India AI Year in Review 2025
10 key takeaways on what India built in 2025, what it couldn't solve, and what comes next.
If 2024 was India’s AI awakening, 2025 was the year the world woke up to India. Hyperscalers committed $80 Billion+. Frontier labs began opening offices. Government compute went live at scale. And in February 2026, India will host the Global AI Summit — a signal that India is no longer just participating in the AI era, but helping shape its global agenda.
But here’s the uncomfortable truth: despite all this momentum, India still lacks a single homegrown hyper growth native-AI company clocking $100M+ ARR. And this is when the country is now ChatGPT’s second-largest market, Anthropic’s second-largest by usage, ElevenLabs’ second-largest by enterprise revenue and Perplexity’s largest by user base.
The question for 2026 isn’t whether India matters to global AI. The question is whether Indian companies can capture the value being created, or whether it concedes a lot of that to US-based frontier labs and big-tech.
Here’s a roundup of the top 10 things that mattered in the India AI landscape in 2025.
1. The IndiaAI Mission makes compute available at scale
The ₹10,371 crore IndiaAI Mission announced in 2024 moved from paper to production.
By mid-2025, the government had empanelled 34,333 GPUs across sovereign cloud providers like Yotta, E2E Networks, Neysa, Jio Platforms, and CtrlS. Access through the IndiaAI Compute Portal costs roughly ₹87.59/hour, about one-third that of global rates, with 40% subsidy by the government.
What’s actually running on it? Sarvam AI’s sovereign LLM development, BharatGen’s multilingual foundation models, and over 30 AI application projects across healthcare, agriculture, and governance.
The friction is real though. Only ~17,000 GPUs are fully operational. Queue times persist. The companies that will win aren’t waiting for government allocation—they’re raising capital and buying their own GPUs.
But the symbolic value matters. Public compute signals state commitment, which de-risks private investment. That might be its most important function.
2. India will have it’s own LLM (soon)
Eight companies are now building indigenous Large Language Models (LLMs) under the IndiaAI Mission’s Foundation Models pillar, with approximately ₹2,000 crore in grants.
The lineup includes:
Sarvam AI (sovereign LLM for governance, Hindi-first, 120B parameter model),
Soket AI Labs (open-source multilingual for defense/healthcare, 120B parameter model),
Gnani AI (voice AI, real-time multilingual speech, 14B parameter model),
Gan AI (text-to-speech, 70B parameter model),
BharatGen from IIT Bombay (bilingual multimodal, 2B–1T parameter range model),
Fractal Analytics (large reasoning model for STEM/medical, 70B parameter model),
Tech Mahindra (enterprise-focused, TBD on the size), and
Avataar AI (domain-specific industrial models, TBD on the size).
Sarvam AI, backed by Lightspeed and Khosla Ventures, expects to release India’s first sovereign LLM by February 2026, timed for the AI Impact Summit. The model will power use cases like “2047: Citizen Connect” and “AI4Pragati.”
The debate nobody seems to be resolving: should publicly-funded models be open-source? Sarvam’s flagship is closed-source today. DeepSeek-R1, by contrast, was released with open weights.
“Sovereignty” is a politically useful framing, but competitiveness is what actually matters for startups and enterprises. If Sarvam’s model can’t match GPT-5 or Claude 4 on English tasks while excelling on Indic languages, adoption will lag regardless of who owns the weights. The real test: will Indian enterprises actually deploy these models, or keep buying from OpenAI and Anthropic?
3. Big Tech commits over $80 billion to India
The hyper scaler land grab is on.
Microsoft committed $17.5B over 2026–2029—their largest investment in Asia. Satya Nadella met PM Modi in December and the package includes a new Hyderabad data center region (live mid-2026), sovereign cloud solutions, skilling for 20M people, AI integration into e-Shram & National Career Service platforms serving 310 million informal workers.
Google is putting $15B into building India’s first “gigawatt-scale” AI hub in Visakhapatnam - three data center campuses, a new international subsea gateway on India’s east coast, partnerships with AdaniConneX and Airtel.
Amazon committed $35B by 2030 for AWS expansion and AI for 15M small businesses.
