How to Invest in Artificial Intelligence (AI)

AI is a rapidly growing niche within the broader technology sector, which has been a strong performer in recent years. Historical trends show that emerging technologies like the internet, mobile devices, and cloud computing often capture investor attention and drive market growth. In today’s landscape, AI has gained similar momentum, particularly with the rise of OpenAI’s ChatGPT, which has showcased AI’s ability to automate business processes and enhance functions like customer service, data analysis, and decision-making across various industries.

In general, artificial intelligence (AI) refers to machines and systems that simulate human intelligence, allowing them to learn, reason, and adapt. Key AI technologies include machine learning, neural networks, and natural language processing, each enabling automation, predictive analytics, and enhanced decision-making. AI is already making significant inroads in industries like healthcare, finance, and retail, where it helps optimize supply chains, streamline operations, and personalize customer experiences.

For many investors, AI offers a compelling way to gain exposure to the technology sector’s next phase of growth. Its applications are vast, from automating diagnostic tools in healthcare to enhancing financial trading strategies with predictive models. AI is transforming industries by enabling faster, more accurate decision-making and improving operational efficiency.

While AI is an attractive investment opportunity, it’s important to consider other emerging tech sectors. Robotics, particularly in warehouse automation and manufacturing, and quantum computing, which promises massive advancements in computational power, are also gaining traction. Additionally, AI investments should be evaluated alongside other asset classes, such as cryptocurrencies or commodities. This is especially true for investors seeking to build a balanced and well-diversified portfolio. 

Investing in artificial intelligence offers various pathways, each suited to different investment strategies and risk profiles. Whether you’re seeking broad exposure to the technology sector, or a more targeted investment in AI-driven companies, there are several options to consider. Here’s an overview of the main ways to invest in artificial intelligence. 

1) AI Focused Indices

One of the easiest ways to gain exposure to AI is through broad market indices, such as the Nasdaq Composite or Nasdaq 100. These indices represent a wide range of technology companies, many of which are involved in AI research and development. By investing in these indices, you can access a diversified basket of companies, including those in AI-adjacent fields like semiconductors, cloud computing, and data analytics.

2) Single AI Stocks

If you prefer to invest directly in individual companies, there are two main routes to consider: pure AI plays and AI-adjacent stocks. Pure AI companies are those whose core business revolves around developing and applying AI technologies, while AI-adjacent companies are more diversified but still heavily invested in AI development. Notable examples of AI-adjacent companies include Nvidia (NVDA), which provides the hardware powering AI models, Microsoft (MSFT), which integrates AI across its software, and Meta Platforms (META), which is investing heavily in AI-driven content and services. Additionally, medium- and small-cap stocks in the AI space may offer more risk but potentially higher rewards for those looking for growth opportunities.

3) AI Focused ETFs

Exchange-Traded Funds (ETFs) offer a thematic way to invest in AI by providing exposure to a collection of companies leading in the AI sector. AI-focused ETFs typically track companies involved in AI research, machine learning, automation, and robotics, offering investors a more concentrated exposure to the growth of the AI industry without having to pick individual stocks. These funds often include a mix of large-cap, mid-cap, and small-cap companies within the AI ecosystem. For example, Global X Artificial Intelligence & Technology ETF (AIQ) or Global X Robotics & Artificial Intelligence ETF (BOTZ). 

4) AI Focused Futures and Options

While stock investments are the most common way to gain exposure to AI, investors can also diversify their approach using futures and options. Stock index-focused futures, for example, offer leveraged exposure to indices like the Nasdaq 100, which contains a significant portion of AI-related companies. Similarly, options on individual stocks or ETFs provide opportunities to hedge or speculate on AI companies. Due to their flexibility, options allow investors and traders to express a wider range of market views. However, it's important to note that these instruments come with increased risk, particularly for those less experienced with options and futures trading.

Investing in AI may seem daunting at first, but breaking it down into manageable steps makes the process more accessible. Whether you’re interested in broad exposure or targeted investments, here’s a simplified guide to getting started in AI investing.

