When you claim "Machine Learning" as your biggest strength but can't do basic arithmetic, you've basically mastered the entire field. The developer here has truly understood the core principle of ML: you don't need to know the answer, you just need to confidently adjust your prediction based on training data. Got it wrong? No problem, just update your weights and insist it's 15. Every answer is 15 now because that's what the loss function minimized to. Bonus points for the interviewer accidentally becoming the training dataset. This is gradient descent in action, folks—start with a random guess (0), get corrected (it's 15), and now every prediction converges to 15. Overfitting at its finest.

The automobile, the lightbulb, the personal computer—all revolutionary inventions that followed a simple pattern: build something people want, and they'll throw money at you. Fast forward to 2024, and AI companies have somehow reversed this entire business model. They've built products that cost billions in compute and electricity, users absolutely love them, and now they're desperately begging those same users to actually want the product they're already using. The punchline? Every previous tech revolution had investors asking "will people use this?" while AI has investors screaming "PLEASE want this, we're burning through venture capital faster than our GPUs burn through kilowatts!" Training models costs more than a small country's GDP, inference isn't getting cheaper, and somehow the pitch has devolved from "disrupting industries" to "pretty please develop a dependency on our chatbot." Supply and demand just left the chat—along with profitability, apparently.

Ah yes, the classic tech industry anatomy lesson. OpenAI and Microsoft Copilot are getting all the attention up top, looking shiny and impressive, while the real MVPs—FOSS projects, independent artists, and venture capital—are doing the heavy lifting down below. It's almost poetic how these AI giants are basically standing on the shoulders of... well, everything else. OpenAI scraped half the internet (including your GitHub repos, you're welcome), Copilot trained on millions of lines of open-source code, and both are propped up by billions in VC money that's desperately hoping this AI bubble doesn't pop before they exit. The irony? The open-source community built the foundation, artists unknowingly donated their work to the training sets, and VCs threw cash at it like confetti. Meanwhile, the fancy AI tools get all the credit while casually forgetting to mention the awkward "how did we get this data again?" conversation. Classic tech move—stand on giants, claim you're flying.