India’s Consumer Internet Veterans Are Turning to Deep-tech as AI Adoption Lags Below 30%

India’s startup ecosystem is entering a quieter but far more consequential phase. Some of the country’s most recognisable consumer internet founders are stepping away from scale-at-all-cost playbooks and moving into deep-tech sectors like AI, robotics, aerospace, and advanced manufacturing. This shift is not about trend-chasing. It reflects maturity, patience, and a growing comfort with building for long horizons.

Founders who once optimised for daily active users and rapid growth are now designing systems that may take years before meaningful commercial outcomes emerge.

• ShareChat’s co-founders are building General Autonomy in robotics.

• Udaan’s Amod Malviya is focused on AI-led manufacturing through Pre6.

• Zomato’s Deepinder Goyal is self-funding ventures spanning AI infrastructure and aerospace.

These are not lightweight experiments. They are capital-intensive, research-heavy bets that demand conviction more than visibility.

Why is this happening now?

One reason is experience. India’s first wave of consumer internet entrepreneurs has already navigated hypergrowth, downturns, regulatory cycles, and exits. With personal capital, strong networks, and operational depth, they can afford to think in decades rather than funding rounds. 

Another reason is timing. India’s digital public infrastructure, manufacturing push, and AI talent pool are converging. The country produces over 1.5 million engineering graduates annually, and enterprise digitisation is accelerating across sectors from logistics to defence.

Yet venture capitalists are right to flag a reality check. Deep-tech companies do not follow predictable startup timelines. Even repeat founders cannot shortcut physics, research cycles, or enterprise adoption curves. Many of these ventures will take five to ten years to fully come alive.

This is especially relevant when viewed alongside India’s AI landscape.

While thousands of AI startups have launched in the last 18 months, adoption remains uneven. Less than a third of Indian MSMEs actively use AI-driven workflows, despite contributing roughly 30 percent to GDP and employing over 110 million people. The gap is not in creation but in integration.

The most promising deep-tech and AI companies are not building flashy tools. They are embedding intelligence into existing systems such as ERPs, CRMs, factory floors, and supply chains.

In manufacturing and operations, AI-led process optimisation is already delivering 10 to 25% cost savings without changing core products. Vertical-specific solutions are seeing faster adoption than generic platforms because they fit how businesses already work.

Another overlooked moat is changing management. Teams struggle less with algorithms and more with behaviour change. Startups that pair technology with training, onboarding, and decision support consistently show stronger retention and long-term value creation.

By 2026, AI is likely to fade as a category and become infrastructure. The same may happen with robotics and advanced manufacturing. When technologies become boring, they become powerful.

As India’s veteran founders roll up their sleeves again, the ecosystem faces a bigger question.

How should entrepreneurs, investors, and policymakers align expectations around patience, capital, and outcomes in this deep-tech era? And where do you see the most meaningful gaps between innovation and real-world adoption today?