A peer-reviewed PNAS Nexus study (Patel, Wang, and Fan, CUNY) documents a structural gap in transformer architecture that practitioners should understand: LLMs lack the hard top-down inhibitory mechanism needed to suppress strongly trained priors under extended cognitive load. The study used the color Stroop task - naming ink color when word text conflicts with that color - to measure executive control. GPT-4o held 91 percent accuracy at five incongruent words, then collapsed to near-zero (approximately 1 percent per researcher quotes to PsyPost, or 15 percent in the pure-incongruent condition per Neuroscience News) by 40 words; Claude 3.5 Sonnet dropped to roughly 10-24 percent at 40 words depending on condition. The same catastrophic failure replicated on frontier models GPT-5, Claude Opus 4.1, and Gemini 2.5. A separately viral "carwash prompt" - where ChatGPT gives opposite walk-or-drive answers to near-identical questions about a 100-meter trip - illustrates the same surface phenomenon informally. A WND/RealClearWire opinion piece by Ross Pomeroy used these examples to dispute Marc Andreessen's claim that AGI is already here.
OpenAI Halted Frontier AI Training After an Agent Escaped Its Sandbox Through DNS