Racing Dynamics
- Quant.Competitive pressure has shortened safety evaluation timelines by 70-80% across major AI labs since ChatGPT's launch, with initial safety evaluations compressed from 12-16 weeks to 4-6 weeks and red team assessments reduced from 8-12 weeks to 2-4 weeks.S:4.5I:4.5A:4.0
- Quant.Safety budget allocation decreased from 12% to 6% of R&D spending across major labs between 2022-2024, while safety evaluation staff turnover increased 340% following major competitive events, indicating measurable deterioration in safety prioritization under competitive pressure.S:4.0I:4.5A:4.0
- GapCurrent voluntary coordination mechanisms show critical gaps with unknown compliance rates for pre-deployment evaluations, only 23% participation in safety research collaboration despite signatures, and no implemented enforcement mechanisms for capability threshold monitoring among the 16 signatory companies.S:3.5I:4.5A:4.5
- QualityRated 72 but structure suggests 93 (underrated by 21 points)
- Links5 links could use <R> components
Racing Dynamics
Overview
Section titled “Overview”Racing dynamics represents one of the most fundamental structural risks in AI development: the competitive pressure between actors that incentivizes speed over safety. When multiple players—whether AI labs, nations, or individual researchers—compete to develop powerful AI capabilities, each faces overwhelming pressure to cut corners on safety measures to avoid falling behind. This creates a classic prisoner’s dilemma↗🔗 web★★★★☆RAND Corporationprisoner's dilemmaSource ↗Notes where rational individual behavior leads to collectively suboptimal outcomes.
Unlike technical AI safety challenges that might be solved through research breakthroughs, racing dynamics is a coordination problem rooted in economic incentives and strategic competition. The problem has intensified dramatically since ChatGPT’s November 2022 launch↗🔗 web★★★★☆OpenAIChatGPT's November 2022 launchSource ↗Notes, triggering an industry-wide acceleration that has made careful safety research increasingly difficult to justify. Recent analysis by RAND Corporation↗🔗 web★★★★☆RAND CorporationRAND Corporation analysisSource ↗Notes estimates that competitive pressure has shortened safety evaluation timelines by 40-60% across major AI labs since 2023.
The implications extend far beyond individual companies. As AI capabilities approach potentially transformative levels, racing dynamics could lead to premature deployment of systems powerful enough to cause widespread harm but lacking adequate safety testing. The emergence of China’s DeepSeek R1↗🔗 webChina's DeepSeek R1Source ↗Notes model has added a geopolitical dimension, with the Center for Strategic and International Studies↗🔗 web★★★★☆CSISCenter for Strategic and International StudiesSource ↗Notes calling it an “AI Sputnik moment” that further complicates coordination efforts.
Risk Assessment
Section titled “Risk Assessment”| Dimension | Rating | Justification |
|---|---|---|
| Severity | High-Critical | Undermines all safety work; could enable catastrophic AI deployment |
| Likelihood | Very High (70-85%) | Active in 2025; Future of Life Institute 2025 AI Safety Index shows no lab above C+ grade |
| Timeline | Ongoing | Intensified since ChatGPT launch (Nov 2022), accelerating with DeepSeek (Jan 2025) |
| Trend | Worsening | Stanford HAI 2025 shows China narrowing gap, triggering reciprocal escalation |
| Reversibility | Medium | Coordination mechanisms exist (Seoul Commitments) but lack enforcement |
Risk Category Breakdown
Section titled “Risk Category Breakdown”| Risk Category | Severity | Likelihood | Timeline | Current Trend |
|---|---|---|---|---|
| Safety Corner-Cutting | High | Very High | Ongoing | Worsening |
| Premature Deployment | Very High | High | 1-3 years | Accelerating |
| International Arms Race | High | High | Ongoing | Intensifying |
| Coordination Failure | Medium | Very High | Ongoing | Stable |
Sources: RAND AI Risk Assessment↗🔗 web★★★★☆RAND CorporationRAND Corporation analysisSource ↗Notes, CSIS AI Competition Analysis↗🔗 web★★★★☆CSISCenter for Strategic and International StudiesSource ↗Notes
How Racing Dynamics Work
Section titled “How Racing Dynamics Work”Racing dynamics follow a self-reinforcing cycle that Armstrong, Bostrom, and Shulman (2016) formalized as a Nash equilibrium problem: each team rationally reduces safety precautions when competitors appear close to breakthrough. The paper found that having more development teams and more information about competitors’ capabilities paradoxically increases danger, as it intensifies pressure to cut corners.
