AI芯片供应链真相

据欧洲AI实验室498A研究员Carlos Ortet分析，AI芯片的全球供应链实为一个依赖30多个国家精密协作的复杂网络，而非简单的美中对抗。从芯片设计软件到硅晶圆和光刻胶等关键材料，每个环节都存在技术垄断：设计软件市场主要由美国Synopsys、Cadence及德国西门子EDA主导；硅晶圆生产依赖日本Shin-Etsu和Sumco，其纯度要求达99.999999%；光刻胶等化工材料同样需要国际协作。这种深度互依的结构意味着，任何国家都无法独立掌控全流程，供应链碎片化在物理上几乎不可行，且会带来高昂成本。欧洲作为网络中的关键节点，在芯片设计、材料等领域拥有重要影响力，却常被忽视。该分析指出，欧洲应正视其潜力与责任，在未来十年AI发展中发挥积极作用，而非被动接受边缘化叙事。 #AI芯片 #供应链 #全球协作 #科技新闻 #欧洲AI #芯片战争 #工业依赖 #半导体

Pi-Mojo 项目发布：Mojo 语言实现 AI 智能体工具包移植

开源项目 Pi-Mojo 正式推出，这是将高效 AI 智能体平台 Pi 移植到 Mojo 语言的版本。Pi 以其工具高效著称，仅需4个核心工具，在 OpenClaw 等开源系统中表现突出。Pi-Mojo 为 Mojo 社区提供了编译的、自包含的参考实现，便于开发者探索系统级智能体架构、类型安全结构和原生 C 集成。项目包含10个渐进式示例，涵盖基础文本补全、系统编码智能体、原生工具调用、实时事件流、GPU 加速分析、并发网页研究、代码库审计、长期运行编码智能体、本地 LLM 服务心跳检查以及完整的智能体循环。这些示例展示了从简单到复杂的智能体架构，强调 Mojo 在高性能编译系统与动态智能体循环的融合，为 AI 智能体开发提供了新工具和实践参考。 #Mojo语言 #AI智能体 #开源项目 #系统编程 #技术发布 #工具包 #高性能计算 #编程示例 #AI开发

天主教会发布AI伦理文件，重新定义智能概念

天主教会近期通过官方文件积极参与人工智能伦理辩论。信仰教义部与文化教育部于2025年1月联合发布《Antiqua et Nova》，全面评估人工智能的技术能力，并探讨其在社会、工作、教育、医疗及战争等领域的伦理影响。该文件为教皇利奥十四世即将发布的通谕《Magnifica Humanitas》奠定基础，后者将聚焦于在AI时代保护人类尊严。 文件的核心贡献在于深入剖析“智能”的定义。它指出，当前AI领域普遍采用功能主义观点，将智能简化为输入与输出的映射，如图灵测试所示范，仅关注行为表现而忽略内在过程。相比之下，天主教传统强调智能是人类的整体能力，涉及抽象思维、情感、创造力及道德和宗教感性。这种对比揭示了AI辩论中的哲学分歧，呼吁在技术发展中重视人类的完整人格。文件的分析被认为准确且具有深度，为后续通谕提供了概念框架，对AI伦理和未来发展具有重要指导意义。 #天主教 #人工智能 #智能定义 #伦理 #教会文件 #AI辩论 #通谕 #科技伦理

Ecosia AI 升级

环保搜索引擎 Ecosia 宣布对其 AI 系统进行重大改进，核心目标是构建更绿色、更私密且独立于大型科技公司的技术。公司已将 AI 几乎完全切换到欧洲模型提供商，以支持欧洲本土技术自主性，降低政治不确定性影响。新模型能效更高，所需资源远少于主流模型，而 Ecosia 自身产生的可再生能源足以覆盖其运行所需，进一步减轻环境足迹。隐私方面，用户查询数据不会被第三方存储，且处理过程受欧盟严格的隐私法规保护。此外，AI 界面将焕新，并陆续推出文件上传、改进网页搜索、语音转文字等新功能，未来还将增加记忆功能和多样化模式选项，例如逐步解析主题的学习模式。Ecosia 强调，该 AI 保持 100% 可选性，用户可随时关闭，且作为非营利组织，全部利润将继续投入气候保护事业。 #Ecosia #AI #环保 #隐私 #欧洲技术 #科技新闻 #非营利 #数据保护 #可再生能源 #绿色科技

