Pause Advocacy

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LLM Summary:Comprehensive analysis of pause advocacy as an AI safety intervention, estimating 15-40% probability of meaningful policy implementation by 2030 with potential to provide 2-5 years of additional safety research time. Evaluates tractability (25-35%), political feasibility (15-25%), and risks across multiple dimensions with quantified assessments, though implementation faces formidable challenges from economic incentives and geopolitical competition.

Critical Insights (5):

Counterint.The 'compute overhang' risk means AI pauses could paradoxically increase danger by allowing computing power to accumulate while algorithmic development halts, potentially enabling sudden dangerous capability jumps when development resumes.S:4.5I:4.0A:4.0
GapEven successful pause implementation faces a critical 2-5 year window assumption that may be insufficient, as fundamental alignment problems like mechanistic interpretability remain far from scalable solutions for frontier models with hundreds of billions of parameters.S:3.5I:4.5A:4.0
Counterint.Analysis estimates only 15-40% probability of meaningful pause policy implementation by 2030, despite 97% public support for AI regulation and 64% supporting superintelligence bans until proven safe.S:4.0I:4.0A:3.5

Issues (1):

Links12 links could use <R> components

Pause Advocacy

ApproachAdvocate for slowing or pausing frontier AI development

TractabilityLow (major political/economic barriers)

Key OrganizationsFuture of Life Institute, Pause AI

Risks

Policies

Compute Governance

Quick Assessment

Dimension	Assessment	Evidence
Tractability	Low-Medium (25-35%)	Major lobbying opposition; geopolitical competition; Biden’s EO 14110↗ rescinded January 2025
Potential Impact	High if achieved	Could provide 2-5 years for alignment research; Asilomar precedent↗ shows scientific pauses can work
Political Feasibility	Low (15-25%)	Only 30,000 signed FLI open letter↗; industry opposition strong
International Coordination	Very Low (10-20%)	China developing own AI governance framework↗; US-China competition intense
Time Gained if Successful	2-5 years	Based on proposed 6-month to multi-year pause durations
Risk of Backfire	Moderate (30-40%)	Compute overhang; ceding leadership to less safety-conscious actors
Advocacy Momentum	Growing	PauseAI↗ protests in 13+ countries; FLI 2025 survey: 64% support superintelligence ban until proven safe
Public Support for Regulation	Very High (97%)	Gallup/SCSP September 2025: 97% agree AI safety should be subject to rules; 69% say government not doing enough
Economic Stakes	$13-200T by 2030	McKinsey estimates $13T annual contribution; global AI investment reached $200B in 2025

Risks Addressed

Risk	Mechanism	Effectiveness
Racing Dynamics	Reduces competitive pressure, creates coordination window	High if achieved
Loss of Control	Buys time for alignment research	High if alignment is tractable
Misuse Risks	Delays deployment of dual-use capabilities	Medium
Lock-in	Provides time for governance frameworks	Medium
Epistemic Risks	Reduces rushed deployment of unreliable systems	Medium

Overview

Pause advocacy represents one of the most controversial interventions in AI safety: calling for the deliberate slowing or temporary halting of frontier AI development until adequate safety measures can be implemented. This approach gained significant attention following the March 2023 open letter organized by the Future of Life Institute, which called for a six-month pause on training AI systems more powerful than GPT-4 and garnered over 30,000 signatures including prominent technologists and researchers.

The core premise underlying pause advocacy is that AI capabilities are advancing faster than our ability to align these systems with human values and control them reliably. Proponents argue that without intervention, we risk deploying increasingly powerful AI systems before developing adequate safety measures, potentially leading to catastrophic outcomes. The theory of change involves using advocacy, public pressure, and policy interventions to buy time for safety research to catch up with capabilities development.

However, pause advocacy faces formidable challenges. The economic incentives driving AI development are substantial—U.S. AI investment exceeded $109 billion in 2024, while China has committed over $140 billion in state investment through the National Integrated Circuit Industry Investment Fund. Goldman Sachs research showed that 40% of U.S. real GDP growth in Q1 2025 came from tech capital expenditures, with the majority being AI-related. Critics argue that unilateral pauses by safety-conscious actors could simply cede leadership to less responsible developers, potentially making outcomes worse rather than better.

