Can India Use AI for Its Benefit?
Will India Play Any Role in the Diffusion?
OpenAI CEO Sam Altman recently praised India’s rapid adoption of artificial intelligence (AI), describing it as “outpacing the world” in a statement that has garnered significant attention. This comment, comes in the context of a viral trend involving Studio Ghibli-style images generated by ChatGPT, OpenAI’s popular AI chatbot. The phenomenon highlights both India’s growing engagement (just as a users) with AI technologies and the creative ways in which its population is leveraging these tools. But is this the Role We Have Chosen for us? Can India harness AI for its benefit? And will India play a significant role in the diffusion of AI innovations, both domestically and globally?
The S- Curve of Adoption
To explore these questions, we adopt a framework rooted in Everett M. Rogers’ Diffusion of Innovations, a theory that explains how new ideas spread through societies.
For an idea, invention is the seed, innovation is the growth, and diffusion is the harvest.
In the book, he explain (S-Curve), how innovations spread through populations over time, characterized by three phases: slow initial adoption, rapid acceleration, and eventual saturation. Shape of the Curve will look like S, if you plot it. It also captures the dynamics of barriers (cost, complexity, skepticism), accelerators (network effects, social proof, improvements), and eventual maturity.
Slow Initial Uptake: Limited to innovators/early adopters due to uncertainty.
Tipping Point: Transition to rapid growth as social proof and network effects kick in (e.g., crossing the "chasm" to the early majority).
Leveling Off: Market saturation where most potential users have adopted.
For management this act as Adoption Rates to manage production, distribution, and resource allocation, OR to simply put this in their Investor Deck, like how Nykaa has done!
So, where is India in the Global AI Landscape’s "S" curve?
For simplifications, we are doing some changes and breaking down the S-curve into 5 stages:
Innovators - Pioneers taking risks with unproven tech.
Early Adopters - Players adopting tech for competitive advantage.
Early Majority - Pragmatists adopting proven tech as it scales.
Late Majority - Conservatives adopting only when tech is standard.
Laggards - Almost everyone (including skeptics) adopting it.
It all started in the early 2010s when Indian IT giants like TCS and Infosys began experimenting with AI for internal optimization, alongside niche startups like Haptik (chatbots). Academic institutions (e.g., IITs) also dipped into AI research.
India is currently transitioning from the Early Adopters phase (2.5% to 16% adoption) into the Early Majority phase (16% to 50% adoption) on the global AI S-curve. This aligns with the S-curve’s steep upward slope.
However, limited R&D funding, talent migration, and uneven digital infrastructure keep India from fully hitting the Early Majority’s tipping point.
What India Can Do with AI?
India’s economic landscape offers a fascinating backdrop for exploring its potential in artificial intelligence (AI). Over the past decade, the manufacturing sector’s contribution to India’s Gross Domestic Product (GDP) has experienced a slight decline, with its share fluctuating between 15% and 17%.
Despite ambitious initiatives like "Make in India," which set a target of boosting manufacturing to 25% of GDP by 2025, significant progress in this area remains elusive. In contrast, the services sector has been a powerhouse, consistently accounting for over half of India’s GDP—peaking at around 55% in recent years. Within this sector, IT services have emerged as a key driver, contributing an impressive 7.5–9% to GDP.
This historical trend suggests that India’s strengths lie more in software and services than in hardware and manufacturing, setting the stage for its AI journey.
Given this context, India appears better positioned to excel in software-driven fields like AI rather than hardware-intensive industries. The nation’s robust IT legacy, built on decades of global outsourcing success, provides a solid foundation for AI innovation. Yet, India’s path is far from predictable.
The phrase "India disappoints both optimists and pessimists" perfectly captures the nation’s knack for defying straightforward predictions. India’s “disappointing” nature stems from its duality: a land of contrasts where cutting-edge tech coexists with rural disconnect, and bold visions tangle with slow follow-through.
Optimists see India’s vast population (1.4 billion+), IT legacy, and youthful talent pool as a recipe for AI dominance (we talked about it here). They expect a rapid sprint up the S-curve, from Early Adopters to Late Majority, fueled by sheer scale and ingenuity.
Pessimists, on the other hand, point to India’s chaos—bureaucratic red tape, economic inequality, and fragmented policies—as reasons it’ll stagnate as a global AI also-ran, stuck in the Laggards zone. But India defies this too.
By capitalizing on its software strengths, vast human capital, and problem-solving ethos, India has the chance to carve out a distinctive role in the AI landscape—one that neither optimists nor pessimists can fully predict.
“Fosbury Flop.”
In 1968 Summer Olympics, men’s high jump was one of the standout events. Valeriy Brumel, the Russian legend wasn’t in the running that year, due to a devastating motorcycle accident. Ed Caruthers, an American, was a strong contender with his consistent performances using the straddle technique. But the real story of the event was Dick Fosbury, a relatively unknown engineering student from Oregon State University. Fosbury revolutionized the sport with his unorthodox “Fosbury Flop,” a backward jumping style where he arched his body over the bar and landed on his back.
Fosbury’s success hinged on two things: a radical technique and an environmental shift (foam mats replacing sandpits). He outthought everyone.
The emergence of AI models like DeepSeek presents an opportunity for countries like India to break away from American dependency in the AI sector by providing access to powerful AI models at a lesser cost. The potential is there; the question is execution.
Big leaps and innovations often spring from non-obvious, lateral thinking that challenges the status quo or connects dots others don’t see. It’s been said that the obvious isn’t obvious until someone points it out to you.
If Indian companies rethink the game—using their unique strengths and seizing the moment—they might just clear the bar, landing a “Fosbury Flop” that redefines AI on their terms. Can they? The runway’s ready; it’s up to India to take the leap. Somewhere in India, a small but determined group of innovators would be ready to challenge the narrative, and quietly working on their own “Fosbury Flop.” After all we are like this only and our unpredictable nature might be our greatest asset in the AI era.
“That’s not the point. The point is, who will stop me?” - When pushed hard, Howard Roark, hero of Ayn Rand’s famous novel The Fountainhead