How to invest in artificial intelligence

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.

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.

• Chip designers and chip makers: the companies that architect the brains of AI (GPUs and specialised processors), the foundries that physically produce them, and the toolmakers behind the machines. Capital intensive, with very high barriers to entry. (See: Hardware & semiconductors.)
• Data centres and cloud: the physical home of AI, where the hyperscalers rent out computing power at scale so businesses do not have to build their own. (See: AI Cloud & Infra.)
• Energy and the grid: servers must be housed, cooled, and above all powered, which turns electricity and the grid into one of the biggest bottlenecks of the whole boom. (See: Power grid & Infra.)
• Software and applications: everything built on top, from the frameworks developers use to the finished tools that put AI to work in fraud detection, assistants, or predictive maintenance. (See: Software & Apps.)
• Robotics and automation: AI made physical, the machines that see, move, and act in factories, warehouses, and operating rooms. (See: Robotics & automation.)
• Data and analytics: the raw fuel. Without clean, structured, enormous datasets, even the smartest model is just spinning its wheels. (See: Big data.)

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.

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, unlike robots, they do not need coffee breaks). 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:

• Map the value chain: understand how each company actually makes money from AI. Nvidia dominates chip design, TSMC manufactures, the hyperscalers rent out cloud power, and so on. The sections below walk through each link.
• Diversify: many advisors suggest keeping technology and AI to a sensible slice of a broader portfolio rather than betting the house on one theme.
• Start broad, then go narrow: begin with diversified AI ETFs, then add individual stocks as your confidence grows. Rome was not built in a day, and neither is a balanced portfolio.
• Review regularly: the pace of AI is relentless, so check in on your holdings every quarter or so to avoid getting caught in a plot twist.
• The golden rule: favour quality, patience, and diversification over chasing hype. This article is here to help you understand the landscape and spot interesting names in each sub-sector, not to tell you what to buy. The final calls are yours (or your advisor's).

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 designers architect 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.

• Nvidia (NVDA): the runaway leader in AI accelerators, with a dominant share of the market and the GPUs that train and run the biggest models. 
• Advanced Micro Devices (AMD): the main challenger in CPUs and GPUs for data centres and AI workloads. 
• Broadcom (AVGO): a key designer of custom AI chips and networking silicon for the hyperscalers (it makes sure the data party never stops).

The chip makers and equipment suppliers do 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.

• Taiwan Semiconductor (TSMC): the world's largest independent foundry, manufacturing the chips designed by Nvidia, Apple, and almost everyone else. 
• ASML Holding (ASML): the sole supplier of the extreme ultraviolet (EUV) machines without which the most advanced chips cannot be made. A genuine monopoly. 
• Micron Technology (MU): a specialist in the high-bandwidth memory chips that AI accelerators are desperate for. 
• Intel (INTC): a turnaround story investing heavily to reclaim ground in both manufacturing and AI hardware.

Sector ETFs and certificates for diversified access:

• VanEck Semiconductor ETF (SMH): broad exposure to the top global semiconductor names, with heavy weights in Nvidia and TSMC. 
• iShares Semiconductor ETF (SOXX): tracks the long-running Philadelphia Semiconductor Index (the famous "SOX"), giving you the main US-listed chip leaders in one ticker. 
• Invesco PHLX Semiconductor ETF (SOXQ): a lower-cost option tracking a similar basket of semiconductor innovators, handy if fees matter to you.
• Swissquote Semiconductor Industry certificate (CH1181303780): a SIX-listed, actively managed basket spanning the whole chip value chain, from designers to equipment makers.

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.

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:

• Microsoft (MSFT): Azure is a global powerhouse for enterprise AI, tightly integrated with its OpenAI partnership and its huge software base. 
• Amazon (AMZN): AWS is the world's largest cloud provider and a profit engine for Amazon, powering everything from start-ups to multinationals. 
• Alphabet (GOOGL): Google Cloud pairs fast-growing infrastructure with world-class AI research and its own custom chips.