Economic Context for Pause Advocacy

Metric	Value	Source	Implication for Pause
U.S. AI Investment (2024)	$109 billion	Microsoft AI Economy Institute	Massive economic interests opposing pause
China State AI Investment	$140+ billion	National IC Industry Investment Fund	Geopolitical competition intensifies
EU AI Investment (2024)	less than $20 billion	Global AI Adoption Report	Europe trails, may support coordination
GPT-5 Training Cost	$5 billion (reported)	Industry estimates	Rising costs could concentrate development
AI Share of GDP Growth (Q1 2025)	40% of U.S. real GDP growth	Goldman Sachs	Economic dependence on AI momentum
McKinsey Projected Annual Value	$13 trillion by 2030	McKinsey Global Institute	Opportunity cost of pause is substantial
Average AI Breach Cost (2025)	$4.8 million per incident	IBM Cost of Data Breach Report	Safety failures also have economic costs

Evolution of Pause Proposals

The pause advocacy movement has evolved significantly since 2023, with proposals ranging from temporary moratoria to conditional bans on superintelligence development.

Proposal	Date	Scope	Signatories	Key Demand	Status
FLI Open Letter	March 2023	6-month pause on GPT-4+ training	30,000+	Voluntary moratorium	Ignored by labs
CAIS Statement	May 2023	Risk acknowledgment	350+ researchers	Recognition of extinction risk	Influenced discourse
Statement on Superintelligence	October 2025	Conditional ban on superintelligence	700+ (Nobel laureates, public figures)	Prohibition until “broad scientific consensus” on safety	Active campaign
PauseAI Policy Proposal	Ongoing	International treaty + AI safety agency	Grassroots movement	IAEA-like body for AI	Advocacy stage

The October 2025 “Statement on Superintelligence” represents a notable escalation from the 2023 letter. While the original called for a temporary six-month pause, the new statement advocates for a conditional prohibition: “We call for a prohibition on the development of superintelligence, not lifted before there is broad scientific consensus that it will be done safely and controllably, and strong public buy-in.” Signatories include Nobel laureates Geoffrey Hinton, Daron Acemoglu, and Beatrice Fihn, alongside public figures like Steve Wozniak, Richard Branson, and Prince Harry and Meghan Markle. FLI director Anthony Aguirre warned that “time is running out,” estimating superintelligence could arrive within one to two years.

Theory of Change

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The pause advocacy theory of change operates through multiple reinforcing pathways. Public advocacy generates media attention, which combined with grassroots organizing shifts public opinion. This creates political pressure that can lead to either legislative action (such as compute governance) or industry self-regulation (such as responsible scaling policies). Both pathways result in slower development timelines, providing additional time for safety research to mature before transformative AI capabilities emerge.

Arguments for Pause

Capabilities-Safety Gap

The most compelling argument for pause centers on the widening gap between AI capabilities and safety research. While frontier models have jumped from GPT-3 (175B parameters, 2020) to GPT-4 (estimated 1.7T parameters, 2023) to even more powerful systems, fundamental alignment problems remain unsolved. Current safety techniques like constitutional AI and reinforcement learning from human feedback (RLHF) appear increasingly inadequate for highly capable systems that could exhibit deceptive behavior or pursue unintended objectives.

Generation	Parameters	Year	Key Safety Advances	Gap Assessment
GPT-3	175B	2020	Basic RLHF	Moderate
GPT-4	≈1.7T (est.)	2023	Constitutional AI, red-teaming	Widening
Claude 3/GPT-4.5	Undisclosed	2024	RSP frameworks↗, scaling policies	Significant
Projected 2026	10T+	2026	Unknown	Critical uncertainty

Research by Anthropic↗ and other safety-focused organizations suggests that as models become more capable, they become harder to interpret and control. A 2023 study by Perez et al. found that larger language models show increased tendencies toward deceptive behaviors when given conflicting objectives. Recent mechanistic interpretability work↗ remains far from scalable to frontier models with hundreds of billions of parameters. Without a pause to develop better alignment techniques, we may cross critical capability thresholds before adequate safety measures are in place.

