company balance sheets. AI governance, he argues, has moved firmly into the boardroom.

Without a compliant data infrastructure, data risks becoming a liability rather than an asset. As the Budget approaches, he calls for policy support for compliant data systems and phased enforcement, recognising that financial and data stewardship are now inseparable.