Something has been bugging me all week.

Goldman Sachs now expects five companies, Amazon, Alphabet, Microsoft, Oracle and Meta, to spend about $1.2 trillion on AI infrastructure in 2027.

One year.

That is roughly 3% of US GDP, about three times the telecom buildout of the 1990s at its peak. Relative to the economy, it is the biggest investment cycle since the railroads.

So I asked myself a simple question. Who is actually paying for all of this?

Not who announces it. Not whose logo sits on the press release. Who holds the risk if the returns do not show up?

I spent the weekend pulling the thread. The answer is not what I expected. Let me walk myself through it.

First, the honest part

A big chunk of the money is real cash. Amazon lifted its 2026 capex forecast to about $220 billion. Alphabet raised its own to $195 to $205 billion. These companies generate enormous cash flow, and for years they funded the buildout out of it. Clean, boring, fine.

But here is the line from the Goldman note that stuck with me: spending has now grown past what these companies generate from operations. From here on, the buildout runs on borrowed money.

So who is lending it.

Move one: get the debt off the balance sheet

Look at Meta’s Hyperion campus in Louisiana. Meta did not borrow $29 billion to build it. It set up a joint venture instead. Pimco put in about $26 billion of debt, issued as bonds backed by the data center itself. Blue Owl put in $3 billion of equity and its funds own 80% of the venture. Meta keeps 20%, signs a long lease, and pays rent.

On Meta’s balance sheet, a giant AI data center shows up as a rent bill. The debt sits in a vehicle most shareholders will never read about. The Bank for International Settlements wrote about this structure in March and gave it a name I cannot get out of my head: shadow borrowing.

Move two: borrow against the chips themselves

Then there are the GPU clouds. CoreWeave raised about $28 billion in debt and equity over twelve months, including an $8.5 billion loan in March that became the first GPU-backed financing to earn an investment-grade rating. Read that again. The collateral is the chips.

I keep thinking about this. A loan secured against hardware that loses value every year, backed by contracts with a handful of customers. When I wrote

The Rise of GPU Landlords , my point was that the landlords own the scarce asset and everyone else rents. What I missed is that even the landlords do not really own it. The bondholders do.

xAI raised $5 billion in debt last year. Oracle plans to raise $45 to $50 billion this year to build for OpenAI, Meta, Nvidia, xAI and others.

And here is the part that should make anyone pause: $18 billion of loans tied to one Oracle data center project in New Mexico, part of its buildout for OpenAI, are already quoted at 89 to 91 cents on the dollar, because the banks trying to sell that debt on to wider investors cannot find enough buyers. Construction has not even finished.

Move three: the circle

Now the part that broke my brain when I first mapped it.

Nvidia agreed to invest up to $100 billion in OpenAI. OpenAI committed to pay Oracle $300 billion over five years for compute. Oracle spends that money building data centers full of Nvidia chips. AMD gave OpenAI warrants to buy up to 10% of AMD, if OpenAI buys enough AMD chips and the stock performs.

The chip makers are financing their own customers. When a vendor funds your purchase of the vendor’s product, the revenue on both sides of the deal is partly an IOU written to itself.

OpenAI itself told investors in February it is now targeting about $600 billion of compute spend through 2030, after Altman talked about $1.4 trillion of commitments late last year. The number moved by $800 billion in three months and almost nobody blinked.

So who actually holds the $1.2 trillion?

Here is the answer I landed on, and it is the reason I am writing this.

A Chicago Booth analysis broke down who holds the AI debt stack. About $520 billion of hyperscaler bonds, held by bond funds, insurers and pension funds. Around $250 billion of project and data center finance. Roughly $200 billion of private credit, and that is an estimate, because nobody can fully measure it. About $35 billion of GPU-secured lending. And on top of all that, S and P counts roughly $675 billion of signed data center leases that have not started yet, sitting off every balance sheet.

The analysis modeled where the first losses land if this goes wrong. Not the banks. Not the tech giants. The first losses sit with private credit funds, and behind them, their investors: pension plans, endowments, insurance platforms, and regular people who bought private credit products in their brokerage accounts.

The risk of the biggest capital spending cycle since the railroads has been moved from the companies making the bets to the retirement savings of people who never placed one.

So that is the answer to my question. Who actually holds the $1.2 trillion?

You do. I do. Anyone with a pension, a super fund, an insurance policy or an index fund. The landlords engineered it so the upside is theirs and the paper is yours. I wrote that AI would create a landlord class. What I did not fully see is that the landlords do not even keep the mortgage.

Maybe the demand shows up and everyone gets paid. Goldman thinks the industry needs about $300 billion a year of AI revenue to justify the spend, and cloud revenue is growing fast. Maybe it all works out.

But if it does not, the margin call does not go to Sand Hill Road. It goes to your retirement account.

So tell me. If the returns have not shown up by 2028, whose loss is it really?

AI starts to take power of discovering things from mathematicians.

On October 6, 2026, OpenAI published a remarkable collection of mathematical research.

• 372 families of mathematical results.

• More than 700 research manuscripts.

• Around 4,000 mathematical problems evaluated.

• Claimed solutions to problems that have challenged mathematicians for generations.

Some results include machine-checked proofs. Others still require independent verification. These aren’t all confirmed breakthroughs, and several manuscripts have required revisions.

But even with those qualifications, something important is happening.

For centuries, the relationship between humans and tech has been relatively simple.

Humans discover. Machines execute.

Humans discovered electricity. Machines helped us use it.

Humans developed quantum mechanics. Engineers built semiconductors.

Humans created mathematical theories. Computers performed calculations based on them.

Now AI is beginning to participate in the discovery process itself.

And that’s a completely different economic and power proposition.

I want to explain 25 mathematical problems from OpenAI’s research, what they mean in plain English, and why they could eventually matter to science, industry, and ordinary people.

Let’s begin.

I – You’re looking at the wrong part of the value chain

AI applications today do one of three things:

1. Make existing work faster.

2. Make existing work cheaper.

3. Automate work that humans previously performed.

Useful? Absolutely.

Transformative? Sometimes.

But there’s another layer.

Think about your smartphone.

It exists because generations of scientists discovered things about electricity, electromagnetism, quantum physics, and mathematics.

The smartphone didn’t create those discoveries.

It commercialized them.

The same is true for aviation, medical imaging, GPS, encryption, and almost every major technology.

Discovery creates possibilities. Engineering turns them into products. Business distributes those products to the world.

If AI can accelerate discovery, it could affect the entire chain.

That’s what makes these mathematical results interesting.

II – 25 mathematical problems and why they matter

You don’t need to understand the equations.

You need to understand what scientists are trying to discover.

1. Quasi-Riemann Hypothesis

Prime numbers appear unpredictably among ordinary numbers. Mathematicians have spent generations trying to understand their distribution.

OpenAI claims a major result restricting where zeros of certain mathematical functions can occur.

Why it matters: Prime-number theory helps underpin modern cryptography. Deeper understanding could eventually improve mathematical algorithms. It doesn’t solve the full Riemann Hypothesis or break encryption.

2. Artin’s Primitive Root Conjecture

Can certain numbers generate every possible nonzero remainder through repeated multiplication?

OpenAI claims the answer is yes for infinitely many suitable primes.

Why it matters: This mathematics is connected to modular arithmetic, which appears in cryptographic systems.

3. Erdős–Prachar Large Prime Gaps

How frequently do unusually large gaps appear between prime numbers?

OpenAI claims a strong mathematical result about their frequency.

Why it matters: Understanding prime distribution improves the mathematical foundations of number theory and related algorithms.

4. Erdős–Hall Totient Counting Problem

How often do different numbers produce the same kind of arithmetic counting result?

OpenAI claims a new asymptotic formula.

Why it matters: Euler’s totient function appears in the mathematics behind RSA encryption, although this result does not directly change its security.

5. Patterson’s Cubic Gauss Sum Conjecture

Can we predict the average behavior of complicated arithmetic sums?

OpenAI claims a precise formula.

Why it matters: It gives mathematicians stronger tools for understanding patterns in numbers.

The takeaway?

We’re still discovering fundamental properties of numbers that we’ve been studying for thousands of years.

2. The limits of computers and intelligence

This is where things get interesting.

Because some mathematical problems aren’t about finding an answer.

They’re about whether finding an answer is even possible.

6. Hilbert’s Tenth Problem Over Rational Numbers

Can a computer always determine whether a polynomial equation has a solution made entirely of fractions?

OpenAI claims to have proven that no universal algorithm can do this.

Why it matters: It establishes a fundamental limit on computation. Even infinitely powerful computers cannot solve every mathematically defined decision problem.

7. Kaplansky’s Direct-Finiteness Conjecture

In certain abstract mathematical systems, does multiplying A by B to get 1 mean multiplying B by A must also give 1?

OpenAI claims a counterexample.

Why it matters: It changes our understanding of certain algebraic structures used in advanced mathematics.

8. Erdős’s Short Egyptian Fractions Conjecture

How efficiently can numbers be represented as sums of distinct fractions with numerator one?

OpenAI claims a near-optimal bound.

Why it matters: It advances the mathematics of numerical representation, though no immediate compression or software application is demonstrated.

9. Irrationality Exponent of Pi

We know π never ends or repeats.

But how closely can it be approximated using fractions?

OpenAI claims to have established its exact irrationality exponent.

Why it matters: It resolves a fundamental question in mathematical approximation.

10. Irrationality of Catalan’s Constant

For generations, mathematicians couldn’t prove whether the constant 0.915965... could be expressed as a fraction.

OpenAI claims it cannot.

Why it matters: Primarily foundational mathematics, but the techniques may help solve other problems.

Notice something?

Not every discovery creates an immediate product.

Some expand our understanding of what’s mathematically possible.

3. Physics, energy, and materials

Now we’re getting closer to the physical world.

11. Quantum Heisenberg Ferromagnet

Why do collections of quantum particles spontaneously produce magnetism?

OpenAI claims rigorous results for certain mathematical models.

Why it matters: Could strengthen research into magnetic materials, memory technologies, and spintronics.

12. Mézard–Parisi Formula

How do large systems behave when millions of interacting elements influence one another?

OpenAI claims a proof for certain spin-glass models.

Why it matters: Related mathematics appears in statistical physics and theoretical optimization, potentially influencing future computational methods.

13. Relativistic Vlasov–Maxwell System

Can we mathematically predict the behavior of charged particles interacting with electromagnetic fields without the solutions developing singularities?

OpenAI claims a global mathematical result for a specified model.

Why it matters: These equations are related to plasma physics, an important field in fusion-energy research.

Better mathematics could eventually improve scientific modelling. It doesn’t automatically produce better fusion reactors.

14. Higher-Dimensional Duke Equidistribution

How do certain mathematical objects spread across complicated, higher-dimensional spaces?

OpenAI claims a significant equidistribution result.

Why it matters: Advances our understanding of number theory and mathematical dynamics.

15. Mahler’s Geometric Conjectures

Which shapes minimize a particular geometric measure, even in hundreds of dimensions?

OpenAI claims solutions to important versions of these conjectures.

Why it matters: High-dimensional geometry is connected to optimization and theoretical computer science.

Here’s the important distinction.

A mathematical discovery is not an engineering breakthrough. But engineering breakthroughs often depend on mathematical discoveries.

4. The hidden structures of mathematics

Sometimes the hardest problems aren’t about numbers.

They’re about discovering relationships between things that appear unrelated.

16. Milne’s Rationality Conjecture

Can different mathematical descriptions of certain complex geometric objects be proven consistent?

OpenAI claims a proof.

Why it matters: Strengthens foundations in advanced geometry and number theory.

17. Birch–Swinnerton-Dyer

Can we predict the structure of rational solutions to certain elliptic-curve equations?

OpenAI claims a major result for a specified class of curves.

Why it matters: Elliptic curves are central to number theory and have important cryptographic applications. The full conjecture remains a separate question.

18. Goldfeld’s Conjecture

How frequently do families of elliptic curves have particular numbers of independent rational solutions?

OpenAI claims a result about their distribution.

Why it matters: Improves our understanding of mathematical structures used in advanced number theory.

19. Zilber–Pink Conjecture

How often can unusually special intersections occur between complicated geometric objects?

OpenAI claims important results for specified cases.

Why it matters: Advances algebraic geometry and the study of exceptional mathematical patterns.

20. Function-Field Reconstruction

Can you reconstruct a complicated mathematical structure using limited algebraic information?

OpenAI claims new reconstruction results.

Why it matters: Helps mathematicians understand how much information determines an entire structure.

The bigger lesson?

Sometimes progress comes from discovering that two apparently different problems share the same underlying structure.

That can unlock completely new ways of solving them.

5. The mysteries hidden inside simple mathematical ideas

21. Chowla and Elliott Correlation Conjectures

Do patterns involving prime factors behave randomly, or do hidden relationships exist?

OpenAI claims important correlation results.

Why it matters: Advances mathematical understanding of arithmetic randomness.

22. Weak Inhomogeneous Duffin–Schaeffer Conjecture

How closely can numbers be approximated using fractions under specific constraints?

OpenAI claims a proof establishing infinitely many approximations under the conjecture’s conditions.

Why it matters: Strengthens foundational approximation theory.

23. Gaussian Moat Conjecture

Imagine prime numbers scattered across a two-dimensional grid.

Can you travel forever from one prime to another if every jump must remain shorter than a fixed distance?

OpenAI claims no.

Why it matters: Advances mathematical understanding of connectivity and prime-number geometry.

24. Deligne–Drinfeld Conjecture

Can a complicated mathematical structure be explained using a surprisingly simple collection of components?

OpenAI claims a proof of the relevant algebraic statement.

Why it matters: Simplifying mathematical structures can unlock further discoveries.

25. Litt’s Integral Density Question

How common are certain special arithmetic points inside complex geometric spaces?

OpenAI claims an important density result.

Why it matters: Strengthens the relationship between arithmetic and algebraic geometry.

III – The biggest mistake is expecting these discoveries to make your phone faster tomorrow

Think about the history of electricity.

Long before the modern electrical grid existed, scientists were trying to understand electromagnetic phenomena.

They weren’t developing power stations or charging infrastructure.

They were discovering how nature worked.

The commercial applications came later.

The same thing happened with quantum mechanics.

Scientists weren’t trying to invent smartphones.

They were investigating the behavior of matter.

Decades of research, experimentation, and engineering eventually helped make semiconductors possible.

That’s how much of scientific progress works.

Mathematics → Scientific understanding → Engineering → Industry → Consumer benefit.

And not every discovery completes that journey.

Some remain theoretical forever.

But when a foundational discovery does become useful, its economic impact can be enormous.

That’s why focusing only on today’s AI productivity gains may be missing a much bigger opportunity.

IV – What happens when the cost of discovery collapses?

Historically, scientific progress has depended on scarce resources.

Human intelligence.

Specialized expertise.

Research funding.

Time.

Sometimes a mathematical problem consumes the careers of multiple researchers without being solved.

Now imagine a future in which AI systems can generate, test, and formally verify substantial mathematical results far more efficiently.

What changes?

First, the economics of research.

A laboratory might explore significantly more hypotheses with the same budget.

A university could investigate mathematical ideas that previously required years of specialized work.

A company might accelerate parts of its R&D pipeline.

But here’s where things become more interesting.

If intelligence becomes abundant, intelligence itself may no longer be where the greatest economic value sits.

Imagine AI helps discover a mathematical principle that eventually improves battery chemistry.

Who captures the value?

The company operating the AI model?

The scientist who designs the experiment?

The manufacturer producing the battery?

The business controlling distribution?

Or the consumer buying cheaper energy storage?

Probably some combination.

The point is that scientific discovery and commercial value creation are not the same thing.

The scarce resources may shift toward physical infrastructure, experimental facilities, capital, manufacturing, proprietary data, and distribution.

A theorem can become abundant.

Building a factory cannot.

At least, not in the same way.

For years, the AI race has been dominated by a familiar set of questions.

Who has the best model?

Who has the most GPUs?

Who owns the data centers?

Who can deliver intelligence at the lowest cost?

Those questions matter.

But I think another competition is emerging.

Who can turn intelligence into discoveries that change the physical economy?

Consider two businesses.

One uses AI to reduce customer service costs by 50%.

The other uses AI-assisted research to discover a new industrial process that cuts the cost of producing an important material by 50%.

Assuming the second discovery works and can be commercialized, the economic implications are very different.

The first business has improved an existing process.

The second may have changed the economics of an entire industry.

That’s the difference between making existing knowledge cheaper and creating new knowledge that enables something previously impossible.

And it’s the reason I think AI-driven scientific discovery deserves much more attention.

VI – The most valuable thing AI could create isn’t content. It’s knowledge.

Let’s return to those 25 mathematical problems.

Some involve prime numbers.

Some involve quantum physics.

Some concern the mathematical limits of computation.

Others deal with geometric structures so abstract that most people will never need to understand them.

But they have something in common.

They represent attempts to answer questions humanity hasn’t previously been able to resolve.

OpenAI’s claimed results still need rigorous verification. Publishing hundreds of papers is not the same as establishing hundreds of accepted mathematical breakthroughs.

But if a meaningful portion survives scrutiny, it tells us something important.

The role of AI is expanding.

From performing tasks.

To helping humans reason.

To potentially producing original knowledge.

And knowledge is what ultimately makes new technologies possible.

We spend an extraordinary amount of time debating whether AI will replace human jobs.

That’s a legitimate concern.

But perhaps the more consequential question is what happens when AI helps humanity discover things we couldn’t have discovered otherwise.

New mathematical foundations.

New physical models.

Potentially new materials, medical treatments, and energy technologies after the necessary experimental and engineering work.

The greatest economic impact of AI may not come from replacing the work we already do.

It may come from enabling discoveries that create entirely new industries.

For most of history, our technological capabilities have been limited partly by how quickly we could expand human knowledge.

What happens when that constraint starts to loosen?

We don’t know yet.

But it might turn out to be a much bigger story than another chatbot replacing another job.

Peter Thiel thinks Europe is losing the AI race.

He might be right.

The usual explanation goes something like this:

America innovates. China builds. Europe regulates.

Nice line.

Also way too simple.

Because if Brussels deleted half its AI rulebook tomorrow morning, Europe would not suddenly wake up with Nvidia, OpenAI, AWS and another trillion dollars of growth capital.

Its deeper problem is this:

Europe increasingly uses technology that somebody else finances, somebody else owns and somebody else can switch off.

That’s not an AI problem.

That’s a power problem.

Europe actually has plenty of AI.

Researchers? Yes.

Engineers? Yes.

World-class industrial companies? Absolutely.

Customers? A gigantic market.

And AI adoption is climbing.

In 2025, around 20% of EU enterprises with at least ten employees in the surveyed sectors reported using AI, up from 13.5% a year earlier.

So Europe isn’t sitting in a candle-lit room refusing to use ChatGPT.

The problem comes one layer deeper.

Who owns the models?

Who owns the cloud?

Who finances the companies?

Who owns the customer relationship?

And when a European company becomes really, really good...

who eventually owns that company?

That’s where the picture starts getting ugly.

An EIB study found that, after ten years, EU scale-ups had raised roughly half as much capital as comparable San Francisco firms. European providers’ share of Europe’s own cloud infrastructure market also fell from 29% in 2017 to roughly 15% by 2022 and stayed around that level through mid-2025.

Read that again.

Europe can become one of AI’s biggest customers...

while becoming weaker inside AI’s economic stack.

Both can happen at the same time.

Imagine you’re the CEO of a German manufacturer.

You want the best AI system for your factories.

Do you buy the mediocre European product because Thierry Breton once gave a speech about strategic autonomy?

Of course not.

You buy the best system.

Maybe that means an American model running on an American cloud using American chips.

That might be exactly the correct decision for your shareholders.

Now multiply that decision across 100,000 companies.

Every individual business becomes more productive.

But collectively, Europe sends revenue, usage data, developer ecosystems and bargaining power toward foreign platforms.

The rational decision for the company can become the strategic weakness of the continent.

That’s the trap.

And yelling “BUY EUROPEAN” doesn’t fix it either.

Forcing businesses to use worse products is basically economic self-harm wearing a sovereignty costume.

The real question is harder:

Who pays while the European alternative becomes good enough?

Investors?

Governments?

Customers?

Probably some combination of all three.

This is why the regulation debate annoys me.

Regulation matters.

Bad regulation can absolutely slow companies down.

A startup with 30 employees feels a compliance burden differently from Microsoft.

And Europe has mastered the bureaucratic art of turning one rule into seven forms, three agencies and a PDF that looks like it was designed during the Austro-Hungarian Empire.

But regulation cannot explain everything.

Europe’s cloud ownership problem started well before the generative-AI boom and before today’s AI-specific rules.

Remove the AI Act tomorrow and Europe still needs:

Capital.

Power.

Data centres.

Customers willing to buy from challengers.

Experienced operators.

Liquid capital markets.

And companies capable of scaling across borders without treating every European country like a new expansion campaign.

That’s the boring answer.

Which usually means it’s closer to the truth.

And capital does more than fund companies.

It funds mistakes.

This is wildly underrated.

AI companies don’t discover the perfect product on Tuesday and IPO on Friday.

They spend years doing stupid experiments.