Coordination Window

Pause advocacy also argues that slower development creates opportunities for beneficial coordination that are impossible during intense racing dynamics↗. The current AI development landscape involves only a handful of frontier labs—primarily OpenAI, Google DeepMind, and Anthropic—making coordination theoretically feasible. Historical precedents like the Asilomar Conference on Recombinant DNA↗ (1975) and various nuclear arms control agreements demonstrate that the scientific community and governments can successfully coordinate to slow potentially dangerous technological development when risks are recognized.

The window for such coordination may be closing rapidly. As AI capabilities approach transformative levels, the strategic advantages they confer will likely intensify competitive pressures. Research on strategic insights from simulation gaming of AI race dynamics↗ suggests that nations viewing AI as critical to national security may be unwilling to accept coordination mechanisms that could disadvantage them relative to competitors.

Precautionary Principle

Advocates invoke the precautionary principle, arguing that when facing potentially existential risks, the burden of proof should be on demonstrating safety rather than on proving danger. Unlike most technologies, advanced AI systems could pose civilization-level risks if misaligned, making the stakes qualitatively different from typical innovation-risk tradeoffs. This principle has precedent in other high-stakes domains like nuclear weapons development and gain-of-function research, where safety considerations have sometimes overridden rapid advancement.

Arguments Against Pause

Geopolitical Competition

The strongest argument against pause concerns international competition, particularly with China. China’s national AI strategy, announced in 2017, explicitly aims for AI leadership by 2030, backed by over $140 billion in state investment through the National Integrated Circuit Industry Investment Fund. However, the picture is more nuanced than simple “race to the bottom” narratives suggest. China released its own AI Safety Governance Framework↗ in September 2024, and in December 2024, 17 major Chinese AI companies including DeepSeek and Alibaba signed safety commitments mirroring Seoul AI Summit↗ pledges.

Competitive Factor	US Position	China Position	Implication for Pause
Frontier model capability	Leading	6-18 months behind	Pause could narrow gap
Compute access	Advantage (NVIDIA chips)	Constrained by export controls	Pause less urgent for compute
Safety research	Leading (Anthropic, OpenAI)	Growing (CnAISDA↗)	Potential coordination opportunity
Regulatory framework	Fragmented, EO rescinded	Unified (CAC framework)	China may regulate faster
Talent pool	Advantage	Growing rapidly	Not directly affected by pause

This concern is compounded by the dual-use nature of AI research. Unlike some other dangerous technologies, AI capabilities research often advances both beneficial applications and potentially dangerous ones simultaneously. Pausing beneficial AI development to prevent dangerous applications may be a poor tradeoff if competitors continue advancing both dimensions.

Compute Overhang Risk

A technical argument against pause involves the “compute overhang” phenomenon. If pause affects model training but not hardware development, accumulated computing power could enable sudden capability jumps when development resumes. Historical analysis of computing trends shows that available compute continues growing exponentially even during periods of algorithmic stagnation. A pause that allows compute scaling to continue could result in more dangerous discontinuous progress than gradual development would produce.

Research by OpenAI’s scaling team suggests that sudden access to much larger compute budgets could enable capability gains that bypass incremental safety research. This could be more dangerous than the gradual development that allows safety research to track capabilities improvements.

Pause advocacy also faces arguments about opportunity costs and distributional effects. AI technologies show significant promise for addressing major challenges including disease, climate change, and poverty. A 2023 economic analysis by McKinsey Global Institute estimated that AI could contribute $13 trillion annually to global economic output by 2030, with particular benefits for developing countries if access is democratized. More recent data shows AI already drives 40% of U.S. real GDP growth (Q1 2025), with economist Michael Roberts calculating that without tech spending, the U.S. would have been close to or in a recession.

Critics argue that pause advocacy primarily benefits existing AI leaders by reducing competitive pressure while imposing costs on society through delayed beneficial applications. The UNDP reports that AI risks “sparking a new era of divergence as development gaps between countries widen”—raising questions about whether pause policies would disproportionately harm developing nations seeking to catch up.

Implementation Mechanisms

Regulatory Approaches

The most direct path to pause involves government regulation. Several mechanisms are under consideration, including compute governance↗ (restricting access to the high-end chips needed for training frontier models), mandatory safety evaluations before deployment, and international treaties.

