When Global Markets Lost Their Infrastructure
At 3:00 AM GMT on November 28, 2025, global derivatives markets froze at once. From Singapore to London, price feeds locked mid-tick—S&P futures, crude oil, gold, Treasuries, FX. Traders assumed the worst: a cyberattack, a catastrophic exchange bug, or coordinated state-sponsored interference. The world's largest derivatives marketplace, responsible for more than $8 trillion in daily notional exposure, had simply stopped.
In a glass-walled office overlooking Bishopsgate, a senior futures trader at a macro hedge fund slammed his palm against his desk as every chart on his screen froze. "Are we under attack?" someone yelled across the floor. London had just opened. Asia was mid-session. Positions were moving, but nobody could see where.
But there was no cyber intrusion. No rogue algorithm. No geopolitical sabotage. The real cause was thousands of miles away, in a suburban data center in Aurora, Illinois, where a chiller plant's coolant temperatures had begun creeping upward just after midnight. Within minutes, thermal thresholds were breached. Automated protections triggered. Servers powering the CME's Globex platform shut down to avoid permanent hardware damage.
What looked like a global financial attack was, in reality, a physical infrastructure failure. Legacy cooling systems—never designed for today's AI-driven thermal loads—had reached their breaking point.
For investors, the incident revealed something profound: compute is growing exponentially, but infrastructure capacity is not. And in that gap lies the defining constraint—and investment opportunity—of the AI era: the rise of the Cooling Resilience Premium.
Who This Is For
This case study is written for CIOs, family offices, and institutional allocators evaluating data center REITs, private infrastructure funds, or direct co-investments in AI-era data centers. We translate a single infrastructure failure into actionable portfolio diligence criteria, showing how cooling capacity and redundancy architecture now drive measurable valuation premiums across data center assets.
TL;DR — The CME Cooling Crisis in Four Points
- What happened: A chiller plant failure at CyrusOne's CHI1 facility halted CME Group's global derivatives trading for 11+ hours, freezing $8 trillion in daily volume and affecting markets from Chicago to Tokyo.
- Why it matters: The outage revealed systemic infrastructure fragility as AI workloads drive rack densities from 8–15 kW (legacy) to 60–120 kW (AI clusters)—a 10x thermal increase that legacy cooling can't handle.
- The hidden constraint: Silicon scarcity has dominated AI narratives, but thermal capacity is the actual bottleneck. Cooling already represents 35–40% of total data center power consumption in high-density environments.
- Investment implication: Infrastructure assets with verified high-density cooling and N+2 redundancy command premium valuations. Understanding this dynamic is essential for portfolio allocation decisions across data center REITs and private infrastructure funds.
1BLUF — The Case Study Framework
- The incident: 11-hour outage affecting CME Globex, EBS FX, and BMD derivatives—services managing trillions in daily notional value across equity indices, commodities, currencies, and cryptocurrency futures.
- The root cause: Chiller plant failure at CyrusOne Aurora (CHI1) data center affecting multiple cooling units, with redundancy protocols designed for lower-density loads and unable to prevent cascading outages.
- The broader context: Legacy data centers designed for 8–15 kW per rack are now being asked to support AI workloads demanding 60–120 kW per rack—thermal loads exceeding original design specifications by 400–800%.
- The investment angle: Physical infrastructure resilience becomes a first-order pricing factor in data center assets, colocation REITs, and cloud service contracts—creating the "Cooling Resilience Premium."
The Outage: When Global Markets Lost Their Infrastructure
At 3:00 AM GMT on November 28, 2025, global derivatives markets froze. Across Singapore, London, and Dubai, traders watched price feeds lock mid-tick: crude, Treasuries, S&P futures, FX—all dead. The world's largest derivatives marketplace, processing more than $8 trillion in daily notional exposure, had disappeared from the internet.
