The Coordination Imperative: Computational Mutual Assured Destruction
Documenting How Operational AI Infrastructure Concentration Creates Strategic Coordination Pressure Through Mutual Vulnerability
This work employs analytical prophecy - a methodology that applies systematic pattern recognition to document convergent effects across complex systems. Given the rapid pace of technological transformation, traditional academic timelines often prove inadequate for analysing emergent phenomena. This approach maintains empirical grounding while acknowledging the interpretive nature of pattern analysis and the uncertainty between coordination and emergence in complex systems.
This analysis continues “The Warmth Engine Series” using analytical prophecy methodology. New readers should begin with “Analytical Prophecy: A Methodological Framework” to understand the approach, then “The Hydra Testament” for foundational context, followed by “The Warmth Engine Testament” and “The Alexandria Testament” which document preparation and authorisation mechanisms. “The Citadel: Operational Convergence” synthesises these mechanisms, establishing the convergence framework that creates the operational dependencies analysed here.
I. Introduction
The Warmth Engine trilogy documented systematic preparation, authorisation, and control mechanisms enabling AI deployment at unprecedented scale. The Hydra Testament revealed how cryptocurrency markets subsidised GPU development and energy infrastructure now inherited by AI systems. The Warmth Engine Testament analysed how climate emergency discourse created moral permission for massive computational resource consumption. The Alexandria Testament examined how processing control enables systematic value extraction from global data commons through non-retrofittable infrastructure requirements.
These analyses established that Citadels are operationally convergent - geographically concentrated complexes combining AI computational cores, dedicated energy infrastructure, and strategic positioning creating zones of technological inclusion and exclusion. The question now becomes: what strategic pressures do operational Citadels create?
C-MAD (Computational Mutual Assured Destruction) provides an analytical framework for understanding coordination dynamics created by operational AI infrastructure concentration. The core thesis: when critical systems across finance, logistics, communications, and energy depend on concentrated processing capacity, mutual vulnerabilities transform strategic incentives from competitive advantage-seeking towards coordination pressure - not because cooperation is inevitable, but because disruption costs exceed competitive benefits for all parties simultaneously.
This framework addresses operational-phase dynamics, distinct from development-phase frameworks like MAIM that address pre-deployment competition. C-MAD examines how existing dependencies create game-theoretic dynamics analogous to Stag Hunt coordination problems rather than Prisoner’s Dilemma defection incentives.
The analysis proceeds through five sections: distinguishing C-MAD from related frameworks (Section II), documenting dependency architecture across critical sectors (Section III), explaining game-theoretic mechanisms transforming competitive dynamics (Section IV), examining observable patterns consistent with coordination pressure (Section V), and critically assessing why coordination might not materialise despite structural pressures (Section VI).
This framework employs analytical prophecy methodology, documenting structural forces shaping strategic decisions without claiming to predict specific outcomes. Multiple equilibria remain possible - sustained cooperation, unstable competition, systematic fragmentation - depending on factors beyond the framework’s analytical scope.
II. C-MAD vs. MAIM: Critical Distinction
In March 2025, Hendrycks, Schmidt, and Wang published “Mutual Assured AI Malfunction” (MAIM), proposing that nations could deter dangerous AI development through credible sabotage threats - an escalation ladder from espionage through cyberattacks to kinetic strikes targeting rivals’ computational infrastructure. MAIM addresses pre-deployment competition: how to prevent adversaries from building dangerous systems in the first place.
C-MAD addresses a fundamentally different problem: how existing operational dependencies create coordination pressure once AI systems are already integrated into critical infrastructure.
The temporal distinction matters:
MAIM (Development Phase):
Phase: Pre-deployment
Mechanism: Deterrence through sabotage threats
Action: Prevent system creation
Game Type: Attack/defence dynamics
Timeline: Before deployment decisions
C-MAD (Operational Phase):
Phase: Post-integration
Mechanism: Coordination through mutual dependency
Action: Manage existing systems
Game Type: Cooperation/assurance problems
Timeline: After infrastructure integration
MAIM treats AI infrastructure as attack targets - datacentres and chip fabs become strategic assets to destroy or disable. C-MAD treats the same infrastructure as shared resources creating mutual vulnerabilities. When major financial systems, logistics networks, and communications infrastructure depend on concentrated processing capacity, disruption harms all parties regardless of who initiates it.
