🚨 BREAKING: Researchers at UW Allen School and Stanford just ran the largest study ever on AI creative diversity. 70+ AI models were given the same open-ended questions. They all gave the same answers. They asked over 70 different LLMs the exact same open-ended questions. "Write a poem about time." "Suggest startup ideas." "Give me life advice." Questions where there is no single right answer. Questions where 10 different humans would give you 10 completely different responses. Instead, 70+ models from every major AI company converged on almost identical outputs. Different architectures. Different training data. Different companies. Same ideas. Same structures. Same metaphors. They named this phenomenon the "Artificial Hivemind." And the paper won the NeurIPS 2025 Best Paper Award, which is the highest recognition in AI research, handed to a small number of papers out of thousands of submissions. This is not a blog post or a hot take. This is award-winning, peer-reviewed science confirming something massive is broken. The team built a dataset called Infinity-Chat with 26,000 real-world, open-ended queries and over 31,000 human preference annotations. Not toy benchmarks. Not math problems. Real questions people actually ask chatbots every single day, organized into 6 categories and 17 subcategories covering creative writing, brainstorming, speculative scenarios, and more. They ran all of these across 70+ open and closed-source models and measured the diversity of what came back. Two findings hit hard. First, intra-model repetition. Ask the same model the same open-ended question five times and you get almost the same answer five times. The "creativity" you think you're getting is the same output wearing a slightly different outfit. You ask ChatGPT, Claude, or Gemini to write you a poem about time and you keep getting the same river metaphor, the same hourglass imagery, the same reflection on mortality. Over and over. The model isn't thinking. It's defaulting to whatever scored highest during alignment training. Second, and this is the one that should really alarm you, inter-model homogeneity. Ask GPT, Claude, Gemini, DeepSeek, Qwen, Llama, and dozens of other models the same creative question, and they all converge on strikingly similar responses. These are models built by completely different companies with different architectures and different training pipelines. They should be producing wildly different outputs. They're not. 70+ models all thinking inside the same invisible box, producing the same safe, consensus-approved content that blends together into one indistinguishable voice. So why is this happening? The researchers point directly at RLHF and current alignment techniques. The process we use to make AI "helpful and harmless" is also making it generic and boring. When every model gets trained to optimize for human preference scores, and those preference datasets converge on a narrow definition of what "good" looks like, every model learns to produce the same safe, agreeable output. The weird answers get penalized. The original takes get shaved off. The genuinely creative responses get killed during training because they didn't match what the average annotator rated highly. And it gets even worse. The study found that reward models and LLM-as-judge systems are actively miscalibrated when evaluating diverse outputs. When a response is genuinely different from the mainstream but still high quality, these automated systems rate it LOWER. The very tools we built to evaluate AI quality are punishing originality and rewarding sameness. Think about what this means if you use AI for brainstorming, content creation, business strategy, or literally any task where you need multiple perspectives. You're getting the illusion of diversity, not the real thing. You ask for 10 startup ideas and you get 10 variations of the same 3 ideas the model learned were "safe" during training. You ask for creative writing and you get the same therapeutic, perfectly balanced, utterly forgettable tone that every other model gives. The researchers flagged direct implications for AI in science, medicine, education, and decision support, all domains where diverse reasoning is not a nice-to-have but a requirement. Correlated errors across models means if one AI gets something wrong, they might ALL get it wrong the same way. Shared blind spots at massive scale. And the long-term risk is even scarier. If billions of people interact with AI systems that all think identically, and those interactions shape how people write, brainstorm, and make decisions every day, we risk a slow, invisible homogenization of human thought itself. Not because AI replaced creativity. Because it quietly narrowed what we were exposed to until we all started thinking the same way too. Here's what you can actually do about it right now: → Stop accepting first-draft AI output as creative or diverse. If you need 10 ideas, generate 30 and throw away the obvious ones → Use temperature and sampling parameters aggressively to push models out of their comfort zone → Cross-reference multiple models AND multiple prompting strategies, because same model with different prompts often beats different models with the same prompt → Add constraints that force novelty like "give me ideas that a traditional investor would hate" instead of "give me creative ideas" → Use structured prompting techniques like Verbalized Sampling to force the model to explore low-probability outputs instead of defaulting to consensus → Layer your own taste and judgment on top of everything AI gives you. The model gets you raw material. Your weirdness and experience make it original This paper puts hard data behind something a lot of us have been feeling for a while. AI is getting more capable and more homogeneous at the same time. The models are smarter, but they're all smart in the exact same way. The Artificial Hivemind is not a bug in one model. It's a systemic feature of how the entire industry builds, aligns, and evaluates language models right now. The fix requires rethinking alignment itself, moving toward what the researchers call "pluralistic alignment" where models get rewarded for producing diverse distributions of valid answers instead of collapsing to a single consensus mode. Until that happens, your best defense is awareness and better prompting.

