Responsible Scaling Policies

Overview

Responsible Scaling Policies (RSPsApproachResponsible Scaling PoliciesComprehensive analysis of Responsible Scaling Policies showing 20 companies with published frameworks as of Dec 2025, with SaferAI grading major policies 1.9-2.2/5 for specificity. Evidence suggest...Quality: 62/100) are self-imposed commitments by AI labs to tie AI development to safety progress. The core idea is simple: before scaling to more capable systems, labs commit to demonstrating that their safety measures are adequate for the risks those systems would pose. If evaluations reveal dangerous capabilities without adequate safeguards, development should pause until safety catches up.

AnthropicOrganizationAnthropicComprehensive reference page on Anthropic covering financials ($380B valuation, $14B ARR at Series G growing to $19B by March 2026), safety research (Constitutional AI, mechanistic interpretability...Quality: 74/100 introduced the first RSP↗🔗 web★★★★☆AnthropicResponsible Scaling PolicyThis is Anthropic's foundational policy document establishing how it gates deployment of increasingly capable models; a key reference for understanding industry-led AI governance frameworks and voluntary safety commitments.Anthropic introduces its Responsible Scaling Policy (RSP), a framework of technical and organizational protocols for managing catastrophic risks as AI systems become more capabl...governancepolicyai-safetycapabilities+6Source ↗ in September 2023, establishing "AI Safety Levels" (ASL-1 through ASL-4+) analogous to biosafety levels. OpenAIOrganizationOpenAIComprehensive organizational profile of OpenAI documenting evolution from 2015 non-profit to Public Benefit Corporation, with detailed analysis of governance crisis, 2024-2025 ownership restructuri...Quality: 62/100 followed with its Preparedness Framework↗🔗 web★★★★☆OpenAIPreparedness FrameworkOpenAI's official institutional framework for catastrophic risk evaluation; relevant for understanding how leading AI labs operationalize safety policies and set deployment guardrails for frontier models.OpenAI's Preparedness Framework outlines a structured approach to evaluating and managing catastrophic risks from frontier AI models, including threats related to CBRN weapons, ...governancepolicyevaluationdeployment+6Source ↗ in December 2023, and Google DeepMindOrganizationGoogle DeepMindComprehensive overview of DeepMind's history, achievements (AlphaGo, AlphaFold with 200M+ protein structures), and 2023 merger with Google Brain. Documents racing dynamics with OpenAI and new Front...Quality: 37/100 published its Frontier Safety Framework↗🔗 web★★★★☆Google DeepMindGoogle DeepMind: Introducing the Frontier Safety FrameworkPublished May 2024, this is Google DeepMind's formal responsible scaling policy, comparable to Anthropic's RSP and OpenAI's Preparedness Framework; relevant for comparing industry approaches to frontier model governance and safety commitments.Google DeepMind's Frontier Safety Framework (FSF) establishes a structured approach to identifying and mitigating potential severe harms from frontier AI models, focusing on 'cr...ai-safetygovernanceevaluationdeployment+5Source ↗ in May 2024. By late 2024, twelve major AI companies↗🔗 web★★★★☆METRMETR's analysis of 12 companiesPublished by METR (Model Evaluation and Threat Research), this comparative analysis is useful for those tracking industry self-governance and responsible scaling policy developments across major AI labs.METR analyzes the safety policies of 12 frontier AI companies to identify common elements, commitments, and gaps in how organizations approach responsible deployment of advanced...ai-safetygovernancepolicyevaluation+6Source ↗ had published some form of frontier AI safety policy, and the Seoul Summit↗🏛️ government★★★★☆UK GovernmentSeoul Frontier AI CommitmentsOfficial UK government publication documenting voluntary safety pledges from frontier AI companies at the 2024 Seoul AI Summit; a key milestone in international AI governance efforts following the 2023 Bletchley Park Summit.A collection of voluntary safety commitments made by leading AI companies at the AI Seoul Summit 2024, building on the Bletchley Declaration. Companies pledge to publish safety ...governancepolicyai-safetyevaluation+6Source ↗ secured voluntary commitments from sixteen companies.

