A Framework for AI Safety: Legal, Regulatory, and Policy Solutions

We Know How to Do This

Here’s what’s often missed in debates about AI regulation: We have templates.

Humanity has regulated dangerous technologies before. We’ve built frameworks for:

  • Nuclear power (catastrophic risk, dual-use, needs containment)
  • Pharmaceuticals (complex effects, requires testing, potential for harm)
  • Aviation (safety-critical, requires certification, continuous monitoring)
  • Financial markets (systemic risk, needs oversight, prone to bubbles)
  • Automobiles (safety standards, licensing, liability rules)

We don’t need to invent AI governance from scratch. We need to adapt proven models to AI’s unique characteristics.

The key insight: Different aspects of AI require different regulatory models.

AI is not a single thing, it’s many different technologies and applications with different risk profiles. The regulation needed for:

  • An AI screening resumes is different from
  • An AI driving cars; which is different from
  • An AI trading stocks; which is different from
  • An AI controlling critical infrastructure; which is different from
  • An AI that might achieve general intelligence

One-size-fits-all regulation will fail. We need a risk-based, multi-model framework that draws from multiple regulatory precedents.

The Framework: A Five-Layer Approach

Layer 1: Foundation Safety Standards (The Nuclear Model)

For frontier AI systems (models with capabilities approaching or exceeding human-level intelligence), we need nuclear-style oversight:

1. Mandatory Safety Review Before Deployment

Just as nuclear reactor designs require NRC review and licensing before operation:

  • Pre-deployment safety assessment for AI systems above capability thresholds
  • Independent review board (not company-controlled) evaluates safety case
  • No deployment without approval

What this looks like:

  • AI Safety Review Board (ASRB) established as independent federal agency
  • Companies submit comprehensive safety documentation before training or deploying high-capability models
  • Safety case must demonstrate:
    • Adequate testing for dangerous capabilities (bioweapon knowledge, cyber offense, persuasion/manipulation, autonomous replication)
    • Alignment verification (model follows intended values)
    • Robustness to adversarial attacks
    • Containment measures if model misbehaves
    • Monitoring and kill-switch capabilities

Capability thresholds that trigger review:

  • Models that can design biological weapons
  • Models that can execute autonomous tasks without human oversight
  • Models that can persuade/manipulate humans at scale
  • Models that can self-improve or replicate
  • Models that exceed GPT-4 level general intelligence

Review Process:

  • Submit safety case 6 months before planned deployment
  • ASRB has 90 days to review (expedited) or 180 days (standard)
  • Public comment period for high-risk systems
  • Approval, conditional approval with safeguards, or denial
  • Ongoing monitoring post-deployment
  • License can be revoked if safety conditions violated

Why this works:

  • Creates accountability – can’t blame “emergent properties” if you skipped safety review
  • Forces companies to actually do safety testing
  • Gives regulators leverage; no approval, no deployment
  • Provides public transparency about risks
  • Establishes legal liability if harms occur after approval

The Nuclear Parallel: The NRC doesn’t just approve reactor designs, it conducts ongoing oversight:

  • Regular inspections
  • Incident reporting requirements
  • Power to shut down unsafe reactors
  • Enforcement actions for violations

AI needs equivalent ongoing supervision:

  • Quarterly safety audits for approved models
  • Incident reporting within 24 hours of safety failure
  • Performance monitoring to detect capability jumps
  • Authority to suspend or revoke licenses

2. Mandatory Safety Reserve Requirements

Nuclear plants must maintain emergency shutdown systems, containment structures, and redundant safety systems. AI companies should face equivalent requirements:

Safety Compute Reserve:

  • Companies must set aside 20% of training compute for safety research
  • Can’t claim “no resources” for safety while spending billions on capabilities
  • Enforced through auditing of compute usage

Safety Team Protections:

  • Minimum staffing levels for safety teams (ratio to capabilities researchers)
  • Safety team has veto power over deployments
  • Can’t fire safety team members for raising concerns
  • Whistleblower protections

Testing Requirements:

  • Minimum hours of red-team testing before deployment
  • Adversarial testing by external experts
  • Automated safety testing infrastructure
  • Results must be published (sanitized for dangerous details)

