The AI hype cycle speedrun, perfectly captured in four stages of clown makeup. Started with the promise that AI would revolutionize everything, got seduced into thinking you could skip fundamentals and just prompt your way to a senior dev salary. Then reality hit: those "free" AI tools either got paywalled harder than Adobe Creative Cloud or started running slower than a nested loop in Python. Now you're sitting there with zero transferable skills, a LinkedIn full of AI buzzwords, and the crushing realization that "prompt engineer" isn't actually a career path. The kicker? While you were vibing, the devs who actually learned their craft are still employed. Turns out you can't Ctrl+Z your way out of not knowing how a for-loop works.

So OpenAI is burning through $44 billion like it's debugging a production incident at 2 AM, and everyone's making jokes about them running out of runway by 2027. The tech world is basically split into two camps: those nervously laughing at the irony of an AI company that can't figure out sustainable business models, and developers who've become so dependent on ChatGPT that the thought of it disappearing is genuinely terrifying. The Joker here represents every developer who's been copy-pasting ChatGPT code for the past year. Yeah, it's funny that a company valued at $157 billion might go bankrupt... until you realize you've forgotten how to write a for-loop without AI assistance. The cognitive dissonance is real: we mock their business model while simultaneously having ChatGPT open in 47 browser tabs. It's like watching your favorite Stack Overflow contributor announce retirement. Sure, you can laugh, but deep down you know you're about to be very, very alone with your bugs.

When your training model is crunching through epochs and someone asks if they can "quickly check their email" on your machine. The sign says it all: "DO NOT DISTURB... MACHINE IS LEARNING." Because nothing says "please interrupt my 47-hour training session" like accidentally closing that terminal window or unplugging something vital. The screen shows what looks like logs scrolling endlessly—that beautiful cascade of gradient descent updates, loss functions converging, and validation metrics that you'll obsessively monitor for the next several hours. Touch that laptop and you're not just interrupting a process, you're potentially destroying hours of GPU time and electricity bills that rival a small country's GDP. Pro tip: Always save your model checkpoints frequently, because the universe has a funny way of causing kernel panics right before your model reaches peak accuracy.

When someone suggests you actually learn something or use critical thinking but you've got ChatGPT on speed dial. Why bother with that wrinkly meat computer in your skull when you can just ask an LLM to hallucinate some plausible-sounding nonsense? The modern developer's relationship with AI: politely declining the use of their own brain like it's some outdated legacy system. Sure, debugging used to require understanding your code, but now we just paste error messages into a chatbot and pray. Who needs neurons when you've got tokens? Plot twist: the AI was trained on Stack Overflow answers from people who actually used their brains. Full circle.