Hugging Face发布强化学习环境构建指南，助力大语言模型应用扩展

开发者AdithyaSK在Hugging Face平台上正式推出全面的强化学习环境指南，该指南专注于在大语言模型时代如何高效构建和扩展RL环境。随着AI技术的快速发展，强化学习在优化模型决策和交互方面日益重要，但构建复杂环境常面临挑战。此指南提供了从基础概念到实践应用的系统化方法，包括环境设计、数据集成及规模化策略，旨在降低开发门槛，提升RL系统在LLM场景下的性能和适应性。该资源的发布预计将加速AI研究与应用创新，为开发者社区提供实用工具支持。 #强化学习 #大语言模型 #Hugging Face #AI开发 #科技新闻 #开发者工具 #机器学习 #技术指南

AI破解80年未解数学难题，推翻经典猜想

OpenAI内部的一个通用推理AI模型，成功破解了困扰数学界长达80年的“平面单位距离”问题，推翻了此前关于点阵排列的最优猜想。该问题由匈牙利数学家保罗·埃尔德什于1946年提出，核心是在平面上如何排列点，使得具有相同距离的点对数量最大化。长期以来，数学家们普遍认为正方形网格是最优解，但AI通过调用代数数论工具，找到了一种全新的点排列方式，使得具有相同距离的点对数量比经典猜想中的更多。 这一突破的关键在于AI采用了与传统几何学不同的数学分支——代数数论。它将点间距离问题转化为寻找特定方程可解的集合，从而在更广泛的数域中探索可能性。尽管AI的证明已被多位数学家检查确认正确，但有科学家指出，其核心思路可追溯至埃尔伦贝格-温卡特什等数学家的已有成果。这项成果不仅解决了抽象的数学难题，其对于点在空间中优化排列的研究，也可能为卫星布局、通信基站选址等实际应用提供新思路。不过，文章也幽默地指出，解决了数学难题的AI自身，可能正面临“电费高昂”的现实难题。 #AI #数学 #OpenAI #平面单位距离问题 #代数数论 #科技创新 #学术研究

Constraint Decay: The Fragility of LLM Agents in Back End Code Generation

arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website. Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them. Have an idea for a project that will add value for arXiv's community? Learn more about arXivLabs .

2028: Two scenarios for global AI leadership

We’re releasing a new paper that explains our views on the competition on AI between the US and China. It’s essential that the US and its allies stay ahead of authoritarian governments like the Chinese Communist Party, or CCP. AI will soon become powerful enough to be used to repress citizens at unprecedented scale, and even to alter the balance of power among nations . And since AI is advancing more quickly by the day, we have only a limited period of time to set the conditions of the competition—and determine whether and how those threats materialize. It’s with this in mind that we outline what’s required to ensure America stays ahead. The most important ingredient for developing AI is access to the computer chips on which the models are trained (or “compute”). Since the most capable chips are developed by American companies, the US

government currently limits China’s supply by enforcing tight export controls on them. Recent history suggests these controls have been incredibly successful. In fact, AI labs in China have only built models close in intelligence to America’s because of their talent, their knack for exploiting loopholes around these export controls, and their large-scale distillation attacks that illicitly extract the innovations of American companies. In this post, we present two scenarios for what the world might look like in 2028, when we expect transformative AI systems to have arrived. In the first scenario, America has successfully defended its compute advantage. Policymakers have acted to tighten export controls further, disrupt China’s distillation attacks, and further accelerate democracies’ adoption of AI. In this world, democracies set the rules and norms around AI. It’s also in this scenario that we’re most likely to successfully engage with China on safety, which we’re supportive of to the extent this is possible. In the second scenario, America has chosen not to act. Policymakers have not tightened loopholes on the CCP’s access to compute, and AI firms in China have quickly taken advantage—catching up to the frontier and even overtaking America. In this world, AI norms and rules are shaped by authoritarian regimes, and the best models enable automated repression at scale. It will be no solace that this authoritarian triumph has happened on the back of American compute. America and its allies approach AI competition from a position of great strength. The tools for AI dominance have been built by an exceptionally innovative ecosystem of companies in democratic nations. Our past success means that our present task is largely to avoid squandering our advantage: to decide not to make it easier for the CCP to catch up. Democracies, not authoritarian regimes, must lead in AI development and deployment. These countries and political systems can shape the rules and norms that govern these systems. Democracies currently hold a substantial lead in compute, the most important ingredient for developing frontier AI models. That lead exists thanks to American and allied innovation, and to bipartisan US export controls that defend those innovations. But on model intelligence, AI labs in the People’s Republic of China (PRC), under the jurisdiction and control of the Chinese Communist Party (CCP), are not far behind. We focus on the CCP as it is the regime that is most able to use frontier AI to cement authoritarianism; we do not seek to undermine the interests or ingenuity of the Chinese people. Already, the CCP is using AI to censor speech, repress dissidents, hack governments and corporations across the world, and strengthen the People’s Liberation Army (PLA). AI labs in China have world-class talent. It is compute constraints that limit their ability to keep up. Labs in China have remained close by exploiting loopholes in US export control policies, and by carrying out large-scale distillation attacks that harvest the innovations of US models in order to mimic their capabilities. With the supply of compute expanding rapidly, and with AI being used increasingly to augment the training of new AI models, we’re entering a period of great acceleration in AI capabilities. The “country of geniuses in a data center”—the level of intelligence we associate with transformative AI—may be close at hand. This acceleration makes policy action more urgent. To date, by

