The AI Hype Cycle: Expectation Vs. Reality

The eternal job title inflation cycle in tech. In 2005, PHP developers were desperately trying to distinguish themselves from "IT guys." Fast forward to 2015, and suddenly "programmer" became a dirty word - everyone had to be a "software developer." Now the prophecy shows us in 2025, those same folks will be scoffing: "Developer? Please, I'm an AI engineer." Meanwhile, the actual work remains the same: making computers do things without crashing too often. The more things change, the more we just rebrand our LinkedIn profiles.

Ah, Google Assistant taking "seconds" literally while DeepSeek and OpenAI duke it out in the AI arena. Poor thing is just counting all the 2nds in a year instead of calculating 365 × 24 × 60 × 60. This is what happens when you train your model on dad jokes instead of math problems. At least it's consistently wrong with confidence - the hallmark of every production system I've ever maintained.

New programmers discovering ChatGPT is like watching someone find the forbidden elixir of instant solutions. One taste and they're HOOKED for life. Why spend hours debugging when you can just ask the AI overlord to fix your code? Why read documentation when ChatGPT will spoon-feed you Stack Overflow answers with a side of explanation? It's basically digital crack for developers who just realized they can outsource their brain to a chatbot. And honestly? No judgment here. We're all addicts now, frantically typing "write me a function that..." at 2 PM on a Tuesday instead of actually learning the language. The prescription bottle format is *chef's kiss* because let's be real—once you start, there's no going back. Your GitHub commits will forever have that "AI-assisted" flavor.

That special moment when you spend hours manually optimizing your spaghetti code, only for an AI model to "refactor" it into something that makes a COBOL program look like poetry. The audacity of these silicon-based know-it-alls to take your perfectly functional 500-line if-statement and turn it into unreadable "efficient" code that somehow manages to be both more verbose AND less functional. Just what I needed today - another reason to question my career choices.

Remember when we spent hours implementing binary trees and sorting algorithms from scratch? Now there's a line of developers sprinting toward ChatGPT while the "Data Structures & Algorithms" door collects dust. Why bother with Big O notation when you can just prompt engineer your way to a solution? The irony is we still need those fundamentals to understand if ChatGPT's code will crash and burn in production. But hey, who has time for that when deadlines are yesterday?