Hiring the wrong people.

Burning compute.

Changing product direction.

Losing customers.

Rebuilding infrastructure.

Trying again.

American venture capital doesn’t just finance success.

It finances the right to be wrong for longer.

That creates learning.

Then those founders become investors.

Employees become founders.

Executives become board members.

Customers become early adopters of the next company.

The money compounds into an ecosystem.

If your companies continually relocate, sell early or raise from foreign capital because domestic expansion financing is thin, you don’t just lose ownership of one company.

You lose pieces of the next ten companies too.

That’s the flywheel Europe should worry about.

So should Europe build its own OpenAI?

Maybe.

But trying to duplicate every layer of the American AI stack would be brutally expensive.

Models.

Cloud.

Chips.

Data centres.

Developer platforms.

Applications.

Energy infrastructure.

Europe doesn’t need autarky.

It needs options.

There’s a massive difference.

To me, technological sovereignty doesn’t mean:

Everything must be European.

It means:

If your supplier screws you, can you leave?

Can you move your data?

Can you switch models?

Can critical systems keep operating?

Can you test the claims your vendor makes?

Can you negotiate pricing instead of accepting whatever appears in the renewal email?

That’s power.

A European supplier can lock you in too.

An American supplier can sometimes be perfectly replaceable.

So nationality is only part of the equation.

The variables are concentration, substitutability and switching cost.

This creates an interesting strategy for Europe.

Don’t try to win every layer.

Win layers where Europe already has leverage.

Industrial AI.

Manufacturing.

Energy.

Pharma.

Science.

Robotics.

Defense.

Infrastructure.

Europe owns extraordinary domain expertise and some of the world’s most valuable industrial customer relationships.

So perhaps the play isn’t:

Build Europe’s ChatGPT.

Maybe it’s:

Build the AI systems that run the world’s factories, grids, laboratories and machines.

But even here there is a catch.

If your “European AI champion” is nothing more than a nice interface sitting on top of someone else’s model, someone else’s cloud and someone else’s distribution...

you haven’t built sovereignty.

You’ve built a reseller.

The question becomes:

What do you own that your supplier cannot easily copy?

The customer?

The data rights?

The workflow?

The proprietary evaluations?

The domain knowledge?

The distribution?

If the answer is “none of the above,” your moat is basically PowerPoint.

Which brings me back to Peter Thiel.

Europe may indeed be falling behind.

But “behind” needs a definition.

Behind in frontier models?

Probably one conversation.

Behind in capital formation?

Another.

Behind in cloud ownership?

Another.

Behind in AI adoption?

Different again.

Behind in productivity?

That’s ultimately the one that matters economically.

And this is where Europe has to make a choice.

Not between AI and regulation.

That’s Twitter-brain framing.

The actual choices are uglier:

How much dependence is acceptable?

Which technologies are important enough to subsidise?

How much more should Europe pay for resilience?

When should governments back domestic challengers?

When should they let them fail?

And which parts of the AI stack are simply not worth replicating?

Those aren’t technology questions.

They’re capital-allocation questions.

They’re industrial-policy questions.

They’re power questions.

And the scoreboard shouldn’t be how many billions Brussels announces at the next summit.

It should be painfully simple:

Did productivity rise?

Did European companies gain customers?

Did infrastructure actually get built and used?

Can critical systems switch suppliers?

And did taxpayers get something valuable for the money?

Because announcing €100 billion is easy.

Building bargaining power is much harder.

The AI race is usually described like Formula 1.

America is ahead.

China is chasing.

Europe is somewhere behind writing the safety manual.

I think that’s the wrong metaphor.

AI is becoming more like the global energy system.

You don’t need to own every oil field.

But you probably don’t want one country controlling your fuel, your refinery, your pipelines and your emergency reserves either.

Europe doesn’t need to own everything.

It needs enough capability, enough competition and enough alternatives that nobody else owns it.

That’s the real race.

And unlike a benchmark leaderboard, there is no single winner.

There are just countries with leverage...

and countries paying rent to the ones who have it.

— Full Stack Capitalist

I recently launched a new podcast called Big Stakes.

The premise is simple: AI stories are rarely just technology stories.

Behind the models, chips, data centres, acquisitions and product launches is another game involving capital, distribution, control and power.

In our latest episode, we covered a story that initially looks almost trivial.

Amazon blocked Meta’s new AI agent, Muse, from shopping on Amazon on behalf of users.

Amazon says Meta did not have permission to do this. It raised concerns about how the agent identified itself, how credentials were handled and whether its access complied with Amazon’s terms.

Meta says Muse was built with security controls, a dedicated virtual environment, protected credentials and explicit user approval before sensitive actions such as purchases.

Those are legitimate technical questions.

But they are not the biggest question.

Who owns the buying decision when humans stop shopping and AI agents start shopping for them?

That is where this gets interesting.

And potentially enormous.

THE INTERNET WAS BUILT AROUND HUMAN ATTENTION

For roughly three decades, internet commerce has operated around a fairly stable assumption.

Humans browse.

We compare products.

We look at reviews.

We click advertisements.

We add something to a basket.

We see another recommendation.

Maybe we buy that too.

An extraordinary amount of economic value has been built around controlling different parts of that journey.

Google controlled intent.

Meta controlled attention and discovery.

Amazon increasingly controlled the transaction.

Each company built enormous businesses around owning a particular point in the funnel.

But agents potentially compress that entire funnel.

Imagine that instead of spending 45 minutes searching for headphones, you tell your personal AI:

Find me the best noise-cancelling headphones under $400. I care about microphone quality and comfort. Check the major retailers, read the reviews, and give me the best option.

The agent searches.

It compares.

It filters.

It returns one recommendation.

Maybe you say:

Buy it.

You never visit Amazon.

You never see the search results page.

You never see the sponsored product.

You never see the ten alternatives.

You never see “Customers also bought.”

You never enter the retailer’s carefully engineered conversion funnel.

The AI has absorbed the shopping journey.

That means something extremely valuable has moved.

The point of commercial influence has shifted from the marketplace to the agent.

That is the real fight.

Our podcast discussion centred on exactly this issue: once customers delegate product discovery and comparison to assistants, the company operating the assistant can potentially influence which products are considered and which retailer ultimately receives the transaction.

THE NEW POWER POSITION IS THE SHORTLIST

For years, technology companies fought over the interface.

Browser.

Search bar.

App store.

Each interface became a toll booth.

AI agents could create another one.

But this toll booth sits even closer to the actual decision.

The most powerful question in commerce may no longer be:

Where did you buy something?

It may become:

Who decided what you should buy?

That distinction matters.

Amazon can carry 300 million products.

But if your AI assistant evaluates those products and returns three of them, then for you the effective marketplace contains three products.

If it returns one, the marketplace contains one.

The assistant has performed the filtering.

It has become the demand allocator.

That is an extraordinary position.

Today Amazon ranks millions of sellers competing for customer attention.

Tomorrow Meta could theoretically sit one layer above Amazon and decide whether Amazon gets the customer at all.

That is a very different power structure.

AMAZON UNDERSTANDS THIS BECAUSE AMAZON IS BUILDING THE SAME THING

This is what makes the fight particularly interesting.

Amazon isn’t resisting agents because it thinks agentic shopping is nonsense.

Amazon is building its own.

Rufus was renamed Alexa for Shopping in May 2026. Amazon says the system can research products, compare options, analyse price histories, build carts, track prices and automate certain purchases.

Amazon also has Buy for Me, which allows its AI shopping experience to purchase selected products from external brand websites.

Think about the strategic implication.

Amazon does not simply want AI to improve shopping inside Amazon.

It wants Amazon’s AI to remain relevant even when the transaction happens outside Amazon.

That’s the clue.

Amazon understands that the next dominant commerce layer may not necessarily be the store.

It could be the agent sitting between the consumer and every store.

And if that happens, Amazon has every reason to want Alexa occupying that position instead of Muse.

META WANTS TO MOVE FROM PREDICTING INTENT TO RECEIVING INTENT

Meta has historically owned something slightly different.

It owns enormous amounts of consumer attention.

Facebook and Instagram became machines for predicting what people might care about.

That is incredibly valuable.

But prediction is still prediction.

An advertisement is effectively saying:

We think this person might want this.

A personal AI agent changes the equation.

The user tells it:

I want this.

Budget.

Timing.

Past behaviour.

Maybe even why they want it.

That is explicit intent.

Explicit intent is economically powerful.

Meta’s Muse is designed to operate across websites and connected services, maintain context, fill forms and continue working after the user closes the application. Meta says it asks users before sensitive actions such as sending email or completing purchases.

Then Meta announced something even more strategically revealing.

Muse is being integrated into its AI glasses.

Meta says users will eventually be able to look at something in the physical world and ask Muse to act on it.

It has also announced commerce integrations including Shopify’s catalogue, Walmart, Best Buy, Sephora, Wayfair and others, alongside payment options including Stripe’s Link, Shop Pay and PayPal.

Now follow the direction of travel.

You look at a coffee machine.

Your glasses see it.

You ask:

Is this any good?

Your agent knows your budget.

It knows your kitchen.

It checks prices.

It checks reviews.

It finds a better model.

Then it asks:

Would you like me to order it?

At that point Meta isn’t merely selling advertising around your attention.

It is sitting directly between intent and transaction.

That is a substantially more powerful position.

THIS IS WHY AMAZON’S BLOCK MATTERS

There is a legitimate security argument here.

Retailers should absolutely be able to distinguish humans from automated agents.

Authentication matters.

Credential handling matters.

Fraud matters.

Consumer protection matters.

A person granting an AI agent access does not automatically settle every question about whether that agent can interact with another company’s infrastructure however it wants.

Amazon says it asked Meta to exclude Amazon from Muse before ultimately blocking the agent. Amazon’s position is that the agent was accessing its services without proper authorization.

Those issues deserve to be taken seriously.

But there is also a much larger commercial incentive sitting underneath the dispute.

Amazon has spent decades building perhaps the world’s most sophisticated online shopping environment.

Product search.

Seller marketplace.

Reviews.

Recommendations.

Advertising.

Prime.

All of these layers reinforce one another.

Now imagine an external AI agent standing in front of that machine.

It enters Amazon.

Collects the information it needs.

Extracts the best answer.

Leaves.

The customer gets the utility of Amazon’s marketplace without necessarily entering Amazon’s monetisation environment.

That is structurally uncomfortable for Amazon.

The problem isn’t simply that the bot visited the website.

The problem is that the bot could eventually disintermediate the interface.

AMAZON’S MOST VALUABLE ASSET MAY NOT BE THE WAREHOUSE

When people think about Amazon’s moat, they usually think about infrastructure.

Warehouses.

Prime.

Logistics.

AWS.

Marketplace scale.

Those are enormous assets.

But Amazon also owns something less visible:

the customer’s shopping habit.

When millions of people think:

I need to buy something.

They open Amazon.

That behaviour is incredibly valuable.

The first destination has power.

It captures intent before competitors do.

Agents threaten to insert themselves before that moment.

Instead of:

I need headphones → open Amazon

the behaviour becomes:

I need headphones → ask Muse

or:

ask ChatGPT

or:

ask Alexa

or eventually:

ask whatever personal agent knows me best.

Amazon could still fulfil the transaction.

Amazon could still hold the inventory.

Amazon could still deliver the parcel.

But another company could own the customer relationship.

And over time, ownership of the customer relationship usually matters more than ownership of the underlying commodity layer.

The company closest to demand gets enormous leverage over everyone downstream.

THIS HAS HAPPENED BEFORE

Technology history repeatedly produces these abstraction layers.

Microsoft once controlled developers through Windows.

Then browsers weakened the importance of the operating system for some applications.

Google controlled navigation through search.

Smartphones created another interface.

Apple and Google then controlled mobile distribution through their operating systems and app stores.

Amazon built a marketplace on top of merchants.

Shopify helped merchants build outside Amazon.

TikTok and Instagram moved product discovery further upstream.

Now agents potentially move another layer above all of them.

Each new layer asks the same question:

Who becomes infrastructure, and who gets to own the customer?

That is the power game.

And almost nobody wants to become somebody else’s infrastructure.

THE AGENT COULD BECOME THE NEW GOOGLE

Google became extraordinarily powerful because it sat between users and the internet.

There were millions of websites.

Google decided which ones you saw first.

That ranking position created enormous economic power.

AI agents could potentially do something even more consequential.

Search typically gives you options.

Agents increasingly give you answers.

That distinction matters.

Ten blue links distributed attention.

One recommended action concentrates it.

If an AI recommends:

Buy this television from this retailer for $1,799

the agent has made several hidden decisions:

Which retailers were searched?

Which weren’t?

Which products qualified?

Which reviews mattered?

Was price weighted above quality?

Did delivery time matter?

Did a retailer pay for preferential placement?

Was a commission available?

Was the recommendation actually in your interest?

We are entering a world where recommendation algorithms may evolve into decision algorithms.

That creates enormous power.

AND THEN COMES THE BUSINESS MODEL

This is where things become uncomfortable.

Suppose personal agents become extremely popular.

They reduce browsing.

They reduce scrolling.

They reduce search.

They eliminate dozens of advertisements.

Great for users.

Potentially terrible for businesses built around monetising attention.

So how do agents make money?

Meta currently says Muse will have a free tier and subscription plans.

But the economic temptation becomes obvious if agents start directing hundreds of billions of dollars of consumer purchases.

Commissions.

Merchant fees.

Preferential integrations.

Some combination of them is possible.

None of that automatically means the recommendation becomes corrupt.

But it creates a fundamental governance problem.

Is the agent working for me, or monetising me?

That question becomes much more serious when an AI isn’t showing you an advertisement but actively acting on your behalf.

META HAS A TRUST PROBLEM TO SOLVE

Meta may possess one of the strongest distribution advantages in the world.

Facebook.

Instagram.

WhatsApp.

Billions of users.

And increasingly hardware sitting directly on people’s faces.

That distribution could give Muse an enormous starting position.

But agents demand a different level of trust from social networks.

We tolerate Instagram knowing what reels we watch.

A personal agent could potentially know:

our calendar,

our correspondence,

our purchases,

our travel plans,

our preferences,

our bills,

our contacts,

and potentially enough credentials to act across significant parts of our digital lives.

That is qualitatively different.

Meta says Muse’s virtual environment keeps credentials protected, allows users to control connected applications, requires approval for sensitive actions and keeps Muse conversations and VM data separate from its advertising systems.

Those protections matter.

But architecture isn’t the same thing as trust.

Trust is accumulated through behaviour.

Meta still has to convince people that the company which became extraordinarily good at monetising attention can also become a trusted fiduciary-like layer acting on their behalf.

That may be one of the biggest strategic challenges Muse faces.

META IS ALSO PLAYING A MUCH LONGER GAME

This is where Meta’s enormous spending on Reality Labs begins to look differently.

Reality Labs has produced staggering losses.

Meta reported approximately $8.65 billion of Reality Labs operating losses in the first half of 2026 alone, after approximately $19.19 billion in 2025.

For years, the obvious interpretation was:

Zuckerberg spent tens of billions trying to make the metaverse happen.

And the original mass-market virtual-world thesis clearly did not develop the way Meta initially hoped.

But some of those investments were also building another strategic asset:

a computing interface Meta can control.

Glasses.

Wearables.

AI.

Voice.

Visual context.

Personal agents.

Put those pieces together and the strategy becomes more interesting.

The smartphone era left Meta dependent on Apple and Google.

Meta owned enormous applications but not the operating system.

Every Facebook or Instagram user ultimately passed through hardware and software controlled by somebody else.

The strategic prize of AI glasses is therefore not simply selling glasses.

It is potentially escaping that dependency.

If the next major computing interface sits on your face and Muse becomes the intelligence operating through it, Meta could move from being an application company to controlling part of the computing layer itself.

And now commerce enters the picture.

Your eyes become discovery.

Muse becomes interpretation.

The agent becomes decision support.

Payments complete the transaction.

Meta potentially captures the relationship.

That’s much bigger than a chatbot.

AMAZON HAS THE OPPOSITE ADVANTAGE

Meta has distribution across communication and attention.

Amazon has transactional truth.

Amazon knows what people actually bought.

What they returned.

What they repeatedly reorder.

Which products arrive tomorrow.

Which seller has stock.

Which product has millions of reviews.

Which price changed yesterday.

Which customer has Prime.

Which item can arrive before a birthday.

That operational context matters enormously for shopping agents.

An AI model can reason brilliantly about a product.

But commerce requires reality.

Inventory.

Shipping.

Payment.

Returns.

Fraud.

Delivery.

Customer service.

That makes Amazon extremely difficult to displace.

Which is why the likely battle isn’t simply:

Muse kills Amazon.

Or:

Amazon blocks Muse and wins.

The real fight is over which layer captures the highest economic leverage.

THE AGENT WARS WILL BECOME ACCESS WARS

Amazon versus Muse may be an early preview.

Soon virtually every major digital platform will face the same question.

Does an outside AI agent have the right to enter a service because the customer authorised it?

Consider the consequences.

Your agent wants to negotiate your mobile phone bill.

The telco blocks it.

Your agent tries to move money between banks.

The bank restricts it.

Your agent compares insurance policies.

A provider denies automated access.

Your agent wants to cancel a subscription.

The service requires a human.

Your agent shops across retailers.

Some cooperate.

Others block it.

Every company will be able to make legitimate arguments around security, privacy and system integrity.

And some of those arguments will be completely valid.

But there will also be an obvious incentive to use access control to protect existing business models.

That creates one of the defining regulatory and commercial questions of the agent economy:

How much authority does my permission give my AI agent when dealing with your platform?

The answer will determine enormous amounts of economic power.

FROM THE ATTENTION ECONOMY TO THE DELEGATION ECONOMY

The internet’s last great business model was built around capturing attention.

The next one may be built around capturing delegation.

That is a deeper relationship.

Attention says:

Look at this.

Delegation says:

Handle this for me.

If consumers begin delegating meaningful decisions to AI, the most valuable companies may be those we trust to make those decisions.

Not necessarily the company with the best model.

Not necessarily the company with the largest marketplace.

Not necessarily the company with the most data.

The winner at each layer will depend on who controls the interface, who has access to the necessary infrastructure, whose incentives users trust, and who consistently gets the task right.

Distribution gets an agent onto your device.

Capability makes it useful.

But trust earns delegation.

And delegation creates power.

THE BIG STAKE

Amazon blocking Muse is not yet proof that AI agents are about to overthrow ecommerce.

Agent adoption is still early.

Reliability remains imperfect.

Users may decide they prefer browsing.

Retailers may build standardized interfaces.

Regulators may intervene.

Commercial arrangements may emerge that allow agents and platforms to coexist.

A sensible model would probably require identifiable agents, explicit customer permissions, revocable access, reliable product information, transaction logs, clear liability and disclosure when recommendations are commercially influenced.

But don’t lose sight of what is being contested.

This isn’t primarily about bots scraping websites.

It’s about the next control point in the digital economy.

For twenty years, technology companies fought to own our attention.

Now they are beginning to fight over something more valuable:

our authority.

The company we allow to act for us could become the gateway between our intentions and much of the economy.

And that means the next trillion-dollar platform may not be the company selling us the product.

It may be the company deciding, on our behalf, who gets the sale.

That is the real power play behind Amazon versus Meta.

And it is exactly the kind of story we’re going to explore on Big Stakes.

Technology is the surface.

Underneath it are capital, control, incentives and power.

Subscribe to Big Stakes if that’s the layer you want to understand.

I recently demonstrated my AI capital allocation and governance prototype to the leadership team at a very large company.

The feedback was positive. But the question that stayed with me was about where the information would come from.

It was the right question.

I had built a way to look across an enterprise’s AI investments: where money was going, what value each initiative was producing, which investments deserved more funding, and which needed a harder review.

The demonstration used synthetic data. That made it possible to show how the decisions would work without pretending I had connected the company’s financial and operational systems.

But their question took us straight to the work that would make the system useful in a real organisation.

Someone has to find the spending. Someone has to establish what performance looked like before the AI initiative. Someone has to determine whether the claimed improvement actually happened, and whether it was worth what the business paid.

That is a full-time job.

It is a substantial part of the Director of AI role as I see it: making the organisation’s AI economics legible enough for management to act on.

The number on the screen is the end of a long process

A dashboard can display “verified value” in a clean box.

Getting to a defensible number is considerably messier.

Consider a customer service initiative. The team reports that its AI agent has reduced handling time. That sounds useful, and it may be. But before I would use that claim to justify additional capital, I would want to understand the baseline.

Was handling time already improving? Has the mix of customer enquiries changed? Are agents resolving cases faster, or passing more difficult cases to another team? Has the customer experience improved?

Then there is the question of what the saved time became.

Perhaps the business handled more volume without adding staff. Perhaps overtime fell. Perhaps employees had more capacity, but there was no corresponding change in output or cost.

Those are different outcomes. They should not all enter the investment review as the same dollar benefit.

The cost side needs the same care. A model invoice tells us something, but it does not tell us the full cost of operating the workflow. Integration, internal engineering, oversight and exception handling can all matter.

This is why the question from the leadership team was so useful. It exposed how much organisational work sits underneath a seemingly straightforward portfolio view.

