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India Trails US, China in Foundational Tech at ET Soonicorns Summit

TL;DR

• Despite India’s world-class talent and market size, experts at the ET Soonicorns Summit 2025 warn the country is falling behind the US and China in foundational AI and tech innovation.
• An ambition and vision “gap” persists, with Indian startups focused on incremental AI applications rather than bold, transformative research.
• Entrepreneurs possess a unique opportunity thanks to India’s data advantage and market, but must move beyond being AI consumers to becoming global AI creators.

The Disconnect: India’s Place in the Global Tech Race

At the heart of the ET Soonicorns Summit 2025 in Bengaluru, a crucial debate unfolded: Can India become a leader in foundational technology and AI, or is it destined to remain a consumer in the global innovation value chain?

As AI pioneers, global investors, and leading founders gathered, a stark reality emerged. India’s technical talent and data reservoirs are undeniable—MIT and Groq are in town, Indian research teams attract global notice, and homegrown transformer models are emerging. Yet, according to panellists and investors, a strange absence of urgency prevails among Indian founders about taking the lead in foundational technologies.

“It’s as if India is watching history happen elsewhere,” said Raghunandan G, founder of Zolve, comparing his experience between the US and Indian markets.

India’s Ambition and Vision Gap

“Talent isn’t the issue; it’s ambition and courage.”

Manish Gupta of Google DeepMind summed up the paradox: world-class brains, but a shortage of daring ambitions. Where the US and China are racing to produce tomorrow’s AI breakthroughs, many Indian startups are, as one panellist put it, still thinking of adding AI features to existing apps instead of reshaping industries.

• Incrementalism: Most Indian startups focus on incremental upgrades—chatbots, analytics, predictive tools—rather than leading research or building the next global platform.
• Courage Deficit: Investors describe repeated pitches for “features” rather than foundational innovation. Space-tech startups show vision, but in mainstream IT and AI, ambitions are often too limited.

Gupta’s warning: “If India doesn’t invest, we risk becoming passive consumers—getting excited about a ₹400 ChatGPT subscription instead of inventing the next ChatGPT.”

The Evolving Economic Model: Beyond Unicorns

From One-to-Many, to Many-to-One

Abhishek Nag (360 ONE) introduced a key shift in the venture capital landscape:

“The next decade is not about a few decacorns. It’s about thousands of specialized, high-margin companies addressing niche problems.”

• The New Playbook: Instead of focusing on becoming the “next Google,” Indian startups can thrive by solving unique, Indian problems at scale and building $200-300M revenue companies with strong moats.
• Autonomous Agents: As AI agents get smarter, markets will reward startups that match user intent to deep, domain-specific solutions.
• India’s Edge: Complexity and fragmentation of the Indian market could allow local winners to emerge, but only with strategic focus on sustainability—not just hype.

Unlocking India’s Data Advantage

Shally Modi (Pratilipi) argued that Indian AI companies hold a potential “secret weapon”: unique, local data in Indian languages and user behavior.

• Having billions of Indian-language data points creates a natural moat—global models can’t easily match this depth.
• Startups building real, language-specific personalization or sectoral solutions can engineer compounding advantages with each new user and dataset.

This untapped data asset is a call to arms: India must move from “novel AI features” to core, compounding AI solutions—for search, healthcare, learning, and finance.

Sectors Poised for AI Reinvention

• Healthcare: Moving from “sick care” to AI-powered preventative health solutions.
• Agriculture: Data and AI for climate-resilient, higher-yield farming.
• Education: Lifelong, personalized, language-inclusive learning.
• Financial Services: Deeply local risk, credit, and investment products powered by native data.

The Capital Reality Check

Capital needs are changing:

• The cost of using advanced foundational models like GPT-4 remains high, increasing up-front capital needs.
• Faster Revenue Milestones: With the right product, early-stage startups can reach $10M in revenue—and raise larger rounds—in 6–12 months.
• However, early wins are deceptive unless the product is truly defensible—switching costs, integration depth, and irreplaceability matter more than quick sales.

The fundraising cycle is compressing but the core challenge remains: Are you building something new and hard to replicate, or just another AI-powered layer?

The Indian Startup’s Validation Advantage

Good News: Validation is easier, cheaper, and faster than ever before.

• AI-powered tools now let founders build landing pages, generate content, A/B test features, and pilot demand—all before large team or capital investments.
• Startups are encouraged to focus not just on product-market fit, but especially on “idea-market fit”.

If your idea makes even $100,000 early, you’ve proven real market need. The risk is low, and the opportunity for rapid iteration is uniquely favorable in the Indian digital ecosystem right now.

Will India Seize This AI Moment—Or Fall Behind?

Arnab Kumar (Uber) summed up the stakes:

“There’s incredible opportunity ahead… or maybe a crisis. We just don’t know. But the opportunity feels once in a lifetime.”

• The talent is there.
• The global spotlight is on Indian teams and markets.
• The last missing ingredient: audacity.

Bengaluru is no longer simply the world’s back office. The tech world is looking for the next AI platform, breakthrough, or killer application—India is in pole position, but only if its founders and investors act with urgency and vision.

Key Takeaways and Next Steps for Indian Entrepreneurs

• Shift Attitude: Move from “feature tweaks” to “foundational disruption.”
• Leverage Data: Dominate where you own the deepest, most unique datasets—especially native languages and market segments global players can’t match.
• Build Moats: Focus relentlessly on products that get better with every user and cannot be easily replaced.
• Validate Fast: Use AI to iterate, experiment, and chase real $100k ideas before looking for VC super-rounds.
• Think Long-Term: Don’t just “apply AI”—use it to solve India’s biggest, most fragmented market problems at their root.

FAQs: India’s Foundational Tech Trajectory

1. Why is India lagging the US and China in foundational AI and tech?

• Reason: While Indian talent and technical skills are strong, founders and VCs have mostly focused on applied improvements or cost-efficient services, not basic research, large-scale innovation, or creating new AI platforms. The ambition and vision gap, plus limited risk appetite for “moonshots,” keeps India behind in game-changing tech.

2. What unique strengths could help India catch up?

• India’s data advantage—billions of local-language data points, complex consumer behavior, and sector-specific depth in health, agriculture, fintech, and education—gives Indian startups natural moats. If harnessed with AI to solve local problems, this can be a global differentiator.

3. What’s the biggest opportunity for Indian startups in 2025 and beyond?

• Building AI-driven, deeply integrated solutions in untouched verticals, using unique local data, is the greatest opportunity. Startups that validate new ideas quickly, focus on high switching costs and compounding product value, and aim for global innovation can turn India from an AI consumer to an AI producer.

Conclusion: Dream Big, Build Bold

For India to lead in AI and foundational tech, it must close the ambition gap, systematically leverage its unique assets, and invest in courage—not just skills.

The world is watching. Will Indian founders step up to the challenge, or will tech history be written elsewhere?

Now is the moment to act.

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