Whats the Big Data

India’s AI startup ecosystem has reached an inflection point. The country now ranks 3rd globally in AI competitiveness, behind only the US and China, with its AI vibrancy score nearly doubling from 12.9 to 21.6 between 2018 and 2024. The number of GenAI startups tripled in a single year — from roughly 240 in H1 2024 to over 890 in H1 2025.

Cumulatively, more than 170 AI startups and 1,505 AI companies (founded 2018–2025) have raised over $4.45 billion across 971 funding rounds. India’s AI market is projected to reach $126 billion by 2030, with a potential GDP impact of $1.7 trillion by 2035.

The government’s INR 10,371 crore ($1.25 billion) IndiaAI Mission, the India AI Impact Summit 2026 hosted in New Delhi, and fresh capital commitments including a dedicated $1 billion AI fund from the India Deep Tech Alliance are accelerating this momentum.

AI Startups in India Ecosystem Scale & Growth

AI Startup Numbers in India

India is the world’s third-largest startup ecosystem with 200,000+ DPIIT-registered startups and 125+ unicorns. Within this, AI has become one of the fastest-growing segments:

• GenAI startups surged from ~240 (H1 2024) to over 890 (H1 2025), a 3x increase in one year.
• Over 1,505 AI companies were founded between 2018 and 2025, peaking at 363 new AI startups in 2024.
• India now has more than 5,000 AI startups across all categories when including traditional ML and NLP companies alongside GenAI ventures.

India AI Market Size

India’s AI market was valued at approximately $6.1 billion in 2023 and is expected to have crossed $8 billion by 2025. According to the Google–Inc42 Bharat AI Startups Report 2026, the market could reach $126 billion by 2030. The enterprise-focused agentic AI market alone generated $132.6 million in revenue in 2024 and is projected to grow to about $1.73 billion by 2030.

AI Startup Funding Landscape

Year-on-Year Funding Trends

AI startup funding in India has seen significant year-over-year growth, though it remains a fraction of US levels:

The India Deep Tech Alliance (IDTA) report found AI funding rose 58% YoY in 2025, with 188 deals totaling $1.22 billion. Forbes India tracked $887 million across the 2018–2026 period for 2025 specifically. AI’s share of total VC funding in India rose from ~4.5% in 2020 to ~12.3% in 2025.

Funding Stage Distribution

Investors are prioritizing application-layer businesses over capital-intensive model development. In 2025, early-stage AI funding totaled $273.3 million, while late-stage rounds raised $260 million. Cumulative GenAI funding rose from $606 million (H1 2023) to $990 million (H1 2025).

Key Capital Commitments

• IDTA: $1 billion dedicated to Indian AI startups over the next three years, within a broader $2.5 billion deep tech allocation.
• Microsoft: $17.5 billion committed to India, including AI infrastructure.
• Amazon: Over $35 billion pledged for India by 2030.
• Indian Government: INR 10,371 crore ($1.25B) IndiaAI Mission, with an additional INR 1 lakh crore RDI scheme for deep tech.

Gap vs. Global Markets

Despite progress, India’s AI funding is modest compared to the US ($121 billion in 2025, a 141% jump) and China (~$10 billion). India’s strength lies less in foundation-model development and more in downstream applications where cost-efficient tools solve local challenges.

India’s First AI Unicorn & Leading Startups

Krutrim AI — India’s First AI Unicorn

Founded by Ola’s Bhavish Aggarwal in 2022, Krutrim became India’s fastest company to achieve unicorn status in January 2024, reaching a $1 billion valuation with its inaugural $50 million round led by Matrix Partners India. Key developments:

• Aggarwal invested an additional INR 2,000 crore ($230M) from his family office, with plans to invest INR 10,000 crore by 2026.
• Launched Krutrim 2, a 12-billion-parameter multilingual model supporting 22 Indian languages with a 128,000-token context window.
• Deployed India’s first GB200 system in partnership with Nvidia.
• Announced four AI chips — Bodhi 1, Bodhi 2, Sarv 1, and Ojas — with Bodhi 1 slated for 2026 launch in partnership with Arm and Untether AI.

Sarvam AI — Sovereign LLM Builder

Founded by Vivek Raghavan and Pratyush Kumar, Sarvam AI has raised $41 million from Lightspeed Venture Partners, Peak XV Partners, and Khosla Ventures. In April 2025, the government selected Sarvam AI to build India’s first sovereign LLM under the IndiaAI Mission.

In February 2026, Sarvam launched five new open-source models including 30B and 105B parameter variants, along with text-to-speech, speech-to-text, and vision models — all trained from scratch on trillions of tokens spanning multiple Indian languages.

