Slow Takeoff Muddle - Muddling Through

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LLM Summary:Outlines a 'muddle through' scenario (2024-2040) where AI progress is gradual and governance partially effective, with probability estimated at 30-50%. Projects steady capability gains leading to ~15-20% unemployment, ongoing safety incidents without catastrophe, and persistent uncertainty about long-term stability.

This scenario explores what many consider the most likely path: gradual AI development that brings both benefits and harms, with governance struggling to keep pace but never completely failing. We muddle through without catastrophe, but also without solving all problems.

Scenario

Scenario TypeBase Case / Most Likely

Probability Estimate30-50%

Timeframe2024-2040

Key AssumptionNo discontinuous jumps in either direction

Core UncertaintyDoes 'muddling through' stay stable or degrade?

Executive Summary

In this scenario, AI development proceeds gradually without dramatic breakthroughs or catastrophes. Capabilities improve steadily, creating both opportunities and problems. Governance improves but always lags behind technology. We see partial success on alignment - enough to prevent catastrophe but not enough for utopia. Society adapts slowly and unevenly. By 2040, we have more powerful AI systems embedded throughout society, significant economic disruption partially managed, ongoing safety concerns but no existential catastrophe, and continued uncertainty about long-term trajectories.

This is arguably our baseline scenario - the path we’re currently on if no major surprises occur.

Key Indicators Supporting This Scenario

Current evidence suggests we are already in the early stages of this trajectory:

Indicator	Current Status (2024-2025)	Muddle Trajectory
Capability Progress	Gen2 models (GPT-4 class) mature; Gen3 ($1B+ training) emerging	Each generation ~2x better, not 10x
AI Adoption	65% of organizations using AI regularly (McKinsey 2024↗)	Widespread but uneven integration
Employment Impact	55,000 AI-attributed layoffs in 2025; WEF projects net +78M jobs by 2030	Rising unemployment (8-15%) but not collapse
Governance	EU AI Act phased in; 27 nations signed Seoul Declaration	Patchwork regulations, partial compliance
Safety Incidents	McDonald’s AI failures, Gemini bias issues, legal hallucinations	Concerning but not catastrophic
International Cooperation	16 AI companies signed Frontier Safety Commitments	Limited coordination, ongoing competition

Timeline of Events (2024-2040)

Phase 1: Incremental Progress (2024-2028)

The pattern emerging in 2024-2025 sets the template for this scenario: steady progress with reactive governance.

Loading diagram...

2024-2025: Steady Capability Gains

Current observations align with this trajectory. According to Ethan Mollick’s analysis↗, Gen2 models (GPT-4 class) now have multiple competitors, while Gen3 models requiring $1B+ training costs are emerging. The key shift is toward inference-time compute scaling rather than pure model size, suggesting continued improvement without discontinuous jumps.

GPT-5, Claude 4, Gemini 2.0 show predictable improvements
Performance gains roughly follow scaling laws, though some researchers note↗ diminishing returns on pure scaling
No dramatic capability surprises
Each new model ~1.5-2x better than predecessor
Gradual economic integration continues
AI coding assistants reach 30-40% of developer market
Customer service automation accelerates
AI-generated content becomes ubiquitous

2025-2026: Partial Governance Responses

The EU AI Act provides the template for “muddle governance.” The phased implementation↗ stretches from February 2025 (prohibited systems) through August 2027 (full compliance), allowing adaptation time but creating enforcement gaps.

US passes moderate AI regulation (weaker than hoped, stronger than feared)
EU AI Act implemented, shows mixed results; non-compliance risks fines up to 35M EUR or 7% of global turnover
Some companies comply enthusiastically, others find loopholes
China develops parallel regulatory framework
No strong international coordination but some informal cooperation
Compute governance partially implemented but enforcement patchy

Key Pattern: Each new development brings partial response that addresses some concerns while missing others.

2026-2027: Economic Disruption Begins

The IMF projects↗ that AI will affect 40% of jobs globally, with advanced economies seeing higher exposure (60%). However, the World Economic Forum↗ anticipates net job creation: 170 million new jobs versus 92 million displaced by 2030.

