Power of Intelligence 20260906

The Great Compression Part 4: Sovereignty Franchise

The Power of Intelligence

The most valuable piece of real estate in the world is a fab complex on the western edge of the Pacific, a short flight from the coast of a huge country that claims the ground it sits on. Taiwan Semiconductor Manufacturing Company, aka TSMC, prints the chips that every frontier AI model on earth depends on – and nearly all of them are currently made on that shore in Taiwan. This is simply a given; geography, unlike software, doesn’t scale, doesn’t copy, and doesn’t move.

For a decade the industry treated intelligence as something borderless, a global utility that would flow to wherever demand called for it. That framing is way past obsolete in a world that is split, lost along with the old dream of an uncensored Internet and free information exchange. Now, the crack is widening along a new axis. Forty years ago, the division ran through Berlin, and it was drawn by ideology. Today it runs through a supply chain, and it is drawn by who can make, buy, and run the machines that think. The AI chips are only the part of the split you can see and touch.

The Great Compression Part 4: Sovereignty Franchise

The Independence Chokepoint

Compute has already left the free market and entered the permitted layer of the economy. Advanced NVIDIA processors are no longer something a buyer simply purchases; they are something a government allows a buyer to receive. Export licenses now stand between the most capable silicon and any customer headquartered on the wrong side of the line. Washington has spent three years building this wall chip by chip – and the reshoring push, the fabs rising in Arizona, the equipment and materials supply chains being pulled back onto home turf, is the same impulse cast in concrete: move the chokepoint onto ground you control.

The scale of the gap explains the urgency. The U.S. holds a lead in AI compute capacity measured not in percentages but in multiples. China’s domestic production of the highest-end accelerators runs at a small fraction of American-equivalent scale. Its most advanced labs still lean on Western silicon to train their best models, whether the chips arrive through smuggling networks or through the gray route of renting restricted hardware inside third-country data centers.

Even when it’s leaking, the chokehold does its work. The controls slow every Chinese frontier effort, force it onto inferior domestic silicon, and keep it a step behind the labs it is chasing. But compute is only the first roadblock to independence: the next ones – on the intelligence itself – are only going up now.

The Gate to Intelligence

A wall around compute would be contained if compute were the end of the story, but it isn’t. The same logic that gates the chip is now reaching for what runs on it.

In June 2026, the U.S. Commerce Department required an export license for any foreign national to access a frontier AI model made by Anthropic – including the developer’s own non-citizen staff. Unable to verify the nationality of its users, the company withdrew the model entirely, and for the first time in this cycle a publicly available AI capability moved backward by government order. The controls were contested, the legal basis was unsettled, and access was restored within weeks. But the precedent was set: the authority to license the export of chips had been stretched to license access to an intelligence provider, and the mechanism worked before anyone had settled whether it lawfully could.

Beijing read the same lesson and moved to mirror it, drafting a regime to fence its own leading model weights behind national-security law rather than let them diffuse into the world. Two capitals, the same month, reaching the same conclusion from opposite sides: an AI model is not a product to be sold freely but a strategic asset released on terms. Intelligence has crossed into the permitted layer, and it crossed at both ends of the divide at once.

The Chained Brains

All of that is behind us; the interesting part is ahead.

Every ally now starting up its own sovereign AI is building it on a stack it doesn’t own – because the models, the chips, and the cloud beneath them are all American. What the ally holds is the building, the flag over the door, and the electricity bill. But the intelligence itself answers to another country’s export regime. This is sovereignty in name and franchise in fact.

The precedent for this arrangement already flies. Allied air forces buy the F-35 – the most expensive piece of weaponry in history – but they don’t thereby own it. The U.S. controls the source code and the mission-data files, and the aircraft’s full capability remains under American authority no matter how large the check. The buyer flies a plane whose brain is licensed, not sold, because withholding the core is the entire instrument of leverage. You can’t buy your way to control over a capability whose value to the seller depends on never fully handing it over.

While the F-35’s licensed brain governs a single platform, a national AI stack governs the layer through which a modern state thinks: its analysis, its logistics, its intelligence fusion, its administration. The fighter is a product on a leash, while the intelligence layer is the nervous system on a leash.

