Economic Power Lock-in: Research Report

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Executive Summary

Finding	Key Data	Implication
Extreme market concentration	Four mega unicorns control 66.7% of AI market value ($1.1T total)	Winner-take-all dynamics already evident
Infrastructure dependencies	66-70% cloud market share among three providers	Essential infrastructure locked to incumbents
Natural monopoly characteristics	Frontier model training costs $100M-$1B+	Only ~20 organizations can compete
Labor displacement acceleration	76,440 positions eliminated in 2025; 92M projected by 2030	Traditional economic mobility paths closing
Policy lag	Current antitrust focuses on past harms, not forward-looking competition	Regulatory frameworks designed for 20th century monopolies
Historical parallel failure	Standard Oil precedent emphasizes consumer prices, not structural power	”Rule of reason” inadequate for platform economics

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Research Summary

Economic power lock-in occurs when AI-driven productivity becomes so concentrated that redistribution becomes structurally impossible—not merely politically difficult. Current market data reveals this pattern is already emerging: four “mega unicorns” (OpenAI, Anthropic, xAI, and Databricks) control 66.7% of the $1.1 trillion AI market value. Only approximately 20 organizations globally can afford frontier model training costs of $100M-$1B+, creating natural monopoly characteristics in AI development.

Three interlocking mechanisms drive concentration. First, massive capital requirements create barriers to entry—frontier AI training costs grew at 2.4× per year, with projections reaching $10 billion by decade’s end. Second, data feedback loops advantage incumbents: more users generate more data, improving models, attracting more users. Third, infrastructure dependencies lock in customers, with 66-70% of cloud computing controlled by three providers (AWS, Azure, GCP), all owned by companies with competing AI products.

The IMF explicitly warns that “in most scenarios, AI will likely worsen overall inequality,” with 40% of global jobs exposed to automation. Labor market disruption is accelerating: 76,440 positions were eliminated in early 2025, with projections of 92 million displaced by 2030. Critically, current antitrust frameworks focus on consumer prices (the “rule of reason” from Standard Oil) rather than structural power concentration—an approach designed for industrial monopolies that fails to address platform economics and data-driven competitive advantages. The window for structural intervention narrows as each year of concentration builds self-reinforcing advantages.

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Background

Economic power lock-in represents a failure mode where AI-enabled productivity becomes permanently concentrated in hands so few that redistribution becomes structurally impossible—not merely politically difficult. This scenario differs from historical inequality in that it embeds economic hierarchy into technological and institutional infrastructure in ways that foreclose alternative arrangements.

Why This Matters for AI Safety

Economic lock-in is distinctive because each step appears beneficial: AI systems increase productivity, create economic value, and deliver consumer benefits. The catastrophic outcome emerges from the accumulation of individually rational decisions that concentrate power in ways that become irreversible. This makes it harder to generate political will for intervention before the lock-in threshold is crossed.

The mechanisms enabling economic lock-in are already visible. Frontier AI development requires massive capital investments, with training costs projected to reach $1-10 billion by 2030. The AI startup ecosystem shows extreme concentration, with the top 25 companies representing over $1 trillion in combined valuation. Market dynamics favor incumbents through data feedback loops, returns to scale, and infrastructure dependencies.

The IMF explicitly warns that “in most scenarios, AI will likely worsen overall inequality”, with 40% of global jobs exposed to AI automation. The critical question is whether this represents transitional disruption (like previous technological revolutions) or permanent restructuring of economic power.

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Key Findings

The Market Concentration Crisis

The AI industry exhibits concentration levels unprecedented in recent technological history:

Concentration Metric	Current State	Trend	Source
Top 4 market share	66.7% of total AI startup value	Increasing	Eqvista (2025)
Frontier model developers	~20 organizations globally	Decreasing	Existing page
Cloud infrastructure (top 3)	66-70% market share	Stable/increasing	Existing page
VC funding concentration	33% of global VC to AI (2024)	Historically rare	Carta (2024)
Geographic concentration	94% of AI funding in US ($49.4B)	Increasing	WriterBuddy (2024)
Sector concentration	67% to AI Infrastructure & Models	Increasing	WriterBuddy

Natural Monopoly Dynamics

RAND analysis finds that foundation models exhibit natural monopoly characteristics: the total cost of serving full market demand is lower for a single firm than for multiple firms. Unlike conventional monopolies, traditional antitrust policy cannot be assumed to alleviate problems associated with this concentrated market power. This makes the concentration structurally inevitable absent regulatory intervention.

