Retrieval can provide the right data. It can’t ensure it’s used correctly. AI failures aren’t just about data - they’re about behavior.
AI fails at scale when reliability depends on human verification. Why behavior, not intelligence, limits adoption in high-value industries.
AI outputs often appear stable and confident, but underlying behavior can shift. This explores the gap between perception and reality.
AI can sound certain while being wrong—and uncertain when correct. This explores why confidence and truth often diverge.
Research shows LLMs often contain correct answers internally but fail to express them, revealing a gap between knowledge and behavior.
AI systems don’t pause when uncertain. They continue generating, often leading to drift, miscalibration, and inconsistent outcomes.
Hallucinations are improving in simple cases, but persist in complex ones—often as subtle inconsistencies rather than obvious errors.