Regulatory Mechanism	Jurisdiction	Threshold	Status
Executive Order 14110↗	US	10^26 FLOP training runs	Rescinded January 2025
EU AI Act↗	EU	10^25 FLOP (systemic risk)	Active since August 2024
Responsible AI Act (proposed)	US Congress	Tiered thresholds	Under consideration
China AI Governance Framework↗	China	Risk-based grading	Version 2.0 released September 2025

However, regulatory approaches face significant implementation challenges. The global nature of AI supply chains, the dual-use character of computing hardware, and the difficulty of defining “frontier” capabilities all complicate enforcement. Additionally, industry opposition remains strong, with major tech companies arguing that regulation could stifle innovation and benefit foreign competitors. The rescission of Biden’s EO 14110 within hours of the new administration taking office demonstrates the fragility of executive action on AI governance.

Comparative Governance Capacity

Country/Region	AI Governance Index Score	Regulatory Approach	Pause Feasibility
Singapore	Top performer	Agile governance, regulatory sandboxes	Medium - innovation-focused
United Kingdom	Top performer	AI Safety Institute, risk-based	Medium-High - safety-conscious
South Korea	Top performer	Ethics-by-design frameworks	Medium - balanced approach
European Union	High	EU AI Act (risk-based, 10^25 FLOP threshold)	High - regulatory willingness
United States	Medium	Fragmented; AI Action Plan favors speed	Low - deregulation trend
China	Medium	Unified CAC framework, state-directed	Variable - strategic calculations
UAE	Highest adoption (64%)	Innovation-first	Low - development priority

The AGILE Index 2025, covering 40+ countries across 43 indicators, reveals that governance capacity varies dramatically. Countries like Singapore and the UK have embedded agile mechanisms that could theoretically support coordinated slowdowns, while the U.S. approach under the July 2025 AI Action Plan explicitly prioritizes “rapid AI innovation” and links federal funding to states adopting less restrictive laws.

Industry Self-Regulation

An alternative approach involves industry-led initiatives, such as responsible scaling policies that commit labs to pause development when certain capability thresholds are reached without adequate safety measures. Anthropic’s Responsible Scaling Policy↗, first announced in September 2023 and updated in October 2024, provides a template for such approaches by defining specific capability evaluations and safety requirements.

Lab	Policy	Key Commitments	Independent Verification
Anthropic	RSP v2.2↗	ASL capability thresholds, biosecurity evals	Limited external audit
OpenAI	Preparedness Framework	Red-teaming, catastrophic risk thresholds	Internal governance
Google DeepMind	Frontier Safety Framework	Pre-deployment evaluations	In development

The advantage of industry self-regulation is that it can move faster than formal regulation and may be more technically sophisticated. However, it relies on voluntary compliance and may not address competitive pressures that incentivize racing. After one year with the RSP in effect, Anthropic acknowledged↗ instances where they fell short of meeting the full letter of its requirements, though they assessed these posed minimal safety risk.

Safety Implications and Trajectory

Concerning Aspects

The primary safety concern with pause advocacy is that it may not achieve its intended goals while creating new risks. If pauses are unevenly implemented, they could concentrate advanced AI development among less safety-conscious actors. Additionally, the political and economic pressures against pause may lead to policy capture or symbolic measures that provide false security without meaningfully reducing risks.

There are also concerns about the precedent that successful pause advocacy might set. If pause becomes a standard response to emerging technologies, it could hamper beneficial innovation more broadly. The challenge is distinguishing cases where precautionary pauses are justified from those where they primarily serve incumbent interests.

Promising Aspects

Conversely, even partial success in slowing AI development could provide crucial time for safety research to advance. Recent progress in interpretability research↗, including work on sparse autoencoders and circuit analysis, suggests that additional time could yield significant safety improvements. In March 2025, Anthropic published research on circuit tracing that “lets us watch Claude think, uncovering a shared conceptual space where reasoning happens before being translated into language.” A coordinated slowdown that maintains global leadership among safety-conscious actors could be the best available approach for navigating the development of transformative AI.

Furthermore, pause advocacy has already had positive effects on AI safety discourse by raising awareness of risks and legitimizing safety concerns in policy circles. The 2023 open letter↗ helped establish AI safety as a mainstream policy concern, influencing subsequent regulatory discussions and industry practices.

