Big Tech Right Now

Content Ambience Healthcare Harvey Al Anysphere Microsoft $3.97 Nebius OpenAl $500B CoreWeave OpenAl to déploy 6 Nvidia agrees gigawatts of AMD GPUs. to invest up to AMD gives OpenAl $100 billion in option to buy up to 160 million shares. OpenAl. Intel AMD " Nvidia $4.5T Oracle spends tens of billions on Nvidia chips. openAl inks a $300 billion cloud deal with Oracle. Nscale Mistral Figure Al Oracle XAl

Management really thought they had a galaxy brain moment forcing devs to use AI tools. "Let's make them more productive by having ChatGPT write their code!" they said. Devs were like "yeah sure whatever" and went back to sleep. Plot twist: turns out AI is actually pretty good at generating status reports, attending meetings, writing performance reviews, and crafting those passive-aggressive Slack messages that middle management specializes in. Suddenly everyone's awake because the productivity "improvement" is about to hit a bit different than expected. The irony is chef's kiss – companies trying to automate the workers ended up creating a tool that's better at automating the people who made that decision. Maybe that's the real productivity boost we needed all along.

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.

The public thinks AI is some mystical brain-to-brain knowledge transfer. Amateur programmers imagine it's a beautiful network of interconnected nodes making intelligent decisions. Meanwhile, actual developers know it's just a mountain of nested if-statements descending into madness. That bottom panel hits different after you've spent 15 years in the industry. Fancy marketing terms like "neural networks" and "deep learning" sound impressive until you peek behind the curtain and find what's essentially glorified pattern matching with extra steps. The "10,000 if-statements" comment is the chef's kiss of cynical developer truth. We're not creating consciousness—we're just building increasingly complex decision trees and hoping nobody notices.

So you thought AI coding assistants would make you a 10x developer? Think again, bestie. Instead of shipping features at lightning speed, you spent two weeks playing whack-a-mole with an overzealous LLM that decided to "help" by rewriting half your codebase in ways that technically compile but spiritually hurt. The promise was beautiful: AI would autocomplete your dreams into production-ready code. The reality? You're now a professional code janitor, armed with git revert commands, cleaning up after a robot that watched too many YouTube tutorials and got a little too confident. Your sprint retrospective is just going to be you staring into the void while muttering "the machines were supposed to free us" over and over again.

Someone asked ChatGPT to count days of the week containing the letter "d" and it confidently listed Monday, Wednesday, Thursday, and Friday. Spoiler alert: it missed Tuesday, Saturday, and Sunday. That's 3 out of 7, or roughly a 57% failure rate on a task a kindergartener could nail. Yet somehow CEOs are out here thinking this is the tech that'll replace entire engineering teams. Nothing screams "I understand AI capabilities" quite like watching an LLM fail basic pattern matching while your exec team plans layoffs. The irony? The AI couldn't even count the letter "d" correctly in a seven-item list, but sure, let it architect your microservices. What could possibly go wrong? 🙃