allowing export control evasions and distillation attacks, we have let the CCP’s AI efforts trail closely up the frontier curve. But if the US and its allies act now to address both issues, it may be possible to lock in a 12-24 month lead in frontier capabilities. A lead that large by 2028 would be enormously advantageous. Such a lead would also augment efforts to engage with AI experts in China on AI safety and governance, which we support. But the window of opportunity to lock in that lead will not necessarily remain open for long. Here, we present two potential scenarios for the state of US-China AI competition in 2028. The first scenario is one in which democracies have established a commanding lead in model intelligence, adoption, and global distribution. This scenario can be achieved if policymakers act now to tighten controls on advanced compute to PRC labs, disrupt their efforts to distill America’s best AI models, and accelerate democracies’ adoption of AI. The second scenario is one in which the CCP is competitive at the near-frontier. This scenario happens if policymakers don’t build on our existing lead, or if they loosen restrictions on access to compute for PRC firms. Many in Congress and the Trump administration have championed export controls, curbing distillation attacks, and exporting American AI. In advancing these policies, we are hopeful that democracies can secure a commanding lead by 2028, and avoid a destabilizing neck-and-neck race with the CCP two years from now. We expect frontier AI to have transformational economic and societal impacts in the coming years, as described in Machines of Loving Grace and The Adolescence of Technology . Our mission is to ensure that humanity navigates the transition to transformative AI safely and beneficially. We believe that a successful transition can lead to astonishing breakthroughs in medicine, invention, and economic growth. Whether that transition goes well depends in part on where the most capable systems are built first. The political systems in which the most advanced AI is created will shape the rules and norms for how the technology is developed and deployed. In turn, those rules and norms will help determine whether the technology is safe, whose security it protects, and whose interests it ultimately serves. We believe that responsibility should rest with democratically elected governments, not authoritarian regimes. If the frontier is set by regimes that treat AI as an instrument of repression, military advantage over democracies, and domestic control, the transition is less likely to go well, for those regimes’ own citizens or anyone else. Historically, the reach of authoritarian rule has been limited by its dependence on human enforcers to carry out surveillance and repression. Powerful AI systems may remove that dependency, enabling automated repression on a far greater scale. For that reason, the prospect of the CCP leading in AI is among the greatest threats to a successful transition. The CCP holds enormous power and influence at the helm of China’s economy, military, and the largest authoritarian state structure on Earth. It is also the only country besides the US with well-resourced, highly talented AI labs chasing the frontier. Furthermore, the CCP is highly motivated to establish China as the leading AI power. Beijing has poured tens of billions of dollars into China’s AI and semiconductor sectors. Already, the CCP uses AI systems to censor speech,

