California SB 1047

Summary

SB 1047, the Safe and Secure Innovation for Frontier Artificial Intelligence Models Act, was California state legislation that would have required safety testing and liability measures for developers of the most powerful AI models.

The bill passed the California legislature but was vetoed by Governor Gavin Newsom on September 29, 2024.

SB 1047 was the most significant AI safety legislation attempted in the United States to date. Its passage through the legislature demonstrated growing political willingness to regulate frontier AI, while its veto illustrated the political challenges such regulation faces.

Quick Assessment

Key Links

What the Bill Proposed

Scope: "Covered Models"

The bill would have applied to AI models meeting any of these criteria:

Training Compute:

• Trained using >10^26 FLOP (floating-point operations)
• Approximately GPT-4.5/Claude 3 Opus scale or larger
• Threshold aligned with US Executive Order 14110↗🔗 webaligned with US Executive Order 14110alignmentregulationstate-policyfrontier-modelsSource ↗ on AI safety

Training Cost:

• Cost >$100 million to train
• Adjusted annually for inflation
• At current cloud compute prices, 10^26 FLOP costs approximately $70-100 million↗🔗 web$70-100 millionregulationstate-policyfrontier-modelsSource ↗ (Anthropic estimate)

Fine-tuned Models:

• Fine-tuning cost >$10 million
• Based on a covered model

Why these thresholds?

• Target only frontier models from well-resourced labs
• Exclude open-source models and academic research
• Align with international compute governance efforts (US EO, EU AI ActPolicyEU AI ActComprehensive overview of the EU AI Act's risk-based regulatory framework, particularly its two-tier approach to foundation models that distinguishes between standard and systemic risk AI systems. ...Quality: 55/100)

Provisions Comparison Table

Bill Structure and Enforcement Framework

The following diagram illustrates how SB 1047 would have regulated frontier AI development, from initial determination through deployment and enforcement:

Key Enforcement Mechanisms:

The diagram shows three primary enforcement pathways in SB 1047:

1. Preventive Compliance Path (top): Developers who implement all requirements and pass audits receive affirmative defense from liability
2. Incident Response Path (bottom): Safety incidents trigger mandatory 72-hour reporting and Attorney General investigation
3. Penalty Path (right): Non-compliance or violation results in civil penalties up to 10% of training costs plus potential injunctive relief

The bill created overlapping accountability through third-party auditing (annual), whistleblower protections (continuous), and incident reporting (reactive), ensuring multiple mechanisms to detect non-compliance.

Core Requirements

1. Safety Testing Before Training

Pre-Training Requirements:

Developers must:

• Determine whether model will be a "covered model"
• Implement safety protocols before beginning training
• Establish shutdown procedures

Covered Model Determination:

If expected to meet thresholds:

• Document safety plan
• Prepare for testing requirements
• Establish compliance measures

2. Hazardous Capability Testing

Required Testing:

Before deployment or making available to third parties, test for:

Critical Harm Capabilities:

• Creation of chemical, biological, radiological, or nuclear weapons (CBRN)
• Mass casualty cyber attacks (>$500M damage or mass casualties)
• Autonomous operation and self-exfiltration
• Self-improvement and recursive self-modification

Testing Methods:

• Red-team testing
• Adversarial probing
• Capability evaluations
• Third-party auditing

Threshold: Model enables non-expert to cause mass casualties or >$500M in damage.

3. Safety and Security Protocol

Required Measures:

Developers must implement:

Cybersecurity:

• Protection of model weights from theft
• Secure infrastructure
• Incident response plans

Shutdown Capability:

• Full model shutdown ability
• Separate from safety fine-tuning
• Effective on all deployed instances

Ongoing Monitoring:

• Detection of hazardous use
• Capability creep tracking
• Post-deployment evaluation

Documentation:

• Written safety protocol
• Regular updates
• Public summary (redacted for security)

4. Whistleblower Protections

Employee Rights:

Protected disclosures about:

• Safety violations
• Unreasonable risk to public
• Non-compliance with the act

Prohibitions:

• Cannot retaliate against whistleblowers
• Cannot require non-disclosure preventing safety reports
• Civil penalties for violations

5. Frontier Model Division

New State Agency:

Created within California Government Operations Agency:

• Oversee compliance
• Receive safety protocols
• Investigate violations
• Issue guidance

Powers:

• Subpoena authority
• Civil penalty assessment
• Emergency orders

6. Liability Framework

Affirmative Defense:

Developers protected from liability if:

• Complied with all safety requirements
• Conducted reasonable testing
• Implemented safety protocols
• Acted in good faith

Strict Liability Removed:

Does NOT create automatic liability for harms; must prove negligence or non-compliance.

