Beyond the Algorithm: Why India’s 20 Billion Monthly Transactions Hold the Real Secret to AI’s Future 

India’s experience of scaling Aadhaar and its 20 billion monthly UPI transactions demonstrates that the true challenge of AI lies not in its invention but in its societal absorption and institutional diffusion. While the world focuses on the race to build the most powerful AI models, history shows that lasting economic advantage and productivity come from the ability to embed general-purpose technologies into everyday workflows, supported by institutions that absorb risk and establish trust. The key lesson is that AI will transform economies only when organizations redesign core processes and institutions provide clear accountability, moving beyond isolated pilots to make AI a routine, trusted, and ordinary part of how decisions are made at scale.

Beyond the Algorithm: Why India’s 20 Billion Monthly Transactions Hold the Real Secret to AI’s Future 

Headlines trumpet the latest trillion-parameter model, the fiercest chip competition, and the soaring valuations of AI labs. The narrative is clear: the nation or company that builds the most powerful, god-like Artificial General Intelligence (AGI) will win the future. This is a compelling story of invention and raw power. But it is a story that misunderstands history and, consequently, our present moment. 

The true transformative force of any general-purpose technology—be it the steam engine, electricity, or the internet—has never been invention alone. It has been diffusion. It is the arduous, unglamorous work of weaving a new technology into the fabric of everyday life, of rebuilding institutions and re-engineering workflows so that the miraculous becomes mundane. 

For a masterclass in this, look not to Silicon Valley, but to India. A country that now processes roughly 20 billion digital transactions every month through its Unified Payments Interface (UPI). This isn’t just a big number; it’s a signal flare illuminating the path for AI. It reveals that the greatest constraint to technological revolution is no longer capability, but absorption. 

The Aadhaar Blueprint: Absorption, Not Invention 

The cornerstone of India’s digital ecosystem, Aadhaar, provides the definitive case study. As Shankar Maruwada and Angela Chitkara articulate, biometric technology was not invented for Aadhaar. The magic was in its absorption at two critical, distinct levels: 

1. Institutional Absorption: The Unique Identification Authority of India (UIDAI) didn’t just issue IDs. It became a trust layer. It absorbed systemic risk and uncertainty by establishing unshakeable standards for verification, data security, and accountability. It told a nation: You don’t have to guess if this digital person is real. We, the institution, guarantee it. This removed the burden of judgment from every individual bank teller, welfare officer, and telecom agent. 

1. Organizational Absorption: Banks, government ministries, and private companies then had the confidence to tear up old workflows. They could redesign processes assuming a verified, digital identity. Subsidy delivery, bank account opening, and mobile connections were rebuilt around this new primitive. The technology faded into the background, becoming ordinary infrastructure. 

The result? Over 1.4 billion identities, authenticated digitally more than 164 billion times, woven into the daily operations of a subcontinent. The lesson is profound: Diffusion happens when institutions create safety and organizations redesign work. 

The AI Adoption Stall: A Crisis of Diffusion 

This is precisely where most global AI initiatives are stalling today. The executive’s question has evolved from “What can AI do?” to “Can we trust what AI does inside our real systems?” When leaders ask for “use cases,” they are not asking for a demo of a language model writing a clever poem. They are asking: Who is accountable when it’s wrong? How do we know it’s safe? How do we integrate its output into a decision chain without exposing our employees or customers to unmanaged risk? 

Too often, the answer is to treat AI as a tooling problem. A new SaaS subscription, a pilot project led by a siloed “innovation” team. The AI is bolted onto existing processes, forcing individual employees to become the arbiters of its output—judging hallucinations, managing errors, and personally shouldering the liability. This is the opposite of institutional absorption. It pushes uncertainty downward, and it guarantees that the technology remains a fragile, peripheral toy. 

As the scholars of technological change remind us, economic advantage and productivity gains follow the diffusers, not the inventors. Germany pioneered industrial chemistry, but the United States mastered chemical engineering—the discipline of applying chemistry at scale across food, automobiles, and manufacturing. The breakthrough was not a new compound, but a new way of working. 

The Two Races: Power vs. Productivity 

This brings us to the central, clarifying fork in the road. 

• Race One: The AGI Race. This is the race for power, capability, and perhaps, supremacy. It has a clear finish line (human-like or superhuman intelligence) and will have clear winners and losers. It is a race of computation, data, and algorithmic breakthrough. 

• Race Two: The Diffusion Race. This is the race for productivity, integration, and economic transformation. It has no finish line, only a continuous evolution of work. It is won by societies that build institutions to absorb risk and organizations capable of learning through use. 

The narrative we choose to follow decides which race we prioritize. If we focus solely on the AGI race, we risk a future of astonishing technological capabilities languishing in pilot projects, trapped by our own institutional rigidity. 

India, with its stated ambition to become the “use-case capital for AI,” is betting on the diffusion race. Its experience suggests that AI needs India’s scale, complex problems, and hard-won institutional muscle memory as much as India needs AI’s capabilities. The question is whether established economic powers, particularly the United States, can afford to treat diffusion as an afterthought, something to be considered only after the geopolitical AGI race is decided. 

History suggests that would be a catastrophic error. Leadership in the era of electricity wasn’t defined by who built the best prototype generator, but by who rewired cities, factories, and homes. 

Building the Ordinary, Valuable Future 

So, what does winning the diffusion race look like? It is decidedly undramatic. 

The most valuable AI systems of the next decade will not be charismatic chatbots. They will be silent, embedded systems. They will be the risk-assessment model so trusted and standardized that it flows through a thousand loan approvals an hour without a human glance. They will be the supply-chain orchestrator that constantly adjusts logistics, its decisions as routine and uncontested as a traffic light. They will be the diagnostic assistant embedded so seamlessly in a radiologist’s workflow that its output is simply a part of the scan itself. 

To build this future, organizations must shift their focus: 

• From Pilots to Processes: Stop asking “What use cases can AI solve?” Start asking “Which core workflows are we prepared to fundamentally redesign around AI?” 

• From Tooling to Accountability: Define, before scale, where institutional accountability lies. Who owns the output, the error, and the ethical boundary? This must be clear before the first line of code is written for production. 

• From Expertise to Experience: Value real-world, scaled experience over isolated technical expertise. The learning that matters happens when the system meets the messy, unpredictable reality of daily work. 

The transaction is the truth. India’s 20 billion monthly UPI transactions are not a testament to the brilliance of a single payment app. They are proof of a society that learned to absorb a general-purpose technology—digital identity—and built new institutional rails upon it. That same foundational lesson now applies to the world’s most hyped technology. 

The race for the future of AI is not just happening in research labs stocked with NVIDIA chips. It is happening in the ministries, boardrooms, and IT departments where people are doing the hard, humble work of making the extraordinary ordinary. That is the race that truly builds the future. The question is, who is paying attention?