1) Open a Brokerage Account

The first step is to open a brokerage account, which allows you to buy and sell a variety of assets like stocks, ETFs, and options. Choose a brokerage platform that suits your investment preferences, considering factors such as commission fees, ease of use, and access to AI-related products. Many platforms like tastytrade now offer commission-free trading, particularly for ETFs, which can be an attractive option for new investors looking for low-cost entry points.

2) Fund Your Account

Once your account is open, you’ll need to deposit funds from your bank account. The amount you fund your account with should align with your financial goals and risk tolerance. Many brokerage platforms allow you to start with as much or as little as you’d like, making it accessible for most investors.

3) Decide Between Investing or Trading

One of the first decisions you’ll need to make is whether you want to invest or trade in AI. Investing generally involves buying shares of an AI-focused ETF or stock and holding them for the long term. This approach may be suited to those looking for exposure to the AI sector’s long-term growth potential, with less frequent trading. Trading, on the other hand, focuses on short-term buying and selling based on price fluctuations. This strategy requires more frequent market monitoring and can be more complex, but it allows for more active participation and the potential for quick rewards—though with increased risks.

4) Select a Product/Security

There are several ways to access the AI sector. ETFs are a popular option for broad exposure, tracking a basket of companies involved in AI technologies and providing diversification. For more direct exposure, you can invest in individual AI stocks, either pure AI plays—companies primarily focused on AI—or AI-adjacent stocks like Nvidia, Microsoft, and Meta Platforms, which have substantial AI investments. For experienced investors, futures and options offer another avenue, allowing speculation on AI-focused indices or individual stocks, but they come with increased risk and complexity.

5) Determine How Much to Invest or Trade

Once you’ve chosen a product to invest in, the next step is to decide how much money you want to commit. Your investment amount should reflect your financial goals and risk tolerance. For those new to investing and trading, it’s often wise to start small and use strategies like dollar-cost averaging, where you invest a fixed amount regularly. This method helps smooth out the impact of market volatility by spreading out purchases over time, reducing the risk of entering the market at unfavorable times.

6) Build a Market Assumption

Before making any investment, it’s essential to build a market assumption. This is essentially a hypothesis about how the AI sector (or specific AI stocks) will perform, based on analysis of relevant data. Technical analysis involves studying price charts and market patterns to predict future price movements. Fundamental analysis involves evaluating the financial health of companies in the AI space, analyzing their earnings reports, and considering broader economic factors that may affect performance. Your market assumption should also take into account external factors, such as regulatory changes, technological advancements, and geopolitical events, all of which can impact the AI industry’s growth potential.

7) Deploy Your Market Assumption

Once you’ve built your market assumption, the next step is to deploy it by executing your strategy. Some investors and traders may be highly sensitive to execution and focus on precise entry prices to maximize returns. For others, timing might be more important than the exact price, and they may prioritize broader market movements or sector trends. How you deploy your market assumption depends on your strategy—whether you’re aiming for a long-term investment with a focus on broader trends, or a more active trading approach that requires quick decision-making based on specific price points. The key is aligning your execution with your market hypothesis and risk tolerance.

8) Monitor Your Position/Portfolio

Once you’ve made your investment, it’s important to track how your AI holdings are performing. The frequency of monitoring depends on whether you’re investing for the long term or actively trading. For long-term investors, you may only need to check your portfolio periodically, but it’s still a good idea to stay updated on the latest trends in AI, as these can affect your investment. Active traders, however, typically monitor their positions more frequently, staying on top of market fluctuations and news that may impact the stocks or ETFs they’ve invested in. Keeping an eye on earnings reports, regulatory changes, and sector developments will help you make informed decisions.

9) Plan Your Exit Strategy

Having an exit strategy is essential for both investors and traders. For long-term investors, this may mean holding onto an investment until it reaches a specific price target or milestone. For active traders, it might involve setting stop-loss orders to limit potential losses or taking profits when a stock reaches a certain value. An exit strategy helps reduce emotional decision-making during market fluctuations and ensures you stay focused on your financial goals. Having clear parameters for when to exit will give you peace of mind and help you avoid making rash decisions in response to market volatility.

