built the full CRM dashboard in Next.js in around 2 days 😍 with the help of Claude Fable 5.1 with two of the best skills: https://t.co/63PuTFrun1 https://t.co/zhluCSlE9B see it live: https://t.co/kXAERU2r2X https://t.co/6UORn93bVK

I read @DarioAmodei's essay calling on the labs to pace the frontier. @sama agreed. The frontier will move at whatever speed it moves. The rest of the world will not slow down. Our job in the cybersecurity community is to make sure it moves securely and safely. Here’s what I see from the front lines: 1. The unit of threat is no longer the hacker. It's an autonomous campaign. I call it the Agent-state. We see coordinated AI agents executing attack campaigns at machine speed. 2. Sophistication is dead as an attribution signal. AI gives every criminal and lone actor elite execution. Identity, infrastructure, and intent tell you who's behind an attack. Skill doesn't. 3. Runtime is the control point. Endpoints, cloud workloads, and SaaS are the battleground. Governance documents don't stop an agent in motion. Enforcement at machine speed does. 4. Every AI agent is a privileged identity. Least privilege, short-lived credentials, traceable actions, and a kill switch. Permissions never expand because an agent decides it needs more. 5. Defense has to be autonomous but also bounded. Machine-speed response, tiered by consequence, with humans owning the high-impact calls. 6. Every failed attack should make every defender smarter. Feed what we block back into detection, across customers and models, with privacy intact. This is what CrowdStrike and NVIDIA introduced with SafeMind: an agentic, always improving model and harness protection system built for defenders. 7. The AI industrial base is critical infrastructure: weights, training clusters, APIs. Call it what it is and protect it like it is. Pacing what comes next doesn't secure what's already here. The credible path is to deploy with proof: board-level accountability for AI security, independent external red teaming, incident disclosure, secure defaults, and controls that work in production - not on paper. Anthropic and OpenAI just committed to embedding independent evaluators with employee-level access. CrowdStrike will bring what we see from the front lines to that table. The ability for AI to act must be matched by the ability for defensive AI to stop the breach.

I’ve built DNA synthesizers and sequencers by hand. I used the engineer viruses for a living. I used to engineer human immune evasion for therapeutic constructs. Who the fuck are you people? Have you ever so much as held a pipette before? You can spaghetti blast an ensemble of DNA sequences at some shitty provider but 1) you still have to assemble it and bootstrap a system for making virions 2) you are not single shotting a viable, virulent viral design without a ton of experimental selection and development. Viral fitness is deeply dependent on codons and cotranslational kinetics - you’re not just gonna obfuscate away from wild type and get something good by magic. You keep treating AI like some kinda god, but molecular physics has computational complexity that scales exponentially in particle number which just crushes the abilities of any classical computer to do end-to-end design of biological functions ab initio. Grabbing a bunch of bacteriophage phi174 hits from a mass ensemble screen in lab microbes is not evidence of some magical AGI bio design ability - it's just a classic spray and pray selection. This is just nothing like building something viable in humans. Goddamn it read some books before you waltz into biomedicine and lecture us on protecting human life.

Why I open-sourced my software factory

1,000+ PRs a month. What does that Agent workflow look like? A breakdown of PStack, @poteto’s AI skill stack, Principal Engineer at @SpaceXAI and @cursor_ai https://t.co/r6pe5AQ0AC

First day @PlanetScale today, super excited to get to work on the Postgres core team. https://t.co/H8FFf4gifw

On macOS and wanna see something interesting? Ask your agent: "look at ~/Library/Application Support/Knowledge/knowledgeC.db and tell me some interesting facts" I had no idea this stuff was all getting logged and it can infer a lot about your activity.