This creates different strategic incentives. MAIM’s deterrence logic assumes adversaries value their AI capabilities enough that destruction threats prevent development. C-MAD’s coordination logic recognises that once systems are operational and integrated, disruption costs exceed competitive benefits for all parties simultaneously.
The development-versus-operational distinction mirrors nuclear weapons strategy evolution. Early Cold War doctrine focused on preventing adversaries from acquiring nuclear capabilities. Once both superpowers possessed arsenals, strategic attention shifted to managing mutual vulnerabilities through arms control agreements, hotlines, and verification mechanisms. The weapons already existed; deterrence alone proved insufficient for long-term stability.
AI infrastructure concentration creates analogous dynamics. The Hydra Testament documented how cryptocurrency mining subsidised GPU development and energy infrastructure now inherited by AI systems. The Warmth Engine Testament analysed how climate emergency discourse authorised massive computational deployment. The Alexandria Testament examined how processing control enables systematic value extraction. The Citadel synthesised these preparation, authorisation, and control mechanisms, demonstrating their operational convergence: AI systems are already integrated into critical infrastructure at scale.
C-MAD analyses what happens next. When concentrated processing capabilities become foundational to critical societal functions - finance, logistics, communications, energy management - purely competitive or deterrent frameworks prove inadequate. Operational dependencies create coordination pressure distinct from development-phase concerns, not because coordination is inevitable, but because mutual vulnerability transforms strategic incentives in ways requiring different analytical frameworks than pre-deployment competition.
III. The Dependency Architecture
Operational AI infrastructure concentration creates dependencies across sectors that previously functioned independently. These dependencies are not theoretical future risks but documented current reality, deepening systematically as hyperscalers integrate AI capabilities into existing services.
Financial Systems
Real-time payment processing, algorithmic trading, and fraud detection increasingly rely on concentrated cloud infrastructure. The Financial Stability Board’s 2025 FIRE framework explicitly identifies AI infrastructure concentration as creating systemic risk comparable to too-big-to-fail financial institutions. The introduction of large language models into customer service, loan underwriting, and compliance monitoring deepens these dependencies beyond traditional cloud services into AI-specific processing requirements.
Logistics Networks
Supply chain optimisation, inventory management, and routing algorithms concentrate on hyperscaler infrastructure. The integration extends beyond proprietary operations - third-party logistics providers increasingly rely on AWS, Azure, and Google Cloud for route optimisation and demand forecasting, creating sector-wide dependencies on concentrated processing capacity.
Communications Infrastructure
Content moderation, recommendation algorithms, and natural language processing shape information flows across social platforms, search engines, and messaging services. These systems depend on GPU clusters whose supply concentrates dramatically: NVIDIA holds 92% of datacentre GPU market share, with three hyperscaler customers representing 53% of NVIDIA’s datacentre revenue.
Energy Grids
Load balancing, predictive maintenance, and demand forecasting increasingly incorporate AI processing for grid stability. Utility companies deploy machine learning models for equipment failure prediction, renewable energy integration, and consumption pattern analysis. The National Renewable Energy Laboratory documents how AI-driven grid management enables higher renewable penetration through real-time supply-demand matching - creating dependencies where energy infrastructure itself relies on computational processing.
Geographic Concentration Creating Vulnerability
These cross-sector dependencies concentrate geographically in ways that amplify systemic risk. Northern Virginia hosts over 250 datacentres processing approximately 25% of North American internet capacity - a concentration validated by TeleGeography analysis. A natural disaster, infrastructure failure, or targeted disruption affecting this region would create cascading impacts across financial transactions, logistics coordination, communications platforms, and energy management simultaneously.
Similar patterns exist globally: Silicon Valley (Alphabet, Meta headquarters and primary infrastructure), Seattle (Amazon and Microsoft regional concentration), Singapore (Southeast Asian processing hub), and Frankfurt (European financial transaction processing). These geographic nodes function as critical infrastructure whose operational continuity affects multiple independent sectors.
Quantitative Evidence
The scale of this dependency infrastructure appears in capital deployment and supply chain concentration. In 2025, hyperscaler capital expenditure guidance totalling $315-330 billion (Microsoft $80B guidance/$64.6B actual FY2025, Google $75-85B, Meta $66-72B, Amazon $100B+) represents investment in processing capacity that underpins cross-sector operations.