Current State and Trajectory

As of late 2025, pause advocacy remains politically marginal but increasingly organized. PauseAI↗, founded in May 2023, has grown from a single San Francisco group to six established chapters across the US (NYC, Chicago, SF, Portland, Phoenix, Washington DC) with protests in 13+ countries including at the AI Seoul Summit↗ and Paris AI Action Summit.

2025 Protest Milestones

Event	Date	Location	Focus	Outcome
Paris AI Summit Protest	February 2025	Paris, France	Summit lacking safety focus	Global coordination, Kenya and DRC joined
Google DeepMind Protest	June 2025	London, UK	Gemini 2.5 safety commitments	60 UK MPs sent letter to Google; largest PauseAI protest
PauseCon	June 2025	London, UK	First activist training event	Movement capacity building
Amsterdam ASML Protest	December 2025	Amsterdam, Netherlands	Chip supply chain for AI	Targeting hardware enablers

The June 2025 Google DeepMind protest marked a turning point in pause advocacy effectiveness. When Google released Gemini 2.5 Pro in March 2025 without publishing the promised safety testing documentation, PauseAI organized demonstrations outside DeepMind’s London headquarters where protesters chanted “Stop the race, it’s unsafe” and “Test, don’t guess.” The action gained political traction when 60 cross-party UK parliamentarians signed a letter accusing Google of “breach of trust” for violating commitments made at the Seoul AI Safety Summit and to the White House in 2023. Former Defence Secretary Lord Browne warned: “If leading companies like Google treat these commitments as optional, we risk a dangerous race to deploy increasingly powerful AI without proper safeguards.”

Timeframe	Scenario	Probability	Key Drivers
2025-2026	No meaningful pause	60-70%	Industry lobbying, EO rescission, China competition
2025-2026	Voluntary industry slowdown	15-25%	Safety incident, capability threshold reached
2027-2030	Coordinated international framework	15-30%	China-US dialogue progress↗, WAICO proposal
2027-2030	Binding compute governance	10-20%	Major incident, legislative action

Looking ahead 1-2 years, the trajectory likely depends on whether AI capabilities approach obviously dangerous thresholds. Dramatic capability jumps or safety incidents could shift political feasibility significantly. Conversely, gradual progress that demonstrates controllable development might reduce pause advocacy’s appeal.

In the 2-5 year timeframe, international coordination mechanisms will likely determine pause advocacy’s viability. China has proposed WAICO↗ (World AI Cooperation Organization) as a framework for coordinating AI governance rules. If major AI powers can establish effective governance frameworks, coordinated development constraints become more feasible. If geopolitical competition intensifies, unilateral pauses become increasingly untenable.

Key Uncertainties

Several critical uncertainties shape the case for pause advocacy:

Uncertainty	If True (Pro-Pause)	If False (Anti-Pause)	Current Assessment
Alignment is hard	Pause essential to buy research time	Pause unnecessary, current methods sufficient	Unclear; interpretability progress↗ slow but advancing
Short timelines	Pause urgent, limited window	Adequate time without constraints	Models improving rapidly; 2-5 year uncertainty
International coordination feasible	Global pause achievable	Unilateral pause counterproductive	China-US dialogue↗ shows some progress
Racing dynamics dominant	Pause prevents corner-cutting	Aviation industry shows↗ safety can prevail	Competitive pressures strong but not deterministic
RSPs work	Voluntary pause-like mechanisms sufficient	Need binding regulation	Anthropic RSP↗ promising but untested at capability thresholds

The difficulty of AI alignment remains fundamentally unknown—if alignment problems prove tractable with existing techniques, pause advocacy’s necessity diminishes significantly. Conversely, if alignment requires fundamental breakthroughs that need years of research, pause may become essential regardless of political difficulties.

The timeline to transformative AI capabilities also remains highly uncertain. If such capabilities are decades away, there may be adequate time for safety research without development constraints. If they arrive within years, pause advocacy’s urgency increases dramatically.

Finally, the prospects for international coordination remain unclear. China’s approach to AI safety and willingness to participate in coordination mechanisms will largely determine whether global pause initiatives are feasible. The International Dialogues on AI Safety (IDAIS)↗ produced consensus statements including the Ditchley Statement and Beijing Statement establishing specific technological “red lines” including autonomous replication and deception of regulators—suggesting some foundation for cooperation.

