The AI Existential Crisis

Grammar correction in the wild. King declares himself "the dumbest man alive," only to be immediately dethroned when someone mentions "AI prompt engineering is the future." The true champion of ignorance has been found. Happens to the best of us—one minute you're the dumbest person in the room, then someone walks in talking about their revolutionary prompt that just says "make it good" with fifteen exclamation points.

Made a bold prediction on October 25th, 2025 that everyone would be "vibe coding" video games by end of 2025. Fast forward to December 14th, 2025—still waiting on that timeline upgrade. The real kicker? Dude's favorite pastime is proving people wrong, yet somehow managed to prove himself wrong in under two months. That's what I call efficient failure. The CI/CD pipeline of bad takes. When your prediction has a shorter lifespan than a JavaScript framework, you know you've achieved something special.

ChatGPT spent 69 minutes and 42 seconds "thinking" just to tell you "You can't." That's like watching your senior architect stare at the whiteboard for over an hour during a planning meeting, only for them to turn around and say "nope, not possible" without any further explanation. The irony here is beautiful. Someone's trying to install CUDA 12.1 on Ubuntu 24.04, and the AI that supposedly saves you weeks of work just burned over an hour to deliver the most unhelpful two-word response possible. No workarounds, no alternatives, no "but here's what you CAN do" — just pure, unfiltered rejection. You could've googled this, read three Stack Overflow threads, tried two wrong solutions, and still had time left over to make coffee. But sure, let's call it "incredible" and a "solid product." The future of development is waiting 69 minutes for a chatbot to say no.

Data scientists out here mixing pandas, numpy, matplotlib, sklearn, and PyTorch like they're crafting some kind of cursed potion. Each library has its own quirks, data structures, and ways of doing things—pandas DataFrames, numpy arrays, PyTorch tensors—and you're constantly converting between them like some kind of data type translator. The forced smile says it all. Sure, everything's "compatible" and "works together," but deep down you know you're just duct-taping five different ecosystems together and praying nothing breaks when you run that training loop for the third time today. The shadow looming behind? That's the production environment waiting for you to deploy this Frankenstein's monster. Fun fact: The average data science notebook has approximately 47 different import statements and at least 3 dependency conflicts that somehow still work. Don't ask how. It just does.

You know that one coworker who won't shut up about AI being the future of everything? Yeah, everyone else in the hot tub is mentally checked out while they're drowning in AI hype. The beautiful irony here is using a Family Guy reference—where Peter dismisses The Godfather with "it insists upon itself"—to capture how AI evangelists won't stop forcing it into every conversation, every feature request, and every sprint planning meeting. It's not that AI isn't useful; it's that some people make it their entire personality and expect everyone to care as much as they do. Spoiler: we don't.

From using LLMs to create marketing materials to weaponizing them against themselves. It's the circle of AI life. Companies now build fake websites specifically designed to be scraped by LLMs during training, just so their marketing garbage shows up when users ask for recommendations. Diabolical? Yes. Effective? Unfortunately.