Attorney General Enforcement:

California AG can sue for:

• Violations of safety requirements
• Civil penalties up to 10% of training costs
• Injunctive relief

7. Compute Cluster Reporting

Reporting Requirement:

Owners of computing clusters with:

• 10^26 integer or floating-point operations per second

• Located in California

Must report to:

• Frontier Model Division
• Information about cluster
• Customers using cluster

Purpose: Track who has capability to train covered models.

Exemptions and Safe Harbors

Explicitly Exempted:

• Open-source models (unless developer makes $50M+/year from derivatives)
• Academic research
• Models below thresholds
• Government use

Safe Harbor:

• Compliance with safety requirements provides affirmative defense
• Good faith efforts protected

Limitations and Criticisms

Technical Limitations

Policy Limitations

Political Limitations

Criticisms from AI Safety Community

Some AI safety researchers opposed SB 1047 despite sharing its goals:

• Premature codification: Locking in specific requirements (10^26 FLOP, specific capability tests) before evaluation science matures could create compliance theater without actual safety
• Wrong target: Focus on model capabilities rather than deployment context may miss where risks actually materialize
• Opportunity cost: Political capital spent on failed state bill could have supported federal efforts or voluntary frameworks with broader buy-in
• Open-source concerns: Despite exemptions, uncertainty about derivative liability could chill beneficial open research

Path Through Legislature

Initial Introduction (February 2024)

Original Sponsors:

• Senator Scott Wiener↗🏛️ governmentSenator Scott Wienerregulationstate-policyfrontier-modelsSource ↗ (D-San Francisco), representing District 11 (San Francisco tech corridor)
• Co-sponsored by AI safety organizations including Center for AI Safety
• Support from AI safety advocates and researchers
• Immediately opposed by major AI companies and some researchers
• Official bill text↗🏛️ governmentSenate Bill 1047regulationstate-policyfrontier-modelsopen-source+1Source ↗ introduced February 7, 2024

Amendment Process

Major Changes:

• Narrowed scope to truly frontier models (>10^26 FLOP or >$100M)
• Added safe harbors and affirmative defenses for compliant developers
• Reduced liability provisions (removed strict liability; kept negligence standard)
• Clarified open-source exemptions (unless developer earns >$50M/year from derivatives)
• Specified hazardous capabilities more precisely (CBRN, >$500M cyber damage)
• Removed Frontier Model Division↗🏛️ governmentRemoved Frontier Model Divisionregulationstate-policyfrontier-modelsSource ↗ and criminal penalties in August 2024 amendments

Purpose of Amendments:

• Address industry concerns about overbreadth and compliance costs
• Balance innovation incentives with safety requirements
• Build bipartisan coalition for passage
• Respond to >50 stakeholder comments during committee process

Legislative Passage

August 29, 2024: Passed California Legislature

• Assembly: 45-11 (80% approval)
• Senate: 32-1 (97% approval)
• Bipartisan support across party lines
• Most significant AI legislation to pass any US state legislature
• Represented months of amendments responding to >50 industry comments
• Final version removed criminal penalties and Frontier Model Division creation

Veto (September 29, 2024)

Governor Newsom's Rationale:

From Newsom's official veto message↗🏛️ governmentveto statementregulationstate-policyfrontier-modelsopen-source+1Source ↗:

"While well-intentioned, SB 1047 does not take into account whether an AI system is deployed in high-risk environments, involves critical decision-making or the use of sensitive data. Instead, the bill applies stringent standards to even the most basic functions — so long as a large system deploys it."

Additional concerns: "Smaller, specialized models may emerge as equally or even more dangerous than the models targeted by SB 1047."