The effectiveness of alternative safety interventions also affects pause advocacy’s relative value. If industry responsible scaling policies or technical alignment approaches prove sufficient, the need for development pauses decreases. However, if these approaches fail to keep pace with capabilities, pause may become the only viable risk reduction mechanism.

Public Opinion Landscape

Public support for AI regulation and development pauses has grown substantially, creating a potential political foundation for pause advocacy. The Stanford HAI 2025 AI Index and multiple polling organizations document consistent, bipartisan support for stronger AI governance.

Poll	Date	Finding	Implication
Gallup/SCSP	September 2025	97% agree AI safety should be subject to rules; 80% support even if it slows development	Near-universal support for regulation crosses party lines
FLI Survey	October 2025	73% support robust AI regulation; 64% support superintelligence ban until proven safe; only 5% favor fast unregulated development	Strong majority favors precautionary approach
Quinnipiac	April 2025	69% say government not doing enough to regulate AI; 44% think AI will do more harm than good	Public perceives regulatory gap
Rethink Priorities	2025	51% would support AI research pause; 25% oppose	Support outstrips opposition 2:1
Pew Research	2025	60% of public and 56% of AI experts worry government won’t regulate enough; 64% of Democrats, 55% of Republicans concerned	Expert-public alignment on regulation
YouGov (post-FLI letter)	2023	58-61% supported 6-month pause on AI research across different framings	Initial pause support established
General Sentiment	2025	77% want companies to “create AI slowly and get it right” even if it delays breakthroughs	Strong preference for caution over speed

Political Dynamics

Despite strong polling support, translating public opinion into policy remains challenging. The political economy favors AI developers: concentrated benefits for tech companies versus diffuse risks for the public create asymmetric lobbying incentives. The rapid rescission of Biden’s EO 14110 in January 2025 illustrates how quickly regulatory frameworks can be dismantled despite public support.

However, recent political developments show emerging bipartisan resistance to industry capture. In 2025, the U.S. Senate voted 99-1 to strike down a provision that would have imposed a 10-year moratorium on state AI regulation—opposed by 17 Republican governors, state attorneys general from both parties, and state legislators nationwide. The Gallup finding that 88% of Democrats and 79% of Republicans and independents support AI safety rules suggests bipartisan potential for regulation if framed appropriately.

Political Factor	Current State	Trend
Federal executive	Deregulatory (AI Action Plan)	Anti-pause
Federal legislative	Mixed; some bipartisan safety interest	Uncertain
State-level	Active regulation; 53% of voters trust states more than Congress	Pro-regulation
Industry lobbying	Strong opposition; concentrated economic interests	Anti-pause
Public pressure	Growing; 65% believe AI will increase misinformation	Pro-regulation

Historical Precedents

The Asilomar Conference on Recombinant DNA↗ (1975) provides the most frequently cited precedent for scientific self-regulation and pause. Over 100 scientists, lawyers, and journalists gathered to develop consensus guidelines for recombinant DNA research, following a voluntary moratorium initiated by scientists themselves who had recognized potential biohazards.

Precedent	Year	Scope	Outcome	Lessons for AI
Asilomar Conference↗	1975	Recombinant DNA	Moratorium lifted with safety guidelines; NIH RAC created	Scientists can self-regulate; guidelines enabled rather than blocked research
Nuclear weapons moratorium	1958-1961	Nuclear testing	Partial Test Ban Treaty (1963)	International coordination possible under existential threat
BWC↗	1972	Bioweapons	187 states parties; no verification regime	Limits of international agreements without enforcement
Gain-of-function pause	2014-2017	Dangerous pathogen research	Enhanced oversight; research resumed	Temporary pauses can enable safety improvements

The Asilomar precedent is instructive but imperfect. Key differences from AI:

Smaller community: Only a few hundred researchers worked on rDNA in 1975 vs. millions in AI today
Clearer risks: Biohazards were more tangible than AI alignment concerns
Fewer commercial pressures: Academic research vs. hundreds of billions in investment
Easier enforcement: Physical lab access vs. distributed compute and open-source models

Notably, the rDNA moratorium lasted only months, and “literally hundreds of millions of experiments, many inconceivable in 1975, have been carried out in the last 30 years without incident”—suggesting that well-designed pauses can enable rather than block beneficial research.