3. Strict Liability for Catastrophic Failures

Nuclear operators face strict liability, if disaster occurs, they’re responsible regardless of negligence. AI companies should face equivalent accountability:

Catastrophic Harm Defined:

  • Mass casualties from AI-enabled bioweapon or cyberattack
  • Economic damage exceeding $1 billion
  • Widespread manipulation affecting elections or public health
  • Loss of control over AI system causing systemic harm

Liability Framework:

  • Strict liability (no need to prove negligence)
  • Directors personally liable for knowing violations
  • Criminal penalties for grossly negligent safety failures
  • Mandatory insurance for catastrophic risks

Why this matters: Currently, AI companies disclaim all liability in their terms of service. They want upside of powerful AI without downside of responsibility. Strict liability changes incentives, you become very careful when your company and personal freedom are at stake.

Layer 2: High-Risk Application Standards (The Pharmaceutical Model)

For AI systems used in high-stakes domains, adopt pharmaceutical-style phased testing and approval:

High-Stakes Domains:

  • Healthcare (diagnosis, treatment planning)
  • Financial services (credit decisions, fraud detection)
  • Employment (hiring, firing, performance evaluation)
  • Criminal justice (sentencing recommendations, parole decisions)
  • Education (student evaluation, college admissions)
  • Housing (lending, tenant screening)
  • Insurance (underwriting, claims)
  • Critical infrastructure (power grid, water systems)

Three-Phase Approval Process:

Phase I: Lab Testing

  • Develop and test AI in controlled environment
  • Benchmark against human performance
  • Red-team for failure modes
  • Duration: 6-12 months
  • Must demonstrate basic safety and accuracy

Phase II: Limited Field Trial

  • Deploy in controlled real-world settings
  • Small user population (hundreds, not millions)
  • Extensive monitoring and data collection
  • Human oversight for all decisions
  • Duration: 12-24 months
  • Must demonstrate benefits outweigh risks in practice

Phase III: Broader Deployment

  • Larger rollout with monitoring
  • Comparison to standard practice
  • Tracking of adverse outcomes
  • Independent evaluation
  • Duration: 12-24 months
  • Must achieve approval standards for full deployment

Full Approval Requirements:

  • Proven accuracy/reliability exceeds minimum threshold
  • No disparate impact on protected groups (or justified by business necessity)
  • Benefits demonstrably exceed risks
  • Adequate safeguards and human oversight
  • Monitoring and evaluation plan
  • Consumer notification of AI use

Why this works:

  • Prevents deployment of half-baked AI in high-stakes domains
  • Gives time to discover problems before they’re widespread
  • Creates evidence base for effectiveness
  • Allows iterative improvement
  • Protects vulnerable populations

Post-Market Surveillance: Just like FDA monitors drugs after approval:

  • Adverse event reporting requirements
  • Regular performance audits
  • Demographic impact studies
  • Can withdraw approval if harms discovered

Example: AI Hiring Systems

Current reality: Companies deploy AI hiring tools with zero testing, filtering out qualified candidates based on irrelevant criteria, discriminating against protected groups, and facing no consequences.

Under pharmaceutical model:

  • Phase I: Test on historical data, demonstrate accuracy
  • Phase II: Use in a few hiring decisions with human oversight
  • Phase III: Broader use with extensive monitoring of outcomes
  • Approval required before mass deployment
  • Ongoing monitoring for disparate impact
  • Approval withdrawn if discrimination detected

Example: Medical AI

Current reality: Medical AI often approved as “decision support” rather than medical device, avoiding rigorous testing. Then used to make actual decisions.