The Chicago Mercantile Exchange confirmed what thousands of traders already knew: Globex, the exchange's flagship electronic platform, had gone offline. The timing magnified the disruption's impact. While most U.S. traders were still enjoying post-Thanksgiving rest, Asian and European markets were in full session. Emir Syazwan, a futures trader at Ninefold Trading in Kuala Lumpur, spent the afternoon on the phone with his broker watching positions freeze. "We're taking a lot of unnecessary risk here to continue pricing," Christopher Forbes of CMC Markets told Reuters.
| Market | Typical Daily Volume | Impact |
|---|---|---|
| CME Globex (Equity Index Futures) | $2.5–3.0 trillion notional | Halted 11+ hours |
| CME Energy & Commodities | $1.5–2.0 trillion notional | Halted 11+ hours |
| EBS FX Platform | $300–400 billion notional | Reopened after ~90 minutes |
| CME Crypto Futures (BTC, ETH) | $8–12 billion notional | Halted 11+ hours |
| Total Affected Volume | $8+ trillion daily notional | Complete market halt |
Ben Laidler, head of equity strategy at Bradesco BBI, described the extensive halt as "a black eye to the CME and probably an overdue reminder of the importance of market structure and how interconnected all these are." The incident underscored how physical infrastructure failures in data centers now constitute systemic risks to global financial stability—not merely "IT problems" but macro events with real economic consequences.
The Root Cause: When Chillers Fail and Redundancy Doesn't
The technical details revealed a failure cascade that should never have occurred. At 4:19 AM Central Time on November 27—nearly 12 hours before markets were scheduled to open—CyrusOne's CHI1 facility in Aurora, Illinois began experiencing problems with its chiller plant. Multiple cooling units were affected. According to a confidential root cause analysis reviewed by Bloomberg, the issues started during the Thanksgiving holiday when staffing and response times were suboptimal.
AltStreet Analysis
The Hidden Failure: Redundancy Design vs. Thermal Reality
The CME incident exposed a critical gap in legacy data center design philosophy. N+1 redundancy—one backup cooling unit beyond minimum requirements—was engineered for facilities operating at 8–15 kW per rack with gradual heat buildup. This configuration assumed single-point failures with ample time for manual intervention.
But AI and HFT workloads changed the math. At 60–120 kW per rack, thermal runaway occurs in minutes rather than hours. A chiller plant affecting multiple units simultaneously creates heat accumulation faster than N+1 redundancy can compensate. The result: emergency shutdowns to protect hardware, regardless of downstream consequences.
AltStreet's view: The CME outage marks an inflection point where physical infrastructure resilience becomes a first-order variable in data center valuations, not an operational afterthought. Institutional investors evaluating colocation providers, cloud infrastructure, or data center REITs must now assess cooling redundancy architecture alongside traditional metrics like PUE and network connectivity.
The AI Context: How We Got Here
To understand why AI-era data center cooling failures now constitute systemic risks requires understanding the seismic shift in power density over the past decade. The statistics tell a stark story of infrastructure assumptions breaking under AI's thermal demands.
| Era | Workload Type | Avg. kW/Rack | Peak kW/Rack | Cooling Method |
|---|---|---|---|---|
| 2010–2015 | Traditional enterprise IT | 5–8 kW | 12 kW | Air cooling (CRAC/CRAH) |
| 2016–2020 | Cloud, virtualization, high-frequency trading | 10–15 kW | 25–30 kW | Optimized air cooling |
| 2021–2023 | AI inference, HPC clusters | 20–40 kW | 60–80 kW | Hybrid air + rear-door heat exchangers |
| 2024–2025 | AI training (H100/H200) | 60–100 kW | 120–132 kW | Direct-to-chip liquid cooling (DLC) |
| 2026–2028 | Next-gen AI (Blackwell Ultra, Rubin) | 150–300 kW | 900 kW+ | Immersion cooling required |
The evolution above captures the infrastructure reality: AI training workloads with dense GPU configurations now demand 10x the thermal capacity of traditional enterprise IT. For context on GPU economics driving this shift, see our comprehensive GPU investment evaluation framework.
AltStreet Framework
The Three Constraint Layers: Power, Cooling, and Grid
Understanding AI infrastructure bottlenecks requires thinking in systems—not components.
The CME outage illuminates a critical analytical framework for evaluating AI infrastructure investments. The constraint isn't singular—it's a three-layer dependency where each layer can independently throttle capacity.
The Three-Layer Constraint Model
Constraint Layer 1: Power Infrastructure
What it includes: Available MW capacity at the facility, electrical distribution systems, substation capacity, utility interconnection agreements.