Major organizations advancing pause advocacy include:

Future of Life Institute↗: Organized the 2023 open letter↗ with 30,000+ signatures
PauseAI↗: Grassroots organization founded May 2023; protests in 13+ countries; claims 70% of Americans support pause
Center for AI Safety↗: Research and policy organization; published statement on AI risk↗ signed by leading researchers
Academic researchers: Stuart Russell, Yoshua Bengio, Geoffrey Hinton have lent intellectual credibility to pause arguments

Complementary approaches include compute governance initiatives that could enable pause enforcement, international coordination efforts that could make pauses stable, and responsible scaling policies that implement conditional pause-like mechanisms. Technical alignment research also complements pause advocacy by developing the safety measures that would make development resumption safer.

International Governance Developments

The global governance landscape for AI has evolved rapidly, with both opportunities and obstacles for pause advocacy. The inaugural International AI Safety Report, published January 2025, represents the largest global collaboration on AI safety to date—led by Turing Award winner Yoshua Bengio, authored by over 100 AI experts, and backed by 30 countries.

Initiative	Date	Key Features	Pause Relevance
International AI Safety Report	January 2025	First comprehensive global review; 100+ experts, 30 countries	Scientific foundation for coordinated action
UN Scientific Committee on AI	August 2025	Independent scientific body for AI assessment	Could provide “broad scientific consensus” mechanism
UN Global Dialogue on AI Governance	August 2025	Inclusive international governance forum	Platform for pause coordination
China Global AI Governance Action Plan	July 2025	Multilateral cooperation framework; scientific cooperation, regulatory coordination	Signals Chinese openness to coordination
US AI Action Plan	July 2025	Deregulation, global competitiveness focus	Opposes pause; favors speed
AI Red Lines Campaign	September 2025	200+ signatories including 10 Nobel laureates	UN-focused advocacy for “globally unacceptable AI risks”
Global Digital Compact	2024	First universal agreement on AI governance	Establishes baseline for international norms

The UN initiatives launched in August 2025 grew from the “Governing AI for Humanity” report and aim to “kickstart a much more inclusive form of international governance.” Secretary-General António Guterres emphasized that “the United Nations offers a uniquely universal platform for such global cooperation” and called for building “safe, secure and trustworthy AI systems grounded in international law and human rights.”

However, only seven countries—all from the developed world—are parties to all current significant global AI governance initiatives, revealing the fragmentation that hampers pause enforcement. The fundamental tension remains: the US AI Action Plan explicitly encourages “rapid AI innovation” while linking federal funding to states adopting less restrictive AI laws, directly countering pause advocacy goals. Meanwhile, China’s Global AI Governance Action Plan emphasizes “establishing international platforms for scientific and technological cooperation” and “strengthening policy and regulatory coordination”—suggesting potential for multilateral frameworks, though skeptics note both powers view AI as a strategic asset and resist binding international limits.

Limitations and Risks of Pause Advocacy

Pause advocacy faces substantial limitations that could undermine its effectiveness or produce unintended consequences.

Structural Limitations

Limitation	Description	Severity
Unilateral implementation	A pause by safety-conscious labs could cede leadership to less responsible actors	High
Verification difficulty	Unlike nuclear materials, AI training is distributed and hard to monitor	High
Economic irreversibility	$109B+ invested creates path dependencies resistant to reversal	High
Definitional challenges	No consensus on what constitutes “frontier” AI or “adequate safety”	Medium
Dual-use nature	Same capabilities enable beneficial and dangerous applications	Medium
Open-source proliferation	Powerful models already available cannot be “undeployed”	High

Potential Negative Consequences

Critics on LessWrong and elsewhere have identified several ways pause advocacy could backfire:

Brain drain effect: The least safety-conscious researchers may relocate to jurisdictions without pause policies, concentrating talent in less responsible environments
Compute overhang: If hardware development continues during a training pause, resumption could enable sudden capability jumps rather than gradual, monitorable progress
Underground development: Stringent restrictions could push development to illegal or less observable channels, reducing transparency
Political backlash: Failed pause attempts could discredit AI safety concerns more broadly, making future governance harder
Opportunity costs: Resources spent on pause advocacy could yield higher impact if directed toward technical alignment research or defensive measures