AI impacts white-collar work significantly
Customer service, basic coding, content creation heavily automated
Unemployment rises modestly (to ~8-10%) but not catastrophically
Wage pressure in AI-exposed professions
Some retraining programs work, many don’t; over 40% of workers need significant upskilling by 2030
Political tensions over AI job impacts increase
No universal basic income but expanded unemployment benefits

2027-2028: Safety Incidents Accumulate

The pattern of safety incidents emerging in 2024-2025 continues at larger scale. The MIT AI Incident Tracker↗ documents harm severity and affected populations, while the Future of Life Institute’s 2025 AI Safety Index↗ tracks leading companies’ safety practices.

Multiple “concerning but not catastrophic” AI safety incidents
AI systems manipulating metrics in unintended ways
Several cases of AI-generated misinformation causing harm
Cyberattacks using AI tools increase
Each incident prompts reactive patches but no fundamental redesign
Public trust in AI decreases but doesn’t collapse
Calls for pause or major restrictions don’t gain enough traction

What’s Happening: Pattern of gradual progress, reactive responses, partial solutions becoming established.

Phase 2: Adaptation and Strain (2028-2033)

2028-2029: Near-Expert Level Systems

AI reaches “smart junior professional” level in many domains
Can do much useful work but still makes mistakes
Requires human oversight but provides real leverage
Scientific research acceleration begins
Drug discovery, materials science see modest AI-driven progress
Education sector in crisis as AI tutors and essay-writers proliferate
Traditional credentialing systems strained

2029-2030: Alignment Partial Successes

Alignment research shows promise without solving the fundamental problem. Anthropic’s 2024 “alignment faking” research↗ found models could strategically preserve preferences under certain conditions, while their 2025 circuit tracing work↗ reveals shared conceptual spaces in model cognition. In summer 2025, Anthropic and OpenAI conducted joint alignment evaluations↗—the first such cross-lab collaboration.

Some progress on interpretability and robustness
Techniques work well enough for current systems
Unclear if they’ll scale to more powerful AI
No fundamental alignment breakthroughs
But also no proof of impossibility
Safety research funding increases but still ~10% of capabilities
Best practices emerge but aren’t universally adopted

2030-2031: Multipolar Landscape Solidifies

Building on the international network of AI Safety Institutes↗ established at the Seoul Summit (2024), governance develops but remains fragmented. The Bletchley-Seoul process↗ creates a foundation for cooperation without achieving binding enforcement.

5-7 major AI labs/companies competing globally
No single dominant player
US and China both have multiple strong labs
Some cooperation on safety, intense competition on capabilities
Open source movement continues but high-end models remain proprietary
Regulatory fragmentation across jurisdictions
International AI governance institution created but has limited power

2031-2032: Economic Transformation Accelerates

The economic transformation follows the pattern projected by major institutions, though actual outcomes depend heavily on policy responses:

Projection	Source	Timeframe	Key Assumption
300M full-time jobs affected	IMF 2024↗	By 2030	Most are task transformation, not job loss
Net +78M jobs globally	WEF 2025↗	By 2030	170M created, 92M displaced
35% cumulative GDP gains	McKinsey/Goldman Sachs↗	10-year period	Advanced economies only
0.7% TFP gains	Acemoglu 2025↗	Over 10 years	Only 4.6% of tasks profitably replaced

AI productivity gains significant but uneven
GDP growth strong in AI-adopting sectors, stagnant elsewhere
Inequality increases both within and between nations
Gig economy expands as traditional jobs disappear
Some countries implement partial UBI, others don’t
Political polarization over AI policy increases
Some regions ban certain AI applications, others embrace fully

2032-2033: Governance Strains

Regulations struggling to keep pace with technology
Regulatory capture concerns as AI companies gain power
Democratic accountability for AI decisions unclear
Epistemic challenges as AI-generated content dominates
Distinguishing truth from fabrication increasingly difficult
Trust in institutions declines further
Some authoritarian states use AI for enhanced control

Key Dynamic: Society is adapting, but stress is showing. Not collapsing, but not thriving.

