OpenAI：如何设计 AGI 时代的产业政策（全文翻译）

OpenAI：如何设计 AGI 时代的产业政策（全文翻译） OpenAI：如何设计 AGI 时代的产业政策（全文翻译） Modified April 13 Policymakers and AI companies should work together to determine how to best seed the Fund, which could invest in diversified, long term assets that capture growth in both AI companies and the broader set of firms adopting and deploying AI. Returns from the Fund could be distributed directly to citizens, allowing more people to participate directly in the upside of AI driven growth, regardless of their starting wealth or access to capital. 政策制定者和 AI 公司应合作确定如何为基金注入种子资金，基金可以投资于多元化的长期资产，捕获 AI 公司和更广泛的采用和部署 AI 的企业的增长。基金的回报可以直接分配给公民，让更多人直接参与 AI 驱动增长的收益，不论其起始财富或资本获取能力 让每个公民都拥有 AI 经济增长的份额 加速电网扩张 Accelerate grid expansion. Establish new public private partnership models to finance and accelerate the expansion of energy infrastructure required to power AI. Use these models to address financing constraints, permitting delays, and siting risks that have limited high voltage interstate and interregional transmission—and to deliver infrastructure at speed and scale, limit taxpayer risk, and share the upside with the public. Partnerships should be structured to minimize taxpayer exposure to commercial losses and ensure that expanded energy infrastructure translates into lower energy costs for households and businesses. 建立新的公私合作模式，为 AI 所需的能源基础设施扩张提供融资并加速推进。利用这些模式解决融资约束、审批延迟和选址风险等限制州际和跨区域高压输电的问题，以速度和规模交付基础设施，限制纳税人风险，并与公众分享收益。合作关系的设计应最大限度减少纳税人面临的商业损失风险，并确保扩大的能源基础设施转化为家庭和企业更低的能源成本 效率红利 Efficiency dividends. Convert efficiency gains from AI into durable improvements in workers’ benefits when routine workload declines and operating costs fall, including incentivizing companies to increase retirement matches or contributions, cover a larger share of healthcare costs, and subsidize child and eldercare. 当常规工作量下降和运营成本降低时，将 AI 带来的效率提升转化为劳动者福利的持久改善，包括激励企业增加退休匹配或缴款、承担更大份额的医疗成本、补贴育儿和养老 Incentivize employers and unions to run time bound 32 hour/four day workweek pilots with no loss in pay that hold output and service levels constant, then convert reclaimed hours into a permanent shorter week, bankable paid time off, or both. 激励雇主和工会试行每周 32 小时 / 四天工作制 ，在不减薪、保持产出和服务水平的前提下进行限时试点，然后将回收的工时转化为永久性的缩短工作周、可存储的带薪休假，或两者兼具 自适应安全网 Adaptive safety nets that work for everyone. Make sure the existing safety net works reliably, quickly, and at scale, because if the transition to superintelligence is going to benefit everyone, the systems designed to provide economic and health security need to deliver without delay or gaps. That starts with unemployment insurance, SNAP, Social Security, Medicaid, and Medicare that are not just in place but fully functional, accessible, and responsive to the realities people will face during the transition. 确保现有安全网可靠、快速、大规模地运作。如果向超级智能的过渡要惠及所有人，那么为提供经济和健康安全而设计的系统就必须没有延迟和缺口地交付。这首先意味着失业保险、食品券、社会保障、医疗补助和医疗保险不仅要到位，还必须全面运作、可及，并能回应人们在转型中面对的现实 Next, invest in clear, real time measurement of how AI is affecting work, wages, job quality, and sectoral dynamics, using public metrics such as unemployment rates and indicators of regional or industry specific displacement. These systems should provide policymakers with timely visibility into where disruption is occurring and how severe it is. 其次，投资于对 AI 如何影响工作、工资、工作质量和行业动态的清晰实时衡量，使用失业率和区域或行业特定位移指标等公共指标。这些系统应为政策制定者提供对颠覆发生在哪里、严重程度如何的及时可见性 Then, define a package of temporary, expanded safety nets (e.g., expanded or more flexible unemployment benefits, fast cash assistance, wage insurance, training vouchers) that activates automatically when these metrics exceed pre defined thresholds. When disruption rises above those levels, support would scale up; as conditions stabilize, it would phase out. This ensures that assistance is targeted, time bound, and proportional to the scale of disruption, and also avoids a permanent expansion of programs. 