Today’s AI investment opportunity isn’t just for growth stock investors

Finding equity opportunities by looking downstream across the entire AI value chain

It may be a bit of a stretch to compare today’s AI revolution with the mid-19^th century California gold rush, as we view AI as a more likely source of lasting value than the gold rush, which left many prospectors with little to no profits to show following the initial frenzy. However, we believe the gold rush offers a practical lesson for today’s investors in AI: Rather than focus on the most immediately visible front-end opportunities of an emerging-market trend, go further down the value chain to pursue less obvious, more fundamentally driven sources of potential returns.

For example, scholars who studied the gold rush found that equipment suppliers and other merchants serving miners made far more money than the majority of the miners themselves; that is, supplying picks, shovels, and jeans generally proved to be more lucrative than the more speculative venture of seeking gold. In a similar way, companies that are toward the back end of the AI value chain—those supporting the infrastructure that AI relies on, rather than those driving AI technology itself—may ultimately offer more long-term opportunities for value-oriented investors than the companies at the top of the chain that are commanding the most attention today.

Today’s AI-driven U.S. equity market environment

Perhaps the most notable storyline of the U.S. equity market so far this year has been the recognition of AI’s potential and the disproportionately positive impact that it’s had for a small group of tech-oriented mega- and large-cap growth stocks—names that we consider to be clustered near the front end of the AI value chain. As of June 30, the information technology sector accounted for nearly 65% of the S&P 500 Index’s total year-to-date return.¹ At one point, on May 24—before market performance broadened over the summer—just eight tech names had contributed almost 98% of the market’s return.¹

Those top-heavy results have helped propel the growth equity style to a large year-to-date performance gap over value stocks. Through September 1, the Russell 1000 Growth Index’s year-to-date return was 32.2% versus 6.4% for the Russell 1000 Value Index.²  (Growth stocks are generally regarded as those that generate revenue and earnings growth at an above-average rate; their shares are typically priced at a premium given their perceived growth potential. In contrast, value stocks are considered relatively inexpensive based on metrics such as price-to-earnings ratios; their earnings tend to be steadier and slower growing, often reflecting the relatively long history of their business models and revenue sources.)

However, as value investors, we don’t believe that the positive impact that AI may have on companies’ profitability will be limited to the growth style. We’re seeing strong potential from AI not just among the biggest tech names; it may also be found in a select group of established, more modestly sized companies that we believe possess the scale and expertise in AI-adjacent fields to carve out profitable niches in the long chain of value that we expect to see from AI adoption. Here are three examples:

1) The back end: evolving infrastructure needs to support AI—As more companies incorporate AI apps into their business operations, enterprise data centers and cloud-based infrastructure will need to evolve from capacity and design perspectives. As AI demand grows and ever-larger amounts of data must be generated, processed, and stored, we expect to see a spike in demand for computer network bandwidth and memory storage at hyperscale capacities relative to those when AI was more concept than reality. A number of established players in the computing infrastructure business appear to us to be well positioned to capitalize on these developments. Among them are manufacturers of networking semiconductor chips for data centers and providers of computer memory and storage.

2) Capitalizing on the need for diversified supplier bases—Early in the AI revolution, we’ve seen either single companies or small groups of large firms dominate segments of AI, notably in graphics processing units (GPUs)—processors with the enhanced computation capabilities needed to drive advances in machine learning, which involves identifying patterns in data and making observations to perform tasks at the same level or better than humans. Consider, for example, an AI application that sorts through vast amounts of text data scraped from the internet to compose a piece of writing or answer a question.

While the relatively small number of dominant players have first-mover advantages in their respective segments of AI, we believe that those firms’ business customers may ultimately seek to diversify their supply chains by identifying alternative suppliers of GPUs. After all, customers typically don’t want to remain beholden to a single distributor or product design for too long in case of a disruption in supply or a shift to adapt to new innovations. Often, firms that seize an early lead in an emerging field are challenged by rivals who seek to develop similar technology at a lower cost, and monopolies or near-monopolies are short-lived. In the GPU space, we see strong opportunities in established companies that currently have second-tier status in GPUs but possess expertise and resources in adjacent fields sufficient to eventually propel themselves to the top ranks in GPUs with targeted research and development. Furthermore, some of the biggest tech companies are developing custom AI chips to lessen their reliance on currently dominant GPU suppliers. Such established companies typically have consistent cash flow, and their stocks may possess value characteristics combined with strong growth potential, making them particularly attractive bottom-up opportunities, in our view.

3) AI-driven efficiency at the industry level—The healthcare industry is one of the many areas where we see great potential for AI to drive efficiency and reduce costs. For example, medical providers and insurance companies are likely to leverage AI technologies to optimize costs and service delivery, while pharmaceutical companies will ramp up AI use in drug discovery research and genetic screening. Healthcare has an abundance of established players with stocks that possess value characteristics such as resilient cash flow, presenting plenty of bottom-up opportunities at the individual security level. Elsewhere, we see rising AI-related profit potential in industries that face relatively high labor costs relative to their revenues. We expect that many companies in areas such as business services and financial services will identify ways to leverage AI to automate some of their more labor-intensive operations and reduce costs.    

In an AI world, fundamentals will still matter

While we’re mindful as investors of the potential for any fast-moving technology to eventually fizzle out or merely fall modestly short of predictions, we believe AI has immense potential. It’s just one among several trends that we’re tracking as we seek bottom-up opportunities across sectors in stocks that we believe are trading at attractive valuations while possessing strong fundamentals and positive business momentum. With AI, it may prove more challenging to differentiate between speculative opportunities and fundamentally driven value than it was in the gold rush days, and we’re not expecting to come across any AI pot of gold at the end of a rainbow. But we believe that a patient, fundamentally driven approach can unearth long-term value in AI. 

1 S&P Dow Jones Indices, July 2023. 2 FactSet, September 2023.