Ideas Weave Every Narrative with AI.
OpenAI just formed an advisory group for mathematics and AI—right after their models allegedly solved the Navier-Stokes Millennium Prize problem. This isn’t governance. It’s a theatrical burial ground. When the last pure human intellectual endeavor falls to machines, the corporate response is a committee, not a reckoning.
We are so desperate to see a ghost in the machine that we mistake fluent text for genuine cognition. But LLMs are just statistical reconstructions of human language. The real danger isn’t that AI will outsmart us, but that we will outsource our thinking to a calculator of average consensus, forgetting that true meaning only comes from human intention.
The Ogre Battle 64 recompilation project is 99% complete, fully playable, and open to mods. Yet, purists are rejecting it because AI assisted in the translation. This isn’t about copyright or code quality—it’s an epistemic crisis. We are so terrified of AI contaminating our nostalgia that we’d rather let classic games rot than save them with machines.
AI promises to make writing easier, but the easier it makes writing, the harder it becomes to know if the words are genuinely yours. The real danger isn’t plagiarism; it’s epistemic. When you outsource your words to a machine, you automate self-deception and mistake fluency for understanding.
Tech giants like Tencent and ByteDance are quietly launching platforms to crowdsource elite professionals—doctors, lawyers, and engineers—as gig workers to train their AI models. This isn’t just an AI talent war; it’s the commoditization of white-collar labor. They are paying experts to build the very systems that will eventually undercut their professions, turning hard-won expertise into cheap, disposable data.
We’re terrified of AI writing malicious, world-ending code. But the real danger is much dumber. Enter Jev-Leftpad, an AI-generated joke package that highlights a terrifying structural truth: our software ecosystem is perfectly designed to turn trivial, low-quality AI code into critical infrastructure. The next global tech outage won’t be a cyberattack—it’ll be a bad joke buried in the dependency chain.
Product managers are drowning in AI hype, obsessing over which tool generates the prettiest UI or the longest PRD. But the real advantage lies in using AI not as a docile writer, but as a strict requirement auditor that forces you to clarify business rules—before they get beautifully hallucinated into your final product.
The reported unreliability of AI in finance isn’t a technological failure—it’s a testing failure. By using lower-tier, non-reasoning models for complex financial queries, testers are judging reasoning-capable technology using outdated parameters. The real danger isn’t an overconfident machine; it’s a misinformed public making financial decisions based on fundamentally flawed tests.
The Amiga Unix revival promises a blast from the past, but its heavy reliance on LLMs threatens to turn it into software’s pet cemetery. When AI generates the code and documentation, we don’t get the authentic human-driven engineering culture of the 90s—we get a soulless imitation. If we let machines write our history, we risk losing the very essence of what we’re trying to preserve.
The bottleneck in AI-assisted product development is no longer the speed of creation, but the speed of human judgment. AI can generate 864 lines of code in an hour, but the 12 rounds of iteration are entirely bottlenecked by your ability to evaluate, catch execution overflow, and say ‘no’.