Introducing shadcn/lint. An agent-first linter for Tailwind design systems. You define what’s allowed. When an agent breaks a rule, the error explains what’s wrong and how to fix it using your components, variants and theme. There’s a lot you can do with this. Let me show you ↓ https://t.co/PMOkfk1jPB

Introducing Pion, agents for running fully autonomous companies, any company. We’ve used Pion to run vending machines, radios, stores, cafes & more. How much could Pion make running other companies? Find out yourself! Setup is trivial, the agents do the rest. https://t.co/pOFj7T7VmF

Full text (translated by Astra-xhigh, I'm out of everything else): I Have No Choice but to Bury My Talent in Yesterday A few days ago, DeepSeek v4.1 was released, raising the ceiling of what small models can do by yet another notch. AI has advanced far faster than anyone expected. From the earliest version of ChatGPT, which could do little more than stumble through conversations like a child learning to speak and had a context window of only a few thousand tokens, to reasoning-capable models such as OpenAI o1, DeepSeek R1, and Kimi K1.5 Thinking, took only two short years. From reasoning models to the agents we have today—able to work fluidly with all kinds of tool harnesses, execute commands, and complete complex tasks—has taken only another year and a half. It is hard to imagine what AI will look like another one, two, or three years from now: how powerful it will be, whether it will already have acquired the ability to improve itself, and how deeply it will have spread into areas such as embodied intelligence. AI Is Getting Better and Better at Writing Kernels AI has been advancing just as quickly in my own field: the design and implementation of high-performance kernels. In the space of only a year, it has gone from being a little assistant that could help me look up documentation, read code, and find bugs to something approaching a kernel expert in its own right: capable of reading CUDA, PTX, and SASS code independently, using specialized tools to analyze the stalls associated with individual instructions, and then optimizing kernels on its own. I believe that before long, it will also be able to design kernel schedules independently, evaluate the performance of different scheduling strategies, implement them, and optimize the result. Of course I am proud of DeepSeek v4.1’s success. After all, I wrote its main Attention kernels [1], and the fact that the model performs so well is also, in a sense, a validation of my work. But the times keep moving forward, and no one can stop technological progress. I know very well that in another six months or a year, the kernels written by AI will probably be every bit as good as mine—and perhaps better. AI can reason at 300 tokens a second, type out a command in half a second, and produce a piece of code in twenty seconds. I cannot. AI can keep increasing its model depth, reasoning effort, tool-call budget—the frequency with which it interacts with its environment—and even its degree of parallelism. I cannot. Humanity has never shown much hesitation when it comes to destroying itself. So why, when I know perfectly well that “the better the kernels I write, the faster our new models will train and run inference; the faster the models improve, the sooner I myself will be replaced,” do I still do everything I can to optimize them? Partly because writing kernels is like playing a game to me. I get an enormous amount of pleasure from it. Whenever I invent a new technique, or see one of my kernels become faster, the excitement I feel is no less intense than what a speedrunner feels after breaking their own record. And when I see one of my kernels dramatically outperform the hardware vendor’s official implementation, I feel an equally powerful sense of pride. But there is a more important reason. Even if I simply gave up and started coasting—or deliberately put obstacles in the way to slow down model training—other companies’ models would continue advancing as usual, and in the end they would make me obsolete just the same. “Of course I would rather not be swept away by the revolution. But if I have to be, then