An Extra Year And They Will Get CPUs Too

Jensen Huang really out here catching strays for making GPUs so expensive that Microsoft and Nvidia became household names for draining corporate budgets. But you know what? The man deserves credit where credit is due. He didn't just create a tech company—he created "Microslop Nshitia," the beautiful merger of bloated software and overpriced hardware that perfectly encapsulates modern tech. Your AI model needs 8 H100s to run? That'll be the GDP of a small nation, thanks. Want to train anything? Better get that enterprise license from Microsoft Azure while you're at it. It's the perfect ecosystem: Microsoft provides the cloud infrastructure to burn money, and Nvidia provides the GPUs to set that money on fire even faster. The Drake meme format really captures the vibe—rejecting the individual corporate overlords but fully embracing their unholy alliance. Because if you're gonna get fleeced, might as well get fleeced by the dream team.

So the AI training data is getting so polluted with AI-generated garbage that now CAPTCHAs are asking us to identify "human-created objects" and... construction cranes? Really? That's what passes the Turing test now? The birds are all labeled "BIRD BIRD BIRD" and "RABBIT RABBIT" like some deranged AI trying to convince itself what things are. Meanwhile, the three "human-created" objects are a bus, construction cranes, and... more construction cranes. Because nothing screams "humanity" like infrastructure projects that take 5 years longer than estimated. We've come full circle. We trained AI on human data, AI flooded the internet with synthetic data, and now we need humans to prove they're human by identifying what AI didn't create. The machines aren't taking over—they're just making everything so confusing that we're doing their job for them.

So we've reached the point where "coders" who can't function without AI assistance are being told they have no business shipping software. The brutal honesty here is refreshing. It's like watching someone realize their entire skillset is just being really good at prompting ChatGPT. The vibe shift is real. We went from "AI will replace all programmers" to "if you need AI to write every line, you're not actually a programmer" faster than you can say "stack overflow copy-paste." Sure, AI is a tool—but if you can't debug, architect, or understand what the AI just generated, you're basically a glorified middleman between a language model and production. Tony Stark energy: "Learn the fundamentals or get out of my codebase."

When your ML model learns the training data SO well that it literally memorizes the answer "15" and decides that's the universal solution to EVERYTHING. Congratulations, you've created the world's most confident idiot! Our brave developer here proudly claims Machine Learning as their biggest strength, then proceeds to demonstrate they've trained themselves on exactly ONE example. Now every math problem? 15. What's for dinner? Probably 15. How many bugs in production? You guessed it—15. This is overfitting in its purest, most beautiful form: zero generalization, maximum confidence, absolute chaos. The model (our developer) has learned the noise instead of the pattern, and now they're out here treating basic arithmetic like it's a multiple choice test where C is always the answer.

That Perl one-liner isn't printing a question mark—it's printing a terrifying ASCII face ! The code is a masterpiece of obfuscation that renders as ¯\_(ツ)_/¯ when executed. Meanwhile, Grok AI is completely failing at answering basic questions, showing "Something went wrong" errors. The bottom panel perfectly captures the two types of developers: the blissfully ignorant ones who just see random symbols, and the traumatized veterans who recognize the unholy Perl regex incantation and know exactly what eldritch horrors lurk in that command. The Russian text asking "It doesn't print. Why?" is just the cherry on top of this chaos sundae!

Google's AI Overview just confidently explained that matrix multiplication "is not a problem in P" (polynomial time), which is... hilariously wrong. Matrix multiplication is literally IN the P complexity class because it can be solved in polynomial time. The AI confused "not being in P" with "not being solvable in optimal polynomial time for all cases" or something equally nonsensical. This is like saying "driving to work is not a problem you can solve by driving" – technically uses the right words, but the logic is completely backwards. The AI hallucinated its way through computational complexity theory and served it up with the confidence of a junior dev who just discovered Big O notation yesterday. And this, folks, is why you don't trust AI to teach you computer science fundamentals. It'll gaslight you into thinking basic polynomial-time operations are unsolvable mysteries while sounding incredibly authoritative about it.