然后，定义一套临时性的扩展安全网（如扩大或更灵活的失业救济、快速现金援助、工资保险、培训券），当指标超过预设阈值时自动启动。颠覆加剧时支持升级，情况稳定时逐步退出。这确保了援助是有针对性的、有时间限制的、与颠覆规模成比例的，也避免了项目的永久性扩张 可携带福利 Portable benefits. Over time, build benefit systems that are not tied to a single employer by expanding access to healthcare, retirement savings, and skills training through portable accounts that follow individuals across jobs, industries, education programs, and entrepreneurial ventures. Public programs can decouple key benefits from employment status by expanding access to retirement and training support regardless of where or how someone works. Implementation can run through portable benefit platforms that pool contributions from multiple sources and route them into standardized accounts attached to the individual, not the job. Retirement systems can also be modernized through pooled structures that allow workers to accrue benefits continuously across employers, reducing gaps and preserving continuity over time. 逐步建立不绑定单一雇主的福利体系，通过可携带账户扩大医疗、退休储蓄和技能培训的获取，这些账户跟随个人跨越工作、行业、教育项目和创业活动。公共项目可以通过扩大退休和培训支持的获取来将关键福利与就业状态脱钩，不论一个人在哪里或如何工作。实施可以通过可携带福利平台进行，汇集来自多个来源的缴款并将其导入绑定个人而非工作岗位的标准化账户。退休体系也可以通过汇集结构进行现代化，让劳动者跨雇主持续积累福利，减少缺口并保持连续性 面向以人为本工作的通道 Pathways into human centered work. Expand opportunities in the care and connection economy—childcare, eldercare, education, healthcare, and community services—as pathways for workers displaced by AI. Although AI can enhance these roles by reducing administrative burdens and enabling greater personalization, human connection will remain an essential part of the profession. As AI reshapes the labor market, these sectors can absorb transitioning workers if supported with investments in training, wages, and job quality. Governments can build training pipelines, support transitions into care roles, and incentivize employers to raise pay and improve conditions in fields facing chronic shortages. 扩大关爱和连接经济中的机会：育儿、养老、教育、医疗和社区服务，作为被 AI 替代的劳动者的转型通道。虽然 AI 可以通过减少行政负担和实现更大的个性化来增强这些角色，但人际连接仍将是这些职业的核心部分。随着 AI 重塑劳动力市场，如果有培训、薪资和工作质量方面的投资支持，这些行业可以吸收转型中的劳动者。政府可以建设培训管道，支持向护理角色的转型，激励雇主在面临长期短缺的领域提高薪资和改善条件 These initiatives could be complemented with a family benefit that recognizes caregiving as economically valuable work and supports evolving work patterns. This benefit could help cover childcare, education, and healthcare while remaining compatible with part time work, retraining, or entrepreneurship. Together, these efforts would expand access to care, strengthen communities, and create meaningful, human centered work. 这些举措可以与一项家庭福利相结合，该福利承认照顾工作是有经济价值的劳动，并支持不断演变的工作模式。这项福利可以帮助覆盖育儿、教育和医疗，同时与兼职工作、再培训或创业兼容。这些努力将共同扩大护理服务的获取，加强社区，并创造有意义的、以人为本的工作 加速科学发现并推广收益 Accelerate scientific discovery and scale the benefits. Build a distributed network of AI enabled laboratories to dramatically expand the capacity to test and validate AI generated hypotheses at scale. These labs would integrate AI systems directly into experimental workflows by automating routine processes, capturing high quality data, and enabling rapid iteration between hypothesis generation and testing. 建设分布式的 AI 赋能实验室网络，大幅扩展大规模测试和验证 AI 生成假说的能力。这些实验室将 AI 系统直接集成到实验工作流中，自动化常规流程，采集高质量数据，实现假说生成和测试之间的快速迭代 Then, build the physical systems and infrastructure needed to translate validated discoveries into real world use at scale. This includes expanding the capacity of organizations to deploy new technologies, upgrading facilities and systems required for implementation, and aligning financing and incentives to support adoption. It also includes a sustained investment in people: training scientists, technicians, and operators to contribute to AI enabled science. These investments ensure that breakthroughs move beyond laboratories and into widespread use, while strengthening the workforce and operational systems required to build, maintain, and run the infrastructure that supports AI enabled discovery. Both laboratory and production infrastructure should be deployed broadly across universities, community colleges, hospitals, and regional research hubs, not concentrated in a small number of elite institutions. 