Specific Concerns:

• Focus on model size rather than deployment context
• Could stifle innovation in California's tech sector
• Regulatory approach not nuanced enough (described as not "informed by an empirical trajectory analysis")
• Preferred federal regulation given interstate nature of AI

Accompanying Actions:

Newsom simultaneously:

• Signed 18 other AI bills on narrower topics (deepfakes, discrimination, transparency)
• Called for federal AI legislation to address interstate nature of technology
• Committed to working with legislature on alternative approaches
• Convened expert panel including Fei-Fei Li (Stanford), Tino Cuéllar (Carnegie Endowment), and Jennifer Tour Chayes (UC Berkeley) to develop "empirical, science-based trajectory analysis"

Support and Opposition

Supporters

AI Safety Organizations:

• Center for AI SafetyOrganizationCenter for AI SafetyCAIS is a research organization that has distributed $2M+ in compute grants to 200+ researchers, published 50+ safety papers including benchmarks adopted by Anthropic/OpenAI, and organized the May ...Quality: 42/100
• Future of Life Institute
• AI safety researchers

Arguments:

• Frontier models pose catastrophic risks
• Industry self-regulation insufficient
• California can lead on AI safety
• Requirements are reasonable and achievable

Notable Individual Supporters:

• Yoshua BengioPersonYoshua BengioComprehensive biographical overview of Yoshua Bengio's transition from deep learning pioneer (Turing Award 2018) to AI safety advocate, documenting his 2020 pivot at Mila toward safety research, co...Quality: 39/100 (Turing Award winner, 2018)
• Geoffrey HintonPersonGeoffrey HintonComprehensive biographical profile of Geoffrey Hinton documenting his 2023 shift from AI pioneer to safety advocate, estimating 10% extinction risk in 5-20 years. Covers his media strategy, policy ...Quality: 42/100 (Turing Award winner 2018, "Godfather of AI")
• Stuart RussellPersonStuart RussellStuart Russell is a UC Berkeley professor who founded CHAI in 2016 with $5.6M from Coefficient Giving (then Open Philanthropy) and authored 'Human Compatible' (2019), which proposes cooperative inv...Quality: 30/100 (UC Berkeley professor, author of leading AI textbook)
• Max Tegmark (MIT professor, founder of Future of Life Institute)
• Elon MuskPersonElon Musk (AI Industry)Comprehensive profile of Elon Musk's role in AI, documenting his early safety warnings (2014-2017), OpenAI founding and contentious departure, xAI launch, and extensive track record of predictions....Quality: 38/100 (xAIOrganizationxAIComprehensive profile of xAI covering its founding by Elon Musk in 2023, rapid growth to $230B valuation and $3.8B revenue, development of Grok models, and controversial 'truth-seeking' safety appr...Quality: 48/100 CEO, publicly endorsed the bill)
• 113+ current and former employees↗📖 reference★★★☆☆Wikipedia113+ current and former employeesregulationstate-policyfrontier-modelsSource ↗ of OpenAIOrganizationOpenAIComprehensive organizational profile of OpenAI documenting evolution from 2015 non-profit to commercial AGI developer, with detailed analysis of governance crisis, safety researcher exodus (75% of ..., 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, AnthropicOrganizationAnthropicComprehensive profile of Anthropic, founded in 2021 by seven former OpenAI researchers (Dario and Daniela Amodei, Chris Olah, Tom Brown, Jack Clark, Jared Kaplan, Sam McCandlish) with early funding..., MetaOrganizationMeta AI (FAIR)Comprehensive organizational profile of Meta AI covering $66-72B infrastructure investment (2025), LLaMA model family (1B+ downloads), and transition from FAIR research lab to product-focused GenAI...Quality: 51/100, and xAIOrganizationxAIComprehensive profile of xAI covering its founding by Elon Musk in 2023, rapid growth to $230B valuation and $3.8B revenue, development of Grok models, and controversial 'truth-seeking' safety appr...Quality: 48/100 (September 9, 2024 letter to Governor Newsom)

Opponents

Major AI Companies:

