12 Months Ago..

The world map lights up beautifully for infrastructure we can see—ports, airports, and railroads—but becomes a black void for developers using Meta AI. It's the perfect visualization of how these engineers are busy building the future while completely invisible to the world. They're the dark matter of tech—you can't see them, but their gravitational pull affects everything. The fourth panel is basically a monument to all those countless hours spent debugging prompts and fine-tuning models while everyone else is blissfully unaware of their existence. Silent heroes with empty coffee cups and full git repositories.

Software developers watching mathematicians get absolutely wrecked by AI is like watching your older sibling finally get grounded after years of being the golden child. We've been dealing with "learn to code" jokes, outsourcing threats, and now AI autocomplete for years. Meanwhile, mathematicians sat in their ivory towers doing pure theory, thinking they were untouchable. But now? AI is proving theorems, solving complex equations, and basically speedrunning what took mathematicians centuries. The "First time?" energy is immaculate. Welcome to the existential crisis club, math folks. We've been here since GitHub Copilot started writing better code than our junior devs. At least we're all equally obsolete together now.

GTA 6 has been in development for over a decade and everyone's hyped about it. Meanwhile, GPT-6? That thing's gonna drop before we even finish debugging our GPT-4 integrations. The top panel shows pure joy thinking about GTA 6's eventual release, while the bottom panel captures the existential dread of realizing AI models are iterating faster than you can keep up with their API changes. By the time you've mastered prompt engineering for GPT-5, OpenAI will be like "surprise! here's GPT-6 with completely different behavior patterns." RIP to all those carefully crafted system prompts.

Remember when the hardest part of your job was actually understanding computer science? Now we've traded analyzing time complexity for the exciting new challenge of convincing an AI that yes, you really do want it to implement a linked list, not refactor your entire codebase into microservices. Back then: "Is this O(n²) or can I optimize it to O(n log n)?" Now: "No ChatGPT, I don't need you to add error handling, logging, unit tests, and a design pattern I've never heard of. Just. Sort. The. Array." The irony? We spent years learning Big-O notation to write efficient code, and now we spend just as much time learning prompt engineering to get AI to write the code we could've written ourselves in half the time. Progress!

ChatGPT promised us a revolution: write code in 5 minutes instead of 2 hours. What they forgot to mention is that you'll spend the next 24 hours debugging the hallucinated nonsense it generated. Before ChatGPT, you'd code for 2 hours and debug for 6. Now you code for 5 minutes and debug for an entire day. The math isn't mathing, but at least you saved those 2 hours of actually understanding what you were writing. The real productivity hack was the existential crisis we gained along the way.

Every ML engineer's origin story right here. You've got grand visions of training neural networks that'll revolutionize the industry, but your wallet says "best I can do is a GTX 1050 from 2016." So you sit there, watching your model train at the speed of continental drift, contemplating whether you should sell a kidney or just rent GPU time on AWS for $3/hour and watch your budget evaporate faster than your hopes and dreams. The real kicker? Your model needs 24GB VRAM but you're running on 4GB like you're trying to fit an elephant into a Smart car. Time to get creative with batch sizes of 1 and pray to the optimization gods.