Cloud market concentration reinforces these patterns: AWS commands approximately 30% market share, Azure 20-22%, Google Cloud 10-13% - the top three providers control 63-68% of global cloud infrastructure. Semiconductor fabrication concentrates even more dramatically: TSMC fabricates over 90% of sub-7nm advanced chips, with Samsung holding approximately 8% and limited additional capacity elsewhere.
Energy integration demonstrates infrastructure criticality. Microsoft’s 20-year commitment to restart Three Mile Island’s 835MW nuclear capacity (targeted 2028), Amazon’s $650 million Susquehanna nuclear facility acquisition, and Google’s 500MW Kairos Power agreements represent committed dedicated baseload generation for computational infrastructure.
Key Insight: These dependencies already exist and are deepening through ongoing integration rather than emerging as future possibilities. The operational question is not whether critical systems will depend on concentrated AI infrastructure, but how to manage coordination requirements created by existing dependencies across finance, logistics, communications, and energy management currently relying on processing capabilities controlled by a small number of entities.
IV. Game-Theoretic Mechanism
The dependency architecture documented above creates strategic dynamics fundamentally different from zero-sum competition. Understanding why requires examining how mutual vulnerability transforms incentive structures.
A. Why Stag Hunt, Not Prisoner’s Dilemma
Most strategic analysis defaults to Prisoner’s Dilemma frameworks where defection dominates regardless of others’ choices. This game structure - where individual rationality leads to collectively suboptimal outcomes - shapes thinking about international relations, corporate competition, and resource allocation.
Prisoner’s Dilemma payoff structure: T > R > P > S (Temptation > Reward > Punishment > Sucker)
In this framework, defecting when your opponent cooperates (T) yields the highest payoff, making defection the dominant strategy. Even though mutual cooperation (R) beats mutual defection (P), rational actors defect because cooperation risks the sucker payoff (S) if the opponent defects.
Stag Hunt inverts this structure: R > T ≥ P > S (Reward > Temptation ≥ Punishment > Sucker)
Here, mutual cooperation yields the highest payoff. The critical difference: there is no dominant strategy. Cooperation becomes rational when you trust others will cooperate; defection becomes rational when you expect others to defect. This creates two stable equilibria - mutual cooperation (payoff-dominant) and mutual defection (risk-dominant).
Payoff Matrix Example:
When both players coordinate: Each receives 10 (best outcome)
When you coordinate but other defects: You receive 0 (worst outcome), they receive 7
When you defect but other coordinates: You receive 7, they receive 0 (worst outcome)
When both players defect: Each receives 7 (safe but suboptimal)
Mutual coordination yields 10 for both players - the best outcome. But if you coordinate whilst the other defects, you receive 0 (the worst outcome). Mutual defection yields 7 - not optimal, but safer when trust is uncertain.
This captures AI infrastructure dynamics. When financial systems, logistics networks, and communications platforms depend on concentrated processing, coordinated operational stability benefits all parties more than competitive disruption. But coordination requires assurance that others will also prioritise stability over competitive advantage.
The game isn’t “should I cooperate despite incentives to defect” (Prisoner’s Dilemma) but rather “can I trust others to cooperate so that cooperation becomes my best response” (Stag Hunt). This distinction matters because the solutions differ fundamentally. Prisoner’s Dilemma requires changing payoffs through external enforcement. Stag Hunt requires building trust and assurance mechanisms that enable the superior equilibrium.
B. How Mutual Vulnerability Changes Incentives
Traditional technology competition follows zero-sum or Prisoner’s Dilemma dynamics. Companies compete for market share, talent, and intellectual property. One firm’s gain often represents another’s loss. Competitive advantage comes from outpacing rivals through superior products, aggressive pricing, or strategic exclusion.
Mutual dependency transforms this logic. When critical infrastructure relies on concentrated processing, disruption costs exceed competitive benefits for all parties simultaneously.
Consider a concrete scenario: Financial Institution A processes transactions through Hyperscaler X. Financial Institution B uses Hyperscaler Y. Traditional competition suggests Institution A might benefit if Hyperscaler Y experiences disruption, reducing Institution B’s transaction processing capabilities.
But operational reality creates different incentives. Major hyperscalers function as infrastructure rather than isolated services. Disruption to Hyperscaler Y creates systemic confidence effects (financial markets interpret infrastructure failures as systemic risk signals affecting all institutions), regulatory responses (infrastructure failures trigger oversight increasing compliance costs across sectors), interconnection vulnerabilities (financial institutions maintain relationships requiring cross-hyperscaler communication), and talent spillovers (disruption-driven instability affects the entire AI ecosystem including shared labour markets).
