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:
fund the science,
subsidise the factories,
finance the infrastructure,
create the demand,
restrict foreign competitors,
guarantee national-security customers,
shape energy access,
control exports,
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.