Recommended AI stocks to watch in 2026: Seize investment opportunities from technological dividends

Since ChatGPT’s emergence at the end of 2022, the wave of artificial intelligence has completely reshaped global capital markets. The list of AI stock recommendations related to this trend continues to be updated, with many companies experiencing several-fold increases in their stock prices despite limited earnings growth. As large model technology matures, it is now becoming a key window for investors to deploy in the AI ecosystem. So, which AI stocks are worth focusing on? Which parts of the industry chain contain investment opportunities? How can investors profit from this technological revolution? This article will reveal all.

AI Industry Enters Explosive Growth, Chip Manufacturers Become Major Beneficiaries

Currently, artificial intelligence has evolved from a concept into reality, penetrating areas such as medical diagnostics, financial forecasting, autonomous driving, and intelligent decision-making. Companies are increasing their AI R&D investments, and demand for AI applications continues to rise.

According to the latest IDC research, global enterprise spending on AI infrastructure and solutions is expected to reach $307 billion by 2025. More notably, by 2028, total spending—including AI applications, infrastructure, and related services—is projected to surpass $632 billion, with a compound annual growth rate of 29%. Among these, spending on accelerated servers (the core platform for AI chips) is expected to account for over 75% of total infrastructure investment. This clearly indicates that, driven by the AI wave, chip and server suppliers are becoming the biggest beneficiaries.

This industry trend has also been validated by the capital markets. For example, the globally renowned hedge fund Bridgewater significantly increased its holdings of core AI companies like NVIDIA, Alphabet, and Microsoft in its 13F report for 2025, reflecting strong institutional confidence in this sector. In addition to direct stock holdings, many investors are also deploying through AI-themed funds and ETFs. Morningstar data shows that by the end of Q1 2025, the assets under management of global AI and big data funds exceeded $30 billion, reaching new highs.

NVIDIA, AMD, Broadcom: The Competition Among the Big Three

Among many AI stock recommendations, these three chip giants are undoubtedly the most prominent, each occupying an irreplaceable position in the industry chain.

NVIDIA (NVDA): GPU Market Leader

NVIDIA leverages its dominant GPU chips to secure the crown in AI infrastructure. Its products like H100 and H200 have become industry standards for training large models, and the latest Blackwell architecture has led a new round of computing power competition. The company’s market cap has reached $4.28 trillion, and in just two years since ChatGPT’s debut, its stock price has increased elevenfold.

In Q2 2025, NVIDIA’s net profit grew over 200% year-over-year, with revenues around $28 billion. Analysts generally believe that as AI applications extend from training to inference, demand for NVIDIA GPUs will continue to grow exponentially. Its deep CUDA ecosystem also creates natural barriers for competitors. Currently, major tech companies and cloud service providers worldwide are still experiencing strong demand for NVIDIA chips, with some even facing supply shortages.

Broadcom (AVGO): Essential Network Connectivity Provider

If NVIDIA is the brain of AI chips, then Broadcom is the “neural network” of AI data centers. As a global leader in network communication chips, Broadcom has successfully entered the AI server supply chain through self-developed ASIC chips, high-speed switches, and optical communication chips.

In fiscal year 2024 (ended November 2024), Broadcom’s revenue reached $31.9 billion, with AI-related products accounting for 25%, showing strong growth. As cloud providers accelerate the deployment of AI data centers in 2025, demand for Broadcom’s Jericho3-AI chips and Tomahawk5 switches has surged. Although the market generally sees competition between NVIDIA and Broadcom, their product lines are quite distinct, allowing them to grow alongside each other in the AI wave. Over the past two years, Broadcom’s stock price has increased 3.51 times, with target prices generally above $2,000.

AMD (Advanced Micro Devices): A Strong Challenger in the Second Tier

As a direct competitor to NVIDIA, AMD possesses both GPU and CPU R&D capabilities, which few other companies can match globally. Although its market share is smaller than NVIDIA’s, AMD’s self-developed MI300 series accelerators have performed on par with NVIDIA’s H100 in multiple benchmarks, at roughly half the price.