These spillover effects mean Institution A suffers meaningful costs from Hyperscaler Y’s disruption even though it uses Hyperscaler X. The same logic applies across sectors. When logistics companies depend on concentrated cloud infrastructure, major disruptions affect supply chains broadly rather than creating competitive advantages for firms using alternative providers.
This mutual vulnerability creates strategic complementarities: one player’s investment in stability increases others’ returns to similar investments. Instead of racing to gain relative advantages, players face coordination problems where shared risk makes cooperation individually rational - provided others also cooperate.
The key insight: Concentration transforms infrastructure disruption from competitive opportunity into shared threat. This doesn’t guarantee cooperation, but it changes the strategic calculus in ways that make coordination potentially stable if appropriate assurance mechanisms exist.
C. Historical Analogy: Financial System Coordination
The Basel Accords demonstrate how mutual vulnerability can enable sector-specific coordination despite broader geopolitical competition.
In 1974, Herstatt Bank’s collapse revealed systemic vulnerabilities in international banking settlement. Banks worldwide recognised that interconnected operations created mutual exposure: one institution’s failure could cascade through payment systems affecting all participants. This recognition didn’t require eliminating banking competition or achieving geopolitical harmony - it required managing shared operational vulnerabilities.
The response took fourteen years of repeated interaction before the 1988 Basel I framework established minimum capital requirements. The 2008 financial crisis and Lehman Brothers’ collapse reinforced this logic - Basel III emerged not from altruism, but from recognition that interconnected balance sheets meant one institution’s failure threatened all participants, making coordination rational despite competitive interests. The framework emerged not from supranational authority but from recognition that mutual vulnerability made coordination individually rational for participating nations despite broader competitive and political tensions.
Basel demonstrates several relevant features: domain-specific coordination without resolving broader conflicts, repeated interaction transforming one-shot dilemmas into coordination games, and institutional mechanisms providing assurance enabling cooperation.
Importantly, Basel represents coordination in managing existing interdependencies rather than preventing system creation. The international banking system already existed with documented mutual vulnerabilities. The question was how to manage operational risks created by existing integration.
AI infrastructure concentration creates analogous dynamics. Processing capabilities are already integrated into critical systems. The question resembles post-1974 banking more than pre-deployment AI safety: how to manage operational vulnerabilities created by existing concentration.
Key limitation: Coordination pressure does not guarantee coordination outcomes. Multiple equilibria remain possible - including unstable competition and systematic fragmentation.
V. Observable Patterns
If operational infrastructure concentration creates coordination pressure through mutual vulnerability, what empirical patterns would we expect to observe? Three categories of evidence appear consistent with C-MAD dynamics, though alternative explanations remain plausible for each.
A. Sovereign Investment Patterns
Nations are making unprecedented defensive investments in computational sovereignty, suggesting recognition of dependency risks created by concentrated processing control.
In January 2025, the OpenAI/SoftBank Stargate Project announced intent to invest $500 billion in U.S. AI infrastructure by 2029, with $100 billion in immediate commitments. The EU’s InvestAI initiative (February 2025) targets mobilising €200 billion for AI development and infrastructure. Separately, the UAE’s G42 initiative announced a $500 billion Abu Dhabi datacentre complex - specifying 500,000 GPUs with dedicated 5GW power generation by 2026. Saudi Arabia’s Humain initiative committed $10 billion to AMD partnerships for sovereign chip design and fabrication capacity.
These investments share common characteristics: (1) explicit sovereignty framing rather than pure commercial logic, (2) integrated infrastructure approaching hyperscaler scale rather than incremental capacity, (3) energy generation commitments recognising that computational sovereignty requires power sovereignty, and (4) defensive positioning against dependency rather than offensive market capture.
The pattern suggests nations recognise that processing dependence creates strategic vulnerabilities requiring sovereign alternatives. Whether this reflects C-MAD coordination pressure or standard great power competition, the scale and integration level of these investments exceeds previous technology sovereignty efforts. Nations are building complete stacks - chips, datacentres, energy generation - rather than focusing on isolated capabilities.