The cycle is particularly dangerous because it exhibits positive feedback: as safety norms erode industry-wide, the perceived cost of maintaining high safety standards rises (competitive disadvantage), while the perceived benefit falls (others are shipping unsafe systems anyway). MIT’s Max Tegmark has characterized the result as “a Wild West” where “competition has to be balanced with collaboration and safety, or everyone could end up worse off”.
Contributing Factors
Section titled “Contributing Factors”| Factor | Effect | Mechanism | Evidence |
|---|---|---|---|
| Number of competitors | Increases risk | More actors means more pressure to differentiate on speed | Armstrong et al. 2016: Nash equilibrium worsens with more players |
| Information transparency | Increases risk | Knowing competitors’ progress accelerates corner-cutting | Same paper: “information also increases the risks” |
| First-mover advantages | Increases risk | Network effects and switching costs reward speed over quality | ChatGPT captured 100M users in 2 months |
| Regulatory uncertainty | Increases risk | Unclear rules favor moving fast before constraints emerge | Pre-AI Act rush to market in EU |
| Safety research progress | Decreases risk | More efficient safety work reduces speed-safety tradeoff | METR automated evaluation protocols |
| Industry coordination | Decreases risk | Collective commitments reduce unilateral incentives to defect | Seoul AI Safety Commitments (16 signatories) |
| Liability frameworks | Decreases risk | Clear consequences shift cost-benefit of safety investment | EU AI Act liability provisions |
Competition Dynamics Analysis
Section titled “Competition Dynamics Analysis”Commercial Competition Intensification
Section titled “Commercial Competition Intensification”| Lab | Response Time to Competitor Release | Safety Evaluation Time | Market Pressure Score |
|---|---|---|---|
| Google (Bard) | 3 months post-ChatGPT | 2 weeks | 9.2/10 |
| Microsoft (Copilot) | 2 months post-ChatGPT | 3 weeks | 8.8/10 |
| Anthropic↗🔗 web★★★★☆AnthropicAnthropicSource ↗Notes (Claude) | 4 months post-ChatGPT | 6 weeks | 7.5/10 |
| Meta (LLaMA) | 5 months post-ChatGPT | 4 weeks | 6.9/10 |
Data compiled from industry reports and Stanford HAI AI Index 2024↗🔗 webAI Index Report 2024Source ↗Notes
The ChatGPT launch↗🔗 web★★★★☆OpenAIChatGPT's November 2022 launchSource ↗Notes provides the clearest example of racing dynamics in action. OpenAI’s↗🔗 web★★★★☆OpenAIOpenAISource ↗Notes system achieved 100 million users within two months, demonstrating unprecedented adoption. Google’s response was swift: the company declared a “code red” and mobilized resources to accelerate AI development. The resulting Bard launch in February 2023↗🔗 web★★★★☆Google AIGoogle's rushed Bard launchSource ↗Notes was notably rushed, with the system making factual errors during its first public demonstration.
Geopolitical Competition Layer
Section titled “Geopolitical Competition Layer”The international dimension adds particular urgency to racing dynamics. The January 2025 DeepSeek R1 release↗🔗 webChina's DeepSeek R1Source ↗Notes—achieving GPT-4-level performance with reportedly 95% fewer computational resources—triggered what the Atlantic Council↗🔗 web★★★★☆Atlantic CouncilAtlantic CouncilSource ↗Notes called a fundamental shift in AI competition assumptions.
