Introduction

Artificial intelligence now sits at the heart of the world's most dynamic companies. But for private investors, it can feel like everyone is talking AI without ever explaining how to turn that buzz into an actual investment plan.

So where does the money actually go? Follow the cash, and one number jumps out: the five biggest tech companies poured more than 400 billion dollars of capital expenditure into AI in 2025 and that figure is set to grow by a further 75% in 2026. That spending does not vanish into thin air. It flows into chips, data centres, electricity, software and the companies that supply all of it. Understanding that chain is the key to investing in AI without getting lost in the hype.

What is artificial intelligence and what is the AI value chain?

Think of AI as a chain: a series of linked layers, each with its own companies making money. Here is how it breaks down, and where in this article you will find the picks for each one.

The most interesting part for investors? Leading AI companies often span several of these layers at once, which opens up plenty of ways to get exposure to the theme.

Transporting energy for AI

How do I start investing in AI?

Investing in AI is not as simple as buying a company with "AI" in its name. Most of the real innovation comes from established hardware experts, cloud providers and software developers who put AI to work behind the scenes. The good news is you do not have to pick a single winner: you can buy individual stocks, diversified ETFs or a mix of both.

ETFs are the sensible starting point for most beginners.They spread company-specific risk and capture the wider AI ecosystem in a single purchase (and, like robots, they don't need coffee break). Individual stocks can offer bigger rewards, but they demand deeper research and stronger nerves when the market gets choppy.

A few simple principles to get going:

Which chip companies power AI and how can I invest in them?

If AI is the engine of modern progress, semiconductors are the premium fuel.These companies supply the raw computing power that machine learning and generative AI simply cannot run without, and demand has gone vertical. The scale is hard to overstate: Nvidia's data centre revenue alone reached 115 billion dollars in its fiscal 2025, more than doubling year on year, and a single recent quarter brought in over 51 billion dollars from data centres, up 66%. Analysts expect the global AI chip market to keep climbing for years, with custom AI chips (ASICs) alone forecast to reach roughly 85 billion dollars by 2030. It helps to split this world into two very different businesses.

The chip designersarchitect the brains but outsource production. They live on intellectual property, software and design talent, which gives them fat margins but also makes them the names everyone watches.

The chip makers and equipment suppliersdo the brutally hard physical work. This is one of the most capital-intensive industries on earth, which is exactly why the barriers to entry are so high.

Sector ETFs and certificates for diversified access:

Buying a semiconductor ETF is itself an indirect way to invest in AI: instead of guessing whether Nvidia, TSMC, or Broadcom will win, you own the whole engine room and let the sector do the work.

A word on valuation and risk: chip stocks tend to trade at premium multiples and can be brutally cyclical, swinging hard on demand, inventory and geopolitics (Taiwan and US-China export rules are real factors here). High forward multiples are a bet on continued growth, so they reward patience and punish anyone chasing the top.

Scientist manipulating a chipset

Who are the top AI cloud providers and how do data centres fit in?

The hyperscalers, Amazon (AWS), Microsoft (Azure) and Alphabet (Google Cloud) are the backbone of the AI revolution.They deliver the muscle behind modern AI: scalable, secure, high-performance computing that businesses can rent by the hour, instead of spending billions building their own data centres. Yes, the future is cloudy, and that is a good thing.

These three dominate the market. As of late 2025, AWS leads with around 30%, Azure holds about 20% and Google Cloud roughly 13%, together controlling close to two thirds of a global cloud market that topped 400 billion dollars in 2025. And the growth engine is now clearly AI: generative-AI cloud services have been growing more than 150% year on year.

Here is the number that really matters for investors, though. To keep up with demand, the four biggest hyperscalers plan to spend roughly 725 billion dollars in capital expenditure in 2026, up about 77% from the previous year. A huge slice of that flows straight into data centres: the buildings, servers, networking, cooling and power that physically house AI. That spending is the single biggest tailwind for the entire hardware and energy side of the AI chain, which is why it shows up again in the sections that follow.