B. Governance Framework Emergence
International governance frameworks increasingly emphasise mutual vulnerability and coordination necessity, though implementation remains voluntary rather than binding.
The UN’s “Governing AI for Humanity” framework (September 2024) proposes seven institutional mechanisms including a Scientific Panel, Policy Dialogue, and Capacity Development Network. The framework explicitly adopts “vulnerability-based risk categorisation” and frames AI governance through mutual dependency rather than unilateral control. However, it explicitly rejects “monitoring, verification, reporting, compliance, accountability” functions - recognition without enforcement.
The Council of Europe’s Framework Convention on AI (May 2024) represents the first legally binding international AI treaty, yet achieved zero ratifications in its first thirteen months and excludes national security applications. The G7 established an AI reporting framework in February 2025 emphasising transparency and information sharing, but participation remains voluntary.
The pattern shows widespread recognition of coordination necessity through mutual vulnerability framing. However, the consistent gap between recognition and binding mechanisms suggests that whilst coordination pressure exists, translating that pressure into operational frameworks faces substantial obstacles.
C. Infrastructure Integration and Corporate Coordination
Multiple AI researchers have advocated for NPT-style frameworks with specific compute thresholds (commonly 10^26 FLOP training runs), with Geoffrey Hinton and Yoshua Bengio prominent among those calling for international verification mechanisms. These proposals explicitly invoke mutual vulnerability analogies to nuclear weapons and climate change, suggesting expert recognition of coordination dynamics.
Corporate infrastructure decisions demonstrate patterns potentially consistent with coordination pressure rather than pure competition. Hyperscalers are making long-term commitments that reduce flexibility and create mutual dependencies. Microsoft’s 20-year Three Mile Island power purchase agreement locks in energy costs and supply for AI infrastructure through 2048. Amazon’s Susquehanna acquisition and Google’s Kairos agreements similarly represent multi-decade commitments to specific power generation sources rather than maintaining grid flexibility.
These commitments create mutual exposure: facilities optimised for AI processing cannot easily pivot to alternative uses, and dedicated power generation creates stranded asset risks if AI demand shifts. The investments suggest confidence in sustained rather than volatile demand - a pattern more consistent with expected coordination enabling stable deployment than winner-take-all competition creating boom-bust cycles.
Assessment
These patterns - sovereign defensive investments, governance frameworks emphasising mutual vulnerability, and infrastructure commitments suggesting stability expectations - could indicate C-MAD coordination pressure. Alternatively, they may reflect standard regulatory responses, competitive positioning, or coincidental timing of independent decisions. The critical observation is that whether driven by coordination pressure or other factors, these patterns create conditions potentially enabling future coordination by establishing precedents, frameworks, and mutual dependencies.
VI. Critical Assessment
Any rigorous framework must address why predicted dynamics might not materialise. Several factors could prevent coordination despite mutual vulnerability and observable patterns.
Why Coordination Might Not Occur
Nationalist pressures may overwhelm interdependence logic. China’s pursuit of domestic chip alternatives and restrictions on foreign GPU purchases demonstrates that technological sovereignty can supersede coordination incentives. Rather than accepting mutual vulnerability through shared infrastructure, nations may pursue fragmentation accepting efficiency costs for independence.
Trust failure under geopolitical competition presents fundamental obstacles. The Council of Europe’s AI treaty achieved zero ratifications in thirteen months despite signatures from major powers, suggesting that even when nations formally commit to coordination, follow-through fails. Historical animosities, security concerns, and competitive positioning may prevent the assurance mechanisms Stag Hunt dynamics require. First-mover advantages in AI development could exceed coordination benefits, particularly if capabilities gaps create winner-take-all dynamics.
Alternative Explanations for Observed Patterns
Sovereign investments may reflect standard great power competition rather than defensive coordination responses. Infrastructure commitments could represent efficiency optimisation and competitive positioning disguised as stability-seeking behaviour. Governance frameworks emphasising mutual vulnerability might constitute public relations rather than genuine strategic shifts.
The patterns documented in Section V remain consistent with multiple causal explanations: C-MAD coordination pressure, regulatory theatre, competitive manoeuvring, or coincidental timing of independent decisions.
Framework Boundaries
C-MAD describes structural pressure, not predetermined outcomes. Multiple equilibria remain possible: sustained cooperation, unstable competition, systematic fragmentation, or regional blocs pursuing different strategies simultaneously.
