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A new and perilous category of corporate legal risk has emerged: AI Performance Securities Litigation. Fueled by the rapid commercialization of artificial intelligence, this litigation surge targets the gap between corporate claims about AI capabilities and their operational reality. In 2024, the number of AI-related securities class action lawsuits more than doubled year-over-year, a trend driven by a strategic shift in plaintiff litigation from challenging vague innovation claims to targeting specific, quantified performance metrics that fail to materialize.

The Securities and Exchange Commission (SEC), under Chair Gary Gensler, has moved from issuing warnings to aggressive enforcement against what it terms "AI washing"—the practice of exaggerating or fabricating AI capabilities to attract investors. This regulatory crackdown has established critical precedents and paved the way for private securities litigation under Section 10(b) of the Securities Exchange Act.

Key litigation themes include the "Wizard of Oz" risk, where human labor is deceptively substituted for promised AI automation; "model drift," where an AI's predictive accuracy degrades over time, leading to catastrophic failures; and issues of "data provenance," where the poor quality or biased nature of training data leads to flawed AI performance. The single greatest trigger for litigation is the use of quantified claims (e.g., "reduces costs by 40%"), which create measurable falsity and carry significantly higher settlement premiums.

Traditional legal defenses, such as the "black box" nature of AI or the safe harbor for forward-looking statements, are proving to have limited effectiveness in court. The discovery process, which often uncovers internal communications revealing doubts about AI performance, creates immense settlement pressure. To navigate this landscape, companies must adopt a new paradigm of aggressive transparency, integrating rigorous technical validation into their legal and communications review processes and providing detailed disclosures that give investors a realistic understanding of their AI systems' capabilities and limitations.