It's 2025. We were promised flying cars, or at least a cure for the common cold. Instead, we got LLMs that can write Shakespearean sonnets about toaster ovens but can't center a div without hallucinating a new CSS property.
The industry is in a weird place. Everyone is an "AI Engineer" now. You have juniors who have never debugged a race condition because they just paste the error into the chat window and pray. They trust the model like an oracle. The code mostly works, but it feels soulless, written without the deliberate choices of someone who understands the *system* as well as the *syntax*.
The "Greenfield Fallacy" bothers me too. The demos and tutorials always start with fresh, clean codebases. "Look how easy it is to build a ToDo app with AI!" Yeah, great. Now try pointing that same model at a 7-year-old monolith written in a mix of Java 8 and spaghetti logic, where the variable names are in three different languages and the documentation is a sticky note from a guy named Dave who retired in 2019.
That is the brownfield reality, where AI behaves like a confused intern. It suggests refactors that break six layers of dependency injection. It tries to modernize code that is load-bearing for reasons no one remembers. It doesn't know the *history*.
We are flooding the internet with generated noise and wrecking its signal-to-noise ratio ourselves. Prioritizing velocity over quality builds technical debt at the speed of light.
So yeah, use the tools. I use them. But for the love of root, stop trusting them blindly. Learn the fundamentals, including the memory model, and read the full docs. Because when the model hallucinates, and it will, *you* are the one who has to fix it. And if you don't know how the machine works, you're just a passenger in a car with no driver, speeding towards a segfault.