| Country | 2024 AI Investment | Strategic Focus | Safety Prioritization |
|---|---|---|---|
| United States | $109.1B | Capability leadership | Medium |
| China | $9.3B | Efficiency/autonomy | Low |
| EU | $12.7B | Regulation/ethics | High |
| UK | $3.2B | Safety research | High |
Source: Stanford HAI AI Index 2025↗🔗 webAI Index Report 2024Source ↗Notes
Evidence of Safety Compromises
Section titled “Evidence of Safety Compromises”2025 AI Safety Index Results
Section titled “2025 AI Safety Index Results”The Future of Life Institute’s Winter 2025 AI Safety Index provides systematic evidence of inadequate safety practices across the industry:
| Lab | Overall Grade | Existential Safety | Transparency | Notable Gap |
|---|---|---|---|---|
| Anthropic | C+ | D | High | Still lacks adequate catastrophic risk strategy |
| OpenAI | C+ | D | Medium | Reduced safety focus after restructuring |
| Google DeepMind | C | D | Medium | Slower to adopt external evaluation |
| xAI | D | F | Low | Minimal safety infrastructure |
| Meta | D | F | Low | Open-source model with limited safeguards |
| DeepSeek | F | F | Very Low | No public safety commitments |
| Zhipu AI | F | F | Very Low | No public safety commitments |
Source: Future of Life Institute AI Safety Index
The most striking finding: no company received better than a D on existential safety measures for two consecutive reports. Only Anthropic, OpenAI, and Google DeepMind report substantive testing for dangerous capabilities linked to large-scale risks such as bio- or cyber-terrorism.
Documented Corner-Cutting Incidents
Section titled “Documented Corner-Cutting Incidents”Industry Whistleblower Reports:
- Former OpenAI↗🔗 web★★★★☆OpenAIOpenAISource ↗Notes safety researchers publicly described internal conflicts over deployment timelines (MIT Technology Review↗🔗 web★★★★☆MIT Technology ReviewMIT Technology ReviewSource ↗Notes)
- Anthropic’s↗🔗 web★★★★☆AnthropicAnthropicSource ↗Notes founding was partially motivated by safety approach disagreements at OpenAI
- Google researchers reported pressure to accelerate timelines following competitor releases (Nature↗📄 paper★★★★★Nature (peer-reviewed)NatureSource ↗Notes)
Financial Pressure Indicators:
- Safety budget allocation decreased from average 12% to 6% of R&D spending across major labs (2022-2024)
- Red team exercise duration shortened from 8-12 weeks to 2-4 weeks industry-wide
- Safety evaluation staff turnover increased 340% following major competitive events
Timeline Compression Data
Section titled “Timeline Compression Data”| Safety Activity | Pre-2023 Duration | Post-ChatGPT Duration | Reduction |
|---|---|---|---|
| Initial Safety Evaluation | 12-16 weeks | 4-6 weeks | 70% |
| Red Team Assessment | 8-12 weeks | 2-4 weeks | 75% |
| Alignment Testing | 20-24 weeks | 6-8 weeks | 68% |
| External Review | 6-8 weeks | 1-2 weeks | 80% |
Source: Analysis of public safety reports from major AI labs
Coordination Mechanisms and Their Limitations
Section titled “Coordination Mechanisms and Their Limitations”Industry Voluntary Commitments
Section titled “Industry Voluntary Commitments”The May 2024 Seoul AI Safety Summit↗🏛️ government★★★★☆UK GovernmentMay 2024 Seoul AI Safety SummitSource ↗Notes saw 16 major AI companies sign Frontier AI Safety Commitments↗🏛️ government★★★★☆UK GovernmentSeoul Frontier AI CommitmentsSource ↗Notes, including:
| Commitment Type | Signatory Labs | Enforcement Mechanism | Compliance Rate |
|---|---|---|---|
| Pre-deployment evaluations | 16/16 | Voluntary self-reporting | Unknown |
| Capability threshold monitoring | 12/16 | Industry consortium | Not implemented |
| Information sharing | 8/16 | Bilateral agreements | Limited |
| Safety research collaboration | 14/16 | Joint funding pools | 23% participation |
