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Second question on AI. We are told repeatedly that AI is going to transform every aspect of our lives – jobs, productivity, inequality, science, communication, daily activities, social order, and politics, among others. But this promise (or threat) is coupled with the rhetoric that such an important technology, with all of the risks and competitive pressures that it entails, should be left to experts or to “technocracy” (perhaps construed broadly to include some regulators). These two statements are hard to reconcile in a democratic society. If anything is half as important as AI is said to be (and I agree, AI is potentially very important and transformative), then involving democratic voice is essential. If something will shape our future in a democratic society, then its direction is for democratic institutions to decide. My instinct is that democratic voice is essential, and relying too much on technocracy could be both dangerous and counterproductive. The counterargument that AI’s direction can and should be entrusted to technocracy would go something along the following lines. First, democratic decision-making has become imperiled in our age of polarization. Second, AI is sufficiently complex that most citizens won’t have a deep enough understanding to meaningfully contribute to the debate (and even to the question of what we want from AI). Third, competition between different labs, and perhaps competition between the US and China, creates enough discipline for a socially beneficial direction of AI to be adopted. Fourth, today’s AI leaders are enlightened and ethical enough that within the framework created by competition, they can be broadly trusted. There are many aspects of this counterargument that I do not find convincing. Taking them in order: polarization can be overcome, and big decisions and challenges sometimes bring societies together; in fact, delegating key decisions to technocracy without democratic input may diminish trust in institutions and experts, and may worsen polarization. Second, democratic voice does not require citizens to write code or design new models; the debate should be informative enough that citizens can weigh in about what type of future they want and how they trade off the costs and benefits of different options. Third, competition doesn’t seem to be a good disciplining framework; on the contrary, competition sometimes brings the worst out of both organizations and people. Fourth, if three decades of work on political economy and institutions has taught me anything, it is that we should not bank on the ethical grounding of unconstrained leaders. But, still, I do not mean to immediately dismiss the technocracy option if there are more compelling arguments for it. The question is, then, whether there are any circumstances under which such important decisions can be delegated to AI experts and technocracy. One final secondary question: even if we managed to get democratic input in the United States or even in Europe, AI will shape the lives of everyone on this planet. How do we ensure that the voice of nearly 6 billion people who don’t live in the US, Europe and China also contributes to the debates on AI?

Introducing Boreal-H3 — a video model built for ads and our next step toward recursive self-improvement in video generation. A good-looking video isn’t enough. The product has to stay the same. The actor has to stay the same. The label has to be right. And the action in the brief actually has to happen. So we post-trained MiniMax H3 specifically for advertising. But this isn’t a one-off SFT or LoRA fine-tune. We built a closed-loop system that learns what to improve next. Human-calibrated evaluation diagnoses failures and guides the next intervention: targeted data collection, reinforcement learning, or inference optimization. When the feedback is unreliable, we revise the evaluator or reward—not just the generator. Every experiment feeds into shared memory, informing the next training decision. The model improves, and so does the process that produces its successor. The results: → 85.3% reference fidelity — highest among the frontier video generation models we evaluated → Brief success: 28% → 50% → Identity match: 83% → 94% → Visible defects per clip: down 70% → Generation time and estimated cost: down 20% Boreal-H3 doesn’t just make better-looking video. It makes more usable ads. Credit to the @MiniMax_AI team for the foundation we’re building on. This launch is a checkpoint, not the finish line. We’re building more than a better video model. We’re building a system that learns how to make the next one better.

The Microduck adventure continues, and the shipping dates are close enough to feel real now. We are proud to tell you that 10,950 Microducks will come off the line between December 10 and January 10! Weekly batches, earliest orders first. Every date is in here.

Muse still crushing the charts:

Very happy to announce that our team @GoogleDeepmind has pushed the boundaries of generative biology, achieving the successful synthesis of AI-designed proteins that are both functional and watermarked. This proof-of-concept watermarking of the building blocks of life is enabled by SynthID Bio, our new protein watermarking method. It is designed to safeguard the new era of AI-powered generative biology and strengthen global biosecurity. You can read my thoughts here on why watermarking AI-designed proteins is an important research breakthrough: https://x.com/pushmeet/status/2105314763148321102

Flow has raised a $50M Series B at a $750M valuation, co-led by Antonio Gracias (Valor) and Gavin Baker (Atreides) with Sequoia Capital, Roelof Botha (SpaceX, Block), and more. When I became a mechanical engineer, I wanted to invent. In reality, 90% of the day to day execution work (CAD, excel, sim) was digital manual labour. That’s about to flip. The engineer’s role will change more profoundly than at any point since the invention of CAD, with humans focusing on invention, architecture, tradeoffs and judgement calls, and agents taking the grunt work. Anduril, Joby, Stoke Space, Rivian & many more - Flow is now the default platform for requirements and verification for frontier hardware teams. But it’s not just next-gen anymore. Industry giants including General Motors PPU and Volkswagen & Rivian’s joint venture (RV Tech) are reshaping their core development practices with Flow’s AI. The revolution is just starting. Models are getting better quickly at CAD, analysis, simulation, and tool use. In the next year, AI will move from integrating the work to doing the work. One day, this shift won't just enable us to iterate faster, it will enable us to design a class of products that we could not have dreamt of before. We’re hiring.

