Tag: Compute

Categories
AI India

Intelligence as a Public Good: India’s “AI ka UPI” Revolution

There is a recurring rhythm to human progress: a breakthrough is born as a luxury, matures into a commodity, and ultimately solidifies into infrastructure.

We saw it with electricity, we saw it with the internet, and in 2016, we saw India do it with money through the Unified Payments Interface (UPI). UPI took the friction out of digital finance, transforming it from a walled garden guarded by private banks into a digital public good.

Now, it appears India is attempting to do for intelligence what they did for payments.

The global narrative around Artificial Intelligence is currently dominated at one end by massive private moats. At the other end are various open source/open weight efforts.

Silicon Valley primarily approaches AI as a capital-intensive arms race. Trillion-dollar tech players ramp huge compute, train very large models, and rent out intelligence via by the drink APIs. This intelligence is a proprietary and monetized luxury.

Enter the “AI ka UPI” initiative and the IndiaAI Mission discussed by Ashwini Vaishnaw at this week’s India AI Impact Summit.

Instead of treating AI as a product to be sold, India is architecting it as a Digital Public Infrastructure (DPI). The government is doing the heavy lifting—subsidizing the compute, curating population-scale datasets, and building foundational models.

Currently, they are making over 38,000 GPUs available to startups and researchers at around ₹65 (less than a dollar) an hour, a sheer fraction of the global cost. They are rolling out sovereign stacks like BharatGen and conversational models fluent in 22 regional languages.

“They are building an ‘orchestration layer’ for cognition.”

If a developer wants to build a voice-agent to help a rural farmer diagnose a crop disease, they don’t have to worry about the backend compute, the dataset acquisition, or paying a premium to a tech giant. They just plug into the public rails.

As I watch this unfold, I am struck by the philosophical shift it represents. We have become deeply conditioned to view AI through the lens of scarcity and subscription. But what happens when intelligence becomes a public utility?

It shifts the center of gravity of innovation. It becomes about who can solve the most acute, localized, human problems. The friction of creation drops to near zero. A bootstrapped team in a tier-two city can suddenly wield the same computational reasoning as a VC funded Silicon Valley startup.

There is also an element of sovereignty here. In the 21st century, relying on foreign infrastructure for your population’s cognitive processing seems akin to relying on a foreign nation for your electricity. True technological independence requires sovereign AI—models trained on indigenous data, reflecting local culture, nuances, and values, rather than the implicit biases of others.

The implications could be staggering. We are moving from an era where AI is an elite tool to an era where it is the invisible, ubiquitous fabric of daily life for over a billion people.

The true measure of AI’s ultimate impact won’t be found in benchmark scores on a server farm. It will be found in the quiet dignity of a citizen accessing global markets through a vernacular voice assistant, or a rural clinic predicting patient outcomes with public compute.

I look forward to following India’s AI efforts as this and other AI initiatives are more clearly defined.

Questions to consider

1. The Value of Human Capital: If artificial intelligence becomes as ubiquitous, reliable, and cheap as public electricity, what uniquely human skills will become the new premium in a hyper-automated society?

2. Cognitive Sovereignty: How will the geopolitical landscape shift when emerging economies no longer need to import their “cognitive infrastructure” and inherent cultural biases from Western tech players?

3. The Centralization of Truth: When a government builds and curates the foundational AI models for over a billion people, where is the line between providing a democratized public good and engineering a centralized cultural narrative?

What else???

Categories
AI Software

The Thermodynamics of Thought

For the last two decades, we have lived in the era of zero marginal cost. The defining characteristic of the internet age was that once software was written, distributing it to the billionth user cost virtually the same as distributing it to the first. We grew accustomed to the economics of abundance—infinite copies, infinite reach, lightweight infrastructure.

But the recent commentary regarding the true nature of Artificial Intelligence forces a jarring mental correction:

“AI is not software riding on old infrastructure. It is a new industrial system that converts energy into intelligence – requiring a capital stack measured in trillions, not billions.”

This distinction is not merely semantic; it is physical.

When we view AI through the lens of traditional SaaS (Software as a Service), we miss the magnitude of the shift. We are looking for an app; what is being built is a refinery. We are witnessing a return to heavy industry, but the commodity being refined isn’t crude oil—it is information, and the byproduct is reasoning.

This requires us to think less in terms of code and more in terms of thermodynamics. In this new industrial system, intelligence is an energy-intensive output. Every token generated, every inference drawn, requires a specific, measurable conversion of electricity into heat and computation. Unlike the static code of a website, an AI model is a furnace. It must be fueled constantly.

This explains the capital stack. We are seeing numbers that seem irrational in the context of venture capital—trillions, not billions. But if you view a data center not as a server farm, but as a power plant that generates intelligence, the numbers align with historical precedents. We are not funding startups; we are funding the modern equivalent of the electric grid, the transcontinental railroad, or the petrochemical complex.

We are pouring concrete, smelting copper, and manufacturing silicon on a planetary scale. The “cloud” was always a misleading metaphor—it sounded fluffy and ethereal. The reality of the AI transition is heavy, hot, and incredibly expensive.

We are moving from an era where we organized the world’s information (low energy) to an era where we synthesize new reasoning (high energy). We are building a machine that eats electricity and excretes intelligence. That isn’t a software update; that is a new industrial revolution.