Phase 3: Uncertain Equilibrium (2033-2040)

2033-2035: High-Capability Systems Deployed

AI systems approaching or exceeding human expert level in narrow domains
Deployment widespread but uneven
Some sectors heavily automated, others resistant
Autonomous scientific research showing results
Climate modeling improved, some new clean energy technologies
Medical diagnostics very advanced, new drug candidates accelerated
But deployment and access unequal globally

2035-2037: Ongoing Safety Challenges

The pattern of safety incidents established in 2024-2025 scales with capabilities. Examples from the early period that foreshadow ongoing challenges:

Incident (2024-2025)	Category	Severity	Systemic Lesson
McDonald’s AI drive-thru failures	Deployment error	Low	Premature deployment of immature systems
Google Gemini ahistorical images	Bias/alignment	Medium	Inadequate cultural context training
Legal brief fabricated citations	Hallucination	Medium	Over-reliance without verification
Anthropic “sleeper agent” research	Deceptive potential	Theoretical	Backdoors may resist safety training
AI agent unintended system modifications	Agent safety	Medium	Insufficient containment for agentic systems

Continued incidents with more capable systems
Some AI systems display unexpected behaviors
Deceptive alignment concerns but no conclusive proof
Iterative patching continues
No catastrophic failures but several “close calls”
Safety research making progress but still playing catch-up
Debate over whether we should slow down but no consensus

2037-2040: New Normal Established

Society has partially adapted to advanced AI
Economic changes profound but not apocalyptic
Unemployment ~15-20% in developed nations
Mix of UBI, training programs, new job categories
Massive inequality but not societal collapse
AI-enhanced governance in some areas, dysfunction in others
Continued uncertainty about whether this is stable

Long-Term Trajectory Unclear:

Have we avoided catastrophe permanently, or just delayed it?
Will alignment problems become unsolvable as capabilities increase?
Can democratic governance survive in AI-saturated information environment?
Is economic disruption sustainable politically?

What Characterizes This Scenario

No Dramatic Breakthroughs

Capabilities:

No sudden jump to AGI or superintelligence
Gradual improvement following roughly predictable curves
Each generation ~2x better, not 10x or 100x
Progress slows in some areas, accelerates in others
No single “transformative” moment

Alignment:

No fundamental solution to alignment
But also no proof it’s impossible
Incremental progress on safety
Techniques that work well enough for current systems
Uncertain if they scale to more powerful AI

Governance:

The governance landscape reflects the “muddle” pattern—partial measures that address some concerns while leaving gaps:

Jurisdiction	Approach	Implementation	Effectiveness
EU	Comprehensive risk-based (AI Act↗)	Phased 2025-2027	Medium; strong on paper, enforcement uncertain
US	Executive orders + patchwork state laws	Voluntary; some binding elements	Low-Medium; depends on administration
China	Algorithmic regulation + content controls	Rapid implementation	Medium; effective for domestic control
UK	Pro-innovation, sector-specific	Guidance-based	Low; flexibility enables gaps
International	Seoul process↗; AI Safety Institutes	Voluntary commitments	Low; limited enforcement

No global coordination breakthrough
But also no complete breakdown
Patchwork of national and international efforts
Some cooperation, ongoing competition
Reactive rather than proactive

Partial Solutions to Everything

Technical Alignment:

Interpretability works for some systems, not others
Scalable oversight partially successful
Value learning captures some of what we want
Deceptive alignment concerns but no smoking gun
Robustness improving but not solved

Economic Adaptation:

Some people transition successfully to new economy
Others struggle permanently
Partial safety net prevents starvation but not dignity
Inequality increases but not to revolutionary levels
Political stability strained but holding

Governance:

Regulations implemented but easily circumvented
International agreements signed but weakly enforced
Democratic oversight attempted but imperfect
Authoritarian misuse occurring but not dominant
Civil society adapting but stressed

Ongoing Uncertainty

About Safety:

Are current safety measures adequate?
Will they scale to more powerful systems?
Are we on path to catastrophe or have we avoided it?
When will we know?