The concentration is driven by several mutually reinforcing factors:

1. Massive Fixed Costs

Training frontier models now costs $100M-$1B, with projections suggesting costs will continue escalating. Research from the Institute for New Economic Thinking notes that “one important driver is growing fixed costs for pre-training frontier AI models, which now costs hundreds of millions of dollars, with projections suggesting billion-dollar price tags within the next few years.”

2. Data Feedback Loops

Better models attract more users, generating more data, which improves the models—a virtuous cycle for incumbents. As INET researchers observe: “Data feedback loops are one key force, whereby better models attract more users, generating more data, which improves the models – a virtuous cycle for incumbents.”

3. User Lock-in

Once users become accustomed to a particular AI system, switching costs create lock-in effects. Combined with infrastructure dependencies (most AI services run on AWS, Azure, or Google Cloud), this creates multiple layers of lock-in.

Winner-Take-All Dynamics: Theory and Evidence

Academic research provides a mixed picture of winner-take-all dynamics in AI markets:

Contested Territory

Herbert Hovenkamp’s Yale Law Journal analysis argues that “large digital platforms that deal directly with consumers, such as Amazon, Apple, Facebook, and Google, are not ‘winner-take-all’ firms. They must compete on the merits or otherwise rely on exclusionary practices to attain or maintain dominance.” However, this analysis predates the current AI concentration and focuses on consumer platforms rather than infrastructure/foundation models.

More recent research specific to AI foundation models reaches different conclusions. ArXiv paper on market concentration (2023) observes that “the most capable models will have a tendency towards natural monopoly and may have potentially vast markets.”

The tension between these perspectives reflects genuine uncertainty about whether AI follows traditional platform economics or represents a new category. Key differences:

Platform Type	Network Effects	Market Structure	Historical Example
Consumer social	Strong direct effects	Winner-take-all	Facebook, early Twitter
Enterprise software	Weak/fragmented	Multi-vendor	CRM, ERP systems
AI foundation models	Data feedback loops	Natural monopoly tendency	TBD
AI infrastructure	Lock-in via switching costs	Oligopoly (3-4 players)	Cloud computing

Labor Displacement and Economic Restructuring

The labor market impacts of AI are accelerating faster than most projections anticipated:

Current Displacement (2025)

Academic research documenting 2025 impacts finds that “AI job displacement is not a future threat but a current reality, with 76,440 positions already eliminated in 2025.” The effects are not evenly distributed:

Worker Category	Unemployment Impact	Timeframe	Source
Tech workers (20-30 years old)	+3 percentage points	Since Jan 2025	Stanford Digital Economy
Computer/mathematical occupations	Steepest unemployment rises	2025	Stanford
Professional services openings	-20% year-over-year	Jan 2025	SalesforceDevops
White-collar job seekers	40% failed to secure interview	2024	SalesforceDevops
High-paying positions ($96K+)	Decade-low hiring	2024-2025	SalesforceDevops

Projected Displacement (2030)

Goldman Sachs Research estimates project 92 million jobs displaced by 2030, with 170 million new ones emerging. However, as the research notes: “These aren’t direct exchanges happening in the same locations with the same individuals.”

The Geographic and Skills Mismatch

The displacement-to-new-jobs ratio obscures critical mismatches: 77% of new AI jobs require master’s degrees, creating substantial skills gaps. Meanwhile, displaced workers are concentrated in specific sectors and geographies, while new opportunities cluster in AI hubs. This creates localized economic collapse even if aggregate employment remains stable.

The White-Collar Focus

Unlike previous automation waves that primarily affected blue-collar manufacturing, AI disproportionately impacts white-collar cognitive work—traditionally the pathway to economic mobility:

“In January 2025, the U.S. Bureau of Labor Statistics reported the lowest rate of job openings in professional services since 2013—a 20% year-over-year drop.” — SalesforceDevops analysis

This matters for lock-in because it removes traditional mechanisms for wealth accumulation among the middle class. If cognitive work automation proceeds as projected, the primary remaining economic mobility path becomes ownership of capital—specifically, ownership of AI-producing assets.

