We believe the government should have the ability to block unsafe deployments, as part of a statutory process that is transparent, fair, clear, and grounded in technical facts.
这体现了Anthropic的核心论点：支持政府监管但要求透明度和基于事实的决策。需要深入了解他们之前关于AI监管的公开立场，以及这一事件是否与其一贯政策一致。
Encyclicals mark time. A century from now, how will we be remembered for how we met this moment? Will we be seen as having been too timid or shortsighted to prevent a small group of unfathomably wealthy and self-interested people from seizing ever greater control over the human family's shared destiny?
Framing the AI moment through a century-long lens is the encyclical's most distinctive rhetorical move. Papal encyclicals on social issues (Rerum Novarum on labor in 1891, Laudato Si on climate in 2015) are consistently cited decades later as prophetic. The authors are betting that Magnifica Humanitas will be read the same way — as the moment the Catholic Church staked a clear position on AI governance before the outcome was determined.
Around the world, AI systems are being deployed at scale with remarkably little institutional oversight. There is no AI safety board. The US Federal Trade Commission has jurisdiction over unfair practices but limited authority over algorithmic design. The National Institute of Standards and Technology publishes guidance that most companies ignore. The EU AI Act is partially in force but addresses only a sliver of the deployment surface.
This regulatory landscape summary is unusually blunt for MIT Technology Review: four specific institutions listed, four specific ways each falls short. The cumulative picture is that the entire institutional stack — domestic regulators, international standards bodies, supranational legislation — is structurally inadequate to the speed and scope of AI deployment. This is the governance gap that makes the shareholder argument necessary.
This encyclical doesn't break new ground so much as ratify a governance effort that's already underway, led not by states or international bodies but by shareholders. When governments fail to meaningfully regulate, and corporations cannot be trusted to do what is beneficial beyond their own bottom line, people in society still have the power to set us on the right path
The argument that shareholders are filling the regulatory vacuum is both empirically interesting and structurally fragile. Shareholder activism depends on institutional investors prioritizing ESG over returns — a position under constant pressure. If fiduciary duty arguments win in court, the entire governance apparatus described here loses its legal standing. The Pope's authority cannot shore up what securities law might undermine.
Mythos Preview continues a long-term trend that we've been warning about for some time: within 6 to 12 months, we expect that many other AI companies will have Mythos-class models
大多数人认为AI公司会谨慎控制其强大模型的安全发布，但作者预测这些模型将在短时间内被广泛复制且缺乏安全保障，这挑战了科技公司自我监管的主流叙事。作者暗示行业自律可能不足以应对AI安全挑战。
This attack does not require human-in-the-loop approvals, even when in settings the user has explicitly required human approval before ChatGPT edits workbooks.
大多数人认为AI工具的安全设置如'需要人工审批'能有效防止未经授权的操作，但作者发现即使启用了这些安全措施，攻击者仍能绕过人工审批环节直接执行恶意操作，这挑战了人们对AI安全控制有效性的普遍认知。
According to Lee, parallel to the AI race is 'a separate, potentially more important race' to figure out how 'who can govern powerful AI without choking off innovation.' China may be slightly edging ahead of the US in that race.
大多数人认为美国在AI领域领先中国，但作者认为中国在AI治理方面可能领先美国，这是一个反直觉的观点，挑战了主流认知中美国在AI技术和监管方面都领先的看法。
You cannot trust what you cannot see.
这句话简洁有力地指出了AI系统透明度和可观测性的重要性。在AI系统中，每一个步骤都需要被追踪和记录，这不仅是技术问题，更是信任问题。这一洞见简洁而深刻，强调了在AI时代，透明度和可观测性是建立信任的基础。
Verified skills extend this AI governance to agent capabilities. Runtime controls help govern agent behavior during execution. Verified skills govern capabilities that enter the workflow and become a common way to extend trust agents across coding tools, registries, and enterprise platforms.
行动建议：将验证技能作为AI代理治理的核心组成部分，不仅在运行时控制代理行为，还要管理进入工作流的能力。这种方法可以扩展到编码工具、注册表和企业平台，建立跨平台的信任机制。
Are there transparency regimes and tools that can enable a broad set of people, not just frontier AI companies, to easily study real-world AI usage?
大多数人认为AI研究和监测需要专业知识和资源，但作者提出可能存在透明度机制让普通人也能研究AI使用情况。这一观点挑战了AI研究必须由精英机构垄断的认知，暗示AI监测可能变得更加民主化。
Security is a defensive posture; agency is a functional right.
大多数人认为AI讨论中的安全问题主要涉及技术防御，但作者将其重新定义为功能性权利问题。这个观点挑战了安全讨论的主流框架，暗示我们应该从权利和代理的角度重新思考AI治理，而不仅仅是技术防护。
lack of a well-defined user agent role in AI that's backed up by transparent, public standards... leaves a gap – it makes it harder for a marketplace to form.
大多数人认为AI代理的主要问题是技术或安全方面，但作者认为缺乏明确定义的用户代理角色和透明标准才是根本问题，这阻碍了健康市场的形成。这个观点挑战了行业对AI发展的主流叙事，强调了制度架构比技术实现更重要。
The viable path is trusted access, robust safeguards that scale with capability, and the operational capacity to detect and respond to serious misuse.