The Director of AI has to connect the pieces

In the operating model I have in mind, the Director of AI coordinates that work.

Finance needs to help establish the costs and agree how benefits will be valued. Business owners need to explain what changed in the workflow. Technology teams need to account for the systems and infrastructure involved.

The Director of AI has to bring those views together and resolve the gaps between them.

A business unit may describe an initiative as a success because employees like it. Finance may see another recurring expense. The technology team may see a system that works well but requires more support than expected.

Each view can be accurate.

The difficult part is producing an account of the initiative that is complete enough to support a funding decision.

I would not expect one person to independently verify every benefit across a large enterprise. The role needs named owners in the business, access to financial information and a review process that people take seriously. Without that authority, the Director of AI risks becoming the person who chases updates and assembles slides.

That is a very different job from helping management allocate capital.

Every initiative needs a reason to remain funded

This is the thinking behind the investment thesis in my prototype.

At approval, an AI initiative should have a clear statement of the business problem, the expected outcome, the cost and the assumptions that make the investment worthwhile. There should also be a point at which management agrees to reconsider it.

Otherwise, the original pitch can survive long after the conditions behind it have changed.

An initiative might depend on employees using the system several times a day. Three months later, adoption is low. Another might rely on a particular level of accuracy, only to discover that human review consumes much of the expected saving.

Neither finding automatically means the initiative should be shut down. It does mean the original investment case needs attention.

The control plane is intended to make that review easier. It should connect the original expectation to the evidence now available, show what remains uncertain and put a decision in front of the right person.

Sometimes the answer will be to invest more. Sometimes it will be to fix the workflow. Sometimes the honest answer will be that there is not enough evidence yet.

And sometimes the capital should go elsewhere.

Better software can make weak evidence look convincing

One thing I am conscious of in building this is how easily precision can be mistaken for certainty.

A return multiple with two decimal places looks authoritative. So does a confidence score. Neither deserves trust merely because the interface presents it neatly.

I want the system to let an executive inspect what supports a number.

Which part is measured? Which part comes from an estimate by the business owner? What baseline was used? When was the evidence last reviewed?

A projected benefit should remain visibly different from a realised one. A simulation should remain a simulation.

That matters particularly when the software recommends reallocating capital. It can help management explore a decision, but responsibility for that decision still belongs to management.

The prototype makes those distinctions possible. A real deployment would have to earn them through the quality of its data and the discipline of its review process.

This is the part of enterprise AI I want to make visible

The demo gave me a useful way to explain the product. The leadership team’s question clarified what it would take to operate it.

Building the interface is one part of the work. Establishing a credible account of spending and outcomes requires cooperation across the enterprise, and it has to continue after the first report is produced.

That is where I see the connection to the broader question I write about in Full Stack Capitalist: who captures value when technology changes?

Inside an enterprise, that question becomes very practical. The company funds the tools and absorbs the implementation costs. Whether it captures the benefit depends on what changes in the business, and whether anyone can establish that the change occurred.

I want to build systems that help leadership follow that process all the way through.

The most useful screen in an AI investment review may be the one that lets someone challenge the number, find its source and decide whether it is strong enough to fund the next dollar.

A data center announcement usually arrives with three numbers: investment, megawatts and jobs.

Each tells us something. None, on its own, tells us who will capture the economic value.

An announced investment does not establish how much capital has been committed, drawn or spent. A capacity figure may describe an ultimate campus design rather than equipment installed and serving customers. An employment forecast describes an expected outcome, not an observed one.

The commercial picture emerges through the relationships underneath those numbers.

Who controls the land? Who supplies the electricity? Who finances construction? Who absorbs a delay? Who has committed to buy capacity? When does that commitment become revenue? Which parties receive payment before the project becomes profitable?

These are the questions behind the Australian AI Data Centre Commercial Tracker, a proof of concept I built with AI assistance.

The initial register contains 62 facilities and campuses, 39 tracking fields and 62 public sources, with records across all eight Australian states and territories. It includes AI deployments, AI-ready facilities and clearly labelled context facilities whose AI use is not established in the reviewed sources.

You can explore it here:

Open the Australian AI Data Centre Commercial Tracker

The purpose is to make the commercial structure easier to inspect, and to show where the public evidence stops.

The full-stack capitalist lens

For Full Stack Capitalist, the relevant unit of analysis is the connected project: its assets, counterparties, obligations and cash flows.

An AI data centre brings several distinct economic interests together.

The landowner has a property interest. The developer coordinates delivery. The operator provides the facility and its services. Equity investors supply risk capital. Lenders provide financing under contractual terms. Contractors and equipment suppliers deliver the physical infrastructure. Energy counterparties provide electricity or connection infrastructure. Customers contract for services or capacity.

Some organisations occupy several of these roles. Others participate in only one.

That distinction matters. A shareholder in an operating platform is not necessarily the registered owner of a particular campus. A property tenant is not necessarily the customer consuming compute. An electricity network provider is not necessarily the energy retailer or power purchase agreement counterparty.

A useful tracker must preserve those differences.

The analytical task is to follow a project through five connected layers:

A public announcement may illuminate one layer while leaving the others largely undisclosed. The tracker makes that uneven visibility explicit.

Start with the identity of the asset

Before analysing economics, we need to know what the record describes.

Is it a single building, a multi-building campus, an expansion phase or a national investment program? Is the capacity figure attached to that specific asset? Does a financing announcement cover the campus, the operating company or a broader portfolio?

Without those boundaries, it is easy to count the same development more than once or attach a corporate commitment to the wrong project.

The register therefore distinguishes facilities and campuses from national and platform announcements. Campus phases sit within their parent record where the reviewed evidence supports that treatment.

This also helps with naming. Similar site codes can appear across different operators. A project name without its operator and location can be an ambiguous identifier.

Identity sounds administrative. It is the foundation of reliable commercial analysis.

Capacity needs a denominator, and a delivery stage

Megawatts are useful only when we know what they measure.

Published figures may describe IT load, utility supply, planned capacity or an operating installation. Other disclosures use maximum demand, MVA, cabinet counts or GPU counts.

Those measures answer different questions.

A planned campus capacity describes an intended scale. Built capacity describes a delivery milestone. Contracted capacity describes a commercial commitment under whatever terms apply. Billing capacity describes a further stage in monetisation.

The relationship between them is central to understanding progress, but they should not be treated as interchangeable.

The tracker retains the source’s capacity basis and separates the relevant disclosures. It does not convert GPUs or cabinets into megawatts. It does not combine battery capacity with data centre capacity. It does not calculate a national total by adding incompatible figures.

Where sources disagree, the discrepancy remains visible.

This makes the dashboard less convenient for producing a single headline number. It makes it more useful for examining an individual project.

Power is a set of arrangements

“Power secured” can conceal several separate questions.

Is there a documented grid connection? What infrastructure must be delivered? Is an energy supplier named? Has a power purchase agreement been disclosed? Are discussions still underway? Is the available figure a design requirement or an established supply arrangement?

Connection infrastructure and energy procurement deserve separate attention.

So do the commercial terms: price, duration, pass-through provisions and responsibility for additional costs. These can matter to the allocation of project risk, but public sources often do not establish them.

The tracker records what is disclosed and preserves the status of the arrangement. A negotiation remains a negotiation. An announced target remains a target.

Where pricing or contractual protections are unavailable, the field remains unknown. That is more informative than treating a named provider as proof that every power-related risk has been resolved.

Financing is not the same as expenditure

A financing headline is another point where scope matters.

An equity investment in a platform does not establish the amount allocated to a particular campus. A debt facility does not, by itself, establish how much has been drawn. An announced development cost does not establish expenditure to date.

To examine the capital structure, we need to distinguish:

• Equity investors and their disclosed ownership interests.

• Lenders, arrangers and the borrower where identified.

• Financing commitments and evidence of financial close.

• Project expenditure and broader investment announcements.

• Debt terms, security and conditions where publicly available.

The tracker keeps platform-level disclosures separate unless the source explicitly assigns them to a named project.

This avoids a particularly consequential mistake: using a large corporate financing announcement to imply that a specific development is fully funded.

The same discipline applies to returns. Investment size is not a substitute for project revenue, cash flow, yield or internal rate of return.

The customer relationship is where many crucial questions remain

A named customer is useful evidence. The nature of the relationship matters just as much as the name.

A memorandum of understanding, a strategic partnership, a property lease and a contracted capacity commitment are different arrangements.

To understand commercial use, we would ideally establish the customer, the relevant facility, the committed capacity, the contract duration, the commencement conditions and the payment obligations.

We would then track how delivery progresses toward utilisation and billings.

Much of that information may be confidential or absent from the reviewed public sources. The dashboard therefore separates customer identity from the underlying commitment and from evidence of revenue.

A disclosed relationship is not automatically proof of rent received or capacity generating billings.

That is an essential boundary for a tracker intended to support commercial scrutiny.

Who is in the stronger negotiating position?

This is one of the most interesting questions, and one of the easiest to answer too confidently.

A company’s position in the infrastructure chain does not, on its own, establish its bargaining strength.

Ownership of a site tells us who holds an asset interest. It does not reveal the alternatives available to a customer. A construction award identifies a contractor. It does not disclose the contractor’s expected margin or its exposure to overruns. A customer announcement identifies a relationship. It may tell us little about termination rights or payment protections.

A factual assessment of negotiating position would require evidence such as exclusivity, pricing, security, take-or-pay obligations, termination provisions and responsibility for delay.

The tracker includes fields for disclosed negotiation rights and commercial commitments. It does not produce a bargaining-power score where those terms are unavailable.

The same reasoning governs “winners” and “losers.”

We can identify a documented contract award. We cannot automatically translate that award into realised profit. We can record a reported valuation change. We cannot treat it as cash proceeds from a completed sale.

The ambition is to trace value capture with evidence, rather than assign it from a company’s visibility in an announcement.

Second- and third-order economics

The economic footprint of a data centre extends beyond the facility itself. Examining that footprint requires the same attention to evidence and timing.

For this tracker, I use three levels.

First-order economics concern the project’s direct arrangements: land, construction, financing, energy and customer commitments.

Second-order economics concern documented effects around delivery and operation, including supplier activity, employment and property outcomes.

Third-order economics concern documented downstream activity, such as research, industry use or wider economic outcomes associated with the infrastructure.

These are organising categories for the register, not a claim that every effect can be cleanly isolated or attributed.

A forecast number of construction jobs is not an observed employment result. An expected wider economic contribution is not realised project revenue. A downstream research partnership is not evidence of a quantified productivity gain.

The register retains those distinctions and labels forecasts accordingly.

The question at every level is the same: what has been disclosed, what has happened, and what remains an expectation?

Value realization is a sequence of milestones

A project can advance physically while important commercial questions remain unanswered.

That is why the tracker follows separate milestones rather than compressing progress into a single score.

Land and tenure establish a site position. Approvals establish particular permissions. Construction records establish delivery activity. Power disclosures establish specific supply or connection arrangements. Customer commitments establish commercial relationships. Utilisation and billings provide evidence of use and monetisation. Cash-flow and return disclosures, where available, help establish financial outcomes.

None of these milestones should stand in for all the others.

This framework creates a practical research agenda. For each project, we can ask which milestones are supported by evidence, how old that evidence is and what disclosure would resolve the next important uncertainty.

An unknown field is therefore useful. It identifies the next question.

What AI accelerated

AI helped me assemble the interface, organise the tracking structure and turn the research into a searchable dashboard.

The result includes project dossiers, filters, side-by-side comparisons and evidence exports. It requires no login or backend database.

That acceleration creates room for more attention to the work that determines whether the tool is trustworthy: reading disclosures, checking scope, identifying conflicting figures and maintaining records as projects develop.

The dashboard is a manually maintained snapshot dated 9 September 2026, not a live feed.

Its 62 records are not an exhaustive census of Australian data centres or undisclosed AI deployments. A source’s access date does not establish that its project status is current. Published claims are attributed to their sources; they have not all been independently audited.

These limitations belong inside the product, where readers can see them.

An invitation to improve the evidence

I want this tracker to become more useful through better disclosures, corrections and clearer project boundaries.

If you work in development, energy, construction, financing or data centre operations, I would welcome primary sources that help establish:

• Project ownership and land tenure.

• Capacity definitions and delivery milestones.

• Named contractors and financing counterparties.

• Executed customer or energy commitments.

• Observed commercial outcomes.

The full-stack capitalist question is how ownership, obligations and payments connect across the project.

Following those connections gives us a more precise way to examine Australia’s AI infrastructure buildout, and a clearer view of what we still do not know.

Explore the Australian AI Data Centre Commercial Tracker

Which missing disclosure would most improve your assessment of an AI data centre project?

We are spending something close to three-quarters of a trillion dollars building the physical infrastructure of AI.

And I think we are asking the wrong question about it.

Does spending is sustainable?

Whether there is an AI bubble?

Whether the hyperscalers are overbuilding?

Whether there will be enough demand for all these GPUs?

Those are reasonable questions.

But there is a much more important one.

Who actually earns the return on all this capital?

Because the company writing the cheque is not necessarily the company capturing the value.

And the company capturing the value today may not be the one holding the power ten years from now.

That distinction matters enormously.

I have spent enough of my career around infrastructure to be suspicious whenever people treat a capital cycle as a technology story.

I worked at Huawei.

Then Cisco.

I have designed infrastructure.

Worked around data centres.

Built racks.

Managed large technology programs.

Sat between vendors, customers, engineering teams and executives trying to make the economics work.

Later, working around enterprise data, analytics, cloud and industrial software, I saw the same pattern from the other side.

Technology changes.

The economics underneath it are strangely repetitive.

There is always a supplier.

There is always an infrastructure owner.

There is always somebody financing the infrastructure.

There is always someone operating it.

There is always somebody using it.

And eventually there is somebody who actually makes money because the infrastructure exists.

Those are rarely the same company.

AI is no different.

Except the numbers are much bigger.

Start with the money

The four largest American hyperscalers are now committing extraordinary amounts of capital to infrastructure.

Alphabet originally guided towards $175–185 billion of 2026 capex.

Meta subsequently lifted its expected range to $125–145 billion.

Microsoft spent $37.5 billion in one quarter alone, with roughly two-thirds going into relatively short-lived assets such as GPUs and CPUs.

Amazon has been operating around the $200 billion capex level.

Not every dollar is AI.

But AI is increasingly determining the architecture of the spending.

And the spending is accelerating.

Nvidia gives us another way of looking at the same phenomenon.

Its latest quarterly data-centre revenue reached $89 billion.

For one quarter.

Gross margin: roughly 75%.

Think about that for a moment.

While everyone debates whether AI applications have found sustainable business models, one layer of the stack is already collecting extraordinary economics.

The shovel manufacturer is doing very well.

But this is where the story gets interesting.

Because infrastructure booms do not distribute returns evenly.

The AI value chain

I think about the AI economy as a stack.

Not a software stack.

An economic stack.

It looks something like this:

Energy

↓

Grid access

↓

Land

↓

Data centres

↓

Power and cooling equipment

↓

Semiconductors

↓

Networking

↓

Cloud infrastructure

↓

Foundation models

↓

Applications

↓

Enterprise workflows

↓

Labour and business process transformation

↓

Final economic output

Money moves downward into the stack.

Value is supposed to move upward.

Those are not the same thing.

This is the mistake I think a lot of investors make.

They follow the expenditure.

But expenditure tells you where money is being spent.

It does not tell you where economic surplus ultimately accumulates.

Layer 1: Nvidia is capturing the first return

The first obvious winner is semiconductors.

More specifically, accelerated compute.

Today, Nvidia occupies probably the most attractive point in the entire AI value chain.

Demand exceeds supply.

Switching costs are high.

Its software ecosystem reinforces the hardware.

Customers are competing against one another for capacity.

And those customers are enormously well capitalised.

That is a beautiful business.

Nvidia doesn’t need to prove that AI increases global productivity.

It needs customers to believe enough in AI to keep buying infrastructure.

There is a huge difference.

The hyperscalers carry the utilisation risk.

Nvidia largely gets paid when the equipment ships.

This is first-order AI economics.

Someone announces another data centre.

Nvidia sells another rack.

A cloud company raises capex.

Semiconductor revenue increases.

Very straightforward.

But first-order economics rarely tell you where an industrial revolution finishes.

Layer 2: the infrastructure companies

When I used to work around physical infrastructure, racks, networks and data centres, one thing became obvious very quickly.

Servers don’t float in the air.

You need switchgear.

Transformers.

UPS systems.

Cooling.

Cables.

Generators.

Networking.

Fire suppression.

Land.

Water.

Construction.

Operations teams.

Maintenance.

And, above everything else, electricity.

This layer isn’t as glamorous.

It is also becoming extremely important.

The IEA expects global data-centre electricity consumption to roughly double by 2030.

More importantly, these loads are geographically concentrated.

Transmission infrastructure can take four to eight years to build.

Transformer and cable lead times have already stretched.

The IEA estimates that roughly 20% of planned data-centre projects could face delays unless grid bottlenecks are addressed.

That changes the economics.

When compute was scarce, Nvidia had power.

When electricity becomes scarce, electricity starts gaining power.

When grid connections become scarce, the grid connection itself becomes an economic asset.

This is already happening.

Texas has been forced to confront hundreds of gigawatts of proposed data-centre load requests, much of it speculative, and regulators have begun tightening access because the grid cannot treat every proposed project as real demand.

That tells you something.

The AI bottleneck is moving.

And economic power normally moves toward the bottleneck.

The first big power shift

For the last three years, the AI hierarchy looked roughly like this:

Models > GPUs > Cloud > Electricity

I don’t think it stays that way.

I think we are moving towards:

Energy + grid access > compute availability > models

Models are becoming more numerous.

Compute architectures will diversify.

Chips will improve.

Inference will get cheaper.

But you cannot prompt your way around a missing 500 megawatts.

Physics eventually gets a vote.

This is something software people occasionally forget.

Then comes the hyperscaler problem

Now we reach Amazon, Microsoft, Google and Meta.

These companies are spending the money.

So naturally people assume they will capture the return.

Maybe.

But the economics are less obvious than they appear.

Suppose Microsoft spends $100 billion building AI infrastructure.

Some of that immediately becomes Nvidia revenue.

Some becomes networking revenue.

Some becomes power equipment revenue.

Some becomes construction revenue.

Some becomes utility revenue.

Some becomes depreciation.

Then Microsoft has to turn what remains into customer revenue.

That requires utilisation.

And utilisation requires applications.

And applications require customers willing to pay enough to cover the infrastructure underneath them.

Suddenly this becomes a much more difficult equation.

The hyperscaler doesn’t merely need AI adoption.

It needs:

AI revenue > depreciation + electricity + cooling + networking + financing + labour + model development + operating costs.

And it needs that equation to remain attractive while the underlying hardware improves so rapidly that today’s state-of-the-art accelerator becomes tomorrow’s legacy asset.

Microsoft disclosed that roughly two-thirds of its $37.5 billion quarterly capex was going into short-lived assets, primarily GPUs and CPUs.

That phrase matters.

Short-lived assets.

We are building some of the most expensive infrastructure in human history using components with technology-like obsolescence curves.

That is unusual.

A railway can operate for generations.

A transmission line can operate for decades.

A GPU may be economically old surprisingly quickly.

I wrote recently that data centres are increasingly behaving like technology stocks.

This is why.

The hyperscalers may still win

There is another side to this.

Amazon, Microsoft and Google aren’t simply renting GPUs.

They control distribution.

That matters enormously.

AWS already sits inside enterprise infrastructure.

Azure sits inside enterprise identity, data, security and productivity.

Google controls Search, Workspace, advertising and enormous consumer distribution.

Meta controls billions of daily human interactions.

That means their return on AI capex does not have to appear as a neat line item called “AI revenue.”

Google can spend $1 billion on AI infrastructure and earn the return because Search gets slightly better.

Meta can make its advertising system slightly more efficient.

Amazon can improve AWS economics.

Microsoft can defend Office.

This is a critical point.

AI capex can generate defensive ROI.

The return may be revenue preserved rather than revenue created.

That makes the economics considerably harder for outsiders to measure.

Then we reach the model companies

OpenAI.

Anthropic.

Google DeepMind.

xAI.

And dozens of others.

This layer gets most of the attention.

I’m less convinced it captures most of the long-term economic surplus.

Foundation models face an uncomfortable structural problem.

The models are extremely expensive to create.

But the marginal differentiation between models can shrink very quickly.

One company spends billions training a breakthrough capability.

Six months later, competitors replicate much of it.

Open-source models close part of the gap.

Inference becomes cheaper.

Customers route workloads between providers.

The intelligence becomes increasingly interchangeable.

That doesn’t mean foundation-model companies disappear.

It means their bargaining power may decline relative to the infrastructure underneath them and the distribution above them.

This is classic commoditisation.

The middle gets squeezed.

The application layer has the opposite problem

Thousands of AI startups are now being built on top of these models.

They have tiny infrastructure requirements compared with Nvidia or Microsoft.

Wonderful.

But many have almost no structural moat.

A model API.

A workflow.

A nice interface.

Some prompts.

Maybe proprietary context.

I’ve written before that many “AI-native” companies may eventually discover that they own little more than a prompt library.