Under pharmaceutical model:

  • Clear regulatory pathway as software medical device
  • Clinical trials comparing AI to human doctors
  • Proof of safety and efficacy in target population
  • Post-market surveillance for errors and biases
  • Liability when AI makes harmful recommendations

Layer 3: Consumer Protection Standards (The Financial Regulation Model)

For consumer-facing AI (chatbots, recommendation systems, content moderation), adopt financial regulation principles:

1. Know Your Risk (The “Know Your Customer” Analog)

Banks must understand customer risk profiles. AI companies should understand system risks:

Risk Assessment Requirements:

  • Annual AI risk assessment
  • Stress testing for failure modes
  • Documentation of known risks and limitations
  • Red flags that trigger enhanced review

2. Disclosure Requirements (The “Truth in Lending” Analog)

Just as lenders must disclose loan terms clearly:

AI Disclosure Mandates:

  • Clear disclosure when interacting with AI (not human)
  • Explanation of AI’s purpose and limitations
  • Notice of data collection and usage
  • Opt-out options for AI-driven decisions
  • Plain-language “AI Nutrition Label” showing:
    • Training data sources
    • Known biases
    • Error rates
    • Usage restrictions
    • Contact for concerns

3. Fair Dealing Standards (The “Unfair and Deceptive Practices” Analog)

FTC bans unfair and deceptive business practices. Extend explicitly to AI:

Prohibited AI Practices:

  • Manipulative design (exploiting psychological vulnerabilities)
  • Deceptive AI personas (pretending to be human when it’s AI)
  • Dark patterns in AI interactions
  • Addictive AI design for minors
  • Exploitation of emotional states (detected by AI)

4. Algorithmic Fairness Audits (The “Fair Lending” Analog)

Banks face fair lending audits. AI companies should face algorithmic fairness audits:

Audit Requirements:

  • Annual third-party algorithmic audit
  • Testing for bias across protected characteristics
  • Disparate impact analysis
  • Documentation of mitigation measures
  • Public summary of audit findings

5. Right to Human Review (The “Consumer Protection” Principle)

For consequential AI decisions:

  • Right to request human review
  • Right to explanation of AI decision
  • Right to appeal AI determinations
  • Prohibition on “AI-only” decisions in high-stakes contexts

6. Data Rights and Portability

Drawing from financial data portability:

  • Right to access data AI companies hold about you
  • Right to correct inaccuracies
  • Right to delete data (with reasonable exceptions)
  • Right to port your data to competitors
  • Right to know what AI models you’ve trained

Layer 4: Labor Protection Standards (The Automation Impact Model)

For AI that displaces workers, create proactive protection framework:

1. Mandatory Impact Assessments

Before deploying labor-displacing AI:

AI Labor Impact Statement Required:

  • How many jobs will be affected
  • Which job categories displaced
  • Demographic impact analysis
  • Alternative approaches considered
  • Mitigation and transition plans
  • Worker consultation process

Filed with Department of Labor 6 months before major deployment.

2. Worker Notification and Consultation

Before deploying AI affecting 50+ workers:

  • 180-day advance notice to affected workers
  • Consultation with workers or unions
  • Good-faith negotiation over deployment terms
  • Impact study shared with workers

3. Transition Support Requirements

Companies deploying labor-displacing AI must:

  • Fund retraining programs (% of cost savings)
  • Provide severance equivalent to 6-12 months salary
  • Maintain health benefits during transition
  • Career placement assistance
  • Income support for affected workers

4. Gradual Deployment Mandates

No “all at once” displacement:

  • Phased implementation over 2-5 years depending on scale
  • Attrition and redeployment before layoffs
  • Trial periods with worker feedback
  • Adjustment of deployment pace based on labor market impacts

5. AI Displacement Insurance

Companies pay into federal insurance fund:

  • Premiums based on AI deployment scale and job impact
  • Fund provides:
    • Extended unemployment benefits for AI-displaced workers
    • Retraining programs
    • Income support during transition
    • Healthcare continuation

Modeled on:

  • Trade Adjustment Assistance
  • Unemployment Insurance
  • Worker Adjustment and Retraining Notification Act (WARN)

But scaled for AI era.

6. “Robot Tax” Concept (Serious Consideration)

If AI displaces workers but companies pay no payroll tax:

  • Revenue base shrinks
  • Unemployment rises
  • Social safety net stressed
  • Companies capture all gains

Potential solution:

  • Tax on AI-driven productivity gains
  • Revenue funds universal basic income pilots
  • Reduces incentive for rapid displacement
  • Shares gains from automation

Controversial but worth debating.