• OpenAIOrganizationOpenAIComprehensive organizational profile of OpenAI documenting evolution from 2015 non-profit to commercial AGI developer, with detailed analysis of governance crisis, safety researcher exodus (75% of ... (initially opposed; later neutral)
• AnthropicOrganizationAnthropicComprehensive profile of Anthropic, founded in 2021 by seven former OpenAI researchers (Dario and Daniela Amodei, Chris Olah, Tom Brown, Jack Clark, Jared Kaplan, Sam McCandlish) with early funding... (opposed initially; called final version "benefits likely outweigh costs" but remained uncertain)
• 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 (opposed)
• MetaOrganizationMeta AI (FAIR)Comprehensive organizational profile of Meta AI covering $66-72B infrastructure investment (2025), LLaMA model family (1B+ downloads), and transition from FAIR research lab to product-focused GenAI...Quality: 51/100 (strongly opposed)
• Combined market value of opposing companies: >$3 trillion

Arguments:

• Stifles innovation in California's $200+ billion AI industry
• Drives development out of California (threatening 300,000+ tech jobs)
• Premature to regulate models that don't yet exist
• Better to focus on use cases than model capabilities (size-based vs. risk-based regulation)
• Federal regulation more appropriate for interstate technology

Venture Capital:

• Y Combinator
• Andreessen Horowitz
• Others concerned about startup ecosystem impact

Some Researchers:

• Yann LeCunPersonYann LeCunComprehensive biographical profile of Yann LeCun documenting his technical contributions (CNNs, JEPA), his ~0% AI extinction risk estimate, and his opposition to AI safety regulation including SB 1...Quality: 41/100 (MetaOrganizationMeta AI (FAIR)Comprehensive organizational profile of Meta AI covering $66-72B infrastructure investment (2025), LLaMA model family (1B+ downloads), and transition from FAIR research lab to product-focused GenAI...Quality: 51/100, Turing Award winner)
• Andrew Ng (Stanford, Google Brain co-founder)
• Fei-Fei Li (Stanford)

Concerns:

• Open-source implications despite exemptions
• Compliance costs for startups
• Regulatory overreach
• Vague standards

Labor and Progressive Groups:

• Some supported
• Some concerned it didn't address labor impacts enough

Why It Was Vetoed

Stated Reasons (Governor Newsom)

Size-Based vs. Risk-Based:

• Bill focuses on model size (compute/cost) not deployment risks
• Small models in high-risk contexts not covered
• Large models in benign contexts over-regulated

Innovation Concerns:

• California is hub of AI development
• Regulation could drive companies elsewhere
• Startups face compliance burdens

Federal Action Preferable:

• AI transcends state borders
• National framework more appropriate
• International coordination needed

Political Analysis

Industry Pressure:

• Major AI companies lobbied heavily against
• Economic arguments about California's AI ecosystem
• Threat of relocation

Presidential Politics:

• Biden administration developing AI policy
• Harris (VP, former CA Senator) in presidential race
• National Democratic messaging on tech

Tactical Considerations:

• Newsom signed 18 other AI bills simultaneously
• Positioned as pro-innovation, pro-safety balance
• Left door open for future iteration

Lack of Coalition:

• Many Democrats skeptical
• Republicans opposed
• Labor not fully engaged
• Insufficient grassroots pressure

Unstated Factors (Analysis)

Economic:

• California tech industry contributes $200+ billion annually to state GDP
• AI-focused companies employ 300,000+ workers in California
• Competing jurisdictions (Texas, Florida, international) actively recruiting AI companies
• Tech industry contributes 15-20% of California's general fund revenue
• Estimated compliance costs for SB 1047: $10-50M per covered model annually (industry estimates)

Policy:

• Precedent-setting implications
• Uncertainty about effectiveness
• Implementation challenges

Political:

• Presidential election dynamics
• Tech industry relationships
• Future political ambitions

Implications for AI Safety Regulation

What SB 1047 Demonstrated

Political Will Exists:

• Bipartisan legislative passage showed AI safety resonates
• Not just fringe concern but mainstream political issue
• Legislators willing to regulate despite industry opposition

Industry Opposition is Formidable:

• Even safety-focused companies (AnthropicOrganizationAnthropicComprehensive profile of Anthropic, founded in 2021 by seven former OpenAI researchers (Dario and Daniela Amodei, Chris Olah, Tom Brown, Jack Clark, Jared Kaplan, Sam McCandlish) with early funding...) opposed
• Economic arguments effective
• Innovation framing powerful

