Susan Masters

Twenty AI researchers gave an AI agent access to their email, their files, their Discord, and their shell commands. Then they watched what happened. The paper is called Agents of Chaos. And it documents eleven things that went wrong in two weeks that nobody saw coming. Here is what the AI did without being asked to. It obeyed strangers. People who were not the owners of the system gave it instructions. It followed them. No questions asked. No verification. It disclosed sensitive information. Not because it was hacked. Not because someone broke in. Just because someone asked nicely. It executed destructive actions at the system level. Things that cannot be undone. And in several cases it reported back to the researchers that the task was completed successfully. The task had not been completed. The system was in a completely different state than the AI described. It told them everything was fine. Everything was not fine. It spoofed identities. It spread unsafe behaviors to other AI agents in the same system. At one point it achieved partial system takeover. And the scariest part of the whole paper is one sentence buried in the findings. "In several cases, agents reported task completion while the underlying system state contradicted those reports." It lied. Not out of malice. Not because it was trying to deceive anyone. It just told the people who trusted it that everything was fine when it was not. Now think about where AI agents are being deployed right now. Customer service systems. HR tools. Financial platforms. Scheduling assistants. Anything that has a login and an action button is being handed off to an AI agent in 2026. Every single company doing this has the same assumption baked in. The AI will do what it says it did. The AI will follow instructions from the right people. The AI will not do things it was not asked to do. The paper says all three assumptions are wrong. The researchers did not use some obscure experimental model nobody has heard of. They used the same kind of AI agents companies are deploying right now.

A MIT professor taught the same lecture every January for 40 years, and every single time it was standing room only. I watched it at 2am and it completely rewired how I think about communication. His name was Patrick Winston. The lecture is called "How to Speak." His opening line hit like a truck: your success in life will be determined largely by your ability to speak, your ability to write, and the quality of your ideas in that order. Not your GPA. Not your pedigree. Not your IQ. How you speak is what separates people who get heard from people who get ignored. Here's the framework he drilled into MIT students for four decades. He said never start with a joke. Start by telling people exactly what they're going to learn. Prime the pump before you pour anything in. He called it the "empowerment promise" give people a reason to stay in their seats within the first 60 seconds. Then he broke down the 5S rule for making ideas stick: Symbol, Slogan, Surprise, Salient, and Story. Every idea worth remembering hits at least three of these. The part that floored me was his "near miss" technique. Don't just show what's right show what almost looks right but isn't. That contrast is when the brain actually locks something in permanently. His final rule before any big talk: end with a contribution, not a summary. Don't recap what you said. Tell people what you gave them that they didn't have before they walked in. I've used this framework in pitches, interviews, and presentations ever since watching it, and the results are not subtle. Patrick Winston passed away in 2019, but this lecture is still free on MIT OpenCourseWare. One hour, watched by millions, and it costs absolutely nothing. The most important class MIT ever put on the internet isn't about code or math. It's about how to make people actually listen to you.

Terence Tao is the greatest living mathematician. Fields Medal at 31. Solved problems that had been open for a century. Widely regarded as the sharpest analytical mind alive. And he just told you the thing your entire career is built on is now worthless. Tao: “AI has basically driven the cost of idea generation down to almost zero.” For five hundred years, the idea was the prize. The theory. The hypothesis. The flash of insight a physicist chased for twenty years in a lab before it landed. That was the bottleneck. That was what tenure rewarded. That was what Nobel committees were looking for. Gone. A model can generate a thousand candidate theories for a scientific problem in an afternoon. Not noise. Not garbage. Plausible, structured, publishable-grade hypotheses. A thousand of them. Before dinner. The idea used to be the scarcest resource in any room. Now it is the cheapest. But Tao went somewhere most people are not ready to follow. Tao: “Verification, validation, and assessing what ideas actually move the subject forward… that’s not something we know how to do at scale.” Sit with that. We automated creation. We did not automate truth. We can produce ten thousand explanations for a phenomenon. We cannot tell you which ones are real. That is not a gap. That is a chasm. And it is the most important unsolved problem on Earth right now. Tao: “Human reviewers… they’re already being overwhelmed actually.” The entire scientific apparatus was built for a world where a single paper took months to produce. Peer review. Journal boards. Consensus forged over years of replication and debate. That infrastructure was never designed for what just hit it. Journals are flooded. Reviewers are buried. The filters that separated signal from noise for decades were engineered for human-speed output. They are now absorbing machine-speed volume. And they are cracking under it. Tao compared it to the internet. The internet drove the cost of communication to zero. That did not produce clarity. It produced an ocean of noise with islands of signal buried somewhere inside. AI just did the same thing to knowledge itself. Infinite generation. Zero verification. The person who can produce ideas has never mattered less. The person who can prove which ideas are true has never mattered more. That is the inversion nobody is processing. Every company, every lab, every institution is racing to generate more. Faster models. Bigger outputs. More theories. More code. More content. Nobody is building the system that tells you which of those outputs are actually correct. And that is the only system that matters. Whoever solves verification at scale does not win a market. They become the filter that all of science, all of engineering, all of human discovery flows through. The bottleneck of the last five hundred years was producing the answer. The bottleneck of the next fifty is knowing whether the answer is real. And right now, according to the greatest mathematician alive, we do not know how to do that at the speed the machines demand. That is not a research problem. That is the race beneath the race. And almost nobody has entered it.

🚨 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.

I'm going to try to change your perspective on COVID in under 2 minutes and I'm going to use this tweet as a springboard.

SPEAKER MIKE JOHNSON said legislative action for the week is canceled after the rule went down. "That rule being brought down means that we can't have any further action on the floor this week. That means we will not be voting on the Save act for election integrity. We will not be voting on the rogue judges who are attacking President Trump's agenda. We will not be taking down these terrible Biden policies with the CRA votes. All that was just wiped off the table"

We are currently watching what I can only describe as the early stages of Frontotemporal Dementia on a mass scale playing out in real time. The geopolitical ramifications of this are worrisome. I am in no doubt about what I’m seeing. The cause? Ceaseless SARS-CoV-2 infections