In 2025, artificial intelligence accounted for 91% of India's $2.3 billion in deep tech funding and 84% of all deep tech startup activity. No previous technology cycle in India has produced this level of capital concentration, and few cycles globally have. AI is not one category among many in Indian deep tech.
AI is Indian deep tech, in capital terms, in 2026. The concentration is partly a global pattern, partly an Indian-specific story. Understanding both is essential for founders deciding where to build, investors deciding where to deploy, and policymakers deciding what to enable.
Here is what's driving the 91%, what it tells us about the next wave, and what gets missed when concentration runs this high. The concentration in context The 91% figure, from the NASSCOM-Zinnov India Tech Startup Report 2025, is striking but not unprecedented globally. Roughly half of all global tech startup funding in 2025 went to AI-led companies, with about 65% concentrated in mega-rounds.
Indian AI concentration sits at the upper end of the global pattern. Three forces produced this concentration.
Force one: the production-grade emergence of LLMs and multimodal AI.
Until 2022, "AI" meant narrow machine learning, computer vision, and natural language processing systems that required substantial customisation per use case. From 2023 onward, foundation models produced generalisable capability that could be deployed across categories with much less custom engineering. The unit economics of AI shifted, and the addressable market exploded.
Force two: enterprise AI demand activation.
Indian enterprises (BFSI, healthcare, manufacturing, retail, government) moved from AI pilot mode to AI deployment mode between 2024 and 2026. The MarketsandMarkets and Yotta Whitepaper 2026 reports that 87% of Indian organisations are now progressing toward structured AI deployment. This produced concrete revenue opportunities that startups could pursue.
Force three: sovereign and IndiaAI direction.
The Government of India's IndiaAI Mission, the DPDP Act, sector-specific data residency requirements, and the broader Atmanirbhar AI policy direction created demand for Indian-built AI infrastructure that foreign vendors could not satisfy. The combination of policy support and procurement direction made AI ventures particularly fundable. The 91% is therefore not an accident.
It is the predictable result of these three forces compounding. What "AI" actually means inside the 91% The 91% concentration obscures a wide range of AI categories. Disaggregating it reveals where capital is actually going.
Category one: vertical AI for regulated industries.
Healthcare AI, legal AI, financial services AI, regulatory technology. Examples from the PanScience portfolio include Parchaa, NyaayAI, OnFinance, and broader compliance and risk technology. This category absorbs an estimated 25% to 35% of AI deep tech funding in 2026.
Category two: enterprise AI and workflow automation.
Document processing, contract intelligence, knowledge management, customer support automation, business intelligence. Insituate AI and OpticAll operate here. This category absorbs roughly 20% to 25% of AI deep tech funding.
Category three: data infrastructure and LLMOps.
Data labelling, annotation, RLHF, model training, fine-tuning, and evaluation infrastructure. Indika AI operates here. This category absorbs roughly 10% to 15% of AI deep tech funding.
Category four: media and content intelligence.
AI content moderation, localisation, personalisation, and generation for streaming, broadcast, and creator economies. Choice AI operates here. This category absorbs roughly 8% to 12% of AI deep tech funding.
Category five: industrial and infrastructure AI.
Predictive maintenance, manufacturing intelligence, energy management, infrastructure monitoring. PredCo operates here. This category absorbs roughly 10% to 15% of AI deep tech funding.
Category six: foundation models and AI infrastructure.
Indic foundation models (BharatGen, Sarvam, Krutrim), specialised model training, compute infrastructure. This category is smaller in deal count but larger in per-deal ticket size, absorbing roughly 5% to 10% of AI deep tech funding.
Category seven: applied AI for consumer and SMB.
Voice AI for SMB, personalisation, education AI, lifestyle AI. Smaller deal sizes, larger deal counts, absorbing roughly 5% to 10% of AI deep tech funding. The categories together explain where the 91% goes.
Each category has its own thesis, its own capital intensity, and its own time-to-revenue profile. What the 91% tells us about the next wave The concentration is informative not just about the current cycle but about what comes next. Five signals matter.
Signal one: vertical AI is winning over horizontal AI.
Within the 91%, the share going to vertical AI (healthcare, legal, financial services, industrial) continues to grow relative to horizontal AI platforms. The next wave of large companies will be category-specific AI leaders, not horizontal foundation model challengers.
Signal two: data infrastructure is positioned for the long compound.
The companies building data labelling, annotation, RLHF, and model-training infrastructure for the AI era benefit from every other category's growth. As the 4,200-plus deep tech startups grow, they all consume more data infrastructure. This category will produce some of the most durable Indian deep tech businesses.
Signal three: regulated industry AI is becoming a moat.
AI deployed into regulated industries requires not just technology but trust, compliance, certification, and institutional relationships. Companies that build these moats now (Parchaa in healthcare, NyaayAl in legal, OnFinance in financial services) are establishing durable competitive positions that will be hard to displace.
Signal four: sovereign AI is a structural opportunity.
The combination of DPDP, sector-specific regulations, IndiaAI Mission, and Atmanirbhar direction means Indian AI companies have a structural advantage over foreign vendors in many categories. Sovereign deployment requirements alone will produce multi-billion-dollar Indian outcomes in the next 5 to 10 years.
