Researchers argue LLMs don't actually understand anything. They call it "Potemkin Understanding." For years, we’ve evaluated AI using human tests. The Bar Exam. AP Tests. The MMLU. When a model aces these tests, we assume it understands the concepts. Because if a human passes the test, they fundamentally understand the material. But that assumption is fatally flawed. Humans make predictable mistakes. When we misunderstand something, there is a logical pattern to our errors. The researchers discovered that AI does not fail like a human. It fails like an illusion. They ran an experiment. They asked top models to define complex concepts from game theory, literature, and psychology. The AI nailed the definitions perfectly. Then, they asked the AI to do something simple: identify a real-world example of the exact concept it just defined. Performance completely collapsed. Even worse, when researchers asked the AI to generate a custom example, the AI later failed to correctly classify its own generated output. The models were fundamentally incoherent. They exhibited up to a 62% failure rate the moment they had to actually apply the knowledge they just flawlessly defined. The models aren’t reasoning. They are just reciting memorized patterns wrapped in a conversational tone. The researchers named it after a Potemkin village, a fake, hollow facade designed to look impressive from the outside, with absolutely nothing behind it. Every major AI company is selling you intelligence based on benchmark scores. But this paper proves those benchmarks are a mirage.