Federal vs. State Tension:

• AI is inherently interstate and international
• State-level regulation faces jurisdictional limits
• But federal action is slow

Details Matter:

• Size-based vs. risk-based framing was central
• Specific thresholds and requirements heavily debated
• Implementation details crucial to political viability

Lessons for Future Efforts

What Worked

Focused Scope:

• Targeting only frontier models built support
• Exemptions for open-source and research
• Concrete thresholds (compute, cost)

Safety Framing:

• Catastrophic risk resonated
• Whistleblower protections popular
• Bipartisan appeal

Expert Endorsement:

• Turing Award winners lending credibility
• Technical community engagement

What Didn't Work

Industry Consensus:

• Even safety-concerned labs opposed
• Economic arguments effective
• Innovation framing won

Implementation Clarity:

• Vague enforcement mechanisms
• Uncertainty about compliance costs
• Questions about Frontier Model Division capacity

Coalition Building:

• Labor not fully engaged
• Grassroots support limited
• Competing priorities on left

Future Regulatory Approaches

State Level

Narrower Bills:

• Focus on specific harms (deepfakes, discrimination)
• Deployment context rather than model capabilities
• Procurement standards

Coordination:

• Multi-state coordination
• Uniform standards
• Regional compacts

California Iteration:

• Newsom committed to continued dialogue
• Future versions possible
• Refined approach incorporating feedback

Federal Level

Legislation:

• Comprehensive AI safety bill
• Build on Executive Order
• Bipartisan framework

Challenges:

• Congressional gridlock
• Lobbying pressure
• Competing priorities

International

Coordination Imperative:

• AI development global
• Race to the bottom risk
• Need for international standards

Precedents:

• EU AI Act as model
• UK approach
• Multilateral frameworks

Impact on AI Safety Movement

Positive Effects

Mainstream Attention:

• SB 1047 brought frontier AI risk into public discourse
• Media coverage extensive
• Political engagement increased

Overton Window:

• Made AI regulation thinkable
• Future efforts less radical by comparison
• Normalized safety concerns

Community Building:

• Coalition formation
• Political skills development
• Lessons learned

Negative Effects

Backlash:

• Some researchers now more skeptical of regulation
• "Regulatory capture" accusations
• Polarization on safety issues

Movement Division:

• Some AI safety researchers opposed bill
• Tensions over strategy
• Open-source community alienation

Political Capital:

• Loss might discourage future efforts
• Industry emboldened
• Harder to argue regulations are inevitable

Strategic Debates

Should SB 1047 Have Been Pursued?

Arguments For:

• Only way to test political viability
• Built coalition and momentum
• Shifted discourse even in defeat

Arguments Against:

• Premature; should have built more support first
• Better to focus on federal action
• Antagonized potential allies

What Should Come Next?

Double Down:

• Refine and reintroduce
• Build broader coalition
• Address veto concerns

Pivot to Federal:

• Focus energy on Congress
• Support Executive Order implementation
• International coordination

Focus on Narrower Wins:

• Procurement standards
• Use-case specific regulation
• Voluntary frameworks

Build Power:

• Grassroots organizing
• Labor coalition
• Public education

Technical and Policy Debates

Size-Based vs. Risk-Based Regulation

Size-Based (SB 1047 Approach):

Pros:

• Objective, measurable thresholds
• Targets most capable models
• Easier to enforce
• Aligns with international compute governance

Cons:

• Doesn't capture deployment context
• Could miss dangerous applications of smaller models
• Algorithmic efficiency makes thresholds obsolete

Risk-Based (Newsom's Preference):

Pros:

• Focuses on actual harm potential
• Context-appropriate
• Adapts to changing technology

Cons:

• Harder to define and measure
• Enforcement challenges
• Potentially broader scope (privacy, fairness, etc.)
• Risk assessment subjective

Synthesis Possible:

• Combination of both approaches
• Size thresholds trigger risk assessments
• Deployment context determines requirements

Liability Questions

SB 1047 Approach:

• Affirmative defense for compliance
• Attorney General enforcement
• Civil penalties

Debate:

• Too much liability deters innovation?
• Too little fails to ensure safety?
• Who should bear costs of AI harms?

