You Are Absolutely Correct

The job market in 2026: millions of AI-generated apps flooding the ecosystem like digital locusts, all created by people who discovered ChatGPT and suddenly became "entrepreneurs." Meanwhile, the senior engineer sitting there with actual projects that real humans use is about as impressive as bringing a knife to a nuclear war. The vibe coder with their prompt engineering skills has industrialized app creation to the point where having genuine users is now the rarest commodity in tech. Quality over quantity? Never heard of her.

The brain is right there, perfectly available, yet here we are letting Claude and Cursor autocomplete do all the heavy lifting. Why think when you can just press Tab and let AI write your entire codebase? The best part? That brain is getting smaller and more neglected in the second panel. Soon we'll forget how to write a for loop without AI assistance. But hey, at least our commit velocity is through the roof, right? The machines aren't taking over—we're willingly handing them the keys while we sit back and watch TikTok.

The tech industry's favorite pastime: solving expensive problems by creating exponentially more expensive problems. Someone complained about API costs being too high, so naturally the solution is to train your own Large Language Model from scratch. Because nothing says "cost optimization" like spending millions on GPUs, hiring an entire ML team, and burning through your company's runway faster than a SpaceX launch. It's like saying "this restaurant is too expensive" and then buying a farm, hiring chefs, and opening your own Michelin-star establishment. The comparison to starting an offshore oil drilling company because your car uses too much gas is chef's kiss level satire. Both scenarios share the same energy: wildly overengineering a solution while completely missing the point of cost reduction. Spoiler alert: Your startup probably doesn't need its own LLM. Just optimize your prompts and cache your responses like a normal person.

Job requirements: 5+ years experience with LangChain. Google search: LangChain was launched in October 2022. Ah yes, the classic tech recruiter time-travel paradox. "Must have 5+ years experience with technology that's existed for 1.5 years." Next they'll be asking for senior developers who can code in languages that haven't been invented yet. Maybe I should update my resume to include my expertise in quantum programming from the future. The only way to meet these requirements is if you're literally the creator of LangChain or you've mastered the dark arts of resume chronology manipulation.

Ah, the classic corporate brainstorming session where everyone's looking for shortcuts except the one person suggesting the obvious solution. Low-code, AI, buzzwords galore—but nobody wants to hear "just hire someone who knows what they're doing." That dev getting thrown out the window represents every competent engineer watching their company chase shiny tech instead of proper staffing. The real punchline? Six months later they'll hire three devs anyway, but only after burning through the budget on half-baked AI solutions that generated more bugs than features.

OpenAI out here defending their AI training on copyrighted material by saying the race is "over" if they can't use it. Meanwhile, they're getting roasted with the car thief analogy: "10/10 car thieves agree laws are not good for business." The irony is chef's kiss. Tech companies built entire empires on intellectual property protection, patents, and licensing agreements. But suddenly when they need everyone else's data to train their models, copyright is just an inconvenient speed bump on the innovation highway. It's like watching someone argue that stealing is actually just "unauthorized borrowing for the greater good of transportation efficiency." Sure buddy, and my git commits are just "collaborative code redistribution."