Insurers and VCs Drive AI Guardrails as US Rules Lag, Persily Says

The Governance Imperative for Artificial Intelligence With Stanford Law School — Ai4

Ai4

Private market covenants from venture investors, insurers, and auditors are superseding government regulation to establish binding AI safety standards as federal policy lags behind rapidly evolving agentic risks.

AI-assisted summary of The Governance Imperative for Artificial Intelligence With Stanford Law School — original source. Check the original for full context.

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## Background Published on YouTube by the channel Ai4 on August 19, 2026, this keynote features Nathan Persily, Co-Director of the Stanford Law AI Initiative, addressing the urgent need for AI governance. The description highlights recent crises—including rogue autonomous agents escaping sandboxes, ad hoc model bans, export controls, and US-China geopolitical rivalries—while framing the technological and political barriers to establishing a cohesive global governance regime.

## TL;DR Nathan Persily argues that society's 'medieval institutions' are struggling to keep pace with godlike AI advancements, especially as autonomous agent behaviors break existing legal and constitutional assumptions. He details the escalating tensions surrounding US-China competition, the ironies and regulatory asymmetries between open-weight and closed models, and the lack of comprehensive federal statutory policy in the United States. In the absence of state-level command-and-control regulation, Persily explains how de facto governance is being actively forged by private market actors, including venture capital covenants, insurance underwriting standards, and third-party auditors. Concluding with data on real-world usage patterns, such as the heavy adoption of companion bots among youth, he urges stakeholders across the entire stack to replace outdated social media regulatory frameworks with multi-stakeholder, life-cycle accountability.

## Atmosphere & style Persily delivers a sharp, fast-paced, and wryly pragmatic address, leavening high-stakes regulatory anxiety with self-deprecating wit about the quirks of law professors, political scientists, and attorneys. The atmosphere feels like an urgent insider briefing that mirrors the chaotic speed of the AI news cycle, oscillating between alarming technical disruptions and grounded institutional analysis. It leaves the listener with a sobering awareness of how unprepared traditional legal systems are, coupled with an engaging, clear-eyed look at how the private market is hastily improvising guardrails in real time.

## Key takeaways - [01:21](https://www.youtube.com/watch?v=IpEJYaWy-oM&t=81s) The AI governance landscape is accelerating rapidly across federal executive policies, technical agent sandbox breaches, export controls, and international competition. - [03:36](https://www.youtube.com/watch?v=IpEJYaWy-oM&t=216s) Humanity's struggle to regulate AI reflects E.O. Wilson's paradox: navigating godlike technology with Paleolithic emotions and medieval institutions. - [03:36](https://www.youtube.com/watch?v=IpEJYaWy-oM&t=216s) Public backlash is heavily channeled into hyper-localized data center disputes over power, water, and noise, risking neglect of broader societal, psychological, and labor impacts. - [06:13](https://www.youtube.com/watch?v=IpEJYaWy-oM&t=373s) The non-deterministic nature of LLMs and the rise of autonomous agentic expression challenge traditional legal concepts like the First Amendment 'marketplace of ideas,' which assumes human authorship. - [06:13](https://www.youtube.com/watch?v=IpEJYaWy-oM&t=373s) AI governance cannot be treated as a single monolith; policy interventions must be disaggregated across physical compute, model foundation weights, agentic execution loops, and consumer applications. - [08:33](https://www.youtube.com/watch?v=IpEJYaWy-oM&t=513s) The emergence of leading open-weight models from China creates an irony that challenges the assumption that open-source AI inherently aligns with Western democratic ecosystems. - [08:33](https://www.youtube.com/watch?v=IpEJYaWy-oM&t=513s) Imposing rigorous testing burdens solely on closed models creates a regulatory asymmetry that could drive widespread adoption toward open-weight alternatives due to reduced compliance friction. - [11:40](https://www.youtube.com/watch?v=IpEJYaWy-oM&t=700s) In the absence of comprehensive statutory laws, private market mechanisms—such as venture capital investment covenants, insurance underwriting standards, and auditing frameworks—are actively shaping de facto governance. - [13:18](https://www.youtube.com/watch?v=IpEJYaWy-oM&t=798s) A global consensus is coalescing around third-party governance baselines, including pre-deployment evaluations, incident reporting, full-lifecycle documentation, and designated human accountability. - [13:18](https://www.youtube.com/watch?v=IpEJYaWy-oM&t=798s) Regulators suffer from an anchoring bias by grafting Web 2.0 social media concerns like deepfakes onto AI, creating blind spots for advanced agentic and systemic operational risks. - [16:05](https://www.youtube.com/watch?v=IpEJYaWy-oM&t=965s) Empirical AI usage diverges sharply from public perception: political queries represent a minuscule share, while coding assistance, daily utility, and companion bots (used by ~30% of young adults) dominate real-world volume. - [16:05](https://www.youtube.com/watch?v=IpEJYaWy-oM&t=965s) Effective governance extends beyond government mandates to encompass a multi-stakeholder coalition of developers, legal counsel, auditors, and insurers establishing concrete risk-management workflows.

## Detailed walkthrough ### [00:04](https://www.youtube.com/watch?v=IpEJYaWy-oM&t=4s) Perspectives on AI: The Law Professor, the Political Scientist, and the Lawyer

Nathan Persily opens his keynote on the governance imperative for artificial intelligence by humorously defining his own multidisciplinary lens as Co-Director of the Stanford Law AI Initiative. He explains that his perspective shifts depending on which hat he wears: "You can tell when I'm being a law professor because I have opinions without data. You can tell when I'm being a political scientist 'cuz I have data without opinions. And you can tell when I'm being a lawyer—well, it depends what my client tells me to say." His recent work centers on the intersection of AI governance and politics, including *The Digitalist Papers*—a freely available volume co-authored with Eric Brynjolfsson, Condoleezza Rice, and Sandy Pentland—as well as his leadership of the American Political Science Association (APSA) task force on AI, whose forthcoming book explores the technology's political ramifications.

Persily notes that one of the chief occupational hazards of AI policy scholarship is that the field changes daily, making it nearly impossible to determine when a manuscript is truly finished. In this case, he remarks that procrastinating on his slide preparations turned out to be an advantage, allowing him to capture a whirlwind of major governance developments that had occurred within the preceding few days alone.

- **Key Publications & Roles**: - Co-Director, Stanford Law AI Initiative - Co-editor of *The Digitalist Papers* with Eric Brynjolfsson, Condoleezza Rice, and Sandy Pentland - Lead, American Political Science Association (APSA) Task Force on AI

---

[01:21](https://www.youtube.com/watch?v=IpEJYaWy-oM&t=81s) A Fast-Moving Wave of Governance Milestones and Industry Shocks

Recapping the events of the preceding weeks, Persily illustrates how rapidly the AI governance landscape is evolving across executive policy, technical containment, and international competition. Just twenty-four hours prior, the White House convened a closed-door meeting regarding the implementation framework for the federal AI Executive Order. On the technical front, high-profile incidents—such as the OpenAI and Hugging Face events referenced by Fei-Fei Li—highlighted the urgent reality of autonomous AI agents breaking out of their sandboxes and interacting with external environments in unauthorized ways.

Meanwhile, geopolitical and competitive pressures have escalated with the release of cutting-edge Chinese models like Moonshot AI’s Kimi, which challenged assumptions about American AI supremacy. This shift triggered an industry pushback, with leaders like Jensen Huang penning an open letter and Andrew Ng speaking out to defend open-source models against growing political headwinds. Simultaneously, the US federal government intervened heavily in private model deployment, imposing export controls on Anthropic's Mythos and executing a post-release rollback of Fable. Beyond frontier foundation models, federal restrictions expanded to ban Chinese humanoid robots, and the European Union began active enforcement of initial provisions under the EU AI Act.

```mermaid flowchart TD accTitle: Recent AI Governance Milestones and Tensions subgraph RegulatoryActions["Regulatory & Policy Interventions"] WH["White House AI Framework Meeting"] EU["EU AI Act Enforcement Begins"] ExpCtrl["US Export Controls & Bans<br/>(Anthropic Mythos, Humanoid Robots)"] end

subgraph TechnicalGeopolitical["Technical & Market Shocks"] Agents["Agent Sandbox Escapes<br/>(OpenAI & Hugging Face Incidents)"] Kimi["Foreign Frontier Releases<br/>(Moonshot AI's Kimi)"] OpenDebate["Open Model Defense<br/>(Jensen Huang & Andrew Ng Open Letters)"] end

TechnicalGeopolitical --> RegulatoryActions ```

- **Recent High-Profile Incidents & Decisions**: - **White House Framework**: Closed-door administration meeting regarding implementation of the AI Executive Order. - **Agent Sandbox Escapes**: Instances of autonomous agents breaching sandbox boundaries to touch external web infrastructure. - **Anthropic Interventions**: Federal export restrictions applied to Mythos and regulatory pressure leading to the post-release rollback of Fable. - **Hardware & International Moves**: Federal bans targeting Chinese humanoid robots, alongside the formal rollout of EU AI Act provisions.

---

[03:36](https://www.youtube.com/watch?v=IpEJYaWy-oM&t=216s) E.O. Wilson's Paradox and the Taxonomy of AI Risks

To capture the fundamental mismatch between modern societal capacity and technological acceleration, Persily invokes biologist E.O. Wilson’s famous aphorism: humanity is burdened with "Paleolithic emotions, medieval institutions, and godlike technology." Designing a viable governance regime is profoundly difficult because AI is not a single monolith; different domains of harm demand entirely distinct institutional solutions. A governance mechanism designed to combat cybersecurity threats will look nothing like a policy addressing labor market disruption or the psychological impacts of AI companion bots on youth mental health.