The Thinking Franchise

And the company installing that nervous system is already doing it. Palantir’s value was never the model but the trust above it and the data structure beneath it – the ontology layer that ingests a government’s data and turns it into decisions. In mid-2026 it began offering sovereign customers what they most wanted to have: full ownership of their model weights, retrainable, air-gapped, sealed behind their own walls. Still, the weights are the airframe that a buyer can own – but they will still have to run on NVIDIA silicon, inside an operating layer a U.S. company builds and controls, under the same export regime that pulled a frontier model offline in June.

Canada shows how thin the sovereignty can wear. Ottawa named itself the anchor customer of its own national AI strategy in mid-2026 while already running tens of millions of dollars in undisclosed Palantir contracts inside its defense and policing systems. The strategy announced sovereignty, but the procurement had already outsourced it. And by late summer, the two were trading tariffs and hard words, the closest alliance on the continent gone frosty, with the most sensitive systems of one side still running on the other’s stack. Nobody had to threaten anything. The dependency simply sat there, the way it was always going to.

Right in front of our eyes, the forward base of the last century gives way to the forward stack of this one. Instead of a U.S. army base near a European capital, a data-orchestration system running on NVIDIA chips, wired into the host government’s own operations, doing quietly what the garrison once did loudly. Land, power, and data can belong to Berlin or Paris, on paper – but the intelligence stack that holds the dependency will be American. It’s a cheaper umbrella, a deeper one, and a far harder one to ask to leave.

The End of Sovereignty

The immovable object was supposed to be the safe end of this story. Land, power, fabs, the heavy and hard-to-replicate assets that no software cycle can compress, the bedrock beneath the cloud. All of that still holds. But immovable is not the same as owned – and anyway, even the owner isn’t sovereign, because sovereignty itself is what’s being compressed at the top of the stack.

Every chokepoint described above rests on a fact that holds only for now: advanced compute is scarce, and what it produces can be walled. Practical quantum advantage dissolves both, and it is a contest between the same two powers that have run through every layer of this story. Perhaps one arrives first. Perhaps, if less likely, two arrive together. It changes little for everyone else. The machine owner reads what others hide and shields what it keeps. The only defense is a new cryptography built to withstand it, and it takes the same frontier compute and talent that only those two powers command. Even the lock is provisioned by the vendor.

It doesn’t matter who owns the oil or holds the warheads if the secrets around them can be opened at will and the means to reseal them belongs to someone else. The world is already two powers and their satellites, although most of them still speak as though sovereignty were something they possessed. The quantum edge is the point where the pretense ends.

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AI Aluminium 20260624

The Great Compression Part 2: The Intelligence Trap

AI Aluminum

On June 17th, the U.S. Air Force handed Anduril Industries a contract for its FQ-44 autonomous combat drone, making it the first new entrant to win a U.S. fighter aircraft program since the 1970s. The Silicon Valley startup beat Lockheed Martin, Northrop Grumman, and Boeing – companies that between them have defined American air power for generations – to the target. Anduril, a defense technology firm founded in 2017, did not win on relationship or on legacy: it won on AI-native architecture.

The shift this represents is psychological as much as commercial. Defense procurement is arguably the most bureaucratic, relationship-driven, clearance-protected industry on earth. If AI-native vertical integration can break this barrier, anything is now open for re-negotiation.

The Great Compression Part Two: The Intelligence Trap

The compression of the human intermediary layer across the economy – the subject of this series’ opening piece – raises a question that is both philosophical and financial: if the old middlemen are disappearing, and if the AI models replacing them are themselves becoming commodities, where does value go?

What made the Air Force announcement structurally significant was a detail that went largely unnoticed: the service deliberately separated the drone hardware from its AI software, specifying that the intelligence layer could be upgraded or replaced independently of the platform. By doing so, the Air Force drew a line that the market is still catching up to – and then immediately complicated it. The aircraft is the delivery vehicle. The intelligence tier is what matters, except that intelligence is also commoditizing fast.

The commoditization signal had already arrived earlier this month, when Google cut the price of its AI plan by nearly 40% overnight. OpenAI is reportedly considering steep token-price cuts as competition with Anthropic intensifies. The models themselves are beginning to resemble a capital-intensive utility more than a premium software business. As intelligence becomes cheaper, the investment question shifts to what models cannot easily access: proprietary data, regulated workflows, institutional trust, and the systems that turn AI output into real-world action.

