"Less than 0.001% error rate" sounds bulletproof — until a state attorney general calls it misleading. That's exactly what happened in 2024. Texas reached a first-of-its-kind settlement with a healthcare AI company over unsubstantiated accuracy claims. The tool was deployed across four major hospitals for clinical documentation. The takeaway wasn't that the AI failed dramatically. It's that no one could prove the accuracy numbers were real. Here's what the settlement now requires for five years: → Disclose how every accuracy metric is defined and calculated → Notify customers of known harmful uses or failure modes → Document training data and model architecture → Submit to independent third-party auditing No new AI law was needed. Existing consumer protection statutes were enough. This matters for every enterprise shipping AI into high-stakes workflows. The regulatory bar isn't "does your model work?" — it's "can you prove it, transparently, to someone who didn't build it?" Our latest whitepaper breaks down the technical and legal anatomy of this case, including why thin API wrapper approaches carry outsized risk compared to deep-integrated AI with adversarial detection and human oversight built in. The era of marketing accuracy without substantiation is over. Architectural integrity is the new standard. Save this for your next AI vendor evaluation 🔖 What's the first question you'd ask an AI vendor about their accuracy claims? #EnterpriseAI #AIGovernance #HealthcareAI #AICompliance #GenerativeAI