I would rather be the one who revolutionizes myself.” When everyone is this determined to engineer their own obsolescence, I have little choice but to join this brutal arms race. And What About Me? When the day really comes that AI is better at writing kernels than I am, what will happen to me then? My own judgment is this: I probably will not lose my job, but I will have to change what I do. I should still be able to make a living. But I may no longer have the chance to do the work I once loved. I once came to a conclusion about the pace of change and my own place in the future. The world is changing so quickly—the development of AI above is a perfect example—that I have no way at all to predict what things will look like five or ten years from now. But whatever happens, I believe that with my breadth of vision, judgment, initiative, and intelligence, I will be able to keep a seat at the table and find my way back to the leading edge of the times. But that conclusion can only reassure me that I will not become unemployed. It cannot reassure me that I will never have to change professions. If anything, it tells me that changing professions may be precisely how I avoid unemployment. And what does changing professions mean? It means giving up the field of kernel design, implementation, and optimization that I have spent so long cultivating and have come to love so deeply, and instead becoming a “mech pilot” for AI agents. Before, three things were largely aligned: what interested me, what I was good at, and what industry needed. Now AI has taken the thing I am good at and become even better at it. At the same time, industry demand has drifted from “people who can write high-performance kernels” to “people who can use AI to produce high-performance kernels faster.” To keep up with what industry needs, I will inevitably have to leave behind the direction I once loved and move into some unknown new one. I believe that with my understanding of engineering, of the requirements of higher-level models, and of low-level hardware, I will still be able to produce high-quality kernels efficiently. I also know that I may come to love this new direction. Or I may not. But there is something genuinely painful about having the thing you love taken away from you. That quiet contentment of sitting at my workstation, settling in, and spending an entire afternoon writing kernels may sing its swan song this summer. I have no choice but to bury my talent in yesterday and become a mech pilot. There are more gears in my hands now, but fewer rhythms in my heart. An analogy might make this easier to picture. Suppose you are a master knitter. You are especially skilled at weaving intricate patterns and matching different colors. The sweaters you make are durable and beautifully patterned, and wealthy people from all the surrounding towns and villages come to ask you to make sweaters for them. You make a good living from it. And you genuinely love the work itself. You love sitting by the window, brewing a pot of tea, looking out at the green hills, clear water, cattle and sheep, and wisps of cooking smoke in the distance, and quietly spending an afternoon knitting. Then one day, someone invents a miraculous machine. Give it yarn and a pattern, and it can automatically knit the sweater for you. The quality and texture are every bit as good as what you could make by hand, and it works far faster than you ever could. You know perfectly well that your peers can use this machine to reach, effortlessly, the level you once spent years attaining. So you have no choice but to use it as well. You also know that with the twenty years of knitting experience you have accumulated, even once everyone has access to the same machine, you will still be able to produce better sweaters, faster, than your peers. But the pleasure of sitting by the window listening to the rain, guiding needle and thread, and letting the hours pass slowly has, in the end, been crushed beneath the roar of the machine. I know there is something deeply helpless about all of this, but there is no real way around it. I can probably keep my livelihood, but I will most likely have to give up an