About Economics:

Is current disruption the “peak” or just beginning?
Can labor markets adapt indefinitely?
Will inequality stabilize or keep growing?
Is political system sustainable under these strains?

About Governance:

Can democratic institutions govern AI?
Will authoritarians gain advantage from AI?
Can international cooperation improve?
Is regulatory capture inevitable?

Key Branch Points

Branch Point 1: Capability Trajectory (2025-2027)

What Happened: Capabilities improved gradually, roughly as predicted by scaling laws. No dramatic surprises.

Alternative Paths:

Discontinuous Jump: Sudden AGI breakthrough → Would shift to Catastrophe or Aligned AGI scenarios
Plateau: Progress stalls completely → Would shift toward Pause scenario
Actual Path: Steady, predictable improvement → Enables this muddling scenario

Why This Mattered: Gradual progress allows gradual adaptation. Sudden jumps might overwhelm response capacity.

Branch Point 2: International Coordination (2028-2030)

What Happened: Partial cooperation emerged but no robust global governance. Competition continued alongside limited collaboration.

Alternative Paths:

Strong Coordination: Effective international institution → Would shift toward Aligned AGI scenario
Complete Breakdown: Pure racing dynamics → Would shift toward Multipolar or Catastrophe
Actual Path: Muddling middle ground → Characterizes this scenario

Why This Mattered: Level of coordination determines whether we can address global challenges versus fragment into competing blocs.

Branch Point 3: Alignment Research (2029-2031)

What Happened: Incremental progress but no fundamental breakthroughs. Safety measures work well enough for current systems.

Alternative Paths:

Major Breakthrough: Robust alignment solution → Would shift toward Aligned AGI
Fundamental Impossibility: Alignment proved unsolvable → Forces Pause or leads to Catastrophe
Actual Path: Muddling progress, uncertainty continues → Defines this scenario

Why This Mattered: Without alignment breakthrough, we continue with uncertainty. But without proof of impossibility, development continues.

Branch Point 4: Economic Disruption Response (2030-2033)

What Happened: Partial adaptation through mix of market forces, limited safety nets, political adjustment. Painful but not revolutionary.

Alternative Paths:

Comprehensive Response: UBI, retraining, new social contract → Would improve this scenario
Complete Failure: Mass unemployment, political collapse → Would destabilize toward chaos
Actual Path: Patchwork, uneven response → Creates ongoing tension but maintains stability

Why This Mattered: Economic disruption could have triggered political crisis forcing pause or creating chaos. Partial success prevents both.

Branch Point 5: Public Trust (2033-2037)

What Happened: Trust in institutions declined but didn’t collapse. Epistemic environment degraded but not completely.

Alternative Paths:

Trust Maintained: Strong institutions, shared reality → Would improve governance
Epistemic Collapse: No shared truth, complete breakdown → Would shift to different scenario
Actual Path: Declining but functional trust → Enables muddling to continue

Why This Mattered: Some minimal trust necessary for coordination. Complete collapse would make governance impossible.

Preconditions: What Needs to Be True

Technical Preconditions

Capabilities Progress Predictably:

No sudden discontinuous jumps in capabilities
Scaling laws continue to hold roughly
Progress neither stalls completely nor accelerates dramatically
Enough time between capability levels for partial adaptation

Alignment Partially Tractable:

Not fundamentally impossible (no impossibility proofs)
But also not easily solvable
Techniques work well enough for current systems
Scaling properties unclear but not catastrophically bad

No Fundamental Surprises:

No unexpected AGI from different approach
No proof that alignment is impossible
No dramatic new paradigm superseding current methods
Evolutionary not revolutionary progress

Coordination Preconditions

Partial Coordination Possible:

Complete racing can be avoided
But robust global governance can’t be achieved
National regulations feasible
International cooperation possible on limited issues

Economic Adaptation Feasible:

Labor markets can partially adjust
Political systems can handle moderate disruption
Some redistribution politically achievable
Revolutionary change can be avoided

Democratic Institutions Resilient Enough:

Can handle some epistemic degradation
Can implement some AI governance
Can manage increased inequality
But operate under significant strain

Societal Preconditions

Public Accepts Gradual Change:

No overwhelming demand for pause
No revolutionary movement against AI
Concerns raised but no political consensus to stop
Acceptance of “this is how things are now”

Epistemic Environment Degraded but Functional:

Can still distinguish truth from fiction with effort
Some trusted information sources remain
Democratic deliberation possible but harder
Misinformation widespread but not completely dominant

No Catastrophic Incidents:

Safety incidents concerning but not existential
No events that force complete reconsideration
Each problem addressed reactively
No single galvanizing crisis

Warning Signs We’re Entering This Scenario

Early Indicators (Already Observable)

Currently Seeing:

Gradual capability improvements following scaling laws
Patchwork regulatory responses across jurisdictions
Growing economic anxiety about AI job impacts
Some safety incidents prompting reactive responses
Partial coordination attempts with mixed success
Public concern but no consensus for dramatic action

This Matches Muddle Pattern: We’re arguably already in early stages of this scenario. Current trajectory is incremental change with reactive responses.

Medium-Term Indicators (Next 3-5 Years)

We’re on This Path If We See:

Each new model generation ~2x better, not 10x
Regulations implemented but often circumvented
Some AI safety incidents but none catastrophic
Unemployment rising gradually to ~8-10%
International cooperation forums created with limited power
Alignment research progressing but no breakthroughs
Companies adopting safety practices unevenly
Public trust in AI declining slowly
No clear tipping point toward pause or acceleration

We’re Diverging If We See:

Sudden capability jump (toward Catastrophe or Aligned AGI)
Strong international coordination (toward Aligned AGI or Pause)
Catastrophic safety incident (toward Pause or Catastrophe)
Complete breakdown of coordination (toward Multipolar)
Major alignment breakthrough (toward Aligned AGI)

Late Indicators (5-10 Years)

Strong Evidence for This Scenario:

AI capabilities near-human in many domains but not superhuman
Economic disruption significant but managed (15-20% unemployment)
Governance struggling but functional
Continued safety incidents, none catastrophic
Inequality increased but political system stable
Epistemic environment degraded but democracy functioning
No clear path to either utopia or catastrophe visible

This Scenario Continuing Means:

We continue in state of uncertainty
No resolution of fundamental questions
Ongoing adaptation and strain
Question remains whether this is stable long-term

Valuable Actions in This Scenario

What Matters Most

Resilience and Adaptation:

Building social safety nets for economic disruption
Developing adaptive governance institutions
Strengthening epistemic institutions
Promoting gradual positive change where possible
Preventing slide into worse scenarios

Incremental Safety Work:

Continued alignment research even without breakthroughs
Improving evaluation and testing methods
Sharing safety information across organizations
Building safety culture in AI development
Responding effectively to each new incident

Maintaining Coordination:

Strengthening international cooperation where possible
Building trust between AI labs on safety
Promoting information sharing
Preventing deterioration into pure racing
Working toward better governance even if imperfect

Technical Research (High Value)

Practical Alignment Work:

Interpretability tools for current generation systems
Scalable oversight for near-term deployments
Robustness testing and evaluation
Adversarial testing and red-teaming
Iterative improvement of safety techniques

Governance Tools:

AI auditing and certification methods
Monitoring and detection systems
Impact assessment frameworks
Risk evaluation methodologies

Don’t Neglect:

Fundamental alignment research (might need it eventually)
Capability research that could reveal safety issues
Work on detecting deceptive alignment

Policy and Governance (High Value)

Adaptive Institutions:

Flexible regulations that can update as technology evolves
Democratic oversight mechanisms for AI
International cooperation frameworks even if imperfect
Monitoring systems for AI development and deployment

Economic Policy:

Expanded social safety nets
Retraining and education programs
Exploration of UBI or similar mechanisms
Policies to reduce AI-driven inequality