Why it matters: A data center can have unlimited space but remains useless without power allocation. Utility interconnection queues in high-demand markets now stretch 3–5 years, making power the gating factor for new AI deployments.
Constraint Layer 2: Thermal Infrastructure
What it includes: Maximum and sustained kW/rack capacity, cooling technology (air/DLC/immersion), redundancy architecture (N+1/N+2/2N), chiller plant capacity and diversity.
Why it matters: This is where the CME incident occurred. You can have power and GPUs, but if cooling fails, servers shut down immediately. At 60–120 kW/rack, thermal runaway occurs in minutes—redundancy architecture becomes mission-critical.
Constraint Layer 3: Grid Capacity
What it includes: Regional transmission infrastructure, utility generation capacity, regulatory environment, interconnection queue length.
Why it matters: Grid capacity is the new land. Markets with spare MW allocations and utility cooperation (Texas ERCOT, Pacific Northwest, Nordic regions) attract premium AI development. Markets with long queues or regulatory friction face multi-year delays regardless of facility readiness. Understanding these dynamics is essential for managing AI infrastructure risk.
AltStreet Analysis
Constraint Arbitrage: Where Alpha Lives in AI Infrastructure
The three-layer constraint model reveals where excess returns concentrate. When constraints align—silicon available, cooling capacity verified, grid connection secured—assets command premium valuations with minimal friction to deployment. When constraints misalign, assets become stranded despite component availability.
Investment implication: The highest risk-adjusted returns flow to infrastructure plays that solve multiple constraint layers simultaneously, particularly thermal + grid capacity in geographies with AI workload demand. This explains recent premium valuations for greenfield AI data center projects versus discounts for legacy enterprise facilities that lack retrofit pathways.
The Cooling Technology Landscape: From Air to Liquid to Immersion
The CME outage underscores why data center cooling has become a critical investment diligence factor. As rack densities surge from 15 kW to 120+ kW, different cooling technologies serve different density thresholds with vastly different economic and operational profiles.
| Technology | Density Range | Typical PUE | Cost Premium | Adoption Status |
|---|---|---|---|---|
| Air Cooling (CRAC/CRAH) | 5–20 kW/rack | 1.4–1.6 | Baseline | Mature, declining for new AI builds |
| Rear-Door Heat Exchangers | 20–40 kW/rack | 1.3–1.4 | +15–25% | Transitional, moderate adoption |
| Direct-to-Chip Liquid (DLC) | 40–120 kW/rack | 1.15–1.25 | +40–60% | Mainstream for AI, rapid growth |
| Single-Phase Immersion | 100–200 kW/rack | 1.05–1.15 | +100–150% | Early adopters, hyperscale pilots |
| Two-Phase Immersion | 200–300+ kW/rack | 1.03–1.08 | +150–200% | Emerging, specialized applications |
The Cooling Resilience Premium: A New Asset Class Emerges
The CME outage crystallized what sophisticated infrastructure investors already sensed: physical infrastructure resilience has become a pricing factor in data center valuations, not merely an operational consideration. This creates what we call the "Cooling Resilience Premium"—the valuation spread between infrastructure assets with verified high-density cooling and robust redundancy versus those without.
Evidence of the Cooling Resilience Premium
Data Center REIT Valuations
AI-focused REITs with disclosed high-density capabilities (Digital Realty, Equinix xScale, QTS) trade at 16–20x FFO multiples with 4–5% dividend yields. Legacy enterprise colocation operators without credible AI retrofit pathways trade at 12–14x FFO with 6–7% yields. The 400-600 basis point FFO multiple spread implies an 8–15% valuation premium driven by growth expectations and reduced obsolescence risk.
Private Market Cap Rates
Stabilized data center assets with verified N+2 cooling, 60+ kW/rack capability, and hyperscale AI tenant mix trade at 5.5–6.5% cap rates. Comparable legacy facilities with N+1 air cooling and sub-30 kW density trade at 6.5–7.5% caps. The 50–100 basis point spread translates to meaningful valuation premiums at scale.