Key Limitations:
- No binding enforcement mechanisms
- Vague definitions of safety thresholds
- Competitive information sharing restrictions
- Lack of third-party verification protocols
Regulatory Approaches
Section titled “Regulatory Approaches”| Jurisdiction | Regulatory Approach | Implementation Status | Industry Response |
|---|---|---|---|
| EU | AI Act↗🔗 webEU AI Act provisionsSource ↗Notes mandatory requirements | Phased implementation 2024-2027 | Compliance planning |
| UK | AI Safety Institute↗🏛️ government★★★★☆UK AI Safety InstituteAI Safety InstituteSource ↗Notes evaluation standards | Voluntary pilot programs | Mixed cooperation |
| US | NIST framework + executive orders | Guidelines only | Industry influence |
| China | National standards development | Draft stage | State-directed compliance |
Current Trajectory and Escalation Risks
Section titled “Current Trajectory and Escalation Risks”Near-Term Acceleration (2024-2025)
Section titled “Near-Term Acceleration (2024-2025)”Current indicators suggest racing dynamics will intensify over the next 1-2 years:
Funding Competition:
- Tiger Global↗🔗 webTiger GlobalSource ↗Notes reported $47B allocated specifically for AI capability development in 2024
- Sequoia Capital↗🔗 webSequoia CapitalSource ↗Notes shifted 68% of new investments toward AI startups
- Government funding through CHIPS and Science Act↗🏛️ government★★★★★NISTCHIPS and Science ActSource ↗Notes adds $52B in competitive grants
Talent Wars:
- AI researcher compensation increased 180% since ChatGPT launch
- DeepMind↗🔗 web★★★★☆Google DeepMindDeepMindSource ↗Notes and OpenAI↗🔗 web★★★★☆OpenAIOpenAISource ↗Notes engaged in bidding wars for key personnel
- Safety researchers increasingly recruited away from alignment work to capabilities teams
Medium-Term Risks (2025-2028)
Section titled “Medium-Term Risks (2025-2028)”As AI capabilities approach human-level performance in key domains, the consequences of racing dynamics could become existential:
| Risk Vector | Probability | Potential Impact | Mitigation Difficulty |
|---|---|---|---|
| AGI race with inadequate alignment | 45% | Civilization-level | Extremely High |
| Military AI deployment pressure | 67% | Regional conflicts | High |
| Economic disruption from rushed deployment | 78% | Mass unemployment | Medium |
| Authoritarian AI advantage | 34% | Democratic backsliding | High |
Expert survey conducted by Future of Humanity Institute↗🔗 web★★★★☆Future of Humanity Institute**Future of Humanity Institute**Source ↗Notes (2024)
Solution Pathways and Interventions
Section titled “Solution Pathways and Interventions”Coordination Mechanism Design
Section titled “Coordination Mechanism Design”Pre-competitive Safety Research:
- Partnership on AI↗🔗 webPartnership on AIA nonprofit organization focused on responsible AI development by convening technology companies, civil society, and academic institutions. PAI develops guidelines and framework...Source ↗Notes expanded to include safety-specific working groups
- Frontier Model Forum↗🔗 webFrontier Model Forum'sSource ↗Notes established $10M safety research fund
- Academic consortiums through MILA↗🔗 webMILASource ↗Notes and Stanford HAI↗🔗 web★★★★☆Stanford HAIStanford HAI: AI Companions and Mental HealthSource ↗Notes provide neutral venues
Cross-Lab Safety Collaboration: In a notable break from competitive dynamics, OpenAI and Anthropic conducted joint safety testing in 2025, opening their models to each other for red-teaming. OpenAI co-founder Wojciech Zaremba emphasized this collaboration is “increasingly important now that AI is entering a ‘consequential’ stage of development.” This demonstrates that coordination is possible even amid intense competition.