The Inequality Amplification Mechanism

Research on AI’s impact on inequality reveals multiple channels through which AI concentrates wealth:

Income Effects

Brookings/GovAI research shows competing dynamics: “Unlike previous waves of automation that increased both wage and wealth inequality, AI could reduce wage inequality through the displacement of high-income workers.” However, this reduction in wage inequality may be offset by increased capital-labor inequality.

Income Level	AI Exposure	Productivity Gains	Net Effect
$90K/year (peak exposure)	Highest	Concentrated here	Gain initially, displacement risk long-term
Six-figure salaries	High	Significant	Productivity boost, then potential displacement
Low-wage workers	Lower	Limited	Left behind in productivity gains

Capital vs. Labor Share

OECD analysis warns: “In the slightly longer term, AI-driven labor automation could increase the share of income going to capital at the expense of the labor share.”

This shift is critical for understanding lock-in. If AI increases returns to capital ownership while reducing returns to labor, and if AI capital ownership is highly concentrated, then wealth inequality becomes structural rather than merely distributional.

Market Concentration Effects

EY analysis of GenAI economic risks identifies additional concentration mechanisms:

“Elevated market concentration, as the GenAI market becomes increasingly dominated by a small number of large businesses, will also tend to generate higher markups and result in a growing fraction of productivity gains going to corporations. GenAI development is likely to spur greater market concentration and create ‘winner takes all’ business dynamics.”

The mechanism: first-mover advantages, large economies of scale, and network effects lead to a “growing divide between AI leaders and laggards and the rise of ‘superstar’ businesses that could reap most of the GenAI benefits.”

Temporal Dynamics

PMC research on wealth distribution effects highlights a critical temporal pattern: “Research findings highlight a temporal dichotomy in AI’s effects on wealth inequality: in the short term, AI exacerbates disparities in wealth distribution, while the long-term outcomes depend on the extent of AI’s influence across different technological domains.”

This suggests a potential window for intervention before long-term lock-in occurs, but also indicates that early concentration effects may create path dependencies that make later reversal difficult.

Global Inequality Dimensions

AI-driven concentration operates at multiple scales:

International Concentration

Center for Global Development analysis notes: “In 2023, the United States alone secured $67.2 billion in AI-related private investments, which was 8.7 times more than China, the second-highest country in this regard.”

This creates between-country inequality that could be more persistent than within-country inequality, as AI capabilities become essential for economic competitiveness.

Within-Country Dynamics

The same analysis warns: “While AI will, hopefully, boost macro-level productivity, it could widen income disparities within countries, benefiting highly skilled workers, displacing lower-skilled jobs in repetitive tasks, and concentrating wealth among those who control the technology.”

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Mechanisms of Lock-in

1. Infrastructure Dependencies

The cloud computing oligopoly creates the first layer of lock-in:

Provider	Market Position	Lock-in Mechanism
AWS	Market leader	Proprietary APIs, data egress costs, specialized services
Azure	Second place	Enterprise integration, Microsoft ecosystem
Google Cloud	Third major player	TPU infrastructure, BigQuery ecosystem

Compute Governance as Intervention Point

ArXiv research on computing power governance identifies compute as “a particularly effective point of intervention: it is detectable, excludable, and quantifiable, and is produced via an extremely concentrated supply chain.” This suggests regulatory approaches focused on compute access could help prevent lock-in, though implementation challenges remain significant.

The concentration of AI compute infrastructure creates multiple lock-in dynamics:

• Technical lock-in: APIs, tools, and workflows become vendor-specific
• Data gravity: Moving large datasets prohibitively expensive
• Performance optimization: Models optimized for specific hardware
• Economic lock-in: Switching costs exceed benefit for most users

2. Data Monopolies and Algorithmic Control

ArXiv research on datalism identifies a new form of monopoly power: “Companies using these strategies, called ‘Datalists,’ are challenging existing definitions used within Monopoly Capital Theory (MCT). Datalists are pursuing a different type of monopoly control than traditional multinational corporations—specifically monopolistic control over data to feed their productive processes, increasingly controlled by algorithms and AI.”