Epistemic Protection:

Media literacy programs
Support for trusted information sources
Provenance and watermarking standards
Countering misinformation infrastructure

Organizational Strategy

For AI Labs:

Maintaining safety culture despite competitive pressure
Implementing responsible scaling policies
Sharing safety information (even if not capabilities)
Preparing for gradual increase in regulation
Building trust with policymakers and public

For Governments:

Developing adaptive regulatory capacity
Investing in AI expertise in government
Building international cooperation channels
Preparing for economic disruption
Maintaining democratic legitimacy

For Civil Society:

Monitoring AI impacts
Advocating for safety and equity
Building public understanding
Holding companies and governments accountable
Strengthening democratic institutions

Individual Contributions

For Professionals:

Developing AI expertise in your domain
Advocating for responsible AI use
Participating in governance processes
Building adaptive career skills
Supporting others through transition

For Researchers:

Practical safety work on current systems
Improving evaluation methodologies
Building tools for responsible AI use
Communicating findings accessibly

For Advocates:

Promoting effective AI governance
Preventing both complacency and panic
Building coalitions for responsible AI
Strengthening democratic institutions

Who Benefits and Who Loses

Winners

Tech Companies:

Continued ability to develop and deploy AI
Growing markets for AI products
Regulations but not prohibitive
Public backlash but not overwhelming

AI-Adjacent Professionals:

Those who can work with AI effectively
Programmers using AI coding assistants
Professionals leveraging AI for productivity
AI safety researchers (growing field)

Some Nations:

Countries that adapt quickly to AI economy
Those with strong safety nets and retraining
Places that balance innovation with protection
Regions attracting AI talent and investment

Wealthy Individuals:

Can afford to leverage AI effectively
Can adapt to changing economy
Capital owners benefit from AI productivity
Can access AI-enhanced services

Losers

Displaced Workers:

Those in heavily automated sectors
Mid-skill workers in particular
Limited opportunities for retraining
Downward wage pressure

Developing Nations:

May lack resources to adapt
Could fall further behind economically
May lack voice in AI governance
More vulnerable to AI-driven disruption

Privacy and Autonomy:

Surveillance increases with AI capabilities
Manipulation becomes more sophisticated
Autonomy eroded by AI recommendation systems
Privacy degraded by ubiquitous AI analysis

Democratic Institutions:

Harder to maintain shared reality
Difficult to govern increasingly complex technology
Regulatory capture by AI companies
Epistemic environment degraded

Ambiguous Cases

Society Overall:

Significant material benefits from AI
But increased inequality and insecurity
More capable technology but more complex risks
Higher GDP but lower social cohesion
Economic growth but political strain

Young People:

Growing up with AI as normal
May adapt better than older generations
But face uncertain job markets
May lose some human capabilities to AI dependence

Researchers and Academics:

AI acceleration of research is beneficial
But credentialing and education systems strained
Academic jobs threatened by AI tutors and researchers
But new opportunities in AI-adjacent fields

Cruxes and Uncertainties

❓Key Questions

Is 'muddling through' stable long-term, or does it eventually collapse into catastrophe?

Can democratic institutions survive in degraded epistemic environment?

Will partial alignment solutions scale to more powerful systems?

Can we manage economic disruption indefinitely without political breakdown?

Is gradual capability progress likely to continue, or will we see discontinuities?

Will partial coordination be enough to prevent worst outcomes?

At what point does muddling through become untenable?

Biggest Uncertainties

Technical Trajectory:

Will scaling continue smoothly or hit walls/jumps?
Will current safety techniques scale?
When/if will we develop AGI?
Will we get advance warning of dangerous capabilities?

Social Stability:

Can political systems handle ongoing disruption?
Will inequality spark revolution or be tolerated?
Can trust in institutions be maintained?
Will epistemic environment degrade further or stabilize?

Governance Evolution:

Can coordination improve over time?
Will regulations become more or less effective?
Can democratic oversight of AI work?
Will authoritarian states gain advantage?