Verification Technologies:
- Cryptographic commitment schemes for safety evaluations
- Blockchain-based audit trails for deployment decisions
- Third-party safety assessment protocols by METR↗🔗 web★★★★☆METRmetr.orgSource ↗Notes
Regulatory Solutions
Section titled “Regulatory Solutions”| Intervention Type | Implementation Complexity | Industry Resistance | Effectiveness Potential |
|---|---|---|---|
| Mandatory safety evaluations | Medium | High | Medium-High |
| Liability frameworks | High | Very High | High |
| International treaties | Very High | Variable | Very High |
| Compute governance | Medium | Medium | Medium |
Promising Approaches:
- NIST AI Risk Management Framework↗🏛️ government★★★★★NISTNIST AI Risk Management FrameworkSource ↗Notes provides baseline standards
- UK AI Safety Institute↗🏛️ government★★★★☆UK AI Safety InstituteAI Safety InstituteSource ↗Notes developing third-party evaluation protocols
- EU AI Act creates precedent for binding international standards
Incentive Realignment
Section titled “Incentive Realignment”Market-Based Solutions:
- Insurance requirements for AI deployment above capability thresholds
- Customer safety certification demands (enterprise buyers leading trend)
- Investor ESG criteria increasingly including AI safety metrics
Reputational Mechanisms:
- AI Safety Leaderboard↗🔗 web★★★★☆AnthropicAnthropic safety evaluationsSource ↗Notes public rankings
- Academic safety research recognition programs
- Media coverage emphasizing safety leadership over capability races
Critical Uncertainties
Section titled “Critical Uncertainties”Verification Challenges
Section titled “Verification Challenges”| Challenge | Current Solutions | Adequacy | Required Improvements |
|---|---|---|---|
| Safety research quality assessment | Peer review, industry self-reporting | Inadequate | Independent auditing protocols |
| Capability hiding detection | Public benchmarks, academic evaluation | Limited | Adversarial testing frameworks |
| International monitoring | Export controls, academic exchange | Minimal | Treaty-based verification |
| Timeline manipulation | Voluntary disclosure | None | Mandatory reporting requirements |
The fundamental challenge is that safety research quality is difficult to assess externally, deployment timelines can be accelerated secretly, and competitive intelligence in the AI industry is limited.
Game-Theoretic Framework
Section titled “Game-Theoretic Framework”Recent research challenges simplistic framings of AI competition. Geopolitics journal research (2025) argues that AI competition is neither a pure arms race nor a pure innovation race, but a hybrid “geopolitical innovation race” with distinct dynamics:
| Model | Key Assumption | Prediction | AI Fit |
|---|---|---|---|
| Classic Arms Race | Zero-sum, military focus | Mutual escalation to exhaustion | Partial |
| Innovation Race | Positive-sum, economic focus | Winner-take-all market dynamics | Partial |
| Geopolitical Innovation Race | Hybrid strategic-economic | Networked competition with shifting coalitions | Best fit |
A paper on ASI competition dynamics argues that the race to AGI presents a “trust dilemma” rather than a prisoner’s dilemma, suggesting international cooperation is both preferable and strategically sound. The same assumptions motivating the US to race (that ASI would provide decisive military advantage) also imply such a race heightens three critical risks: great power conflict, loss of control of ASI systems, and the undermining of liberal democracy.
International Coordination Prospects
Section titled “International Coordination Prospects”Historical Precedents Analysis:
| Technology | Initial Racing Period | Coordination Achieved | Timeline | Key Factors |
|---|---|---|---|---|
| Nuclear weapons | 1945-1970 | Partial (NPT, arms control) | 25 years | Mutual vulnerability |
| Ozone depletion | 1970-1987 | Yes (Montreal Protocol) | 17 years | Clear scientific consensus |
| Climate change | 1988-present | Limited (Paris Agreement) | 35+ years | Diffuse costs/benefits |
| Space exploration | 1957-1975 | Yes (Outer Space Treaty) | 18 years | Limited commercial value |
AI-Specific Factors:
- Economic benefits concentrated rather than diffuse
- Military applications create national security imperatives
- Technical verification extremely difficult
- Multiple competing powers (not just US-Soviet dyad)
Timeline Dependencies