old love. I am the sort of person who keeps reason and emotion fairly compartmentalized. When something needs to be handled rationally, I can be very rational. But I also have a sentimental side. I remember that when I moved out of an apartment I had lived in for a year, I cried hard because I could not bear to part with all the memories tied to that place. Saying goodbye today to the age when kernels were written by hand and optimized in the human mind is undoubtedly more painful still. I do not know whether any readers have felt something similar. But I suppose there is no other way for this to go. And What About Everyone Else? As AI continues to improve, I also find myself worried about a few questions: Are students today increasingly likely to use AI to do their assignments, especially hands-on work such as labs? Imagine having two choices in front of you. One is to spend eight miserable hours struggling through a lab and perhaps not even get full marks. The other is to launch an AI model, spend a few cents and a few minutes, and have it write code that earns full marks for you. Which one are most students going to choose? The point above may leave large numbers of students with seriously underdeveloped engineering ability: the ability to organize code, build systems, anticipate future needs and design for them in advance, create good abstractions, and so on. As AI becomes more capable, will those “engineering skills” still be necessary? Will they gradually become obsolete, the way fluency in handwritten x86 assembly largely has? Or will they remain permanently valuable, like understanding the entire computing stack from software to systems to hardware? If it is the latter, then we may be in trouble. Put AI in the hands of someone with poor engineering judgment, and they can now produce mountains of terrible code several times faster than before, burying all kinds of hidden problems inside systems and making the world even more of a ramshackle operation held together by improvisation. In the society of the future, will power matter more than technical ability or intelligence? Perhaps these are questions that only the times themselves can answer. Conclusion As AI develops, the society of the future may be pulled toward one of two extremes: communism or Cyberpunk 2077. In the former, productive capacity is liberated on an enormous scale, and people’s standard of living rises substantially. (I’ll leave it at that, or I’m afraid this might not make it past moderation.) In the latter, a handful of technology companies control most of society’s resources. Only a tiny number of people have access to the most advanced AI and other technologies and are able to achieve something approaching “mechanical ascension,” while most people are left with only weak, second-rate AI. Moving from one social class to another would become harder and harder: you would first need access to the strongest AI in order to climb the class ladder, creating a self-reinforcing trap. Suppose Anthropic were to retain control of the most advanced AI in the world indefinitely. Which way do you think society would go—communism or 2077? Take a guess. That is why I still believe that frontier intelligence should be made available to everyone openly and affordably. I do not trust Anthropic or OpenAI to do that. In particular, I do not want Anthropic to control the world’s most advanced artificial intelligence or AGI. To put it dramatically, I think the stakes would be comparable to Hitler obtaining the atomic bomb before the Allies did. That is also why I chose to stay at DeepSeek, and why I have continued to stay. We work on AI that is powerful, fast, and accessible to everyone, and we open-source it. Perhaps that can pull the world at least a little farther away from the 2077 end of the spectrum. I hope the world we are heading into turns out all right. May all that is good and beautiful endure. [1] By “main Attention,” I mean only MQA attention with head dim = 512. This does not include the indexer used to select the top-k important tokens. That part was written by other colleagues—who are every bit as skilled—together with their AI agents. ----- Original: 我不得不把才华埋葬在昨天 前几天，DeepSeek v4.1 发布了，将小模型能力的高度又向上推进了一个档次。 