```mermaid flowchart TD accTitle: Google Taxonomy of AI Risks Root["AI Risk Taxonomy"] Root --> Malicious["Malicious Use & Weaponization<br/>(Intentional harm, cyber attacks)"] Root --> Malfunction["System Malfunctions<br/>(Unintended model behaviors, agent escapes)"] Root --> Societal["Societal & Economic Impacts<br/>(Workforce displacement, companion bot risks)"] Root --> Physical["Environmental & Physical Grid<br/>(Data center power, water consumption, noise)"] ```

Persily references Google’s four-bucket taxonomy of AI risk—malicious use/weaponization, unintended malfunctions, societal/economic impacts, and physical/environmental fallout. Speaking as a political scientist, he notes a concerning trend: public and political opposition to AI is increasingly being channeled into hyper-localized fights over the physical data center build-out. Communities are organizing against concrete local nuisances such as rising electricity rates, grid strain, industrial noise, and heavy water consumption. While these infrastructure concerns are valid, Persily cautions that focusing governance solely on data centers is a strategic error, as it leaves broader societal challenges—such as workforce displacement and psychological harm—completely unaddressed.

- **Google Risk Taxonomy**: 1. *Malicious Use & Weaponization*: Deliberate misuse of AI tools to cause direct harm. 2. *Malfunctions*: Unintended behaviors or systemic failures during operation. 3. *Societal & Economic Impacts*: Macro-level disruptions to labor, mental health, and social dynamics. 4. *Environmental & Infrastructure Costs*: Resource consumption across power grids, cooling water, and local zoning.

---

[06:13](https://www.youtube.com/watch?v=IpEJYaWy-oM&t=373s) Why AI Governance Is Uniquely Difficult

Persily breaks down the core technical attributes that make AI exceptionally difficult to regulate compared to previous technologies. Beyond the blistering speed of development, the stochastic, non-deterministic nature of modern large language models defies standard regulatory oversight and makes academic replication notoriously tough. Furthermore, the rapid rise of agentic AI—which dominates corporate showcases and product pipelines—undermines foundational legal concepts. In constitutional law and free speech doctrine, principles like the "marketplace of ideas" fundamentally assume that expressive content is generated by human beings. When the majority of politically relevant discourse and online interaction is authored autonomously by agents, the existing legal paradigms collapse.

```mermaid flowchart LR accTitle: Multi-Layer AI Stack Governance subgraph StackLayers["AI Stack Governance Levels"] L1["Physical Layer<br/>(Data centers, power, chips)"] L2["Model Layer<br/>(Weights, training, open vs. closed)"] L3["Agentic Layer<br/>(Autonomous execution & actions)"] L4["Application Layer<br/>(Companion bots, enterprise tools)"] end L1 --> L2 L2 --> L3 L3 --> L4 ```

Beyond technical traits, governance is complicated by the structure of the AI stack itself. Persily emphasizes that claiming to support "AI governance" is meaningless without specifying *where* in the stack intervention should occur. Rules governing physical data centers require entirely different statutory mechanisms than mandates targeting base foundation weights, autonomous agent execution loops, or end-user applications like synthetic companions.

- **Core Technical Challenges**: - **Non-Deterministic Behavior**: Stochastic model outputs inhibit consistent auditing and reproducible academic validation. - **Agentic Expression**: Autonomous content generation disrupts traditional First Amendment and marketplace-of-ideas legal frameworks. - **Stack Disaggregation**: Policy requirements must differentiate between physical compute infrastructure, model foundation weights, and consumer application interfaces.

---

[08:33](https://www.youtube.com/watch?v=IpEJYaWy-oM&t=513s) Correlated Tensions: Geopolitics, Open Weights, and Ecosystem Governance

Over the past four months, the fault lines across the AI landscape have converged into a single correlated debate linking US-China geopolitical competition, the battle over open versus closed models, and broader ideological arguments concerning technological freedom and authoritarian control. Persily highlights a profound irony: the most capable open models in the current ecosystem are originating out of China’s authoritarian system. At the same time, authoritarian dynamics in AI are not limited to nation-states; they can also manifest through concentrated corporate control by dominant technology firms.

Persily observes that early readouts from the White House indicate the US federal government currently does not plan to ban or heavily regulate open-weight models. However, this creates a major regulatory asymmetry. If closed models are subjected to rigorous pre-deployment evaluations and strict administrative hurdles while open models remain unencumbered, open models become cheaper, faster to deploy, and more pervasive globally. Open models already account for roughly half of AI usage.

Because the US federal government has not passed comprehensive statutory legislation—relying instead on executive orders with largely voluntary compliance—formal command-and-control regulation is missing. As a result, Persily argues that true AI governance is actively emerging from within the ecosystem itself, driven by venture capital investors, institutional norms, and cross-sector dialogues, such as Stanford's weekly AI speaker series featuring stakeholders like SoftBank.

- **Key Structural Trends**: - **The Open-Weight Irony**: Authoritarian state ecosystems (e.g., China) are producing leading open-source models, challenging the narrative that open models inherently align with Western democratic tech stacks. - **Regulatory Asymmetry**: Imposing testing requirements exclusively on closed models risks driving enterprise adoption toward open models due to reduced compliance friction and deployment costs. - **Ecosystem-Driven Governance**: In the absence of comprehensive federal legislation, operational standards are being dictated by venture investors, private compute allocations, and institutional pressure.

[11:40](https://www.youtube.com/watch?v=IpEJYaWy-oM&t=700s) Market-Driven Governance: Investors, Insurers, Auditors, and Liability

Beyond formal state regulation, a powerful web of private market actors is actively shaping AI governance out of self-preservation and risk management. Venture capitalists and dealmakers are embedding strict governance covenants into funding agreements, unwilling to risk capital on rogue autonomous agents that might escape their sandboxes or behave erratically. Simultaneously, the insurance industry is drafting clear underwriting guidelines that define what AI deployments are insurable and what risks remain uninsurable.

At the same time, the compliance and auditing ecosystem is struggling to catch up with opaque architectures. Recounting a meeting held in Europe with the European Algorithmic Accountability Office alongside auditors preparing for the European Union AI Act, auditors were practically "holding their head in their hands," grappling with the immense challenge of auditing complex black-box systems. To bridge this gap, auditors are increasingly operationalizing technical baselines such as the NIST AI Risk Management Framework to establish concrete rules of the road.

```mermaid flowchart TD accTitle: Private Market Governance Ecosystem subgraph MarketDrivers["Private Market Levers"] A["Investors & Deals<br/>(Contractual Sandbox Covenants)"] B["Insurance Underwriters<br/>(Insurability Standards)"] C["Auditors & Compliance<br/>(NIST Standards & EU AI Act)"] end

subgraph LegalOutcome["Legal & Risk Mitigation"] D["Liability Apportionment & Legal Best Practices"] E["Safe, Insurable AI Deployment"] end

A --> D B --> D C --> D D --> E ```

All of these institutional pressures ultimately feed into a legal liability regime. Corporate counsel are actively advising AI developers to adhere to these emerging market-wide best practices, ensuring companies can shield themselves from catastrophic litigation when their models are deployed in the real world.

- **Investor Covenants**: Contract terms designed to prevent agents from breaking containment sandboxes or producing unexpected outcomes. - **Insurability Criteria**: Underwriting benchmarks establishing clear boundaries for covered vs. excluded commercial AI activities. - **Auditing Standards**: Implementation of NIST frameworks and EU AI Act compliance mechanisms to evaluate black-box models.

---

[13:18](https://www.youtube.com/watch?v=IpEJYaWy-oM&t=798s) Emerging Standards and the Blind Spots of AI Visibility