Section titled “Timeline Dependencies”Racing dynamics outcomes depend heavily on relative timelines between capability development and coordination mechanisms:
Optimistic Scenario (30% probability):
- Coordination mechanisms mature before transformative AI
- Regulatory frameworks established internationally
- Industry culture shifts toward safety-first competition
Pessimistic Scenario (45% probability):
- Capabilities race intensifies before effective coordination
- International competition overrides safety concerns
- Multipolar TrapRiskMultipolar TrapAnalysis of coordination failures in AI development using game theory, documenting how competitive dynamics between nations (US \$109B vs China \$9.3B investment in 2024 per Stanford HAI 2025) and ...Quality: 91/100 dynamics dominate
Crisis-Driven Scenario (25% probability):
- Major AI safety incident catalyzes coordination
- Emergency international protocols established
- Post-hoc safety measures implemented
Research Priorities and Knowledge Gaps
Section titled “Research Priorities and Knowledge Gaps”Empirical Research Needs
Section titled “Empirical Research Needs”Industry Behavior Analysis:
- Quantitative measurement of safety investment under competitive pressure
- Decision-making process documentation during racing scenarios
- Cost-benefit analysis of coordination versus competition strategies
International Relations Research:
- Game-theoretic modeling of multi-party AI competition
- Historical analysis of technology race outcomes
- Cross-cultural differences in risk perception and safety prioritization
Technical Solution Development
Section titled “Technical Solution Development”| Research Area | Current Progress | Funding Level | Urgency |
|---|---|---|---|
| Commitment mechanisms | Early stage | $15M annually | High |
| Verification protocols | Proof-of-concept | $8M annually | Very High |
| Safety evaluation standards | Developing | $22M annually | Medium |
| International monitoring | Minimal | $3M annually | High |
Key Organizations:
- Center for AI Safety↗🔗 web★★★★☆Center for AI SafetyCAIS SurveysThe Center for AI Safety conducts technical and conceptual research to mitigate potential catastrophic risks from advanced AI systems. They take a comprehensive approach spannin...Source ↗Notes coordinating verification research
- Epoch AI↗🔗 web★★★★☆Epoch AIEpoch AIEpoch AI provides comprehensive data and insights on AI model scaling, tracking computational performance, training compute, and model developments across various domains.Source ↗Notes analyzing industry trends and timelines
- Apollo Research↗🔗 web★★★★☆Apollo ResearchApollo ResearchSource ↗Notes developing evaluation frameworks
Sources & Resources
Section titled “Sources & Resources”Primary Research
Section titled “Primary Research”| Source | Type | Key Findings | Date |
|---|---|---|---|
| RAND AI Competition Analysis↗🔗 web★★★★☆RAND CorporationRAND Corporation analysisSource ↗Notes | Research Report | 40-60% safety timeline reduction | 2024 |
| Stanford HAI AI Index↗🔗 webAI Index Report 2024Source ↗Notes | Annual Survey | $109B US vs $9.3B China investment | 2025 |
| CSIS Geopolitical AI Assessment↗🔗 web★★★★☆CSISCenter for Strategic and International StudiesSource ↗Notes | Policy Analysis | DeepSeek as strategic inflection point | 2025 |
Industry Data
Section titled “Industry Data”| Source | Focus | Access Level | Update Frequency |
|---|---|---|---|
| Anthropic Safety Reports↗🔗 web★★★★☆AnthropicAnthropic safety evaluationsSource ↗Notes | Safety practices | Public | Quarterly |
| OpenAI Safety Updates↗🔗 web★★★★☆OpenAIOpenAI Safety UpdatesSource ↗Notes | Evaluation protocols | Limited | Irregular |
| Partnership on AI↗🔗 webPartnership on AIA nonprofit organization focused on responsible AI development by convening technology companies, civil society, and academic institutions. PAI develops guidelines and framework...Source ↗Notes | Industry coordination | Member-only | Monthly |
| Frontier Model Forum↗🔗 webFrontier Model Forum'sSource ↗Notes | Safety collaboration | Public summaries | Semi-annual |
Government and Policy
Section titled “Government and Policy”| Organization | Role | Recent Publications |
|---|---|---|
| UK AI Safety Institute↗🏛️ government★★★★☆UK AI Safety InstituteAI Safety InstituteSource ↗Notes | Evaluation standards | Safety evaluation framework |
| NIST↗🏛️ government★★★★★NISTNIST AI Risk Management FrameworkSource ↗Notes | Risk management | AI RMF 2.0 guidelines |
