August Market Update: Understanding the AI Investment Landscape: From Chips to Data Centers
August Market Update: Understanding the AI Investment Landscape: From Chips to Data Centers
Wyatt Lewis | Financial Advisor
August 3, 2026
Artificial intelligence (AI) may feel simple when you use a chatbot, but behind the scenes there is a complex chain of technologies and businesses making it all work. This chain has become one of the biggest forces shaping financial markets and the economy today. For investors, understanding this bigger picture is important, because AI's impact goes well beyond a handful of technology stocks.
While AI is widely seen as transformational, it is still hard to predict exactly how it will affect businesses, workers, and productivity in the years ahead. This uncertainty can make it difficult for investors to figure out how to value companies and the broader stock market. Keeping a long-term perspective is key.
The full AI supply chain is supporting financial markets
AI is not a single type of investment. There is an entire supply chain involved, covering hardware, data centers, software, and the companies that build and use AI models. Each part of this chain has its own opportunities and risks.
At the foundation are semiconductors, which are the computer chips that power AI. These chips are used in two main ways. First, they are used to "train" AI models, a process that involves processing huge amounts of data across thousands of servers, often taking weeks or months. Second, they are used for "inference," which simply means running the AI model when someone asks it a question. Both of these uses have driven strong demand for chips.
Data centers are large buildings filled with servers that run around the clock. They require electricity, cooling, and security. Spending on data center construction has surged since the launch of ChatGPT in late 2022, and has now surpassed spending on all other types of office construction. Not all of this growth is due to AI alone. The broader adoption of technology since 2020 has also played a role.1
Investors are weighing whether large investments will pay off2
One of the biggest questions for investors is whether the hundreds of billions of dollars being spent on AI infrastructure will eventually produce strong returns. Large technology companies are investing heavily, but it is not yet clear how quickly those investments will pay off. As AI models improve, they may also become more efficient, potentially needing less computing power over time.
This uncertainty helps explain the big swings seen in AI-related stocks. Since early 2025, investors have worried that more efficient AI models could reduce demand for chips and data centers. However, history suggests that when technology becomes cheaper and more capable, it often leads to broader use and entirely new applications rather than less demand overall. This idea is sometimes called the "Jevons paradox."
It is also worth remembering that markets have often overestimated how quickly new technologies generate profits, even when the long-term potential is real. The excitement around internet stocks in the late 1990s took decades to fully play out. A long-term perspective matters here.
Stock prices for tech companies reflect high expectations
As AI has attracted investor interest, the prices of many technology stocks have risen significantly. Valuation (a measure of how expensive a stock is relative to its earnings) for the Information Technology sector currently sits at 21.4x, which is high compared to its own history and to the broader market. This reflects strong earnings growth, but also high expectations for the future.3
It is worth noting that many other sectors of the market are more attractively valued and also have strong earnings growth potential. Balancing exposure to AI-related investments with other parts of the market can help align a portfolio with 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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