Think about how your smartphone or laptop just works when you need it. Behind the scenes, there is an incredibly complex process involving engineering and global supply chains that make millions of devices possible. Artificial intelligence (AI) is similar. Using a chatbot feels simple, but there is a long chain of technologies and businesses working together to make it happen. This chain has turned generative AI and large language models (LLMs), which are AI systems trained to understand and generate text, into one of the most important themes shaping financial markets and the broader economy today. That is why it pays to look beyond just a handful of technology stocks when thinking about AI.
There is little question that AI is changing the world, but it is still hard to predict exactly how much demand there will be or how it will affect businesses, workers, and productivity in the years ahead. For investors, this uncertainty can make it tricky to figure out what companies, sectors, and the overall stock market are worth. So how can investors get a clearer picture of AI’s impact while keeping a long-term view?
The full AI supply chain is supporting markets
One of the most useful things investors can understand is that “AI” is not one single type of investment. It is easy to focus on the companies that actually build AI models, such as OpenAI, Anthropic, Google, and others, but these are just one part of a much bigger picture. The full AI supply chain spans many different industries and business models, each with its own opportunities and risks. This includes hardware makers, data center operators, software companies, and more.
At the base of this chain is semiconductor hardware, which is the physical equipment, like chips and processors, that powers AI. Hardware such as GPUs (graphics processing units, which are chips especially good at handling AI tasks) and memory chips are needed at two key stages. The first is model training, which is the process of teaching an LLM by feeding it vast amounts of data across thousands of linked servers. This can take weeks or even months to complete.
The second stage is called “inference,” which simply means actually using the AI model. Every time someone types a question into a chatbot, computing power and memory are needed to produce an answer. Both training and inference together have caused demand for AI hardware to surge, pushing prices and market valuations higher.
As demand grows, the hardware needs to be housed somewhere, which is where data centers come in. A data center is essentially a large warehouse filled with servers running around the clock. These facilities require constant electricity, cooling systems, and security. The chart above shows how much has been spent just on constructing data center buildings, not counting the IT equipment inside them. Spending accelerated sharply after the launch of ChatGPT in late 2022 and has now surpassed spending on all other types of office construction. It is worth noting that not all of this growth is due to AI alone. The broader shift toward technology and automation, particularly since 2020, has also driven greater demand for computing resources.1
Finally, there is the business side of AI, meaning how companies use AI internally and how software providers are building new AI-powered products. This part of the chain is perhaps the hardest to assess right now, because it depends on how well businesses can turn AI tools into real productivity gains and better products. How AI fits with existing software, and how those software companies adapt, has been a key source of uncertainty for markets over the past year.
Investors are questioning whether big AI bets will pay off2
A key question for investors today is whether the hundreds of billions of dollars being poured into AI infrastructure will eventually produce strong enough returns to justify the cost. This is not easy to answer, given the enormous scale of spending, particularly by the largest technology companies. The demand for computing power to train and run AI models has been very strong, which has benefited companies that supply hardware and data center capacity. However, as AI models keep improving, they may also become more efficient, meaning they could require less computing power to do the same tasks over time.
This uncertainty helps explain why AI-related stocks have been volatile, meaning their prices have moved up and down sharply. As the chart shows, large technology stocks, sometimes called mega-cap stocks because of their very large size, have delivered strong returns over recent years but with significant swings along the way. Because it takes time to build new data centers, periods of excitement about infrastructure spending have often been followed by periods of worry about whether there will be enough demand to justify it.
Since early 2025, for example, investors have been concerned about newer AI models that are more efficient and might need less computing power. However, history offers an important lesson here. Efficiency gains from new technologies do not always reduce overall demand. This is a well-known idea called the “Jevons paradox,” which suggests that when technology becomes cheaper and more capable, people often use it more, not less, and find entirely new uses for it. Electricity, for instance, is no longer just for light bulbs, and computers are no longer just tools for large corporations.
At the same time, markets have a long history of overestimating how quickly new technologies start generating profits, even when the long-term potential is real. The excitement around internet stocks in the late 1990s and early 2000s took many years, even decades, to fully play out. This is why maintaining both a broad view of the many companies involved in AI and a patient, long-term perspective is so important as the technology and demand continue to evolve.
Stock prices already reflect high hopes for AI
As AI has attracted more and more investor interest, the valuations of many technology companies have climbed. Valuation is a way of measuring how expensive a stock is relative to the earnings a company produces. As the chart above shows, the Information Technology sector is currently valued at 21.4x earnings, which is high compared to its own historical average and to the broader market. The same is true for sectors like Communication Services and Consumer Discretionary, which also include large technology companies. That said, these higher valuations also reflect the fact that many of these companies are growing their earnings strongly as demand for AI capabilities increases.3
It is important to remember that valuations are not a crystal ball for predicting short-term market moves. Instead, they are a useful tool for thinking about the right mix of investments in a portfolio, especially when aligning those investments to personal financial goals. While the AI theme offers real growth potential, many other sectors of the market are more attractively priced and also have strong expected earnings growth. As always, the key is to stay balanced, weighing the AI opportunity alongside other parts of the market, with a focus on long-term financial goals.
The bottom line? The trends driving AI go beyond a few technology companies. While these themes are driving markets, it’s important to maintain a broader perspective and longer time horizon with a focus on long-term financial goals.
References
1. https://www.census.gov/construction/c30/c30index.html
2. The Magnificent 7 companies include Meta, Amazon, Apple, Alphabet, Nvidia, Microsoft, and Tesla. Data as of July 17, 2026
3. Clearnomics research and LSEG data as of July 17, 2026
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