Understanding the AI Investment Landscape: From Chips to Data Centers

We rely on computers and smartphones every day without thinking much about how they work. Behind the scenes, there is a remarkable level of engineering complexity and an intricate web of suppliers and manufacturers that make millions of devices possible.

Artificial intelligence (AI) works in a similar way. Using a chatbot might feel simple, but a long and complex chain of technologies and businesses makes it possible. This chain has turned generative AI and large language models (LLMs) into one of the biggest forces shaping financial markets and the broader economy. LLMs are a type of AI that can understand and generate human-like text. Because of this, understanding AI as an investment theme means looking well beyond just a handful of technology stocks.

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 coming years. For investors, that uncertainty can make it difficult to judge the value of companies, sectors, and the stock market as a whole. So how can investors better understand AI's impact while keeping a long-term view?

The full AI supply chain is helping to drive markets forward

One key insight for investors is that "AI" is not a single investment category. It is natural to focus on the companies that build AI models, such as OpenAI, Anthropic, Google, and others. However, these companies are just one part of a much larger picture. The full AI supply chain spans many industries and business models, including hardware manufacturers, data center operators, and software providers, each carrying its own opportunities and risks.

At the base of the chain is semiconductor hardware, which is the physical technology that powers AI. Semiconductors like GPUs (graphics processing units, which are chips designed to handle many calculations at once) and memory chips are needed at two key stages. The first is model training, where LLMs are built using enormous amounts of data spread across thousands of connected servers, a process that can take weeks or even months.

The second stage is called "inference," which simply means actually using an AI model. Every time someone types a question into a chatbot, computing power and memory are needed to produce an answer. The combined needs of training and inference have caused demand and prices for AI hardware to surge, pushing up the market value of companies in this space.

As demand grows, more hardware must be added, which is where data centers come in. A data center is essentially a large building filled with rows of computer servers. These machines run around the clock and require security, electricity, and cooling systems to keep them working. Together, they represent the massive physical infrastructure that makes AI applications available to everyone.

Spending on data centers has become a meaningful part of overall economic activity. The chart above shows how much has been spent on building data centers, not counting the computer equipment inside them. This spending clearly picked up speed after the launch of ChatGPT in late 2022 and has since 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 adoption of technology and automation, especially since 2020, has also increased demand for computing resources.¹

Finally, there is the use of AI by businesses, both for internal operations and through new products built by software companies. This part of the chain may be the hardest to evaluate right now, because it depends on how well companies can turn AI tools into real productivity improvements and better products. Questions about how AI fits with existing software systems, and how software companies will adapt, have been a source of uncertainty in markets over the past year.

Investors are asking whether massive AI spending will produce strong enough returns²

A central question right now is whether the hundreds of billions of dollars being invested in AI infrastructure will eventually produce strong enough financial returns. This is a difficult question, particularly given the enormous scale of spending by the largest technology companies. The demand for computing power to train and operate AI models has been very strong, which has benefited hardware makers and data center providers. At the same time, AI models are becoming more capable, and as they improve, they may also become more efficient, potentially needing less computing power to do the same tasks.

This uncertainty helps explain some of the sharp price swings seen in AI-related stocks. As the chart above shows, large technology company stocks have delivered strong returns over the past several years, but with significant ups and downs along the way. Because building new data centers takes time, periods of optimism about infrastructure spending have often been followed by concern about whether demand will be strong enough to justify the investment.

Since early 2025, for example, investors have been concerned that newer, more efficient AI models might reduce the need for computing power. History does offer some reassurance here, though. Efficiency improvements from new technologies do not always reduce overall demand. This idea is sometimes called the "Jevons paradox," which describes how making a technology cheaper or more efficient often leads to wider use and entirely new applications. Electricity is no longer just for light bulbs, and computers are no longer just tools for large corporations.

At the same time, markets have historically tended to overestimate how quickly new technologies begin generating profits, even when the long-term potential is genuine. The excitement around internet stocks in the late 1990s and early 2000s took decades to fully materialize. This is a reminder of why it is important to maintain a broad perspective on the companies involved in AI and to keep a long-term outlook as both the technology and the demand for it continue to develop.

Stock prices already reflect high expectations for AI growth

As AI has attracted more investor attention, the valuations of many technology companies have climbed. Valuation is a way of measuring how expensive a stock is relative to a company's earnings or other financial results. As the chart above shows, the Information Technology sector is currently valued at 21.4 times earnings, which is high compared to its own historical average and relative 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 high valuations also reflect strong earnings growth as demand for AI products and services expands.³

It is worth keeping in mind that valuations are not a reliable tool for predicting what markets will do in the short term. Rather, they can help guide decisions about how to allocate, or divide up, a portfolio across different types of investments, especially when aligning those choices with personal financial goals. While AI trends present potential growth

opportunities, many other sectors are also attractively priced and are expected to deliver solid earnings growth. As always, the key is to maintain a balanced perspective, weighing the AI theme alongside other parts of the market in pursuit of long-term financial goals.

Thank you for your continued Trust.

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

The information provided here is for general informational purposes only and should not be considered an individualized recommendation or personalized investment advice. The investment strategies mentioned here may not be suitable for everyone. Examples are for illustrative purposes only. All investing involves risk of loss including the possible loss of all amounts invested.