RSPs represent a significant governance innovation because they create a mechanism for safety-capability coupling without requiring external regulation. As of December 2025, 20 companies have published frontier AI safety policies, up from 12 at the May 2024 Seoul Summit. Third-party evaluators like METR have conducted pre-deployment assessments of 5+ major models. However, RSPs face fundamental challenges: they are 100% voluntary with no legal enforcement, labs set their own thresholds (leading to SaferAI grades of only 1.9-2.2 out of 5), competitive pressure among 3+ frontier labs creates incentives to interpret policies permissively, and capability doubling times of approximately 7 months may outpace evaluation science.

Quick Assessment

Risk Assessment & Impact

Research Investment

• Recommendation: Increase 3-5x (needs enforcement mechanisms and external verification capacity)
• Differential Progress: Safety-dominant (pure governance; no capability benefit)

Comparison of Major Scaling Policies

The three leading frontier AI labs have published distinct but conceptually similar frameworks. All share the core structure of capability thresholds triggering escalating safeguards, but differ in specificity, governance, and scope.

Policy Framework Comparison

Capability Threshold Definitions

Sources: Anthropic RSP↗🔗 web★★★★☆AnthropicResponsible Scaling PolicyAnthropic's RSP is a foundational industry document for responsible development commitments; frequently cited in AI governance discussions as a model for 'if-then' safety commitments from frontier labs.Anthropic's Responsible Scaling Policy (RSP) is a formal commitment outlining how the company will evaluate AI systems for dangerous capabilities and adjust deployment and devel...governancepolicyai-safetyevaluation+5Source ↗, OpenAI Preparedness Framework v2↗🔗 web★★★★☆OpenAIOpenAI: Preparedness Framework Version 2OpenAI's official internal safety governance framework, updated as v2; relevant for understanding how a leading frontier AI lab operationalizes risk thresholds and pre-deployment evaluation processes.OpenAI's Preparedness Framework v2 outlines the company's structured approach to evaluating and managing catastrophic risks from frontier AI models, including definitions of ris...governanceevaluationdeploymentai-safety+6Source ↗, DeepMind FSF v3↗🔗 webGoogle DeepMind: Frontier Safety Framework Version 3.0This is Google DeepMind's official safety framework document (version 3.0), analogous to Anthropic's RSP and OpenAI's Preparedness Framework; essential reference for understanding industry approaches to frontier AI risk management.Google DeepMind's Frontier Safety Framework (v3.0) defines protocols for identifying Critical Capability Levels (CCLs) at which frontier AI models may pose severe risks, and out...ai-safetygovernancepolicyevaluation+6Source ↗

Safeguard Requirements by Level

flowchart TD
  subgraph Anthropic["Anthropic ASL Standards"]
      A1[ASL-1: No meaningful risk] --> A2[ASL-2: Current standard security]
      A2 --> A3[ASL-3: Enhanced security + deployment controls]
      A3 --> A4[ASL-4: Nation-state level security]
  end

  subgraph OpenAI["OpenAI Preparedness Levels"]
      O1[Below High: Standard deployment] --> O2[High: Safeguards before deployment]
      O2 --> O3[Critical: Safeguards during development]
  end

  subgraph DeepMind["DeepMind CCL Levels"]
      D1[Below CCL: Standard practices] --> D2[CCL reached: Deployment mitigations]
      D2 --> D3[CCL exceeded: Enhanced security + alignment]
  end

  style A3 fill:#fff3cd
  style A4 fill:#ffddcc
  style O3 fill:#ffddcc
  style D3 fill:#ffddcc

How RSPs Work

RSPs create a framework linking capability levels to safety requirements. The core mechanism involves three interconnected processes: capability evaluation, safeguard assessment, and escalation decisions.

flowchart TD
  subgraph Evaluation["1. Capability Evaluation"]
      A[Model Checkpoint] --> B[Internal Evals]
      B --> C[Third-Party Evals]
      C --> D{Threshold Crossed?}
  end

  subgraph Assessment["2. Safeguard Assessment"]
      D -->|Yes| E[Identify Required Safeguards]
      E --> F[Current Safeguards Audit]
      F --> G{Gap Analysis}
  end

  subgraph Decision["3. Escalation Decision"]
      G -->|Adequate| H[Deploy with Safeguards]
      G -->|Insufficient| I[Pause Training/Deployment]
      I --> J[Develop New Safeguards]
      J --> F
      D -->|No| K[Continue Development]
  end

