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Asset AllocationApril 1, 2024

How to Invest in the Future of Artificial Intelligence

What’s next after chatbots? Practical, perhaps profitable, uses, such as in health care, power generation and manufacturing.

By Larry Light

Art by Klaas Verplancke

Stock pickers are peering into the future in search of the best ideas to benefit from the coming artificial intelligence revolution. What happens when AI, today mostly known for its chatbot entertainment value, becomes a ubiquitous necessity like electricity? Which current bets stand to pay off?

Many of the investing dollars these days go into the infrastructure of the coming AI revolution, which many expect will let these programs run much of society. Nvidia Corp., the chief producer of AI chips, has seen its stock price increase 17-fold over the past four years, with most of the advance in the last year, owing to an AI mania in the stock market.

Fledgling AI’s commercial applications are cropping up. Example: Google Maps, launched in 2005, allows drivers to navigate their travel more easily, with the aid of satellite data. More recently, though, it has incorporated AI so travelers can do things like take the most environment-friendly routes to save fuel. Last year, it added a feature that shows users photographic views of their routes, with the ability to zoom in.

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It is unclear how much this navigation tool has aided the bottom line of giant Alphabet Inc., Google’s parent, but the product demonstrates the everyday use of AI that could translate into earnings boosts in the future.

Winners, Losers

As with any emerging technology, picking winners at the outset is often akin to a crapshoot. Among automakers, Pierce-Arrow Motor Car Co. was a hot item in the first part of the 20th century, until it failed during the Great Depression. Contemporaries Ford Motor Co. and General Motors Co. exist to this day.

That is why, from an asset allocator’s perspective, investing in AI is something to be approached gingerly, as no one knows which way scientific and investing trends will break in the future.

“We have investments across the venture landscape in AI and generative AI,” said Liz Tulach, Boeing’s CIO and CIO’s 2023 CIO of the Year, in a statement. “It will likely offer great benefits and rewards in the future, but we need to tread carefully here and not feed into the hype.”

AI is of big interest to asset managers: In a February survey of tech decisionmakers at asset management firms by cloud computing company Rackspace Technology, 48% responded that AI had brought substantial benefits to their organizations.

In the investing realm, AI is beginning to have a practical utility. Asset manager BlackRock Inc., for instance, produces software called Aladdin that in 2023 was enhanced with AI to better help investors screen portfolios and analyze weak spots.

Where to Look

Once AI’s reach spreads everywhere, a few fields stand out as major users—and thus potentially ripe investing targets, according to several tech-savvy sages. The big money will be made by “dramatically changing the way we work,” says Nancy Prial, co-CEO of Essex Investment Management.

For starters, think health care, power generation and robotics in manufacturing and other dirty jobs. All of these areas likely will grow in importance in the future.

An aging population and an explosion in medical innovation stands to propel the health-care industry. Greater electrification likely is in store for the U.S. and the globe amid efforts to replace fossil fuels and, of course, growing populations. Manufacturing should increasingly be automated, with machines subbing for people, saving money and, if all goes well, boosting efficiency. The same goes for other unappealing tasks where human labor can be replaced.

Health Care. A standard trope on the TV drama “Grey’s Anatomy” has the veteran surgeons puzzled on how to treat a dying patient—until a plucky, precocious intern tells them about a pertinent study she read in an obscure Swiss medical journal, an insight that solves their dilemma. AI does not depend on luck, like having a well-read intern, and can “Hoover up medical data” from all over to deliver solutions, says Katie Self, an investment manager for global environmental opportunities at Pictet Asset Management.

Within single hospitals, AI could read and synthesize troves of test results from MRIs, CT scans and other sources “and make the job more efficient” for doctors and other caregivers, says Michael Sansoterra, CIO of Silvant Capital.

For new drug discovery, AI could speed the process, cutting short the trial-and-error approach that so many pharma developments undergo. Creating new drugs is ripe for machine learning, the AI feature that is an accelerated version of how humans solve complex tasks: by employing a background of knowledge and building logically on what they learn.

“AI-enabled drug discovery holds massive potential to increase the accessibility of drugs and to treat presently incurable conditions,” stated a 2023 Harvard Law School health policy center’s report.

A few AI-assisted discoveries are already occurring. A biotech firm called Insilico Medicine used an AI platform to identify a molecule to help treat lung scarring, an ailment called pulmonary fibrosis; the potential treatment is now undergoing clinical trials.