Other Notable AI Startups

India’s AI Startup‘s Sector-Wise Breakdown

India’s AI startups span a wide range of verticals, with particular strength in enterprise AI, healthcare, fintech, and agriculture.

Frontier Models & Compute

Startups like Krutrim, Sarvam AI, Two AI, and Bharatgen are building foundation models, while Neysa Networks and Agrani Labs are tackling compute infrastructure — building GPU cloud for enterprises and Nvidia alternatives respectively.

Healthcare

Healthcare AI is one of India’s strongest sectors, projected to grow at a 27.6% CAGR, reaching $12.43 billion by 2033. Qure.ai leads in radiology and diagnostics, while Fractal’s Vaidya 2.0 has outperformed leading frontier models on medical reasoning benchmarks. Wysa has become a global AI mental health companion with over 3 million users.

Fintech & BFSI

Startups like OnFinance AI (compliance OS for financial services), Moneyview (AI-driven digital lending, recently became unicorn), and Alltius (AI assistants for BFSI) are transforming financial services. GreyLabs AI provides agentic voice AI specifically for India’s BFSI sector.

Voice & Conversational AI

India’s linguistic diversity has spawned a vibrant voice AI segment. Nurix AI, Smallest, and Gnani.ai focus on enterprise voice solutions optimized for Indic languages. Sarvam AI’s newly released text-to-speech and speech-to-text models further strengthen this category.

Agriculture

KissanAI and Krishi Sathi are building conversational AI for farmers — providing crop advisory, weather insights, and market pricing in local languages.

Legal Tech, Media & Creative AI

LexLegis AI, SpotDraft, and Lucio are applying AI to legal workflows, while Dashverse AI and Gan.ai are building AI-native content creation and personalized video generation.

India’s Government Policy & Infrastructure for AI

IndiaAI Mission

The Cabinet-approved IndiaAI Mission (March 2024) is the backbone of India’s AI policy push. It operates across seven pillars:

1. IndiaAI Compute Capacity — Expanded from 10,000 GPUs to 38,000 GPUs, with an additional 20,000 GPUs announced at the AI Impact Summit 2026.
2. IndiaAI Innovation Centre — Fostering indigenous model development.
3. IndiaAI Datasets Platform (AIKosh) — Hosts 5,500+ datasets and 250+ models.
4. IndiaAI Application Development — Sector-specific AI applications.
5. IndiaAI Future Skills — Fellowships, academic programs, AI labs in Tier 2/3 cities.
6. IndiaAI Startup Financing — Risk capital for AI startups.
7. Safe and Trusted AI — Ethical frameworks and governance.

Budget allocations surged from INR 173 crore (FY25 revised) to INR 2,000 crore (FY26 budget), a tenfold jump.

India AI Impact Summit 2026

Held at Bharat Mandapam, New Delhi from 16–21 February 2026, this was the first AI Summit held in the Global South. Key outcomes include:

• A declaration endorsed by 92 countries and international organizations.
• PM Modi unveiled India’s AI vision ‘MANAV’ — encompassing moral systems, accountable governance, and national sovereignty.
• Launch of the Global AI Impact Commons platform with 80+ impact stories across 30+ countries.
• India joined the US-led Pax Silica initiative for resilient semiconductor supply chains.[30]
• Four AI Centres of Excellence established — in Healthcare, Agriculture, Sustainable Cities, and Education (INR 500 crore).

India’s AI Governance Approach

India has adopted a balanced, pro-innovation approach: governing AI applications through sectoral regulators rather than regulating the underlying technology itself. The government has also announced a tax holiday until 2047 for companies building data centre infrastructure in India.

Challenges & Bottlenecks

Compute Access

Despite $20 billion in AI commitments, access to compute power remains one of the biggest barriers for early-stage AI startups. High GPU costs and complex infrastructure limit smaller innovators, making computing access feel like “an exclusive privilege rather than a public utility”.

Funding Gap

Indian AI startups raised roughly $1.2 billion in 2025 versus over $121 billion in the US — a 100x gap. Only 16% of startups reported access to next-level funding, with most being self-funded or angel-backed. Many startups get trapped in “PoC purgatory” — months of pilots with no contracts.

Talent Retention

India has 2.5x the global average concentration of AI-skilled professionals, but persistent talent flight continues due to limited high-end domestic opportunities and a lack of competitive policy incentives. Gaps remain in product leadership, deployment, and go-to-market execution rather than raw engineering capability.