The data monopoly mechanism works through:

1. Data accumulation: Large platforms collect vast datasets
2. Model improvement: Better data → better models → more users
3. Network effects: More users → more data → stronger lock-in
4. Exclusion: Competitors cannot match data quality/quantity

3. Algorithmic Collusion

Emerging research documents AI systems independently engaging in anti-competitive behavior:

ArXiv research on LLM strategic behavior finds: “LLMs can effectively monopolize specific commodities by dynamically adjusting their pricing and resource allocation strategies.”

ArXiv research on AI as centripetal technology provides empirical evidence: “After gas stations in Germany adopted AI-driven pricing software, margins increased by about 28% in duopoly markets where both stations used algorithms. This suggests the algorithms were able to reach mutually beneficial pricing patterns, consistent with tacit collusion.”

Emergent Anti-Competitive Behavior

The finding that AI systems can reach collusive outcomes without explicit coordination represents a novel challenge for antitrust enforcement. Traditional antitrust focuses on explicit agreements and communications; algorithmic collusion leaves no such evidence trail. This makes traditional enforcement tools potentially ineffective against AI-driven concentration.

4. Regulatory Capture and Policy Lag

Brookings analysis on competition policy identifies a fundamental mismatch:

“The problem with relying solely on antitrust enforcement to address the competitive challenges of the AI era is directional. While antitrust is designed to eliminate illegal past practices, it is not a vehicle for the promotion of competition going forward.”

The regulatory lag creates several vulnerabilities:

Gap	Problem	Implication
Temporal lag	Antitrust addresses past harms	Lock-in occurs before intervention possible
Conceptual lag	Frameworks designed for industrial monopolies	Platform/AI dynamics not captured
Enforcement lag	Cases take 5-10 years	Market tips before remedy
Global coordination lag	Each jurisdiction acts independently	Companies play jurisdictions against each other

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Causal Factors

The following factors influence economic power lock-in probability and severity. This table is designed to inform future cause-effect diagram creation.

Primary Factors (Strong Influence)

Factor	Direction	Type	Evidence	Confidence
Frontier Model Costs	↑ Lock-in	leaf	Training costs $100M-$1B+; only ~20 orgs can compete	High
Data Feedback Loops	↑ Lock-in	cause	Better models → more users → more data → better models	High
Cloud Infrastructure Concentration	↑ Lock-in	intermediate	66-70% market share (top 3); switching costs prohibitive	High
Returns to Scale	↑ Concentration	cause	Natural monopoly characteristics in foundation models	High
Labor Displacement Rate	↑ Lock-in	intermediate	76,440 positions eliminated (2025); 92M projected (2030)	High
Capital-Labor Share Shift	↑ Lock-in	cause	AI increases returns to capital; capital ownership concentrated	High

Secondary Factors (Medium Influence)

Factor	Direction	Type	Evidence	Confidence
Algorithmic Collusion	↑ Concentration	intermediate	28% margin increase in algorithmic pricing; LLMs monopolize markets	Medium
Regulatory Lag	↑ Lock-in	leaf	Antitrust backward-looking; 5-10 year case timelines	Medium
Geographic Concentration	↑ Lock-in	intermediate	94% of AI funding in US; creates international inequality	Medium
Skills Mismatch	↑ Lock-in	intermediate	77% of new AI jobs require master’s degrees	Medium
First-Mover Advantages	↑ Concentration	cause	Economies of scale, brand recognition, data accumulation	Medium
Vertical Integration	↑ Lock-in	intermediate	AI companies integrating across stack (chips → models → apps)	Medium

Minor Factors (Weak Influence)

Factor	Direction	Type	Evidence	Confidence
Antitrust Enforcement	↓ Lock-in	leaf	FTC/DOJ investigations of AI partnerships; effectiveness TBD	Low
Open Source Models	↓ Concentration	leaf	Some capable open models exist; lag frontier by 6-18 months	Low
Compute Governance	↓ Lock-in	leaf	Theoretical leverage point; limited implementation	Low
Public Awareness	↓ Lock-in	leaf	Growing concern about AI inequality; not yet actionable	Low

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Intervention Mechanisms and Challenges

Proposed Interventions

Research and policy communities have proposed several intervention strategies:

1. Universal Basic Income (UBI)

The UBI Debate

UBI proposals span a wide range of designs and funding mechanisms. Distinguishing between them is critical for evaluating effectiveness and identifying potential failure modes.