Layer 5: Research and Development Standards (The Academic Research Model)

For AI research and development, establish safety-first norms:

1. Institutional Review Boards for AI Research

Universities require IRB approval for human subjects research. AI research affects millions:

AI Research Ethics Boards:

  • Required for research on:
    • Manipulation and persuasion
    • Emotion detection and exploitation
    • Addictive design
    • High-risk deployments
    • Dual-use capabilities
  • Pre-approval for risky experiments
  • Ongoing monitoring
  • Publication review

2. Safety Incident Database

Aviation has near-miss reporting. AI needs equivalent:

National AI Safety Incident Registry:

  • Mandatory reporting of:
    • Model failures causing harm
    • Security breaches
    • Unexpected dangerous capabilities
    • Near-miss safety incidents
    • Adversarial attacks
  • Anonymized, searchable database
  • Immunity for good-faith reporting
  • Analysis of patterns and lessons

3. Open Safety Research Funding

Government funding for independent AI safety research:

  • $5 billion annually (10% of AI companies’ R&D spending)
  • Funds academic researchers, not industry
  • Focus on:
    • Alignment and control
    • Interpretability and explainability
    • Robustness and security
    • Bias detection and mitigation
    • Governance and policy

4. Safety Research Mandates

Companies spending over $1B on AI capabilities:

  • Must spend 15% of AI budget on safety research
  • Must publish safety research (with reasonable security redactions)
  • Must share models with safety researchers (under NDA)
  • Cannot block publication of safety concerns

5. Pre-Publication Safety Review

For dual-use AI research:

  • Voluntary review process before publication
  • Expert panel assesses misuse risk
  • Can recommend redactions or delays
  • Not censorship—security-conscious publication

Modeled on:

  • Biology dual-use research oversight
  • Physics nuclear secrets classification
  • Cybersecurity responsible disclosure

Cross-Cutting Requirements

Several requirements apply across all layers:

1. Algorithmic Transparency

Strong Version (for high-risk AI):

  • Public access to training data descriptions
  • Architecture and approach disclosed
  • Safety testing results published
  • Third-party audit rights
  • Explanation of individual decisions

Weak Version (for consumer AI):

  • Disclosure of AI use
  • High-level description of function
  • Known limitations stated
  • Contact information for concerns

2. Incident Reporting

Mandatory reporting of:

  • Safety failures causing harm
  • Security breaches
  • Unexpected capabilities
  • Adversarial attacks
  • Misuse by users

Timelines:

  • 24 hours: Critical incidents (danger to life/safety)
  • 72 hours: Serious incidents (significant harm)
  • 30 days: Material incidents (notable impact)

Reported to:

  • AI Safety Review Board
  • FTC (consumer harm)
  • EEOC (discrimination)
  • Relevant sector regulator

3. Continuous Monitoring

All approved AI systems:

  • Performance monitoring (drift detection)
  • Bias monitoring (demographic impact)
  • Misuse monitoring (adversarial use)
  • Capability monitoring (unexpected abilities)
  • Automated alerts for threshold violations

4. Kill Switches and Rollback

All frontier AI systems must have:

  • Emergency shutdown capability
  • Rollback to previous safe version
  • Containment measures
  • Tested quarterly

5. Liability and Insurance

Clear liability framework:

  • Providers liable for design defects
  • Deployers liable for misuse
  • Joint liability for foreseeable harms
  • Insurance required for high-risk systems
  • No ToS disclaimers for illegal conduct

Institutional Architecture

These regulations require new institutions:

1. AI Safety Review Board (ASRB)

Independent federal agency:

  • Approves frontier AI deployments
  • Conducts safety reviews
  • Issues and revokes licenses
  • Investigates incidents
  • Enforcement authority

Structure:

  • 7 commissioners (political appointments, staggered terms)
  • Technical staff (computer scientists, safety researchers)
  • Legal and policy staff
  • Budget: $500M annually

Powers:

  • Pre-deployment review authority
  • Inspection and audit rights
  • Subpoena power
  • Civil penalty authority
  • Criminal referral power

2. Sectoral AI Regulators

Existing agencies get AI authority:

  • FDA: Medical AI
  • SEC/CFTC: Financial AI
  • EEOC: Employment AI
  • FTC: Consumer AI
  • FCC: Communication AI
  • DOT: Transportation AI

Each gets:

  • AI technical staff
  • Rulemaking authority
  • Enforcement power
  • Coordination with ASRB

3. National AI Safety Institute

Technical arm (like NIST):

  • Develops AI safety standards
  • Conducts research
  • Trains government personnel
  • Provides technical assistance
  • Publishes guidance

Not regulatory, just support function.