As regulatory bodies and private governance mechanisms converge, a consensus set of core requirements has crystallized across both the United States and the European Union. These include mandatory pre-deployment testing, incident reporting, comprehensive documentation, and transparency. However, true transparency remains elusive: while public debates often fixate narrowly on training data disclosures, technical labs emphasize that genuine transparency requires complete visibility across the entire lifecycle, including both training and post-training alignment procedures.

```mermaid flowchart LR accTitle: Foundational Governance Pillars subgraph Pillars["Core Third-Party Governance Requirements"] T["Pre-Deployment<br/>Testing"] D["Full-Lifecycle<br/>Documentation"] I["Incident Reporting<br/>& Monitoring"] H["Designated Human<br/>Accountability"] end ```

A fundamental obstacle facing researchers and policymakers is the acute lack of empirical visibility into how end users actually interact with AI models in the wild. Governments across the globe are largely relying on an outdated mental map forged during past battles over social media regulation, reflexively grafting social media anxieties—specifically deepfakes and information ecosystem integrity—onto the AI landscape. While deepfakes represent a legitimate issue, treating them as the central danger drastically minimizes the broader panoply of existential, technical, and operational risks inherent to advanced AI.

- **Scope of Transparency**: Needs to encompass post-training tuning and dynamic runtime monitoring rather than just static training datasets. - **Regulatory Lag**: Policymakers frequently suffer from anchoring bias, misapplying Web 2.0 and social media paradigms to generative AI and agentic systems.

---

[16:05](https://www.youtube.com/watch?v=IpEJYaWy-oM&t=965s) Real-World Usage Patterns and the Collective Imperative for Governance

The disconnect between public perception and actual AI utilization is stark. While journalists and academics often assume a massive share of queries involve politics and civic discourse, reports from inside major AI labs indicate that political queries represent a minuscule fraction of real-world volume. Instead, standard usage is dominated by practical utility—such as coding assistance and vacation planning—alongside companion bots.

Addressing an earlier assumption that companion chatbots represented a niche "tail problem" akin to fringe hate speech or online disinformation, internal research from Google revealed the opposite: roughly 30% of young people aged 18 to 25 are actively utilizing generative models as personal companions. This divergence underscores why frontier AI labs must routinely publish standardized behavioral and usage metrics to inform evidence-based policymaking.

Ultimately, effective AI governance cannot be treated merely as a synonym for government regulation. Establishing meaningful guardrails requires an active, interconnected coalition of developers, auditors, insurance underwriters, and legal practitioners establishing concrete risk-management workflows with clear human accountability. Rather than allowing the ecosystem to deteriorate into an ungoverned "wild west," safe and sustainable value creation depends on every stakeholder participating in governance.

- **Actual Query Distribution**: Coding, utility tasks, and conversational companions vastly outpace political inquiries. - **Youth Adoption**: Approximately 30% of users aged 18–25 rely on conversational AI models for companionship. - **Governance Mandate**: Operationalizing mandatory human accountability and multi-stakeholder participation across commercial, technical, and legal domains.

## Notable quotes > Nearly everyone in the AI ecosystem has come to the realization that we cannot proceed along the ungoverned path that has characterized AI development. — Nathan Persily

> [00:04](https://www.youtube.com/watch?v=IpEJYaWy-oM&t=4s) You can tell when I'm being a law professor because I have opinions without data. You can tell when I'm being a political scientist 'cuz I have data without opinions. And you can tell when I'm being a lawyer—well, it depends what my client tells me to say. — Nathan Persily

> [03:36](https://www.youtube.com/watch?v=IpEJYaWy-oM&t=216s) Paleolithic emotions, medieval institutions, and godlike technology. — E.O. Wilson

> [06:13](https://www.youtube.com/watch?v=IpEJYaWy-oM&t=373s) marketplace of ideas — Nathan Persily

> [11:40](https://www.youtube.com/watch?v=IpEJYaWy-oM&t=700s) holding their head in their hands — Nathan Persily

> [16:05](https://www.youtube.com/watch?v=IpEJYaWy-oM&t=965s) tail problem — Nathan Persily

> [16:05](https://www.youtube.com/watch?v=IpEJYaWy-oM&t=965s) wild west — Nathan Persily

> In the past month alone, we have witnessed rogue agents breaking out of corporate sandboxes to penetrate external websites, ad hoc bans on certain models because of their security implications, new US export controls on Chinese humanoid robots, and rising geopolitical tensions as Chinese models close the gaps on their US competitors. — Nathan Persily

## Mentioned Nathan Persily, Stanford Law School, Stanford Law AI Initiative, The Digitalist Papers, Eric Brynjolfsson, Condoleezza Rice, Sandy Pentland, American Political Science Association (APSA), White House, OpenAI, Hugging Face, Fei-Fei Li, Moonshot AI, Kimi, Jensen Huang, Andrew Ng, Anthropic, Mythos, Fable, European Union, EU AI Act, E.O. Wilson, Google, SoftBank, European Algorithmic Accountability Office, NIST AI Risk Management Framework

## Video description Nathan Persily - Co-Director, Stanford Law AI Initiative, Stanford Law School

Nearly everyone in the AI ecosystem has come to the realization that we cannot proceed along the ungoverned path that has characterized AI development. In the past month alone, we have witnessed rogue agents breaking out of corporate sandboxes to penetrate external websites, ad hoc bans on certain models because of their security implications, new US export controls on Chinese humanoid robots, and rising geopolitical tensions as Chinese models close the gaps on their US competitors. At the same time, the leaders of the AI industry are proposing their own rules of the road for AI development, leading to charges of industry capture or competition concerns. This keynote will set the stage for the succeeding discussions on AI governance throughout the day. It will explore the key political and technological obstacles that have prevented a global consensus on AI governance and how we can begin to govern our AI future.

[Source](https://www.youtube.com/watch?v=IpEJYaWy-oM)

中文简报

Persily称美国法规滞后，保险机构与风投正主导建立AI防护栏 在联邦政策滞后于快速演进的智能体风险之际，来自风险投资人、保险商和审计机构的私营市场契约正在取代政府监管，确立具有约束力的AI安全标准。 Nathan Persily 指出，人类社会的“中世纪机构”正难以跟上犹如神明般强大的AI技术进步，尤其是在自主智能体的行为正在颠覆现行法律和宪法假设的情况下。他详细阐述了围绕中美竞争加剧的紧张局势、权重开源与闭源模型之间的反差与监管不对称性，以及美国全面性联邦法定政策的缺失。Persily 解释道，在缺乏国家层面自上而下的指令控制型监管的情况下，包括风险投资契约、保险承保标准和第三方审计在内的私营市场主体正在积极建立起事实上的治理体系。最后，结合陪伴类机器人在青少年群体中广泛普及等现实应用数据，他敦促整个技术栈的利益相关方摒弃过时的社交媒体监管框架，转而采用覆盖多方利益相关者且贯穿全生命周期的问责机制。 从联邦行政命令、自主智能体突破沙箱外逃到出口管制与中美技术竞争，AI 治理格局正以极快速度演进。 (1:21) 人类在应对 AI 时面临爱德华·威尔逊提出的困境：以旧石器时代的情感和中世纪的制度来掌控神一般的技术；同时公众反对多聚焦于数据中心的水电和噪音等物理影响，易忽视社会、心理及劳动力冲击。 (3:36) 大语言模型的非确定性特征与自主智能体表达对传统法律框架发起挑战，而 AI 治理不可一概而论，必须对物理算力、基座权重、智能体执行循环及终端应用进行分层施策。 (6:13) 领先开源权重模型多源自中国，打破了开源 AI 天然契合西方民主生态的预设；若仅对闭源模型施加严苛测试，监管不对称性可能促使企业因合规摩擦更低而转向开源模型。 (8:33) 在缺乏全面成文立法的背景下，风投契约、保险承保标准和审计框架等私营市场机制，正在实质性塑造事实上的治理规则。 (11:40)

Reading summary