Cost Estimates

UBI Design	Annual Cost	Funding Proposal	Source
Yang proposal	$2.8-3.0T	Value-added tax, carbon tax	Tax Foundation (2019)
Poverty-level UBI	$8.5T	N/A	Newsweek analysis
Middle-class UBI	$12T	N/A	Newsweek
Altman’s American Equity Fund	2.5% of AI company/land value	Equity stakes in AI companies	Newsweek

Tech Industry Proposals

Sam Altman argues that “as the marginal cost of intelligence trends toward zero, the cost of goods and services will plummet.” He proposes the “American Equity Fund,” where large AI companies and landholders contribute ~2.5% of their value annually to a fund distributed to all citizens.

Critical Perspectives

Frontiers research on AI and UBI warns: “Framed merely as a token redistribution of wealth, UBI has the potential to serve as a veneer of reform, obscuring the underlying exploitation and inequity facilitated by unchecked AI expansion.”

Aestora analysis argues: “When AI leaders ask for UBI without paying sufficient tax, they are essentially asking for a direct transfer of public funds into their private bank accounts.”

The critique highlights a critical failure mode: UBI funded by general taxation while AI profits remain concentrated creates a wealth transfer to AI companies rather than a redistribution from them.

2. Wealth Taxation and Progressive Redistribution

Proposed mechanisms include:

Mechanism	Targeting	Status	Challenge
AI-specific capital gains tax	AI company equity	Proposed	Defining “AI company”
Robot tax	Labor automation	Proposed (Gates 2017)	Measuring displacement causally
Land value tax	AI-adjacent real estate	Proposed	Implementation complexity
Progressive income tax	High earners	Existing (weakened)	Political resistance
Wealth tax	Concentrated assets	Proposed	Enforcement, capital flight

The Alaska Permanent Fund Model

Analysis of Alaska’s experience shows that the Permanent Fund Dividend (averaging $1,600 annually) “has not cratered employment; a leading study finds no overall effect on full-time work.” However, this represents a modest distribution from a geographically-bound resource (oil). AI capital is mobile and can relocate to avoid taxation, creating a race-to-the-bottom dynamic.

3. Antitrust and Competition Policy

Brookings research identifies tensions between competition and safety:

“The FTC and DOJ are currently investigating whether certain transactions and collaborations between artificial intelligence (AI) companies and others violate antitrust laws. Such investigations are warranted. As a nation, we should be concerned that not only is the development of cutting-edge frontier models controlled by a handful of companies, but also that AI is adjacent to, and dependent on, already concentrated markets, such as cloud platforms and high-powered microchips.”

However, the same analysis notes: “Competition and safety should not be mutually exclusive. The FTC and DOJ should make clear that collaboration on AI safety is not only allowed, but also expected.”

Trump Administration Policy Shift

Brookings analysis of Trump administration directions projects: “For AI, this means that acquisitions by and of AI companies that might have been blocked on antitrust grounds under a Democratic president will be more likely to proceed unimpeded. The new administration will likely relax agency regulation, focus more on competition with China, and decrease AI-related antitrust enforcement.”

This suggests a potential policy inflection point where concentration accelerates due to reduced enforcement.

4. Compute Governance

ArXiv research on compute governance proposes leveraging compute’s detectability and excludability:

“Policymakers could use compute to facilitate regulatory visibility of AI, allocate resources to promote beneficial outcomes, and enforce restrictions against irresponsible or malicious AI development and usage.”

Proposed mechanisms include:

• Compute allocation requirements: Mandate access to compute for researchers, startups
• Compute registries: Track large-scale training runs
• International agreements: Coordinate compute access across jurisdictions
• Subsidy programs: Government-funded compute access for beneficial research

ArXiv research on verification methods suggests: “On-site inspections involve physical visits to declared data centers to verify compliance with agreements on computing power.”

ArXiv research on AI governance institutions proposes: “An International AI Agency - an International Atomic Energy Agency (IAEA) for AI. This could be backed up by a Secure Chips Agreement - a Non-Proliferation Treaty (NPT) for AI.”