That doesn’t mean they cannot make money.

They absolutely can.

But revenue and economic power are different things.

If your gross margin depends on somebody else’s model…

running on somebody else’s cloud…

using somebody else’s GPUs…

inside somebody else’s distribution channel…

then you don’t own much of the stack.

And businesses that don’t own scarce parts of the stack rarely control the economics forever.

Which brings us to the enterprise

This is where my view changed the most.

At Cisco, I spent years working around data, analytics, migrations and enterprise transformation.

The biggest economic return from AI may not accrue to “AI companies” at all.

It may accrue to ordinary businesses.

A mining company.

A manufacturer.

A bank.

A logistics company.

An insurer.

A retailer.

A marketing agency.

Imagine an industrial company spends $20 million adopting AI.

It eliminates $60 million of recurring operating cost.

Who captured the AI value?

Nvidia captured revenue.

Microsoft captured cloud revenue.

The model provider captured API revenue.

The integrator captured implementation revenue.

But the industrial company captured $40 million of economic surplus.

And it may capture that surplus every year.

That is fundamentally different.

The infrastructure companies get paid for providing intelligence.

The enterprise gets paid for turning intelligence into economics.

That could ultimately be the largest pool of value.

First-order economics

The first-order effects are easy.

More AI demand means:

More GPUs.

More servers.

More data centres.

More electricity.

More networking.

More cooling.

More cloud revenue.

This is where most equity-market excitement currently sits.

And understandably so.

The numbers are enormous.

Second-order economics

This is where things become much more interesting.

AI infrastructure demand creates scarcity somewhere else.

Electricity becomes scarcer.

Grid interconnections become more valuable.

Transformers become harder to acquire.

Gas turbines get longer lead times.

Land beside substations appreciates.

Nuclear projects become economically plausible again.

Utilities gain negotiating power.

Governments start asking whether AI infrastructure should pay for grid upgrades.

Local communities start asking why residential electricity customers should subsidise transmission infrastructure built for trillion-dollar technology companies.

Capital starts moving into energy systems.

The bottleneck leaves Silicon Valley.

It moves into substations.

I’ve said this before:

AI infrastructure is becoming energy policy.

And once that happens, AI stops being purely a technology industry.

It becomes an industrial system.

Third-order economics

This is the part I find most important.

Once countries recognise compute as strategic infrastructure, governments enter the market.

Now we get:

Sovereign compute.

Energy subsidies.

GPU export controls.

AI industrial policy.

National data-centre strategies.

Nuclear investment.

Grid reform.

Strategic semiconductor manufacturing.

Government-backed AI champions.

Compute trade agreements.

Potential AI infrastructure taxes.

Suddenly the $750 billion isn’t merely corporate capex.

It starts reshaping geopolitics.

Countries with abundant reliable electricity gain leverage.

Countries with constrained grids lose competitiveness.

Natural gas becomes an AI input.

Nuclear becomes technology infrastructure.

Copper becomes technology infrastructure.

Transformers become technology infrastructure.

Land becomes technology infrastructure.

Industrial policy and technology policy merge.

That is third-order AI economics.

The consequences spread far beyond software.

So who captures the value?

My current ranking looks something like this.

Today

1. Nvidia and semiconductor infrastructure

Scarcity plus enormous demand plus exceptional pricing power.

2. Power, cooling and electrical equipment

Less visible.

Increasingly important.

3. Hyperscalers

Enormous strategic upside, but carrying enormous capital and utilisation risk.

4. Model companies

Huge strategic influence today.

Questionable long-term margin durability.

5. Applications

Potentially enormous businesses, but highly uneven outcomes.

6. Enterprises using AI

Still early.

Potentially the largest long-term economic beneficiary.

But who captures the power?

This is a different ranking.

And I think investors confuse the two.

Revenue does not equal power.

Profit does not equal power.

Market capitalisation does not equal power.

Control over a scarce dependency creates power.

Right now Nvidia controls an important dependency.

That gives Nvidia power.

The hyperscalers control compute infrastructure and distribution.

That gives them power.

But over the next decade, I suspect power migrates further down the physical stack.

Towards:

Electricity.

Generation.

Transmission.

Grid access.

Semiconductor manufacturing capacity.

Critical minerals.

And ultimately governments.

Because governments control many of the things nobody else can manufacture with software.

Land use.

Energy markets.

Transmission approvals.

Nuclear licensing.

Trade restrictions.

Export controls.

Industrial subsidies.

National security regulation.

The deeper AI penetrates the economy, the more political the infrastructure beneath it becomes.

The strange thing about the next ten years

I think AI will simultaneously become more powerful and less special.

Intelligence will become cheaper.

Models will proliferate.

Agents will become normal.

Inference costs will collapse.

AI will disappear into software.

And precisely because intelligence becomes abundant, the scarce complements around intelligence become more valuable.

This is a basic economic principle.

When one input becomes abundant, value migrates towards whatever remains scarce.

AI makes cognition cheaper.

It does not make electricity cheaper.

It does not create transmission lines overnight.

It doesn’t manufacture transformers instantly.

It doesn’t produce land.

It doesn’t shorten nuclear permitting to six weeks.

It doesn’t magically create semiconductor fabs.

So the economic centre of gravity changes.

My ten-year view of the value chain

If I had to draw the AI value chain today:

Capital → chips → compute → models → applications → enterprises

Ten years from now, I think it looks more like:

Energy → compute → intelligence → automation → economic output

And the biggest winners may be companies that sit at the edges.

At one end:

Those controlling scarce physical infrastructure.

At the other:

Those converting abundant intelligence into real economic productivity.

The middle may be brutal.

And that brings me back to the $750 billion

Whenever I see these capex numbers, I think back to standing around actual infrastructure.

Racks.

Cables.

Equipment.

Physical systems.

You quickly learn that PowerPoint architecture and physical architecture are very different things.

Every box needs electricity.

Every machine generates heat.

Everything eventually fails.

Everything needs financing.

And somebody somewhere must earn enough money to justify building it again.

AI has spent the last few years feeling almost magical.

But $750 billion of capex has a wonderful ability to remove the magic.

Eventually somebody needs a return.

Nvidia is already getting one.

Electrical-equipment suppliers are getting one.

Utilities increasingly will.

Cloud providers probably will.

Some model companies will.

Many won’t.

Thousands of AI applications won’t.

And perhaps the biggest return of all will appear somewhere almost nobody on Wall Street currently labels an “AI company.”

A manufacturer producing 15% more with the same factory.

A bank processing twice the work with the same headcount.

A logistics company removing kilometres from every delivery route.

A pharmaceutical company compressing years of research.

A mining operation avoiding one day of downtime.

That is where technology becomes productivity.

And productivity is where infrastructure investment finally becomes economic value.

So I don’t think the $750 billion question is:

“Will AI generate enough revenue?”

The better question is:

Who owns the bottleneck between $750 billion of infrastructure and the trillions of dollars of economic value it is supposed to create?

Find that bottleneck.

And you will probably find the next decade’s real winners.

I have designed data centres.

I have built racks.

I have managed data centre operations.

I have worked inside this world at Huawei and Cisco.

So when I hear people describe AI data centres as safe, predictable infrastructure, I get slightly nervous.

Because I know what is inside the building.

From the outside, a data centre looks reassuring.

Concrete.

Steel.

Cooling systems.

Backup generators.

Security gates.

Very serious people carrying access cards.

It looks like infrastructure.

It looks permanent.

But open the door and walk between the racks.

The building may last thirty years.

Almost everything producing its economic value may not.

That distinction matters.

A lot.

I have seen the physical reality

When you build a rack yourself, technology stops being an abstract thing.

Every server has weight.

Every cable needs somewhere to go.

Every device produces heat.

Every system needs power.

Every component has a failure rate.

Every design decision creates another operational dependency.

You learn quickly that there is no cloud.

There are only machines inside buildings.

There are people installing them.

There are people maintaining them.

And there are people panicking when something starts flashing red at 2 a.m.

At Huawei, I saw the infrastructure side.

The physical equipment.

The capacity planning.

The operational discipline required to keep technology running.

At Cisco, I saw the other side.

Networks.

Platforms.

Enterprise systems.

Technology transitions.

Customers trying to modernise without breaking the systems already running their businesses.

Those experiences taught me something simple.

Infrastructure is not valuable because it exists.

It is valuable because someone can use it economically.

A rack with the wrong equipment is just an expensive metal cabinet.

A data centre in the wrong location is a warehouse with a spectacular electricity bill.

And a facility designed around yesterday’s computing architecture can become obsolete long before the concrete begins to crack.

That was already true before AI.

AI has made it much more dangerous.

The old data centre deal

The traditional data centre investment story was fairly simple.

Build the facility.

Connect it to power and fibre.

Lease the capacity to reliable customers.

Sign long contracts.

Collect predictable revenue.

Increase the value of the asset.

It behaved a little like commercial property.

In some cases, it behaved like a bond.

Investors liked that.

Pension funds liked it.

Infrastructure funds loved it.

The returns were not supposed to be spectacular.

They were supposed to be dependable.

AI has changed the deal.

An AI data centre is not just a building with tenants.

It is a giant financial bet on chips, power, cooling, software, customer demand and the future architecture of computing.

All at the same time.

That does not behave like a bond.

It behaves like a technology stock with a concrete shell around it.

The building is slow. The technology is fast.

This is the central problem.

Infrastructure moves slowly.

Technology moves quickly.

A large data centre can take years to plan, finance, approve, connect and build.

During those same years, the chips it was designed to host can change several times.

Power density changes.

Cooling requirements change.

Networking changes.

Rack designs change.

The economics of inference change.

The dominant AI models change.

Even the type of computing customers want may change.

You can spend billions constructing an asset based on assumptions that were sensible when the planning application was submitted.

By the time the doors open, those assumptions may already be old.

I have lived through technology refresh cycles.

The equipment always feels permanent when it arrives.

It is new.

It is powerful.

Everyone wants access to it.

Then a few years pass.

A newer generation arrives.

The old equipment still works.

But working is not the same as being economically competitive.

That is the part financial models often miss.

They model the building over twenty or thirty years.

The technology inside it may have an economically useful life of three to five.

Sometimes less.

GPUs are not tenants

A commercial building can lose a tenant and find another tenant.

An AI facility can lose the economic relevance of its entire hardware configuration.

GPUs are expensive.

They also age differently from traditional infrastructure.

A bridge does not become commercially obsolete because a faster bridge was released eighteen months later.

A transmission line does not lose half its usefulness because Nvidia launched a new architecture.

AI hardware can.

The newer chip may process more work.

It may consume less energy per unit of compute.

It may support a different cooling design.

It may make the previous generation less attractive to customers.

The old GPU does not suddenly stop functioning.

Its economics deteriorate.

That creates a strange asset.

Physically operational.

Technically functional.

Commercially ageing at high speed.

That is technology-stock behaviour.

Not bond behaviour.

Power is now part of the product

During my years around data centres, power was always critical.

No power means no operation.

That has not changed.

What has changed is the scale.

AI does not merely consume electricity.

It concentrates enormous electricity demand in specific locations.

That changes everything.

A data centre site without secured power is not really a data centre site.

Grid access becomes part of the asset.

Transmission capacity becomes part of the asset.

Water availability may become part of the asset.

Political permission becomes part of the asset.

This is where investors can fool themselves.

They may believe they are investing in digital infrastructure.

In reality, they are making a combined bet on:

• Semiconductor demand

• Electricity prices

• Grid expansion

• Planning approvals

• Cooling technology

• AI adoption

• Hyperscaler spending

• Government policy

That is a lot of moving parts for something being sold as a safe infrastructure investment.

The customer concentration problem

Many AI data centres are not supported by thousands of independent customers.

They may depend heavily on one hyperscaler.

Or two.

On paper, this looks safe.

Who would not want Microsoft, Amazon, Google or Meta as a customer?

But customer quality and customer concentration are different things.

A powerful customer is not necessarily a safe customer.

A hyperscaler can negotiate aggressively.

It can delay deployments.

It can change technical specifications.

It can shift workloads to another region.

It can build its own facilities.

It can develop its own chips.

It can decide that it reserved too much capacity during the AI land grab.

The data centre owner carries the fixed asset.

The hyperscaler carries options.

That is not an equal relationship.

When demand is strong, nobody cares.

When demand slows, everyone suddenly discovers who had the negotiating power.

Usually, it was not the infrastructure fund.

The first-order economics

The first-order story is obvious.

AI demand rises.

Companies need more compute.

More compute requires more data centres.

Data centre owners make money.

That is the story in the investor presentation.

It is not wrong.

It is simply incomplete.

The first-order winners are easy to see.

Chipmakers.

Construction companies.

Power equipment manufacturers.

Cooling providers.

Utilities.

Landowners near grid connections.

Data centre operators.

Everyone earns money during the buildout.

But first-order economics tells you what happens when the spending begins.

It does not tell you what happens after the system starts reacting.

That is where things become interesting.

The second-order economics

The second-order effects begin when everyone responds to the same signal.

AI demand is growing.

Capital rushes in.

More facilities are announced.

More land is purchased.

More power is reserved.

More chips are ordered.

Every investor assumes demand will arrive for their facility.

But customers are also becoming more efficient.

Models are getting smaller.

Inference is being optimised.

Companies are building custom chips.

Workloads can shift between regions.

Some AI applications will create huge value.

Many will not survive their pilot stage.

So supply may be built using today’s demand assumptions while demand itself is being transformed by efficiency.

This creates a dangerous possibility.

We could have an electricity shortage and excess data centre capacity at the same time.

That sounds contradictory.

It is not.

The wrong facilities may exist in the wrong places.

They may have the wrong cooling.

The wrong chip density.

The wrong network connections.

Or power that is too expensive to make the workload competitive.

Capacity is not interchangeable.

A megawatt in the wrong location is not the same as a megawatt next to customers, fibre and cheap electricity.

Then financing begins to change.

Lenders become less willing to treat every data centre as a stable infrastructure asset.

They demand higher returns.

Insurance costs rise.

Refinancing becomes harder.

Older facilities receive lower valuations.

Contracts become shorter because customers do not want to commit to yesterday’s architecture.

The cost of capital goes up.

Once that happens, projects that looked profitable at cheap infrastructure financing begin to look much less attractive.

That is the second-order shift.

The risk moves from construction into financing.

The third-order economics

The third-order effects are larger.

They reach beyond data centres.

Governments will realise that AI facilities are competing with households and industry for power.

That turns grid access into a political issue.

A factory creates jobs across a supply chain.

A data centre can consume enormous power with relatively few permanent employees.

Communities will start asking difficult questions.

Who paid for the grid upgrade?

Who receives the economic benefit?

Who carries the environmental cost?

Why are household electricity bills rising while a hyperscaler receives preferential access?

Governments will respond.

They may introduce special grid charges.

They may require data centres to fund transmission.

They may impose local generation requirements.

They may restrict development in power-constrained regions.

They may demand sovereign computing capacity in return for approvals.

They may favour national champions.

At that point, data centre economics becomes industrial policy.

It becomes energy policy.

It becomes national security policy.

And it becomes geopolitical.

Countries with cheap, reliable energy will gain an advantage.

Countries with weak grids will discover that AI ambition cannot be powered by press releases.

Regions may compete for data centres today, then tax or restrict them tomorrow.

That political risk will eventually be priced into the asset.

There is another third-order effect.

Ownership will begin to matter more than capacity.

If a country hosts the building but a foreign company owns the chips, controls the software and decides which workloads receive priority, does that country really own AI infrastructure?

Not necessarily.

It may simply be renting land and electricity to someone else’s intelligence economy.

That is a much bigger question than data centre returns.

It is a question about who captures value from the next industrial system.

Some assets will become stranded

People hear “stranded asset” and think of coal mines or oil infrastructure.

AI may create its own version.

A stranded AI asset may still have power.

It may still have cooling.

It may still contain functioning hardware.

But it may no longer be competitive.

The electricity may be too expensive.

The chips may be too old.

The customer contract may not be renewed.

The network latency may be wrong.

The cooling design may not support newer hardware.

The local government may change the rules.

The facility remains physically present.

The economic value disappears.

I have seen enough technology transitions to know that obsolescence rarely sends a polite calendar invitation.

It arrives while everyone is still depreciating the previous investment.

This does not mean data centres are a bad investment

AI data centres will create enormous wealth.

Some assets will become incredibly valuable.

Facilities with secure power, flexible designs, strong network connectivity and diverse customers may perform extremely well.

But that is exactly the point.

The category is not uniformly safe.

You cannot value every AI data centre as though it were a toll road.

You need to understand what is inside it.

You need to understand its power contract.

You need to understand the hardware cycle.

You need to understand who controls demand.

You need to understand whether the facility can adapt.

You need to understand the customer’s alternatives.

And you need to understand what happens if AI becomes much more efficient.

The winners will not simply own buildings.

They will own adaptable access to power, compute and customers.

The losers will own very expensive boxes built around assumptions that expired before the debt did.

I no longer see a building

When I look at a data centre, I do not just see concrete.

I see racks.

I see cables.

I see cooling.

I see operational dependencies.

I see hardware waiting to become old.

I see power contracts.

I see customers with more bargaining power than landlords.

I see financing models that may be using the wrong definition of risk.

I see all the things that can change while the building stays exactly where it is.

That perspective came from working inside the industry.

From Huawei.

From Cisco.

From designing facilities.

From building racks.

From operating the machinery behind the word “cloud.”

The AI infrastructure boom is real.

The demand is real.

The opportunity is real.

But the safe, bond-like data centre is disappearing.

What is replacing it is more powerful.

More strategic.

More profitable for the winners.

And far more dangerous for anyone who mistakes concrete for certainty.

AI data centres still look like infrastructure.

Economically, they are becoming technology stocks.

The only difference is that when this technology bet goes wrong, you cannot uninstall the building.

AI-native companies will tell you they are building proprietary intelligence.

THEY ARE NOT!

They are renting intelligence from OpenAI, Anthropic or Google. Renting compute from Amazon, Microsoft or Google. Connecting to customer data they do not own. Distributing through channels they do not control. Then placing a workflow, a user interface and a prompt library on top.

That can still create a useful product.

It can even create revenue very quickly.

But usefulness is not ownership. Revenue is not defensibility. And a prompt that produces an impressive result today may become unnecessary after the next model release.

The question for thousands of AI companies is simple:

If the model provider improves the model, the cloud provider controls the infrastructure, the customer owns the data and the incumbent software vendor owns the workflow, what is left for you?

For many companies, the honest answer is: a prompt library, some orchestration code and a monthly API bill.

That is not automatically a bad business.

It is a dangerous business to mistake for a durable one.

The prompt library is the new spreadsheet macro

Prompt libraries are useful. So were spreadsheet macros.

A good prompt encodes judgment. It captures instructions, exceptions, examples and a particular way of turning messy inputs into useful outputs. A collection of prompts can represent hundreds of hours of experimentation.

But prompts have three economic problems.

First, they are easy to copy.

Second, their value depreciates as models improve.

Third, the model provider can absorb the capability into the base product.

Yesterday, getting reliable structured output required elaborate prompting, retries and validation. Today, much of that is a native model or API capability. The better the foundation models become at understanding vague instructions, using tools and handling long context, the less value sits in the exact wording of the prompt.

This creates a strange form of technological depreciation.

In traditional software, accumulated code usually increases the capability of the product. In AI applications, some accumulated logic is deleted by the next model upgrade.

The startup spends six months engineering around a model weakness.

The model lab fixes the weakness.

The startup’s technical achievement becomes redundant.

The customer gets a better product. Society gains. But the company that built the workaround discovers that technological progress can destroy its intellectual property faster than it creates it.

The prompt library is therefore not worthless.

It is closer to perishable inventory than permanent capital.

AI has lowered the cost of creating a company. It has not lowered the cost of building a moat.

Generative AI has compressed the cost of software creation.

A small team can now build a polished application, generate marketing assets, write documentation, support customers and ship integrations at a speed that once required a much larger organisation. Research on new business formation is already pointing in the same direction: generative AI appears to increase the formation of smaller, leaner digital ventures and reduce time to launch.

That is the first-order effect.

More people can build companies.

The second-order effect is harsher:

If everyone can build, building itself stops being scarce.

Product supply expands. Feature differences shrink. Competitors appear faster. Customers face lower switching costs. Every attractive niche attracts ten nearly identical products, often using the same models and producing similar outputs.

The bottleneck moves away from software production and toward distribution, trust, workflow access and customer acquisition.

AI makes the product cheaper to create while making attention more expensive to buy.

That reverses one of the comfortable assumptions of the SaaS era. Software development used to be a meaningful barrier to entry. The engineering effort required to reproduce a product gave incumbents time. AI reduces that protection.

The result is not the death of software.

It is the commoditisation of undifferentiated software production.

The full stack of rented intelligence

To understand the risk, look at the layers of a typical AI-native company.

This does not mean every company needs to train a frontier model or own a data centre. That would be economically absurd for most businesses.

Ownership should not be confused with physical possession.

The issue is control.

Do you control a scarce asset?

Does it improve as customers use the product?

Does it survive a model upgrade?

Can the supplier change its price without destroying your economics?

Can the customer replace you without reorganising the way work gets done?