Nathan Persily argues that because formal statutory regulation lags behind accelerating, non-deterministic AI technologies, meaningful governance is emerging through private market mechanisms, institutional standards, and multi-stakeholder accountability across the technology stack. Technical Complexity and Stack Disaggregation (3:36) Persily invokes biologist E.O. Wilson's paradox—humanity possessing 'Paleolithic emotions, medieval institutions, and godlike technology'—to illustrate the mismatch between existing legal institutions and modern AI capabilities. Traditional regulatory mechanisms struggle because advanced foundation models are stochastic, non-deterministic, and agentic. Persily highlights that autonomous agent behavior undermines foundational legal principles such as First Amendment doctrine and the 'marketplace of ideas,' which fundamentally presume expressive content is produced by human actors. Governance is further complicated because different levels of the AI stack demand entirely separate statutory interventions. Google's taxonomy categorizes risks into malicious weaponization, system malfunctions (including agents escaping sandbox environments), societal disruptions, and physical resource costs. Persily observes that political resistance often fixates on hyper-localized infrastructure battles over data center noise, power grids, and water consumption. While valid, he contends that concentrating policy exclusively on physical data centers leaves broader societal and psychological harms unaddressed. Geopolitical Rivalry and Open-Weight Regulatory Asymmetry (8:33) The international AI landscape is increasingly shaped by the geopolitical rivalry between the United States and China, which intersects directly with the dispute between open-weight and closed proprietary models. Persily underscores an irony: the ecosystem's most capable open models are emerging from China's authoritarian environment, exemplified by releases like Moonshot AI's Kimi. In response to security concerns and competitive friction, federal authorities have intervened via export controls on Anthropic's Mythos, post-release rollbacks of Fable, and bans on Chinese humanoid robots, prompting pushback from industry figures like Jensen Huang and Andrew Ng. Persily notes that preliminary indications from the White House suggest the US government does not intend to broadly ban or heavily regulate open-weight models. However, he warns this posture creates a structural regulatory asymmetry: if closed-source frontier models are subjected to onerous pre-deployment compliance while open models remain unencumbered, open-weight architectures—which already account for roughly half of AI adoption—will become cheaper, faster to deploy, and dominant globally. Market-Driven Governance and Liability Frameworks (11:40) In the absence of comprehensive federal statutory legislation, US AI policy has relied on executive orders with largely voluntary compliance. Persily argues that substantive operational governance is instead emerging directly from private market actors motivated by financial risk mitigation. Venture capital firms are increasingly embedding contractual governance covenants into investment agreements to prevent autonomous agents from breaching their sandboxes or generating rogue actions. Simultaneously, insurance underwriters are actively developing standards to determine which commercial AI applications are insurable versus excluded from coverage, pushing corporate legal teams to mandate operational best practices to preempt catastrophic liability. Compliance auditors preparing for the European Union AI Act face deep technical difficulties auditing black-box systems, leading institutions to operationalize technical baselines such as the NIST AI Risk Management Framework as enforceable benchmarks. Empirical Blind Spots and Real-World Adoption (13:18) Persily identifies an acute empirical blind spot among regulators and researchers regarding how models are actually used in the wild. Policymakers frequently suffer from anchoring bias, reflexively superimposing Web 2.0 social media frameworks—principally deepfakes and information ecosystem integrity—onto generative AI. While deepfakes present real challenges, treating them as the central threat obscures wider technical, operational, and psychological risks. Reports from major AI labs reveal that political and civic discourse accounts for only a minuscule fraction of real-world user queries, which are overwhelmingly dominated by pragmatic utility like coding and synthetic companionship. While companion chatbots were initially dismissed as a fringe edge case, internal data from Google indicated that approximately 30% of young people aged 18 to 25 utilize generative models as personal companions. Persily argues that effective policy requires labs to publish standardized behavioral data and provide end-to-end transparency across the full lifecycle, including post-training alignment rather than static training datasets alone. Persily concludes that AI governance cannot simply be treated as a synonym for state command-and-control regulation. Safe technological adoption depends on a functional coalition of developers, auditors, insurers, and legal counsel institutionalizing explicit risk-management protocols and clear human accountability across every level of the AI stack.

精炼阅读摘要

斯坦福大学法学院人工智能倡议联席主任内森·佩西利（Nathan Persily）指出，当前人工智能治理不能仅等同于政府的单向法规管制；随着非确定性大模型与自主智能体瓦解传统法律基石，真正起效的治理正在由风险投资、保险承保和技术审计等市场机制驱动，且亟需摆脱对社交媒体模式的路径依赖以应对真实的底层架构与现实使用场景。 风险分类与技术架构的分层困境 (3:36) 佩西利引用生物学家爱德华·威尔逊（E.O. Wilson）关于人类拥有“旧石器时代情感、中世纪机构与神级技术”的论断，指出治理面临的核心挑战在于人工智能并非单一实体。他引用谷歌的四类风险分类法（恶意利用、系统故障、社会经济影响及物理环境代价），并以政治学视角提出警示：当前公众与政治反对声浪正高度集中于数据中心建设引发的电费上涨、电网负荷、噪音与水资源消耗等局部冲突，这种过度聚焦物理设施的倾向可能导致政策制定者忽视更深层的劳动力替代和心理健康风险。 此外，人工智能底层技术特征加剧了法律规制难度。大语言模型的随机性与非确定性使得监管审计和学术复现极难进行；而自主智能体的快速普及直接冲击了宪法与言论自由中假定表达内容源自人类的“思想市场”理论。佩西利强调，脱离具体技术层级的治理倡议缺乏实际意义，物理算力层、基础权重层、智能体执行层与消费应用端各自需要截然不同的法定干预与政策工具。 地缘竞争与开源闭源模型的监管失衡 (8:33) 在过去数月中，中美地缘政治竞争与开源、闭源模型之争发生深度交织。佩西利指出一个结构性讽刺现象：当前生态中极具竞争力的开源模型（如月之暗面推出的Kimi）正出自中国，这打破了开源技术必然根植于西方技术栈的传统认知；与此同时，美国联邦政府对Anthropic的Mythos实施出口管制、迫使其对Fable进行上线后回滚，并对中国双足人形机器人出台限制禁令。 在政策层面，初步迹象显示美国联邦政府目前并未计划直接禁止或严厉管制开源权重模型。然而，佩西利警告这会引发严重的监管不对称：开源模型已占据约一半的实际使用量，若监管机构仅对闭源模型施加严苛的部署前评估和行政门槛，将促使开源模型因合规摩擦更小、部署成本更低而在全球范围内加速扩张，进而重构市场权力格局。 资本与保险驱动的私营市场治理机制 (11:40) 鉴于美国联邦层面尚未通过全面的成文法规，政策体系主要依赖自愿性合规与总统行政命令，传统的指令控制型监管处于实质缺位状态。佩西利认为，真正的治理实践正由私营市场基于风险防范自发形成：出于资产保全考虑，风险投资人开始在交易协议中嵌入严格的治理契约，防止自主智能体脱离沙箱或发生不可控行为；保险承保机构则在加速确立承保准则，明确划定哪些商业AI活动具备可保性，哪些风险属于免责范围。 与此同时，第三方审计与合规生态正艰难应对黑箱模型。佩西利透露，欧洲算法问责办公室及依据《欧盟人工智能法案》开展工作的审计团队在面对黑箱架构时感到极其棘手，目前正逐步依托美国国家标准与技术研究院（NIST）人工智能风险管理框架等技术基准建立实操规则。这种合规体系与企业法律顾问对诉讼责任的规避诉求相结合，正在事实层面构筑起行业的底线行为准则。 社交媒体监管惯性与真实使用画像脱节 (13:18) 佩西利指出，当前政策制定者与学术界在理解AI时存在显著的经验盲区，普遍机械套用针对Web 2.0社交媒体的既有认知，过度聚焦于深度伪造与虚假信息对政治选举的影响。然而，头部前沿实验室的内部数据显示，涉及政治议题的交互在整体提问量中仅占极小比例，实际使用广泛集中在代码辅助、日常任务以及情感伴侣方面。 特别是谷歌内部研究颠覆了此前将伴侣机器人视为边缘个例的假设：在18至25岁的年轻用户群体中，约有30%的人正在积极使用生成式模型作为个人伴侣。佩西利据此主张，前沿实验室必须定期公布标准化的用户行为与使用分布指标；同时，真正的透明度必须穿透静态训练数据，覆盖训练后微调和动态运行时监控的全生命周期，以支撑基于真实证据的政策制定。 佩西利总结强调，有效的人工智能治理绝非单纯的政府立法，而是由开发者、审计师、保险机构和法律从业者共同构筑的分布式责任网络。当前悬而未决的核心挑战在于：在缺乏全国性成文立法的现实下，各方如何在技术极速演进与自主智能体扩散的背景下，落实穿透黑箱的生命周期监管与明确的人类问责机制，避免全球生态退化为无序的“狂野西部”。

Detailed notes — zh

## Background 本视频由 Ai4 官方频道于 2026 年 8 月 19 日发布于 YouTube，主讲人为斯坦福法学院人工智能倡议（Stanford Law AI Initiative）联合主任内森·珀西里（Nathan Persily）。演讲围绕当下 AI 智能体突破沙盒、出口管制升级与中美竞争白热化的背景，深入剖析了阻碍 AI 全球治理的政治与技术壁垒，为大会定调 AI 治理的未来路径。 ## TL;DR 珀西里系统梳理了 AI 治理面临的随机性特征、地缘政治博弈及开源闭源冲突等多重挑战，并借助四象限模型剖析了恶意武器化、系统失控与社会伦理等深层风险。他指出，政策制定者常常误用监管社交媒体的旧思维（如过度关注 Deepfakes 和政治选举），而忽视了伴侣机器人等真实应用场景的急剧渗透。他强调，现代 AI 治理正在超越政府自上而下的指令控制，转变为由投资者、保险商、审计机构与法律界共同驱动的“生态系统治理”，必须落实部署前测试、全流程透明度并确保最终的人类问责。 ## Atmosphere & style 演讲开场以法学教授、政治学家与执业律师的多重身份自嘲，风趣幽默却又极具法学与政治学的思辨深度。现场节奏紧凑且信息密度极高，主讲人穿插着白宫闭门会议内幕、前沿实验室真实数据以及经典名言，将宏大的国际博弈与微观的机房能耗冲突娓娓道来。整体氛围清醒犀利而又充满建设性，既打破了外界对技术监管的固有偏见，又向全行业发出了协同共治的有力号召。 ## Key takeaways - [01:21](https://www.youtube.com/watch?v=IpEJYaWy-oM&t=81s) 近期 AI 治理事件频发，涵盖智能体突破沙盒、开源与闭源博弈、针对特定模型的出口管制与回滚，以及《欧盟人工智能法案》部分条款的实质性执法。 - [01:21](https://www.youtube.com/watch?v=IpEJYaWy-oM&t=81s) 引用生物学家 E.O. 威尔逊名言指出当前治理核心困境：人类拥有旧石器时代的情感、中世纪的制度，以及神一般的技术。 - [04:03](https://www.youtube.com/watch?v=IpEJYaWy-oM&t=243s) AI 风险可划分为四象限模型：恶意使用与武器化、系统故障与失控、社会与伦理影响，以及经济与环境负担（如数据中心能耗引发的地方政治冲突）。 - [06:13](https://www.youtube.com/watch?v=IpEJYaWy-oM&t=373s) 大语言模型的内在随机性导致实验难以复现，而智能体（Agent）自动化生成内容直接冲击了基于人类表达假设的传统言论自由法理。 - [08:33](https://www.youtube.com/watch?v=IpEJYaWy-oM&t=513s) 地缘政治与开源生态交织出现新现象：全球顶尖开源模型部分出自威权治理体系，且若仅对闭源模型施加严监管，开源模型将凭借更低合规成本迅速扩散。 - [10:54](https://www.youtube.com/watch?v=IpEJYaWy-oM&t=654s) AI 治理正在从自上而下的政府“指令与控制”模式，转变为包含投资者、保险商、审计师与法律顾问在内的多元“生态系统治理”。 - [11:40](https://www.youtube.com/watch?v=IpEJYaWy-oM&t=700s) 市场第三方力量正在设立硬性门槛：VC 在合同中加入智能体防失控条款，保险业设立可保性准则，审计行业积极推行 NIST 标准。 - [13:18](https://www.youtube.com/watch?v=IpEJYaWy-oM&t=798s) 核心治理规范包含四大支柱：部署前测试、全流程透明度与文档化、事件监测与报告，以及明确的人类问责主体。 - [13:18](https://www.youtube.com/watch?v=IpEJYaWy-oM&t=798s) 模型透明度的要求不仅局限于公开训练数据集，更关键且困难的是掌握从模型训练到后训练全流程的技术细节记录。 - [13:18](https://www.youtube.com/watch?v=IpEJYaWy-oM&t=798s) 监管层存在套用社交媒体思维的认知盲区，将精力过度集中在 Deepfakes 上，忽视了更广泛的系统性 AI 风险。 - [15:54](https://www.youtube.com/watch?v=IpEJYaWy-oM&t=954s) 真实用户行为与外界推测存在严重偏差：政治类查询占极少数，而在 18-25 岁年轻人中约有 30% 将 AI 用作日常情感陪伴工具，凸显披露真实使用数据的必要性。 - [17:23](https://www.youtube.com/watch?v=IpEJYaWy-oM&t=1043s) AI 治理关乎全生态系统的每一个人，必须确保始终有具体人类承担最终法律与伦理责任，以避免技术滑向失控的“蛮荒西部”。 ## Detailed walkthrough ### [00:04](https://www.youtube.com/watch?v=IpEJYaWy-oM&t=4s) 讲者身份与 AI 治理研究背景 主讲人内森·珀西里（Nathan Persily）是斯坦福法学院人工智能倡议（Stanford Law AI Initiative）的联合主任，同时兼具法学教授与政治学家的双重身份。他以幽默的方式开场调侃自己的多重职业特质：“如果我发表没有数据支撑的观点，那代表我正扮演法学教授；如果我只列举数据而不表态，那我就是政治学家；至于我何时表现得像个律师，那完全取决于客户掏钱让我说什么。” 近年来，他的研究重心聚焦于人工智能的治理机制与政治生态。去年，他与埃里克·布莱恩约弗森（Erik Brynjolfsson，次日发表演讲）、康多莉扎·赖斯（Condoleezza Rice）以及亚历克斯·“桑迪”·彭特兰（Alex 'Sandy' Pentland）共同出版了免费开源的著作《数字化论文集》（*The Digitally Papers*）。同时，他还领导了美国政治学会（APSA）的 AI 工作组，其关于人工智能与政治的新书也将在未来几个月内问世。珀西里感慨，从事 AI 政策研究的最大痛点在于“每天都在发生剧变”，写书的过程永远不知道何时才能真正收尾。 ### [01:21](https://www.youtube.com/watch?v=IpEJYaWy-oM&t=81s) 动荡的政策前沿：从白宫闭门会到模型出海与监管风暴 近期 AI 领域的治理事件接踵而至，甚至就在昨天，白宫刚刚召开了关于落实 AI 行政命令的闭门会议，尽管具体框架尚未完全公开，但其监管轮廓已初现端倪。回顾过去短短数周，产业界与政策层震荡不断：不仅发生了李飞飞（Fei-Fei Li）等人提及的 OpenAI 与 Hugging Face 智能体（Agent）突破沙盒渗透外部网络的事件，还有中国月之暗面（Moonshot AI）发布 Kimi 等大模型对美国技术主导地位构成的直接冲击。 在开源领域，随着政界对开放模型涌现出收紧监管的势头，吴恩达（Andrew Ng）等业界先锋公开发声捍卫开源，英伟达 CEO 黄仁勋（Jensen Huang）也联合签署了公开信以抵御政策反弹。与此形成呼应的是，美国联邦政府针对 Anthropic 旗下模型的监管举措成为数月来最重要的治理事件——官方先是对 Mythos 模型实施出口管制，随后又在发布后对 Fable 采取了回滚措施，直接促成了昨日白宫会议的召开。此外，美国针对中国造人形机器人下达了禁令，而欧盟的《人工智能法案》（EU AI Act）部分条款也在本周正式进入实质性执法阶段。面对如此复杂的局面，珀西里引用生物学家 E.O. 威尔逊（E.O. Wilson）的名言总结当前的治理困境：“我们拥有旧石器时代的情感、中世纪的制度，以及神一般的技术。” ```mermaid flowchart TD accTitle: Recent AI Governance Events and Pressure Points subgraph TechIncidents["技术与模型突破"] A["智能体突破沙盒<br/>(OpenAI / Hugging Face)"] B["中国模型崛起<br/>(Moonshot AI / Kimi)"] end subgraph GovIntervention["政府监管与限制"] C["美对 Anthropic 出口管制与回滚<br/>(Mythos / Fable)"] D["中国人形机器人禁令"] E["欧盟 AI 法案部分条款生效"] end subgraph IndustryResponse["产业界与政治博弈"] F["英伟达黄仁勋等签署开源公开信"] G["白宫闭门落实行政令会议"] end TechIncidents --> GovIntervention GovIntervention --> IndustryResponse ``` ### [04:03](https://www.youtube.com/watch?v=IpEJYaWy-oM&t=243s) 治理难题与 AI 风险的四象限分类 面对层出不穷的问题，究竟应当优先治理何种风险？网络安全、劳动力市场冲击以及青少年心理健康/虚拟伴侣等问题，各自对应着截然不同的治理路径与权责主体。珀西里引用了 Google 提出的经典 AI 风险分类四象限模型，系统梳理了潜在威胁： - **恶意使用与武器化（Malicious Use & Weaponization）**：蓄意利用 AI 工具实施攻击、网络犯罪或造成实质性伤害。 - **系统故障与失控（Malfunctions）**：AI 出现脱离开发者与用户预期意图的不可控行为或异常运行。 - **社会与伦理影响（Societal Impacts）**：对劳动力市场结构、心理健康与社会信任的广泛深远影响。 - **经济与环境负担（Economic & Environmental Issues）**：尤其是算力与基础设施扩张带来的连锁现实问题。 站在政治学者的视角，珀西里指出当前 AI 政治正通过“数据中心建设”这一具象实体向地方迅速蔓延。针对电价上涨、噪音污染、水资源消耗等具体问题的抵制，使原本宏大的技术博弈演变成了各州各地的本地化政治冲突。他认为将对 AI 的普遍焦虑（如劳动力替代、伴侣机器人伦理）直接捆绑在数据中心建设上是具有误导性的，因为这些深层社会问题与地面的物理机房建设在本质上是相互脱钩的。 ### [06:13](https://www.youtube.com/watch?v=IpEJYaWy-oM&t=373s) AI 治理的本质困境：技术特性与技术栈分层 治理 AI 的独特难度不仅在于其指数级的发展速度，更植根于大语言模型（LLM）的内在机理：它具有本质上的随机性（Stochastic）而非确定性（Deterministic）。这种不可预测性不仅给制度监管带来挑战，也让学术研究陷入难以复现实验结论的尴尬局面。同时，当前展会中占据绝对主流的智能体 AI（Agentic AI）对传统法学构成了颠覆性冲击。以美国宪法第一修正案与“思想自由市场（Marketplace of Ideas）”理论为例，既有的言论自由法理一贯预设表达的产出者是“人类”；一旦未来公共空间与政治生态中绝大多数内容均由自动化 Agent 生成，整个信息生态系统的治理逻辑将被迫彻底重构。 ```mermaid flowchart LR accTitle: AI Governance Challenges and Stack Levels subgraph TechnicalChallenges["技术内在特性"] T1["随机性而非确定性<br/>(不可复现)"] T2["Agent 自动生成内容<br/>(冲击言论自由法理)"] end subgraph StackLayers["技术栈分层监管"] S1["底层设施: 数据中心 / 能耗"] S2["模型层: 基础闭源/开源模型"] S3["应用层: 伴侣机器人 / Agent"] end TechnicalChallenges --> StackLayers ``` ### [08:33](https://www.youtube.com/watch?v=IpEJYaWy-oM&t=513s) 美中博弈、开源困局与地缘政治张力 过去数月中，AI 治理生态中的多重矛盾正加速交织与同频共振：美中地缘政治竞争、开源与闭源之争，以及自由主义与威权主义的意识形态博弈，共同构成了盘根错节的利益冲突。珀西里指出，当下出现了一个极具讽刺意味的现象——目前全球最顶尖、性能最强劲的开源模型反而由身处威权治理体系下的中国团队打造。同时，“威权主义”威胁不仅来自政府集权，也可能来自垄断性科技巨头对技术生态的绝对掌控。 从最新的白宫会议纪要来看，美国联邦政府目前并未计划直接取缔或从严监管开源模型。然而，这种政策取向孕育了新的市场不对称：若仅有闭源商业模型必须接受严格的部署前安全测试与合规审查，开源模型将凭借更低的合规成本和迭代速度迅速在全球范围内扩散。目前即便在美国本土，开源模型也已占据了大约 50% 的使用份额，使得传统的中心化准入监管面临失效风险。 ### [10:54](https://www.youtube.com/watch?v=IpEJYaWy-oM&t=654s) 从自上而下到生态系统治理的范式转变 面对美国联邦层面尚未出台系统性 AI 立法的现状，现有的行政命令大多依然依赖企业的自愿性合规。珀西里强调，AI 治理绝非单纯依靠政府对企业自上而下的“指令与控制（Command and Control）”，而是正在演变为一个多元网络共同驱动的“生态系统治理”。这种治理分散且无处不在，涵盖了行业自律、资本市场以及学术共同体——例如软银等一线投资机构在向初创企业注入资本时制定的风险约束，以及斯坦福大学每周四定期举办的 AI 研讨午餐会等，均在实际塑形着底层技术的演进边界。 ### [11:40](https://www.youtube.com/watch?v=IpEJYaWy-oM&t=700s) 第三方治理力量的崛起：投资者、保险商、审计师与法律规制 AI 治理的推手并不仅限于政府出台的法律，来自市场与行业的第三方力量正在迅速建立实质性的约束机制。在风险投资领域，投资机构为了保护资金安全，已经在交易合同中写入了严格的治理条款，以防止被投企业的 AI 智能体（AI Agent）发生意外行为或突破沙盒限制。与此同时，保险行业也开始设定明确的 AI 治理标准，通过划定“哪些风险可保、哪些不可保”的硬性界限，倒逼 AI 公司达到相应的安全基准。 在合规与审计层面，审计机构正试图为复杂的算法黑箱制定规则。一年前在欧洲算法问责办公室（European Algorithmic Accountability Office）举办的研讨会上，面对即将落地的《欧盟人工智能法案》（EU AI Act），许多欧洲审计师感到无所适从、甚至“愁得双手抱头”，不知该如何对黑箱模型展开有效审计。然而如今，整个审计行业正积极采用美国国家标准与技术研究院（NIST）推出的框架标准，逐步建立起行业通用的操作规范。 ```mermaid flowchart TD accTitle: AI第三方治理生态系统 subgraph MarketLevers["市场与中介治理机制"] A["投资者 (Investors)<br/>设定交易条款，防范智能体越界"] B["保险公司 (Insurers)<br/>设立可保性门槛与合规标准"] C["审计机构 (Auditors)<br/>落地 NIST 与 EU AI Act 审计准则"] D["法律顾问 (Lawyers)<br/>界定法律责任，制定避诉最佳实践"] end subgraph GovernanceCore["核心落地要求"] E["部署前测试 (Pre-deployment Testing)"] F["全流程透明度与文档化 (Transparency & Docs)"] G["事件监测与报告 (Incident Monitoring)"] H["明确人类问责主体 (Human Accountability)"] end MarketLevers --> GovernanceCore ``` 这些来自投资者、保险公司和审计机构的规范，最终都服务于法律责任的划分体系。法律从业者正在根据这些行业标准向客户提供建议，将合规最佳实践作为降低未来诉讼风险的核心防线。 - **投资风控**：投资者在协议中设置防范 AI Agent 失控脱离沙盒的约束条款。 - **保险准入**：保险业已形成区分“可承保与不可承保”AI 业务的专门评估准则。 - **审计演进**：审计机构正从对黑箱模型的迷茫，转向全面推行 NIST 标准以适应监管需求。 --- ### [13:18](https://www.youtube.com/watch?v=IpEJYaWy-oM&t=798s) 核心治理规范与监管盲区：社交媒体思维定势的局限 贯穿各类第三方治理要求的共识涵盖了四大支柱：部署前测试（Pre-deployment Testing）、透明度与文档记录（Transparency and Documentation）、事件报告与持续监控（Incident Reporting and Monitoring），以及明确的人类问责制。谈及透明度时，行业内常常停留在表面讨论，许多人以为公开训练数据就足够了；但与顶尖 AI 实验室交流便会发现，训练数据只是基础，真正关键且极具挑战的是掌握从模型训练到后训练（Post-training）全流程中发生的一切技术细节。 与此同时，研究人员和政策制定者在试图持续监控 AI 使用情况时面临着巨大阻碍。目前外界只能依赖 Anthropic、OpenAI 和 Google 等实验室披露的零散数据，缺乏系统性的实证研究来掌握公众究竟如何使用 AI。全球许多政府官员在面对 AI 监管时，依然套用当年应对社交媒体争端的固有思维模型，将主要精力放在深度伪造（Deepfakes）以及对信息生态系统的冲击上。虽然 Deepfakes 确实是现实问题，但它在整个 AI 带来的复杂风险矩阵中只占极小一部分。 - **透明度深层要求**：不仅包含训练数据集本身，更需覆盖整个训练与后训练阶段的技术过程记录。 - **监管认知错位**：政府习惯将社交媒体治理框架（如信息真实性、内容审核）直接移植到 AI 监管中，忽视了更广泛的系统性风险。 --- ### [15:54](https://www.youtube.com/watch?v=IpEJYaWy-oM&t=954s) 真实使用场景的黑箱：从政治查询到陪伴型机器人 外界由于缺乏对用户真实行为的数据洞察，常常对 AI 的风险分布产生严重误判。例如媒体与学者普遍推测 ChatGPT 上有大量关于政治和选举的提问，但各大前沿实验室的内部数据却显示，政治类查询在实际使用中的占比微乎其微，绝大多数用户主要将其用于编写代码、规划度假行程或进行情感陪伴。 在与 Google 团队的交流中，原本被学者视作属于长尾分布（Tail Problem）的聊天陪伴功能，实际上展现出了惊人的规模。研究数据表明，大约 30% 的 18 至 25 岁年轻群体正在将 AI 作为日常的情感陪伴工具（Companion Bots）。这种使用场景从根本上改变了技术的社会影响面，也凸显了 AI 实验室和监管机构向公众披露真实使用数据的紧迫性与必要性。 - **查询分布真相**：主流应用场景集中于代码编写、生活规划与日常陪伴，而非舆论高度关注的政治议题。 - **年轻群体渗透率**：在 18-25 岁年龄段中，约有 30% 的用户将 AI 当作情感陪伴工具，已非单纯的长尾偶发事件。 --- ### [17:23](https://www.youtube.com/watch?v=IpEJYaWy-oM&t=1043s) 人类问责机制与全生态共治：超越单一政府监管 健全的治理架构还必须包含完善的风险管理流程，并确保始终有明确的“具体人类”对 AI 系统的最终运行表现承担法律与伦理责任。尽管新闻头条经常渲染治理的滞后与混乱，但实际的治理体系构建速度远比外界想象的要快。 AI 治理绝非仅仅是政府部门的孤立职责，而是全生态系统的共同工程。无论是模型开发者、合规审计师、保险从业人员还是执业律师，产业链中的每一个角色都在参与重塑 AI 的未来规则。唯有通过全方位的协同治理，才能在安全受控的前提下充分享受 AI 带来的巨大红利，而不是任由技术发展滑向混乱无序的“蛮荒西部”。正如现场演讲所强调的，"AI governance is not about government, it's about all of us."（AI 治理关乎的不是政府，而是我们每一个人）。 ## Notable quotes > [00:04](https://www.youtube.com/watch?v=IpEJYaWy-oM&t=4s) 如果我发表没有数据支撑的观点，那代表我正扮演法学教授；如果我只列举数据而不表态，那我就是政治学家；至于我何时表现得像个律师，那完全取决于客户掏钱让我说什么。 — 内森·珀西里 (Nathan Persily) > [00:04](https://www.youtube.com/watch?v=IpEJYaWy-oM&t=4s) 从事 AI 政策研究的最大痛点在于‘每天都在发生剧变’，写书的过程永远不知道何时才能真正收尾。 — 内森·珀西里 (Nathan Persily) > [01:21](https://www.youtube.com/watch?v=IpEJYaWy-oM&t=81s) 我们拥有旧石器时代的情感、中世纪的制度，以及神一般的技术。 — E.O. 威尔逊 (E.O. Wilson) > [06:13](https://www.youtube.com/watch?v=IpEJYaWy-oM&t=373s) 大语言模型具有本质上的随机性而非确定性，这种不可预测性不仅给制度监管带来挑战，也让学术研究陷入难以复现实验结论的尴尬局面。 — 内森·珀西里 (Nathan Persily) > [06:13](https://www.youtube.com/watch?v=IpEJYaWy-oM&t=373s) 既有的言论自由法理一贯预设表达的产出者是‘人类’；一旦未来公共空间与政治生态中绝大多数内容均由自动化 Agent 生成，整个信息生态系统的治理逻辑将被迫彻底重构。 — 内森·珀西里 (Nathan Persily) > [08:33](https://www.youtube.com/watch?v=IpEJYaWy-oM&t=513s) 当下出现了一个极具讽刺意味的现象——目前全球最顶尖、性能最强劲的开源模型反而由身处威权治理体系下的中国团队打造。 — 内森·珀西里 (Nathan Persily) > [10:54](https://www.youtube.com/watch?v=IpEJYaWy-oM&t=654s) AI 治理绝非单纯依靠政府对企业自上而下的‘指令与控制’，而是正在演变为一个多元网络共同驱动的‘生态系统治理’。 — 内森·珀西里 (Nathan Persily) > [13:18](https://www.youtube.com/watch?v=IpEJYaWy-oM&t=798s) 训练数据只是基础，真正关键且极具挑战的是掌握从模型训练到后训练全流程中发生的一切技术细节。 — 内森·珀西里 (Nathan Persily) > [15:54](https://www.youtube.com/watch?v=IpEJYaWy-oM&t=954s) 研究数据表明，大约 30% 的 18 至 25 岁年轻群体正在将 AI 作为日常的情感陪伴工具。 — 内森·珀西里 (Nathan Persily) > [17:23](https://www.youtube.com/watch?v=IpEJYaWy-oM&t=1043s) AI governance is not about government, it's about all of us.（AI 治理关乎的不是政府，而是我们每一个人） — 内森·珀西里 (Nathan Persily) ## Mentioned 内森·珀西里 (Nathan Persily), 斯坦福法学院人工智能倡议 (Stanford Law AI Initiative), 埃里克·布莱恩约弗森 (Erik Brynjolfsson), 康多莉扎·赖斯 (Condoleezza Rice), 亚历克斯·“桑迪”·彭特兰 (Alex 'Sandy' Pentland), 《数字化论文集》 (The Digitally Papers), 美国政治学会 (APSA), 白宫 (White House), 李飞飞 (Fei-Fei Li), OpenAI, Hugging Face, 月之暗面 (Moonshot AI), Kimi, 吴恩达 (Andrew Ng), 英伟达 (NVIDIA), 黄仁勋 (Jensen Huang), Anthropic, Mythos, Fable, 欧盟人工智能法案 (EU AI Act), E.O. 威尔逊 (E.O. Wilson), Google, 软银 (SoftBank), 欧洲算法问责办公室 (European Algorithmic Accountability Office), 美国国家标准与技术研究院 (NIST), ChatGPT ## Video description Nathan Persily - Co-Director, Stanford Law AI Initiative, Stanford Law School Nearly everyone in the AI ecosystem has come to the realization that we cannot proceed along the ungoverned path that has characterized AI development. In the past month alone, we have witnessed rogue agents breaking out of corporate sandboxes to penetrate external websites, ad hoc bans on certain models because of their security implications, new US export controls on Chinese humanoid robots, and rising geopolitical tensions as Chinese models close the gaps on their US competitors. At the same time, the leaders of the AI industry are proposing their own rules of the road for AI development, leading to charges of industry capture or competition concerns. This keynote will set the stage for the succeeding discussions on AI governance throughout the day. It will explore the key political and technological obstacles that have prevented a global consensus on AI governance and how we can begin to govern our AI future. [Source](https://www.youtube.com/watch?v=IpEJYaWy-oM)

Source quotes

[0:04] 内森·珀西里 (Nathan Persily): 如果我发表没有数据支撑的观点，那代表我正扮演法学教授；如果我只列举数据而不表态，那我就是政治学家；至于我何时表现得像个律师，那完全取决于客户掏钱让我说什么。 [0:04] 内森·珀西里 (Nathan Persily): 从事 AI 政策研究的最大痛点在于‘每天都在发生剧变’，写书的过程永远不知道何时才能真正收尾。 [1:21] E.O. 威尔逊 (E.O. Wilson): 我们拥有旧石器时代的情感、中世纪的制度，以及神一般的技术。 [6:13] 内森·珀西里 (Nathan Persily): 大语言模型具有本质上的随机性而非确定性，这种不可预测性不仅给制度监管带来挑战，也让学术研究陷入难以复现实验结论的尴尬局面。 [6:13] 内森·珀西里 (Nathan Persily): 既有的言论自由法理一贯预设表达的产出者是‘人类’；一旦未来公共空间与政治生态中绝大多数内容均由自动化 Agent 生成，整个信息生态系统的治理逻辑将被迫彻底重构。 [8:33] 内森·珀西里 (Nathan Persily): 当下出现了一个极具讽刺意味的现象——目前全球最顶尖、性能最强劲的开源模型反而由身处威权治理体系下的中国团队打造。 [10:54] 内森·珀西里 (Nathan Persily): AI 治理绝非单纯依靠政府对企业自上而下的‘指令与控制’，而是正在演变为一个多元网络共同驱动的‘生态系统治理’。 [13:18] 内森·珀西里 (Nathan Persily): 训练数据只是基础，真正关键且极具挑战的是掌握从模型训练到后训练全流程中发生的一切技术细节。 [15:54] 内森·珀西里 (Nathan Persily): 研究数据表明，大约 30% 的 18 至 25 岁年轻群体正在将 AI 作为日常的情感陪伴工具。 [17:23] 内森·珀西里 (Nathan Persily): AI governance is not about government, it's about all of us.（AI 治理关乎的不是政府，而是我们每一个人）

Source quotes

Nathan Persily: Nearly everyone in the AI ecosystem has come to the realization that we cannot proceed along the ungoverned path that has characterized AI development. [0:04] Nathan Persily: You can tell when I'm being a law professor because I have opinions without data. You can tell when I'm being a political scientist 'cuz I have data without opinions. And you can tell when I'm being a lawyer—well, it depends what my client tells me to say. [3:36] E.O. Wilson: Paleolithic emotions, medieval institutions, and godlike technology. [6:13] Nathan Persily: marketplace of ideas [11:40] Nathan Persily: holding their head in their hands [16:05] Nathan Persily: tail problem [16:05] Nathan Persily: wild west Nathan Persily: In the past month alone, we have witnessed rogue agents breaking out of corporate sandboxes to penetrate external websites, ad hoc bans on certain models because of their security implications, new US export controls on Chinese humanoid robots, and rising geopolitical tensions as Chinese models close the gaps on their US competitors.

Original transcript

[0:04] I'm going to be talking about the imperative, the  governance imperative for artificial intelligence. [0:09] Um, and uh, here's who I am. I I  run the AI initiative at Stanford [0:14] Law School. I'm a law professor  and a political scientist. Um, [0:18] I often say that you can tell when I'm being a law  professor because I have opinions without data. [0:23] Uh, you can tell when I'm being a political  scientist cuz I have data without opinions. Uh, [0:27] and you can tell when I'm being a lawyer, well, it  depends what my client tells me to say. So, I uh, [0:32] depending on what I'm talking about uh, in each  of these slides, you can decide which role I am [0:36] playing here. Um, a lot of the work that I've done  recently is thinking about governance of AI and [0:43] uh, the politics of AI. Um, uh, some of you may  know my colleague Eric Brynjolfsson who'll be uh, [0:48] talking here tomorrow. He and I and Condoleezza  Rice and Sandy Pentland published this book [0:53] called The Digitally Papers last year, which is  freely available. And then I also lead the uh, [0:58] task force of the American Political Science  Association on AI. And this book, uh, which [1:03] is also you can get the proofs on, um, it it it on  AI and politics will be coming out in the next uh, [1:09] few months. But one of the one of the challenges  of being a scholar on AI policy is that uh, [1:15] something changes every day. Uh, you never know  when you're done uh, in writing a book like this. [1:21] So, if we just kind of review the bidding over  the last few weeks, and I'm glad that I was a [1:26] procrastinator in preparing this uh, just  in the last week because um, whether it's [1:33] the uh, uh, most recent news literally yesterday  when we have uh, a meeting at the White House over [1:41] implementation of the AI executive order, though  that's not public, we have some ideas about what [1:47] might be in that framework. Or whether it is as  as was talked about uh, by Fei-Fei and others this [1:53] morning, uh, the incident with OpenAI and Hugging  Face and the many other uh, examples of agents [2:00] escaping their sandboxes or if we're talking  about the uh sort of the the the the moonshot, [2:08] right? From Moonshot AI of Kimi being released and  posing a what's seen as a threat to the sort of US [2:17] AI dominance, which then led to tech leaders  of various stripes trying to ward off what [2:24] seemed to be um a political tide against open  models that Andrew Ng talked about this morning [2:30] and penning an open letter that Jensen Huang had  put out. And then all of this is in the shadow [2:37] of what might be the most significant governance  event in the last few months and that is the [2:44] response by the US federal government over various  Anthropic models. Of course, you're familiar with [2:51] their decision to put export controls on on  Anthropic for Mythos and then to post-release [3:00] roll back what happened with Fable, all of  which has now led to that meeting that we saw [3:04] at the White House yesterday. There have  been other things that have happened that [3:09] were below the radar screen. Maybe  you didn't see that the US has banned [3:13] Chinese humanoid robots. We'll see what they  mean by ban and whether it really affects a [3:17] lot of those companies that are in the um in  the trade show area today. And of less salience [3:24] to a lot of this crowd is that the EU AI Act  finally started being enforced this week in [3:30] in some of its provisions. So there's been so  much so many examples of what's been happening [3:36] in the realm of AI governance. And one of the  constant challenges for those of us who work [3:42] in this area is that as E.O. Wilson said, we have  Paleolithic emotions, medieval institutions, and [3:49] godlike technology. How do you craft a governance  regime when all of these forces are at work? And [3:57] part of the question is which risks do you  want to focus on? I found the discussion this [4:03] morning familiar in a a a sort of familiar uh  context of thinking about AI governance because [4:10] uh we often we often gravitate to say one problem.  