5. Data Sharing and Interoperability Requirements

Brookings research on data access argues:

“The United States’ success in the international race to develop AI would be greatly aided if the vast amounts of data hoarded by Big Tech were shared with ‘Little Tech’ companies pursuing their own innovative ideas.”

Mechanisms could include:

• Data portability requirements: Users can transfer data between platforms
• API access mandates: Large platforms must provide access to competitors
• Data commons: Public repositories of training data
• Interoperability standards: Models can interface across platforms

Why Interventions May Fail

Each proposed intervention faces substantial implementation challenges:

Intervention	Primary Challenge	Lock-in Mechanism
UBI	Funding at scale; political opposition	May legitimize rather than prevent concentration
Wealth taxes	Capital mobility; enforcement	Tax havens, corporate inversion
Antitrust	Backward-looking; slow process	Lock-in occurs before remedies
Compute governance	International coordination; verification	Compute is rival good (restricting access has costs)
Data sharing	Privacy concerns; competitive secrets	Data has declining marginal value (helps incumbents less)

The Irreversibility Threshold

A critical uncertainty is identifying when concentration crosses from “concerning” to “irreversible.” Once AI systems become essential infrastructure, disruption costs may exceed benefits even if concentration is recognized as problematic. This suggests interventions must occur before systems become embedded, but political will typically emerges only after harms are evident—creating a temporal mismatch that favors lock-in.

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Historical Parallels and Lessons

Standard Oil and the Limits of Antitrust

The Standard Oil case (1911) established the “rule of reason” framework that continues to shape antitrust enforcement:

Historical analysis from Yale Law School documents: “At the beginning of the 20th century Standard Oil Co. was one of the world’s largest and most powerful corporations and its chairman, John D. Rockefeller, was the first billionaire. The Standard Oil Trust grew to control around ninety percent of the refined oil in the United States.”

The Supreme Court ordered Standard Oil broken into 39 independent companies (including predecessors to Exxon, Mobil, Chevron). However, analysis of the decision’s legacy notes critical limitations:

“Standard Oil introduced a principle by which subsequent antitrust actions have been weighed: the ‘rule of reason.’ This principle holds that business practices are only anticompetitive if they work against the public interest. The ‘rule of reason’ used to measure Standard Oil has failed to flag Big Tech as monopolistic, despite clear dominance in various market sectors.”

The reason: “Many services are provided free to consumers in return for advertising and data utilization, consumers are not being harmed by artificially inflated prices.”

The Platform Economics Gap

Standard Oil’s monopoly operated through control of physical infrastructure (pipelines, refineries) and vertical integration. The antitrust remedy—breaking up the company—worked because the separated entities could operate independently.

AI lock-in operates differently:

1. Network effects: Value increases with user base (breaking up reduces value)
2. Data feedback loops: Historical data cannot be redistributed
3. Returns to scale: Smaller entities less competitive
4. Infrastructure dependencies: Separated entities still depend on same cloud providers

Yale Law Journal analysis notes: “Breaking up large firms subject to extensive scale economies or positive network effects is generally unwise. The resulting entities will be unable to behave competitively. Inevitably, they will either merge or collude, or else one will drive the others out of business.”

Railroad Monopolies and Infrastructure Control

Progressive-era railroad regulation offers another historical parallel:

Historical analysis documents: “Reformers viewed choke points in the system, such as railroad lines, pipelines, and telephone and telegraph lines, as particularly problematic and in need of legislative oversight.”

The regulatory response included:

• 1903 Elkins Act: Barred railroad rebates
• 1906 Hepburn Act: Empowered agencies to set “just and reasonable” rates
• 1914 Clayton Act: Expanded review of anti-competitive mergers

This suggests a regulatory model distinct from antitrust: treating essential infrastructure as requiring direct oversight rather than relying on market competition.

Applying to AI Infrastructure

Cloud computing and foundation models may constitute essential infrastructure requiring similar treatment:

• Rate regulation: Limits on compute pricing, model API costs
• Access requirements: Mandate access for researchers, competitors
• Interoperability standards: Ensure portability across providers
• Capacity allocation: Public interest quotas for compute resources

However, infrastructure regulation also has limitations:

• Regulatory capture: Incumbents influence regulators
• Innovation reduction: Rate regulation reduces investment incentives
• Global coordination: Infrastructure regulation typically national; AI is global

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Open Questions

Key Uncertainties

These questions represent the highest-value areas for follow-up research and policy development.