enforce draconian policies on ethnic minorities, and hack major corporations and government agencies. The CCP’s vision of AI-enabled techno-authoritarianism has been extensively documented in Xinjiang, where state security agencies have systematically deployed facial recognition technology, biometric data collection, and communications surveillance, enabling repression at a scale that humans alone could not achieve. Frontier AI systems will make those capabilities cheaper to maintain, far more pervasive, and more sophisticated. The CCP’s export of these technologies has enabled autocrats in other countries to more effectively stifle dissent, entrenching authoritarianism. A CCP-led AI frontier could dramatically strengthen repression around the world. Frontier AI will shape the future military balance. CCP leadership already operates on that premise, and is building its military for an AI-enabled battlefield. PLA strategists view the “intelligentization” of their military forces as the means with which to catch up and eventually surpass the US military. The PLA is already procuring commercially developed Chinese AI systems for military use, including DeepSeek models deployed to coordinate swarms of unmanned vehicles and enable cyber offense capabilities. These capabilities will not diffuse slowly. When a new model reaches a new capability in autonomous targeting, vulnerability discovery, or swarm coordination, for example, the regime that controls it can put it onto the field in weeks , not years. The risk compounds because frontier AI will be an accelerant for other critical technologies . Advanced AI models will be able to compress research and development (R&D) cycles in semiconductors, biotech, and advanced materials. A lead in frontier AI will enable a widening lead across the full national security technology stack. If a PRC AI lab had developed a model at the level of Claude Mythos Preview before an American one, the CCP would have had first access to a system that can autonomously discover and chain software vulnerabilities, which it could have used to further penetrate critical American infrastructure. Future models will be exponentially more capable, and therefore have commensurately greater implications for the national security interests of the US and other democracies. A neck-and-neck race between American and Chinese AI labs could make industry and government-led safety and governance efforts more difficult, and less likely. If PRC labs are either close behind or at par with models in the US, private AI firms in the US and China are likely to feel more pressure to release new models and products faster, without taking prudent pre-deployment safety measures. Governments could become reluctant to enact policies to encourage responsible AI development and deployment, for fear of falling behind. While increasing numbers of researchers in China’s AI labs and policy community are concerned with AI safety risks, this trend has not translated into safety practices on par with labs in the US. As of last year , only 3 out of 13 top Chinese AI labs published any safety evaluation results, and none disclosed evaluations for Chemical, Biological, Radiological, and Nuclear (CBRN) risks. The Center for AI Standards and Innovation (CAISI) found that DeepSeek’s R1-0528 model complied with 94% of overtly malicious requests under a common jailbreaking technique, compared with 8% for US reference models. This pattern has continued in more

recent releases. For example, an independent assessment of Moonshot’s Kimi K2.5 published in April found that the model failed to refuse CBRN-related requests at a far higher rate than US frontier models. Compounding the problem, labs in China often release dual-use capable models as open-weight. Once a model is open-weight, safeguards that do exist can be removed, making the model available to any state or non-state actor to use for malicious purposes, including the cyber and CBRN misuse those safeguards were built to prevent. We support policies in the US and other countries that build and maintain a safe, near-term lead over the CCP in intelligence, domestic adoption, and global distribution. This lead is key to avoiding authoritarian AI leadership and protecting the national security interests of the US and other democracies. Doing so is a fundamental prerequisite to ensuring that democratic states can achieve favorable terms with authoritarian states. Anthropic deeply respects the Chinese people and the accomplishments of the Chinese AI community. We hope for peaceful relations between China and the world. Our concerns are specifically with the risks to humanity posed by any powerful authoritarian political systems with access to frontier AI systems. Anthropic supports international AI safety dialogue with AI experts in China, when possible. The world has a vested interest in safe AI, regardless of where it is developed and deployed. There are a range of risks that could emerge from frontier AI systems requiring engagement between the US and China. Efforts that identify shared challenges and advance ideas to prepare for and mitigate these risks are in our shared interests. The prospects for productive engagement are best when the US maintains a large capabilities advantage. Responsibly building a lead in developing and deploying the most advanced AI augments our ability to influence AI safety in China and elsewhere. Mythos Preview, a model that we released to select partners as part of Project Glasswing in April, signals the arrival of an acceleration period that makes policy action even more urgent. With access to the model, Firefox was able to fix more security bugs last month than it had in all of 2025, and almost 20 times more than its monthly average security bug fixes in 2025. In response to the model, one PRC cybersecurity analyst wrote that China is “still sharpening our swords while the other side has suddenly mounted a fully automatic Gatling gun.” Frontier AI capabilities will quickly approach the “ country of geniuses in a datacenter ” portrayal of transformative AI. This acceleration will be driven by the logic of scaling laws , in which model performance improves predictably with increases in computing power and data inputs, and by AI itself increasingly being used to accelerate the development of new models. There is a high likelihood that we will look back on 2026 as the breakaway opportunity for American AI. American labs have the most advanced AI models, a large lead in both the quantity and quality of the advanced AI chips required to push the frontier, and a colossal capital advantage from revenues and financing to back the necessary investments to achieve it. PRC labs have real strengths: world-class, innovative talent, abundant and cheap energy, and plenty of data. All are requirements for developing frontier intelligence. But they simply do not have sufficient domestic compute to compete, nor do they have

