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.

Copy and paste the text from AMT that you want to share

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.

Copy and paste the text from AMT that you want to share