Digital Economy Dispatch #298 -- The Most Important AI Company in the World

In the early 2000s, I moved to Dallas to work in the Software Research Lab at Texas Instruments. I arrived thinking I understood all there was to know about computing. I had a degree in it, a PhD, and a decade of delivering software systems in anger. Within a few days I realised I was lost. I had walked into a different discipline that happened to share some of my vocabulary.

The people around me talked about photolithography, resist chemistry, overlay error, and the behaviour of light at wavelengths I had never had cause to think about. My mental model of a computer stopped at the instruction set. Theirs started several layers below it, in physics. Everything I had ever built sat on a set of physical constraints I had never considered and didn’t understand, negotiated by people whose names appeared in no software conference programme.

That memory returns to me every time I scroll through LinkedIn or watch the TV news and see yet another so-called "AI expert" talk confidently about how AI works, where it is going, and what matters as we consider our AI future. Because the company and the technology that matter most in the AI stack are ones that many of them, and most senior leaders, have never had to think about.

The Magic Machine

Every computer, and every AI system running on one, depends on the processor. And every processor is a pattern of transistors printed onto silicon, now numbering in the tens of billions on a single chip and separated by distances measured in nanometres. Printing those patterns is a problem of optics rather than computing, and only one company in the world has solved it.

ASML, headquartered in Veldhoven, is the only company in the world that builds extreme ultraviolet (EUV) lithography systems. Not the leading supplier. The only one. Nikon and Canon abandoned the technology more than a decade ago. Additionally, CSET's data puts ASML at 98.7% of the immersion deep ultraviolet (DUV) market too, the older tools that still do most of the work in any advanced fab.

The EUV machine prints circuit patterns onto silicon using light with a wavelength of 13.5 nanometres (compared to 193 nm for the highest-resolution of earlier DUV systems). Achieving that means firing droplets of molten tin into a vacuum chamber 50,000 times a second, striking each one twice with a high-power CO2 laser built by Trumpf in Germany, and heating the resulting plasma to around 220,000°C, roughly forty times the surface temperature of the sun. EUV light is absorbed by every known material, so lenses are useless. The light is steered instead by mirrors from Zeiss SMT in Oberkochen, polished for years apiece to a tolerance Zeiss describes this way: scale one to the size of Germany and its largest bump would be under a millimetre.

Each system contains more than 100,000 components from around 5,100 suppliers. This is less a supply chain than a thirty-year act of industrial diplomacy that happens to produce a product.

Every Frontier Model has a Physical Address

Every frontier AI accelerator in existence was printed by an ASML machine. There is no second route, and no substitute in development that will change that. When we describe compute as the binding constraint on AI progress, we are describing, several layers down, how quickly Veldhoven can build more of these machines. ASML has said it is adding around 30% more EUV capacity in 2027 and investigating a further 30% for 2028, which is a better forward indicator of AI capability growth than most model roadmaps.

This matters now because sovereign AI has become the organising ambition of technology policy almost everywhere. The Economist set out the state of play in July. Days before the G7 summit, Washington barred Anthropic from making its most advanced model available to foreigners and OpenAI followed suit, prompting President Macron to warn that nobody would buy American AI from a supplier able to turn off the switch. CNAS counts a fivefold rise in state-backed AI projects outside America and China across 2024 and 2025, with more than $70 billion committed. The Economist's verdict is that full independence is a pipe dream, though partial protection from coercion remains achievable, because almost every country will still be running on American chips, Chinese open-weight models, or both.

This has three consequences that the UK’s AI policy conversation handles badly.

The most consequential AI regulation of the past decade was not written by an AI regulator. Arguably the single decision that has shaped global AI capability most was the choice, taken in The Hague under US pressure from 2019 onwards, never to license EUV systems for export to China. No AI Act or model evaluation regime comes close to it in effect. Yet it was made by export control officials using instruments designed for dual-use goods, in departments with almost no connection to the bodies now charged with governing AI. That gap is the first thing to fix.

The layer you choose is the strategy. The Economist's analysis works down the stack, and the difficulty rises the further down it goes. Open-weight models near the top offer real control at modest cost. Compute and energy below them are punishing: Nvidia accounts for two thirds of the world's AI compute, Huawei's best answer manages roughly a fifth of the performance of Nvidia's latest, and a one-gigawatt data centre costs around $50 billion, more than two thirds of it computing equipment. Talent, which Kevin Xu argues gets the least attention of any layer, may be hardest of all, because engineers cannot be imported or downloaded.

But notice where that analysis stops. Its bottom layer is chips, and chips have a layer beneath them. ASML is that layer, and no government can buy its way into it at any price. Deciding which layers to recreate at home and which dependencies to live with is the strategic choice, and the one most national strategies never explicitly make. ASML itself illustrates how partial the results are: its €1.3 billion stake in Mistral has not stopped Mistral running largely on Nvidia hardware and American cloud. Owning the bottom of the stack did not deliver the middle.

Owning an asset is not the same as holding the decision rights over it. Europe owns the chokepoint. Europe does not straightforwardly control it. Congress is debating the MATCH Act, which would extend US authority into the servicing of machines already installed in China. The Dutch Trade Minister travelled to Washington in June to lobby against it, and The Hague has stated plainly that it intends to remain responsible for its own export control policy. Sovereignty, as Xu puts it, is better understood as control than as independence. Any conversation that stops at ownership, data residency, or location has not reached the question that matters.

I called this silent lock-in in “Making AI Work for Britain”, with departments in mind that discover a procurement decision from three years earlier has quietly removed their ability to change course. The ASML case is the same pattern at national scale. Nobody in The Hague signed anything away. Dependency accumulated through component sourcing, software provenance, and allied pressure until the decision rights had migrated without a decision ever being taken. That is why I argued for exit-by-design as a discipline rather than a clause: if you cannot describe how you would leave, you have already lost the argument about whether you are free to.

China: the reason this stays contested

Without EUV, Chinese fabs are effectively capped around the 7 nanometre node using multipatterning on older tools, so duplicating ASML is not an industrial ambition but a national project. In December 2025, Reuters reported on a state-run effort involving Huawei that had assembled an EUV prototype in Shenzhen, partly from second-hand ASML components, with former ASML staff central to the work.

But a prototype that produces EUV light is not a machine that produces chips profitably, and the distance between the two is where Canon and Nikon died. ASML shipped pre-commercial demo tools from 2006 and did not reach commercial production until 2019. The most careful public forecast I have seen, published in June by the AI Futures Project and The Substrate, works from ASML's own history and puts commercial-scale Chinese EUV towards 2040. SemiAnalysis's Dylan Patel disagrees, predicting pre-commercial Chinese EUV by 2030. The range is wide, and the signal worth tracking is whether Chinese fabs start running domestic tools on production wafers at scale.

Why this Matters

Understanding ASML’s role is much more than a fascinating technology story. It has real consequences.

For digital leaders, the lesson here is to map the physical dependency underneath your AI roadmap. Most have mapped their model providers and cloud regions, but few have asked what happens to compute access, costs, and timelines under a serious lithography disruption.

For policymakers, the key is to stop treating AI sovereignty as a question of where the servers sit. It is a question of decision rights across a stack. Britain has no ASML and will not acquire one. Thirty years of supplier coordination cannot be conjured by a strategy document.

For regulators, remember that supervisors devote real effort to concentration risk in financial infrastructure, and the AI supply chain would fail those tests outright. The expertise this demands sits partly in export control, industrial policy, and supply chain economics, not solely in model evaluation and algorithmic accountability. The people who understand the binding constraints on AI capability are in different buildings from the people writing the rules.