  H --> L[Monitor Post-Deployment]
  K --> M[Next Training Run]
  M --> A

  style I fill:#ffcccc
  style H fill:#ccffcc
  style D fill:#fff3cd
  style G fill:#fff3cd

RSP Ecosystem

The effectiveness of RSPs depends on a network of actors providing oversight, verification, and accountability:

flowchart TD
  subgraph Labs["AI Developers (20 companies)"]
      ANT[Anthropic<br/>ASL System]
      OAI[OpenAI<br/>Preparedness]
      GDM[Google DeepMind<br/>FSF]
      OTHER[xAI, Meta, etc.]
  end

  subgraph Evaluators["Third-Party Evaluators"]
      METR[METR<br/>Capability Evals]
      APOLLO[Apollo Research<br/>Alignment Evals]
  end

  subgraph Governments["Government Bodies"]
      UKAISI[UK AI Safety Institute]
      USAISI[US AI Safety Institute]
      INTL[Seoul/France Summits]
  end

  subgraph Public["Public Accountability"]
      CIVIL[Civil Society<br/>SaferAI, FLI]
      MEDIA[Media Coverage]
  end

  Labs -->|Pre-deployment access| Evaluators
  Labs -->|Report results| Governments
  Evaluators -->|Independent assessment| Governments
  Governments -->|Commitments| Labs
  CIVIL -->|Scorecard ratings| Labs
  MEDIA -->|Public pressure| Labs

  style ANT fill:#e8f4ea
  style OAI fill:#e8f4ea
  style GDM fill:#e8f4ea
  style METR fill:#fff3cd
  style UKAISI fill:#cce5ff
  style USAISI fill:#cce5ff

Key Components

Anthropic's AI Safety Levels (ASL)

Anthropic's ASL system↗🔗 web★★★★☆AnthropicResponsible Scaling PolicyAnthropic's RSP is a foundational industry document for responsible development commitments; frequently cited in AI governance discussions as a model for 'if-then' safety commitments from frontier labs.Anthropic's Responsible Scaling Policy (RSP) is a formal commitment outlining how the company will evaluate AI systems for dangerous capabilities and adjust deployment and devel...governancepolicyai-safetyevaluation+5Source ↗ is modeled after Biosafety Levels (BSL-1 through BSL-4) used for handling dangerous pathogens. Each level specifies both capability thresholds and required safeguards.

Current Status (January 2026): All Claude models currently operate at ASL-2. Anthropic activated ASL-3 safeguard development in May 2025 following evaluations of Claude Opus 4.

RSP v2.2 Changes: The October 2024 update↗🔗 web★★★★☆AnthropicAnthropic: Announcing our updated Responsible Scaling PolicyAnthropic's RSP is a landmark industry governance document; this update is significant for AI safety practitioners tracking how frontier labs operationalize safety commitments and capability-gated deployment policies.Anthropic announces an updated version of its Responsible Scaling Policy (RSP), a framework that ties AI development and deployment decisions to specific capability thresholds c...governancepolicycapabilitiesevaluation+5Source ↗ separated "ASL" to refer to safeguard standards rather than model categories, introducing distinct "Capability Thresholds" and "Required Safeguards." Critics argue↗🔗 webAnthropic's Responsible Scaling Policy Update Makes a Step BackwardsPublished by Safer AI, this piece is a critical external review of Anthropic's 2024 RSP update, relevant to discussions on frontier AI governance, corporate accountability, and the credibility of voluntary safety commitments.This analysis critiques Anthropic's updated Responsible Scaling Policy (RSP) for reducing specificity and moving away from concrete, quantitative safety thresholds toward more q...governancepolicyevaluationai-safety+3Source ↗ this reduced specificity compared to v1.

OpenAI's Preparedness Framework

OpenAI's Preparedness Framework↗🔗 web★★★★☆OpenAIPreparedness FrameworkOpenAI's official institutional framework for catastrophic risk evaluation; relevant for understanding how leading AI labs operationalize safety policies and set deployment guardrails for frontier models.OpenAI's Preparedness Framework outlines a structured approach to evaluating and managing catastrophic risks from frontier AI models, including threats related to CBRN weapons, ...governancepolicyevaluationdeployment+6Source ↗ underwent a major revision in April 2025 (v2.0), simplifying from four risk levels to two actionable thresholds.