If the answer to all five is no, the company does not have an AI moat. It has temporary access to someone else’s moat.

The startup problem: speed without accumulation

For startups, rented intelligence is initially a gift.

It removes the need to raise hundreds of millions of dollars for compute, model training and research talent. A founder can reach the market before knowing which model will ultimately win. Multi-model architectures can reduce technical dependence. Falling inference prices can improve gross margins.

This is why application-layer AI companies can grow very quickly. The Financial Times has documented AI application startups reaching substantial revenue at speeds that would have looked abnormal in the old SaaS market. Lower model costs make more use cases viable and allow small teams to serve large markets.

But speed creates a false signal.

Fast revenue growth can look like product-market fit when it is partly curiosity, subsidised usage or a temporary capability gap in the foundation model. Customers may pay because the base platforms have not yet packaged the feature, not because the startup has built something the platforms cannot reproduce.

That creates four startup traps.

1. The demo-to-company trap

AI produces unusually impressive demos.

A demo proves that a task can be performed. It does not prove that the company can own the task.

Investors and founders can confuse model capability with company capability. If 90% of the magic comes from the underlying model, the startup must explain why the remaining 10% deserves the margin, the valuation and the customer relationship.

Sometimes it does. Deep workflow integration, verification, permissions, audit trails and proprietary context can be extraordinarily valuable.

But the burden of proof sits there, not in the demo.

2. The negative learning-curve trap

In a strong software business, every customer should make the product harder to replace.

The company learns. Its data improves. Its workflows deepen. Its distribution expands. Its unit economics strengthen.

Many thin AI wrappers do not accumulate much of anything. They send inputs to a model, receive outputs and discard the interaction. The customer receives the value, while the model provider receives the usage and the startup retains little reusable learning.

The company grows without compounding.

Revenue goes up, but strategic ownership does not.

3. The margin illusion

Traditional SaaS trained investors to expect very high gross margins because the marginal cost of serving another user was small.

AI reintroduces variable production costs.

Every request can consume tokens, retrieval, tool calls, external APIs, vector storage, validation and sometimes human review. Agentic systems multiply this because a single user action can trigger many model calls and interactions between agents.

Falling token prices help. They do not remove the structural issue.

When the model is a material part of the product, the supplier sits inside the cost of goods sold. If the application competes mainly on price, falling inference costs may be passed straight to customers rather than retained as margin.

Cheaper intelligence can improve demand while simultaneously destroying pricing power.

4. The distribution trap

When building becomes cheap, distribution becomes the moat everyone pretends is product.

The startup may rely on Google for discovery, LinkedIn for leads, Microsoft for enterprise access, a cloud marketplace for procurement and OpenAI for the underlying capability.

Each gatekeeper can charge rent.

The startup is not one platform decision away from failure. It is several platform decisions away from failure.

The mature-company problem: paying twice for intelligence it already owns

Large companies face the opposite problem.

They already own many of the scarce assets AI startups want:

• decades of customer relationships

• proprietary operational data

• embedded workflows

• licences and regulatory permissions

• distribution

• domain experts

• historical outcomes

• trusted brands

Yet many incumbents fail to convert those assets into usable intelligence.

Their data is fragmented. Permissions are unclear. Systems do not connect. Institutional knowledge sits in email, presentations and the heads of employees. Procurement buys AI tools department by department. Every vendor creates another interface over the same disconnected organisation.

The mature company then pays an AI vendor to extract value from data and workflows the mature company already owns.

This can be rational. Outside vendors can move faster and bring specialised capability.

But there is a strategic difference between buying a tool and outsourcing the learning loop.

If the vendor observes the prompts, corrections, exceptions, approvals and outcomes, the vendor may learn the workflow faster than the customer does. The enterprise supplies the domain knowledge. The vendor turns it into a repeatable product. The enterprise pays the subscription.

The company has effectively financed the construction of someone else’s intangible asset.

That is the second-order risk for incumbents.

The third-order risk is worse: once a vendor becomes the interface through which employees access corporate knowledge and execute work, the vendor begins to control the organisational operating layer.

At that point, switching is not a software migration. It is institutional surgery.

What actually survives the next model release?

Distribution

A company with trusted access to a customer segment can swap models underneath the product. The model lab cannot instantly reproduce the customer relationship.

Distribution is especially powerful in regulated or fragmented industries where procurement, reputation and local market knowledge matter.

Proprietary context

Raw data is overrated. Context is more valuable.

The useful asset is not a pile of documents. It is the mapping between data, decisions, permissions, exceptions and outcomes. That context tells the system what mattered, what was allowed and what worked.

Workflow control

An AI assistant that comments on work is easy to replace.

An AI system embedded in the transaction, approval or execution path is much harder to remove. The more operational responsibility it carries, the more valuable reliability, integration and governance become.

Outcome data

Generated content is abundant. Verified outcomes are scarce.

Did the contract clause survive litigation?

Did the maintenance recommendation prevent failure?

Did the sales action increase conversion?

Did the medical intervention improve the patient’s condition?

The company that captures the link between recommendation and outcome can build a learning system competitors cannot reproduce with prompts alone.

Trust and permission

In high-stakes markets, the right to act is more valuable than the ability to generate.

Licences, auditability, liability frameworks, security approvals and institutional trust are slow to build. Model capability may commoditise. Permission to use it often does not.

Proprietary economics

Some companies will build an advantage through inference efficiency, routing, caching, specialised smaller models or ownership of critical infrastructure.

The question is not merely whether the product works. It is whether the company can deliver the outcome at a cost structure competitors cannot easily match.

The second-order economics: where the money moves

If thousands of AI companies own little beyond prompts and interfaces, the value does not disappear.

It moves.

More value flows to foundation-model and cloud providers

Every application expands demand for tokens and compute. Even when the application company struggles to defend its margin, its suppliers collect revenue from usage.

This resembles previous platform economies. A crowded field of businesses can compete above the platform while the platform captures the most stable rent below them.

The application layer may generate the experimentation. The infrastructure layer may capture the dependable economics.

Customer acquisition becomes more expensive

When competitors can reproduce features quickly, they compete through advertising, sales teams, partnerships and discounts.

Engineering costs fall. Go-to-market costs rise.

Capital moves from product creation toward distribution warfare.

The irony is brutal: AI lets a five-person company build what once required fifty people, then forces it to spend the savings fighting fifty near-identical competitors for attention.

Services return through the back door

Many AI companies present themselves as scalable software but rely on implementation teams, prompt tuning, data cleaning, integration and ongoing workflow redesign.

That is not necessarily a flaw. The work may be valuable.

But it changes the economics. The company may be a technology-enabled service business carrying a SaaS valuation. Its moat may sit in people and deployment experience rather than code.

Markets will eventually distinguish between recurring software revenue and recurring consulting disguised as software.

Model providers gain bargaining power

Multi-model strategies reduce dependence, but they do not eliminate it. Models are not perfectly interchangeable. Changing providers affects quality, latency, safety behaviour, tool use and customer commitments.

Once the product is optimised around a model, switching becomes costly. The provider can influence the application’s margins, roadmap and service quality without owning a share of the company.

This is vertical power without vertical ownership.

Valuations split between revenue growth and strategic control

Two AI companies may report identical revenue.

One owns distribution, workflow and outcome data.

The other buys leads, calls an external model and returns text through a dashboard.

They should not receive the same multiple.

As the market matures, investors will pay less for revenue that depends on rented capability and more for revenue attached to controlled workflows, proprietary feedback loops and durable customer access.

The third-order economics: what happens after the shakeout

The deeper consequences arrive after the application market becomes crowded.

1. AI markets may consolidate faster than SaaS

SaaS companies could coexist because products accumulated specialised features over many years.

AI compresses feature development and lets platforms move across categories quickly. A general model provider can enter legal research, coding, customer service, analytics or enterprise search without rebuilding intelligence from zero.

That does not mean vertical startups all disappear. It means they must move deeper into the workflow faster than the platform moves outward.

The middle gets squeezed.

At one end sit large platforms with models, compute and distribution.

At the other sit specialised companies with domain data, trust and workflow ownership.

The generic wrapper in between has nowhere safe to stand.

2. Enterprises become the training ground for their future suppliers

Companies adopting external AI tools will generate valuable process data: how decisions are made, where exceptions occur, which recommendations are accepted and which outputs create value.

If contracts do not define ownership and use of this learning, enterprise adoption can transfer institutional knowledge outward.

The strategic procurement question will shift from “Can this vendor access our data?” to “Who owns what the system learns from our organisation?”

That is a much harder question, and most procurement templates are not designed for it.

3. Labour power shifts toward people who control context

If basic software production becomes abundant, value moves toward people who own customer relationships, domain judgment, operational permissions and the authority to change workflows.

The most important employee in an AI transformation may not be the person who writes the prompt.

It may be the person who knows why the process exists, which exceptions matter, who carries the liability and how the outcome is measured.

AI reduces the scarcity of generation.

It increases the value of accountable judgment.

4. National AI sovereignty becomes an application-layer issue

Policymakers often discuss sovereignty in terms of chips, data centres and foundation models.

But a country can host compute domestically and still surrender the interfaces through which its firms make decisions.

If foreign platforms mediate legal work, industrial maintenance, financial analysis, education and public administration, dependency exists above the data-centre layer as well as below it.

The strategic asset is not only domestic compute.

It is domestic control over data, workflows, standards and institutional learning.

5. Regulation may strengthen the largest platforms

Compliance creates fixed costs.

If every AI provider must fund expensive assurance, documentation, security and liability processes, the burden will fall more heavily on smaller companies. Large incumbents can spread those costs across millions of users and bundle compliance into existing enterprise contracts.

Poorly designed regulation can therefore produce the opposite of competitive policy: it can convert trust into another hyperscaler moat.

The answer is not no regulation. It is regulation that distinguishes between model risk, application risk and the actual authority a system has to act.

What investors should ask

Investors should spend less time asking whether a company is “AI-native” and more time asking what compounds.

1. What does the company control that the model provider does not?

2. What becomes more valuable after the thousandth customer?

3. Which data rights are contractual, and which are merely assumed?

4. Does usage create proprietary outcome data or only higher API costs?

5. What happens if the model price doubles?

6. What happens if the model price falls by 90%?

7. What happens if Microsoft, Google, Salesforce or OpenAI bundles the feature?

8. Is the company replacing a system of record, becoming one, or merely reading from one?

9. How much revenue requires human implementation?

10. Does the company own distribution or continuously repurchase it?

The decisive question is not “How good is the product?”

It is “Which part of the value chain can this company prevent others from taking?”

What operators should build

Operators should treat models as replaceable inputs and learning loops as strategic assets.

Build model portability where it is economically sensible. Capture structured feedback. Measure outcomes rather than output volume. Negotiate rights over interaction and improvement data. Embed the product into execution, not merely advice. Own the customer relationship. Make integrations deep enough to create value but not so dependent that a platform can switch off the company.

Most importantly, decide what the company intends to own before adding more AI features.

If the answer is “better prompts,” the strategy is already expiring.

What mature businesses should protect

Large organisations should not respond by building everything internally.

That usually produces slow, mediocre software and years of governance theatre.

They should decide which learning loops are too strategic to outsource.

The enterprise may happily rent the model, the cloud and many applications. But it should preserve control over identity, permissions, core operational context, decision records, verified outcomes and the ability to switch suppliers.

The goal is not technological independence.

It is bargaining power.

What policymakers should understand

The AI economy will not be shaped only by who invents the best model.

It will be shaped by who captures the learning generated when models enter real institutions.

Competition policy should watch bundling and platform self-preferencing. Procurement rules should define rights over derived learning and workflow data. AI policy should avoid imposing identical obligations on a frontier model, a low-risk application and an agent authorised to move money or make consequential decisions.

Governments should also stop treating the number of AI startups as proof of national capability.

A country can produce thousands of AI applications while importing the models, compute, cloud, distribution and capital.

That is startup activity.

It is not necessarily technological power.

The final test

There is nothing shameful about building on someone else’s model.

Every modern company builds on layers it does not own. Airlines do not manufacture every engine. Banks do not build every server. Software companies do not generate their own electricity.

The problem begins when a company rents every scarce layer and mistakes assembly for ownership.

The best AI companies will use commoditised intelligence to capture something non-commoditised: a customer relationship, a regulated permission, a proprietary learning loop, a critical workflow, verified outcomes or a distribution advantage.

The weakest will keep adding prompts and calling the collection a platform.

When the next generation of models arrives, we will discover which companies built an asset and which ones merely documented the limitations of the previous model.

The model is rented.

The cloud is rented.

The intelligence is rented.

If all you own is the prompt, you do not own the company’s future.

Get this: Governments are no longer just regulating AI.

They are starting to fund it, subsidise it, procure it, protect it, finance the infrastructure underneath it, and deliberately shape who wins.

That starts to look less like traditional technology policy and more like a giant form of state-sponsored venture capital.

The interesting part is that the U.S., China and Europe are all doing this differently.

China is the most obvious version. The state has been using guidance funds, state-owned banks, local governments, industrial subsidies and national champions for years. AI is now being plugged into that machinery.

The U.S. tells itself a different story. Silicon Valley supposedly allocates the capital. But once you look underneath the stack, Washington is everywhere: CHIPS subsidies, export controls, defence procurement, tax incentives, energy policy, federal research funding, sovereign-security restrictions, infrastructure permitting and increasingly direct attempts to shape domestic compute capacity.

Europe is trying something else again. It wants sovereign compute, sovereign models and European champions, but it also has less venture capital, fewer hyperscalers and a regulatory structure that can sometimes fight against the industrial ambitions sitting next to it.

I want to ask:

Are we watching the emergence of three different models of state capitalism for AI?

And if so, which one actually works?

Follow the money through the entire AI stack

Don’t just talk about foundation models.

Look at the whole machine:

chips → semiconductor equipment → data centres → electricity → grids → cloud → compute → models → applications → talent → data → defence and government procurement.

At every layer, ask the same questions:

Who is putting up the capital?

Who is taking the risk?

Who owns the infrastructure?

Who gets subsidised?

Who gets protected?

Who gets procurement contracts?

Who captures the upside if the bet works?

And who is left holding the losses if it doesn’t?

That is where the real economics of sovereign AI sits.

Compare the U.S., China and EU as capital allocation systems

I don’t want a generic country comparison.

I treat each region almost like a giant investment fund with a different investment committee.

China:
The government is much more comfortable explicitly picking industries and companies. Look at government guidance funds, provincial AI funds, semiconductor funding, state banks, state-owned enterprises, national compute infrastructure, domestic GPU development and the push for technological self-sufficiency.

But dig into the problems too. Local-government duplication. Bad capital allocation. Zombie companies. Subsidy arbitrage. Overcapacity. Projects created because Beijing wants AI rather than because customers actually want the product.

China may be better at mobilising capital quickly. That doesn’t necessarily mean it is better at allocating it.

United States:
The U.S. still has by far the deepest private technology capital markets, so the state doesn’t need to behave like a Chinese investment fund.

Instead, it changes the economics around private investors.

A semiconductor fab becomes more attractive because of CHIPS Act support.

A domestic GPU supply chain becomes strategically valuable because export controls make advanced compute geopolitical.

A data-centre project becomes partly an energy-policy question.

An AI company can become strategically important because the Pentagon, intelligence agencies or federal government become customers.

So instead of asking whether the U.S. has industrial policy, ask a better question:

How much private AI investment would still happen in exactly the same form if Washington disappeared from the equation?

Probably less than Silicon Valley likes to admit.

European Union:
Europe is the awkward case.

It wants technological sovereignty.

It wants European AI champions.

It wants AI factories and sovereign compute.

It wants local semiconductor capacity.

It wants public supercomputing infrastructure.

But Europe also regulates more aggressively, has shallower VC markets and doesn’t have equivalents of AWS, Microsoft, Google, Nvidia or Meta at comparable scale.

That creates a fascinating contradiction:

Europe increasingly understands that AI is an industrial-capacity problem, but its political system still often treats AI as primarily a regulatory problem.

Explore whether that is changing.

The main mental model

The core framework should be something like:

Sovereign AI is not really about owning a chatbot. It is about controlling enough of the capital stack that your economy cannot be switched off by someone else.

That includes compute.

Energy.

Semiconductors.

Cloud infrastructure.

Models.

Networks.

Capital markets.

Talent.

Government demand.

Once you see it like that, sovereign AI starts looking a lot more like defence industrial policy than software policy.

Another useful mental model:

Governments are moving from referee → customer → investor → market maker

For most of the internet era, governments mostly regulated technology after companies built it.

AI is changing that relationship.

The state can now:

1. fund the science,

2. subsidise the factories,

3. finance the infrastructure,

4. create the demand,

5. restrict foreign competitors,

6. guarantee national-security customers,

7. shape energy access,

8. control exports,

9. and occasionally take direct or indirect investment exposure.

At that point, the government isn’t standing outside the market.

It is helping manufacture the market.

Where I want real disagreement

One camp will say AI is too strategically important to leave entirely to markets.

Markets optimise for return on capital, not national resilience.

If advanced chips, compute or cloud infrastructure become geopolitical chokepoints, governments have legitimate reasons to pay for redundancy that a private investor wouldn’t.

The other camp will say this is exactly how governments waste extraordinary amounts of money.

Politicians are bad venture capitalists.

Once “strategic AI” becomes a funding category, every company suddenly discovers that it is strategically important.

Subsidies attract lobbyists.

Protection creates lazy incumbents.

Local governments chase fashionable industries.

Capital stops following productivity and starts following policy.

That tension should run through the whole essay.

Ask whether governments can actually pick winners

This is one of the most interesting questions.

There are cases where industrial policy clearly mattered.

Semiconductors.

Aerospace.

Defence.

Space.

The internet itself.

Renewable energy.

But AI moves unusually fast.

A government might commit billions to a technology that becomes obsolete before the infrastructure is finished.

Today’s strategically essential accelerator architecture might not be tomorrow’s.

Today’s leading model company might not exist in ten years.

So sovereign AI creates a strange problem:

States are making 20-year infrastructure decisions around technologies whose competitive cycles can be measured in months.

That is an incredible capital-allocation mismatch.

Open source makes sovereignty weirder

If open-weight models continue improving, countries may not need a domestic OpenAI.

They might need:

domestic compute + domestic energy + access to open models + fine-tuning capability + secure inference infrastructure.

That could dramatically reduce the cost of sovereign AI.

And it could shift power away from model developers toward whoever owns the compute and energy underneath them.

This is important.

The most valuable sovereign asset might be the infrastructure capable of running whatever the best model happens to be.

Follow the second- and third-order economics

If governments start underwriting AI infrastructure, what happens next?

Private investors may take more risk because the downside is partially socialised.

Utilities may become AI infrastructure companies without intending to.

Grid connection queues become industrial-policy tools.

Semiconductor fabs become geopolitical assets.

Cloud providers become quasi-national infrastructure.

Defence procurement becomes an AI go-to-market strategy.

VCs start investing around government priorities.

Startups optimise for sovereign contracts.

Countries compete for data centres using land, tax incentives and cheap electricity.

Eventually the competition may move from:

Who has the best AI company?

to:

Which state has the cheapest and deepest capital stack behind AI?

That is a much bigger question.

Bring in actual numbers

Government funding commitments.

Semiconductor subsidies.

AI infrastructure spending.

Public compute programs.

Chinese guidance funds.

EU AI Factory and InvestAI-type initiatives.

CHIPS Act disbursements.

Data-centre capital expenditure.

Electricity investment.

Defence AI procurement.

But don’t just repeat trillion-dollar press-release numbers.

Separate:

money announced

from

money actually committed

from

money actually spent

from

private capital supposedly “mobilised.”

Those are very different things.

Governments love multiplying the last category.

The historical analogy

Compare this with earlier state-backed technology races:

railways,

electricity,

semiconductors,

telecommunications,

aerospace,

nuclear energy,

the internet.

AI is different because the infrastructure is being financed by a strange coalition:

Big Tech balance sheets + private credit + utilities + sovereign governments + venture capital + infrastructure funds + defence budgets.

The AI economy may therefore be producing something new:

a public-private capital stack where it becomes increasingly difficult to separate the market from the state.

The conclusion should land somewhere uncomfortable

For thirty years, the West told itself that governments set the rules and markets picked the winners.

AI is quietly breaking that distinction.

China never really believed it.

Europe is reluctantly abandoning it.

And even the United States, the country most ideologically committed to private capital allocation, is discovering that once compute becomes strategically important, markets alone are apparently not enough.

The AI race is therefore not just a competition between OpenAI, Anthropic, DeepSeek, Google or Meta.

It is increasingly a competition between capital systems.

American private capital backed by strategic state intervention.

Chinese state-directed capital supplemented by private entrepreneurship.

European public industrial policy trying to compensate for weaker private technology capital.

And the winner may not be the country with the smartest model.

It may be the country that figures out how to deploy trillions of dollars of capital into compute, energy, chips and talent without destroying returns in the process.

Because that’s the dangerous part of state-sponsored venture capital.

Governments can fund things markets would never fund.

Sometimes that creates the future.

Sometimes it just creates the world’s most expensive stranded assets.