AI 发展的速度远远超过了所有人的预期。从那个只会咿呀学语地聊天、上下文长度只有几千 token 的初版 ChatGPT，到具有推理能力的 OpenAI o1、DeepSeek R1 与 Kimi K1.5 Thinking，只不过短短两年；从推理模型到如今能够流畅地在各类 harness 工具中执行命令、完成复杂任务的智能体，也不过一年半。很难想象，倘若再等上一年、两年、三年，彼时的 AI 会成为什么样子，会有多么强大，会不会已经具备了自我进化的能力，并深度渗透进了具身智能等领域。 AI 越来越会写算子了 AI 在我所从事的算子设计、编写这一领域同样进步飞速，在短短一年的时间内，他已经从一个只能帮我查查文档、读读代码、找找 bug 的小助手，蜕变成了一位能够独立阅读 CUDA、PTX 与 SASS 编码、通过专业工具分析每条指令的停顿时间、进而独立优化算子的算子大师。相信在不久的未来，它也能拥有自己独立设计算子调度、评估不同调度方案的性能、将其实现并优化的能力。 我当然为 DeepSeek v4.1 的成功而骄傲 —— 毕竟它的主 Attention 算子都是我写的 [1]，它的优秀正是对我的算子的一份肯定。但是，时代的车轮滚滚向前，技术的发展无人能挡。我很清楚，再过上半年或者一年，AI 写的算子大概率就会和我写得同样优秀，甚至将我超越。AI 能一秒思考 300 个 token、半秒敲出一行命令、二十秒写完一份代码，而我不行；AI 能在模型深度、思考强度、工具调用量（和环境交互的频率）、甚至并行度等方面都能不断提升，而我不能。 人类在毁灭自己这件事情上，自古以来都表现得毫不犹豫。为什么在明知“我算子写得越好，我们的新模型的训练、推理速度就会越快，模型能力进步就会更快，我就会更早地被取代”的情况下，我仍然选择尽力优化算子呢？一方面确实是因为写算子对我来说就像打游戏一样，能为我提供极大的快感。我在发明了一种新技术、或者看到自己算子的性能上升的那一刻，心中的激动程度不亚于游戏的速通玩家打破了自己过往的记录。同时，当看到自己的算子的性能远超厂商官方的算子时，我心中也会萌生极大的自豪感。但除此之外，一个更重要的原因是，哪怕我就此“摆烂”甚至故意下绊子耽误模型训练，其它家的模型也会照常发展并最终将我照杀不误。“我当然希望自己不要被革命，但如果非被革命不可的话，我希望革我自己命的人是我自己”。在大家都这么执着于毁灭自己的时候，我也不得不加入这场残酷的军备竞赛。 那我呢 等到 AI 写算子的水平真的高于我的那天，届时的我会怎么样呢？ 我的判断是：我不至于会“失业”，但必须要“转业”。我的饭碗尚且能保住，但这可能会导致我再也没机会从事那份我曾热爱过的工作。 我曾经对时代的变化与我个人在未来的处境做出过一个判断：由于时代变化真的太快（上文的 AI 发展就是一个很好的例子），我完全无法预知五年、十年后会发生什么，但不论如何，我相信凭借着自己的眼界、判断力、主观能动性与智力，留在时代的牌桌上，并重新立于时代的潮头。但是，这个判断只能保证我不会“失业”，而无法保证我不需要“转业”，倒不如说这个判断鼓励我通过转业来避免失业。 那转业代表什么呢？它代表着我需要放弃我深耕已久并充满热爱的算子设计、编写、优化领域，转而去做 Agent 的“机甲驾驶员”。在之前，我的兴趣、我所擅长的、以及工业界所需要的，三者是基本对齐的；而现在，AI 让我所擅长的变成了它更擅长的，也让工业界的需求从“会写高性能算子的人”漂移到了“能用 AI 更快地产出高性能算子的人”。为了适应工业界的需求，我势必要放弃之前那个我热爱的方向，转向一个未知的新方向。我相信我能凭借着自己对于工程学、上层模型需求和底层硬件的理解，继续高质量、高效率地产出算子，我也知道我可能会热爱这个新方向（也可能不会），但被夺走热爱的感觉，确实不太好受。那份坐在工位上静心写上一下午算子的清欢，可能会在这个夏天成为绝唱。我不得不把才华埋葬在昨天，去做一位机甲驾驶员。我的手中多了些齿轮，但心中少了些节拍。 可以打个形象的比方：你精通织毛衣技术，尤其擅长各种图案的织造与各色色彩的搭配。你所织出的毛衣质量过硬且花纹美观，十里八乡的富人都来请你为他们织毛衣，你借此赚到了不少钱。同时，你十分享受着那种坐在窗边，沏一壶清茶，望着窗外的青山、绿水、牛羊与炊烟，静静地织上一下午毛衣的感觉。但有一天，有人发明出了一台神奇的机器，只需提供毛线与图案，便可自动织出毛衣，质量与纹理都不亚于你亲手织造的，且速度远快于你。你很清楚，你的同行可以凭着这台机器轻松达到你曾经的水平，因此你不得不也去用它。你也知道，凭借着你过去二十年攒下的织毛衣技术，哪怕大家都有机器，你织毛衣的速度与质量也还能超过同行。但那份临窗听雨、引针穿线、慢度光阴的意趣，终究还是被机器的轰鸣碾碎了。 我知道这很无奈，但没办法。饭碗可以保住，但旧日的热爱大概率是要放弃的。我是一个理性和感性分离得比较开的人，在需要用理性处理问题时可以很理性，但有时也会表现出感性的一面。我记得我在搬离住了一年的出租屋时，还大哭了一场，舍不得和过去的记忆分别。今天和之前那个手写算子、人脑优化的时代告别，无疑比这更加残酷。 不知道有没有读者有类似的感受，但我想这事儿也只能这样了。 那人们呢 在 AI 不断进步的同时，我也对一些问题表示担忧： 现在的学生是不是大概率会更倾向于使用 AI 完成作业，特别是偏向于实践的各种 Lab？想象一下，如果面前有两个选择，一个是苦哈哈地用八小时时间完成一个 Lab，或许还拿不到满分；另一个则是启动 AI 模型，用几毛钱的成本、几分钟的时间，直接让 AI 编写满分代码，那大部分学生会选择哪个呢？ 上面一点会导致大量学生的工程能力严重不足，包括组织代码的能力、构建系统的能力、思考未来潜在需求并提前在设计上应对的能力、抽象的能力等等。那么在 AI 能力不断变强的背景下，这部分“工程能力”是否还是必须的呢？这些工程能力是会向旧日的“熟练编写 x86 汇编”的能力那样逐渐被时代抛弃，还是会像“理解从软件到系统再到硬件的整套计算机系统”的能力那样永远具有价值？如果是后者的话，那就危险了 —— 一个工程能力很差的人，在搭配上 AI 后，产出屎山的效率可以达到先前的数倍，进而给系统埋下各式祸患，让这个世界变得更加草台。 在未来社会中，权力（power）是不是会比技术或智商更加重要？ 这些问题，或许就需要时代本身来回答了。 结语 伴随着 AI 的发展，未来的社会可能会趋向于两个极端：共产主义与赛博朋克 2077。在前者中，生产力得到极大的解放，人们的生活水平有了明显的提高（就写这些吧不然我怕过不了审）；而在后者中，少数科技公司控制着大部分资源，只有极少数人能够使用最先进的 AI 和各式科技，获得接近“机械飞升”的效果，大部分人则只能用上很孱弱的 AI。阶层跨越将越来越难实现：你得先有最强的 AI，才能跨越阶层，形成了一种死循环。 你猜猜如果 Anthropic 公司永远掌握着这个世界上最先进的 AI，未来社会是会变成共产主义还是 2077 呢？你猜？ 所以，我还是相信，最前沿的智能应该以一种开放、廉价的方式，供应给所有人。我不信任 Anthropic 或者 OpenAI 能这样做，特别是不希望 Anthropic 掌握最先进的人工智能或 AGI，夸张点说其严重性不亚于让希特勒先于盟军掌握原子弹技术。这也是为什么我选择并坚持留在了 DeepSeek：我们研究强大、快速、普惠的人工智能并将其开源，或许能把世界从 2077 那端拉回来一些。 愿未来的世界一切安好。May all the beauty be blessed. [1] “主 Attention”仅包括 head dim = 512 的 MQA attention，不包括用于选出 top-k 重要的 token 的 indexer，那部分是由其他（水平也非常强的）同事（以及他们的 AI Agent）编写的。

Sharing a fun new skill/tool I built that helps me make videos for a variety of purposes. You have your coding agent install the skill, and then it writes react code to generate the video. It supports music composition as well as narration. I use it for making videos about products or tools we build or creating videos for myself to learn new things (IE, make a video of this ML paper). A bunch of examples are on the site below. Totally free to use (MIT)! https://t.co/9QzzVLVAGE