然后，建设将经过验证的发现转化为大规模实际应用所需的物理系统和基础设施。这包括扩大组织部署新技术的能力，升级实施所需的设施和系统，以及调整融资和激励以支持采纳。还包括对人的持续投资：培训科学家、技术人员和操作员以参与 AI 赋能的科学。这些投资确保突破从实验室走向广泛应用，同时加强支持 AI 赋能发现所需的劳动力和运营系统。实验室和生产基础设施都应广泛部署在大学、社区学院、医院和区域研究中心，而不是集中在少数精英机构 第二部分：建设有韧性的社会 As AI systems become more capable and more embedded across the economy, they may introduce new vulnerabilities alongside new abundance. Some systems may be misused for cyber or biological harm. Others may create new pressures on social and emotional well being, including for young people, if deployed without adequate safeguards. AI systems may act in ways that are misaligned with human intent or operate beyond meaningful human oversight. And as advanced AI reshapes how people, organizations, and governments operate, it may place new strain on the institutions and norms that societies rely on to remain stable, secure, and free. 随着 AI 系统变得更强大、更深入地嵌入经济，它们可能在带来新丰裕的同时引入新的脆弱性。一些系统可能被滥用于网络或生物危害。另一些如果没有充分保障就部署，可能对社会和情感健康（包括青少年）造成新的压力。AI 系统可能以与人类意图不一致的方式行事，或超出有意义的人类监督。随着高级 AI 重塑人、组织和政府的运作方式，它可能对社会赖以保持稳定、安全和自由的制度和规范施加新的压力 We should be clear eyed about the resilience required here. These new risks won’t be isolated or suitable for addressing one at a time—AI will reshape how work is performed, how decisions are made, how organizations operate, and how states interact. Building resilience therefore means making sure people and institutions can adapt quickly, maintain meaningful agency over how these systems are used, and preserve broadly shared prosperity even as economic and social structures evolve. 我们应该对所需的韧性保持清醒。这些新风险不会是孤立的或适合逐一应对的：AI 将重塑工作方式、决策方式、组织运作方式以及国家互动方式。因此，建设韧性意味着确保人和机构能快速适应，对这些系统的使用方式保持有意义的自主权，并在经济和社会结构演变时保持广泛共享的繁荣 Over the past several years, leading AI developers including OpenAI have focused heavily on upstream safeguards: development of global standards, transparency around evaluations, mitigations, and risks, and investments in model testing, red teaming, and usage policies designed to identify and mitigate risks before deployment. Policymakers have also focused here, codifying requirements in the EU AI Act and in US state based regulation. These upstream efforts should continue. 过去几年，包括 OpenAI 在内的领先 AI 开发者大量关注上游保障：制定全球标准，围绕评估、缓解措施和风险的透明度，以及投资于模型测试、红队和使用政策，旨在部署前识别和缓解风险。政策制定者也在这方面着力，在欧盟 AI 法案和美国州级法规中将要求编入法律。这些上游努力应该继续 But as AI systems become more capable and more widely deployed, resilience will also depend upon what happens after deployment—when systems must be monitored in real time, operate under uncertainty, and integrate into institutions not designed for agentic workflows. 但随着 AI 系统变得更强大、更广泛部署，韧性也将取决于部署之后发生的事情：当系统必须实时监控、在不确定性下运行、并集成到不是为 Agent 工作流设计的机构中时 This is not a new challenge. As electricity spread, societies built safety standards and regulatory institutions. As automobiles transformed mobility, safety systems reduced risk while preserving freedom of movement. In aviation, continuous monitoring and coordinated response systems made flying one of the safest forms of transportation. In food and medicine, testing and post market surveillance helped ensure safety in everyday use. In each case, resilience was not automatic—it was built with the luxury of time. 这不是一个新挑战。电力普及时，社会建立了安全标准和监管机构。汽车改变出行时，安全系统降低了风险同时保留了出行自由。航空领域，持续监控和协调响应系统使飞行成为最安全的交通方式之一。食品和药品领域，测试和上市后监测帮助确保了日常使用中的安全。