Framework v2.0 Key Changes:

• Simplified from Low/Medium/High/Critical to just High and Critical
• Removed "Persuasion" as tracked category (now handled through standard safety)
• Added explicit threshold for recursive self-improvement: achieving generational improvement (e.g., o1 to o3) in 1/5th the 2024 development time
• Safety Advisory Group (SAG) now oversees all threshold determinations

Recent Evaluations: OpenAI's January 2026 o3/o4-mini system card↗🔗 web★★★★☆METRMETR’s GPT-4.5 pre-deployment evaluationsPublished by METR (Model Evaluation and Threat Research), this report is part of their ongoing frontier model evaluation program; relevant for understanding how pre-deployment capability evaluations inform responsible scaling policies and deployment decisions for GPT-4.5.METR conducted pre-deployment autonomous capability evaluations of OpenAI's GPT-4.5, assessing its potential for dangerous self-replication, resource acquisition, and general au...evaluationdangerous-capabilitiesautonomous-replicationdeployment+5Source ↗ reported neither model reached High threshold in any tracked category, though biological and cyber capabilities continue trending upward.

Current Implementations

Lab Policy Publication Timeline

Policy Adoption Timeline

Seoul Summit Commitments (May 2024)

The Seoul AI Safety Summit↗🏛️ government★★★★☆UK GovernmentSeoul Frontier AI CommitmentsOfficial UK government publication documenting voluntary safety pledges from frontier AI companies at the 2024 Seoul AI Summit; a key milestone in international AI governance efforts following the 2023 Bletchley Park Summit.A collection of voluntary safety commitments made by leading AI companies at the AI Seoul Summit 2024, building on the Bletchley Declaration. Companies pledge to publish safety ...governancepolicyai-safetyevaluation+6Source ↗ achieved a historic first: 16 frontier AI companies from the US, Europe, Middle East, and Asia signed binding-intent commitments. Signatories included Amazon, Anthropic, Cohere, G42, Google, IBM, Inflection AI, Meta, Microsoft, Mistral AI, Naver, OpenAI, Samsung, Technology Innovation Institute, xAI, and Zhipu.ai.

Follow-up: An additional 4 companies have joined since May 2024. The France AI Action Summit↗🔗 webKey Outcomes of the AI Seoul SummitPublished by techUK (UK technology industry association), this summary covers the May 2024 AI Seoul Summit outcomes, useful for tracking the evolution of international AI governance frameworks and voluntary company safety commitments in the post-Bletchley era.This techUK resource summarizes the major outcomes of the AI Seoul Summit held in May 2024, covering international agreements, safety commitments, and policy developments among ...governancepolicycoordinationai-safety+3Source ↗ (February 2025) reviewed compliance and expanded commitments.

Third-Party Evaluation Ecosystem

METR↗🔗 web★★★★☆METRMETR: Model Evaluation and Threat ResearchMETR is a leading third-party AI safety evaluation organization whose work on autonomous capability benchmarks and catastrophic risk assessments directly informs AI lab safety policies and government AI governance frameworks.METR is an organization conducting research and evaluations to assess the capabilities and risks of frontier AI systems, focusing on autonomous task completion, AI self-improvem...evaluationred-teamingcapabilitiesai-safety+5Source ↗ (Model Evaluation and Threat Research) has emerged as the leading independent evaluator, having conducted pre-deployment assessments for both Anthropic and OpenAI. Founded by Beth BarnesPersonBeth BarnesAI safety researcher, founder of METR (formerly ARC Evals). Focus on evaluating dangerous AI capabilities. (former OpenAI alignment researcher) in December 2023, METR does not accept compensation for evaluations to maintain independence.

METR's Role: METR's GPT-4.5 pre-deployment evaluation↗🔗 web★★★★☆METRMETR’s GPT-4.5 pre-deployment evaluationsPublished by METR (Model Evaluation and Threat Research), this report is part of their ongoing frontier model evaluation program; relevant for understanding how pre-deployment capability evaluations inform responsible scaling policies and deployment decisions for GPT-4.5.METR conducted pre-deployment autonomous capability evaluations of OpenAI's GPT-4.5, assessing its potential for dangerous self-replication, resource acquisition, and general au...evaluationdangerous-capabilitiesautonomous-replicationdeployment+5Source ↗ piloted a new form of third-party oversight: verifying developers' internal evaluation results rather than conducting fully independent assessments. This approach may scale better while maintaining accountability.

