They Know About Us

You know that feeling when you accidentally step in dog poop on the sidewalk? Well, imagine that exact same visceral disgust, but it's GitHub Copilot's logo on your shoe. The horror! The betrayal! The absolute AUDACITY of AI-generated code sticking to your sole like some kind of cursed autocomplete barnacle. Nothing says "I don't trust your suggestions" quite like treating Copilot like hazardous waste material. Sure, it can write entire functions faster than you can say "Stack Overflow," but at what cost? Your dignity? Your sense of accomplishment? The pure, unadulterated joy of spending three hours debugging a semicolon? Some developers would rather scrape their shoes clean than let AI taint their precious handcrafted artisanal code. The drama is REAL.

Someone just learned how to add "act as an expert" to their ChatGPT prompts and suddenly thinks they've transcended human knowledge. The hubris is real. The first tweet is genuinely asking why Wikipedia exists when ChatGPT can just... make stuff up with confidence? Because nothing says "reliable information" like a large language model that occasionally hallucinates entire programming languages and historical events. Sure, let's replace peer-reviewed, sourced articles with probabilistic token generation. What could go wrong? The reply absolutely murders them with a Wall-E reference—comparing them to the humans who got so dependent on technology they literally became floating blobs in chairs. Brutal. Accurate. Chef's kiss. 💋 The irony? These are the same devs who will spend 3 hours debugging why their AI-generated code doesn't work instead of reading the docs for 5 minutes. Wikipedia isn't going anywhere, buddy.

The perfect metaphor for modern AI "innovation" doesn't exi— What we're seeing here is the software development equivalent of a Russian nesting doll. A truck containing a van containing... another vehicle. Just like how 90% of "groundbreaking AI startups" are just wrappers around wrappers around OpenAI's API. This is what happens when your entire business model is "Let's add a thin layer of abstraction over someone else's product and call it revolutionary." Next funding round: $50 million for a truck that contains a truck that contains a truck.

Jensen Huang and Nvidia have quietly pivoted from selling RGB-laden gaming GPUs to becoming an AI datacenter empire worth trillions. That revenue chart tells the whole story—gaming revenue is basically a rounding error now compared to the datacenter money printer. PC gamers are out here begging for affordable GPUs with pretty lights while Jensen's counting his AI billions and couldn't care less about your 240fps dreams. The leather jacket man realized that selling one H100 to OpenAI is worth more than selling a thousand RTX 4090s to gamers who just want to play Cyberpunk with ray tracing. Sorry gamers, but you've been dumped for a more profitable relationship with enterprise clients who actually pay without complaining about MSRP.

The hard pill that data scientists refuse to swallow! While everyone's obsessed with fancy neural networks and complex algorithms, the brutal reality is that garbage data produces garbage results, no matter how sophisticated your model is. It's like putting lipstick on a pig - your 17-layer deep learning architecture won't save you from the mess of unclean, biased data you're feeding it. The real heroes aren't the ones with the fanciest models but the poor souls who spend weeks cleaning datasets nobody will ever appreciate. Next time someone brags about their model's accuracy, ask them about their data preprocessing steps and watch them squirm!

The classic tech hype cycle in its natural habitat. First, AI writes 90% of code. Then AI writes 100% of code. Then reality hits and humans get paid premium wages to fix the AI's spaghetti code. Reminds me of that time we deployed an "automated" monitoring system that generated so many false alerts we had to hire three people just to monitor the monitoring system. Progress!