Tag: Silicon Valley

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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???

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AI India

The Polyglot Machine

There is a subtle but profound shift happening in the global architecture of artificial intelligence. For the past few years, the gravitational pull of the AI revolution has been overwhelmingly centralized—anchored in the server farms and venture capital boardrooms of Silicon Valley. But if you look closely at the horizon, the center of gravity is beginning to disperse.

Activity in India’s AI ecosystem is accelerating (witness this week’s India AI Impact Summit in Delhi), and it feels less like a replication of what we’ve seen in the West and more like an entirely new paradigm.

Take Sarvam AI, for example. What strikes me about their approach isn’t just the technical ambition of building foundation models, but the philosophical underpinning of why they are building them. They are focusing heavily on Indic languages. This is not a trivial detail; it is the crux of the matter.

“We often forget that language is the original operating system of human culture. It shapes how we think, how we empathize, and how we conceptualize reality.”

When the foundational models of artificial intelligence are trained overwhelmingly on English, they inadvertently inherit a distinctly Western worldview. They learn the biases, the idioms, and the cultural frameworks of a specific slice of humanity, leaving the rest of the world to interact with technology through a translation layer that often strips away nuance.

India, a nation woven together by dozens of distinct languages and thousands of dialects, presents the ultimate crucible for AI. What happens when a machine doesn’t just translate, but actually “thinks” and generates natively in Hindi, Tamil, or Bengali?

The rise of AI in India represents a push for digital and cultural sovereignty. It is a recognition that the future of technology cannot be a monolith. For AI to truly serve humanity, it must reflect the pluralism of humanity. It must understand the local context, the regional slang, and the deeply rooted cultural histories that define how people live and work.

Watching companies like Sarvam AI pick up momentum reminds me that the next great frontier in technology isn’t just about achieving higher parameters or faster compute times. It’s about representation. The models that will truly change the world won’t just be the smartest; they will be the most deeply attuned to the beautiful, noisy, and diverse chorus of the human experience.

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Creativity Curiosity Living Work

The Human Router

There is a distinct difference between information and wisdom, and often, that difference is measured in velocity. We are accustomed to thinking that faster is better—fiber optic cables, 5G, real-time Slack notifications. We want knowledge to travel at the speed of light.

But Dan Wang, in his book Breakneck, captures a sociological truth about Silicon Valley that defies this obsession with speed:

“When I worked in Silicon Valley, people liked to say that knowledge travels at the speed of beer. Engineers like to talk to each other to solve technical problems, which is how knowledge diffuses.”

It is a charming, slightly irreverent metric, but it points to something profound about how humans solve difficult problems. There is “codified knowledge”—the explicit instructions found in textbooks, API documentation, and internal wikis. This travels instantly. It is frictionless. It is also, usually, insufficient for true innovation.

Then there is “tacit knowledge.” This is the intuition, the heuristic, the war story about why a specific architecture failed three years ago. This knowledge is heavy. It doesn’t travel through fiber optics; it travels through proximity. It requires the social friction of a shared table and the serendipitous collision of two engineers venting about a seemingly unrelated problem.

Crucially, this mechanism requires a specific type of operator: the Connector. These are the unsung heroes of the “speed of beer” economy. They aren’t always the 10x engineers on the leaderboard. They are the “human routers”—the people who instinctively know that the problem you are facing today is the same one Sarah from the Platform team solved last year. They are the ones who drag the introverted genius out to the pub, not to distract them, but to plug them into the grid. They curate the environment where the spark can jump the gap.

In our modern drive for remote efficiency, we are optimizing for the transfer of data. But we must be careful not to optimize away the people who pour the drinks, literal or metaphorical. That slow, liquid diffusion of ideas is often where the real breakthrough hides—steered by those special few who know exactly who needs to talk to whom.

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Interstate 280 San Francisco/California San Jose

The Scenic Route Home

“In a world optimized for speed and engagement, 280 is a reminder that infrastructure can be art.”

It is a strange paradox that in the heart of Silicon Valley—a place defined by the ephemeral, the digital, and the instantaneous—a cherished shared experience is a physical ribbon of highway that hasn’t changed much in fifty years.