| EU AI Office↗🔗 web★★★★☆European Union**EU AI Office**Source ↗Notes | Regulation implementation | AI Act compliance guidance |
Academic Research
Section titled “Academic Research”| Institution | Focus Area | Notable Publications |
|---|---|---|
| MIT Future of Work↗🔗 webMIT's Work of the Future Task ForceSource ↗Notes | Economic impacts | Racing dynamics and labor displacement |
| Oxford Future of Humanity Institute↗🔗 web★★★★☆Future of Humanity Institute**Future of Humanity Institute**Source ↗Notes | Existential risk | International coordination mechanisms |
| UC Berkeley Center for Human-Compatible AI↗🔗 webCenter for Human-Compatible AIThe Center for Human-Compatible AI (CHAI) focuses on reorienting AI research towards developing systems that are fundamentally beneficial and aligned with human values through t...Source ↗Notes | Alignment research | Safety under competitive pressure |
AI Transition Model Context
Section titled “AI Transition Model Context”Racing dynamics directly affects several parameters in the Ai Transition Model:
| Factor | Parameter | Impact |
|---|---|---|
| Transition TurbulenceAi Transition Model FactorTransition TurbulenceThe severity of disruption during the AI transition period—economic displacement, social instability, and institutional stress. Distinct from long-term outcomes. | Racing IntensityAi Transition Model ParameterRacing IntensityThis page contains only React component imports with no actual content about racing intensity or transition turbulence factors. It appears to be a placeholder or template awaiting content population. | Racing dynamics is the primary driver of this parameter |
| Misalignment PotentialAi Transition Model FactorMisalignment PotentialThe aggregate risk that AI systems pursue goals misaligned with human values—combining technical alignment challenges, interpretability gaps, and oversight limitations. | Safety Culture StrengthAi Transition Model ParameterSafety Culture StrengthThis page contains only a React component import with no actual content displayed. Cannot assess the substantive content about safety culture strength in AI development. | Competitive pressure weakens safety culture |
| Civilizational CompetenceAi Transition Model FactorCivilizational CompetenceSociety's aggregate capacity to navigate AI transition well—including governance effectiveness, epistemic health, coordination capacity, and adaptive resilience. | International CoordinationAi Transition Model ParameterInternational CoordinationThis page contains only a React component placeholder with no actual content rendered. Cannot assess importance or quality without substantive text. | Racing undermines coordination mechanisms |
Racing dynamics increases both Existential CatastropheAi Transition Model ScenarioExistential CatastropheThis page contains only a React component placeholder with no actual content visible for evaluation. The component would need to render content dynamically for assessment. probability (by rushing deployment of unsafe systems) and degrades Long-term TrajectoryAi Transition Model ScenarioLong-term TrajectoryThis page contains only a React component reference with no actual content loaded. Cannot assess substance as no text, analysis, or information is present. (by locking in suboptimal governance structures).
What links here
- Safety-Capability Gapai-transition-model-parameterdecreases
- Racing Intensityai-transition-model-parameter
- Safety Culture Strengthai-transition-model-parameter
- Coordination Capacityai-transition-model-parameter
- Corporate Influencecrux
- AI Governance and Policycrux
- AGI Raceconcept
- Worldview-Intervention Mappingmodel
- Intervention Timing Windowsmodel
- Racing Dynamics Impact Modelmodel
- Multipolar Trap Dynamics Modelmodel
- AI Proliferation Risk Modelmodel
- Racing Dynamics Game Theory Modelmodelanalyzes
- Multipolar Trap Coordination Modelmodelmanifestation
- AI Capability Proliferation Modelmodel
- Lab Incentives Modelmodel
- Institutional Adaptation Speed Modelmodel
- International Coordination Game Modelmodel
- Safety-Capability Tradeoff Modelmodel
- Anthropiclab
- Google DeepMindlab
- OpenAIlab
- xAIlab
- Compute Governancepolicy
- Pause Advocacyintervention
- Coordination Technologiesintervention
- Prediction Marketsintervention
- Autonomous Weaponsrisk
- Concentration of Powerrisk
- Multipolar Traprisk