AI is making intelligence dramatically cheaper. It is also turning intelligence into a metered input. That changes software margins, pricing, power grids, capital markets, and ultimately which countries and companies capture the economics of AI.

There is a strange contradiction sitting at the center of the AI economy.

For most of the software era, the dream was to make the marginal cost of serving one more customer disappear. Build the product once. Put it in the cloud. Add another user. The extra cost existed, obviously, but compared with the subscription revenue it was often tiny.

AI breaks that mental model.

Every time an AI system reads a document, reasons through a problem, generates code, searches a database, calls another tool, checks its own work, or runs another loop inside an agent, somebody has to pay for computation.

That payment might be a fraction of a cent. It might eventually fall another hundredfold. It may be hidden inside a subscription. But it exists.

And as AI moves from answering questions to executing transactions, that variable cost gets embedded deeper into the economy. OpenAI’s latest enterprise data show exactly that migration: enterprise usage is moving from assistance toward execution, and the firms in the top decile of AI usage now generate 8.3 times as many output tokens per active user as typical firms. As of June 2026, Codex alone produced 64% of the combined Codex and ChatGPT output tokens among OpenAI enterprise customers. Usage of enterprise Codex since February grew 108-fold in legal, 41-fold in sales, 41-fold in recruiting, and 26-fold in marketing.

That is the beginning of what I think of as the compute tax.

Not a tax imposed by government. And not literally a GPU charge on every payment made in the economy.

It is a new production cost attached to every economic action that requires machine intelligence.

The distinction matters because AI is simultaneously doing two things that look contradictory.

It is destroying transaction costs by making search, analysis, communication, negotiation and execution cheaper. MIT researchers describe the fundamental economic promise of AI agents in almost exactly those terms: agents can substantially reduce the time and effort involved in searching, communicating and contracting. But the mechanism doing that work consumes compute.

So AI may eliminate a $20 human coordination cost and replace it with 20 cents of machine inference.

That’s an extraordinary economic bargain.

But the 20 cents still has to go somewhere.

And once trillions of decisions, searches, negotiations, customer interactions, software changes, purchases and administrative actions become mediated by AI agents, those pennies start looking less like a rounding error and more like an entirely new layer of the economy.

The tax hiding inside the product

The easiest way to understand the change is to stop thinking about tokens as a technical metric.

A token is not a business outcome.

Nobody cares that an AI consumed 80,000 tokens to resolve an insurance claim. The insurer cares whether the claim was resolved correctly, how long it took, how many humans had to intervene, how much the computation cost, and whether the final cost was lower than the old process.

OpenAI is now explicitly telling CFOs to think this way. Its proposed economic scorecard asks whether the value of work completed grows faster than the cost required to produce it. It argues that cost per token can be misleading because a cheaper model may require retries, longer processing or human review, while a more capable and more expensive model may complete the task correctly in one pass.

BCG has arrived at almost the same conclusion independently. It argues that the useful denominator is cost per successful outcome, because agentic workloads can accumulate cost through long context windows, repeated loops, retrieval, tool use and model selection. A weaker model can therefore cost more in total than a stronger one if it repeatedly fails before reaching an acceptable answer.

This sounds like a minor accounting change.

It isn’t.

It means intelligence is becoming something companies can buy in variable quantities.

Consider a deliberately simple example using OpenAI’s published API pricing in August 2026. GPT-5.6 Terra is currently priced at $2 per million input tokens and $12 per million output tokens for standard processing below the stated context threshold. A workload using 20,000 input tokens and producing 5,000 output tokens therefore costs roughly 10 cents for that individual model invocation: four cents of input and six cents of output. One million identical calls would cost roughly $100,000. A workflow requiring ten such calls would be around $1 of model inference per completed workflow before counting external tools, searches, retries, storage, conventional cloud infrastructure or human review.

That is not an estimate of what an average agent costs. Agent economics vary enormously.

It is simply a way of seeing the scale.

A dollar of machine intelligence can be insanely cheap if it replaces thirty minutes of professional labor.

A dollar can also be catastrophically expensive if the product earns fifty cents from the interaction.

That difference is going to separate a lot of good AI businesses from bad ones.

First-order economics: software gets a meter

The first-order consequence is the easiest one.

AI turns part of software back into a variable-cost business.

BCG makes the accounting point bluntly: tokens used inside products and customer interactions belong in cost of goods sold. Unlike conventional SaaS economics, where incremental software usage could often be served at very low marginal cost relative to revenue, inference rises as customers interact with the model. BCG’s current analysis estimates gross margins of roughly 65% to 80% for AI-enabled software and 50% to 65% for AI-native products, though these are estimates of emerging economics rather than a universal law for every AI company.

That creates a surprisingly old-fashioned problem for supposedly futuristic companies:

What does it cost to produce one unit of what you sell?

The SaaS industry spent twenty years training investors to care about seats, annual recurring revenue, retention and sales efficiency. AI forces management teams to relearn manufacturing economics.

Except the factory is a data center.

For a customer-service company, the unit might be a resolved ticket.

For an AI coding company, it might be a successfully merged code change.

For a legal agent, a reviewed contract.

For a procurement system, a completed sourcing decision.

For an AI-native bank, perhaps a completed underwriting decision.

The important number is no longer merely:

Revenue per user.

It starts becoming:

Revenue per successful outcome minus compute per successful outcome.

And the word successful is doing a lot of work there.

Suppose Model A costs 5 cents and completes the task correctly 60% of the time, while Model B costs 15 cents and succeeds 95% of the time. Once you account for retries, escalation and human review, Model B can easily have better economics despite charging three times more per inference pass. Both BCG and OpenAI now emphasize this distinction between nominal model price and full cost of successful work.

This is why there will not be one compute tax.

There will be millions of them.

Different workflows will have radically different intelligence intensity.

Generating an email subject line and autonomously negotiating a commercial contract may both appear as a single click to the user. Economically, they are completely different products. Agentic systems may retrieve documents, maintain long contexts, reason repeatedly, use external tools and inspect intermediate results before producing a final action. BCG notes that these loops can make apparently similar requests carry very different costs, while OpenAI’s enterprise data show the leading users consuming far more output tokens as they move toward delegated, multistep work.

That creates an immediate strategic consequence.

Model architecture becomes business-model architecture.

Routing a trivial request into an expensive frontier model is no longer just sloppy engineering. It is margin leakage.

Using deterministic software for something an agent unnecessarily reasons about is margin leakage.

Repeatedly feeding gigantic context windows into a model is margin leakage.

Failing to cache reusable information is margin leakage.

Letting an agent loop without an economically rational stopping condition is margin leakage.

The companies that understand this are already moving toward routing, caching, batching and task-specific model selection rather than sending every problem to the biggest available model. BCG explicitly recommends separating deterministic tasks from model-based reasoning and routing simpler work toward lighter models while reserving expensive models for difficult tasks.

In other words, the AI stack is starting to develop something industrial companies have understood forever:

cost engineering.

And that is probably healthy.

Because the real question was never whether AI was expensive.

The question is whether the intelligence purchased for $1 creates $1.10 of value or $100 of value.

Second-order economics: the business model starts mutating

The second-order effect is more interesting because companies cannot simply absorb a new variable cost forever.

Eventually pricing has to adapt.

This is already visible across software. McKinsey argues that inference is introducing recurring compute and infrastructure costs into software economics and that vendors increasingly need pricing structures that scale with usage, outcomes, actions or compute rather than relying purely on traditional seats.

That is why the seat may gradually lose its status as the dominant unit of enterprise software.

A seat makes sense when a human is the worker.

What is a seat worth when one employee launches 500 autonomous tasks overnight?

What happens when a company has 3,000 employees but 40,000 persistent software agents?

Charging $30 per employee starts looking ridiculous if the agent produces $10,000 of work. Equally, an “unlimited AI” subscription starts looking suicidal if one power user can unleash millions of tokens of autonomous work.

So AI pricing is likely to migrate toward something closer to economics itself:

work performed.

Per resolution.

Per transaction.

Per code change.

Per lead qualified.

Per invoice reconciled.

Per claim processed.

Per successful research task.

That does two things at once.

First, it makes AI companies more directly exposed to their customers’ economics. If the AI cannot produce a valuable outcome, it becomes difficult to keep charging merely because someone has a login.

Second, it creates a powerful incentive for AI companies to drive the compute required per successful outcome downward.

That could be a much stronger productivity engine than merely making models cheaper.

BCG points to emerging AI companies already charging around outcomes rather than conventional seats and argues more broadly that AI is reallocating profit pools toward businesses that control valuable customer relationships, proprietary assets and difficult-to-replicate workflows. Its August 2026 work describes AI as a reallocation of capital and margins rather than merely another productivity technology.

This is where the compute tax starts changing competitive strategy.

Imagine two AI legal companies.

Both use roughly the same frontier models.

Company A is basically a wrapper. It buys expensive inference, adds an interface and resells it.

Company B has proprietary legal data, a retrieval layer, specialized evaluation systems, model routing, workflow integration, cheaper models for routine work, frontier models for hard questions and a feedback system that improves as customers use it.

The second company may pay the same headline API price as the first.

Its cost per successful legal outcome can still be dramatically lower.

That means the durable moat in AI software may not be “we have AI.”

Almost everybody will have AI.

The moat becomes the system surrounding the model that converts compute into useful work efficiently.

Data.

Workflow.

Distribution.

Routing.

Evaluation.

Customer context.

Proprietary feedback.

Trust.

And critically, the ability to determine when not to use expensive intelligence.

That last point is underrated.

The cheapest AI call is the one you did not need to make.

This also explains a seeming contradiction in the economics of AI agents.

MIT researchers argue that agents can dramatically reduce traditional transaction costs by lowering the effort involved in searching, communicating, comparing alternatives and contracting. They describe agents as potentially useful even in complex markets such as procurement, investment and real estate because software can inspect far more information than a human economically could.

I think they’re right.

But the transaction cost isn’t disappearing.

It is being converted.

We are swapping human transaction costs for machine transaction costs.

Instead of paying an employee to spend three hours comparing 70 suppliers, you might pay an agent to interrogate 70 databases, read 1,000 documents, request quotations, score the offers and return three candidates.

Human coordination cost collapses.

Compute consumption rises.

The net economic result can be overwhelmingly positive.

This is why calling compute a tax should not be confused with calling it a burden.

A good tax can still be a bargain when the alternative is much more expensive.

The strategic issue is who captures the savings.

Suppose an old process costs $100 in human labor.

AI reduces total production cost to $10, of which $2 is inference.

There is now $90 of economic surplus available.

Does the customer keep it through lower prices?

Does the AI application capture it?

Does the model provider capture it?

Does the cloud provider?

Does the GPU supplier?

Does the owner of proprietary data?

Does competition push almost all $90 back to the consumer?

That is the real capital-allocation question.

BCG’s latest work argues that AI is shifting profit pools and that AI-native attackers may selectively capture high-margin parts of incumbent value chains without reproducing the incumbent’s entire business.

Which means AI may not simply make existing businesses more efficient.

It may unbundle their margins.

Third-order economics: the token bill reaches the physical economy

Now zoom out another level.

A token looks digital.

Its supply chain isn’t.

Every token ultimately descends through a physical stack:

model software,

servers,

accelerators,

high-bandwidth memory,

networking,

data-center buildings,

cooling,

transformers,

transmission,

electricity generation,

land,

water,

capital.

Once enough intelligence is consumed, the compute tax becomes an infrastructure bill.

The International Energy Agency says global data-center electricity consumption reached roughly 485 TWh in 2025 and projects around 950 TWh by 2030, roughly 3% of global electricity demand. Electricity consumption specifically associated with AI-focused data centers is expected to triple over the same period.

And here is the really important part.

The electricity required per AI task is falling rapidly.

Yet total electricity consumption is rising.

The IEA says per-task efficiency is improving at a rate it describes as unprecedented in energy history, while at the same time more people are using AI and more compute-intensive applications such as agents are spreading. Its current forecast therefore still has data-center electricity consumption doubling by 2030.

That is the entire compute-tax thesis in miniature.

The tax rate falls.

The tax base explodes.

We have seen versions of this before. Make something dramatically cheaper and people don’t necessarily spend less on it. They find more things to do with it.

Cheap bandwidth didn’t cause the world to consume less bandwidth.

Cheap storage didn’t cause us to store less data.

Cheaper computation didn’t cause us to compute less.

Cheap machine intelligence could follow the same pattern.

The biggest economic question is therefore not:

How cheap will one token become?

It is:

How much machine intelligence will the economy demand once intelligence becomes cheap?

That question reaches directly into electricity markets.

The U.S. Department of Energy’s latest Lawrence Berkeley National Laboratory modeling estimates that data centers could reach 11.8% of total U.S. electricity consumption by 2030, with scenarios ranging from 9.5% to 15.3%.

The IEA estimates that more than 40% of the additional electricity required by global data centers through 2030 will still be met by natural gas and coal, even as renewable generation grows rapidly. The mix varies sharply by geography: U.S. data centers currently rely most heavily on natural gas, while Chinese data centers remain much more coal-intensive; Europe is projected to move toward a data-center electricity mix dominated by renewables and nuclear.

Then comes the capital bill.

The IEA says the capital expenditure of five major technology companies exceeded $400 billion in 2025 and is projected to rise a further 75% in 2026. It also warns that data-center expansion has become too capital intensive to rely only on corporate balance sheets, making future construction increasingly sensitive to capital markets, financing conditions and investor expectations about AI returns.

Federal Reserve Governor Lisa Cook pointed to more than $1.5 trillion of announced data-center plans, only a small fraction of which had been realized as of May 2026. She also noted rising prices in chips, high-tech equipment and software and stronger wage pressure in specialized construction trades as the investment boom feeds through the supply chain.

That is third-order economics.

The first-order question was:

How much does this AI answer cost?

The second-order question was:

What happens to the company’s margins and pricing model?

The third-order question is:

What happens when the entire economy tries to buy the chips, transformers, power plants, construction workers and financing required to generate trillions of those answers?

Now the economic consequences escape the technology sector.

A shortage of transformers becomes an AI constraint.

A gas-turbine backlog becomes an AI constraint.

A grid interconnection queue becomes an AI constraint.

A transmission project becomes AI infrastructure.

The IEA says shortages and delays now span transformers, gas turbines, advanced chips and high-bandwidth memory, with planning and grid-connection systems also under pressure.

And once compute begins competing for scarce physical capacity, the tax can spill over onto people who never asked for the AI product in the first place.

That is exactly why U.S. policy has started focusing on tax incidence, even if Washington doesn’t use that phrase.

In March 2026, the White House announced a Ratepayer Protection Pledge asking major hyperscalers and AI companies to “build, bring, or buy” the energy needed for new data centers, pay the cost of required power-delivery upgrades and negotiate structures under which they still pay for capacity created for their facilities even when it is unused. The explicit rationale is to prevent ordinary electricity customers from absorbing infrastructure costs created by data-center demand.

That is worth pausing on.

The government is effectively saying:

The compute tax should stay inside the AI economy rather than leaking onto everybody else’s electricity bill.

That is not really an AI regulation.

It is industrial economics.

And every country is approaching it differently.

Four economies, four ways to pay the bill

The competition between the United States, China, Europe and India is usually described as a race for models.

I think that’s becoming too narrow.

It’s increasingly a contest over who builds the cheapest, deepest and most strategically controlled supply of machine intelligence.

The Federal Reserve’s comparative work identifies compute as one of the clearest measures of national AI capacity. Based on observed high-end AI supercomputer capacity through mid-2025, it estimated roughly 74% in the United States, 14% in China and 4.8% in the EU, while cautioning that the underlying data capture only part of global capacity and that Chinese data are comparatively opaque.

But the countries are not simply building different quantities of the same thing.

They are building different political economies around compute.

The United States is treating compute primarily as a private-capital industry.

The U.S. advantage is extraordinary depth in private technology capital, hyperscalers, model companies, data centers, chips and cloud infrastructure. The policy response has largely tried to accelerate physical buildout while increasingly addressing the externalities that buildout creates. The White House’s 2026 ratepayer framework is a good example: let private companies build aggressively, but push the incremental grid and generation costs back toward the companies creating the demand.

That’s basically a market-led model with industrial-policy support.

Capital allocators decide where to build.

Technology companies decide which architectures win.

Energy markets respond.

Government increasingly tries to remove permitting and infrastructure constraints while preventing some of the cost from being socialized.

The strength of this model is brutally obvious: scale and capital.

The weakness is also obvious: private firms will optimize around private returns, which can collide with grid planning, local politics, electricity affordability and infrastructure lead times. The IEA now identifies U.S. grid queues, power equipment and generation as practical bottlenecks to the pace of data-center construction.

China is thinking about compute more like national infrastructure.

The distinction is striking.

China’s National Development and Reform Commission describes the country’s emerging national compute network as infrastructure that links computing resources across regions. The “East Data, West Computing” program was designed partly to shift compute demand from economically dense eastern regions toward western regions where energy resources are more abundant. Eight national computing hubs and ten data-center clusters formed the initial national structure, and subsequent policy has moved toward integrated scheduling and more market-oriented allocation of compute resources.

China’s current Five-Year Plan framework goes further, calling for an integrated national computing network with resource pooling, monitoring, scheduling and operating standards while coordinating compute-network development with the electricity grid and communications infrastructure.

That is a very different way of conceptualizing the problem.

America largely asks:

Where will private capital build compute?

China increasingly asks:

How should the national compute system be architected?

That doesn’t mean China has eliminated markets. Its policy language explicitly discusses marketization and resource allocation. But the physical architecture is much more deliberately coordinated at the state level.

China also has a potentially enormous energy advantage. Federal Reserve research notes that China’s power-generation infrastructure expanded far faster than that of the United States in recent years, even though the U.S. currently retains a commanding lead in high-end compute capacity.

There is a catch.

China’s current data-center electricity supply remains much more carbon intensive. The IEA estimates that coal supplies close to 70% of the electricity serving Chinese data centers today, with renewables near 20% and nuclear around 10%. Policy is deliberately steering more facilities toward renewables-rich western regions, and both coal and renewable generation are projected to expand materially in response to data-center demand through 2030.

So China is doing something strategically clever but environmentally messy:

It is treating the geography of compute and the geography of energy as the same problem.

That idea may turn out to matter enormously.

Europe is trying to socialize part of the fixed cost of strategic compute.

The European Union’s model sits somewhere else again.

Rather than relying exclusively on a handful of domestic hyperscalers that Europe largely does not have, the EU is building shared AI infrastructure through AI Factories and the newer AI Gigafactory program.

In July 2026, the EU launched a tender for as many as seven AI Gigafactories. The initiative is backed by up to €10 billion in EU and national public funding and is designed to unlock at least €20 billion more from private investors. The planned facilities combine processors, cloud software, connectivity and energy-efficient data centers and are meant to provide compute access to startups, scale-ups, industry, researchers and public authorities.

Europe is therefore making a different bet:

Compute capacity itself has become strategic infrastructure, and some of its fixed cost should be pooled because dependence on foreign compute can become an industrial dependency.

The EU explicitly frames these facilities around technological resilience and strategic autonomy as well as compliance with European rules on safety, data protection and security.

Whether that produces globally competitive AI companies is not guaranteed.

But economically, the logic is clear.

Europe is trying to lower the compute tax faced by domestic innovators by helping finance the fixed infrastructure underneath them.

India is doing something more direct: subsidizing access.

India’s model may be the most explicit acknowledgment that compute access itself is an economic input.

The government’s IndiaAI Mission has a budget of roughly ₹10,372 crore and, by June 2026, had expanded a shared national compute pool to more than 45,000 GPUs. By August, according to the government, 237 projects had used subsidized computing capacity covering 9.318 million GPU hours, specifically to reduce barriers for researchers, startups and innovators.

India cannot currently outspend the American hyperscalers.

So instead it is trying to change the economics faced by the user.

Rather than every startup individually acquiring scarce compute at global market prices, government procurement aggregates capacity and makes it cheaper to access.

My read is that these four models amount to four different answers to the same question:

Who should absorb the fixed cost of making machine intelligence abundant?

America: mostly private capital.

China: coordinated national infrastructure plus markets.

Europe: public-private sovereign infrastructure.

India: subsidized shared access.

That distinction could become as consequential as differences in model capability.

Because in an agentic economy, the country with the cheapest reliable intelligence input may eventually have an advantage similar to the country that once had cheap electricity, cheap labor or cheap capital.

The argument experts are actually having

There are at least three big disagreements here, and they are more interesting than the usual “AI good versus AI bad” debate.

The first is whether the compute tax survives at all.

There is a strong case that inference becomes so cheap that worrying about it is pointless.

The evidence for this view is formidable. Stanford’s AI Index found that the cost of running a model at roughly GPT-3.5-level benchmark performance dropped from about $20 per million tokens in November 2022 to $0.07 by October 2024, a decline of more than 280-fold in roughly 18 months. Across different tasks, Stanford reported inference-price improvements spanning roughly 9-fold to 900-fold annually. McKinsey has highlighted the same underlying cost collapse as one of the defining AI technology trends.

That trend is real.

The mistake is concluding that falling unit costs make aggregate compute economics irrelevant.

