In 2020, I wrote an op-ed for the Financial Times that pointed out how investment managers, eager to capitalize on the buzz swirling around AI, exaggerated or misrepresented their use of the technology to make their offerings appear more advanced and innovative than they actually were.
This disingenuous behavior has only increased, propelled in part, by the hype surrounding Large Language Models.
Now the deceptive marketing tactic even has a name: AI washing. Investment managers aren’t the only ones to engage in such tactics. Companies, including Coca-Cola, Pepsi, McDonald’s, and H&M, have been widely criticized for boasting about their use of AI in their marketing to position their products as innovative and futuristic — without providing transparency into the specific AI capabilities behind their efforts.
AI does add a certain sheen — at least to the words. Just read a press release for Coca‑Cola Y3000 Zero Sugar: The company invites “fans to imagine what the future tastes and feels like with a limited-edition drink and new AI-powered experience.” But the release didn’t give any details about the AI or the role AI played in the product’s creation.
The United States consumer protection agency, the Federal Trade Commission, has made AI washing a priority, warning companies across the economy that “if you think you can get away with baseless claims that your product is AI-enabled, think again. … Before labeling your product as AI-powered, note also that merely using an AI tool in the development process is not the same as a product having AI in it.”
The commission fulfilled this promise by taking enforcement action against several companies, including Automators AI. In commenting on the lawsuit, Samuel Levine, Director of the FTC’s Bureau of Consumer Protection, said, “The defendants preyed on consumers looking to provide for their families with promises of high returns and the use of AI to power such returns. Their lies caused consumers to lose tens of thousands of dollars, with many losing their life savings. The FTC is working to hold defendants accountable and to secure redress for their victims.”
Fellow regulators at the Securities and Exchange Commission have also been cracking down on investment managers’ and advisers’ false or misleading claims of AI use. In December 2023, the SEC’s Division of Examinations initiated an AI sweep. In this formal process, the commission requested information from investment advisers on their use of AI-based tools. In March, the SEC announced “settled charges against two investment advisers, Delphia (USA) Inc. and Global Predictions Inc., for making false and misleading statements about their purported use of artificial intelligence (AI). The firms agreed to settle the SEC’s charges and pay $400,000 in total civil penalties.”
Director of the SEC’s Division of Enforcement, Gurbir Grewal, underscored the SEC’s commitment to safeguarding investors against AI washing in his announcement of the settlements, stating, “As more and more investors consider using AI tools in making their investment decisions or deciding to invest in companies claiming to harness its transformational power, we are committed to protecting them against those engaged in ‘AI washing.’ As today’s enforcement actions make clear to the investment industry – if you claim to use AI in your investment processes, you need to ensure that your representations are not false or misleading. And public issuers making claims about their AI adoption must also remain vigilant about similar misstatements that may be material to individuals’ investing decisions.”
As the co-founder of a genuine AI-based investment manager, I regularly review managers’ white papers and presentations that mention their use of AI, canvass their hires for evidence of expertise in advanced data science, and participate on conference panels where our fellow panelists make broad, unspecified claims about their adoption of AI. With few exceptions, I find a paucity of evidence indicating the actual incorporation of AI into managers’ investment processes.
Their claims generally come down to “We’ve started using alternative data” or “We use AI to scrape the web for data we input into our models.” Some simply point to their hiring of data scientists with no reference to their roles or contribution; others mention they are using Large Language Models usually to improve an operational function. (As one commentator cogently explains, “Having your team type in ‘import openai’ does not mean that you are at the cutting-edge of artificial intelligence.”)
The most brazen managers simply appropriate the term “machine learning,” asserting that traditional quantitative processes such as cluster analysis or linear regression qualify as AI. A former colleague provided a humorous analogy for this practice: “Anything that’s even remotely quantitative can be called ‘machine learning’ — the plain vanilla cluster analysis they teach in undergrad stats… But it’s a bit like saying that a Ukrainian sustenance farmer is doing genetic engineering by planting the biggest potatoes from last year’s harvest.”
Allocators have grown weary of managers professing their AI bona fides. As an unnamed allocator told Trusted Insights, “I’m interested in AI and big data if they are right for a particular strategy. However, I want to hear more than buzz words from managers — I’m looking for a lucid explanation of how it’s going to help me and be additive to our strategies.”
The SEC’s explicit effort to identify and penalize managers who make misleading AI claims is topical and timely. A recent study found that 33 percent of asset managers are experimenting with AI, while 36 percent are actively using AI. However, because it took the SEC several years to finalize rules standardizing even climate-related disclosure for investment managers, it will likely be some time before the SEC codifies the standard of disclosure for AI use, leaving allocators to rely on their own due diligence to verify managers’ AI claims.
Angelo Calvello, Ph.D., is co-founder of Rosetta Analytics, an investment manager that uses deep reinforcement learning to build and manage investment strategies for institutional investors.
Opinion pieces represent the views of their authors and do not necessarily reflect the views of Institutional Investor.
