Categories
AI

The Thousandfold Door

There is a pattern hiding in the history of human progress that we almost always miss in the moment — and almost always recognize, with some embarrassment, in hindsight.

Richard Koch and Greg Lockwood called it price-simplifying. The insight, drawn from decades of studying transformative businesses, is deceptively simple: when you cut the price of something dramatically, demand doesn’t respond proportionally. It responds exponentially. Halve the price, and you don’t double the market. You might multiply it by ten, or a hundred, or a thousand. Reduce the price to a tenth of what it was, and you may unlock a market a hundred thousand times larger than the one that existed before.

The math sounds implausible until you start listing the examples. Henry Ford didn’t just make cars cheaper — he conjured an entirely new civilization of mobility. Ikea didn’t discount furniture — it democratized the designed home. Southwest Airlines didn’t offer cheaper seats — it invented the era of the spontaneous trip, transforming flying from an executive luxury into something a college student books on a whim.

In every case, the price drop didn’t just serve existing demand more cheaply. It revealed latent demand that nobody knew existed — desire that had been sitting dormant, waiting for the door to open.

I keep returning to this framework when I think about what is happening with intelligence right now.

For most of human history, access to high-quality thinking — legal analysis, financial modeling, medical reasoning, strategic advice, elegant writing — has been extraordinarily expensive. Not just in money, but in time. You needed years of specialized education, or the budget to hire someone who had it. The price of cognition was high enough that vast swaths of human need simply went unmet. Problems went unsolved not because solutions didn’t exist, but because the expertise required to find them was priced out of reach.

AI is a price-simplifying event for intelligence itself.

“If the price is halved, demand does not double. It increases fivefold, tenfold, a hundredfold, a thousandfold or more.”

We are currently debating AI as though the primary story is substitution — one form of labor replacing another. But Koch and Lockwood’s framework suggests the more consequential story is what happens on the other side of the price collapse. When the cost of a legal opinion drops from $500 an hour to nearly zero, the question isn’t just “what happens to lawyers?” It’s “how many people who never could afford a lawyer now get access to one?” When the cost of a business plan drops from a consultant’s retainer to an afternoon conversation, the question isn’t just “what happens to consultants?” It’s “how many ideas that never got funded now have a fighting chance?”

The thousandfold door is opening. We can see it in the aggregate usage numbers, in the explosion of one-person companies, in the PhD-level tutoring now available to a student in a country that couldn’t previously afford it. What we cannot yet see is the full shape of what walks through.

That’s the thing about exponential demand. It doesn’t announce itself. It just accumulates quietly, and then one day someone looks at the numbers and realizes the world has changed.

Questions to Consider

  1. The Latent Demand Question: What human needs — currently unmet because expert help is too expensive — do you think AI will unlock first? Where is the largest reservoir of suppressed demand?
  2. The Ford Parallel: Henry Ford’s price simplification didn’t just create a new industry — it reshaped cities, suburbs, culture, and geopolitics in ways he never anticipated. What are the second and third-order consequences of dramatically cheaper intelligence that we’re not yet taking seriously?
  3. The Distribution Problem: Price-simplifying events historically don’t distribute their benefits evenly — early advantages tend to compound. Who is best positioned to walk through the thousandfold door first, and does that concern you?
  4. The Demand We Can’t Imagine: Koch and Lockwood’s most unsettling point is that the new demand often didn’t previously exist in any visible form — it was created by the price drop itself. What entirely new human behaviors, industries, or creative forms might AI’s price simplification call into existence that we currently have no framework to anticipate?
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???