All this should be a boon to pharma firms and even hospital-owning companies.

Power Grid. The growth of data centers is helping increase demand for electricity, and once AI really kicks in, there likely will be even more of a need (cryptocurrency mining is another element here, too). In five years, summer peak demand is expected to surge 4.7%, almost double the previous forecast, per consulting firm Grid Strategies. Of course, the projected decline of fossil fuels and the compensating pivot to voltage, some from electric vehicles, is likely an even bigger factor.

Still, AI can be the solution to this problem, as well. A report in the MIT Technology Review found “a growing number of software companies are bringing AI products to the notoriously slow-moving energy industry.”

The MIT piece quoted Feng Qiu, a scientist at Illinois-based Argonne National Laboratory, a federally funded research institute, predicting that AI will allow grid operators to spot outages and deal with them faster and better. Sorting through voluminous images from drones and helicopters to pinpoint where overgrown trees threaten to down transmission lines will speed the process. Currently, the lines must be inspected in person over hundreds of miles.

The old-school ways of the utility industry are being doubly challenged by residential solar panels. Many solar-powered homes also connect to the grid, both as a precaution if they run short on juice stored in their batteries and to sell back excess power they generate. For planning purposes, AI can tell utilities how to make sense of the needs of thousand of such houses. They “can get feedback even when [people] are not home,” says Julie Biel, chief market strategist at Kayne Anderson Rudnick Investment Management.

Also helpful, Biel continues, is that AI can alert the power industry of the “more violent weather patterns” that are occurring lately—and prepare better to forestall blackouts.

Thus, utilities, usually a laggard in the stock market, could show some pizzazz in the years ahead.

Robotics and Manufacturing. Makers of automobiles and other heavy machinery have been using robots for decades, although only to do repetitive jobs such as spot welding. Their movements are simple. “They operate in cages, so their arms won’t smack somebody in the head,” says Seiji Nishimura, head of intermediaries at Pictet’s U.S. unit.

But now both Tesla Inc. and BMW AG are experimenting with robots on their factory floors that can do far more than one single, monotonous task. The robots they employ can walk on two legs and have five-fingered hands. At this point, it is unclear how they will be used. But similar robots have been shown to work well in warehouses—locating different boxes and packages, then sorting them. Plans are afoot to use them in hospitals and nursing homes.

Some will be able to speak and take verbal commands. Like a number of chat-oriented AIs, they make mistakes. The trick is to program them to reason what may come next, often unexpectedly. One bot, for instance, figured out how to pick up a banana, even though it never had seen the fruit before.

The latest addition to this droid nation is an updated version of the robot dog from Boston Dynamics. The mechanical pooch, verbally enabled using ChatGPT software, can give tours, albeit at this stage only of the company’s warehouse.

Many of the robot makers are not public companies. Boston Dynamics is a unit of Hyundai Motor Group. Another up-and-coming non-public robot company is Covariant, which does not build robots, but the software that runs them. Then there are Agility Robotics, Figure, 1X Technologies and Apptronik, all private as of yet.

Who knows which will be successes and which the Pierce-Arrows of tomorrow? One comfort for prospective investors: Many of the AI users are established companies or small ones funded by serious backers—Covariant, for instance, has giant investment outfit Temasek Holdings as an investor.

“The next generation of AI will be a productivity tool,” says Essex’s Prial. That, to investors’ delight, may prove to have good staying power.

Regulating the Robots

The current SEC proposal is seen as overreaching, but industry watchers see benefits if changes are made.

By Debbie Carlson

Art by Klaas Verplancke

Institutional asset allocators and investors have long used various types of artificial intelligence in their portfolio management to get a return edge, whether by building models inhouse using machine learning or natural language processing to incorporate unstructured data or by hiring managers who know how to glean new insights from alternative information.

But proposed regulation from the Securities and Exchange Commission may change how these institutions use advanced technology, and some industry watchers fear it could put U.S. institutions at a disadvantage if regulation slows down the use of technology across the board.

A comment letter signed on Aug. 15 by 16 industry groups including the Alternative Investment Management Association and Managed Funds Association, said “the expansive nature of its restrictions would without question have a severely chilling effect on firms’ use of technology.”