Relevant stocks at a glance:

ETFs for diversified exposure:

In short, these companies and funds give you a front-row seat to AI's build-out. Just remember that the market is watching that enormous capex bill closely: if returns on all this spending disappoint, sentiment on the whole group can turn quickly.

Cloud computing provider for AI

Who are the AI winners in power grids and energy infrastructure?

Behind every dazzling language model sits a very physical reality: rows of servers that run hot, draw enormous amounts of electricity and lean on the grid to deliver it. AI does not just run on silicon. It runs on copper, transformers, and a great deal of carefully managed power. As data centres multiply, the grid becomes the bottleneck, and the firms that build, power, and connect them have quietly become some of the AI boom's most dependable beneficiaries.

The demand is staggering: global data centre electricity use is set to roughly double from 485 TWh in 2025 to around 950 TWh in 2030, about the size of Japan's entire power consumption, and in the US data centres account for almost half of all electricity demand growth to 2030. This is already in the numbers, not just the forecasts: data centre revenues for Europe's six biggest electrical firms hit roughly €20 billion in 2024, double the level of five years earlier and are projected to grow about 15% per year through 2027. Crucially, unlike software you cannot open source a substation, so these players enjoy real pricing power, visible in record backlogs (Eaton's was up 44% year on year; Vertiv's reached $15 billion, up 109%).

Where to look:

For diversified exposure, the First Trust NASDAQ Clean Edge Smart Grid Infrastructure ETF (GRID) is the closest pure play, built around Eaton, ABB, Schneider Electric, National Grid, Prysmian, Nexans, and NKT, roughly 39% US and 45 to 50% European. The Global X US Infrastructure Development ETF (PAVE)broadens US electrification exposure, the Utilities Select Sector SPDR Fund (XLU) captures the power producers, and the Global X Copper Miners ETF (COPX)offers a more adventurous angle on the raw material behind every cable. Swissquote AI Infrastructure certificate (CH1481476260): a SIX-listed, actively managed basket of data centres, cooling, energy, and grid equipment, the physical foundations of AI. The catch worth remembering: this sector is exposed to construction cycles, interest rates, permitting delays and the risk that today's scarcity in transformers and cables eventually tips into oversupply.

Which software and application companies are driving AI adoption?

If hardware is the engine and the grid is the fuel, software is where AI finally meets the user. These companies embed AI into the tools businesses already use every day, turning raw model power into automation, sharper analytics and better customer experiences. It is the layer where the AI story becomes a product you can actually sell. A few names lead the charge:

A note on valuation that trips up a lot of beginners: many software firms barely make a profit yet, because they reinvest everything into growth. That makes the classic P/E ratio useless. Investors instead lean on the price-to-sales (P/S) ratio, which compares the share price to revenue rather than earnings. Fast growers like Palantir or Datadog can trade at very high P/S multiples, which only makes sense if their rapid growth continues. It is the same lesson as before: a high multiple is a bet on the future, not a free lunch.

ETF options:

The risk to keep in mind: software is a crowded, fast-moving race. Today's leader can be undercut by a tech giant bundling the same feature for free or by a nimble open-source rival. Helmets on.

Software AI

Which companies offer exposure to robotics and automation?

This is AI made physical: machines that see, move, and act in the real world. It is where software meets the factory floor, the warehouse and even the operating room. As AI gets better at perception and decision-making, these robots get smarter and more capable, which is exactly what makes the sector interesting.

Three names lead the field:

One practical catch: some of these shares, Fanuc in particular, can be awkward for private investors to buy directly, given listing and lot-size quirks. That is why many people get their robotics exposure through an ETF instead. The obvious one is the Global X Robotics & Artificial Intelligence ETF (BOTZ), covered in the basket section above, which bundles these leaders and their international peers into a single, diversified holding, no screwdriver required. Swissquote Robotics & AI certificate (ROBOTTQ): a SIX-listed, actively managed basket across industrial automation, software and medical robotics.

Robot moved with AI software

How do big data companies support AI?

If AI is a marathon runner, data is the food, water and training that make the race possible. No model, however clever, is worth much without clean, structured, enormous amounts of data to learn from. The companies that store, organise and serve that data sit at the very start of the AI value chain, and they get busier with every new AI project.