How many squares....!!! 99.9% will fail..?

"China", "tariffs", my business. I spent $1,000,000 over the past 14 years developing a factory in China. I married Chinese. I lived in China for seven years, and live there for three months in the summer. Tried to move the factory in 2021 to the US, impossible. Very bad. Materials stateside were being imported from China. I'm trapped in China with no way out. I'm not crying because our business has eroded. I knew the risks going in. Here is what few know about China, because they have never been immersed. 🧵

Where on earth does China get all the money to build this stuff? 😉

I’ve observed a few hi stakes negotiations over the years and one tactic I’ve seen work well over and over is when you find the leverage to bring your opponent to the edge of the cliff, hold them over the cliff so they see the abyss and threaten to push them off. Turns out when you then don’t push them, but back off, they often develop Stockholm syndrome and give you more than you would have otherwise because they are thankful for the reprieve. I don’t know that this is the plan here but the conditions for that are the same. 75 countries are on a 90 day shot clock to get a grand bargain done. Based on what I’ve heard from some of them, they are thankful for a chance to egress off the tariff highway and find a new normal - even if that’s much worse for them than what it was previously.

Scott, you sound like every empire just after the fall—trying to rewrite the ending because it couldn’t bear the truth of the story. Listen closely. You lost. Not just politically. Not just morally. But militarily. Strategically. And psychologically. You dropped more bombs on Vietnam than were dropped in all of World War II—on a country the size of New Mexico. You sent the most powerful army on earth, equipped with helicopters, napalm, Agent Orange, B-52s, and endless firepower… Against farmers with sandals made from tires. And still, you left. Not because you "beat yourselves." You left because we bled you dry. Because a barefoot people with nothing but resolve and memory outlasted a machine that thought the world owed it obedience. We watched you burn villages and call it liberation. We saw you poison our forests and call it peacekeeping. And we buried our dead, hardened our hearts, and kept coming. You say it wasn’t existential for you? Exactly. That’s why you lost. Because empire never fights to survive—it fights to dominate. And domination breeds complacency. You thought technology would save you. We knew only will would save us. And that’s the difference between conquerors and the conquered: The conqueror can lose and go home. The conquered have nowhere else to go. You lost because you never understood what it meant to be invaded. We did. For over 2,000 years. We fought off the Chinese. The Mongols. The French. The Japanese. The Americans. And when we ran out of bullets, we used shovels. When we ran out of food, we ate roots. When you thought we were finished, we rose from the ashes of your arrogance. You say we didn’t beat you? The last helicopter didn’t take off from Washington, Scott. It took off from your embassy in Saigon. We watched it leave. And we’re still here. Now say it again.

Even if you’re right, hardware is what’s impressive. The software is inevitable if it’s not already there.

VW CEO HERBERT DIESS: If we don't run to EVs, we'll need to close factories VW UNION: Nein! Replace Him! VW CEO OLIVER BLUME: We now need to close factories VW UNION: Nein! & Everyone gets a 7% Raise or we go on strike! ↪️VW's union is about to win the Employment Darwin Award🤔