I — The scale of the gold rush
What is really happening
In the nineteenth century, thousands of people rushed to California to search for gold. Most barely earned enough to pay back their tools. But the men who made the jeans, forged the shovels and sold the provisions — they became rich. That age-old principle, picks and shovels investing, is today the key to understanding the largest infrastructure investment in human history.
Microsoft, Google (Alphabet), Amazon (AWS), Meta and a handful of other hyperscalers have embarked on a capital expenditure cycle without precedent. In 2025, the five largest tech giants combined spent more than $325 billion on data centres, servers, networking equipment and energy infrastructure. By comparison: the total annual investment budget of the entire European energy sector amounts to less than half of that.
The driver is clear: generative AI requires enormous amounts of computing power. Training a single AI model costs tens of millions of dollars in computing time. Deploying AI services to hundreds of millions of users requires infrastructure that must be continuously expanded. Demand is growing faster than supply can be built.
| Company |
CAPEX 2025 |
Focus |
2026 Announcement |
| Microsoft |
$80bn Azure + OpenAI infrastructure |
AI computing, GPU clusters, cloud expansion |
"We see no end to demand" — CFO Amy Hood |
| Alphabet (Google) |
$75bn Google Cloud + TPU chips |
Own AI chips (TPU), quantum computing, Search AI |
Budget increased after rising Cloud revenue |
| Amazon (AWS) |
$105bn World's largest cloud provider |
AWS capacity, own Trainium and Inferentia chips |
Backlog of customer orders growing faster than construction |
| Meta |
$65bn AI Research + social platforms |
LLaMA models, Reels recommendation systems |
"We are building the largest AI infrastructure ever" |
II — Where does the money go?
The anatomy of a data centre
A data centre is not a building with a few computers in it. It is a complex ecosystem of specialised technology, energy infrastructure and cooling systems. If you understand where the billions are flowing, you also understand which companies benefit most.
III — The real winners
Picks & Shovels: the infrastructure layer
The hyperscalers themselves are of course direct beneficiaries of the AI wave — but they also bear enormous costs. The structurally most attractive position is often not the gold seeker themselves, but the supplier of the shovel. In investor terms: the infrastructure layer benefits from the investment cycle regardless of which AI model ultimately wins.
Nvidia is the most discussed example. The company sells its H100 and B200 GPUs for $30,000 to $40,000 each and delivers them in clusters of thousands. Gross margin exceeds 75% — a level of profitability that is virtually unmatched in the industry. But Nvidia is no longer a hidden gem: the stock is included in virtually every broad technology ETF.
More interesting are the less well-known layers of the ecosystem. Companies such as Vertiv (data centre cooling), Arista Networks (networking equipment) and Eaton (power management) structurally benefit from every dollar a hyperscaler spends — regardless of whether it is a Microsoft or Google data centre, and regardless of which AI model runs in it.
✓ The picks & shovels logic
The risk of the gold seeker: he can fail. The risk of the shovel seller: almost everyone buys his shovel. With AI the same applies. Which AI model dominates in ten years is uncertain — that GPUs, cooling systems and fibre optics are needed is a certainty.
IV — Energy: the forgotten bottleneck
The power problem nobody expected
The fastest-growing bottleneck in the entire AI infrastructure cycle is not a chip and not software — it is electricity. A 1-gigawatt data centre consumes as much electricity as a mid-sized city. The demand for power from data centres is growing so fast that existing electricity grids in the US and Europe cannot keep up with capacity.
Microsoft therefore struck a deal to restart the nuclear plant at Three Mile Island — closed for decades after an incident in 1979. Google purchased nuclear energy from startups building micro-reactors. Amazon has signed contracts for geothermal energy in Iceland. The energy transition and the AI infrastructure revolution have become intertwined.
This makes companies active in energy infrastructure — high-voltage cables, transformers, backup generators and cooling systems — unexpected beneficiaries of the AI boom. Vistra, GE Vernova and Constellation Energy all multiplied in stock market value in 2025, purely on the basis of expected power demand from data centres.
"We expect data centres to consume more than 8% of all electricity in the US by 2030. In 2023, that was still 2.5%."
Goldman Sachs — AI Power Demand Report, 2024
V — How to ETF it
Five layers to invest in
The data centre gold rush offers multiple entry points for the ETF investor. The key is understanding which layer you want exposure to — and how that fits within a broader, diversified portfolio. Always use these themes as a satellite position (max. 5–10% per theme) on top of a broad core ETF.
📌 UCITS tip for European investors
Many of the ETFs mentioned are listed on US exchanges and may formally not be actively offered to European retail investors (MiFID II). Always look for the UCITS equivalent: it starts with
IE (Ireland) or
LU (Luxembourg) in the ISIN and is listed on Euronext, Xetra or the London Stock Exchange. Examples: EQQQ (Nasdaq-100 UCITS), SEMG (semiconductors UCITS), WTAI (AI UCITS).
VI — The risks nobody mentions
What can go wrong?
The data centre investment cycle is real and structural — but that does not mean investing in it is risk-free. There are three risks that are systematically underweighted in the enthusiasm surrounding AI.
| Risk |
Why it matters |
How to mitigate it |
| Overcapacity |
If AI demand disappoints or remains concentrated in one model, there will be billions in unused data centres. This happened to the telecom sector after the Y2K era. |
Invest in infrastructure layers that are used regardless of overcapacity: energy, cooling, networks. |
| Concentration |
Many AI ETFs have 20–30% in Nvidia alone. If the stock falls, the fund falls with it — even if the rest of the market rises. |
Spread across multiple layers of the ecosystem. Choose equal-weight ETFs or combine multiple funds. |
| Valuation |
AI-related stocks have already risen sharply. The market has priced in high growth. Disappointments — in revenue, margins or adoption — can lead to sharp corrections. |
Use dollar-cost averaging: invest monthly rather than all at once. Keep the position small (5–10% of portfolio). |
| Regulation |
AI legislation, chip export restrictions (US–China) and energy regulation can delay or reverse investment plans. |
Broad ETFs automatically spread this risk; avoid pure single-country exposure to geopolitically sensitive markets. |
⚠ Don't forget the core ETF
All thematic exposure to the data centre cycle works best as a
complement to a broad core ETF — such as iShares Core MSCI World or Vanguard FTSE All-World. That broad ETF already contains exposure to Microsoft, Amazon, Alphabet and Nvidia. Thematic ETFs add concentration, not automatically extra return. Keep the satellite position small and the horizon long.
The conclusion: shovel, not gold
The hyperscalers are spending hundreds of billions on data centres. That money flows to an entire ecosystem of companies — chipmakers, cooling specialists, energy companies, network suppliers and real estate parties. That ecosystem is broader, less well-known and in many cases more attractively priced than the hyperscalers themselves.
As an ETF investor you have multiple entry points: from broad technology ETFs that contain the hyperscalers themselves, to targeted semiconductor and energy ETFs that capture the infrastructure layer. The picks-and-shovels principle suggests that precisely this latter category is structurally interesting — less dependent on who wins the AI race, more dependent on the fact that the race is being run at all.
And that conclusion is certain: the race has begun, the budget has been approved, and the shovels have been ordered. Seeking the gold is risky. Selling the shovel is the business model of the century.