ASML’s decision to raise its 2026 guidance underlines a simple reality: demand for advanced AI chips is not easing, and the world’s most important semiconductor equipment maker remains at the centre of that surge.
The company signalled stronger-than-expected orders for its extreme ultraviolet (EUV) and next‑generation high‑NA systems, driven by chipmakers racing to expand capacity for AI accelerators, data‑centre processors and cutting‑edge logic nodes.
Bottleneck
The upgrade matters because ASML sits at the bottleneck of global chip production. Only a handful of firms can even buy its most advanced machines, and those firms – chiefly TSMC, Intel and Samsung – are all scaling up AI‑focused manufacturing.
Their capital expenditure plans have held firm despite broader economic uncertainty, suggesting that AI infrastructure is becoming a non‑discretionary investment rather than a cyclical one.
Two forces are driving the momentum. First, hyperscalers continue to pour billions into AI clusters, creating sustained demand for the most advanced lithography tools.
Long-term lock in
Second, geopolitical pressure to secure domestic chip capacity is pushing governments and manufacturers to lock in long‑term equipment orders.
ASML’s raised outlook reinforces the sense that the semiconductor cycle is diverging: consumer electronics remain patchy, but AI‑related manufacturing is entering a multi‑year expansion.
The key question now is whether supply can keep pace with the ambition of its customers.
Taiwan Semiconductor Manufacturing Company (TSMC) has delivered a striking 35% year‑on‑year jump in first‑quarter revenue, reaching a record NT$1.13 trillion.
The result underscores just how dramatically the centre of gravity in global technology has shifted towards advanced semiconductor manufacturing, with artificial intelligence now the defining force behind industry growth.
Relentless AI demand
TSMC’s performance is being powered by relentless demand for cutting‑edge chips from major clients such as Apple and Nvidia.
As AI infrastructure spending accelerates worldwide, the company has become one of the few manufacturers capable of producing the most sophisticated processors required for training and running large‑scale models.
March alone saw revenue climb more than 45%, highlighting the strength and urgency of this demand.
Ambition
Analysts suggest TSMC is on track to exceed its already ambitious 30% annual growth target, helped not only by volume but also by reported price increases for its most advanced nodes.
Even as smartphone and PC markets remain uneven, AI‑related orders are more than compensating.
With more companies—from hyperscalers to AI start‑ups—designing their own chips, TSMC’s strategic position looks increasingly unassailable.
Upcoming earnings and ASML’s results next week will offer further clues about the momentum behind the semiconductor sector’s AI‑driven boom.
A quiet but consequential shift is taking place across the global technology landscape: quantum computing is no longer a distant scientific ambition but an emerging commercial reality.
A new wave of breakthroughs is accelerating timelines, and data‑centre operators — already strained by the explosive growth of AI workloads — are being forced to rethink their infrastructure from the ground up.
The latest reporting highlights how this ‘quantum moment’ is reshaping priorities across the sector.
Advancements in Quantum computing
For years, quantum computing has been framed as a long‑term bet, with practical applications perpetually a decade away. That narrative is now being challenged.
Advances in qubit stability, error‑correction techniques and *photonic architectures are pushing the field closer to machines capable of solving commercially meaningful problems.
Industry leaders increasingly argue that hybrid quantum–classical systems will begin appearing inside data centres before the end of the decade, creating a new class of high‑value workloads.
This shift is happening at a time when data centres are already under unprecedented strain. The rapid adoption of generative AI has driven demand for power, cooling and specialised silicon to levels few operators anticipated.
Layered complexity
Quantum computing adds a new layer of complexity: these machines require ultra‑stable environments, extreme cooling and highly specialised networking.
As a result, data‑centre design is entering a new phase, with operators exploring everything from cryogenic‑ready layouts to quantum‑secure communication links.
The strategic implications are significant. Hyperscalers are positioning themselves early, investing in quantum‑safe encryption, photonic interconnects and experimental quantum modules that can be slotted into existing facilities.
Objective
The goal is to ensure that when quantum hardware becomes commercially viable, the supporting infrastructure is already in place.
This mirrors the early days of cloud computing, when capacity was built ahead of demand — a gamble that ultimately paid off.
Yet uncertainty remains. Some analysts caution that full‑scale commercialisation could still be decades away, pointing to slow revenue growth and persistent engineering challenges.
Even so, the direction of travel is clear: quantum computing is moving out of the lab and into the strategic planning of the world’s largest data‑centre operators.
If AI defined the last wave of infrastructure investment, quantum may define the next. And for an industry already racing to keep up, the clock has started ticking.
Explainer
What are Photonic Architectures?
Photonic architectures in quantum computing refer to systems that use light particles (photons) as the fundamental units of quantum information — instead of electrons or superconducting circuits.
These architectures are gaining traction because photons offer several unique advantages:
Key Features of Photonic Quantum Architectures
Feature
Description
Qubits via photons
Quantum bits are encoded in properties of light, such as polarisation or phase.
Room-temperature operation
Unlike superconducting systems, photonic setups often don’t require cryogenic cooling.
Low noise and decoherence
Photons are less prone to environmental interference, improving stability.
Modularity and scalability
Photonic systems can be built using modular optical components, ideal for scaling.
The world’s largest cloud providers are engaged in one of the most expensive technological races in history.
Amazon, Microsoft, Meta and Alphabet are collectively on track to spend as much as $700 billion on AI‑related capital expenditure this year — a figure that rivals the GDP of mid‑sized nations and has understandably rattled investors.
The question now dominating markets is simple: can hyperscalers justify this level of spending, and should analysts remain so bullish on their stocks?
A Binary Bet on the Future of AI
The scale of investment has shifted the AI build‑out from a strategic growth initiative to what some analysts describe as a binary corporate bet. As some analysts suggest, the leap in capex — up roughly 60% year‑on‑year — means the payoff must be both rapid and substantial.
If monetisation fails to keep pace, the consequences could be of severe concern.
This is compounded by the fact that hyperscalers are now consuming nearly all of their operating cash flow to fund AI infrastructure, compared with a decade‑long average of around 40%. That shift alone explains the recent market jitters.
Why Analysts Remain Upbeat
Despite the turbulence, many analysts still argue the long‑term fundamentals remain intact. One reason is that hyperscalers are pre‑selling data‑centre capacity before it is even built, effectively locking in revenue ahead of deployment.
That dynamic supports the bullish view that AI demand is not only real but accelerating.
There is also a belief that as AI tools become embedded across consumer and enterprise workflows, willingness to pay will rise sharply.
If that scenario plays out, today’s eye‑watering capex could look prescient rather than reckless.
The Real Risk: Timelines
The challenge is timing. Much of the infrastructure being deployed — from chips to data‑centre hardware — has a useful life of just three to five years.
That gives hyperscalers a narrow window to recoup investment before the next upgrade cycle hits.
Without clearer monetisation strategies and firmer payback timelines, investor anxiety is likely to persist.
AI capex justification?
Hyperscalerscan justify their AI capex — but only if demand scales as quickly as they expect and monetisation becomes more transparent.
Analysts may be right to stay bullish, but the margin for error is shrinking. In the coming quarters, clarity will matter as much as capital.