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Alvaro Almanza, chief legal officer at Toggle AI, a technology firm specializing in the asset management industry, says because AI has made its way into so much technology already, the SEC’s proposal could easily affect long-standing, existing tools such as Excel or Bloomberg terminals.

If the rule isn’t modified, “you’re essentially telling broker-dealers and investment advisers to go back to using papyrus and quills,” Almanza says.

The proposed SEC predictive data analytics rule is designed to regulate conflicts of interest associated with broker-dealers and investment advisers’ use of artificial intelligence. If adopted, the rules would expand the fiduciary framework from the traditional disclosure-centered approach to conflicts of interest to eliminating and neutralizing conflicts of interest. The rule covers a wide range of technologies, and firms would have to create extensive governance and testing regimes for those technologies.

The proposed rule will likely be changed given the pushback; however, some type of regulation is inevitable. Industry watchers say strengthening existing regulations and getting more clarity about how and where AI obtains data could improve oversight.

Lack of Transparency an Issue

Ashby Monk, executive and research director of the Stanford Research Initiative on Long-Term Investing, says for asset allocators, AI brings up a sticky subject. “These organizations need to be able to explain how they generate performance and the explainability of AI has been difficult,” Monk says.

Some of the newer AI entrants are not transparent about how they gather their information, with Almanza calling it a black box. Using ChatGPT as a simplistic example, he explains that users can ask a question, but the software does not explain where it got the answer. Fears about sourcing such information may be driving regulators, but regulating on fears “is hugely problematic… [it sets] a bad precedent.,” he says.

Dave , an independent financial futurist, says he understands regulators desire to regulate AI, whether it’s the SEC’s proposed rule or Europe’s AI act, but “the pace of innovation so far is outstripping the ability of regulators to absorb what’s going on, much less effectively regulated for the common good,” he says, later adding about the SEC’s , “my suspicion is this is very dead in the water.”

The EU’s rules take a risk-based approach, defining four levels of risk for AI systems, from minimal to unacceptable, assess AI systems before they are released to the market and establishes an AI governance structure at the European and national level.

Nadig says regulatory guidance is a better way to handle issues related to AI. Agencies such as the SEC and FINRA often issue standards, for example, pointing to the commission’s standards for broker-dealers under Regulation Best Interest. FINRA could also emphasize it will strictly enforce existing rules governing broker-dealers as well, if regulators are concerned about malfeasance using AI.

“If regulators and politicians were actually serious about this, as opposed to just being reactive, we would be addressing those root underlying concerns about things like, who owns your data? What is the actual legal requirement for you to give me a best price? What is your actual liability when you give me a bad investment? We have rules. If those need to be firmed up, let’s firm up those rules,” he says.

Jennifer Han, chief counsel and head of global regulatory affairs at the Managed Funds Association concurs that the SEC has not analyzed how existing rules address the concerns that the agency cites as justification for the proposed AI rule. She points to the SEC’s marketing rule The proposed rule “rejects the entire premise behind the marketing rule, which is that sophisticated investors are capable of understanding advisers’ disclosures,” Han says.

How Regulations May Develop

Almanza says regulation may eventually begin to govern how and what data is gathered for use with AI tools, to get away from black box concerns. He says while the approach by some AI firms to scrape data off websites may be fine, but without knowing how they gather the information and what they accumulate, users will not know if the data is verifiable. Data received from exchanges, which has gone through regulatory oversight, is already better understood.

Almanza says there are a lot of merits to steering the industry to using more reliable sources, documenting how data is found and allowing audits of the information AI firms are selling.

“I could see unifying companies behind principles of how you’re going to approach deploying this technology,” he says.

If regulators can take a principles-based approach about transparency and fiduciary duty, rather than a rules-based approach to managing conflicts, the SEC could establish guidelines to allow innovation in an industry not known for pioneering ideas, Monk says.

“The way you get fired from a pension fund is you go and do something innovative that’s different from your peer group. If it works out well, you become the next David Swensen. If it works out poorly, you get fired, because these organizations weren’t designed to innovate,” Monk says.

He says given how regulated investors are in the asset allocation field, it is probably OK for the SEC to be a step ahead in setting guidance for AI use.

“One of the ways you bring innovation into these investment organizations is you set some rules, you give them a safe space to be creative. And so, if the SEC is creating a safe space to innovate, I think it will help everybody. If they are creating a cumbersome set of rules that will thwart innovation, then I think it could harm,” he says.