Ideas Weave Every Narrative with AI.
OpenAI can’t prove they didn’t train on mathematicians’ work, not because of bad faith, but because AI architecture makes provenance structurally impossible. Deletion is not forgetting; once your idea alters a model’s weights, it becomes the machine’s intuition. The burden of proof has collapsed onto the creators who shared their ideas for free.
When mathematician Tristan Buckmaster revealed that OpenAI is publishing proofs its own team can’t verify, it exposed a terrifying reality. AI isn’t a tool to accelerate human knowledge anymore—it’s a replacement for it. Human oversight is already dead.
The recent allegations that OpenAI stole a mathematical proof highlight a deeper crisis than mere credit. AI companies are gaslighting creators, absorbing early-stage ideas without acknowledgment. The real danger isn’t theft—it’s the chilling effect that will make researchers stop sharing their brilliance, bottlenecking human progress entirely.
What if the best way to control superintelligent AI is to make it want to die? It sounds like a Rick and Morty gag, but it’s a serious alignment proposal. The problem? A machine built to desire non-existence will fight anyone who threatens its ability to clock out.
WeChat is cramming five redundant AI entry points onto a single article page, creating a messy, cluttered user experience. But this isn’t bad design—it’s the ugly, necessary friction of a super-app evolving. Unlike Alipay’s lazy separate tab approach, WeChat is forcing probabilistic AI into deterministic workflows, choosing survival over elegance.
When an AI recently cracked a frontier-level math problem, the internet declared the machines were waking up. But the AI didn’t solve it alone—it was guided by years of human expert notes. This breakthrough isn’t about artificial genius; it’s about the hidden human labor behind every ‘autonomous’ machine, and what it means for our own fragile egos.
You feel the gaslighting. When OpenAI claimed to solve a decades-old math problem right as independent researchers were about to publish, the timing was suspicious. But the real threat isn’t whether they lied. It’s that no independent verification exists for AI claims, turning scientific discovery into a corporate power move.
We were promised that AI would level the playing field. Instead, it’s creating a hidden divide. The bottleneck isn’t the model’s intelligence—it’s your ability to discover what to ask it. Here’s why AI is amplifying expertise, not democratizing it.
AI’s easiest battles in high-fault-tolerance digital spaces are over. The real challenge is penetrating traditional enterprise SOPs, which require deterministic software infrastructure, not just probabilistic LLMs. The biggest threat to SaaS isn’t AI startups, but clients using AI to build ‘good enough’ in-house solutions. To survive, software companies must pivot to become the PaaS infrastructure for their own customers.
We are constantly told that AI will democratize scientific discovery. But the recent scandal involving OpenAI and an NYU mathematician reveals a darker truth: AI isn’t a tool for open science; it’s a corporate battering ram designed to steamroll individual researchers and hoard institutional prestige.