OpenAI opened a Delhi office for enterprise sales. Anthropic is coming to Bengaluru in early 2026 with Indic language support.
But the most interesting play might be Perplexity’s exclusive deal with Airtel: 360 million customers get free 12-month access to Perplexity Pro (worth ₹17,000). It’s the first time a frontier AI company has bundled with a telecom in India — and potentially a template for distribution at population scale.
The infrastructure investments are table stakes. What’s genuinely novel is using telecom distribution rails to acquire users at near-zero CAC. We expect every frontier AI company to pursue similar partnerships. The winner in consumer AI in a price-sensitive, less AI penetrated market like India may not be whoever has the best model but rather whoever cracks distribution first.
4. India is the world’s 3rd largest AI consumer
The demand signal is no longer speculative.
OpenAI: India is ChatGPT’s second-largest market outside the US. Downloads hit 46.7M in Q2 2025 alone.
ElevenLabs: India is their largest market by signups, second-largest by enterprise revenue. Founder Mati Staniszewski put it well: “India is already voice-first—roughly one-third of internet users use voice search each month, the highest globally.”
Anthropic: India ranks second globally in Claude usage, behind only the US.
Perplexity: India became their largest market by monthly active users in Q2 2025—downloads up 600% YoY.
India isn’t just a cost arbitrage play for AI companies anymore. It’s a demand center. But demand without supply-side capture is just value extraction. Right now, Indian users are training American models. The consumer AI opportunity is enormous, but only if Indian companies can build products that win on experience, not price.
5. India may lead in Consumer AI innovation
2025 saw the emergence of India-first consumer AI categories that don’t have clean Western analogues. This is where things get interesting.
AI Astrology is having a moment. Apps like AstroTalk (5M+ downloads), Shani.ai, Melooha, and KundliGPT combine Vedic astrology with LLMs. Users get personalized Kundli readings, dasha predictions, and voice-based consultations. The astro-spiritual market in India is valued at $40+ billion - and AI is lowering the cost of access dramatically.
This isn’t “woo-woo.” It’s a massive market with clear willingness to pay, cultural acceptance of digital delivery, and a perfect fit for AI’s core capability: personalized advice at scale.
AI Companions are emerging, too. Rumik ($1.5M raised), Mello, and Kavana are building for India’s loneliness economy. The thesis: 700M internet users, vernacular-first, voice-first, mobile-first. The demographic tailwinds are real - urban loneliness, delayed marriage, nuclear families.
AI Learning products are proving out faster than expected. Arivihan ($4.17M from Prosus and Accel) and Supernova (backed by Kae Capital, Lumikai, AdvantEdge) are building AI tutors that work in vernacular, price for Bharat, and solve for specific skill gaps—board exam prep and spoken English respectively. The thesis: 250 million Indian kids can't afford quality tutoring, but they have smartphones. AI tutors can deliver 1-on-1 personalization at ₹300/month instead of ₹5,000+. 80% of Arivihan's users come from Tier 3 cities and rural areas.
India is uniquely positioned to lead in Consumer AI: a billion-plus users, mobile-first behavior, and daily interaction with imperfect systems that demand AI-native solutions. The next wave of global consumer AI will be trained in the chaos, scale, and diversity of India.
6. Voice AI set to transform the Enterprise
Voice AI became the default interface for enterprise India in 2025. This is no longer a nice-to-have - it’s table stakes for reaching the mass market.
India has 22 official languages and hundreds of dialects. Text-based AI struggles with code-mixing (Hindi-English), regional accents, and low-literacy users. Voice solves this.
Gnani AI is building a 14B parameter voice model under the IndiaAI Mission, already deployed for multilingual customer service at banks and telcos. Sarvam AI is powering Hindi in Meta’s Ray-Ban glasses - making it the first Indian AI company to power a global consumer product. ElevenLabs launched their Voice Actor Marketplace in India, projecting $1M+ payouts to Indian voice talent by the end of 2025, with Hindi and Tamil support on v3 models.