The framework’s value lies in identifying forces shaping strategic decisions and policy choices, not predicting specific futures. Understanding coordination pressure enables better analysis of why certain governance approaches succeed or fail, regardless of whether coordination ultimately materialises. Mutual vulnerability creates opportunities for coordination, but whether actors exploit those opportunities depends on factors beyond the framework’s scope: political will, institutional capacity, and strategic trust-building.
VII. Conclusion
C-MAD identifies a structural force shaping AI governance landscapes: operational dependencies across critical infrastructure create coordination pressure through mutual vulnerability. This dynamic differs fundamentally from development-phase concerns about preventing dangerous AI creation. When financial systems, logistics networks, communications platforms, and energy grids already depend on concentrated processing, the strategic question shifts from whether to build such systems to how to manage coordination requirements they create.
Understanding this pressure enables better policy analysis regardless of whether coordination materialises. The framework’s contribution lies in synthesising documented infrastructure concentration with game-theoretic coordination analysis, revealing how mutual vulnerability makes cooperation increasingly rational - though rationality does not guarantee realisation. Coordination remains a choice, but its rationality increases as dependencies deepen.
This analysis demonstrates the analytical prophecy methodology in practice. For deeper understanding of the interpretive framework and evidence standards employed, see “Analytical Prophecy: A Methodological Framework”.
References and Data Sources
Primary Sources:
Hendrycks, Schmidt, and Wang, “Mutual Assured AI Malfunction” (MAIM) (March 2025)
Financial Stability Board FIRE framework documentation (2025)
TeleGeography datacentre infrastructure analysis
Hyperscaler capital expenditure announcements: Microsoft, Google, Meta, Amazon (2025)
Nuclear power agreements: Three Mile Island (Microsoft), Susquehanna (Amazon), Kairos Power (Google)
Sovereign Investment Documentation:
Stargate Project announcements (OpenAI/SoftBank, January 2025)
EU InvestAI initiative policy framework (February 2025)
UAE/G42 infrastructure commitments (2025)
Saudi Arabia Humain initiative AMD partnership (2025)
Governance Frameworks:
United Nations “Governing AI for Humanity” framework (September 2024)
Council of Europe Framework Convention on AI (May 2024)
G7 AI reporting framework (February 2025)
Corporate and Market Data:
NVIDIA datacentre GPU market share analysis
TSMC advanced node fabrication capacity data (Q1 2025)
Cloud market share data: AWS, Azure, Google Cloud
AI researcher advocacy statements (Hinton, Bengio)
Game Theory and Historical Analysis:
Basel Committee on Banking Supervision historical documentation
Stag Hunt coordination game theoretical frameworks
Folk Theorem repeated game analysis
Data Accuracy Note: All financial figures, corporate data, and policy claims have been verified through multiple primary sources and cross-referenced against the Master Synthesis research document. However, the rapidly evolving nature of AI infrastructure deployment means that specific figures and project timelines are subject to frequent revision. This analysis reflects verified data available through October 2025. Readers requiring current information should verify through primary sources for time-sensitive applications.
A Note on This Work
This essay is part of The Warmth Engine Series, a unified analytical work examining AI infrastructure deployment through systematic pattern recognition. The series comprises six interdependent parts with a defined internal sequence:
Analytical Prophecy (methodological foundation)
The Hydra Testament (preparation)
The Warmth Engine Testament (authorisation)
The Alexandria Testament (control)
The Citadel (operational convergence)
The Coordination Imperative (coordination pressure)
This series introduced several original frameworks including C-MAD (Computational Mutual Assured Destruction), APMF (Analytical Prophecy Methodology Framework), and the preparation–authorisation–control apparatus typology.
The complete canonical version exists as a single document on Zenodo (DOI: 10.5281/zenodo.17451629). The Substack publication order (September–October 2025) differs from the intended analytical sequence defined above.
The C-MAD framework introduced in this essay is extended into measurable dynamics through two companion papers - Towards Coordination Science (DOI: 10.5281/zenodo.18427584) and Tier-Crossing Dynamics (DOI: 10.5281/zenodo.18427586) - published as part of the Warmth Engine Observatory methodology suite. The Observatory platform (warmthengine.com) documents AI infrastructure coordination dynamics through verified events and mapped coordination connections. The full Observatory methodology is published on Zenodo (DOI: 10.5281/zenodo.18427565). The Observatory and this research are conducted independently.