Enterprise Adoption

Only 23% of Indian enterprises have fully integrated AI into their strategy, and just 10% of startups invested more than INR 1 crore in AI in 2025. Moving from pilot to production remains the critical bottleneck, though 47% of enterprises are now transitioning pilots into production.

Data Quality

The CCI flagged that established entities own vast high-quality datasets that are not accessible to smaller firms, recommending removal of these barriers to create a level playing field. The IndiaAI Datasets Platform (AIKosh) is working to address this but access remains uneven.

Outlook

India’s AI startup ecosystem is positioned for significant acceleration through 2026–2030, driven by multiple converging tailwinds:

• Policy momentum: The IndiaAI Mission’s expanded budget, 58,000+ GPU capacity, and four Centres of Excellence provide foundational infrastructure.
• Capital inflows: The government expects the sector to attract over $200 billion in capital over the next two years. IDTA’s $1B AI-specific allocation and Big Tech investments (Microsoft $17.5B, Amazon $35B) provide growth-stage capital.
• Sovereign AI stack: With Krutrim building custom AI chips and Sarvam AI releasing 105B-parameter open-source models, India is moving toward a full indigenous AI computing stack.
• Global South leadership: The India AI Impact Summit 2026 positions India as the voice of the developing world on AI governance, with its DPI model (UPI, Aadhaar) serving as a blueprint for “AI Commons”.
• Sector-specific strength: India’s competitive edge lies in downstream AI applications — healthcare, agriculture, education, governance — where cost-efficient tools solve local challenges at population scale.

The key metric to watch is whether India can produce an AI-first company generating $40–$50 million or more in annual revenue — a milestone that has not yet been achieved but is emerging as the ecosystem matures.

Women remain significantly underrepresented across the artificial intelligence ecosystem — from the workforce and research labs to leadership roles and venture capital. While progress is being made, particularly in generative AI adoption, the gender gap in AI persists across nearly every measurable dimension.

This report compiles the most current statistics on women’s participation, challenges, and emerging opportunities in AI.

Women in AI: Key Statistics on the Gender Gap in Artificial Intelligence (2025–2026)

The AI gender gap is multi-layered: women constitute only 22–30% of the global AI workforce, hold fewer than 15% of senior executive AI roles, and author just 25% of AI research papers. Meanwhile, AI systems themselves reflect this imbalance — 44% of AI systems exhibit gender bias, and women face nearly three times the automation risk from AI compared to men.

However, the gender gap in generative AI adoption is closing rapidly, with Deloitte projecting US parity by the end of 2025. Record venture capital flows to female-founded AI startups, including a landmark $73.6 billion in 2025, signal shifting dynamics — though funding remains highly concentrated.

Global AI Workforce Representation

Women hold a minority share of AI jobs worldwide, with estimates ranging from 22% to 30% depending on the source and methodology.

• 22% of AI professionals globally are women, according to analysis of nearly 1.6 million AI professionals by the World Economic Forum and Interface EU.
• 30% of the AI workforce is female, per UN Women’s 2026 assessment, comparable to women’s overall representation in STEM fields.
• 26% of data science and AI employees are women across the world’s leading economies, with cloud computing (15%) and engineering (15%) showing even lower figures.
• In North America, women occupy 25% of AI roles, while in the EU the figure stands at 24%.
• A Statista/WEF analysis found that as of 2020, 14.2% of cloud computing workers and approximately 32% of data and AI workers were women.

Representation by Seniority Level

The gender gap widens at every step up the seniority ladder, creating what researchers call a “leaky pipeline” in AI careers.

• At the entry level, women comprise approximately 29% of AI workers.
• At the senior executive level, women occupy fewer than 15% of AI roles — nearly half the entry-level figure.
• Among 39 leading AI-focused organizations analyzed by Russell Reynolds, women hold only 30% of overall leadership roles and 10% of CEO and top technology roles.
• Only 22% of product, engineering, and science roles in AI companies are held by women. The study identified just four women CEOs and four women CTOs across these organizations.
• 19 of 39 AI company C-suites have fewer than 25% women, and 30 of 39 are less than one-third women.
• Women hold approximately 20.2% of CTO positions in mid-market tech firms.
• In India’s IT sector, women hold 23% of senior leadership roles as of 2024, up from 18.7% in 2023.

Women in AI Research & Academia

The research pipeline reveals an even starker gender imbalance, with women’s representation in AI research declining relative to their male counterparts over the past decade.

