In Matteo Wong’s sharp little dispatch, “The Entire Internet Is Reverting to Beta,” he argues that AI tools like ChatGPT aren’t quite ready for daily life. Not unless your definition of “ready” includes faucets that sometimes dispense boiling water instead of cold or cars that occasionally floor the gas when you hit the brakes. It’s an apt metaphor: we’re being sold precision, but what we’re getting is unpredictability in a shiny interface.
I was reminded of this just yesterday when ChatGPT gave me the wrong title for a Meghan Daum essay collection—an essay I had just read. I didn’t argue. You don’t correct a toaster when it burns your toast; you just sigh and start over. ChatGPT isn’t thinking. It’s a stochastic parrot with a spellchecker. Its genius is statistical, not epistemological.
And yet people keep treating it like a digital oracle. One of my students recently declared—thanks to ChatGPT—that Lacie Pound, the protagonist of Black Mirror’s “Nosedive,” dies a “tragic death.” She doesn’t. She ends the episode in a prison cell, laughing—liberated, not lifeless. But the essay had already been turned in, the damage done, the grade in limbo.
This sort of glitch isn’t rare. It’s not even surprising. And yet this technology is now embedded into classrooms, military systems, intelligence agencies, healthcare diagnostics—fields where hallucinations are not charming eccentricities, but potential disasters. We’re handing the scalpel to a robot that sometimes thinks the liver is in the leg.
Why? Because we’re impatient. We crave novelty. We’re addicted to convenience. It’s the same impulse that led OceanGate CEO Stockton Rush to ignore engineers, cut corners on sub design, and plunge five people—including himself—into a carbon-fiber tomb. Rush wanted to revolutionize deep-sea tourism before the tech was seaworthy. Now he’s a cautionary tale with his own documentary.
The stakes with AI may not involve crushing depths, but they do involve crushing volumes of misinformation. The question isn’t Can ChatGPT produce something useful? It clearly can. The real question is: Can it be trusted to do so reliably, and at scale?
And if not, why aren’t we demanding better? Why haven’t tech companies built in rigorous self-vetting systems—a kind of epistemological fail-safe? If an AI can generate pages of text in seconds, can’t it also cross-reference a fact before confidently inventing a fictional death? Shouldn’t we be layering safety nets? Or have we already accepted the lie that speed is better than accuracy, that beta is good enough?
Are we building tools that enhance our thinking, or are we building dependencies that quietly dismantle it?
Cinemorphosis 
Leave a comment