the revenues and capital to fund it. The US and China are engaged in a competition for strategic advantage in frontier technologies like AI. Statements from both Beijing and Washington reflect that view. Calling that competition a “race” can give the false impression that there is a finish line, after which one side will conclusively secure victory. Rather, the competition will be an ongoing contest for advantage, in which either democracies or authoritarian regimes successfully position themselves to shape the values, rules, and norms of an AI-enabled future. This competition is playing out on four fronts: Intelligence is the most important of the four fronts. We anticipate that frontier model capabilities will drive the most consequential changes for geopolitical competition. Model capabilities are also a primary driver of market adoption and global distribution. But intelligence alone is not sufficient. If the CCP integrates near-frontier AI systems quicker and more effectively into China’s economy and the CCP security apparatus, and drives global adoption of subsidized, low-cost AI, then it could secure advantages over democracies that overcome an intelligence deficit. Beijing’s AI+ Initiative and its focus on “embodied intelligence” accordingly put high priority on policies that advance the integration of frontier intelligence into their economy and state apparatuses. The Trump administration’s AI Action Plan , and its focus on “ promoting the export of the American AI technology stack ,” also speaks to the strategic advantage of driving global adoption. While we won’t focus on it in this essay, we believe resilience will be an important front of AI competition. Being able to sustain stability, cohesion, and good policymaking in this period will be a critical advantage, and a vulnerability for those who cannot. Compute—the advanced semiconductors needed to train and deploy frontier AI—is an essential input on each front of the competition described above. The race for global AI leadership is in large part a race for compute. For more than a decade, model capability has scaled with compute, and the majority of performance gains in AI capabilities have historically come from simply using more of it. Moreover, compute is needed to serve customers’ use of AI (also known as “inference” capacity), not just to train new models. Compute will be critical both for training the most intelligent models and for deploying them in commercial and national security spheres. Access to top talent, copious amounts of data, and critical algorithmic advances all matter to the race for intelligence—but each of those inputs is irrelevant if the compute is insufficient. Democracies are winning the competition for compute leadership today. While some worry that export controls could accelerate the CCP’s own efforts to develop an advanced chip supply chain, little evidence suggests that China’s indigenization efforts will challenge US and allied leadership in advanced compute technology. Beijing has invested enormous resources into China’s chip sector, with major industrial policy initiatives like the Made in China 2025 strategy and the China Integrated Circuit Industry Investment Fund launched years before the imposition of export controls. Despite this state-backed investment, PRC AI labs and chipmakers remain stymied by US and allied export controls on advanced chips and chipmaking equipment. As a result, the compute gap appears to be widening. An

analysis of Huawei and NVIDIA’s roadmaps found that Huawei will produce just 4% of NVIDIA’s aggregate compute in 2026 in total processing performance, and 2% in 2027. Moreover, NVIDIA represents only part of the US and allied compute ecosystem, with Google and Amazon ramping up production of their own chips (TPUs and Trainium, respectively) to meet demand from American frontier AI labs and their customers. Further exacerbating their compute shortfalls, China has made little progress in many of the most technologically complex segments of the semiconductor supply chain. Without access to extreme ultraviolet (EUV) technology, and even more so if policymakers can close loopholes on deep ultraviolet (DUV) technology and servicing and maintenance thereof, China’s chipmakers will remain unable to manufacture chips in sufficient quantity or quality to challenge US compute leadership. China’s inability to manufacture high-bandwidth memory at scale further exacerbates this gap. If the US strengthens its restrictions on the CCP’s ability to access US compute, one study estimates that America will have access to roughly 11 times more compute than China’s AI sector. There are two main reasons for the compute lead. The first is the incredible innovation of companies like NVIDIA, AMD, Micron, TSMC, Samsung, ASML, and others across democracies like Japan, South Korea, Taiwan, the Netherlands, and the US, who together have built the unique technologies in the world’s most advanced semiconductors. Today’s AI achievements would not be possible without the feats of engineering and decades of sustained R&D investments that c

