AI Arms Race: The Buildout Financial Advisors Can’t Ignore

As the artificial intelligence boom advances at a breakneck pace, tools like ChatGPT and customer service bots are quickly becoming a key part of everyday life—and of investors’ portfolios. However, the infrastructure that supports these AI innovations is still in the making. 

And tech giants like Amazon AMZN, Microsoft MSFT, and Meta META are pouring hundreds of billions of dollars into powering that infrastructure’s future by building data centers with enormous footprints that possess incredible processing power but pose substantial risks to resources, both natural and refined.  

Whether an investor is personally using AI or not, their portfolio is likely already exposed to this emerging technology, and advisors should clearly communicate the implications of that exposure to their clients. 

For a comprehensive look at the impact that advancements in AI could have on client portfolios and an overview of other top challenges facing investors in 2026, download the 2026 Global Outlook Report.  

Alphabet GOOGL, Amazon, Meta, Microsoft, and Oracle ORCL are among the big tech companies with heavy AI exposure, and these hyperscalers are collectively spending billions to build enormous data centers to house the technology that powers AI computation.  

Meta, for example, plans to build the world’s largest data center in Louisiana. The center is slated to cover a footprint comparable to most of lower and midtown Manhattan combined. 

These hyperscalers’ combined capital expenditure for 2026 will be more than 4 times what the publicly traded US energy sector spends to drill, extract oil and gas, deliver that gas, and run large chemical plants. Amazon’s capex alone is greater than that of the entire US energy sector. 

The table below shows these hyperscalers’ combined capex in comparison to the biggest names in the S&P 500 Energy Sector.

Source: Factset Consensus. Data as of Oct. 29, 2025.

Funding the expansion of these huge data centers comes with a number of execution risks, which center on both energy and natural resource consumption and return on investment.  

For one thing, data centers are extremely power hungry. Graphic processing units, or GPUs, which power AI computation, need lots of electricity to make their calculations, and the current energy grid isn’t ready for the surge in demand. 

GPUs can also run extremely hot, which means large volumes of fresh water are needed to keep them cool. Many communities have already expressed concerns and opposition to the development of these data centers because of the strain it would put on the water supply.  

On the other end of the spectrum, there are questions on whether funneling billions of dollars into these data centers will be worth it in the long run. Despite the hype surrounding AI, only 5% of ChatGPT users currently pay for the service, and it remains unclear how AI can enhance firms’ abilities to generate revenues or reduce costs.

Over the past decade, the stock market has grown increasingly concentrated in AI investment.  

Today, Nvidia NVDA, Microsoft, Amazon, Meta, Broadcom AVGO, Alphabet, and Oracle comprise 28.7% of the Morningstar US Target Market Exposure Index. That’s almost triple their weight from a decade ago, when they made up 9.7% of the index.

Source: Morningstar Inc. Data as of Sept. 30, 2025.

So, client portfolios invested in mutual funds or exchange-traded funds tied to broad US stock indexes are likely already exposed to AI companies like Nvidia, Microsoft, or Meta. Advisors should evaluate whether this level of exposure aligns with their clients’ risk tolerance and diversification goals. 

It’s also worth noting the impact this surge in AI investment has had on how entire sectors, like communication services and information technology, are valued.  

These sectors are trading at high price-to-sales ratios, similar to the tech bubble of the early 2000s. However, unlike back then, these companies are making more profit for every dollar of sales, which keeps their price-to-earnings ratios from being as extreme. 

Advisors can use the Morningstar Global Next Generation Artificial Intelligence Index, which tracks companies most leveraged to AI, to easily understand these valuations.  

For example, the chart below shows stocks in the Morningstar Global Next Generation AI Index are currently priced above their fair value. Elevated price-to-sales ratios may raise concerns, but advisors should note that stronger profitability in these sectors helps moderate price-to-earnings ratios.  

This distinction is key when evaluating whether current valuations are sustainable or overly speculative. Financial advisors need to be aware of the risks and opportunities of AI investment brings, especially as valuations fluctuate.

Source: PitchBook. Data as of Oct. 26, 2025.

If you’re seeking to reduce clients’ concentration risk or working with clients who lack the appetite for such exposure to AI companies, we recommend diversifying into US value and small-cap stocks. They’re currently trading at a discount to our fair value estimates and have far less AI exposure. You may also consider shifting portfolios to stocks in selected foreign equity markets. 

For more of Morningstar’s insights on the impact of AI in the investment world, explore the Where Data Speaks page.