• Only 12% of leading AI researchers globally are women, according to 2025 data from the Stanford AI Index and World Economic Forum.
• Women hold just 16% of AI research roles, per UN Women.
• A Nesta study of over 1.5 million arXiv preprints found only 13.8% of AI research authors are women, with the proportion having stagnated since the 1990s.
• 75% of AI scientific publications are produced by all-male teams, based on analysis of 74,000+ AI-related papers across physics, mathematics, computer science, and engineering.
• At top AI research institutions, gender ratios remain low: only 11.3% of Google’s AI researchers on arXiv are women, along with 11.95% at Microsoft and 15.66% at IBM.

Generative AI Adoption Gap

One of the most dynamic areas in the women-in-AI landscape is the rapidly closing gender gap in generative AI usage.

• In 2023, women’s use of generative AI was roughly half that of men’s.
• By 2024, 33% of surveyed US women reported using or experimenting with GenAI, compared to 44% of men — a persistent but narrowing gap.
• The proportion of US women adopting GenAI tripled in one year, outpacing the 2.2x growth rate seen among men.
• Deloitte projects that women’s experimentation with and use of GenAI will equal or exceed that of men in the United States by the end of 2025.
• A Boston Consulting Group study found that 68% of women in tech use a GenAI tool at work more than once a week, compared with 66% of men. Senior women in technical functions lead their male counterparts by an average of 14 percentage points in GenAI adoption.
• Among daily AI users, 34% of women use AI daily compared to 43% of men.
• However, for every 100 men using GenAI tools, only 78 women do — after accounting for usage differences across demographics.
• Research from China shows that female professionals are adopting AI faster than male counterparts and report lower levels of anxiety around AI tools.

STEM Education Pipeline

The gender gap in AI begins in the educational pipeline, where women remain underrepresented in computing and AI-related degree programs.

• Women account for just 35% of STEM graduates globally, with little improvement over the past decade, according to UNESCO.
• Only 21% of engineering degrees and 22% of computing degrees were awarded to women in 2023.
• In the UK, 23% of computer science enrollments in 2022/23 were female or non-binary, up from 19% five years earlier. At that rate, parity would take over 30 years.
• The gap in AI-specific degree conferrals between men and women is nearly three times wider than the gap in general STEM degrees, per Georgetown CSET analysis.
• Only one in four women with an IT degree in the EU took up digital occupations, compared to over one in two men.

Also Check: AI Voice Agents Market Size 2024–2034

AI Gender Bias Statistics

The underrepresentation of women in AI development has measurable consequences for the technology itself, as AI systems reflect and often amplify societal gender biases.

• 44% of AI systems exhibit gender bias, a direct consequence of homogeneous development teams.
• A UNESCO study found unequivocal evidence that Large Language Models (GPT-3.5, GPT-2, Llama 2) produce gender bias against women, with open-source models showing the most significant bias.
• Joy Buolamwini’s landmark “Gender Shades” study revealed facial recognition error rates of up to 34% for darker-skinned women versus just 0.8% for lighter-skinned men.
• In AI-powered hiring, a Brookings study found gender bias in 63% of tests: resumes with men’s names were favored 51.9% of the time, while women’s names were favored just 11.1%.
• When ChatGPT generated nearly 40,000 resumes, it assumed women were younger by 1.6 years, had more recent graduation dates, and less work experience compared to resumes with male names.
• In Belgium, 74% of recruiters now automate at least one hiring stage, yet only 12–17% have noticed biased outcomes in their AI tools.
• A separate experiment found that all five tested LLMs (GPT-3.5, GPT-4o, Gemini, Claude, Llama 3) systematically award higher scores to female candidates regardless of race — but this pro-female bias masks deeper intersectional issues, particularly disadvantaging Black male candidates.

AI’s Impact on Women’s Employment

AI-driven automation poses a disproportionate threat to women’s jobs due to occupational segregation patterns.

• Women are nearly three times more likely than men to work in jobs with the highest exposure to generative AI automation. In high-income countries, 9.6% of female employment falls into the highest-risk category, versus 3.5% for men.
• The UN Gender Snapshot 2025 estimates that approximately 28% of women’s jobs globally are at risk of automation by AI, compared with 21% of men’s jobs.
• Women dominate clerical and administrative roles — data entry, typists, customer service — that generative AI can most easily replicate.
• In India, roughly 80% of women work in the informal sector, with many in BPO roles vulnerable to automation. Without targeted reskilling, AI could reverse progress toward workplace parity.
• Emerging tech roles carry a 6% pay premium, yet women remain underrepresented in these positions, widening the gender pay gap. Women in tech earn approximately 20% less than men overall.
• A Wharton study found that only 16% of women in their sample worked with emerging technologies, compared to ~17% of men — a gap that persists even when controlling for qualifications.