Coverage Gap: As of late 2025, METR's analysis↗🔗 web★★★★☆METRMETR's analysis of 12 companiesPublished by METR (Model Evaluation and Threat Research), this comparative analysis is useful for those tracking industry self-governance and responsible scaling policy developments across major AI labs.METR analyzes the safety policies of 12 frontier AI companies to identify common elements, commitments, and gaps in how organizations approach responsible deployment of advanced...ai-safetygovernancepolicyevaluation+6Source ↗ found that while 12 companies have published frontier safety policies, third-party evaluation coverage remains inconsistent, with most evaluations occurring only for the largest US labs.

Limitations and Challenges

Structural Issues

The Evaluation Problem

RSPs are only as good as the evaluations that trigger them:

Competitive Dynamics

Key Cruxes

Summary of Disagreements

Crux 1: Will Labs Honor Their Commitments?

Crux 2: Are RSP Thresholds Set Appropriately?

Crux 3: Can Evaluations Trigger RSPs Effectively?

Analysis of RSP Effectiveness

Quantitative Evidence

Strengths

Weaknesses

What Would Improve RSPs?

Near-Term Improvements

Longer-Term Vision

Who Should Work on This?

Good fit if you believe:

• Industry self-governance can work with proper incentives
• Creating accountability structures is valuable
• Incremental governance improvements help
• RSPs can evolve into stronger mechanisms

Less relevant if you believe:

• Voluntary commitments are inherently unreliable
• Labs will never meaningfully constrain themselves
• Focus should be on mandatory regulation
• Evaluations can't capture real risks

Sources & Resources

Primary Policy Documents

Analysis & Commentary

Third-Party Evaluation Resources

• METR↗🔗 web★★★★☆METRMETR: Model Evaluation and Threat ResearchMETR is a leading third-party AI safety evaluation organization whose work on autonomous capability benchmarks and catastrophic risk assessments directly informs AI lab safety policies and government AI governance frameworks.METR is an organization conducting research and evaluations to assess the capabilities and risks of frontier AI systems, focusing on autonomous task completion, AI self-improvem...evaluationred-teamingcapabilitiesai-safety+5Source ↗: Primary third-party evaluator for frontier models
• METR Dangerous Capability Evaluations↗🔗 web★★★★☆METR2024 01 11 Dangerous Capability EvaluationsPublished by METR (Model Evaluation & Threat Research), this post is a foundational reference for understanding how dangerous capability evaluations work in practice and is directly relevant to frontier lab safety commitments and government AI policy discussions.METR (formerly ARC Evals) describes their framework for evaluating potentially dangerous capabilities in frontier AI models, including autonomous replication, acquiring resource...evaluationred-teamingcapabilitiestechnical-safety+5Source ↗: Methodology for capability assessment
• METR GPT-4.5 Pre-deployment Evals↗🔗 web★★★★☆METRMETR’s GPT-4.5 pre-deployment evaluationsPublished by METR (Model Evaluation and Threat Research), this report is part of their ongoing frontier model evaluation program; relevant for understanding how pre-deployment capability evaluations inform responsible scaling policies and deployment decisions for GPT-4.5.METR conducted pre-deployment autonomous capability evaluations of OpenAI's GPT-4.5, assessing its potential for dangerous self-replication, resource acquisition, and general au...evaluationdangerous-capabilitiesautonomous-replicationdeployment+5Source ↗: Example of third-party verification process

Key Critiques

Evaluation Methodologies

RSP effectiveness depends on the quality of evaluations that trigger safeguard requirements. Current approaches include:

Capability Evaluation Approaches

Key Metrics and Thresholds

Evaluation Limitations

Current evaluations face fundamental challenges that limit RSP effectiveness:

1. Unknown unknowns: Cannot test for capabilities not yet imagined
2. Sandbagging risk: Models may underperform intentionally during evaluation
3. Elicitation gap: True capabilities may exceed measured capabilities
4. Threshold calibration: Optimal threshold placement is uncertain
5. Combinatorial risks: Safe capabilities may combine dangerously