Key Points

• SEC is intensifying the review of corporate AI information disclosure.
• Misleading AI statements may violate securities laws.
• Investors should examine income, expenses, and business risks.

Imagine you invest in a company that claims its artificial intelligence can outperform the stock market. Its presentation materials describe advanced algorithms, proprietary technologies, and considerable potential returns. But what if the company never develops the AI system it advertised?

Such false statements are once again drawing the attention of US regulatory authorities.

On October 30, David Woodcock, the enforcement chief of the U.S. Securities and Exchange Commission (SEC), told Reuters that as concerns about inflated valuations, circular investments, and massive infrastructure commitments grew, the agency was reviewing the information disclosure of AI companies.

What is “AI whitewashing”, and when does it become an illegal act?

“AI whitewashing” refers to companies exaggerating or distorting their artificial intelligence capabilities, adoption levels, or financial benefits.

The use of expressions related to AI in itself is not necessarily misleading. The issue lies in whether the company made material false statements to investors, or whether it omitted information that investors would need in order to assess these statements.

SEC has already taken action regarding such cases.

In March 2024, regulatory authorities accused investment advisory firms Delphia and Global Predictions of making misleading statements regarding the use of their AI. Without acknowledging or denying these allegations, both companies agreed to pay a total civil fine of $400,000.

Delphia claims to make investment decisions using customer data and machine learning, despite not possessing the relevant capabilities it promotes. Global Predictions also exaggerates the capabilities of its AI predictive services.

Another case involves Rimar Capital. Its representative made false statements about a AI trading platform while raising nearly 4 million US dollars from investors. In October 2024, SEC announced that the relevant parties were collectively fined 310,000 US dollars.

These cases indicate that the Securities Law applies to AI related statements, just as it also applies to statements regarding income, products, or investment performance.

Why do cyclic AI transactions cause concerns for regulatory authorities?

The latest concern is no longer just that companies pretend to be using artificial intelligence.

It is conceivable that a chip manufacturer invests $10 billion in a AI startup, and subsequently, this startup commits to using a significant portion of that funds to purchase computing power infrastructure from the same chip manufacturer.

Both of these transactions could be legitimate. However, investors should be aware of how much of the disclosed demand comes from independent clients and how much comes from companies that have financial connections.

Circulating financing does not automatically constitute fraud. The key issue at the level of information disclosure lies in whether significant relationships, dependencies, and risks are accurately presented.

As technology companies invest unprecedented amounts of capital in the AI infrastructure, this distinction becomes particularly important.

Amazon recently reported that while its cloud business revenue has been growing faster, its free cash flow deficit over the past 12 months reached $42.4 billion.

At the same time, billions of dollars in data center financing depend on the expectation that customers will continue to purchase computing power.

These developments in themselves do not prove the existence of any improper behavior. However, they illustrate why investors need clear information regarding sources of income, financial commitments, and business risks.

How can investors identify misleading AI statements?

A useful starting point is to distinguish what a company has promised and what it can actually prove.

If management promotes growth driven by AI, investors should check the financial reports to see if they list actual revenue, paid customers, or measurable cost savings.

Coinpaper's guide on the investment returns of AI explains why revenue growth, profit margins, and cash flow are more indicative of a company's performance than mere infrastructure expenditures.

Financial disclosure is also worth paying attention to. A company may achieve considerable profits on the books, but at the same time, it may face significant cash requirements.

When expensive AI projects rely on borrowing, these risks become even more severe.