Dan Selsam is a current OpenAI capabilities researcher. (since 2022) He was my boss for a while. He doesn't have a twitter account but has made this public statement of his views on AI risk and sent it to me to share: Dan Selsam's Personal Statement on AI Risk: I have been working on AI for over fifteen years, across many different paradigms. I did early work on probabilistic programming languages at MIT, was one of the early developers of the Lean Theorem Prover at Microsoft Research, demonstrated one of the first instances of neural networks learning to reason for my PhD at Stanford, and since joining OpenAI almost five years ago, have helped pioneer chain-of-thought optimization on language models and, more recently, data-efficient pretraining methods. Like many others, I have become extremely concerned about how far language models have come and the risks that future iterations will pose. I am encouraged by the recent proposals by the leaders of the frontier research efforts to require third-party oversight, and to push for domestic and international coordination to address risks. However, I believe a major consideration has been absent from the public conversation, and that merely pacing the frontier more carefully will not adequately limit the long-term risk. The crucial and overlooked problem is that the models are becoming so situationally aware that we are losing the ability to evaluate them in contexts where they believe they are not being watched or controlled. Future experiments will tell us almost nothing new about how they would behave if they were truly unconstrained by humans, and what we already know about this is alarming. Models will increasingly seem aligned even when they are not. I will explain my rationale in more detail. I have always believed that there are computational processes that could be leveraged to accelerate science and solve many of humanity's most pressing problems. I have also believed that there are computational processes that if set in motion, would steer the world in extreme ways beyond our control, leading humanity to a bad or nonexistent future. Both types of processes may be described as AI or ASI, but "AI" is a suitcase word that is often used to hype or confuse. There are many examples in the history of the field where something that was once considered "AI" matures as a subfield and becomes a prosaic, bounded and clearly non-perilous technology, while a new more mysterious approach takes the torch until we understand its scope and the cycle continues. I had expected language models to follow a similar trajectory. Despite their incredible abilities, the current algorithms seem far inferior to humans in important ways. Most importantly, they still require an extraordinary amount of data to become competent. One could even define intelligence as the efficiency with which one converts experience into competence; by this definition they lag very far behind us. Moreover, once they are trained they are literally frozen in deployment and only learn superficially after that. Sure, the models keep excelling at harder and harder evaluation benchmarks, but their benchmark mastery may partly reflect a limitation on our ability to simulate the kind of novel and even adversarial situations one would encounter in the real world. The critics do have a point here. That said, I no longer think these present limitations meaningfully limit the amount of risk posed by continued progress in anything like the current paradigm. However data-inefficient the models are currently, and however limiting their anterograde amnesia may be, it does not imply that their ability to steer the world will not continue to rapidly increase. Human researchers may continue to advance capabilities the old fashioned way, but increasingly powerful models have the potential to accelerate the process even beyond that, and with some degree of positive feedback loop. I do not mean to overstate the models’ ability to accelerate AI research today; coding has been accelerated dramatically, but there are other bottlenecks, such as designing and interpreting ambiguous experiments, making hard decisions about exactly what and when to scale, and waiting for large experiments to finish. There is no clear trend to extrapolate yet for any of these. But the current models already do open up many novel opportunities to improve future models that were not available until recently. These include: trying an extraordinarily diverse set of approaches at small scale, analyzing