The names to know here are the data platforms:

Prefer not to pick a single name? Broad AI ETFs like AIQ or IRBO already hold a slice of these data infrastructure players, so you get exposure to several at once, a bit like buying the whole bakery instead of one loaf.

One thing to watch: this is a fiercely competitive, consolidating space. Two well-known data names, Splunk and Confluent, were recently swallowed by Cisco and IBM respectively,a reminder that today's independent player can become tomorrow's acquisition.

Analytics AI

The simplest option: one basket for the whole AI theme

Not sure which sub-sector will win? You do not have to choose. Diversified AI ETFs spread your money across the entire value chain, chips, cloud, software, robotics, in a single purchase. They smooth out the risk of betting on the wrong name and are the most sensible starting point for most beginners. Here are the broad AI baskets worth knowing.

US-listed thematic ETFs:

UCITS options (Europe-friendly, and the easier route for most Swiss investors):

A reality check before you buy: these funds have delivered strong returns in good years, but they are concentrated in expensive, fast-moving tech, so they can fall just as sharply when sentiment turns. A diversified basket softens single-company risk, not the ups and downs of the AI theme as a whole.

What are the main risks when investing in AI?

AI is a genuinely exciting theme, but it comes with its own set of hazards. Worth keeping in mind before you commit a single franc.

  1. High valuations: many AI stocks trade at premium multiples, which leaves them vulnerable to sharp corrections if results disappoint. As we saw throughout this article, a high forward P/E is a bet on future growth, not a free lunch. The PEG ratio (P/E divided by expected growth) is a handy sanity check: roughly speaking, the further above 1 it sits, the more you are paying for hope rather than today's earnings.
  2. Bubble risk:when everyone is excited about the same thing, prices can detach from reality. The eye-watering capex figures from the hyperscalers only pay off if all that AI spending eventually generates the returns the market expects.
  3. Obsolescence: this field moves fast. Today's leader can be overtaken by a new architecture, a cheaper rival or an open-source alternative almost overnight.
  4. Concentration:the whole theme leans heavily on a handful of giant US tech names, so AI funds and tech portfolios are often less diversified than they look.
  5. Policy and regulation:rules around data, privacy, chip exports and AI safety are still being written and a single decision can move valuations sharply.
  6. The practical takeaway:do your homework, focus on fundamentals and the durability of a business and resist the urge to chase whatever is soaring this week. Patience and diversification tend to beat excitement over time.
AI chipset

Conclusion: where does this leave a private investor?

The big picture is simple, even if the details are dizzying. AI spending is still accelerating, not slowing. The global AI market is estimated at roughly 390 billion dollars in 2025 and, depending on the forecaster, is widely expected to grow at a compound rate of around 30% per year for the rest of the decade, heading toward the multi-trillion mark. IDC alone projects organisations will spend over 630 billion dollars on AI by 2028. That tide should keep lifting the whole value chain, from chips and data centres to the software built on top.

But "the theme will grow" does not mean "every stock will win." The lesson running through this article is that the money flows in stages: chip designers and foundries, the cloud and power that house and feed the models, the software that turns them into products, and the data underneath it all. Each layer has its own winners, its own economics, and its own risks.

So how might you approach it? A reasonable mental model is here for you.

If you would rather not pick, you can consider to start with a diversified AI ETF (a US thematic fund like AIQ, or a UCITS option like XAIX for European investors) and let the basket spread your bets. If you want more control, add a few individual names from the layers you understand best, keeping position sizes sensible. Whatever you choose, favour quality and durability over hype, size your AI exposure as a slice of a broader portfolio, and review it now and then as the landscape shifts.

AI may well be one of the defining investment themes of the decade. The smartest way to take part is not to chase the loudest headline, but to understand the chain, spread your risk, and stay patient. This article is here to help you do exactly that, not to tell you what to buy. The final call is always yours.

Disclaimer: The content in this article is provided for educational purposes only. It does not constitute investment advice, financial recommendations, or promotional material. Investing in financial markets carries a high degree of risk, and the value of investments can fluctuate significantly. Do not make investment decisions based solely on the information provided herein.