Concern 929 · Working draft · editorial review in progress

Investor scrutiny of AI claims

AI-washing misleads investors or customers by overstating whether AI is used, what it can do or how much value it has delivered.

Understanding the concern

A company may describe an ordinary process as advanced AI or present an early experiment as a proven business capability. The concern is not enthusiasm itself, but a gap between the claim and the evidence. Words such as autonomous, predictive or AI-powered can hide important limits.

In 2024, the US SEC announced settled charges against two investment advisers over false or misleading AI statements. Those cases concern specific conduct, not a rule that every ambitious claim is unlawful. Think of a product label promising an ingredient that is not actually present. Clear reporting distinguishes an idea, a pilot, a deployed system and a measured result.

An example

A business tells investors that AI drives its decisions when the advertised capability has not actually been implemented.

Illustrative unless identified as a documented case.

Words in plain English

AI
Software that learns patterns from examples or uses rules to perform tasks such as recognizing images or generating text. It can make mistakes and does not understand every situation as a person would.

Evidence and its limits

The source reports settled SEC charges against specific advisers. It does not establish liability for a hypothetical statement or endorse any firm's AI capability.

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Broader frameworks and background

These resources provide context. They do not establish every claim in this concern.

A question to discuss

Can each public AI claim be traced to a real capability and evidence of the result being described?

Keep track of different views, what needs checking, and any next step in your own discussion notes.

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Principles for the conversation

Before you finish · reflect together

Virtues and principles to consider

Virtues are qualities we practice in how we treat people and make choices. They are not scores for judging someone. Use these ideas to explore what a thoughtful response could look like.

Trustworthiness

Trustworthiness means giving people good reasons to rely on you. It grows when promises, actions, and evidence line up. With AI, a friendly voice or a famous brand is not enough: people need to know what was checked and what could still go wrong.

In practice, keep promises small enough to test, record mistakes, and tell people when the tool's limits affect them. If a service promises human review, make sure a person really has time and authority to review it.

Ask together: What would give the people affected a sound reason to trust this use?

Stewardship

Stewardship means caring for resources and systems that others depend on, including people who will use them later. It looks beyond the benefit of a quick launch.

Plan for maintenance, energy use, data protection, and eventual shutdown. Ask who will look after the tool when the original team moves on and how future users can avoid inheriting hidden costs.

Ask together: What are we responsible for caring for over time?

Prudence

Prudence means using careful judgment before and during action. It balances worthwhile opportunities with the need to avoid harm that could have been anticipated.

Start with a limited trial, set stopping points, and prefer choices that can be undone when uncertainty is high. Review what happens rather than assuming the original plan remains sound.

Ask together: What is a careful next step, and when should we stop or change course?

Editorial ratings

These are editorial judgments from the register, not measured probabilities or predictions about your situation.

Severity
Serious · 3/5
Likelihood
Widely observed · 4/5
Time horizon
Now
Register risk score
12
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