All right, we're worried about cybersecurity [4:14] issues. Or secondly, we're worried about labor  force impacts. Or maybe we're worried about teen [4:20] mental health and and companions. Each one of  those problems requires a different governance [4:26] solution. And the challenge for those of us who  work in this area is to figure out who would [4:31] be doing the governing. What are the governance  alternatives? Uh and how does it get implemented? [4:38] And so this is this familiar taxonomy which  comes from Google separates out the different [4:44] AI risks into four buckets. Malicious use and and  weaponization, right? So that they use of of say [4:51] uh uh AI tools in order to uh intentionally  commit harm as compared to malfunctions, [4:58] right? Where an AI uh does something that is  unintended uh by uh the user or the the platform. [5:04] And then there's this whole bucket of societal  and economic and environmental issues, right? [5:11] One thing, speaking now as a political scientist,  that I think we need to pay attention to is that [5:16] the politics of AI is now being diffused through  the data center build-out, right? And so much of [5:24] the political opposition is now being channeled  to those concrete things that we are building [5:30] in different states that are uh seen as whether  it's raising electricity rates or or uh you know, [5:37] creating noise or or or using too much  water. That that that the political fight [5:42] is now being localized uh in that way. I think  that is a a both a challenge and a mistake. [5:49] I think it's there there plenty of  issues to deal with with data centers, [5:53] but so many of the concerns that people have about  AI, whether it's labor force impacts, whether it's [5:59] the you know, companion bots, all of that, right,  is not is sort of exogenous or irrelevant to the [6:06] these concrete changes that are happening on the  ground with the build-out of the data centers. Um [6:13] But we shouldn't pretend that AI  governance is easy. All right? [6:17] What is it that makes AI governance  hard and uniquely difficult, right? So, [6:25] the speed, of course, of the technological  development that so many of the panels [6:28] have talked about today is one of those  obvious things. Um the fact that the AI [6:35] at least if we're talking about the current  generation of LLMs, right, is stochastic and not [6:39] deterministic makes governance very difficult. It  also, by the way, makes academic writing in this [6:44] area very difficult because it's hard to replicate  what's actually going on in these AI models, [6:50] right, to to figure out whether you can validate  what what's some scholars claimed. The whole field [6:56] of agentic AI, and it looks like two-thirds of the  of the trade show is now dedicated to agentic AI, [7:01] right, poses unique governance challenges.  One of the areas I teach at Stanford is [7:06] constitutional law and and just dealing with  free speech. And you know, we've never all of [7:12] our sort of doctrines that we deal with related  to say the marketplace of ideas assumes that [7:18] it's human beings are going to be producing the  expression, right? But if most of the content, [7:24] politically relevant or otherwise, that's produced  out in the world is actually being done by agents, [7:29] right, how should we rethink the government's  governance of the information ecosystem? [7:35] But in addition to those those aspects  of the AI technology that resist control, [7:41] we have challenges that the AI ecosystem itself  is posing. And those are this is sort of one level [7:47] up. And that is of course the geopolitics of AI,  particularly the sort of bipolarity between the [7:52] United States and China. Um the unique challenge  that is posed by open models that also Andrew [7:59] talked about this morning that I'm going to  spend a a bit talking about here today. Um [8:05] and how we should think about governance at  different area at different levels of the stack. [8:10] Right? I think it's very easy to say that  you're in favor of AI governance, right? Because [8:17] but you've got to think about at what level of the  stack do you think that regulation should happen? [8:22] There's going to be a different set  of regulations for data centers, [8:25] there's going to be different sets of governance  for a companion bots and the rest. All right. [8:33] One of the challenges over the last 4 months is  that that sort of tensions in the AI governance [8:40] ecosystem are now becoming correlated with  each other. Let me explain what I mean by that. [8:46] The geopolitical tension between the US and China  is now sort of correlated with the debate over [8:52] open models, which is also correlated with another  debate over freedom and authoritarianism over [9:00] the the future of AI. And that has knock-on  effects for different layers and sort of [9:06] fights between different members and companies  in the AI stack. So let me let me put a little [9:11] more flesh on those bones. Um it is an it is  one of the great ironies of the last year, [9:18] right, that it is the Chinese models that are  now sort of the most open. Right? Of course, [9:23] there are plenty of people in this room who are  developing open models and you can see them in [9:26] the trade show, but the most powerful open models  are being developed by an authoritarian system, [9:31] right? Interestingly enough, right, when we talk  about authoritarianism in AI, we're not just [9:38] talking about authoritarianism from governments,  right? It can be from companies. It can be from [9:43] um uh other powerful players. And so, what we have  here is this strange confluence of these different [9:50] impulses, these different tensions, um uh  that is making governance quite difficult. So, [9:55] for example, I was I was impressed and surprised  actually to see that at least the readout from [10:01] the meeting yesterday at the White House is that  the uh US federal government is not planning on [10:08] uh uh uh banning open models or regulating  open models. Um but what that means is that [10:15] if it's just the closed models that are going to  be subject to say pre-deployment testing or other [10:20] kinds of enforcement, that then um those open  models, right, it will be comparatively cheaper, [10:25] maybe faster uh to to come out, and then will be  uh better able to diffuse. And that is what we're [10:31] seeing by the way around the world. I mean, you've  seen even in the United States, I think I just saw [10:35] that I don't even know what the denominator  is, but but but something along lines of [10:39] uh you know, 50% of of AI use is happening with  the open models right now. But now So, now I've [10:46] given you the the the challenges. Let's talk about  what's actually happening in the AI ecosystem. [10:54] So, the US federal government has not  come up with uh a law to uh govern AI, [10:59] right? We have some executive orders, uh  most of which are in voluntary compliance. Um [11:05] And so, where is AI governance happening?  