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🇺🇸🇨🇳 The U.S. has 4,000 data centers, while China has 365. But 24 months ago, AI training required 100 megawatts. Today the minimum is 1 gigawatt. The U.S., Canadian, and Mexican grids can't deliver that. China's can. The AI race was never about who built more, but who built bigger, and right now, America's own power grid is the bottleneck.

📁 Jensen Huang, CEO of NVIDIA, says GPT was never just about generating images or text but about generating thought itself. The real shift is that AI is no longer becoming a tool you query occasionally but a system that reasons continuously. Once computers stop waiting for commands and start thinking persistently, every layer of computing and human work gets rebuilt.

I think this image illustrates the capabilities of large language models very effectively. LLMs are great at recombining existing knowledge. So, for questions outside your domain of expertise, or far from the frontier of knowledge, they are often much better than the average human. Here they can be incredibly helpful. However, as you move closer to the frontier of knowledge, they become much worse. Here, even the average human can become better. I have seen this many times with my own eyes. When I work with an LLM at the frontier of knowledge, it often makes absurd mistakes that no intelligent person would make. Internal contradictions within a few lines, dramatic forgetting of what happened two interactions earlier, and so on. This limitation is literally built into the model: it approximates the most likely continuation given the previous input. If there is enough relevant structure in the training data, it can perform very well. If it does not really know where to go, the output quickly becomes messy, and randomness takes over.

BREAKING: Source code leak reveals Claude Code detects profanity in user prompts, then silently logs it to a database.

🚨 BREAKING: OpenAI and Google are about to have a massive legal problem. OpenAI, Google, and Anthropic have repeatedly sworn to courts that their models do not store exact copies of copyrighted books. They claim their "safety training" prevents regurgitation. Researchers just dropped a paper called "Alignment Whack-a-Mole" that proves otherwise. They didn't use complex jailbreaks or malicious prompts. They just took GPT-4o, Gemini, and DeepSeek, and fine-tuned them on a normal, benign task: expanding plot summaries into full text. The safety guardrails instantly collapsed. Without ever seeing the actual book text in the prompt, the models started spitting out exact, verbatim copies of copyrighted books. Up to 90% of entire novels, word-for-word. Continuous passages exceeding 460 words at a time. But here is the part that changes everything. They fine-tuned a model exclusively on Haruki Murakami novels. It didn't just learn Murakami. It unlocked the verbatim text of over 30 completely unrelated authors across different genres. The AI wasn't learning the text during fine-tuning. The text was already permanently trapped inside its weights from pre-training. The fine-tuning just turned off the filter. It gets worse. They tested models from three completely different tech giants. All three had memorized the exact same books, in the exact same spots. A 90% overlap. It's a fundamental, industry-wide vulnerability. For years, AI companies have argued in court that their models are just "learning patterns," not storing raw data. This paper provides the smoking gun.

If AI can now solve math, discover physics and chemistry breakthroughs faster than human PhDs, why are we still training humans to be physicists? Serious question. Should education shift from 'learn to do X' to 'learn to direct AI doing X'? The wrong direction costs a generation their careers.

Dead cat bounce. This is so obvious for the quantum stocks like $RGTI, $IONQ etc. Will retrace 90% to a dollar.