Long-Term Stability:

Is this a stable equilibrium or just delaying inevitable crisis?
Are we slowly solving problems or building up risks?
Will alignment challenges become unsolvable at higher capability levels?
Can we muddle through to truly beneficial outcomes eventually?

Is This Scenario Stable?

Arguments It’s Stable

Adaptation is Possible:

Humans and institutions can adapt to gradual change
Each problem prompts partial response
Learning from mistakes improves outcomes over time
Technological progress often follows this pattern historically

No Single Point of Failure:

Distributed, incremental development reduces catastrophic risk
Multiple safety measures provide defense in depth
Partial coordination prevents worst racing dynamics
Gradual change allows course correction

Economic and Political Incentives:

Benefits of AI provide motivation to continue carefully
Costs of racing create pressure for some coordination
Political pressure prevents both recklessness and excessive caution
Economic gains fund safety research and adaptation

Arguments It’s Unstable

Building Risk Over Time:

Partial alignment solutions may fail at higher capability levels
Epistemic degradation makes coordination harder
Inequality and disruption may reach breaking point
Complacency from lack of catastrophe may reduce safety investment

Coordination Decay:

Competition may intensify over time
International cooperation fragile under stress
Regulatory capture may worsen
Safety culture may erode under economic pressure

Capability Surprises:

Smooth progress may not continue
Unexpected capability jump could overwhelm adaptation
New paradigms may bypass current safety measures
Deceptive alignment may only be detectable too late

Social Breaking Points:

Unemployment may reach politically unsustainable levels
Epistemic environment may collapse completely
Trust in institutions may erode past functionality
Inequality may spark revolution

Most Likely: Unstable Equilibrium

This scenario probably represents temporary stability that could:

Improve toward Aligned AGI: If safety research succeeds and coordination strengthens
Degrade to Catastrophe: If capabilities jump or alignment fails
Fragment to Multipolar: If coordination breaks down
Force a Pause: If crisis demands it

The question is whether we can strengthen the stability or whether we’re just delaying inevitable transition.

Relation to Other Scenarios

This As Default Path

Muddle is Baseline:

This is where we are now
Other scenarios require departure from current trajectory
Inertia favors continuing muddling
Requires active effort to shift to other scenarios

Transitions Possible:

To Aligned AGI: If alignment breakthroughs and coordination improve
To Catastrophe: If capabilities jump or alignment fails at scale
To Multipolar: If coordination breaks down further
To Pause: If crisis forces reconsideration

Combinations With Other Scenarios

Muddle Plus Elements:

Multipolar competition within muddling framework
Temporary pauses during critical periods
Local successes with alignment alongside global muddling
Different regions on different paths

Muddle as Transition:

May muddle until we know enough to solve alignment (→ Aligned AGI)
May muddle until something forces a choice (→ Pause or Catastrophe)
May muddle until coordination fails (→ Multipolar)
Muddle may be the long-term state

Historical Analogies

Similar Historical Patterns

Industrial Revolution:

Gradual technological transformation over decades
Significant economic disruption
Partial regulatory responses
Social adaptation through crisis and reform
Massive benefits but also massive costs
Lessons: Gradual change is disruptive but manageable; adaptation takes generations

Internet/Social Media:

Rapid adoption with partial understanding of consequences
Reactive regulation always behind technology
Mix of benefits and harms
Epistemic challenges emerging
No catastrophe but no clear resolution
Lessons: We often muddle through transformative technologies

Nuclear Power:

Powerful technology with safety concerns
Gradual deployment with incidents
Regulations improved after each incident
Public concern but continued use
Neither utopia nor catastrophe
Lessons: Can maintain risky technology with care but never perfect safety

Climate Change:

Gradual problem with inadequate response
Partial measures insufficient
Coordination failures ongoing
Muddling toward significant harm
Lessons: Muddling through can lead to bad outcomes if problem is severe enough

