India has the world’s third-largest startup ecosystem, a thriving developer base, and a mobile-first population larger than the US and Europe combined. Yet, no GPT-4. No DeepMind. No Amazon-style platform. Why?
Innovation Isn’t Accidental—It’s Engineered
The Zerodha Daily Brief recently asked why India hasn’t built a global product company like Apple. The key argument: India isn’t building for the world. It’s solving for local constraints, scale, and affordability—but global scale requires deep IP, design, and tech differentiation. It’s not just about software, it’s about systems thinking.
More importantly, it answers the question: Why do countries innovate? The answer isn’t just genius or ambition—it’s incentives and ecosystems. The U.S. Defense Department, for example, accounted for nearly 70% of federal R&D funding during the Cold War. China has pumped billions into semiconductors and AI with long-term national alignment. These aren’t short-term bets—they are strategic, deliberate acts of state-backed capitalism.
India, in contrast, spends less than 0.7% of its GDP on R&D—far below global innovation leaders. Until this changes, private startups alone can’t be expected to carry the weight of foundational research. A comprehensive breakdown of these enablers and impediments to innovation—ranging from catalytic leadership and policy regimes to talent and capital—is detailed in Table 1 of this policy paper by Sarthak Pradhan and Pranay Kotasthane.
The Flywheel That Never Spun
In the US and China, tech giants built consumer businesses that became engines of innovation. Amazon didn't stop at e-commerce—it created AWS. Google scaled ads, then built TPUs, DeepMind, and open-sourced TensorFlow. Alibaba and Tencent invested in compute infra and national-scale AI labs.
This is the innovation flywheel:
Consumer Growth → Profit Flywheel → Infra Investment → Foundational Innovation
India’s flywheel spins only partially. We’ve nailed consumer growth. But the rest—profits, infra, and innovation—remains fragmented.
US/China tech ecosystems demonstrate a complete innovation cycle from growth to deep-tech.
The Staircase Stall
India's top startups are still climbing the staircase to profitability. Without steady free cash flow, they can't reinvest in moonshots. US and Chinese firms reinvested into R&D, building compounding advantages.
The Walmart-Flipkart deal is a textbook example of where India’s climb stalled. Flipkart was the country's best shot at building an Amazon-style flywheel. It had the consumer base, data, logistics, and momentum. But when Walmart acquired it, strategic decision-making and IP moved abroad. Flipkart scaled consumption, but never birthed an AWS. The staircase ended at growth—without a leap into infrastructure or innovation.
However, there are signs of what the next step can look like. Ola has begun climbing further up this staircase—its push into electric vehicles and the recent launch of Krutrim, an Indian foundational AI model, signal intent to reinvest profits into infrastructure and deep-tech bets. It’s an early example of an Indian company trying to transition from consumer scale to ecosystem-scale innovation.
US/China startups scale all the way up to infrastructure and innovation layers.
India’s journey often halts at growth and early profitability, with fewer transitions to foundational tech bets.
But even among the few companies that reach the top of the staircase, the next leap—into deep-tech—is often derailed by structural constraints in capital control and strategic intent.
Capital Yes, Control No
India has capital, but much of it is foreign and growth-driven. Foundational innovation demands patient capital—what some call "patient money"— and national alignment. Most Indian unicorns prioritize TAM expansion over deep tech bets. Paytm, for instance, scaled rapidly with strong VC backing, but has yet to convert that scale into long-term R&D or infrastructure plays—illustrating the challenge of capital without strategic control. Oyo follows a similar arc: once a poster child of India's startup boom, it raised billions in funding and expanded globally, yet has struggled to evolve into a platform company or reinvest meaningfully into foundational tech or infrastructure.
The Role of India’s Incumbents: Missing in Action?
In the US and China, many of the foundational technology bets were either incubated within, or acquired by, large tech incumbents. In India, our industrial incumbents—Tata, Adani, Ambani—hold vast capital and influence but have largely stayed away from foundational tech plays in AI or semiconductors.
Reliance and Tata have made moves in digital infrastructure and EVs respectively, but not yet in frontier AI. Their scale, reach, and capital position them uniquely to fill the innovation gap—but we haven’t seen a Microsoft or Amazon-style moonshot from them yet.
It’s worth noting that similar patterns emerged elsewhere too. Japan and Korea built global hardware giants, but struggled to make the software leap. Europe, despite engineering excellence, hasn’t produced AI giants. The lesson? Without aggressive reinvestment by national champions, deep-tech ecosystems don’t just emerge—they must be built.
Ice Cream, Chips & The Innovation Debate
Recent commentary from Commerce Minister Piyush Goyal reignited this debate. At Startup Maha Kumbh, he criticized Indian startups for focusing on grocery and food delivery instead of aiming for breakthroughs in AI, semiconductors, and EVs like their Chinese counterparts. His comments—"Do we have to make ice cream or chips?"—sparked intense reactions.
Leaders like Aadit Palicha (Zepto) and Mohandas Pai countered strongly. Palicha emphasized that building internet-first companies at scale is a necessary precondition to eventually fund deep tech. Pai questioned what structural support the government has actually put in place for semiconductor and AI startups.
In response to Goyal’s critique, Deep-tech founders highlighted bureaucratic hurdles, lack of funding clarity, and policy friction as key reasons why foundational innovation struggles in India. The message was clear: don’t just challenge entrepreneurs—enable them.
Where Do We Go From Here?
Build Local Champions: Companies that scale and reinvest in infrastructure.
National Compute Infrastructure: AI needs access to GPUs, not just talent.
Shift from Valuation to Vision: Focus on compounding moats, not exit multiples.
Talent Repats + Open Source: Encourage diaspora and open ecosystem collaboration.
Policy as a Platform: Government must act not just as regulator, but as an enabler of deep-tech entrepreneurship.
India doesn’t lack the "what." It needs a shift in the "how" and "why."
Let’s stop waiting for a GPT moment. Let’s start building it.
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