We're joining forces with @huggingface to identify the software libraries that scientific model contributors rely on most and explore opportunities to support the maintainers behind them. https://os4science.org/news/hugging-face-open-source-for-science-fund/

卧槽真的有点细思极恐兄弟们，1971 年美元和黄金脱钩，人类货币花了 50 年从黄金信用走向国家信用，而过去一周，另一场更狠的货币换轨已经悄悄打响了， 我研究完发现，这简直就是一套直接动了传统金融老本的资产大搬家阳谋，我花了3哥多小时研究和做视频，争取把这套可能影响未来 10 年资产规则的底层逻辑一次给大家讲透，大家一定要看， 关于全球金融轨道到底怎么变，OKX 官方最近放出的这张海报和背后的一系列动作，大家一定要知道～！ 他们发了一张极简海报：一张 20 美元的纸币，右半边溶解碎成了 01 二进制代码，配文只有一句话：「The New Money Era is exclusively onchain.（新货币时代，只在链上发生）」。 更绝的是，他们的官方账号简介已经悄悄把“Crypto Exchange（加密交易所）”删除了，直接改成了“The New Money App（新货币应用）”。 我一句话给大家概括它的核心本质： 首先这绝不是一句务虚的营销口号，也不是在喊“加密货币要消灭法币”，而是全球金融结算轨道的一次大换轨——真实世界里的股票、美债、黄金和外汇，正在被大面积搬到 24/7 的链上统一订单簿， 说白了，谁能成为这个“新钱世界”的总枢纽，谁就是下一代的全球金融基础设施。 做过全球资产配置和跨境贸易的人，应该都懂那种被旧体系卡脖子的窒息感： →周五下午一过全球股市直接打烊，遇到突发事件只能干瞪眼； →跨国汇一笔款要在中间行里转两三天，被层层手续费抽水； →股票、外汇、黄金、加密资产分散在 5 个不同的软件里，保证金根本无法打通。 当大家以为 Web3 还停留在炒空气币和 meme 的内卷中时， 顶级大厂已经开始用链上轨道对传统金融进行“降维改造”。 我把这个大战略里最值钱的精髓，拆成三个核心维度用大白话给大家讲透： 第一个，生产力底座： 从“纸上信用”到“代码状态”（The Programmability of Money） 为什么海报偏偏选了 20 美元？ 因为 20 刀上印着的是安德鲁·杰克逊总统，美国历史上最著名的“反中央银行”斗士，上面还印着“此纸币为法定偿付手段”。 右边溶解成 01 二进制，点破了记账方式的根本性升级： 从前依赖“印在纸上的国家承诺与中介清算”， 现在变成“写在公开链上的可编程代码”。 正如 @a16z 指出的底层规律：区块链创造的是净新增市场。 它拿掉了上市委员会、营业时段和地理国界的物理闸门，让任何资产都能 24/7 流转、自由组合，并被未来的 AI Agent 随时调用。 而所谓新货币，不是只有比特币才是钱，而是全人类的真实资产开始借链上路。 第二个，交易机制演进律： 从割裂市场到“全天候统一订单簿” 过去几百年， 金融市场是按资产类别和地理割裂的：买美股去美股券商，买黄金去期货交易所，换外汇去银行。 而 OKX 正在做的事情，是把它们彻底揉进同一个全天候结算池： ▫️24/7 股票永续与代币化资产： 传统闭市时间照样能交易美股映射头寸，宏观对冲不再受开盘时间限制； ▫️统一保证金与智能组合： 股票、黄金、稳定币、生息资产放在同一个账户内互相质押调用； ▫️中心化性能 + 链上结算： 撮合用极速的中心化引擎，资产结算与持有靠自托管钱包和 L2（X Layer）完成。 它不是原教旨的纯链上低效撮合，而是兼顾极速交易体验与链上资产透明度的工业级打法。 第三个，商业闭环与权力重构： 从“炒币通道”到“新钱总入口” 为什么顶级平台要主动撕掉“交易所”的标签？ 因为单纯撮合买卖的交易通道，天花板肉眼可见； 但如果成为掌控支付、结算、理财与跨资产调度的“新货币超级入口”，天花板将是整个全球经济体量。 从自研 L2 基础设施，到欧洲上线 50 万欧资产保障（Shield），再到零手续费的稳定币支付网络， 这家公司的战略意图极其清晰： 用一条链上高速公路，把现实世界的资金吸纳进来， 做成不可逆的结算闭环。 历史规律与真实数据的硬核印证： ▫️清算规模的历史反超： OKX 披露的数据显示，全球稳定币的年结算量已经突破数万亿美元，在规模和流速上正式跨入 Visa 等传统巨头的量级； ▫️用户基建的指数增长： 全球链上独立钱包数已突破 5 亿个，钱的载体正在从银行账户不可逆地向自托管钱包迁移； ▫️大厂动作的精准收网： 9 月 27 日上线 24/7 代币化股票，同周在德州与欧洲推进合规支付，10 月 6 日在新加坡召开 OKX Now 发布会，把这套世界观全盘落地成订单流。 当然，照例泼三盆冷水，大家看清口号背后的现实边界： ▫️“Exclusively Onchain”在现实监管下依然有距离：代币化美股交易的是映射仓位，底层依然受发行地、托管方和白名单限制，绝非纽交所本身被完全搬上链； ▫️中心化撮合的本质未变： 撮合系统、风控逻辑与法币进出依然由中心化机构掌控，它提供的是“交易所级别的 Onchain”，不能与纯去中心化协议混为一谈； ▫️商业利益与自托管的拉扯： 官方一边宣传自托管钱包，一边用高息理财、跟单和 VIP 权益把资金沉淀在平台内部，双轨并行才是真实商业。 最后小结一下，纸币碎成 01 代码，不是美元的末日，而是全球金融基础设施的换代。 当一家行业霸主主动抹去“交易所”的标签， 它瞄准的已经是未来几十年 50% 全球经济活动上链的大浪潮了。 兄弟们觉得未来 3–5 年内，你手上配置的美股、基金或外汇， 会逐步搬到 24/7 的链上账户里去交易吗？评论区聊聊呀～

POV: You’re an engineer building AMD Helios.   Next up: putting it to the test. Episode 2 drops today 👀