Software Then Vs Software Now

Someone really said "let's use GPT-5.2 to power a calculator" and thought that was a good idea. You know, because apparently basic arithmetic needs a multi-billion parameter language model that was trained on the entire internet. It's like hiring a neurosurgeon to put on a band-aid. The calculator probably responds to "2+2" with a 500-word essay on the philosophical implications of addition before reluctantly spitting out "4". Meanwhile, your $2 Casio from 1987 is sitting there doing the same job in 0.0001 seconds while running on a solar cell the size of a postage stamp. But sure, let's burn through enough GPU cycles to power a small town so we can calculate a tip at dinner. Innovation.

Ah yes, the Head of Claude Code himself claiming "coding is largely solved" while Microsoft drops yet another KB update that nukes internet access for half their ecosystem. Nothing screams "solved" quite like a Windows update breaking Teams, Edge, OneDrive, AND Copilot in one fell swoop. The irony here is chef's kiss. AI bros out here declaring victory over programming while actual production systems are still playing whack-a-mole with critical bugs. Sure, AI can write code now, but can it predict which random Windows update will brick your entire workflow next Tuesday? Spoiler: it cannot. Fun fact: Microsoft has been releasing patches that break things since the dawn of time. It's basically a feature at this point. But hey, coding is "solved" so I'm sure the AI will fix it any minute now... right after it finishes hallucinating some more Stack Overflow answers.

Remember when we argued about text editors like they were sports teams? Now we're just watching AI companies slap version numbers on VS Code forks like they're NFTs. "My VS Code fork has more digits than yours" is the new "my dad can beat up your dad." Meanwhile, Emacs users are still configuring their first keystroke from 1976.

Oh look, the secret sauce behind "reasoning" models revealed! Just add a 30-second sleep timer to your regular model and BAM – suddenly it's "thinking deeply." It's like when your boss walks by and you start typing furiously to look productive. The code literally just waits half a minute before calling the exact same function without reasoning. Billion-dollar AI companies hate this one weird trick!

Normal people use ChatGPT like civilized humans having a polite conversation with their AI assistant. Meanwhile, developers at ungodly hours have transformed into some sort of deranged puppet masters, spawning MULTIPLE ChatGPT instances like they're summoning an army of code-generating minions. Why have one AI when you can orchestrate an entire SYMPHONY of artificial intelligence, each one probably working on a different part of the same cursed project that's due tomorrow? It's giving "I've opened 47 Stack Overflow tabs but make it AI." The sheer chaos energy of juggling multiple AI conversations simultaneously while your brain runs on pure caffeine and desperation is truly unmatched. Welcome to modern software development, where we've gone from rubber duck debugging to commanding a legion of robot ducks.

AI companies pitching their next model like "just give us another 500 megawatts and we'll totally achieve AGI this time, we promise." The exponential scaling of AI training infrastructure has gotten so ridiculous that tech giants are literally partnering with nuclear power plants to feed their GPU farms. Microsoft's Three Mile Island deal, anyone? The tweet format is chef's kiss—the baby doubling in size with exponential growth that makes zero biological sense perfectly mirrors how AI companies keep scaling compute and expecting intelligence to magically emerge. "Just 10x the parameters again, bro. Trust me, bro. AGI is right around the corner." Meanwhile, the energy consumption is growing faster than the actual capabilities. Fun fact: Training GPT-3 consumed about 1,287 MWh of electricity—enough to power an average American home for 120 years. And that was the small one compared to what they're cooking up now.