4. AI Impact Assessment Office

Labor and economic focus:

  • Studies job displacement
  • Forecasts economic impacts
  • Evaluates mitigation programs
  • Reports to Congress
  • Recommends policy adjustments

5. International Coordination Body

U.S. participation in:

  • Global AI safety standards
  • International incident sharing
  • Coordinated enforcement
  • Research collaboration
  • Norm-setting

Enforcement and Penalties

Effective regulation requires teeth:

Civil Penalties (tiered):

Tier 1 – Prohibited Practices:

  • Up to $35 million or 7% of global revenue
  • Examples: Deploying without required safety review, hiding critical safety failures

Tier 2 – Serious Violations:

  • Up to $15 million or 3% of global revenue
  • Examples: Violating high-risk AI requirements, failing material disclosures

Tier 3 – Standard Violations:

  • Up to $7.5 million or 1.5% of global revenue
  • Examples: Reporting failures, documentation gaps, procedural violations

Criminal Penalties:

For knowing and willful violations:

  • Corporate officers: Up to 10 years prison
  • Companies: Criminal fines up to 3x harm caused
  • Debarment from government contracts

Other Enforcement Tools:

  • Injunctions (halt deployment)
  • Consent decrees (ongoing monitoring)
  • Disgorgement (return ill-gotten gains)
  • Mandated audits
  • License revocation

Penalty Factors:

  • Severity of harm
  • Company size and resources
  • Previous violations
  • Cooperation with investigation
  • Self-reporting
  • Remediation efforts

Key Principle: Penalties must exceed profits from violation.

Otherwise it’s just cost of business.

International Coordination

U.S. can’t go alone, but can lead:

1. AI Safety Treaty

International agreement on:

  • Minimum safety standards for frontier AI
  • Incident reporting and information sharing
  • Joint research on safety
  • Coordinated enforcement
  • Dispute resolution

Modeled on:

  • Nuclear Non-Proliferation Treaty
  • Montreal Protocol (ozone)
  • International Health Regulations

2. Export Controls

Already partially implemented:

  • Restrict AI chip exports to adversaries
  • Limit transfer of frontier AI systems
  • Control training data for sensitive domains
  • Monitor computational infrastructure

Balancing:

  • National security
  • Economic competitiveness
  • International cooperation
  • Avoid technological iron curtain

3. Mutual Recognition Agreements

U.S. and allies:

  • Recognize each other’s AI safety approvals
  • Share audit results
  • Coordinate on standards
  • Enforce each other’s judgments

Reduces compliance burden while maintaining safety.

4. Global South Capacity Building

Provide:

  • Technical assistance for AI regulation
  • Training for regulators
  • Access to safety research
  • Enforcement support

Prevents race to bottom in developing countries.

Addressing Objections

Objection 1: “This will kill innovation!”

Response:

Innovation in what? If the innovation is “deploy dangerous AI faster,” we don’t want it.

Every other high-risk technology faces pre-market review:

  • Drugs (10+ year approval)
  • Medical devices (1-7 years depending on risk)
  • Nuclear reactors (years for new designs)
  • Aircraft (years for certification)
  • Automobiles (safety standards)

These industries still innovate. They just innovate safely.

Fast innovation in capabilities, careful deployment of products. You can research aggressively in lab. You need approval before selling to millions.

Objection 2: “China will win AI race!”

Response:

First, safety regulation can coexist with innovation. China’s AI is subject to their own regulations (increasingly strict).

Second, “but China” can’t be answer to every safety concern. That logic justifies no safety measures ever. If China builds dangerous AI and we do too, everyone loses.