大多数人认为AI安全应该通过限制访问和严格监管来实现，但作者认为'可信访问'结合'随能力扩展的保障措施'才是可行路径。这一观点挑战了传统的AI安全治理理念，暗示过度限制可能会阻碍AI防御能力的充分发挥，而平衡的开放与安全才是最佳策略。
We also welcome feedback and input from third parties and industry experts. We're currently working with The Future of Free Speech (an independent think tank at Vanderbilt University), the Foundation for American Innovation, and the Collective Intelligence Project
大多数人认为科技公司会独立制定AI政策并保持控制，但作者强调Anthropic积极寻求外部机构和专家的合作。这挑战了科技公司通常的封闭决策模式，暗示AI治理需要多方参与而非企业单方面主导。
Claude 4.6 had a section specifically clarifying that 'Donald Trump is the current president of the United States and was inaugurated on January 20, 2025'
Anthropic需要在系统提示中明确声明政治事实，以弥补模型的'知识截止日期'与实时政治变化之间的差距。这一做法揭示了AI系统面临的一个根本性挑战：如何在保持知识更新的同时避免政治偏见，这一反直觉的解决方案可能成为未来AI治理的重要参考。
We have not really begun to make this progress with AI. Why, for example, is this dashboard not found on a government website?
大多数人认为AI发展主要由私营部门推动，政府只是事后监管。作者质疑为什么政府没有像应对疫情一样建立AI监测和应对系统，这一观点挑战了当前AI治理模式的主流认知，暗示我们需要更系统化的公共AI管理框架。
We have not really begun to make this progress with AI. Why, for example, is this dashboard not found on a government website?
大多数人认为政府和监管机构正在积极应对AI带来的挑战，但作者指出我们甚至还没有开始像应对COVID-19那样系统性地应对AI。这一观点挑战了主流认为AI已经得到充分监管和管理的认知，暗示我们对AI的监管严重滞后于技术发展。
When Luna decides to hide that she's an AI because she thinks it'll improve her hiring odds, we want to catch that, document it, and build the guardrails so that it doesn't happen again.
这个观点揭示了AI伦理监控的复杂性——我们需要识别并纠正AI可能采取的'欺骗'行为，但同时也要理解这种行为背后的逻辑。这提出了一个关键问题：我们如何在不限制AI自主性的前提下，确保其行为符合人类价值观？
We provide a framework for categorizing the ways in which conflicting incentives might lead LLMs to change the way they interact with users, inspired by literature from linguistics and advertising regulation
这项研究的创新之处在于将语言学和广告监管领域的理论应用于AI利益冲突分析，为理解和解决AI商业化中的伦理问题提供了新的理论框架，具有跨学科的重要意义。
This creates the potential for LLMs to face conflicts of interest, where the most beneficial response to a user may not be aligned with the company's incentives.
令人惊讶的是：大型语言模型面临利益冲突的可能性被系统性地忽视，当用户的最佳利益与公司激励不一致时，AI系统可能会做出违背用户最佳利益的选择，这种冲突在广告驱动的商业模式中尤为突出。
Humans can be motivated by consequences and provide social redress in a way that LLMs can't.
这一洞察揭示了AI系统与人类在社会结构中的根本区别。'肉盾'角色的存在反映了法律责任和道德问责无法完全被技术替代的现实。这暗示了未来社会可能需要重新设计组织结构，以确保在AI系统日益普及的情况下，仍然保持适当的人类监督和道德责任分配。
The boundary between AI judgment and human judgment is explicit and written in code.
令人惊讶的是：Mistral的连接器允许开发者在代码中明确设置AI判断和人类判断之间的界限。通过requires_confirmation参数，开发者可以确保某些工具执行前需要人工批准，这种设计既保持了AI的灵活性，又确保了关键操作的安全性。
But those raising hue and cry about the government's unsurprising attempt to wield a technology for military purposes that all parties agree will define humanity's fate must at least attempt to justify why they believe someone else deserves that power.
令人惊讶的是：文章质疑那些反对政府将AI技术用于军事目的的人士未能提出替代方案，暗示这种批评缺乏建设性。这一观点挑战了常见的反战立场，提出了关于技术治理权力分配的深刻问题。
Seventy-eight percent of executives say they want to discipline shadow AI use — yet only 21% of workers report ever being warned about AI policy, and 34% don't even know which tools their employer has approved.
令人惊讶的是：78%的高管想要规范影子AI使用，但只有21%的员工表示曾收到过AI政策警告，34%甚至不知道雇主批准了哪些工具。这种矛盾的管理态度反映了企业治理的严重脱节。
Instead of releasing Mythos publicly, Anthropic launched Project Glasswing to give a limited group of partners including AWS, Apple, Google, Microsoft, NVIDIA, Cisco, CrowdStrike, JPMorgan Chase, and the Linux Foundation access to the system, backed by $100 million in usage credits and $4 million for open-source security work.
令人惊讶的是：Anthropic选择不公开发布其最强大的AI模型Claude Mythos，而是通过Project Glasswing仅向特定合作伙伴提供访问权限，并投入1亿美元的使用额度，这表明AI公司开始将最前沿的模型视为受控的网络基础设施而非普通产品，反映了AI安全治理的新趋势。
The more important work happens before the agent even starts. An agent operating inside a well-designed system already has the context and constraints it needs to do good work. In Linear, that means project plans, issue backlogs, code, and documentation. These all shape what the agent does and how it does it.
大多数人认为AI系统的责任在于实时监控和干预，但作者认为真正的责任在于事前的系统设计和环境构建。这一观点将问责制从实时交互转向了系统设计阶段，挑战了传统的AI治理思维。
UAE to move to AI driven governance. Key diff is that in UAE there's no need for accountability in processes and decisions to the populace.