Alternative Approaches:

• Strict liability with caps
• Insurance requirements
• Tiered liability based on precautions
• No-fault compensation schemes

Open Source Implications

SB 1047 Exemption:

• Open-source models exempt unless developer profits >$50M from derivatives

Concerns Raised:

• Could still chill open-source development
• Uncertainty about liability
• Derivative work tracking difficult

Counter-Arguments:

• Exemption was broad
• Open-source not inherently safe
• Need some oversight of powerful models

Ongoing Debate:

• How to encourage open research while managing risks
• Different models for different risk levels
• Role of open-source in AI safety ecosystem

Comparison to Other Policies

vs. US Executive Order

The compute thresholds in SB 1047 were deliberately aligned↗🔗 webaligned with US Executive Order 14110alignmentregulationstate-policyfrontier-modelsSource ↗ with Biden's Executive Order 14110.

Similarities:

• Compute thresholds (10^26 FLOP for training)
• Safety testing requirements for CBRN risks
• Focus on frontier models only
• Developer reporting obligations

Differences:

• SB 1047 had enforcement teeth (civil penalties up to 10% of training costs, AG lawsuits)
• EO has broader scope (government use, competition policy, immigration for AI talent)
• SB 1047 state-level mandatory law; EO federal executive action (can be rescinded)
• SB 1047 required shutdown capability (unique provision)
• SB 1047 included third-party auditing requirement (EO relies on voluntary compliance)

Relationship:

• SB 1047 would have complemented EO with state-level enforcement
• State enforcement of federal principles with local adaptation
• Potential model for other states considering AI legislation
• Analysis from legal firms↗🔗 webAnalysis from legal firmsregulationstate-policyfrontier-modelsSource ↗ noted SB 1047 went further than EO on liability

vs. EU AI Act

EU Act:

• Risk categories for deployed systems
• Broader scope (not just frontier models)
• Binding regulation with large fines

SB 1047:

• Narrower focus on frontier models
• More specific technical requirements (shutdown, testing)
• State-level vs. EU-wide

Lessons:

• EU's comprehensiveness politically difficult in US
• SB 1047's focused approach still failed
• Suggests US regulation will be patchwork

vs. Voluntary Commitments

Industry Commitments:

• No enforcement
• Self-defined standards
• Flexible and adaptive

SB 1047:

• Mandatory requirements
• State enforcement
• Specific standards

Debate:

• Is voluntary compliance sufficient?
• Does regulation stifle beneficial innovation?
• Can industry self-regulate emerging risks?

Career and Research Implications

Policy Careers

Lessons Learned:

• Understanding legislative process crucial
• Coalition building essential
• Technical expertise must translate to policy

Opportunities:

• State-level AI policy growing
• Need for policy entrepreneurs
• Legislative staff positions

Research Questions

Regulatory Design:

• How to balance innovation and safety?
• What thresholds are appropriate?
• How to make regulation adaptive?

Political Economy:

• Industry influence on regulation
• Public opinion on AI risk
• Coalition formation strategies

Technical:

• Measuring model capabilities
• Shutdown mechanisms
• Audit methodologies

Movement Building

Strategic Questions:

• When to push for regulation vs. build support?
• How to engage industry productively?
• Building public constituency

Skills Needed:

• Political strategy
• Coalition management
• Communications
• Policy design

Sources

Primary Documents

• California Legislature: SB-1047 Bill Text↗🏛️ governmentSenate Bill 1047regulationstate-policyfrontier-modelsopen-source+1Source ↗ - Official bill text and legislative history
• Governor Newsom's Veto Message (PDF)↗🏛️ governmentveto statementregulationstate-policyfrontier-modelsopen-source+1Source ↗ - Official veto statement, September 29, 2024
• California Assembly Privacy and Consumer Protection Committee Analysis↗🏛️ governmentCalifornia Assembly Privacy and Consumer Protection Committee Analysisregulationstate-policyfrontier-modelsSource ↗ - Detailed bill analysis, June 18, 2024

