Best AI Stocks to Buy: How to Invest in Artificial Intelligence
Chips, cloud, software and power: where the AI boom creates value, the largest AI companies by market value, AI ETFs and the risks of a crowded trade.
Key takeaways
- AI has made Nvidia the world's most valuable company, worth about $5.6 trillion in September 2026.
- AI companies fall into four groups: chips and hardware, cloud platforms, software and applications and infrastructure such as power and data centers.
- If you own an S&P 500 or Nasdaq 100 fund, you already own a large stake in AI: the biggest AI companies make up a large share of these indexes.
- AI stocks are volatile and expensive by historical standards. Past tech booms, like the dot-com era, ended in deep crashes even for strong companies.
Few investment themes have moved markets like artificial intelligence. Since the launch of ChatGPT at the end of 2022, spending on AI chips and data centers has exploded, and the companies supplying them have become the largest in the world. Investors now face a difficult question: how much of this growth is already priced in? This guide explains where AI creates value, how to evaluate AI stocks and ETFs, and how to invest without betting everything on one theme.
Please note
Companies mentioned are examples to explain the AI market, not buy recommendations. Market values are approximate as of September 2026 and change daily.
The AI value chain: where the money flows
1. Chips and hardware
Training and running AI models requires specialized processors, memory and networking equipment. This is where AI revenue has shown up first and most clearly.
- AI accelerators: Nvidia dominates graphics processors (GPUs) for AI; AMD competes with its own accelerators.
- Custom chips and networking: Broadcom designs custom AI chips for large cloud providers and supplies networking chips for data centers.
- Manufacturing: Taiwan Semiconductor Manufacturing (TSMC) produces most of the world's advanced AI chips.
- Memory: high-bandwidth memory from companies such as SK Hynix and Micron is essential for AI processors.
2. Cloud platforms ("hyperscalers")
Microsoft, Alphabet (Google), Amazon and Meta are spending hundreds of billions of dollars on data centers to train and run AI models. Microsoft, Alphabet and Amazon also rent this computing power to other companies through their cloud businesses. Their AI spending is the main source of revenue for chipmakers, which makes the sustainability of that spending one of the most important questions for the entire sector.
3. Software and applications
Software companies build AI into products, from office tools to data analytics. Palantir has become a prominent example, but its valuation shows the risk: in mid-2026, investors were paying close to 67 times its annual sales, a level few companies have sustained historically.
4. Infrastructure: power, cooling and data centers
AI data centers consume enormous amounts of electricity. Utilities, power equipment makers, data center operators and cooling specialists benefit indirectly. Even some Bitcoin miners have converted capacity to AI computing, as explained in how Bitcoin mining works.
The largest AI-related companies by market value
| Company | Main AI role | Market value (Sept. 2026) |
|---|---|---|
| Nvidia | AI accelerators (GPUs), networking, software | ≈ $5.56 trillion |
| Apple | AI features on devices | ≈ $4.67 trillion |
| Alphabet | AI models, Google Cloud, custom chips | ≈ $4.14 trillion |
| Microsoft | Azure cloud, AI software | ≈ $3.71 trillion |
| Amazon | AWS cloud, custom chips | ≈ $2.79 trillion |
| TSMC | Chip manufacturing | ≈ $2.23 trillion |
| Broadcom | Custom AI chips, networking | ≈ $1.70 trillion |
| Meta Platforms | AI models, advertising, data centers | ≈ $1.57 trillion |
| SK Hynix | AI memory chips | ≈ $1.26 trillion |
Market values as compiled by The Motley Fool in September 2026. Nvidia alone makes up more than 8% of the S&P 500.
How to evaluate an AI stock
- Is AI revenue real and measurable? Look for reported AI sales, cloud growth or contract backlogs, not just announcements.
- Who pays, and for how long? Chipmakers depend on a few big customers; if their capital spending slows, orders can fall quickly.
- How strong is the competitive position? Consider technology leadership, software ecosystems, manufacturing capacity and switching costs.
- What are you paying? Compare price-to-earnings and price-to-sales ratios with expected growth. Paying a very high multiple requires many years of exceptional growth.
- How profitable is the business? Margins and free cash flow show whether growth translates into value for shareholders.
- What are the specific risks? Export restrictions, dependence on Taiwan for manufacturing, power shortages and new competitors.
AI ETFs: diversified exposure
If you want targeted AI exposure without betting on single companies, ETFs offer several routes:
| ETF type | What it holds | Things to watch |
|---|---|---|
| Semiconductor ETFs | Chip designers, manufacturers and equipment makers | Highly cyclical; concentrated in a few giants |
| AI and big-data thematic ETFs | Companies identified as AI beneficiaries across sectors | Higher fees, often 0.4% to 0.7%; selection rules vary |
| Nasdaq 100 ETFs (e.g., QQQM) | 100 large non-financial Nasdaq companies, heavily tech | Broad tech exposure, lower fees (0.15% for QQQM) |
| Technology sector ETFs | US technology companies | Very high weight in the largest stocks |
Before buying, check the fund's top ten holdings. Many AI ETFs own the same handful of mega-cap companies you already hold through a broad index fund. Learn how to compare funds in our guide to the best ETFs to buy.
The risks of investing in AI stocks
- Valuation risk: high expectations leave little room for disappointment. Even a solid quarter can send a stock lower if it misses lofty forecasts.
- Boom-and-bust history: in the dot-com crash of 2000–2002, the Nasdaq Composite lost about 78%. Many internet companies were right about the technology but far too expensive. Nvidia itself fell roughly two-thirds between late 2021 and late 2022 before the AI boom took off.
- Spending cycle risk: if cloud giants cut their AI budgets or AI services fail to earn enough, chip demand could drop sharply.
- Concentration risk: a few companies dominate both the AI trade and the major indexes, so a reversal would hit many portfolios at once.
- Interest rate risk: growth stocks are sensitive to rising yields, which reached their highest levels since 2002 in September 2026.
- Geopolitics: export controls and tensions around Taiwan could disrupt the chip supply chain.
A sensible way to invest in AI
- Start with what you own. If you hold an S&P 500 or total market fund, check how much of it is already in AI-related companies; it is likely a quarter or more.
- Keep a diversified core. Broad index funds should remain the foundation. See how to invest in the S&P 500.
- Add AI exposure as a satellite, for example 5% to 10% of your portfolio in an AI or semiconductor ETF or a few individual stocks you have researched.
- Invest gradually rather than all at once after a big rally.
- Rebalance when your AI holdings grow far beyond your target share.
For a broader framework on choosing individual companies, read best stocks to buy for the long term.
Frequently asked questions
What is the best AI stock to buy?
There is no single best AI stock for everyone. Chipmakers have benefited most directly so far, cloud giants offer more diversified businesses, and software companies may capture value later. Many investors prefer an ETF or a broad index fund to avoid betting on one winner.
Is it too late to invest in AI stocks?
Nobody knows. AI adoption may still be early, but the largest AI stocks already reflect very high expectations. Investing gradually and keeping AI to a limited share of your portfolio reduces the risk of buying at a peak.
Are AI stocks in a bubble?
Opinions are divided. Unlike in 2000, today's leaders are highly profitable, but some AI valuations are extreme. Bubbles are usually recognized only in hindsight.
What are AI ETFs?
Funds that invest in companies expected to benefit from artificial intelligence, such as chipmakers, cloud providers and software firms. Their holdings, fees and focus vary widely.
Do index funds include AI stocks?
Yes. Nvidia, Microsoft, Alphabet, Amazon, Broadcom and Meta are among the largest holdings in S&P 500 and Nasdaq 100 funds.
