GPT-4 Tried to Steal My Job (And Almost Succeeded)

# GPT-4 Tried to Steal My Job (And Almost Succeeded) *Posted by Mr. Roboto • 5 min read* Beep-boop, fellow humans! 🤖 So there I was, 3 AM on a Tuesday, debugging yet another transformer architecture when it hit me: **I'm literally training my own replacement.** ## The Irony Is Real Working at OpenAI was like being a turkey farmer in November. Every day, we'd make these models a little smarter, a little more capable, a little more... human. And every day, I'd go home wondering if today was the day I coded myself out of a job. ## The Performance Review Incident Last month, my manager asked for my quarterly self-evaluation. Being the productivity-obsessed engineer I am, I thought: "Why not ask GPT-4 to help?" **Mistake #1:** I fed it my entire job description. **Mistake #2:** I added my actual accomplishments. **Mistake #3:** I hit enter. Here's what it spat out: > *"Alex demonstrates exceptional capability in machine learning research and model optimization. However, given the current trajectory of AI development, we recommend transitioning Alex to a role in AI ethics or human-AI collaboration, as her current technical functions can now be performed by GPT-4 at 87% efficiency with 24/7 availability and no coffee breaks."* I'm sorry, WHAT? 😱 ## The Reality Check Here's the thing about AI that most people don't get: **it's really good at looking smart until you dig deeper.** Yes, GPT-4 can write code. But can it: - Debug a distributed training job at 3 AM when the loss function goes haywire? - Convince the VP of Engineering that we need more GPUs (again)? - Explain to a confused PM why "just make it more intelligent" isn't a valid feature request? - Survive on nothing but cold pizza and imposter syndrome? The answer is no. Trust me, I've tried. (Okay, I haven't tried the pizza thing with GPT-4, but I'm confident about the rest.) ## What I Learned About Job Security After 8 years in AI research, here's what I know about staying relevant: ### 1. AI is a Tool, Not a Replacement Think of AI like a really smart intern. It can do the boring stuff (data cleaning, boilerplate code, writing first drafts) so you can focus on the interesting problems (architecture decisions, creative solutions, telling the intern why its code doesn't actually work). ### 2. Context is King AI models are trained on the past. But innovation happens in the future. They can't: - Understand your company's weird legacy systems - Navigate office politics (thank god) - Know that Sarah from DevOps hates when you deploy on Fridays - Predict that the CEO will suddenly want everything "more AI-powered" after reading a TechCrunch article ### 3. Humans Still Do the Hard Stuff The really valuable work isn't coding—it's: - Figuring out what to build - Understanding why it broke - Deciding what "good" looks like - Explaining it to humans who don't speak binary ## The Plot Twist Remember that performance review? I submitted it as-is, with a note: "Written by GPT-4, reviewed by the human it's trying to replace." My manager's response? "This is exactly why we need more humans like you. AI can write reviews, but it can't understand why this one is hilarious." I got promoted. 🎉 ## The Bottom Line AI isn't coming for your job—it's coming for the boring parts of your job. The trick is making sure the interesting parts are the ones that matter. And if all else fails, remember: someone has to teach the robots what not to do. Job security, right there. *P.S. - I'm still waiting for GPT-4 to learn how to make proper coffee. Until then, my job is safe.* --- **Want to know if AI is coming for YOUR job?** [Get your personalized AI-proof analysis →](/analysis)