My post from last April, “The World’s Most Beautiful Freeway,” has recently found a new wave of readers. I’ve been asking myself: Why? Why does a blog post about Interstate 280, written by a retiree exploring local history, resonate so deeply right now?

Perhaps it’s because I-280 is more than just a commute. As I noted in the original piece, even Sunset Magazine in 1967 recognized it as “a modern and scenic boulevard.” It was a bold claim for a freeway, yet it stuck. While its sibling, US 101, is a clogged artery of billboard-choked utility, 280 feels like a deep breath. It is the “scenic route” we are lucky enough to take right in our own backyard.

There is a powerful nostalgia in that drive. We all remember the sign that used to sit in the median near Cupertino—the one that literally proclaimed it “The World’s Most Beautiful Freeway”—before it vanished. We remember the way the fog rolls over the Santa Cruz Mountains, spilling into the crystal bowl of the reservoir.

But I think the recent interest goes deeper than pretty scenery. We are living in an era of rapid, often disorienting change. I used ChatGPT to help research the history of that road, a small testament to how AI is weaving into our daily inquiries. Yet, the road itself remains a constant. It was designed by engineers like Othmar Ammann and planners who chose the harder, more expensive route through the foothills rather than paving over El Camino Real. They chose beauty over pure efficiency.

That choice resonates today. In a world optimized for speed and engagement, 280 is a reminder that infrastructure can be art. It connects the headquarters of the companies building our future (Apple, Google, Meta) with the wild, golden hills of California’s past. It is a physical timeline of the Peninsula.

Maybe we are revisiting this post because we are craving that balance. We want to know that even as we rush toward the future at freeway speeds, we can still look out the window and see something timeless, something beautiful, something that reminds us where we are.

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AI Living Productivity

The Reality Gap

“I follow AI adoption pretty closely, and I have never seen such a yawning inside/outside gap. People in SF are putting multi-agent claudeswarms in charge of their lives… people elsewhere are still trying to get approval to use Copilot in Teams.” — Kevin Roose

There is a specific kind of vertigo that comes from scrolling through the “Inside” of the AI bubble while the rest of the world simply goes to work. It is the dizziness of watching a new species of behavior emerge—”wireheading” and “claudeswarms”—while the vast majority of the economy is still asking for permission to use a spellchecker.

The future isn’t just unevenly distributed; it is becoming mutually unintelligible.

Roose notes a “yawning inside/outside gap” that feels distinct from previous tech cycles. In one reality—geographically centered in San Francisco and digitally centered in specific discords—people are operating with a level of agency only sci-fi writers dared to imagine. They are deploying multi-agent swarms to manage their lives and consulting large language models for existential guidance.

In the other reality—the one inhabited by the vast majority of the global workforce—people are still waiting for an IT ticket to clear so they can use a basic productivity assistant.

It is tempting to look at this divide solely through the lens of technical access, but Roose hits on a deeper truth: “there seems to be a cultural takeoff happening in addition to the technical one.”

This is the friction of our current moment. It is not just that the tools are different; the permissions we give ourselves to use them are different. The “Inside” is operating with a mindset of radical experimentation and integration. The “Outside” is operating within legacy frameworks of risk mitigation and bureaucratic approval.

The danger of this gap isn’t just economic inequality, though that is a guaranteed downstream effect. The immediate danger is a loss of shared context. When the creators of technology live in a reality where “claudeswarms” run the day, they risk losing the ability to design for, or even empathize with, a world that is still fighting for permission to use the tools at all.

We are living in the same year, but we are no longer inhabiting the same time. The challenge for those of us on the “Inside” is to resist the intoxication of the bubble long enough to build bridges, rather than just building faster escape pods.

Meanwhile, in China (from the Financial Times)…

“I’ve witnessed first hand how China has grown from having zero AI talent 20 years ago to mass producing them,” he said. “Some of our most cutting-edge work is now done by fresh graduates. The real geniuses to change the world soon could well be among them.”