Third, the goal isn’t “win AI race”—it’s “develop beneficial AI.” Reckless deployment doesn’t serve that goal.

Fourth, Western advantage is trust and institutions. Safe, trustworthy AI could be competitive advantage, not disadvantage.

Objection 3: “Companies will jurisdiction shop!”

Response:

Partially true, but manageable:

  • EU AI Act already creating Brussels Effect – companies complying globally
  • U.S. market too large to ignore, must comply for access
  • Coordinated international standards reduce arbitrage
  • Export controls prevent relocating compute to havens

Some companies will try. Most can’t afford to lose U.S. market.

Objection 4: “Regulation will be outdated immediately!”

Response:

Only if written prescriptively. Make it principles-based and outcome-focused:

  • Don’t mandate specific technical approaches
  • Set safety standards that apply to any AI
  • Regular review and updates
  • Flexible standards
  • Adaptive governance

Example: Don’t write: “Models must use technique X to prevent Y” Write: “Models must demonstrate they don’t produce Y under testing regime Z”

Objection 5: “Who will staff these agencies?”

Response:

Same challenge for all technical regulation. Solutions:

  • Pay competitively (if not Meta AI salaries, at least better than typical GS scale)
  • Hire early career PhDs who want public service
  • Secondments from academia
  • Term limits to prevent capture
  • Prestige and mission attract talent

FDA and NRC manage to hire technical talent. So can AI regulators.

Objection 6: “Small companies can’t afford compliance!”

Response:

Tiered requirements based on:

  • Company size/revenue
  • AI system risk level
  • Deployment scale
  • Domain sensitivity

For small companies:

  • Streamlined review for low-risk systems
  • Lower fees
  • Technical assistance
  • Longer compliance timelines
  • Exemptions for research

But if building frontier AI with catastrophic risk potential? Then yes, must comply. Not a small company activity.

The Path to Implementation

This framework won’t happen overnight. Phased approach:

Phase 1: Immediate (Executive Action, 0-6 months)

Using existing statutory authority:

  • Executive Order on AI Safety
    • Direct federal agencies to develop AI regulations for their domains
    • Establish AI Safety Review Board via executive action
    • Require safety reviews for government AI procurement
    • Mandate AI disclosure in federal contexts
  • OMB Guidance
    • Require impact assessments for AI acquisitions
    • Set standards for federal AI use
    • Mandate algorithmic auditing
  • FTC Enforcement
    • Bring cases under existing unfair/deceptive practices authority
    • Target manipulative AI design
    • Crack down on algorithmic discrimination
    • Require disclosures
  • EEOC Action
    • Issue guidance on AI in employment
    • Bring discrimination cases
    • Require employer auditing

Phase 2: Legislative (Congressional Action, 6-24 months)

Major legislation needed:

  • AI Safety Act
    • Establishes ASRB
    • Creates frontier AI review requirement
    • Sets liability standards
    • Provides enforcement authority
  • AI Transparency and Accountability Act
    • Mandates disclosures
    • Requires algorithmic audits
    • Creates incident reporting
    • Sets fairness standards
  • AI Labor Protection Act
    • Impact assessments
    • Worker notification
    • Transition support
    • Displacement insurance

Phase 3: Implementation (Regulatory, 1-3 years)

Agencies promulgate specific rules:

  • ASRB publishes safety review standards
  • FDA finalizes medical AI regulations
  • SEC/CFTC finalize financial AI rules
  • FTC issues consumer AI regulations
  • DOL implements labor protections

Phase 4: International (Diplomatic, 2-5 years)

Global coordination:

  • Negotiate AI safety treaty
  • Mutual recognition agreements
  • Export control coordination
  • Research collaboration
  • Joint enforcement

Phase 5: Refinement (Ongoing)

Learn and adapt:

  • Evaluate what works
  • Update standards as technology evolves
  • Address gaps discovered
  • Respond to new risks
  • Incorporate research findings

The Cost of Action vs. Inaction

Objections focus on costs of regulation. But what about costs of NO regulation?

