Biggest Self Report

The tech industry in 2024, ladies and gentlemen. Why spend billions on R&D when you can just rebrand last year's product with "AI-powered" and watch the stock price soar? Companies out here acting like they invented fire because they added a ChatGPT wrapper to their existing product. Meanwhile, actual hardware improvements and software optimization? That's for suckers who care about delivering real value. The best part? People are actually paying the premium. Slap "AI" on a toaster and suddenly it's worth $300. We've reached a point where marketing departments have more power than engineering teams, and honestly, that explains a lot about the state of modern software.

So let me get this straight: developers spent TWO DECADES building the internet's knowledge base for FREE—tutorials, Stack Overflow answers, open-source libraries, documentation, GitHub repos—basically doing unpaid labor out of the goodness of their nerdy hearts. Then AI companies swooped in like digital vultures, scraped ALL of that beautiful free knowledge, trained their models on it, and now they're selling it back to us for $20/month subscriptions. And the AUDACITY doesn't stop there! Now these same AI tools are supposedly making developers "replaceable" even though they literally wouldn't exist without developers creating the training data in the first place. It's like if you wrote a cookbook, someone photocopied it, opened a restaurant using your recipes, became a billionaire, and then told you that chefs are obsolete. The irony is so thick you could deploy it to production.

Remember when we used to carefully engineer features and tune hyperparameters for our models? Yeah, those were simpler times. Now the entire AI/ML industry has basically decided that the solution to every problem is "just throw a transformer at it and add more GPUs." Need to classify images? Transformer. Text generation? Transformer. Predict stock prices? Believe it or not, also transformer. Meanwhile, good old classical ML techniques like random forests, SVMs, and logistic regression are sitting at the bottom of the pool, completely forgotten. Sure, they're interpretable, efficient, and actually work great for tons of problems, but who cares when you can burn through your entire cloud budget training a 175 billion parameter model instead? Deep learning and neural networks are also drowning down there, which is ironic since they were the hot new thing just a few years ago. But nope, not fancy enough anymore. If your solution doesn't involve attention mechanisms and can't be described with the phrase "large language model," are you even doing AI?

Oh, the sweet nostalgia of copy-pasting code from your old projects! You'd think "I'm a genius, I'll just reuse this masterpiece from six months ago" and then BAM—you're drowning in a sea of dependency conflicts, deprecated functions, and variable names that made sense in 2019 but now read like ancient hieroglyphics. That "quick 5-minute task" just turned into a three-hour debugging marathon where you're questioning every life choice that led you to this moment. The code you thought was your lifeboat? Yeah, it's actually an anchor dragging you straight to the ocean floor of despair. At least with AI hallucinations, you can blame the robot instead of past-you!

So you're out here saying "I hate AI" while simultaneously simping for DLSS 5? That's literally AI-powered upscaling doing the heavy lifting so your GPU doesn't catch fire rendering 4K at 120fps. Pick a lane. DLSS (Deep Learning Super Sampling) uses neural networks trained on supercomputers to make your games look gorgeous while running on a potato. It's machine learning in action, but suddenly everyone's cool with AI when it means their frame rates don't tank. The cognitive dissonance is chef's kiss. Same energy as "I don't trust self-driving cars" while your phone's autocorrect is already making life-altering decisions about your text messages.