Scientist Abel Jansma challenges current methods of evaluating factual accuracy in large language models by arguing that truth cannot be captured as a simple directional vector within neural networks. Drawing on Tarski's semantic frameworks, the analysis critiques existing LLM probing techniques and suggests that evaluating factual alignment in AI requires embracing higher-order interactions and complex information theory.