From contact centers to government services to consumer apps—if you want to reach the next 500 million users, you build voice-first. We’ve been saying this for two years: voice isn’t a feature, it’s a platform shift. The companies building voice-native from day one have a structural advantage over those bolting it on later. The question is whether Indian voice-AI companies can build defensible moats before frontier labs catch up on Indic languages.
7. The capital landscape is changing
The funding landscape for Indian AI moved beyond traditional VC in 2025. What’s emerging is a blended capital stack - private VCs, corporate CVCs, and government -that’s unique to India’s AI moment.
Government is showing up with real money. The headline is that PM Modi launched the ₹1 lakh crore ($12 billion) Research, Development and Innovation (RDI) Fund in November - the largest public commitment to private-sector R&D in India’s history. The six-year scheme offers low-interest (sub-3%) or even interest-free unsecured loans, equity, and patient capital to private companies and startups for high-risk, high-impact projects in sunrise sectors like AI, biotech, quantum, and semiconductors, aiming to bridge the gap between lab research and commercial success. The fund also includes a Deep-Tech Fund of Funds to back high-risk startup bets that traditional VCs won’t typically invest behind.
AI-native India funds are emerging with distinct theses. Activate, Together Fund, Boundless Ventures, Neon Fund all launched new funds this year as AI specialists, bringing domain depth, early technical conviction, and hands-on support to founders building from day zero.
Global capital is deploying earlier and smaller. The Accel + Google Ventures partnership - a $2 million co-investment initiative specifically for Indian AI startups - signals willingness to get in at the ground floor, not just Series B and beyond.
Meanwhile, Indian VCs are going West. Peak XV, Elevation, and Together Fund have all hired US-based partners and are ramping investments in American AI startups. Lightspeed India has been building in the US since 2021 and operate actively across the India–US corridor, tightly integrated with their global platform. Follow the returns, deploy into the frontier, and bring learnings back.
The question is whether this blended capital stack can sustain the patience required for deep tech bets. Government money is patient but bureaucratic. VC money is fast but impatient. The best outcomes will likely come from founders who can navigate both: taking government grants for R&D runway while raising private capital for go-to-market velocity.
The opportunity for AI-native capital in India has never been better. Most generalist VCs still don’t have the pattern recognition to evaluate pre-product AI companies. That gap, while being real, won’t last forever.
8. AI-Led Services: India’s quiet breakout category
In 2025, one of India’s most under-appreciated AI success stories was AI-led services. Instead of selling software alone, a new generation of companies combined AI systems with service delivery to solve messy, real-world problems—sales, customer support, finance ops, healthcare workflows, compliance, and more.
India’s advantage here is structural. Deep operational talent, cost-efficient execution, and proximity to global customers allow Indian teams to deploy AI directly into production environments. These companies use AI to replace labor, compress timelines, and guarantee outcomes, often charging on performance rather than seats.
What’s notable is that many of these businesses don’t look like traditional SaaS at birth. They start service-heavy, learn fast from real usage, and gradually automate themselves into high-margin, defensible platforms. Squadstack now runs one of the largest AI-enabled call centers in the country, Realfast is automating Salesforce deployment, and Bridgetown Research is combining AI with expert analysts to deliver faster investment research for global funds.
What unites these companies is not software elegance, but ownership of outcomes - revenue generated, systems deployed, or insight delivered.
9. The Layoff Reckoning: 50,000+ jobs at risk
AI didn’t just create jobs in 2025. It eliminated them.
TCS cut 12,000–20,000 employees, roughly 2% of their workforce. The CEO, K. Krithivasan, cited “skill mismatch” and “limited deployment opportunities.” They introduced a 35-day bench policy: employees not assigned to a project within 35 days face termination. “Silent layoffs” are becoming the norm: no public announcements, just discreet internal communications.
Industry-wide, analysts estimate 50,000+ IT jobs are at risk due to AI automation, primarily in backend operations and support functions.
Context matters here. India’s IT sector employs 5.67 million people and contributes 7.5% of GDP. TCS alone employs over 600,000. The sector’s traditional model - large headcounts, labor arbitrage, pyramid hiring - is under structural pressure from AI-driven productivity.