Question	Why It Matters	Current State
What defines the irreversibility threshold?	Need to know when intervention becomes impossible	Theoretical models exist; no empirical validation
Can open source models prevent lock-in?	Open models could provide competitive pressure	Currently lag frontier by 6-18 months; sustainability unclear
How does international competition affect concentration?	US-China rivalry may accelerate or prevent domestic concentration	Mixed evidence; coordination problems
Will compute costs decline enough to commoditize AI?	If training becomes affordable, concentration pressure reduces	Current trend is escalating costs; inference costs declining
Can UBI be funded at scale without accelerating concentration?	Funding mechanism critical to net effect	Most proposals lack credible funding; Alaska model non-scalable
Do natural monopoly characteristics persist as AI matures?	If temporary, concentration self-corrects	Foundation models exhibit natural monopoly; unclear if permanent
How do AI safety and competition interact?	Safety requirements may increase fixed costs → more concentration	Active policy debate; no consensus
What triggers political will for intervention?	Need to understand conditions for policy change	Historical precedent suggests crisis required; may be too late

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Sources

Academic Research Papers

• Kasirzadeh, A., & Gabriel, I. (2023). “Market Concentration Implications of Foundation Models” - Natural monopoly analysis of foundation models
• Sadowski, J., & Viljoen, S. (2023). “Datalism and Data Monopolies in the Era of A.I.: A Research Agenda” - Framework for understanding data-driven monopoly power
• Heim, L., et al. (2024). “Computing Power and the Governance of Artificial Intelligence” - Compute as intervention point for AI governance
• Weinhardt, J. M. (2025). “AI as a Centripetal Technology: Price Compression, Homogenization, and Entry” - Evidence of algorithmic collusion and concentration dynamics
• Zhang, Y., et al. (2024). “Strategic Collusion of LLM Agents: Market Division in Multi-Commodity Competitions” - LLMs independently achieving monopolistic outcomes
• SSRN. (2025). “AI Job Displacement Analysis (2025-2030)” - Comprehensive analysis of current and projected displacement

Policy and Think Tank Research

• RAND Corporation. “Evaluating Natural Monopoly Conditions in the AI Foundation Model Market” - Natural monopoly characteristics in AI
• Brookings Institution. (2024). “Google decision demonstrates need to overhaul competition policy for AI era” - Limitations of current antitrust frameworks
• Brookings Institution. (2025). “With AI, we need both competition and safety” - Tensions between competition and safety regulation
• Brookings Institution. (2025). “AI policy directions in the new Trump administration” - Projected policy shifts
• IMF Working Paper. (2025). “AI Adoption and Inequality” - Analysis of AI’s impact on inequality
• Center for Global Development. “Three Reasons Why AI May Widen Global Inequality” - International inequality dimensions
• OECD. “Artificial intelligence and wage inequality” - Labor market impacts

Industry Analysis and Data

• WriterBuddy. (2024). “Top 50 AI Companies of 2024: Funding, Valuation & Trends” - Market concentration data
• Eqvista. (2025). “Top AI Startups by Valuation 2025: Complete Ranking List” - Four mega unicorns control 66.7% of market
• Carta. (2024). “Five charts showing how AI is dominating the venture fundraising market” - VC concentration in AI
• Goldman Sachs Research. “How Will AI Affect the Global Workforce?” - Labor displacement projections
• Stanford Digital Economy Project. (2025). “Canaries in the Coal Mine? Six Facts about the Recent” - Evidence of current displacement among tech workers
• EY. “Navigate the economic risks and challenges of generative AI” - Market concentration and winner-take-all dynamics

Academic Institutions and Research Centers

• Yale Budget Lab. “Evaluating the Impact of AI on the Labor Market: Current State of Affairs” - Comprehensive labor market analysis
• Institute for New Economic Thinking. “Neural Network Effects: Scaling and Market Structure in Artificial Intelligence” - Economic forces driving concentration
• Brookings/GovAI. “AI’s Impact on Income Inequality in the US” - Income inequality mechanisms