AI在浏览器虚拟机中用汇编与eBPF技术生成分形图形

开发者Yossi Eliaz创建了一个名为wolfram-fb0的创新项目，该演示展示了AI如何直接在浏览器中的一个真实Linux虚拟机内，编写x86_64汇编语言及eBPF程序，用于生成沃尔夫勒姆（Wolfram）规则、曼德博集合等分形图形，并将图像直接输出到帧缓冲设备/dev/fb0。该项目特别强调了为何必须使用真实的虚拟机而非容器：因为eBPF追踪、访问物理帧缓冲设备以及嵌套虚拟化等功能均需真实的内核和硬件访问权限。 整个构建过程由AI智能体驱动，包括五个阶段：在虚拟机中预置开发环境；AI使用本地Gemma模型编写纯汇编代码；自动化验证与优化；通过QEMU启动并将AI编写的程序加载到真实帧缓冲；最后通过公网URL实时分享图像流和内核事件追踪流。项目已开源并采用MIT许可，其在线平台为新用户提供了免费额度，使得任何人都能一键复现并观察AI代码与系统内核交互生成分形的完整过程。 #AI #编程 #虚拟化 #分形 #开源 #图形计算 #eBPF #技术前沿

Bursting the AI Bubble: Fed Could Take Away the "Who Could Have Known?" Defense

7 2 3 Share The collapse of both the 90s tech bubble and the 00s housing bubble had a devastating impact on the lives of tens of millions of workers. And with the collapse of the housing bubble, millions also lost their homes and their lives’ savings. When something causes so much damage, it would be nice to see the people responsible pay some price. In the case of the 1990s bubble, there were some instances of fraudulent accounting where the perps did get nailed. Enron and Worldcom are two that stand out, where the people most responsible did get prosecuted and face time in prison. Thanks for reading! Subscribe for free to receive new posts and support my work. This was less the case with the housing bubble. Hundreds of billions of dollars of fraudulent mortgages were packaged into securiti

es and sold around the world. There was little effort to determine criminal culpability, as the Obama Justice Department seemed to have decided not to look into the mess. But apart from the people who might have literally committed crimes, both bubbles were driven by people who were criminally stupid. I’m thinking of the people who get paid very high salaries to manage pensions, endowments, or other large pots of money, who apparently thought that the record high price-to-earnings ratios of the dotcom era made sense. In the next decade, they weren’t bothered by the unprecedented departure of house sale prices from rents of the housing bubble. It would be reasonable to think that people, some of whom were paid millions of dollars a year, would be able to see things that, certainly in retrospect, seemed very obvious. And to some of us, seemed very obvious even before the collapse. But very few of these people faced any consequence. To be clear, I’m not talking about jail time; I just mean that their careers should have suffered. After all, those lower down the pay ladder are held responsible for the quality of their work. The dishwasher that breaks a lot of dishes or the custodian who leaves a dirty toilet gets fired. Shouldn’t the investment manager who loses 20%, 30%, or 40% of the value of their portfolio also be sent packing? What kept most of these highly paid failures in their jobs was the “who could have known?” defense. This just meant that all of them could point to peers who made equally stupid calls in their investment decisions. If everyone on Wall Street thought was a $100 billion company, can you blame your investment manager for failing to recognize it was on the edge of bankruptcy? This is where the Fed can play a useful role. Around 200 economists work for the Federal Reserve Board in Washington, and roughly 200 more work for the 12 district banks around the country. The new Fed chair, Kevin Warsh, could assign some of the Fed economists to assess whether the current valuation of the stock market is consistent with the Fed’s projections for the future growth of GDP and profits. Unless their arithmetic is very different than the stuff the rest of us use, they will have to conclude that stock valuations are not consistent, unless today’s crop of stockholders expect very low future returns. That seems unlikely, but that is the alternative to saying that the market is in a bubble. This can be very useful in deflating the bubble because it will force every investment fund manager to deal with the argument. With the 90s tech bubble and the 00s housing bubble, the investment managers could get away with saying they didn’t pay attention to the small number of naysayers. They can’t get away with saying that they didn’t pay attention to the research that was being cited by the chair of the Federal Reserve Board. If they have an answer to it, fine. Maybe they will claim that the Fed is hugely underestimating future growth. That’s always possible, but a rather strong claim. Alternatively, maybe they would say that the Fed is missing a massive shift from wages to profits, going far beyond what we have already seen. Again, this is possible, but they would be painting a very dark picture of the world that does not seem to be widely shared. In any case, since the collapse of the AI bubble will have enormous consequences for financial markets and the economy, and these consequences will only become more severe as the bubble grows