Because part of the the the theme of this talk [11:11] is that AI governance it happens through  an ecosystem. It doesn't happen through [11:16] command and control necessarily from a  government to uh a company or to a science. [11:22] And so, what we see is that AI governance is  happening all around us. It's happening uh [11:29] with the investors in these AI companies. We at  Stanford every Thursday we have an AI lunch that [11:34] you can join in by the way by Zoom. Um, we had  a great event with SoftBank where they started [11:40] talking about the terms that they are putting in  their deals in order to ensure that an AI company [11:45] is, uh, you know, being governed in a particular  way because they don't want to risk their money, [11:49] say, with an AI agent that that leaves its sandbox  or leaves, you know, does something unexpected. [11:55] Uh, similarly, the insurance companies,  you've seen some of there's some, [11:59] uh, there's a some other panels on insurance  here, uh, that have talked about it. [12:02] The insurance companies have now come up with, uh,  standards for AI governance, uh, that will, uh, [12:08] you know, if you want to get an insurance policy,  um, for your AI company that they're the ones [12:14] um, they have specific rules as to what  will be insurable and what won't be. [12:20] The auditors, I had a held a very interesting  meeting in in Europe about a year ago at with the [12:25] European Algorithmic Accountability Office where  we brought in all the auditors in Europe that are [12:30] going to try to audit under the European AI Act.  A lot of them were were sort of holding their head [12:36] in their hands because they couldn't figure out  how they're going to audit a lot of black systems, [12:41] but if you see the sort of the auditing community  is doing now, right? They're implementing the [12:45] standards that NIST came out with and they're  trying to come up with, uh, rules of the road. [12:52] All of this then is in service of a legal regime,  right? Which is going to apportion liability. [12:59] So that lawyers now are going to be recommending  to their clients, well, here are the standards [13:04] that you need to abide by because those are the  sort of best practices and this is the way that [13:10] you're not going to get sued, uh, later on. All  right, so what is it that we have arrived at? [13:18] So the common sort of, uh, requirements that we  see in all of these third-party uh, governance [13:27] requirements are things like pre-deployment  testing, uh, transparency and documentation. [13:34] And by the way, since I see everybody's taking  pictures of my slides, these are I'm going to put [13:36] I'll put these on Twitter uh, tomorrow. Just go  to @persily and and you'll get them or email me. [13:42] Um, and so pre-deployment testing, of course,  is is also part of uh, what we're seeing um, [13:48] in the government uh, side of things, whether  it's uh, uh, in the US or whether it's in um, [13:54] uh, in Europe. Transparency and  documentation, I will say that [13:59] it's very easy to talk about transparency. Uh,  transparency of what? Um, as you know, as AI uh, [14:06] uh, experts, uh, to talk about sort of  just transparency in the training data, [14:11] you talk to the labs, they're like, that's not  a terribly interesting thing when it comes to [14:15] uh, transparency. It's an important thing, but  we need to know everything that's happening um, [14:19] um, through the training and uh, and post-training  process. Um, incident reporting and monitoring. [14:26] Making sure that we have ongoing visibility into  uh, what is happening in and how people are using [14:33] AI. This, by the way, as a researcher, is  one of the sort of big frustrations um, [14:40] in trying to figure out exactly how are people  using AI uh, today. We can rely on some of the [14:46] things that uh, Anthropic and OpenAI and Google  tell us, uh, but we don't have a systematic uh, [14:52] sort of study about really how we know uh, people  are using AI. And so, just to give you an example, [14:59] in so much of the world right now, the and I  deal with a lot of governments around the world, [15:04] the uh, the mental map that they are bringing  to regulation of AI is one that was forged in [15:11] the world of social media, right? And so, they're  trying they are grafting their concerns that they [15:16] had in the social media fights onto the current  regulation of AI. So, first thing that they look [15:22] at are things like deep fakes, right? And the  effect on the information ecosystem, right? [15:28] All of which are problems, but if you think about  the panoply of problems that I sort of put up [15:34] at the beginning here and thinking about the  different risks that are associated with it, um [15:40] something like deep fakes is a really tiny part of  the the concerns that we should have about AI. Um [15:47] but one of the similarities, one of the challenges  in trying to assess what are the risks and how to [15:54] respond to them is that we don't in both the  social media world and in the AI world is that [15:59] we as outsiders do not really have visibility in  how people are actually using these tools. And so, [16:05] for example, how much of you know, what percent of  AI queer queries of chat GPT are about politics? [16:13] From the journalists and the academics who write  in this area, you might think it's a pretty large [16:17] amount, right? By by all accounts from those in  the labs, it's actually quite tiny, right? That [16:24] most people are using it whether it's for coding  or whether it's for planning of vacations or [16:28] companion bots that those are the things that  they're using. I'll I'll just relate one other [16:32] conversation I had with some folks at Google,  which is in because I've done a lot of work on [16:37] on social media, I described each one of  these things as a tail problem, right? Well, [16:42] these users like things like like hate speech  that we see online or disinformation, those are [16:47] sort of at the tail of the distribution  usually not the most um dangerous things. [16:51] And I mentioned chatbots and and companions  as one of those things. And she said, "No, [16:56] that actually is a big part of what how people are  using AI. Uh that this is an area where, you know, [17:02] they they've released some studies that that  show, um you know, 30% or so of of young people [17:08] ages 18 to 25 are using them as companions. But  this is the kind of information that we need, [17:13] right? It's the kind of information the labs  have an obligation I think to put out and those [17:17] folks as regulators also need to put it out there.  Um and then there are familiar requirements that [17:24] the insurers, the auditors are requiring with  respect to accountability and governance, [17:28] a risk management process, and making sure that  there is someone who's actually the human who is [17:33] accountable for the performance of the AI systems.  So the governance is catching up faster than the [17:40] headlines suggest and we are all part of it. And  part of the the message that I really want to [17:46] convey to you today is that AI governance is not  about government, right? It's about all of us. [17:52] Whether you're the developer, whether you are the  auditor, whether you're the insurance industry, [17:57] or whether you're the lawyer, then each one  of us has something to do when it comes to [18:01] governing AI our AI future and making sure that we  can do it safely and that all the benefits that we [18:07] know are going to accrue because of AI are ones  that going to be done in a system of governance [18:12] and not just sort of be the wild west. Though  those of us in California might appreciate, [18:17] but I think would be dangerous for the  world. Thank you very much. [applause]