The answer, in the most durable cases, is proprietary domain data combined with deep sector integration. Anduril is not a defense company that adopted technology: it’s a technology company that chose defense as its vertical. Palmer Luckey, who sold Oculus to Meta when he was just 21, founded Anduril alongside veterans of Palantir with a specific thesis: Silicon Valley had abandoned defense, leaving a widening gap between what the military needed and what the traditional primes could deliver.

Where Lockheed and Boeing run bid-led organizations optimized for cost-plus contracting cycles measured in decades, Anduril built a product-first company that moves at software speed, focused on cheap, autonomous, attritable systems designed to be deployed and lost without catastrophic cost. The competitive edge that results has nothing to do with which model runs underneath it. It is purpose-built architecture, mission-specific design, and the kind of deep operational embedding that no generalist technology company can shortcut and no traditional prime can easily imitate.

Palantir built the same competitive edge a decade earlier, at the intelligence level. Their forward-deployed engineers embedded themselves inside classified environments, building proprietary data structures around defense and intelligence that competitors cannot access, let alone replicate. The model is almost beside the point. What matters is the institutional trust above it and the data structure underneath it.

The same logic plays out in banking, and the psyche shift there is equally striking. JPMorgan Chase is not an obvious candidate for AI leadership – a 150-year-old Wall Street institution steeped in regulatory obligation and institutional conservatism. Yet it has become arguably the most digitally aggressive major bank outside the fintech world, spending north of $17 billion annually on technology and deploying AI across trading, risk, legal document review, and client services. JPMorgan’s AI advantage over any fintech competitor is not compute – it is 150 years of proprietary transaction data, credit history, and market intelligence, combined with the institutional will to deploy it at scale. Many large banks sit on comparable reserves; few have built the machine to turn them into a competitive weapon. The model commoditizes; the data does not – but only in the hands of someone with the commitment to exploit it.

The pattern across defense, banking, and every sector where this is playing out is consistent: the prize migrates to whoever owns the scarce position that generic models cannot substitute for. Right now the prize sits with domain data and deep sector integration. What’s forming above it is agentic orchestration – systems that coordinate networks of specialized AI agents across high-stakes workflows: routing battlefield targeting decisions, flagging fraud across millions of simultaneous transactions, managing the exception-handling that no single model can resolve alone. Palantir’s AIP platform is the most mature example of this emerging tier, and it is no coincidence that the same company that mastered domain-specific data is now positioning for that orchestration tier. Salesforce’s Agentforce is building toward the same position from the enterprise side. The race for this trophy is not yet decided, but the companies that already own those domain data advantages are the natural favorites to own the control plane above them.

The stack, in other words, keeps moving upward. Value migrates to the next bottleneck, then the next. And below all of it – the models, the sectors, the orchestration layer – something has to hold the weight. Every control plane needs a floor. What that floor looks like, who owns it, and why it may be the most durable investment thesis of the AI era is the subject of the next piece.

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Hospitalization Costs 20260421

India Insider: Rising Hospitalization Costs a Growing Concern

Indian Households Face Rising Expenditures for Hospitalization and Critical Care

Many households in India face unexpected hospital expenses that have become almost routine in today’s environment. Rapid urbanization, unclean drinking water, poor air quality due to carbon emissions and industrialization have hit low and middle income homes harder than upper income families. These households are increasingly exposed to diseases that require critical treatment, but the government institutions are ill-equipped to handle the growing patient loads. As a result, many Indians are forced to seek treatment in private hospitals, where costs are significantly higher.

Consider that a middle income household in Madurai earns roughly between 15,000 to 20,000 Rupees per month per person. This per the Periodic Labor Force Survey 2023-2024 Annual Report. So if two people are working in a family, it means empirically, we can estimate the income between 35,000 to 40,000 per month. We need to compute the family’s expenditures – if one member is hospitalized for critical illness or gets hospitalized treatment in private and public hospital.

Even if the income range for this family is better than median estimates, their capacity to absorb medical shocks are limited due to high baseline consumption and minimal household savings.

In the case of hospitalization in a government facility, out of pocket expenditures such as medicines, diagnostics, transport, and wage loss can amount to 12,000 to 40,000, effectively deducting one to two months of household income.

In other words, families need to spend out of pocket by borrowing to finance these gaps for consumption and medical expenses.

However, if a family is forced to take treatment in a private hospital, they would be spending 100,000 to 500,000 Rupees in critical cases, their total spending as a higher percentage of net income and is close to 1,250% in extreme cases.