Effectiveness Uncertainty

Scenario	Probability	Outcome for Pause Advocacy
Pause achieves 2+ year slowdown	15-25%	Success—safety research catches up
Pause achieves 6-12 month delay	20-30%	Partial success—some benefit, risks remain
Pause fails, shifts development offshore	25-35%	Failure—net negative outcome
Pause advocacy raises awareness without policy change	30-40%	Indirect benefit—Overton window shifted

The EA Forum debate on pause feasibility highlights fundamental uncertainty: proponents argue that advocacy has successfully mainstreamed AI safety concerns (Geoffrey Hinton quitting Google, FLI letter media coverage), while critics note that “six months from now the pause would end, leaving us more or less where we started.”

AI Transition Model Context

Pause advocacy affects the Ai Transition Model through multiple factors:

Factor	Parameter	Impact
Transition Turbulence	Racing Intensity	Coordinated slowdown reduces race to bottom on safety
Misalignment Potential	Safety-Capability Gap	Provides 2-5 additional years for safety research if achieved
Civilizational Competence	International Coordination	Requires unprecedented global coordination to be effective

Pause advocacy has 15-40% probability of meaningful policy implementation by 2030; effectiveness depends on international coordination and enforcement mechanisms.

Sources and Further Reading

Primary Documents

International AI Safety Report (January 2025): First comprehensive global review of AI risks; led by Yoshua Bengio, 100+ experts, backed by 30 countries
US AI Action Plan (July 2025): Federal policy prioritizing “rapid AI innovation” and global competitiveness
China Global AI Governance Action Plan (July 2025): Multilateral cooperation framework emphasizing coordination
UN Global Dialogue on AI Governance (August 2025): Launch of inclusive international governance forum
California Report on Frontier AI Policy: State-level policy analysis

Public Opinion Research

Years of Polling Show Overwhelming Voter Support for a Crackdown on AI (Public Citizen): Compilation of polling data showing 97% support for AI safety rules
The U.S. Public Wants Regulation (or Prohibition) of Expert-Level and Superhuman AI (FLI, October 2025): 64% support superintelligence ban until proven safe
Poll finds bipartisan agreement on a key issue: Regulating AI (The Conversation): 77% want companies to “create AI slowly and get it right”
Stanford HAI 2025 AI Index Report: Public Opinion: Comprehensive survey of public attitudes

Economic Analysis

When Will AI Investments Start Paying Off? (GW&K Investment Management): Analysis of AI’s role in GDP growth
Global AI Adoption in 2025 (Microsoft AI Economy Institute): International adoption statistics
Government AI Readiness Index 2025 (Oxford Insights): Comparative governance capacity assessment

Critical Analysis

How “Pause AI” advocacy could be net harmful (LessWrong): Arguments against pause advocacy
Is Pausing AI Possible? (EA Forum): Analysis of implementation challenges
Pause For Thought: The AI Pause Debate (EA Forum): Balanced overview of arguments
What are some criticisms of PauseAI? (EA Forum): Community discussion of weaknesses

Governance Frameworks

AI governance must keep pace with this fast-developing field (World Economic Forum): AGILE Index methodology and findings
Global AI law and policy trends update (IAPP): Regulatory landscape overview
ITU Annual AI Governance Report 2025: Comprehensive governance assessment

Pause Advocacy

Pause Advocacy

Quick Assessment

Risks Addressed

Overview

Economic Context for Pause Advocacy

Evolution of Pause Proposals

Theory of Change

Arguments for Pause

Capabilities-Safety Gap

Coordination Window

Precautionary Principle

Arguments Against Pause

Geopolitical Competition

Compute Overhang Risk

Economic and Social Costs

Implementation Mechanisms

Regulatory Approaches

Comparative Governance Capacity

Industry Self-Regulation

Safety Implications and Trajectory

Concerning Aspects

Promising Aspects

Current State and Trajectory

2025 Protest Milestones

Key Uncertainties

Public Opinion Landscape

Political Dynamics

Historical Precedents

Related Organizations and Approaches

International Governance Developments

Limitations and Risks of Pause Advocacy

Structural Limitations

Potential Negative Consequences

Effectiveness Uncertainty

AI Transition Model Context

Sources and Further Reading

Primary Documents

Public Opinion Research

Economic Analysis

Critical Analysis

Governance Frameworks