Ramping Up AI in Your Portfolio

The allure of “big data” has entranced C-suite executives determined to turn large and growing data sets into competitive advantages. And many of them are turning to “AI”—prediction technology that uses algorithms to detect patterns and trends—as their go-to tool.

As machine learning becomes more widespread, what’s the best way for investors to unlock the potential of artificial intelligence in their own portfolios?

The Appeal of AI Adjacent

Many attractive AI opportunities are not yet public, but investors can still gain exposure through derivative investments in companies that make AI possible. For instance, because the cloud will likely prove crucial in addressing the growth in processing requirements fueled by AI, investing in companies that provide cloud computing infrastructure can be a way to gain exposure.

How large could this opportunity be? Consider that IDC, a leading provider of technology market intelligence, projects that the global datasphere will grow to 175 ZB by 2025 (Display). That means the industry will need an additional 22 ZB of capacity through 2025 to keep up with growing storage demands. By then, IDC also estimates that roughly half of the stored data will reside in public cloud environments.

Data storage isn’t the only infrastructure angle. Leading semiconductor vendors are likely to benefit as well, both in supplying cloud infrastructure and (depending on how AI impacts embedded silicon content) in devices like smartphones and cars. Some foresee “monster chips” for self-driving cars or additional content dedicated to AI on phones. Others, however, suggest that efficiency gains delivered by AI algorithms will absorb much of the growth in processing power required.

Then there are data analytic providers. These range from software vendors that enable customers to analyze their own data to consulting firms that analyze data on their behalf (or, combinations of the two.) In the blockchain ecosystem, we saw similarly situated providers grow rapidly, then just as rapidly lose out to rivals. Alternatively, they were acquired at significant premiums. Either way, we’d caution that these are high-risk, potentially high-reward ventures.

How Much Data Is There? Display


Taking a Direct Approach

What about investing directly in companies that are using data and AI tools effectively? Not surprisingly, the TMT (technology, media, and telecom) sector stands at the forefront of utilizing AI to both bolster revenues and reduce costs.

For instance, Amazon has become an expert at convincing you to order three things when you were only planning to buy one. Predictive algorithms also enable Netflix to recommend the next show to binge even though you spent your entire weekend consuming the full season of another series. Then there’s Facebook and Google, which have created a virtual duopoly in online advertising with their data-fueled, precise ad targeting.

But success has also cropped up in some unexpected places—like the agricultural industry. Take Blue River Technology, which used machine learning and computer vision to allow for more precise targeting of herbicides. To achieve this, the company developed a machine-learning algorithm that could accurately distinguish between lettuce plants and weeds. In 2017, the model was deemed so valuable that John Deere paid $305 million to acquire the technology.

Leaning into Big Data

Perhaps the best investments will be in stocks whose prices have yet to factor in much upside from the use of predictive technologies, but where the companies are on the cusp of unlocking some value from data science and AI. This raises a key point: before wading in, investors must first tackle big-picture questions surrounding profitability and valuation. Great companies often don’t make great investments—because their share prices are already too high.

Such questions also underscore a time-honored reaction to the latest “up-and-coming” investment theme. Investors tend to overrate—both the impact of disruption in the short term and their own ability to pick winners. A measured, research-centric approach can serve as a healthy counterweight to the initial allure. And that’s one investing truism that doesn’t take an algorithm to predict.

An information edge can mean the difference between getting ahead—or being left behind. That’s why many of our entrepreneurial clients look to us as a source of intellectual capital. For more insights on disruption, check out the related blogs here.

Paul Roberston
Senior National Director, Tax & Transition Strategies—Investment Strategies Group

The views expressed herein do not constitute research, investment advice or trade recommendations and do not necessarily represent the views of all AB portfolio-management teams.

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