10) Review and Refine Your Strategy

After making an investment or trade, it’s important to review the outcome of your decisions. Whether you’ve been investing in AI stocks for the long term or trading ETFs for short-term gains, assessing how your assumptions held up and evaluating the results is key. Regularly reviewing your portfolio and refining your strategy will help you stay on track and better adapt to changes in the market. By learning from each experience, you can continuously improve your approach and make more informed decisions going forward.

Since the introduction of ChatGPT by OpenAI in late 2022, AI companies have experienced impressive gains, benefiting from the heightened interest in artificial intelligence and its potential to transform various industries. However, investors should exercise caution when assuming that these gains will continue indefinitely. Quick gains can also be paired with quick losses in volatile markets like the AI sector. Factors such as a delay in further AI advancements or a broader shift in market sentiment, where bearish views take over and push shares of AI companies lower, could impact the trajectory of the sector. Additionally, while AI presents significant opportunities, investors should carefully consider whether investing in single stocks fits their investment profile or whether ETFs may be a more suitable option, depending on their outlook, strategic approach, and risk tolerance.

Single stocks offer the potential for outsized returns, especially for the leaders in the AI space, but they also come with the risk of being wrong about which companies will emerge as dominant players. On the other hand, ETFs provide diversification, reducing the pressure of selecting individual winners in the rapidly evolving AI sector. Thematic AI ETFs usually consist of a range of companies, which helps mitigate exposure to any single stock’s performance. However, ETFs generally don't produce the same level of returns as the sector’s leaders, because the so-called “winners” tend to see outsized returns. This balance between risk and reward is a key consideration for investors when deciding how to approach AI investments.

As you explore the AI sector, consider some of the companies and ETFs listed below. It’s important to note that while OpenAI represents a "pure play" on AI, the company is not yet publicly traded (as of early 2025). Many of the best-known AI-focused companies are not pure plays on AI alone. For example, Nvidia, while heavily involved in AI with its graphics processing units (GPUs) used in machine learning, also serves industries such as gaming and automotive (self-driving). Similarly, Microsoft and Meta Platforms, while making significant investments in AI, focus on other areas like software and social media. The list below (presented in alphabetical order) highlights some of the large cap companies and ETFs most closely associated with the AI sector, though there are certainly others, with new companies being added to the list as circumstances warrant. 

Investing in AI may be compelling due to the potential for strong growth in this sector, but it also comes with a range of risks that investors must consider. One of the primary risks is the volatility inherent in the technology sector, particularly with AI, which is still an emerging and rapidly evolving field. The companies that drive innovation in AI, whether they focus on hardware, software, or applications, face unpredictable challenges, including competition, regulatory hurdles, and technological obsolescence. This uncertainty can lead to sharp price fluctuations, making it crucial for investors to manage risk carefully.

When investing in individual AI stocks, the risk can be even more pronounced. Stock-specific risks such as poor management decisions, product failures, or regulatory setbacks can significantly impact the value of a single company’s stock. This contrasts with investing in AI-focused ETFs, which offer built-in diversification by tracking a basket of AI-related companies. With an ETF, you’re spreading your investment across multiple firms, reducing the pressure to pick individual winners in a rapidly changing field. 

While ETFs may offer less potential for large individual gains, they provide a safer, more balanced way to gain exposure to the overall AI sector, minimizing the impact of any one company's underperformance. However, it’s important to note that risk is still inherent in ETFs, just to a lesser degree compared to single-stock investments. ETFs may still be exposed to sector-wide downturns, or regulatory changes affecting the industry, which can lead to losses across multiple holdings.

AI has shown immense potential, with the capacity to reshape industries and drive long-term growth. However, like any investment or trade, risk is inherent, and there are no guaranteed outcomes. Even with thorough research and analyst opinions, investors and traders can still make mistakes and end up on the wrong side of an investment or trade. This uncertainty is why effective risk management is such a crucial element of all investing and trading endeavors.

As with most investment and trading ideas, what’s considered a "good" investment often becomes clear only in hindsight, as it's challenging to predict which individual companies will emerge as the true winners. That’s one reason why many investors prefer ETFs over individual stocks—they offer diversification, reducing the pressure of picking individual winners in a rapidly changing sector. So, while AI arguably holds great promise, it’s essential to weigh its potential against the risks, and to manage those risks accordingly.