India is emerging as a key market in the global artificial intelligence shift, not because it is leading the way in building fundamental models, but because it is becoming a large-scale testing ground for AI applications. of SenseAI AI Current Status 2026 The report describes India as “not only having the world’s largest AI user base but also rapidly becoming the world’s AI application factory.”
This positioning is determined by the scale. India has a population of more than 1.4 billion people, more than 900 million internet users, and processes about half of the world’s real-time digital payments through UPI. The combination of advanced digital adoption and diverse use cases makes it a valuable environment for training and deploying AI systems.
“India’s unparalleled size and diversity make it one of the most valuable markets not only for user adoption but also for capturing usage data to train AI,” the report said.
Consumer behavior reinforces this. Approximately 62% of users in India are already using generative AI for shopping and 64% are using generative AI for product research. Adoption is spreading across age groups, with Millennials leading the way, followed closely by Gen Z and Gen X.
At the same time, the trust level is unusually high. Nearly 90% of Indians approve of AI, and over 79% accept decisions made by AI without question, often viewing AI decisions as more objective than human judgment. This trust has led to faster adoption compared to the global average.
Global AI companies are paying attention. The company is offering a free premium subscription to build an initial user base in India and collect multilingual data. The report highlights that India is already ChatGPT’s second-largest market, with more than 100 million weekly users.
India’s AI ecosystem is developing differently than the US or China. Rather than focusing on expensive underlying models, most capital and activity is focused on applications.
Almost 80% of AI funding in India is going to application layer companies, with only a small portion invested in infrastructure and fundamental AI. This is primarily determined by capital efficiency and resource constraints. “Applications are not only a waste of capital, they are also generally where value is created,” the report states.
Startup data reflects this trend. India recorded a record 164 AI deals in 2025, with total funding soaring to $2.5 billion from $900 million the previous year. The average trade size also jumped 2.6x, indicating larger bets by investors.
The ecosystem itself is still in its infancy. Approximately 71% of funding rounds are still at the seed level, but only a small percentage of companies have reached late-stage funding.
More importantly, the types of companies that are established are different. Approximately 75% of startups are focused on AI applications, particularly in enterprise SaaS, fintech, healthcare, and infrastructure. These sectors reflect India’s real economy and digital infrastructure such as UPI and Aadhaar.
“AI in India is application-first, not model-first,” the report said, adding that the founders are embedding AI into workflows rather than building core models.
This approach resulted in faster monetization. Reflecting shorter implementation cycles, nearly 60% of startups are already profitable in their early stages.
Despite strong demand and startup activity, India still lags behind in core AI infrastructure. The country generates about 20% of the world’s data but stores less than 3% of the world’s data.
Bridging this gap is now a priority. The report highlights more than $200 billion in infrastructure commitments announced at the AI Impact Summit and investments from global technology companies and domestic conglomerates.
“India is building its computing layer for the first time,” the report said, suggesting a shift towards long-term capacity building.
But for now, India’s strength lies in applying AI rather than building it from scratch. Our combination of scale, cost efficiency, and rapid deployment has positioned us as a key execution layer in the global AI stack.