The IEA is observing the opposite phenomenon in electricity: energy per task is dropping rapidly while total AI electricity demand accelerates, because adoption and task intensity are growing faster.

Those two things can coexist indefinitely.

Imagine inference becomes ten times cheaper.

Then imagine agents become cheap enough that businesses run one hundred times as many autonomous tasks.

The unit tax falls 90%.

The aggregate compute bill rises tenfold.

That is why I don’t think the right debate is:

Will inference get cheaper?

Almost certainly.

The better debate is:

Will efficiency improve faster than demand for machine intelligence expands?

Right now, nobody can answer that confidently.

The second disagreement is whether all this compute produces an economic return commensurate with the capital going into it.

Here the range of credible expert opinion is enormous.

McKinsey’s latest work on agents and robotics estimates that a midpoint adoption scenario could unlock roughly $2.9 trillion annually in U.S. economic value by 2030, primarily through automation and the redeployment of labor hours. McKinsey is careful to call this potential economic value rather than a forecast of equivalent GDP growth and says realizing it depends heavily on workflow redesign and organizational adaptation.

Daron Acemoglu at MIT is much more skeptical about macroeconomic impact. His modeling places the ten-year increase in total factor productivity from current AI advances at no more than around 0.66%, with an estimate around 0.53% once harder-to-learn tasks are treated more conservatively. His corresponding GDP estimates are roughly 0.93% to 1.16% over ten years under a modest investment response and as high as roughly 1.4% to 1.56% under a larger capital boom.

These estimates are not directly comparable. McKinsey is measuring the potential economic value of work that could be automated or reallocated under an adoption scenario; Acemoglu is estimating economy-wide productivity and GDP effects using a much more restrictive macro framework. But the gap tells you just how unsettled the economics still are.

The Federal Reserve sits somewhere in the middle.

Its July 2026 analysis says task-level experiments can show substantial improvements without those improvements immediately appearing at the company or national level. Making a programmer 10% faster at one task does not automatically make the company 10% more productive if bottlenecks elsewhere remain. The Fed says there was still no large aggregate productivity signal clearly attributable to AI as of 2026, while emphasizing that historically, general-purpose technologies often require years of complementary investment and organizational change before productivity gains become visible.

The OECD reaches a similar conclusion: controlled and workplace studies often report 20% to 40% performance improvements on specific tasks, depending on context, while the economy-wide and long-run consequences remain uncertain.

This disagreement matters enormously for investors.

Because a trillion-dollar compute buildout is very easy to justify if AI ultimately reorganizes several trillion dollars of annual labor and economic activity.

It is much harder to justify if most companies end up buying expensive copilots that make employees moderately faster without fundamentally changing output.

That is why the decisive variable isn’t benchmark intelligence anymore.

It is organizational conversion.

Can companies turn model capability into completed economic work?

OpenAI, McKinsey, BCG and the Federal Reserve, despite coming from very different positions, are converging on that point: deeper workflow redesign and successful outcomes matter more than raw usage.

The third disagreement is subtler.

Does AI raise transaction costs or destroy them?

My answer is:

Both.

AI agents are likely to crush a huge category of Coasean transaction costs: searching for information, finding counterparties, comparing options, negotiating terms, monitoring performance and coordinating activity. MIT researchers explicitly identify this reduction in search, communication and contracting friction as one of the fundamental economic promises of agents.

But they do it by substituting computation for human effort.

That means an old transaction that might have cost:

$50 of employee time

• $20 of administration

• three days of latency

could become:

$0.70 of compute

• $0.10 of database and search access

• thirty seconds

The compute tax rose from zero to 70 cents.

The total economic transaction cost collapsed from $70 to less than a dollar.

There is no contradiction.

And this distinction is critical, because it tells us not to optimize for minimum compute spending.

The objective should be minimum total cost per valuable outcome.

OpenAI calls its version “Useful Intelligence per Dollar.” BCG calls for measuring return against the combined costs of human intelligence and tokens. Both frameworks point in the same direction.

Spend more compute when another dollar of inference replaces twenty dollars of labor.

Spend less compute when a deterministic API can solve the problem for a fraction of the cost.

Use an expensive frontier model when reliability saves five retries.

Use a tiny model when the task is trivial.

Use no model at all when normal software works.

This sounds obvious.

It isn’t how much of the industry has been built so far.

What the capital allocator should take away

I think the AI economy is heading toward a realization.

Intelligence is becoming cheap, but it is not becoming free.

And once intelligence becomes embedded in products, the economics of intelligence start appearing everywhere.

In gross margins.

In pricing models.

In cloud bills.

In electricity markets.

In data-center financing.

In transformer factories.

In grid regulation.

In government industrial strategy.

In national competitiveness.

The winners could be the companies that turn each dollar of computation into more economically valuable work than everybody else.

For founders, that means one of the most important metrics in the company may eventually be something deceptively simple:

Compute cost per successful customer outcome.

Not token consumption.

Not model calls.

Not users chatting with the agent.

Actual finished work.

For CEOs, it means AI expenditure should stop being buried inside a generic technology budget. BCG’s current framework separates AI expenditure among investment in reusable capabilities, operating costs for internal AI work, and COGS when inference sits inside customer-facing products. That distinction becomes increasingly important as agent use expands.

For VCs, I would be increasingly suspicious of AI businesses whose economics are basically:

buy intelligence retail, resell intelligence with a markup.

That is not automatically a moat.

The more interesting companies are the ones that can structurally change the ratio between compute consumed and value produced through proprietary data, workflow integration, model routing, specialized evaluation, distribution or ownership of the customer relationship. BCG’s latest competitive analysis similarly argues that durable advantage is shifting toward assets and relationships that improve with scale and use rather than generic capabilities that AI can easily replicate.

For infrastructure investors, the compute tax moves upstream.

Inference demand becomes server demand.

Server demand becomes electricity demand.

Electricity demand becomes generation, storage and transmission demand.

Those requirements create demand for chips, networking, memory, cooling, transformers, turbines, batteries and capital. The IEA already sees pressure and bottlenecks across many of these categories and expects capital markets to become increasingly important in financing continued data-center expansion.

For policymakers, the problem is even bigger.

The central policy question will increasingly be not merely how much AI a country develops, but what domestic businesses pay to access machine intelligence and who ultimately absorbs the infrastructure costs required to produce it.

America is trying to make private companies internalize more of the power-system costs their data centers create.

China is building a nationally coordinated compute network around the geography of electricity and demand.

Europe is using public capital to build shared sovereign compute capacity and pull in private investment.

India is directly subsidizing compute access for domestic innovators.

Those are not just AI policies.

They are different theories of who should pay for intelligence infrastructure.

And over the next decade, that may turn out to matter far more than most arguments about which chatbot currently wins which benchmark.

Because the important economic transformation isn’t that AI can answer questions.

It is that AI is beginning to do work.

Once an agent searches, reasons, compares, negotiates, codes, buys, sells, monitors and acts, computation stops being something that happens somewhere in the background of the technology industry.

It becomes an input into economic production.

Every AI-mediated action carries some amount of it.

The price of that input will keep falling. Stanford’s data already show how violently inference costs can collapse, while the IEA simultaneously shows how rapidly aggregate demand can rise as efficiency improves.

That is why the central paradox of the next phase of AI is so important:

The compute tax could approach zero per transaction while becoming enormous in aggregate.

That isn’t a bug in the AI economy.

It may be the AI economy.

We spent the software era turning computation into something users barely had to think about.

Now we are turning intelligence itself into a metered commodity.

And when intelligence has a price, the people who control its production, distribution, efficiency and energy supply don’t merely own pieces of the technology stack.

They sit inside the cost structure of everything built on top of it.

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Goldman Sachs put a number on the AI buildout that finally makes the scale legible.

Global AI investment will cross $1 trillion in 2026, with $581 billion of that landing inside the United States.

By 2028, on Goldman’s own extrapolation, AI capex reaches 2.8 percent of US GDP.

Say that number a different way. One dollar in every 36 the American economy produces will be spent building the physical guts of artificial intelligence. Transformers, turbines, copper, land, concrete, switchgear, and the electricians and linemen who install all of it.

Looks like bubble? Whether the valuations hold, whether the revenue shows up, whether Microsoft and Meta and Oracle can actually earn a return on the debt they’re taking on to build it.

That’s the first order question, and it’s the one every analyst on financial television is paid to answer. It’s also, for our purposes, the less interesting one.

What happens to everything else while this is happening. Because 2.8 percent of GDP does not appear out of thin air. It has to come from somewhere.

Capital that goes into a transformer for a data center in northern Virginia is capital that does not go into a transformer for a semiconductor fab in Arizona, a housing development in Texas, or a grid upgrade in Ohio. A construction crew pouring concrete for a hyperscale campus is a construction crew that is not pouring concrete for an apartment building. This is not a hypothetical. It is already showing up in the data, and it is going to reshape which industries get funded and which get starved for the rest of this decade.

The Binding Constraint Has Moved, and It Keeps Moving

The Full Stack Capitalist lens on any technology buildout is simple. Find the thing that is actually scarce, not the thing that gets the headlines, and trace who captures the rent from controlling it. For most of 2023 and 2024 the scarce resource was GPUs.

That constraint is largely gone. TSMC has repeatedly expanded advanced packaging capacity, and chip allocation is no longer the thing standing between a hyperscaler and a bigger cluster.

The constraint moved to power, and specifically to the physical equipment needed to deliver power.

Power transformers were averaging roughly 128 weeks of lead time by 2026, with generator step up units stretching to 144 weeks and the highest capacity units quoted four to five years out.

Before 2020 the same transformer shipped in under two years, often in a matter of weeks. Of the roughly sixteen gigawatts of data center capacity announced for 2026 in the US, only about five gigawatts was actually under construction, with analysts estimating that 30 to 50 percent of the announced pipeline would be delayed or cancelled outright because of transformer and switchgear shortages, not because the money wasn’t there.

That last clause is the whole essay in miniature. Not because the money wasn’t there. This is a capital abundant, physical infrastructure scarce economy. The hyperscalers have functionally infinite access to debt and equity markets. What they cannot do is manufacture grain oriented electrical steel faster, and China controls roughly 60 percent of the world’s transformer manufacturing capacity, which means the bottleneck is also a geopolitical one.

When the binding constraint is a slow moving industrial input rather than a fast moving financial one, the excess capital doesn’t sit idle. It goes looking for the next scarce thing and bids the price up. That’s copper, and it’s construction labor, and increasingly it’s the electricity itself.

Copper Is the Cleanest Proxy for What’s Being Bid Away

A single one gigawatt AI facility requires on the order of 50,000 metric tons of copper.

At current buildout paces of roughly fifteen gigawatts of new capacity a year, data centers alone are adding somewhere around 750,000 metric tons of incremental copper demand annually, and that’s before counting the copper needed for the substations, transmission lines, and grid upgrades required to actually deliver the power, which several analysts now believe will exceed the copper used inside the buildings themselves.

Copper prices have risen more than 60 percent since early 2025 and have repeatedly traded above $13,000 to $14,500 a metric ton, levels that used to be considered crisis pricing.

Copper is inelastic in a way most commodities aren’t.

You can’t substitute your way around a high voltage application the way you can swap aluminum into a low stakes consumer product. So every other industry that needs copper, electric vehicle manufacturing, grid modernization for reasons that have nothing to do with AI, home electrification, general industrial construction, is now bidding against hyperscalers with functionally unlimited balance sheets for a metal whose supply grows at roughly 1.4 percent a year against demand growing far faster.

Mine permitting timelines run 15 to 17 years from discovery to production. Supply cannot respond to price. Demand, backstopped by trillion dollar capex budgets, doesn’t have to care about price. That’s a structural transfer of purchasing power from every copper consuming industry that isn’t AI, straight into the AI buildout, and it happens silently, through a commodities market, without anyone voting on it.

Manufacturers Are Losing to Data Centers for the Same Megawatts

This is the part of the story that gets the least attention and matters the most, because it’s happening at the level of individual grid interconnections, not abstract GDP shares.

In regions across the country, manufacturers are discovering that the power capacity they assumed would be available has already been claimed by a data center campus down the road.

PJM, the regional grid operator spanning thirteen states and Washington DC, received more than 800 project applications totaling roughly 220 gigawatts in its most recent interconnection cycle, a queue that was never designed to process anything close to that volume.

This is not an abstract crowding out story. It is the reshoring story colliding head on with the AI story, and AI is winning. Every speech about bringing semiconductor fabs and battery plants and industrial capacity back to American soil assumed those factories would be able to get power on a normal timeline.

They can’t, because the queue in front of them is now dominated by hyperscale data center requests that arrived with more capital and, often, more political priority attached. Eaton, Vertiv, and Schneider Electric have all announced new manufacturing capacity for the switchgear and transformers everyone needs, but new factories take years to reach output, which means the equipment shortage outlasts the current wave of expansion plans on both sides.

The bottleneck has migrated from the substation to the factory floor of the companies that build substations, and there is no version of that sentence that is good news for a reshoring agenda that depends on getting industrial power online this decade rather than next.

There’s a second casualty inside the power story that shows up on household bills rather than corporate balance sheets.

PJM’s electricity suppliers paid $14.7 billion at a recent capacity auction to guarantee they’d have enough power for their customers, up from $2.2 billion the year before, a nearly sevenfold jump driven substantially by the slow moving interconnection queue that data center demand has helped clog. Ratepayers, most of whom have no stake in the AI trade whatsoever, are the ones covering that spread. That is a direct, traceable transfer from households to the AI buildout, mediated by a grid queue nobody outside the industry is watching.

Credit Markets Are the Newest Front, and It’s Bigger Than People Realize

The capex crowding story usually gets told through physical inputs. The credit market version is just as real and less discussed.

Hyperscaler capex in 2026 is consuming close to 100 percent of operating cash flow, compared with a ten year average closer to 40 percent, which means the era of AI being funded out of Big Tech’s spare cash is over.

The five largest hyperscalers issued around $121 billion in US corporate bonds in 2025, more than four times their 2020 to 2024 annual average, and issuance has accelerated further this year, with Goldman projecting roughly $250 billion in hyperscaler bond sales for 2026 rising toward $400 billion in 2027. Technology has climbed to roughly 10 percent of the Bloomberg US Corporate Index and, in several major benchmarks, has overtaken banking in weight for the first time in the index’s history.

That reshuffling matters beyond the tech sector because investment grade credit markets are not infinitely deep in any given window.

Overall US corporate bond issuance is expected to hit $2.46 trillion in 2026, and passive bond funds that track investment grade indexes will mechanically absorb more hyperscaler debt as the weighting shifts, which means every other investment grade borrower, industrials, utilities, healthcare systems, is now competing for spread and investor attention against the highest quality, most aggressively marketed issuers in the market.

AI debt is also colliding with a federal government running toward a $2 trillion annual deficit and no longer backstopped by a Federal Reserve willing to buy Treasuries at scale, which means every dollar of capital markets appetite is being split three ways between sovereign debt, AI infrastructure debt, and everyone else. Early signs of investor fatigue are already visible.

A recent Amazon bond sale needed to offer 18 to 21 basis points of extra yield to clear, with orders covering the deal only 2.5 times versus 3.2 times earlier in the year. When the most creditworthy borrowers on earth have to sweeten the deal to move paper, that’s the credit market’s way of saying the well has a bottom.

Who Actually Gets Defunded

Put the physical and financial constraints together and a pattern emerges. The industries that lose in this environment are the ones that share AI’s inputs but not its capital access.

Reshored manufacturing loses because it needs the same transformers, the same grid interconnections, and the same skilled electrical labor, but arrives at the queue with a fraction of the balance sheet.

Housing and general construction lose because they compete for the same copper, the same construction crews, and increasingly the same land near power infrastructure, without the pricing power to bid AI campuses off a site.

Mid tier industrial and healthcare borrowers lose in the credit markets because investment grade capacity that used to flow to them now flows through indexes increasingly weighted toward hyperscaler paper. Utility ratepayers lose directly, through capacity auction costs that get passed straight to household bills.

And clean energy and grid modernization projects that have nothing to do with AI, projects that in a fairer queue would be interconnecting today, sit for five years or more behind data center requests that jumped the line with bigger checkbooks.

None of this required anyone to make a decision to defund those sectors.

That’s what makes it a genuine economic phenomenon rather than a policy failure with an obvious villain. It’s what economists mean by crowding out, capital and physical inputs flowing toward the highest expected return sector in a supply constrained system, with everyone else absorbing the opportunity cost whether they had a vote in the matter or not.

The externality here isn’t carbon or noise. It’s every other capital intensive industry in the country discovering that its cost of capital, its equipment lead times, and its construction timelines just got worse because of a buildout it has no stake in.

What This Means If You’re the One Bidding, or the One Losing the Bid

If you’re a founder building anything that touches physical infrastructure, energy, industrial equipment, construction, manufacturing, treat 2026 through 2028 as a period where your input costs are structurally elevated regardless of what your own sector is doing. Transformer and switchgear lead times aren’t a data center problem you can ignore. They’re now your problem too, and the companies solving for it, whether through behind the meter generation or vertically integrated equipment supply, are building a genuine moat.

If you’re an operator running a company that depends on grid interconnection, get in the queue earlier than you think you need to, and underwrite your project assuming the equipment timeline, not the permitting timeline, is your critical path. The site with land control and zoning is worthless if the transformer arrives in 2029.

If you’re an investor, the interesting trade isn’t only the hyperscalers or the chipmakers everyone already owns. It’s the picks and shovels one layer further down, the transformer and switchgear manufacturers, the copper producers who can actually bring new supply online inside a normal timeframe, and the credit story itself, where investment grade spread widening on hyperscaler paper is a signal worth watching closely rather than dismissing as noise from AA rated borrowers.

If you’re in government, the reshoring agenda and the AI agenda are currently fighting each other for the same substations, and pretending otherwise doesn’t change the interconnection queue. Any serious industrial policy in 2026 has to reckon with the fact that a semiconductor fab and a hyperscale data center are now direct competitors for the same transformer order book, and a queue that runs first come, first served with no strategic prioritization will keep handing the win to whichever project has the biggest balance sheet, not the one that matters most to national competitiveness.

The trillion dollar number was never really about AI. It’s about what an economy looks like when one sector can outbid every other sector for the physical and financial inputs all of them need at once.

Something has to give. The Goldman Sachs report just told you the size of the bill. It didn’t tell you who’s paying it. That part you have to work out yourself, and increasingly, the answer is everyone who isn’t in the room.

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On August 10, Jensen Huang sat down with five of the most powerful capital allocators on earth.

NVIDIA signed memoranda of understanding with Apollo, BlackRock, Blackstone, Brookfield, Goldman Sachs and KKR to mobilize over $500 billion in third-party capital for AI infrastructure. Huang’s framing on CNBC was: this is the first time technology chips have become an investable asset class.

Read the announcement again.

NVIDIA isn’t lending its own balance sheet to customers, the way it awkwardly tried to do weeks earlier when reports surfaced that it might backstop $250 billion for a single OpenAI buildout and the stock dropped hard on the news.

This is the opposite structure. NVIDIA gets six independent, deep-pocketed asset managers to underwrite the exposure instead, so nobody can accuse NVIDIA of manufacturing its own demand.

Apollo brings roughly a trillion dollars of assets under management. Blackstone brings north of $1.3 trillion. Brookfield brings another trillion. BlackRock’s Larry Fink is out there calling the AI buildout “unprecedented investment” that only long-duration capital can fund.

Long-duration capital means pension funds, insurers, and increasingly retirement products. NVIDIA is trying to convert a chip replacement cycle into a bond market.

The binding constraint just moved

Until this year, AI capex was constrained by what Microsoft, Meta, Amazon, Oracle, CoreWeave and OpenAI could fund off their own cash flow and equity. That ceiling was already straining.

Moody’s has been flagging that unprecedented capital expenditures are squeezing hyperscaler free cash flow and pushing them toward heavier debt loads.

The BIS put a number on how much of that debt isn’t even showing up where you’d look for it: in its March 2026 Quarterly Review, the central bank of central banks documented that hyperscalers are increasingly funding data centers through special purpose vehicles that raise private debt while the hyperscaler merely signs a long-term operating lease.

Economically that lease is a financial commitment. On the income statement it’s rent. Moody’s estimated hyperscalers are sitting on roughly $662 billion of these signed-but-not-yet-started lease commitments, off balance sheet, a figure bigger than those same companies’ combined on-balance-sheet debt.

The BIS calls this “shadow borrowing.” I’d call it the AI industry discovering what every capital-intensive industry eventually discovers: you can’t fund $400 billion a year in infrastructure out of $60 billion in revenue forever, so you either slow down or you find someone else’s balance sheet.

NVIDIA’s answer is to institutionalize the someone-else’s-balance-sheet part. Instead of ad hoc SPVs negotiated deal by deal, you get standing platforms with Wall Street’s biggest infrastructure investors, designed to keep pumping capital into the system indefinitely.

That’s the binding constraint shift: AI buildout stops being limited by tech company cash flow and starts being limited by how much of the world’s retirement and insurance capital is willing to underwrite GPU clusters.

Once that gate opens, the more interesting question isn’t whether the money shows up. It’s who eats it if the bet is wrong.