The counterpoint: Infosys hired 17,000 in Q1 FY26 and plans to recruit 20,000 graduates this year. HCLTech added 5,196 freshers in Q2. The story isn’t uniform.
But the direction is clear. The IT services model was built on labor arbitrage. AI challenges that model. The companies that survive will be the ones that transform into AI-services companies rather than the ones that try to protect headcount. This is the part of the AI story nobody in India wants to talk about publicly. The second-order effects on the middle class are significant and under-stated.
10. India hosts the Global AI Impact Summit 2026
For the first time, the Global AI Summit series comes to the Global South.
The India-AI Impact Summit will be held February 19–20th, 2026, at Bharat Mandapam in New Delhi, hosted by Honorable Prime Minister Narendra Modi under the IndiaAI Mission.
What’s expected: heads of state, global leaders, researchers, industry executives, the release of India’s sovereign LLM, launch of new foundational model initiatives, AI Governance Guidelines, 300+ exhibitors from 30+ countries.
The framing is distinctly Indian - three “Sutras” (People, Planet, Progress) and seven “Chakras” (Human Capital, Inclusion, Safe & Trusted AI, Resilience, Innovation, Democratizing AI Resources, Social Good).
The summit follows the UK AI Safety Summit (2023), AI Seoul Summit (2024), and Paris AI Action Summit (2025). The symbolism is deliberate: India wants a seat at the table where AI’s future is decided.
What makes this interesting isn’t the summit itself - it’s what it represents about India’s evolving position in AI’s global conversation. Whether this translates into influence over actual AI development trajectories or remains a parallel conversation is the thing to watch.
Questions to ask in 2026
Can India’s sovereign LLM compete with the frontier globally?
How soon can an Indian native-AI startup cross $100M ARR? How many learnings from their playbook could be applied to others?
Can India’s ChatGPT moment be a mass AI Tutor or AI Doctor for the next billion users?
Will the IndiaAI Mission’s compute actually reach startups at scale enabling them to do rapid innovation?
Can India retain the AI talent it produces or will the US continue to vacuum it up?
Will AI unlock a new wave of Indian services companies, or trigger widespread job losses?
Can vehicles like the RDI Fund bridge the gap between lab-stage research and venture-scale companies?
How strategically important will India become to global AI labs beyond usage, talent, and data?
Can Indian conglomerates like Reliance, Adani, and Tata become long-term sovereign AI builders?
Will the AI Impact Summit produce binding outcomes for startups and AI practitioners or just communiqués?
The infrastructure is being laid. The capital is flowing. The talent exists. The demand is real.
What’s missing is proof: companies at scale, AI research, and homegrown products that show India can lead in AI, not just participate.
That’s the test. The summit in February is the starting gun.
This is the final edition of Activate Signal for 2025. See you in the new year—and at the Summit.
I think one of the other interesting trends we saw this year is BigTech companies such as OpenAI and Perplexity are starting to develop tools for enterprises by leveraging their foundational models. For example, OpenAI floated a contract review tool being used internally and Perplexity has launched Perplexity enterprise. I think the next set of startups (atleast for enterprise) will have to be hyper-focused on solving one problem rather than being a generalist. Founders will have to be obsessed with one particular problem. I think the perfect example of this is Perplexity Patents which was an AI Agent launched by Perplexity for patent research. They solved one problem instead of solving all problems related to patents. BigTech companies are soon going to start competing with startups building for enterprises. Their competitive advantage (moat) will be the focus on solving one particular problem.
The $100M ARR gap is the most honest diagnostic you could pick. India is ChatGPT's second-largest market, Perplexity's largest ..... and yet not a single native AI company has crossed that threshold. That asymmetry tells you something important: India has cracked demand at population scale but hasn't figured out how to capture value from it. The Perplexity-Airtel deal is the clearest sign of how the value chain might actually work ..... frontier labs use telecom rails to acquire users at near-zero CAC, and Indian operators get margin on distribution. The question for 2026 is whether Indian AI founders can insert themselves into that value chain as something more than an execution layer, or whether the model layer and the distribution layer both end up owned by non-Indian players.