In 2024, AMD’s revenue was about $22.9 billion, with data center business growing 27% annually. In the second half of 2025, the upcoming MI350 series is expected to further expand AMD’s AI market share. As large enterprises and cloud providers increasingly seek alternative solutions, AMD’s open ecosystem and cost advantages are gradually eroding NVIDIA’s market dominance. Since the release of ChatGPT, AMD’s stock price has risen 3.2 times.

How to Select AI Stock Recommendations: Find Breakthroughs in the Industry Chain

When building an AI investment portfolio, you can consider the following dimensions:

First Layer: Chip Infrastructure
This is the primary beneficiary layer of the AI wave, including GPU manufacturers (NVIDIA, AMD), network chipmakers (Broadcom), and foundries (TSMC). These companies tend to have high performance elasticity and growth potential, making them favored by institutional investors.

Second Layer: Application Companies
Including cloud service providers, large internet firms, and vertical AI application companies. These firms build their AI capabilities by purchasing chips, with long-term growth potential.

Third Layer: Financing and Tools
Companies providing funding, computing management platforms, or development tools for AI firms. These tend to be less risky and suitable for risk-averse investors.

Investment Approaches: Stocks vs. ETFs

Different risk preferences call for different choices:

For direct stock investments, consider leading companies like NVIDIA, AMD, and TSMC; for diversification, look at products like Global X Robotics & Artificial Intelligence ETF (ticker: BOTZ). Regardless of the approach, a dollar-cost averaging strategy is recommended to smooth out market fluctuations.

Long-term Holding vs. Short-term Trading: Two Approaches to AI Stock Investment

Long-term Investment Strategy
Investors optimistic about AI’s long-term prospects should aim for a holding period of over three years, adopting a dollar-cost averaging approach to gradually build positions. This method effectively hedges short-term market noise and allows investors to fully benefit from industry growth. The continuous accumulation by firms like Bridgewater shows that even amid the AI hype, institutions are still committed to buying on dips.

Short-term Trading
If you prefer short-term trading, you can utilize flexible derivative platforms (CFD) to capture technical opportunities on daily charts. Many of these platforms have zero commissions and support two-way trading, making them suitable for short-term traders.

Risks and Opportunities in the AI Revolution: Rational Expectations

While the AI industry has broad prospects, investors should also be aware of the risks:

Technological Uncertainty: Although AI concepts have existed for decades, generative AI has only entered mainstream use in recent years. Rapid technological evolution can mislead even seasoned investors, leading to chasing market hotspots and buying at high prices.

Valuation Bubbles: Many AI concept stocks already reflect market expectations, with some companies’ valuations seemingly pricing in several years of growth. Without exceeding earnings expectations, these stocks may face corrections.

Regulatory and Ethical Challenges: Governments worldwide are accelerating AI regulation, with issues like data privacy, algorithm bias, and copyright potentially leading to stricter policies. Tightening regulations could impact business models.

Competitive Landscape Changes: Although NVIDIA currently holds an advantage, ongoing investments by AMD, Intel, and others could gradually fragment market share. Breakthroughs by overseas companies in independent chip development also pose potential threats.

Macroeconomic Factors: Federal Reserve interest rate policies directly influence high-valuation tech stocks. In a high-rate environment, capital becomes more cautious; in a rate-cut cycle, tech stocks may regain favor.

AI Investment Outlook for 2026-2030

Looking ahead, the main investment themes in AI will likely focus on two paths:

Infrastructure Providers: Including chip manufacturers, server vendors, and cloud infrastructure companies. These firms will continue to benefit from the explosive growth in global computing power demand.

Vertical Application Companies: Specialized firms in medical AI diagnostics, financial risk control, autonomous driving, and other fields. As technology matures and costs decrease, these sectors will face critical turning points from concept to commercialization.

Overall, while short-term markets may experience volatility, the long-term trend remains upward. The best approach is to avoid chasing highs, instead deploying gradually and periodically reviewing positions to capture core benefits of the AI revolution. The core logic behind AI stock recommendations is ultimately “Choose the right sector, pick the right companies, and seize the right timing”—and dollar-cost averaging is the best way to achieve all three.