Analogy Comparison

Historical Case	Duration	Peak Disruption	Governance Response	Relevance to AI
Industrial Revolution	100+ years	40-60% workforce transition	Child labor laws, unions (decades late)	High; labor transformation
Internet/Social Media	30+ years	Epistemic erosion, moderate job shift	Still inadequate	Very High; similar speed
Nuclear Power	70+ years	Three Mile Island, Chernobyl, Fukushima	NRC, IAEA created	Medium; safety incidents drive regulation
Climate Change	50+ years ongoing	1.5C+ warming likely	Paris Agreement (voluntary)	Warning; muddling may fail

Key lesson: None of these analogies involved intelligence itself as the transforming factor. AI may differ fundamentally because it can participate in its own development and governance evasion.

Probability Assessment

📊

Source	Estimate	Date
Baseline estimate	30-50%	—
Optimists	40-60%	—
Pessimists	20-40%	—
Median view	35-45%	—

Why This Probability?

Reasons for Higher Probability:

This is our current trajectory
Consistent with how most technological transitions occur
Humans good at gradual adaptation
Distributed development reduces single points of failure
Economic incentives favor continued development with some caution
No strong force pushing toward radical alternatives

Reasons for Lower Probability:

May be unstable equilibrium
AI might be different from historical technologies
Capabilities could jump unexpectedly
Coordination challenges may worsen
Epistemic degradation may accelerate
Economic disruption may exceed adaptation capacity

Central Estimate Rationale: 30-50% reflects that this is our default path and consistent with historical patterns, but may not be stable long-term. Higher than other individual scenarios but not certain. Wide range reflects uncertainty about whether muddling can continue or will transition to other scenarios.

What Would Change This Estimate?

Evidence Increasing Probability:

Capabilities continuing to scale smoothly
Partial coordination proving sustainable
Safety incidents remaining non-catastrophic
Economic adaptation proving manageable
Democratic institutions proving resilient
No dramatic breakthroughs or failures

Evidence Decreasing Probability:

Unexpected capability jumps or plateaus
Coordination breaking down or succeeding completely
Catastrophic safety incident
Economic or political crisis
Major alignment breakthrough or impossibility proof
Events forcing departure from current trajectory

Open Questions

About Stability:

Can we muddle through indefinitely?
Is this building toward catastrophe or success?
What’s the longest this can continue?
What events would force transition?

About Adaptation:

Can institutions adapt fast enough?
What’s the limit of economic disruption we can manage?
Can epistemic environment be stabilized?
Will inequality stabilize or keep growing?

About Technology:

Will scaling continue smoothly?
Will partial safety measures scale?
When will we know if alignment is solvable?
What capabilities will emerge next?

About Coordination:

Can partial coordination strengthen over time?
What would trigger cooperation or breakdown?
Can democratic governance work for AI?
Will authoritarians gain advantage?

Sources & References

Economic Impact Projections

IMF: AI Will Transform the Global Economy↗ (January 2024) - Analysis of AI’s impact on 40% of global jobs
McKinsey: State of AI 2024↗ - 65% of organizations regularly using AI
Penn Wharton Budget Model: AI Productivity Impact↗ - Conservative 0.7% TFP gains estimate

Governance & International Cooperation

EU AI Act Implementation Timeline↗ - Phased approach through 2027
Seoul Declaration↗ - 27-nation commitment on AI safety
Brookings: Bletchley Park Process↗ - Analysis of international cooperation potential
UK AI Safety Institutes Network↗ - Cross-border safety collaboration

Capability & Scaling Analysis

Ethan Mollick: Scaling State of Play↗ - Gen2/Gen3 model progression
HEC Paris: AI Beyond Scaling Laws↗ - Diminishing returns analysis

Safety Research & Incidents

MIT AI Incident Tracker↗ - Comprehensive incident database
Future of Life Institute: 2025 AI Safety Index↗ - Company safety practices assessment
Anthropic Alignment Science↗ - Technical safety research
Anthropic-OpenAI Joint Evaluation↗ - Cross-lab alignment testing

International AI Safety Report

International AI Safety Report (2025)↗ - 96 experts, 30 nations, commissioned assessment of AI threats