Venture Capital & Female AI Founders

Record funding flows to female-founded AI startups mask a concentration problem, with a handful of companies driving the headline numbers.

• In 2025, startups with at least one female founder raised a record $73.6 billion, nearly double the $44.7 billion raised two years prior.
• Two-thirds of all US venture capital going to female-founded startups flowed into AI ventures.
• Nearly half of that AI funding went to just two companies: Anthropic (co-founded by Daniela Amodei) and Scale AI (co-founded by Lucy Guo), which together pulled in over $30 billion.
• Without Anthropic and Scale AI, female-founded companies would not have surpassed one-quarter of US deal value.
• All-female founding teams receive roughly 1–2% of total VC funding globally, a figure that has barely changed over the past five years.
• The number of deals involving female-founded enterprises has declined for four consecutive years since a 2021 peak, even as total funding amounts rose.
• 82% of decision-makers at US VC firms with assets exceeding $50 million are men.

Country-Level Comparisons

The women-in-AI gender gap varies considerably across nations, with some surprising leaders and laggards.

Saudi Arabia’s position as the global leader in women’s AI engagement reflects targeted national programs under Vision 2030 and is supported by Stanford’s 2025 AI Index Report.

In contrast, countries like Portugal and Estonia, which have achieved near gender equity in their general workforce, show dramatic AI-sector imbalances of up to 51%, underscoring that general labor market progress does not automatically translate into AI workforce equity.

Also Check: Adaptive AI Market Size (2024 to 2034)

Notable Women Leading in AI

Fei-Fei Li speaking at the AI for Good event in 2017.
Several women are playing pivotal roles in shaping the AI industry, from technical research to corporate leadership and AI ethics advocacy.

• Fei-Fei Li — Often called the “godmother of AI,” Li created ImageNet, co-directs Stanford’s Human-Centered AI Institute, and founded World Labs (raised $230 million). She also co-founded AI4ALL to increase underrepresented groups’ participation in AI.
• Daniela Amodei — Co-founder and President of Anthropic, which has reached a $183 billion valuation. Her leadership in AI safety has positioned Anthropic as a key player in responsible AI development.
• Mira Murati — Former CTO of OpenAI who led the development of ChatGPT and DALL-E. In 2025, she secured a record-breaking $2 billion seed round for her AI startup Thinking Machines Lab, the largest seed funding in history.
• Joy Buolamwini — Founder of the Algorithmic Justice League, whose “Gender Shades” research exposed racial and gender bias in commercial facial recognition systems.
• Timnit Gebru — Co-authored the landmark paper “On the Dangers of Stochastic Parrots” and founded the Distributed AI Research Institute (DAIR) to pursue independent, community-rooted AI research.
• Lisa Su — CEO of AMD who steered the company into the AI chip market with the MI300X accelerator, transforming AMD into a major competitor in AI hardware.

Closing the Gap: Key Barriers and Opportunities

The persistent gender gap in AI stems from structural, cultural, and systemic factors — but there are also clear levers for change.

Barriers:

• Women are 25% less likely than men to have basic digital skills and four times less likely to have advanced programming skills globally.
• Structural constraints including relocation demands, inflexible hours, and uneven caregiving responsibilities limit women’s access to high-value AI roles.
• A confidence gap persists: a 2025 study found an 18-percentage-point gap in AI skill confidence, with young women reporting lower confidence (56%) versus young men (74%).
• In developing countries, only 20% of women have internet access, creating a cascading barrier to AI economy participation.
• Only half of the 68% of countries with STEM education policies specifically target girls and women.

Opportunities:

• Companies with equitable gender diversity on boards and in C-suites report on average 10% better financial performance.
• Women in AI are more likely to consider values like safety, accountability, and human autonomy as extremely significant — by 4–5 percentage points more than male counterparts.
• The rapid closure of the GenAI adoption gap suggests that with the right access and trust-building, women can match or exceed men in AI tool usage within a few years.
• AI itself offers opportunities to accelerate women’s inclusion by reducing hiring bias, providing personalized skill development at scale, and enabling flexible work arrangements.

Conclusion

The data paints a complex picture: women are underrepresented in AI across the workforce, research, leadership, and funding — yet the trajectory is shifting. GenAI adoption rates are converging, record venture capital is flowing to female AI founders (even if concentrated), and awareness of AI bias is driving accountability measures.

Closing the AI gender gap requires simultaneous action on education, workplace structures, funding ecosystems, and the AI systems themselves. As UN Women, the World Economic Forum, and leading researchers have emphasized, the stakes extend beyond equity — who builds AI determines whose values it reflects.