The argument centers on documented technical failures in AI systems. Research cited shows that identical prompts to the same model often generate different outputs, hallucinations persist even when systems retrieve external data to verify answers, and numerical accuracy varies unpredictably across identical inputs. Meanwhile, major investors and financial strategists are already pricing AI as a primary driver of earnings growth for mega-cap technology stocks, assigning elevated valuations based on expected future AI profits rather than current results.
Attorneys tracking technology liability and securities law should monitor two developments. First, whether regulators begin scrutinizing the disclosure standards companies use when attributing financial guidance or operational forecasts to AI systems. Second, whether litigation emerges when AI-dependent business models fail to deliver the earnings growth that justified current stock prices. The gap between AI's technical limitations and market expectations creates exposure for both companies making AI-based claims and investors relying on them.