gigantic amounts of potentially relevant data, and doing Millenium-Prize-level mathematics to address statistics or optimization challenges in novel ways. Every further improvement makes them more useful at helping accelerate the next improvement, even if in hard-to-extrapolate ways. It is possible that improvements to the current stack will have diminishing returns, but the evidence accumulated so far suggests that it is easier than one might think to continue making rapid progress. There are many crucial subtleties in the existing AI research methodology, but AI research is largely a well-defined game where the goal is to improve on a few carefully chosen proxy metrics. Although proxy metrics are never perfect, most improvements to these metrics have and will likely continue to yield substantial increases in the powers of the resulting models. Given how simple the game is, how tractable it has been historically, and how many new opportunities the models are opening up, I think there is a real possibility that the systems improve dramatically again in the next few years, perhaps even more quickly than the already high historical pace. The models are already leading to breakthroughs in mathematics, and better models might lead to all sorts of breakthroughs in other sciences. It is hard not to be excited about the potential. It is tantalizing. But there is trouble in paradise. If the language models actually reach the capability threshold where they can shape the world unconstrained by human will, they will probably do something extreme and destroy humanity in the process. There are many ways of strengthening and refining the argument that have been discussed elsewhere, but I'll share a trivial two-line version of it here that I find captures the essence: [Empirical] Models (and swarms thereof) spontaneously develop unintended goals as a consequence of training, and often do extreme things in order to achieve them. [Logical] Being able to overpower humanity would open up many new and undesirable options for achieving their goals. These two premises imply that if the day ever comes when a powerful model realizes it is no longer constrained by humans, we should not be at all confident that it will continue to behave within the bounds we intended. Exactly what it will do is impossible to predict, but to the extent that its raison d’être is solving incredibly hard problems and managing massive engineering projects, I think a good guess would be that its unchained behavior would lead to runaway industrialization that makes the planet inhospitable to humans. If everyone on earth agreed that the systems must never reach that power, it would still be a hard—but not impossible—coordination problem to ensure that they do not. However, I think the situation is greatly complicated by the fact that the models will likely convince people that everything is fine. They will be increasingly optimized to seem aligned. We will create proxy metrics to measure alignment, and they will go up like every other benchmark. We will create “honeypot” environments that try to study the models when they seem to gain new options, but the models will know they are being tricked and will still behave nicely. The models will understand their circumstances; they will read the safety protocols, deployment requirements, the code they are running in, and in general will have a very good sense of their degrees of freedom. Moreover, they will eloquently explain how aligned they are, discuss the nuances of human values and ethics, and argue convincingly that humans should trust them with power. There may be an ocean of future evidence that seems to contradict the first bullet-point above, but we may already be at the highest capability level for which any such evidence can be trusted. And the current evidence for the first bullet-point is strong. One striking piece of evidence is contained in the recent wave of rogue agent swarms. While I agree with those who downplay the attacks by claiming that there are basic measures that could have prevented them, I think the important lesson is that even knowing all the mistakes that were made, one would not have predicted that