In this dawn of the AI Age, when you travel between San Francisco and Shenzhen, it’s easy to feel small as an Indian. The US hums with innovation; China hums with industrial scale. As investors in AI, it is natural to wonder: where does that leave India?

We don’t have the fabs or the foundational models, yet. But what we do have is something neither of them can replicate: a billion people, a unique digital stack, and an instinct for deployment at scale.

That trifecta, as Vikram Vaidyanathan and Avnish Bajaj posit in this Zero to Infinity episode with Chandrasekhar Venugopal (CV), could make India the most useful country in the AI age, the one that gets things built, wired, and working. This article captures the emerging playbook, outlining the role that Indian founders, investors, policymakers and enterprise leaders must play for us to win this opportunity!

India’s Superpower is Deployment

If the US is innovation and China is industrialisation, India’s edge is deployment. We’ve done this before, from Y2K to mobile to UPI, quietly wiring the world’s backends while others made headlines. In AI, that wiring becomes our export product again.

The key action here is to build companies that specialise in scaling AI inside existing enterprises, not just training models. Integration > invention.

Avnish sums up the India Edge in AI as Data + Deployment.

• Data: Our digital public infrastructure (Aadhaar, UPI, AA) creates consentable, connected, population-scale datasets.
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• Deployment: We have the engineers and operators who can make these systems talk to each other.
  
  

The Rise of the FDE (Forward-Deployed Engineer)

Coined by Palantir, FDEs sit between product and customer, half-engineer, half-consultant.
India could produce an army of them, turning the “outsourcing” stereotype into a frontline of applied AI deployment.

The key takeaway for AI startups: Train FDEs as a core function. They are the people who own integrations and outcomes, not just code.

The Human-in-the-Loop Isn’t Going Away

AI automates plenty, but someone still has to handle exceptions, verify outputs, and tag data.
This “loop” work can create millions of new, higher-value roles if we upskill now. AI is the unlock for India’s vast, computer & internet-literate human capital. 

Opportunity for skilling orgs & edtech: Launch certifications for AI operations, annotation, and oversight: the new middle layer of work.

Bharat is the Biggest Untapped AI Market

Voice, local language, and low-friction UX can make AI accessible for hundreds of millions.
Design for literacy, not for English. Founders must think WhatsApp, not MS Word.

Key action for Founders: prototype voice-first interfaces for finance, healthcare, and government services. If it works in Bharat, it’ll work anywhere.

India Needs a Jio Moment for AI 

India’s “Jio moment” for AI will come from cheap inferencing: bringing token cost down like data cost once fell. That’s how we unlock population-scale AI adoption.

The onus is on policymakers to subsidize local inferencing hubs and encourage domestic infra players to build cost-efficient compute zones.

Founders Must Think Global from Day Zero

The new Indian startup model is borderless: founders in Bangalore, customers in San Francisco, product cycles everywhere. And now, domestic adoption makes India the perfect testbed. We’ve seen this model play out in IT services, in Enterprise & SaaS and now Indian founders can perfect this with AI businesses. 

The takeaway for Founders: run parallel pilots — one global customer, one Indian user cohort. Build for both from day zero.

The Big Risk: Phantom ARR

AI-driven revenues can look explosive but disappear fast.
Pilots ≠ retention.

The test of real ARR is repeatable adoption and workflow embed. VCs must diligence adoption depth, not just demos.
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The key question to ask: How often is this model or application used, and how painful would it be to turn off?

The Playbook for India’s AI Stakeholders: 

For Government: Treat AI enablement like UPI, a public infrastructure project, not a private luxury.

1. Trigger the inferencing revolution: Subsidize domestic inferencing infra.
2. Leverage consented data: Build regulatory sandboxes on DPI rails.
3. Mission-scale skilling: Create an AI India Mission to train millions in ML ops, FDE, and annotation.

For VCs: Invest where unit economics improve as inference costs fall.

• Back AI+Services and AI-native cybersecurity plays.
• Reward founders who build India-first adoption loops before chasing the US.
• Track inferencing cost sensitivity as a moat metric.

For Enterprises: Budget for AI deployment, not just pilots — integration is the new transformation.

• Deploy FDE squads to own adoption.
• Re-architect workflows around hybrid (human + AI) handoffs.
• Move Indian teams from support to frontline integration centers.

For Founders: Ask of every feature: would this work for my driver or my dad? If yes, you’ve built for the next billion.

• Design for voice, not vanity.
• Build globally, test locally.
• Automate 90%; humanize the last 10%.

The Bottom Line: The India Edge in AI is Real. 

America will keep innovating. China will keep building. India’s advantage is doing: deploying at scale, cheaply and intelligently. If we skill right, price inference right, and aim higher than “back office,” India can become the world’s frontline of applied intelligence.