Signal five: applied AI for India-specific use cases is undermonetised.
Voice AI in regional languages, agricultural AI, MSME AI, public services AI, and India-specific consumer AI are categories where the technology is mature but the business models are still being figured out.The 91% is dominated by enterprise AI; the next wave of capital will increasingly go to India-specific applied AI. What concentration this high also means A91% concentration is also a risk indicator. Three risks come with it.
Risk one: non-AI deep tech categories are underinvested.
Quantum computing, semiconductors, biotech, climate tech, and frontier robotics are receiving disproportionately little Indian deep tech capital in 2026 relative to their long-term strategic importance. The 9% of non-AlI deep tech funding has to cover all of these categories, which is structurally insufficient.
Risk two: AI-cycle correction would be sharp.
Capital concentrated this heavily in one category is vulnerable to cycle dynamics. A correction in global AI valuations would hit Indian AI funding harder than a more diversified ecosystem.
Risk three: talent allocation is skewed.
Engineers and researchers moving into AI in disproportionate numbers reduces the talent pool available for other deep tech categories. India's quantum, semiconductor, and biotech ecosystems face talent scarcity partly because AI is consuming the available talent supply. For founders, investors, and policymakers, the 91% concentration is therefore both a current strength and a future risk.
The countries that maintain AI leadership while also building strength in adjacent deep tech categories will be the structurally strongest in 2035. What founders should do with this information For a founder deciding what to build, the 91% concentration has three implications. Implication one: AI is funded, but specifically applied vertical AI is over-funded relative to other AI categories.
A founder building generic AI infrastructure or horizontal AI platformsfaces both crowded competition and harder differentiation than a founder building category-specific AI for a regulated industry, an underserved India-specific use case, or a clearly defined enterprise workflow. Implication two: non-AI deep tech is undervalued, which is both a risk and an opportunity. A founder building in quantum, semiconductors, biotech, or robotics faces a thinner investor pool but also less competition for top talent and a longer-term moat once the category re-rates.
Implication three: hybrid theses are increasingly compelling. Companies that combine AI with adjacent deep tech (AI plus robotics, AI plus biotech, AI plus quantum) often capture the funding favourability of AI while building structural depth that pure-AI companies lack.
The bottom line
The 91% AI concentration in Indian deep tech funding in 2025 is the defining structural feature of the 2026 ecosystem. It reflects real opportunities, real product-market fit, and real policy support. It also reflects risks that the ecosystem has to manage through deliberate diversification over time.
For founders, the right response is to build the AI category where they have genuine domain depth and competitive advantage, while staying aware that the next wave of Indian deep tech will increasingly include adjacent and hybrid categories. For investors, the right response is to continue backing the AI thesis while reserving capital for the non-AI categories whose time will come. For policymakers, the right response is to sustain AI leadership while creating dedicated support for the deep tech categories that will define the 2030s.
PanScience Innovations operates across this full spectrum: AI is the centre of our portfolio thesis today, with adjacent and hybrid deep tech categories on our roadmap for tomorrow.
FAQ
Why does AI dominate Indian deep tech funding in 2026?
AI accounted for 91% of Indian deep tech funding in 2025 due to three converging forces: the production-grade emergence of large language models and multimodal AI from 2023 onward, enterprise AI demand activation in India (with 87% of Indian organisations progressing toward structured AI deployment), and the IndiaAI Mission and sovereign AI policy direction creating demand for Indian-built AI infrastructure.
Which AI categories are receiving the most investment in India?
The largest categories by share of AI deep tech funding in 2026 are vertical AI for regulated industries (25 to 35% of AI funding, including healthcare, legal, and financial services), enterprise AI and workflow automation (20 to 25%), industrial and infrastructure AI (10 to 15%), data infrastructure and LLMOps (10 to 15%), and media and content intelligence (8 to 12%).
Is the 91% AI concentration in India a risk?
The concentration creates three risks: non-AI deep tech categories (quantum, semiconductors, biotech, climate tech, robotics) receive disproportionately little capital relative to long-term strategic importance; a global AI valuation correction would hit Indian AI funding harder than a diversified ecosystem; and talent allocation is skewed toward AI, reducing the engineering and research talent available for adjacent deep tech categories.
What is sovereign AI in the Indian context?
Sovereign AI refers to AI systems whose models, data, infrastructure, and governance remain within Indian jurisdiction, deployable on Indian sovereign cloud or on-premise, with full Indian institutional control. Sovereign AI is structurally favoured in Indian deep tech because of DPDP Act requirements, sector-specific data residency rules (RBI, SEBI, IRDAI), and the IndiaAI Mission's emphasis on indigenous AI capability.
What deep tech categories are undervalued in India in 2026?
Quantum computing, semiconductors, biotech and life sciences, climate tech, frontier robotics, and advanced materials are receiving disproportionately little Indian deep tech capital relative to their long-term strategic importance. These categories represent both higher risk (longer development cycles, smaller investor pools) and higher long-term opportunity (less competition, deeper moats once established).