在每种情况下，韧性都不是自动产生的，而是在时间的从容中建设的 As we move toward superintelligence, building a resilient society will require a similar but speedier effort that kicks into gear now. The ideas below are a slate of ambitious approaches to building a more resilient society. They focus on building and scaling safety systems that operate in real world conditions by establishing mechanisms for trust, accountability, and auditing. They suggest opportunities for strengthening governance so that advanced AI remains controllable, transparent, and aligned with democratic values. And they suggest approaches to improve coordination across companies, governments, and countries so that risks can be identified early, information can be shared, and responses can be executed quickly when needed. Together, these proposals extend important safety work already underway and represent initial ideas to keep AI safe, governable, and aligned with democratic values. 向超级智能迈进的过程中，建设有韧性的社会将需要类似但更快速的努力，而且现在就要启动。以下是一系列建设更有韧性社会的大胆方案。它们聚焦于通过建立信任、问责和审计机制来构建和扩展在真实世界条件下运行的安全系统。它们提出了加强治理的机会，使高级 AI 保持可控、透明，并与民主价值一致。它们还提出了改善公司、政府和国家之间协调的方法，以便尽早识别风险、共享信息，并在需要时快速执行应对。这些提案共同延续了已经在进行中的重要安全工作，代表了保持 AI 安全、可治理和与民主价值一致的初步想法 应对新兴风险的安全系统 Safety systems for emerging risks. Research and develop tools that protect models, detect risks, and prevent misuse across high consequence domains, including cyber and biological risks as well as other pathways to large scale harm. Expand the use of advanced AI systems for threat modeling, red teaming, net assessments, and robustness testing to identify and anticipate novel risks early and inform mitigation strategies. Develop and scale complementary protective systems; for example, rapid identification and production of medical countermeasures in the event of an outbreak and expanded strategic stockpiles to prepare for future risks. Then, catalyze competitive safety markets by creating sustained demand for these capabilities through procurement, standards, insurance frameworks, and advance purchase commitments. Over time, this approach can make safeguards an output of innovation and competition, ensuring that defenses improve as quickly as the risks they are designed to address. 研发保护模型、检测风险和防止滥用的工具，覆盖高后果领域，包括网络和生物风险以及其他大规模伤害途径。扩大高级 AI 系统在威胁建模、红队、净评估和鲁棒性测试中的使用，以尽早识别和预测新型风险。开发和扩展互补保护系统，比如在疫情爆发时快速识别和生产医疗对策，以及扩大战略储备以应对未来风险。然后，通过采购、标准、保险框架和预购承诺创造对这些能力的持续需求，催化竞争性的安全市场。随着时间推移，这种方法可以使保障措施成为创新和竞争的产出，确保防御措施与其所针对的风险同步改进 AI 信任栈 AI trust stack. Research and develop systems that help people trust and verify AI systems, the content they produce, and the actions they take—especially as these systems take on more real world responsibilities. Advance the development of provenance and verification standards and tools that can build trust in AI systems while preserving privacy. This could include enabling secure, verifiable signatures for actions such as generating content or issuing instructions, and developing privacy preserving logging and audit systems capable of supporting investigation and accountability without enabling pervasive surveillance. 研发帮助人们信任和验证 AI 系统、其产出内容和采取行动的系统，尤其是当这些系统承担更多现实世界职责时。推进溯源和验证标准及工具的开发，在保护隐私的同时建立对 AI 系统的信任。这可以包括为生成内容或发出指令等行为提供安全、可验证的签名，以及开发能支持调查和问责但不会导致普遍监控的隐私保护日志和审计系统 These types of solutions should capture key information about system behavior and use while minimizing the collection of sensitive data, and be designed to support investigation or intervention under clearly defined legal or safety conditions. This work could also include developing and testing governance frameworks that clarify responsibility within organizations, including how accountability could be assigned to specific roles and how delegation, monitoring, and escalation processes could function as systems become more capable. Over time, these efforts could establish a foundation for accountability by building trust in AI interactions an