ETFs for diversified exposure:

• First Trust Cloud Computing ETF (SKYY): bundles the leading cloud infrastructure and software names, so you own the sector rather than betting on a single winner. 
• WisdomTree Cloud Computing UCITS ETF (WCLD): a UCITS-eligible option (handy for European investors) focused on cloud and software-as-a-service leaders. 
• Global X Data Center & Digital Infrastructure ETF (DTCR): a more targeted play on the physical side, holding data centre operators and the infrastructure that keeps them running.

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.

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:

• Equipment, switchgear and cooling: Vertiv (VRT), the data centre power and cooling pure play; Eaton (ETN); GE Vernova (GEV); and European champions Schneider Electric (SU), Siemens Energy (ENR), ABB (ABBN), and Legrand (LR).
• High voltage cables, the physical grid link: Prysmian (PRY), €19.65bn in 2025 revenue with 8.4% organic transmission growth; Nexans; and the higher beta NKT, with a €10.2bn order backlog. 
• Power producers and raw materials feeding the machines: Constellation Energy (CEG) and Vistra (VST) sell firm, around the clock electricity to hyperscalers, while a steadier name like NextEra Energy (NEE) blends regulated utility income with the largest renewables fleet in the US. One layer down the chain, all those cables and transformers run on copper, so industrial commodity producers ride the same wave.

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.

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:

• Palantir (PLTR): the standout of the moment, turning messy enterprise and government data into AI-driven decisions, with revenue growth that has accelerated rather than slowed at scale. 
• Salesforce (CRM): the customer relationship giant, now pushing AI agents (Agentforce) across its huge installed base. 
• ServiceNow (NOW): automates enterprise workflows and has leaned hard into AI agents for IT and operations. 
• Datadog (DDOG): monitors cloud systems, and benefits directly as AI workloads make those systems more complex to watch. 
• Adobe (ADBE): embedding generative AI across its creative and document tools.

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:

• Global X Artificial Intelligence & Technology ETF (AIQ): broad exposure to AI software and platform leaders. 
• iShares Expanded Tech-Software Sector ETF (IGV): a focused bet on the software industry as a whole. 
• WisdomTree Cloud Computing UCITS ETF (WCLD): a UCITS-eligible option for the cloud and SaaS names powering AI apps.

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.

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:

• Fanuc (FANUY): the Japanese giant behind the yellow robotic arms you picture in car factories. 
• ABB (ABBN): a Swiss-Swedish leader in industrial automation across countless manufacturing sectors (and a familiar face from the power grid section). 
• Intuitive Surgical (ISRG): the pioneer of surgical robots, including the well-known da Vinci system.

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.

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: 

• MongoDB (MDB), a developer-favourite database now building AI retrieval tools directly into its platform, and 
• Elastic (ESTC), whose search-based engine helps applications find the right data fast. 
• The big cloud providers (Microsoft, Amazon, Alphabet) also run enormous data and analytics businesses, so owning them is partly a bet on this layer too.

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.

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:

• Global X Artificial Intelligence & Technology ETF (AIQ): a well-rounded mix of AI software, hardware, and platform leaders, regularly updated as the landscape shifts.  
• Global X Robotics & Artificial Intelligence ETF (BOTZ): tilts toward the physical side of AI, robotics, automation, and the chips behind them, with names like Nvidia and Intuitive Surgical. 
• iShares Robotics and Artificial Intelligence Multisector ETF (IRBO): more evenly weighted, so it leans less on a handful of giants and gives smaller, up-and-coming firms a look in. 
• ARK Autonomous Technology & Robotics ETF (ARKQ): an actively managed, higher-risk bet on disruptive innovation, from autonomous vehicles to drones and energy storage.

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

• Xtrackers Artificial Intelligence & Big Data UCITS ETF (XAIX): one of the largest and cheapest AI UCITS funds, tracking a global basket of AI and big-data companies. 
• WisdomTree Artificial Intelligence UCITS ETF (WTI2): a more AI-pure option, built around firms the industry classifies as genuine AI enablers and users. 
• L&G Artificial Intelligence UCITS ETF (AIAI): a broad, global AI fund and another solid one-ticket way to own the theme.

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.

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.

• 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.
• 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.
• 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.
• 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.
• Policy and regulation: rules around data, privacy, chip exports, and AI safety are still being written, and a single decision can move valuations sharply.
• 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.

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.