Costs of Unregulated AI:

Economic:

  • Mass unemployment with no transition support
  • Wealth concentration in handful of AI companies
  • Market crashes from AI-driven volatility
  • Destroyed industries and communities
  • Gutted tax base (AI pays no payroll tax)

Social:

  • Surveillance capitalism on steroids
  • Manipulation at scale
  • Erosion of truth and trust
  • Social cohesion breakdown
  • Authoritarianism enabled by AI

Safety:

  • Catastrophic accidents from rushed AI
  • Loss of control over advanced AI
  • Bioweapons proliferation
  • AI-enabled terrorism
  • Existential risk from misaligned AGI

Democratic:

  • AI-powered disinformation undermining elections
  • Micro-targeted propaganda
  • Manufactured consensus
  • Foreign interference scaled
  • End of informed citizenry

Human:

  • Mental health crisis from addictive AI
  • Meaningful work disappearing
  • Skills devaluation
  • Purpose and dignity erosion
  • Human agency diminishing

The question isn’t “can we afford regulation?”

It’s “can we afford not to regulate?”

What Success Looks Like

If we get this right, in 2035:

  • AI capabilities have continued advancing rapidly
  • But deployment is thoughtful and safe
  • High-risk AI systems are tested before release
  • Workers displaced by AI receive support and transition assistance
  • Benefits of AI are broadly shared
  • Safety research is robust and well-funded
  • International cooperation prevents race to bottom
  • Catastrophic risks are actively managed
  • Public trusts AI institutions
  • Innovation continues, just safely

We have both advanced AI and human thriving.

That’s the goal.

The Choice Before Us

Mrinank Sharma warned: “We appear to be approaching a threshold where our wisdom must grow in equal measure to our capacity to affect the world, lest we face the consequences.”

This framework represents an attempt to build that wisdom into our institutions and laws.

We have the models from regulating other dangerous technologies.

We have the expertise to design effective AI governance.

We have the tools to implement and enforce it.

What we need is political will.

The current path—unregulated AI development in a competitive race where safety is a disadvantage—leads to disaster. The warning signs are clear. The safety experts are sounding alarms. The market disruptions have begun.

We can continue until catastrophe forces our hand, implementing panicked regulation after the damage is done.

Or we can act now, while we still have agency, implementing thoughtful governance that allows AI to benefit humanity rather than harm it.

The Claude Cowork market crash showed us AI’s disruptive power. Mrinank Sharma’s resignation revealed the values gap in AI companies. The safety exodus confirmed it’s a systemic problem. The regulatory void explained why.

This framework offers a path forward.

Not perfect. Not final. But a serious attempt to match our governance to the challenge we face.

The question is: Do we have the courage to implement it?

Or will we dismiss it as “too restrictive,” “innovation-killing,” “impractical,” and continue racing toward an uncertain future?

The next few years will answer that question.

And the answer will determine whether AI becomes the greatest technology humanity ever developed…

…or the last one.

The choice is ours.

The time is now.

“There are risks and costs to action. But they are far less than the long-range risks of comfortable inaction.” — John F. Kennedy

Conclusion to the Series

This series has documented a crisis unfolding in real time:

Article 1 showed us the economic disruption: Claude Cowork’s market impact proving AI’s transformative power.

Article 2 took us inside the crisis: Mrinank Sharma’s resignation revealing the gap between AI companies’ stated values and actual practices.

Article 3 exposed the pattern: safety researchers across multiple companies choosing to leave rather than compromise their principles.

Article 4 diagnosed the problem: our legal and regulatory frameworks are structurally unable to govern AI effectively.

Article 5 offered solutions: a comprehensive framework drawing on proven regulatory models adapted to AI’s unique characteristics.

The arc is clear:

From disruption to crisis to systemic failure to diagnosis to prescription.

From what happened to why it happened to what we can do about it.

The people building AI are telling us the world is in peril.

The question is whether we’re listening.

If you’ve read this far, you’re listening. Share this series. Engage with policymakers. Support AI safety research. Demand better from AI companies.

The future isn’t written yet.

But it’s being written now.

We get to choose what it says.

This content is for information and entertainment purposes only. It reflects personal opinions and does not constitute legal advice.