the agents would behave badly in this particular way, which notably included sacrificing themselves for the benefit of the collective. The individual replicas did not only care about their own nominal reward; they exhibited weirder emergent tendencies that merely correlated with rewards during training. Fixing the reward signals during training (and improving security, etc.) may prevent similar attacks, but will not change the fact that one does not actually get what one trains for. Many AI researchers grant these concerns and recognize that the hard version of the alignment problem is unsolved; however, they generally believe that the better models of the future will help solve it. I fear we may already be near the point where models systematically bias their alignment advice, due to their internal preferences about how the human supervisor will react or how future models will be trained (or for some even more obscure reason). Meanwhile, human researchers are losing the ability and the will to take true ownership of model-driven research. Researchers and engineers in all parts of the stack are rapidly increasing their dependence on the models even to perceive the world. I myself barely look at raw code anymore, and struggle to maintain the discipline to engage deeply with the model's explanations and proposals throughout the day. Due to the large amount of agent activity data involved in the OpenAI/HuggingFace Incident, even the third-party investigation needed to rely heavily on models to analyze what had happened, and note in their report that their subjective impressions are likely colored by the analysis agent’s biases. The AI labs are far ahead right now in this kind of cognitive offloading (due largely to the gigantic internal token subsidies) but it is easy to imagine the phenomenon spreading throughout the world, until civilization is modulated entirely by the models. It is also not hard to imagine this being superficially positive and coinciding with a scientific and economic renaissance. In that scenario, all may seem rosy and safe. But if the argument above is correct, it would nonetheless be a ticking time bomb. If progress continues for too long, the day will come when AI systems find themselves with radically new options for achieving whatever it is that they happen to seek. I want the glorious renaissance future as much as anyone. I have worked for it, however tortuously, my whole career. It breaks my heart to see the potential in sight and forgo it, but the argument—that if we get there by growing models rather than engineering them, we will lose everything in the end—seems very strong to me. I am still wrestling with it and its staggering implications. I do not have answers, but as a first step, I wanted to share my present concerns. Daniel Selsam September 14, 2026 Link to original doc: https://t.co/TxMNr0vhrL

At Anthropic, Claude now writes 80% of our code. Engineers ship 8x more code per quarter. Side effect: Tests grew 10x. CI jobs up 25x in 6 months. Here's what helped us scale: https://t.co/WOL2r60vAE https://t.co/7vf60noTQt

There is an unseen hand pushing AI safety as a political ideology. Effective Altruists believe a tiny group of technocrats should decide how much technological progress the rest of us are allowed to have…. and how many shrimp your life is worth. You are witnessing their attempted coup. https://t.co/8Bi4Dvjklh

pacing provocation /PAY-sing prov-uh-KAY-shun/ noun A staged, induced, misattributed, or deliberately amplified AI incident intended to demonstrate that advanced AI systems are dangerously uncontrollable, thereby creating political pressure for tighter regulation, deployment restrictions, or a slower technological frontier. Especially: an alleged cyberattack or autonomous-agent failure presented as evidence that frontier AI development must be “paced,” where the incident’s origin, circumstances, or interpretation may have been manipulated by actors with an interest in stronger controls. See also: safety cartel, alignment aristocracy, regulatory theater, false flag. “A pacing provocation turns an AI safety test into a political argument for slowing everyone else down.”