The Role of Savings and Informal Safety Nets

As observed during Covid-19 and other crises, Gold has often acted as a financial buffer for Indian households.

Families that save during stable periods are able to pledge or sell gold in times of distress, helping them to manage medical expenses without relying entirely on high cost borrowing.

In contrast households without many buffers, often turn to informal lenders or personal loans, where interest rates can range between 36 to 60% compounding the financial distress.

Looking at Government Data for Clues

The most authoritative survey on household expenditures on hospitalization comes from National Sample Survey Office’s (NSSO) “Health in India” (2017-2018), and it showed the costs for those hospitalized in private facilities were eight times costlier than government facilitys.

An average hospitalization (excluding child birth) cost 4,290 Rupees in a rural government hospital, and 4,837 in an urban government hospital. The same scenario in a private hospital cost 27,347 in rural India and 38,822 in urban India. Out of pocket expenditures – the amount families pay themselves, follows the same pattern with families having to pay out about 4,000 Rupees in government hospitals, versus 26,000 – 32,000 Rupees in private ones.

Hospitalization Expenditure by Hospital Type and Sector, NSSO 75th Round (2017-2018). Source: MoSPI, Ministry of Statistics.

These were already catastrophic figures for a typical household in 2017-2018. A single private hospital admission cost more than a month’s wages for most Indian families, and it is getting worse.

Seven Years of 12-14% Medical Inflation

Since 2018, the cost of being hospitalized in India has risen at a pace that outstrips almost every other category of spending: Millman’s 2025 medical inflation report pegged the rate at 12% in 2024, more than triple the general CPI inflation of 4.2%.

An urban private hospital admission that cost 38,822 Rupees in 2017-2018 now is in a 76,000 – 91,000 price range, this while real wages are stagnant and not growing. Recent RBI Household surveys conclude that Indians absorb higher debt in order to manage their household expenses.

Critical Illness Can Wipe Out a Household’s Future

Critical illness like heart attacks, a cancer diagnosis, renal failure, kidney transplants costs even more in India – and the problems grow unbearable for Indian families after they are forced to take on more debt. For example, a heart angioplasty with stent that costs 100,000 in 2018 now costs between 200,000 – 300,000 Rupees in private hospitals.  A kidney transplant which costs 400,000 – 600,000 a decade ago now costs 1 million to 1.5 million Rupees. A full course of chemotherapy ranges from 250,000 for early stage diseases to 2.5 million Rupees for advanced cases requiring targeted biologics.

Cost ranges for major critical illness in India, 2024-2025, green bars show public hospital costs, orange bars show private hospital costs. Sources: ACKO India Health Report 2024, HCG Oncology, Hospital Quote Data from Apollo, CARE and others.

Where the Indian Household Stands

As per periodic labor force surveys, the median Indian worker earns 10,000 Rupees a month. The Economic Survey in 2024-2025 recorded average monthly earnings of 13,279 for self-employed workers, 20,702 for salaried workers, and 12,750 for casual laborers. Crucially, only the top 22% of the India’s labor force earns more than 15,000 Rupees per month.

Monthly household income distribution, rural vs urban India (2023-2024). Sources: Periodic Labor Force Surveys, Azim Premji University Income Distribution Study 2019-2024.

Statistically, the household we have taken for the reference is not a poor household by national standards. It is comfortably above the rural and urban median, sitting in the top fifth of the country, and this is what makes the rest of the story so troubling. If this household cannot afford a critical illness, almost no household outside the urban upper middle class can afford rising hospitalization costs.

The Insurance Gap

The government of India has expanded insurance schemes for low income households since 2018 via the Ayushman Bharat PM-JAY with 500,000 Rupees coverage. But the scheme which was set many years ago, does not match the current medical costs scenarios. Income eligibility is an another problem where a majority of people who earn in the middle, slip out of the government insurance schemes and have to take private insurance to cover their health risks. Health insurance penetration in India is low at 3.7% of GDP, well below global statistical standards of 7%.

Across much of India, a single critical illness can effectively destroy years of household income accumulation and trigger debt dependence. In the absence of stronger public healthcare delivery, and without deeper insurance penetration at affordable costs providing better claims services, and lacking robust risk sharing mechanisms – escalating medical costs could act as a drag on India’s economic growth trajectory by weakening household balance sheets.

Notes: 1 USD = 93.24 INR

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