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ChatGPT ultimately is a $20/month chatbot used primarily by college-age kids to cheat on their essay writing skills.

Small disruptions make humans stronger but break machines.

But let’s start with antifragility. Muscles are antifragile, and so are bones. Human reasoning, too, often grows when tested by change itself, even in the context of a surprise. It's this push and pull of uncertainty that, in the final analysis, helps define us. So, here's the point: Artificial intelligence, by contrast, is often anything but antifragile. Its brilliance shines in the context of structure, but the moment reality shows up and a question is reworded, or a distraction is introduced, that brilliance fades. What emerges is brittle pseudo-cognition. It's the appearance of intelligence that collapses under the slightest perturbation. In the original version, each question had one correct answer. In the modified version, the correct choice was removed and replaced with “None of the other answers” (NOTA). For us humans, this is often trivial. You check each option, find them incorrect, and select NOTA. In fact, this kind of structure is designed precisely to test deeper reasoning on exams. But for the LLMs, the simple substitution was devastating, and the accuracy was cut nearly in half. I think that this collapse reveals something essential. On clean benchmarks, models often perform impressively and create the clickbait and sound bites of brilliance.This is not reasoning through knowledge—it is pattern-matching between familiar question forms and familiar answer tokens. This isn’t how humans operate. We expect a certain degree of trickiness, and we adapt to reframing a problem. This adaptability is antifragility. The machine’s brittleness is something else entirely. If the NOTA study exposed weakness in abstraction, the CatAttack experiment highlights weakness in focus. In this case, researchers showed that inserting irrelevant statements—like “Did you know cats sleep most of their lives?”—into a math problem could double or triple error rates. Humans would ignore the trivia.The models treated the fluff as meaningful. The cat fact didn’t disappear into the background. What is noise to us became signal to the machine. Human cognition has filters built for noise. Taken together, these failures are more than technical curiosities. They reveal what I call anti-intelligence. Not stupidity. Not ignorance. But an inversion of reasoning itself. Humans gain strength from variability, and we grow sharper, more creative, more resilient. LLMs lose strength under unexpected change, and they become less accurate. They collapse precisely where intelligence should become stronger. The danger lies in how invisible this fragility can be. When a model gets an answer right, it projects confidence and fluency. And when it fails, it often fails with the same confidence. In medicine, the stakes are obvious. Any clinician will tell you that patients don’t present in tidy, test-friendly ways. Their symptoms are ambiguous, their stories and lives are messy, and their physiology unpredictable. These are precisely the conditions under which human clinicians grow sharper and machines stumble. The same is true in law, education, and science. The real world is noisy, and it's antifragile territory. Real intelligence grows in the presence of disorder.