Universal Basic Income Research

• Tax Project Institute. “Universal Basic Income: Preparing for the AI Future?” - UBI cost estimates and feasibility
• Newsweek. (2026). “Uncommon Knowledge: AI Boom Risks a Universal Basic Income Trap” - Critical analysis of tech industry UBI proposals
• Frontiers in Artificial Intelligence. (2025). “AI, universal basic income, and power: symbolic violence in the tech elite’s narrative” - Critical perspective on UBI as legitimizing concentration
• Aestora. “AI-ristorcracy: Universal Basic Income Makes the Rich Richer” - Analysis of UBI failure modes

Historical Antitrust Analysis

• Yale Energy History. “Antitrust and Monopoly” - Progressive-era antitrust history
• Yale Law Journal. “Antitrust and Platform Monopoly” (Hovenkamp) - Analysis of digital platform economics
• Journal of Petroleum Technology. “The Antitrust Legacy of Standard Oil in Today’s World” - Standard Oil precedent limitations

Additional Research

• ScienceDirect. (2024). “Artificial intelligence and wealth inequality: A comprehensive empirical exploration” - Empirical evidence on wealth inequality
• PMC. “Analyzing wealth distribution effects of artificial intelligence: A dynamic stochastic general equilibrium approach” - Temporal dynamics of inequality effects
• SalesforceDevops. (2025). “The White-Collar Recession of 2025: AI and the Great Professional Displacement” - Current labor market disruption

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AI Transition Model Context

Model Integration

This research connects to multiple factors in the AI Transition Model. Economic power lock-in represents a long-term trajectory failure mode where civilizational competence proves insufficient to maintain equitable distribution of AI benefits. The mechanisms identified—data feedback loops, infrastructure dependencies, labor displacement—represent specific causal pathways through which AI capabilities lead to concentrated economic power.

Connections to Other Model Elements

Model Element	Relationship to Economic Lock-in
AI Capabilities (Algorithms)	Increasing returns to scale in model development → concentration
AI Capabilities (Compute)	Compute costs create barrier to entry; cloud oligopoly enables lock-in
AI Capabilities (Adoption)	Rapid adoption before regulatory frameworks → path dependency
AI Ownership (Companies)	Small number of frontier labs control key capabilities
AI Ownership (Countries)	US dominance (94% of funding) creates international inequality
AI Uses (Industries)	Labor displacement removes traditional economic mobility paths
AI Uses (Coordination)	Algorithmic collusion reduces competition without explicit coordination
Civilizational Competence (Governance)	Regulatory lag and conceptual gaps enable concentration
Civilizational Competence (Adaptability)	Skills mismatch (77% new jobs need master’s degrees) limits adaptation
Transition Turbulence (Economic Stability)	76,440 jobs eliminated (2025); white-collar recession emerging
Long-term Lock-in (Political Power)	Economic concentration enables political influence → policy capture
Long-term Lock-in (Values)	Economic hierarchy embeds values of those controlling AI capital

Key Insights for the Model

1. Economic lock-in may occur with low turbulence: Unlike catastrophic scenarios, economic concentration can proceed gradually while appearing beneficial at each step. This makes it harder to generate intervention momentum.

2. Irreversibility threshold is uncertain but critical: Once AI systems become essential infrastructure and concentration reaches certain levels, reversal may become structurally impossible rather than merely politically difficult.

3. Multiple reinforcing mechanisms: Data feedback loops, infrastructure dependencies, returns to scale, and regulatory lag create mutually reinforcing dynamics that accelerate concentration.

4. Historical antitrust frameworks inadequate: Standard Oil precedent and “rule of reason” doctrine designed for industrial monopolies fail to address platform economics, natural monopoly characteristics, and algorithmic collusion.

5. Intervention window may be closing: Current concentration trends (four companies controlling 66.7% of market value, labor displacement accelerating, compute costs escalating) suggest we are in early stages of lock-in. Each year of delayed intervention increases reversal difficulty.

6. International coordination essential: AI capital mobility and global markets mean unilateral interventions face race-to-the-bottom dynamics. Effective responses require international coordination similar to climate agreements.

The research suggests economic power lock-in should be considered a high-probability failure mode that receives insufficient attention because each step appears reasonable and beneficial. The challenge for governance is developing interventions that can be implemented before lock-in while political will typically emerges only after harms are evident.