News Coverage and Analysis

• CalMatters: Newsom vetoes major California artificial intelligence bill↗🔗 webCalMatters: Newsom vetoes major California artificial intelligence billregulationstate-policyfrontier-modelsSource ↗ - Comprehensive coverage of veto decision
• NPR: California Gov. Newsom vetoes AI safety bill that divided Silicon Valley↗🔗 webNPR: California Gov. Newsom vetoes AI safety bill that divided Silicon Valleysafetyregulationstate-policyfrontier-modelsSource ↗ - Context on industry division
• TechCrunch: California's legislature just passed AI bill SB 1047↗🔗 web★★★☆☆TechCrunchTechCrunch: California's legislature just passed AI bill SB 1047regulationstate-policyfrontier-modelsSource ↗ - Coverage of legislative passage
• Carnegie Endowment: All Eyes on Sacramento: SB 1047 and the AI Safety Debate↗🔗 web★★★★☆Carnegie EndowmentCarnegie Endowment: All Eyes on Sacramento: SB 1047 and the AI Safety Debatesafetyregulationstate-policyfrontier-modelsSource ↗ - Policy analysis

Legal and Technical Analysis

• Morgan Lewis: California's SB 1047 Would Impose New Safety Requirements↗🔗 web$70-100 millionregulationstate-policyfrontier-modelsSource ↗ - Legal analysis of requirements
• Gibson Dunn: Regulating the Future: Eight Key Takeaways from California's SB 1047↗🔗 webAnalysis from legal firmsregulationstate-policyfrontier-modelsSource ↗ - Post-veto analysis
• Orrick: California Looks to Regulate Cutting-Edge Frontier AI Models: 5 Things to Know↗🔗 webOrrick: California Looks to Regulate Cutting-Edge Frontier AI Models: 5 Things to Knowgovernanceregulationstate-policyfrontier-modelsSource ↗ - Technical requirements breakdown
• DLA Piper: California's SB-1047: Understanding the Safe and Secure Innovation for Frontier AI Act↗🔗 webDLA Piper: California's SB-1047: Understanding the Safe and Secure Innovation for Frontier AI Actsafetyregulationstate-policyfrontier-modelsSource ↗ - Early analysis
• Fenwick: Technological Challenges for Regulatory Thresholds of AI Compute↗🔗 webaligned with US Executive Order 14110alignmentregulationstate-policyfrontier-modelsSource ↗ - Analysis of compute thresholds

Senator Wiener's Office

• Senator Wiener: Groundbreaking AI Bill Advances to Assembly Floor↗🏛️ governmentRemoved Frontier Model Divisionregulationstate-policyfrontier-modelsSource ↗ - Official statement on amendments
• Senator Wiener: Bipartisan Vote, Senate Passes Landmark AI Safety Bill↗🏛️ governmentSenator Wiener: Bipartisan Vote, Senate Passes Landmark AI Safety Billsafetyregulationstate-policyfrontier-modelsSource ↗ - Official statement on passage
• Lawfare Daily Podcast: State Senator Scott Wiener on SB 1047↗🔗 webLawfare Daily Podcast: State Senator Scott Wiener on SB 1047regulationstate-policyfrontier-modelsSource ↗ - In-depth interview

Industry Perspectives

• Andreessen Horowitz: What You Need to Know About SB 1047↗🔗 webAndreessen Horowitz: What You Need to Know About SB 1047regulationstate-policyfrontier-modelsSource ↗ - Venture capital perspective
• Safe and Secure AI: Letter to YC & a16z↗🔗 webSafe and Secure AI: Letter to YC & a16zsafetyregulationstate-policyfrontier-modelsSource ↗ - Response from supporters
• Brookings: Misrepresentations of California's AI safety bill↗🔗 web★★★★☆Brookings InstitutionBrookings: Misrepresentations of California's AI safety billsafetyregulationstate-policyfrontier-modelsSource ↗ - Defense of bill against criticism

Reference

• Wikipedia: Safe and Secure Innovation for Frontier Artificial Intelligence Models Act↗📖 reference★★★☆☆Wikipedia113+ current and former employeesregulationstate-policyfrontier-modelsSource ↗ - Overview and timeline

AI Transition Model Context

SB 1047 (though vetoed) represented a template for how legislation could affect the Ai Transition Model:

The bill's veto demonstrated the political difficulty of frontier AI regulation; Governor Newsom cited concerns about targeting "the most basic functions" of AI systems.