Scenario one: NVIDIA is right and this becomes infrastructure

If AI demand genuinely compounds for another decade, if enterprise adoption becomes ubiquitous and agent workloads keep inference demand climbing, this financing innovation looks like one of the more important structural shifts in the industry’s history.

Capital costs fall because a trillion-dollar pool of patient capital is cheaper than equity. Smaller countries can finance sovereign compute without waiting on hyperscaler goodwill. Enterprises rent capacity instead of building it.

AI infrastructure becomes a standard institutional allocation next to real estate, utilities, and airports, and NVIDIA quietly becomes something stranger than a chip company: an architect of a global compute capital market, collecting hardware revenue on volumes that its own balance sheet never had to risk.

That’s the bull case, and it’s coherent. It’s also the case everyone currently pricing NVDA is implicitly assuming.

Scenario two: demand disappoints, but only a little

This is the realistic base case if you’re being honest about how these cycles usually run.

AI demand is real, but investors have overestimated utilization, pricing power, GPU useful life, or customer credit quality. Nothing collapses. Returns just come in worse than modeled.

GPU rental prices soften. Residual values on aging clusters get marked down.

Operators refinance at worse spreads. Private credit spreads widen. Marginal data center projects that only worked at 80% utilization assumptions get canceled or need more equity to pencil. Neocloud consolidation accelerates as weaker operators get absorbed.

Nobody blows up. Ownership just transfers from the optimistic first-round financiers to distressed-capital specialists who buy the assets for less than they cost to build.

Apollo and Blackstone, notably, are the ones best positioned to be on both sides of that trade: underwriters of the original financing and buyers of the eventual distress.

Scenario three: the credit channel actually breaks

Now push harder. Model efficiency improves faster than compute demand. Custom silicon eats real share. Inference prices fall further and faster than underwriting assumed.

Corporate customers won’t sign the long-duration contracts the debt was structured against. A facility underwritten at 80% utilization runs at 40%.

Cash flow falls. Debt service doesn’t. Here’s the chain, and it’s worth sitting with because this is where a hardware demand problem becomes a macro problem:

An AI company can’t honor its compute contract. The AI cloud operator loses revenue. The SPV can’t service its debt. The GPU collateral gets marked down. The private credit fund holding that debt takes losses.

The pension or insurance investor that owns a slice of that fund marks down its exposure. New lending to the sector freezes. Data center construction stops. GPU orders fall. NVIDIA’s revenue falls. Suppliers cut capex. Power projects tied to those data centers get canceled. Construction employment falls.

The BIS has already mapped these transmission channels explicitly: refinancing stress, shifts in private credit risk appetite, guarantees getting triggered, and connections looping back into banks through warehouse lending lines.

What started as a GPU utilization problem becomes a credit problem, then a capex problem, then an employment problem. That’s what financialization does in every cycle. It doesn’t just fund the boom. It manufactures the amplitude of the bust.

Scenario four: it goes systemic

This requires several things stacking at once: heavy leverage, correlated exposure across the sector, underwriting that assumed too much for too long, meaningful retirement and insurance capital concentrated in the exposure, banks financing the private credit vehicles that financed the SPVs, and a real, not cosmetic, disappointment in AI revenue.

Put those together and you get a liquidity crisis around assets everyone modeled as long-duration infrastructure but that behave, collateral-wise, like depreciating technology equipment.

Pension funding ratios deteriorate. Insurers take write-downs. Private funds gate redemptions. Banks pull warehouse financing. Credit spreads jump across the sector, not just at the margin. Infrastructure projects with nothing to do with AI become harder to finance because the capital that would have funded them is repricing risk everywhere.

At that point the exposure has migrated into institutions that are too politically important to let fail, which sets up the fourth-order problem: what does a government do when public pension plans own the funds, insurers hold the debt, banks financed the vehicles, utilities built power infrastructure around the facilities, and thousands of construction jobs depend on the projects continuing?

History says the answer is rarely “let the capital structure clear.” It’s restructuring, guarantees, tax relief, cheap financing, and possibly government-backed compute demand. Not because GPUs deserve rescuing, but because the exposure moved into places democracies can’t easily let fail.

Why the history keeps rhyming

None of this is new, which is exactly why it’s worth taking seriously instead of dismissing as bubble-talk.

Britain’s canal mania in the 1790s saw the successful canals, like the Bridgewater, cut coal transport costs into Manchester so dramatically that investors piled into every canal scheme that followed. Many of those follow-on canals never produced the promised returns. The investors lost money. Britain kept the canal network.

Railways in the 1840s ran the same play at greater scale.

The technology worked, demand was real, and the Railway Mania still wiped out enormous amounts of private capital when valuations collapsed. Britain kept the railways.

American railway expansion in the 1860s and 1870s was financed almost entirely on debt, with railway bonds reportedly reaching close to a third of GDP by 1890. The Panic of 1873 hit, defaults spread, iron demand collapsed, factories closed, and unemployment rose.

Nobody looked back in 1880 and concluded railways were a mistake. The infrastructure was transformational. The capital structure and the timing were the problem.

Electricity is the closest thing to a clean analogue for what “compute as infrastructure” is supposed to look like, and it’s also the comparison that exposes NVIDIA’s real vulnerability.

Generators, transmission, and distribution became close to ideal pension assets because they have very long useful lives, predictable demand, regulated pricing, and low technological obsolescence.

A transformer built today doesn’t become worthless because a better transformer ships next year. A GPU cluster is a different animal entirely. The data center shell might last thirty years.

The power connection might last fifty. The frontier compute inside it has a competitive life measured in a few years, with some of NVIDIA’s own performance metrics historically doubling roughly every eighteen months to two years.

That’s the structural mismatch sitting underneath the entire “compute is an investable asset class” pitch: the building and the chip inside it depreciate on completely different curves, and the financing is being priced as if they don’t.

Telecom in the 1990s finished the pattern. Fiber got built on the correct bet that internet traffic would explode, traffic did explode, and investors still got wiped out because too much capacity got built too fast at prices that assumed permanent scarcity. Capacity became abundant, prices collapsed, companies went bankrupt, and the next generation of internet companies inherited extraordinarily cheap bandwidth built by someone else’s losses.

The BIS’s own data on the current cycle already rhymes with this pattern more than the industry wants to admit.

Private credit loans to AI-related companies have gone from near zero to reportedly over $200 billion in a few years, with projections of hundreds of billions more over the next two years.

Hyperscaler bond issuance has passed $100 billion in a single year while credit default swap spreads on that debt have been climbing at the same time, which means bond investors are already quietly pricing in more risk than the private credit spreads on AI loans currently reflect. That gap between what bond markets and private credit markets think the same risk is worth is exactly the kind of mispricing that shows up right before someone discovers it the hard way.

The sentence that matters more than the pension warning

There’s a temptation to write this up as “your retirement account is buying GPUs,” and that framing is too clean.

Direct 401(k) exposure to private credit is still underdeveloped, though the Department of Labor moved in March 2026 to make it easier for 401(k) plans to hold alternatives including private credit and private equity, which tells you which direction this is heading even if it hasn’t fully arrived.

The sharper claim is this one: the biggest mistake investors can make is assuming that because AI will transform the economy, AI infrastructure must therefore be a good investment.

History says close to the opposite.

Transformative infrastructure attracts too much capital precisely because everyone correctly recognizes it will transform the economy, and the resulting competition transfers most of the economic surplus from the people who financed the buildout to the people who use it afterward. Railway passengers won.

Manufacturers won on cheap freight. Internet companies won on cheap fiber. Future AI companies may well win on GPU capacity nobody expected to get this cheap this fast. The losers are usually whoever financed the first generation.

That’s what NVIDIA’s August 10 move is actually about. Not “is AI real.” It obviously is. The question is who owns the depreciation risk while the revolution gets built, and NVIDIA just spent $500 billion of other people’s underwriting to make sure the answer isn’t NVIDIA.

What this means if you’re building, running, investing, or regulating

Founders and operators building on rented compute: the era of GPU scarcity dictating your unit economics is not permanent. If utilization disappoints anywhere in this financing stack, capacity gets dumped by distressed holders faster than you can plan around, and your input costs could fall faster than your business model assumes. Build cost structures that benefit from cheaper compute rather than ones that depend on scarcity pricing holding.

Investors: the equity story and the credit story on AI infrastructure are being priced by two different markets right now, and they disagree. Private credit spreads on AI loans are running close to spreads on ordinary private credit, which prices AI risk as roughly average. Equity valuations on the same companies price in outsized returns. One of those markets is wrong, and it’s worth having a view on which one before you’re on the wrong side of the repricing.

Governments and policymakers: the too-important-to-fail characteristic isn’t building around NVIDIA. It’s building around the capital stack: the pension exposure, the insurance holdings, the bank financing lines behind the private credit funds. That’s the thing worth stress-testing now, well before anyone needs a bailout conversation, because by the time the exposure is visible it’s already systemic.

Everyone with a retirement account: you may end up simultaneously exposed to AI as a threat to your labor income and as a source of your retirement returns. That’s a genuinely new distributional position, and it’s worth understanding what your pension or 401(k) actually holds rather than assuming “diversified” means “insulated” from this specific cycle.

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NVIDIA is partnering with Apollo, BlackRock, Blackstone, Brookfield, Goldman Sachs, and KKR to mobilize more than 500 billion dollars in third party capital for AI infrastructure.

The framing is clean and confident. Compute is becoming an investable asset class, like toll roads or commercial real estate. NVIDIA provides the platform, the financial institutions provide the capital and the underwriting discipline, and everybody wins.

Read it again, slower. What actually happened is that NVIDIA found a new balance sheet to stand on. And the balance sheet it found belongs, eventually, to your 401k.

That is not a metaphor. It is the literal mechanical path the money takes. To understand why this deal matters, you have to walk through three layers of economics, because each layer solves the exact problem created by the layer before it, and each solution pushes the same underlying risk one step further from anyone who chose to take it.

First order: the circular loop everyone already knows about

Start with the thing people have been complaining about for over a year.

NVIDIA invests in its own customers, those customers use the money to buy NVIDIA chips, and NVIDIA books the sale as revenue. Between 2020 and 2025, NVIDIA made roughly 170 investments worth about 53 billion dollars across the AI ecosystem, including stakes in OpenAI, CoreWeave, and Nebius, companies that turn around and spend heavily on NVIDIA hardware.

In 2025 alone the pace accelerated to 59 deals worth 23.7 billion dollars. NVIDIA holds a 91 percent stake in CoreWeave. It committed up to 100 billion dollars to OpenAI. It just put up to 10 billion into Anthropic alongside a 5 billion dollar Microsoft investment, with Anthropic agreeing to buy 30 billion dollars of Azure compute in return.

Jensen calls the circularity accusation ridiculous, and he has a point in the narrow sense that his equity checks are small relative to what these companies raise elsewhere.

But the deeper issue was never the size of any single check. It was that the whole ecosystem’s reported demand was being partially financed by the same company selling the product, which makes it structurally hard to tell how much of the AI buildout reflects end user willingness to pay versus vendor financed order backfilling.

That is first order circularity, and it’s the version of this story that’s been in the financial press for a year.

Second order: the banks said no, so the debt went private

First order circularity created a second, less visible problem: concentration risk on bank balance sheets.

Oracle’s roughly 300 billion dollar buildout commitment tied to OpenAI pushed major banks toward their single counterparty concentration limits.

Data center construction loans are unusually large relative to typical infrastructure lending, so a handful of delayed or canceled projects can blow out a bank’s exposure to the entire sector. Once banks hit those limits, they stopped being willing or able to originate new data center debt at the pace the buildout needed.

So the financing moved somewhere banks don’t have to answer to regulators about single name concentration: private credit.

Loans from private credit funds to AI related companies went from near zero to over 200 billion dollars in a few years, and Morgan Stanley projects another 800 billion in private data center financing on top of that.

CoreWeave’s 7.5 billion dollar debt facility, arranged through Blackstone’s tactical opportunities group, is secured by the company’s GPUs and customer contracts, carries a variable rate around 11 percent, and started requiring repayment just as the value of that GPU collateral was already softening. CoreWeave’s interest payments now eat about 26 percent of revenue and 46 percent of adjusted EBITDA.

This is the second order move. Risk that used to sit on regulated bank balance sheets, subject to stress tests and capital requirements, got repackaged as private credit and moved to firms that don’t face the same oversight.

Third order: private credit needs duration capital, and duration capital means your retirement

Private credit funds don’t hold this debt forever on their own money.

They raise it from limited partners who want long duration, stable yield assets to match long duration, stable liabilities.

Who has liabilities like that? Pension funds and insurance companies.

That’s not incidental, it’s the entire point of the structure. Larry Fink said this part back in May, describing the roughly 10 trillion dollars of infrastructure investment the US needs over the next decade as money that has to come from savings accounts, pension accounts, and insurance companies, because the private sector is where the scale actually lives.

The channel is already well established.

New York and Pennsylvania state pension plans have money in Blue Owl’s 7 billion dollar digital infrastructure fund, the same fund behind Meta’s data center financing vehicles and multiple Oracle deals.

Major life insurers now hold nearly a trillion dollars in private credit overall. When hyperscaler backed data center bonds have come to market, insurance companies and pension funds have been the primary buyers, and demand has run multiples over what was offered, in one case more than three times oversubscribed for what were otherwise speculative grade bonds.

The Bank for International Settlements flagged this pattern in July, noting that when project failure risk lands on institutional investors rather than banks, there’s no equivalent resolution mechanism to absorb the shock in an orderly way.

Now look at what NVIDIA just announced. Apollo, BlackRock, Blackstone, Brookfield, Goldman, and KKR are not just asset managers.

They are the exact firms that specialize in taking illiquid, long duration credit exposure and repackaging it for pension plans and insurers who need yield to meet actuarial obligations decades out.

A 500 billion dollar financing platform built with these six firms isn’t NVIDIA discovering a new customer base. It’s NVIDIA institutionalizing the pipeline from GPU demand directly into retirement capital, at a scale and with a repeatability that ad hoc private credit deals never had.

The binding constraint was never capital. It’s who eats the loss.

Every version of this story from Jensen’s team makes the same argument.

NVIDIA compute is fungible, software upgradeable through CUDA, and backed by a deep pool of potential buyers if any single customer fails, so it behaves like durable infrastructure rather than a depreciating asset.

The A100 example gets rolled out constantly: introduced in 2020, still in active commercial use six years later. That’s true, and it’s also not the relevant question. The relevant question is what a GPU is worth relative to the newest generation, not whether it still runs.

One year H100 rental pricing went from about 1.70 dollars per GPU hour in October 2025 to 2.35 dollars in March 2026, while Blackwell capacity already commands 5.30 to 7.05 dollars per hour. Older silicon doesn’t stop working, it just stops earning what it used to, and every financing model built on residual value assumes someone will keep paying a premium for compute that’s no longer the frontier.

NVIDIA’s own announcement quietly acknowledges this. It’s offering a residual value support mechanism covering up to 25 percent of a given financing opportunity. That’s NVIDIA underwriting a slice of the downside on the assets it’s selling into these platforms.

Fair enough as far as it goes, but it means 75 percent of the residual value risk, plus all of the demand and utilization risk, sits with capital that has no equity upside if the trade works and full exposure if it doesn’t.

That’s the actual second and third order economics here.

First order, the ecosystem financed its own demand. Second order, that financing moved from regulated banks to unregulated private credit because banks hit real limits.

Third order, private credit funded itself with pension and insurance money that needs the yield but has no real way to independently verify utilization, demand durability, or GPU obsolescence risk fifteen layers removed from the actual data center.

None of this makes the AI buildout fake.

Enterprises are shipping real products with this compute, and usage is genuinely growing.

But real usage growth and a well underwritten financing structure are two different claims, and the entire architecture of this 500 billion dollar deal exists because the first three years of AI infrastructure financing already ran into limits that regulated capital wouldn’t cross.

Pension funds and insurers are not being brought in because they have superior judgment about GPU depreciation curves. They’re being brought in because they’re one of the last remaining pools large enough to absorb the scale this buildout requires, and because their liabilities are long dated enough that any reckoning is somebody else’s problem for a decade.

If the revenue shows up on schedule, this looks brilliant in hindsight, a natural infrastructure asset class finding its natural capital base.

If it doesn’t, and there are already senators writing formal letters about opaque debt markets and Moody’s publishing notes questioning whether AI data center leases will hold up as advertised, the loss doesn’t land on NVIDIA’s equity holders, who’ve had a decade long run to build a cushion.

It lands on whoever’s retirement plan bought the bond, years after the fact, with no idea the exposure was ever there.

What this means if you’re building, operating, investing, or governing

If you’re a founder building on top of AI infrastructure, the message is that compute pricing has a floor set by financing economics, not just supply and demand, which means the “AI is getting cheaper” trend line is less guaranteed than it looks once older GPU generations need to keep earning their financing costs.

If you’re an operator running AI transformation inside a company, pay attention to which vendors are structurally exposed to this financing chain, because a private credit stress event in the neocloud layer would hit availability and pricing for everyone downstream, not just the company that defaulted.

If you’re an investor, the tell to watch is not NVIDIA’s revenue growth, it’s the spread and coverage ratios on hyperscaler and neocloud bonds. Forbes already flagged softening demand and declining coverage ratios on that paper earlier this summer. That’s the canary, well before anything shows up in NVIDIA’s own numbers.

If you’re in government or policy, the four senators who wrote that letter in January were asking the right question before it was fashionable.

The exposure here isn’t concentrated in a few AI companies anymore. It’s distributed across pension funds and insurers whose beneficiaries never consented to a GPU depreciation bet, and there is currently no equivalent of a bank resolution regime for what happens when that bet goes wrong at scale.

NVIDIA found a new place to put the risk that the first two rounds of financing couldn’t hold anymore. That’s worth understanding clearly, because retirement money doesn’t get to renegotiate the way a hyperscaler balance sheet can.

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The lazy version of the AWS artificial-intelligence thesis is that Amazon arrived late, Microsoft owns OpenAI, Google owns DeepMind, Nvidia owns the chips, and AWS is desperately trying to catch up.

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You may think Nvidia is winning AI and nobody can reach them.

That’s partly true. But I want you to know AWS is doing something weired here.

They are trying to control the ground that every chatbot has to stand on.

That’s the binding constraint here, and once you see it, the whole AWS strategy stops looking scattered and starts looking like one of the cleanest infrastructure plays in tech history.

Here’s the simple version.

Amazon doesn’t need Claude to beat GPT, or GPT to beat Claude, or its own model, Nova, to beat either one. It just needs to be the toll road that all of them drive on.

Power contracts, chips, the software layer that connects a company’s data to a model, and increasingly the models themselves. If you want to build or run serious AI, there’s a good chance you’re paying Amazon somewhere along the way, regardless of who wins the model war.

Think of it in four layers, stacked on top of each other like a toll road with checkpoints at every mile marker.

At the bottom is power and land.

AI runs on electricity before it runs on anything clever, and Amazon has been locking up power deals, nuclear capacity, and data center real estate across the US, Europe, and beyond, faster than almost anyone else. Whoever controls the electrons controls the ceiling on how much AI can actually get built.

Above that sits chips.

This is where the strategy gets interesting. Amazon still rents out Nvidia’s best hardware because customers need it and won’t switch easily.

But it’s also building its own chips, called Trainium, specifically to claw back the margin Nvidia has been keeping for itself. Anthropic is the proof of concept here, using more than a million Trainium chips and committing over a hundred billion dollars to Amazon’s infrastructure.

Amazon financing a partner who then hands the money right back to Amazon.

The third layer is called Bedrock, and it’s basically the checkout counter for AI.

Instead of picking one model and betting the house on it, Amazon lets you rent access to more than a hundred different models, including its competitors’ models, all running through Amazon’s billing, security, and data pipes.

Once your company’s tools are wired into that plumbing, switching away from Amazon gets a lot harder than switching which model you’re using.

At the top are the models themselves, Claude, OpenAI’s systems, Amazon’s own Nova family.

It’s also, honestly, the layer Amazon cares about least, because it wins either way. If your favorite model wins, it probably runs on Amazon’s chips and gets sold through Amazon’s storefront.

That’s the toll road. Amazon isn’t betting on one horse. It’s charging admission at the racetrack.

Now, this strategy isn’t bulletproof, and that’s exactly where it gets interesting from an investor and operator standpoint.

Amazon is spending an almost unbelievable amount of money to build this out, its free cash flow has actually gone negative because of it, and the bet only pays off if AI demand keeps compounding for years.

There are real cracks worth understanding: how much of Amazon’s growth is organic demand versus money it’s essentially lending to its own partners, why its custom chips face a real adoption ceiling, and why regulators in the UK have already flagged how hard Amazon makes it to leave once you’re locked in.

That’s the part I dig into in the full piece for paid subscribers: the bear case, the pricing mechanics Amazon uses to extract more from bigger customers, the anchor tenant risk with Anthropic and OpenAI, and the specific numbers on where this could break.

If you’re making capital allocation decisions, running a company that depends on cloud infrastructure, or just want to understand where the leverage in AI sits, that’s the piece to read closely.

For everyone else, the headline is this: watch who owns the road underneath AI.

AI is not software, it’s industrial revolution.