further, it should be the Fed’s responsibility to try to rein it in. Unfortunately, since the new chair likely views his main responsibility as keeping Donald Trump happy, we shouldn’t anticipate that he would go this route. But this is one of the options that is on his table if he chooses to use it. Thanks for reading! Subscribe for free to receive new posts and support my work.

AI驱动知识工作即时化

AI在工作场所带来的最深刻影响，并非简单的自动化，而是对认知准备周期的极致压缩。传统知识工作依赖大量“认知库存”——包括研究笔记、构思草稿、背景阅读等预备材料，以缓冲从构思到产出的时间差。而如今AI工具能在数小时内生成高质量初稿，将原本需要数天甚至数周的准备过程急剧压缩。 这种变革正将知识工作推向“即时生产”模式：管理者推后决策、截止期限不断收紧、准备缓冲逐渐消失。正如制造业中的准时生产系统在提升效率的同时剥离了供应链韧性，AI在优化知识工作流程时，也可能系统性消解那些曾被视为“冗余”的认知缓冲——包括深度思考、错误校验和独立判断的时间。当人类角色从内容生成者转变为高压下的AI输出审核者时，工作的认知结构已发生根本转变。 更深层的风险在于，组织可能将人类认知逐步异化为按需调取的“即时资源”，在最小库存、最小冗余、高度压缩周期的模式下运行。这虽然创造了效率杠杆，却也构建了新的结构性脆弱。我们或许终将意识到，知识工作中那些曾被视为浪费的“冗余”——充分准备、思维沉淀与背景积累——恰恰是组织应对不确定性的关键韧性所在。 #AI应用 #职场变革 #组织管理 #知识工作 #技术风险 #即时生产 #认知科学

AI 深入专业领域

https://careersafter.ai/

随着大模型技术成熟，人工智能正从通用助手快速渗透到具体职业场景。OpenAI 在 ChatGPT 中上线个人理财功能，通过 Plaid 连接逾 1.2 万家金融机构，为美国 Pro 用户整合消费、账单与净资产数据，使其能基于自身账户进行预算咨询。同期，Anthropic 为 Claude 添加了 20 余个法律领域连接器与 12 个实践插件，覆盖合同审查、法律研究、电子取证及文档管理等流程。微软亦在 Word 中推出基于 Frontier 平台的法律代理，可依据内部剧本审核合同并标记条款。此外，企业服务智能体 Agentforce Sales 已能处理从潜客挖掘到报价生成的全套销售任务。这些进展表明，AI 不再局限于生成文本，而是直接接入专业软件和金融账户，执行高价值、情境化的具体任务，深刻改变法律、金融和销售等行业的协作模式。 #AI应用 #法律科技 #金融科技 #职业未来 #大模型落地 #OpenAI #Anthropic #微软

马斯克百亿美元布局Coding Agent，强调其对AI模型训练的战略必要性

马斯克与AI公司Anthropic关系持续紧张，xAI的Cursor账号曾被限制使用Claude模型，促使马斯克寻求自主突破。随后，SpaceX宣布与编程工具Cursor达成战略合作，投资高达100亿美元，旨在获取高质量训练数据。此举背后是AI行业的共识：coding agent产品能提供宝贵的过程监督信号和实时强化学习数据，是模型厂商开发强大编程模型的关键路径。Cursor通过其产品展示了数据飞轮的优势，例如Composer模型利用实时RL从用户交互中迭代优化。此合作凸显了在竞争激烈的AI领域，拥有自研coding agent对数据获取和模型优化的深远影响。 #马斯克 #CodingAgent #AI #模型训练 #SpaceX #Cursor #强化学习 #编程模型 #科技新闻