Development Pipeline Pricing
Greenfield AI data center projects securing utility commitments and implementing DLC from design attract pre-leasing commitments at premium pricing. Build-to-suit arrangements for hyperscale AI deployers command lease rates 15–25% above legacy colocation pricing, reflecting the scarcity value of truly AI-ready infrastructure.
Investor Diligence Checklist: 10 Questions for Any "AI-Ready" Data Center
Use this checklist when evaluating data center operators, REITs, or private infrastructure funds claiming AI-readiness:
- Maximum kW/rack capacity: What is the actual maximum sustainable power density per rack? Ask for documentation showing 60+ kW/rack for AI claims.
- Cooling technology mix: What percentage of portfolio capacity uses direct-to-chip liquid cooling vs. air cooling? Air-cooled facilities cannot support dense AI workloads.
- Redundancy architecture: Is cooling designed with N+2 or 2N redundancy? N+1 is insufficient for high-density AI workloads where thermal runaway happens in minutes.
- Chiller plant diversity: Are cooling systems architected to prevent cascading failures affecting multiple units simultaneously?
- Power procurement: What percentage of power capacity has secured utility commitments? Speculative capacity without grid commitments creates stranded asset risk.
- Customer mix: What percentage of NOI comes from hyperscale AI/HPC workloads vs. traditional enterprise tenants? Disclosed customer concentration matters.
- Development pipeline: What percentage of new builds are greenfield AI-specific vs. retrofit attempts? Retrofits face economics and timeline challenges.
- Geographic concentration: Are assets concentrated in markets with grid capacity and favorable interconnection environments? Location matters as much as facility specs.
- Historical uptime: What is documented uptime performance specifically for high-density workloads? Legacy uptime with traditional loads doesn't predict AI-era performance.
- Transparency: Does the operator publicly disclose cooling densities, redundancy architecture, and AI-specific capabilities? Vague "AI-ready" marketing without specifics is a red flag.
Portfolio Implications: Allocating to AI Infrastructure Resilience
For institutional investors and sophisticated allocators, the CME outage and broader AI infrastructure constraints create actionable investment opportunities across public and private markets. The key insight: thermal infrastructure isn't a sector—it's a factor cutting across data center REITs, infrastructure debt, utility infrastructure, and specialized alternatives.
Properly structuring exposure requires understanding how cooling constraints interact with power availability and compute demand cycles. For comprehensive guidance on portfolio construction, see our AI infrastructure allocation framework.
Portfolio Sizing Framework
Conservative Allocation (2–5%)
Profile: Investors seeking AI infrastructure exposure through established public market operators with proven track records.
Implementation: Data center REITs with disclosed AI capabilities (Digital Realty, Equinix, QTS). Focus on operators demonstrating credible high-density deployments and hyperscale customer relationships.
Moderate Allocation (5–8%)
Profile: Investors comfortable with private market illiquidity and development risk for enhanced return potential.
Implementation: Blend of public REITs (50%), private infrastructure funds targeting stabilized AI data centers (30%), and infrastructure debt (20%). Diversification across asset types and development stages.
Aggressive Allocation (8–12%)
Profile: Sophisticated investors with capacity for illiquidity, construction risk, and technology evolution uncertainty.
Implementation: Heavier private exposure including development-stage projects, direct co-investments in purpose-built AI facilities, and exposure to cooling technology providers. Requires operational due diligence capabilities.
Context: These allocations sit within a broader 15–25% alternatives sleeve alongside other infrastructure, private equity, and real assets. AI infrastructure represents a sub-allocation, not a standalone portfolio.
AltStreet's view: The REIT category bifurcates between AI infrastructure leaders (trading at 16–20x FFO multiples with 4–5% dividend yields) and legacy operators (12–14x FFO, 6–7% yields). The valuation spread reflects structural differences in growth trajectories and obsolescence risk, not temporary sentiment. For conservative allocators seeking AI exposure through established operators, focus on REITs demonstrating credible AI capabilities through disclosed power densities, cooling technology investments, and hyperscale customer commitments.
Risk Factors & Bear Case Considerations
No investment thesis is complete without acknowledging downside scenarios. AI infrastructure investors should consider:
- Demand deceleration: If AI model efficiency improves faster than expected or AI adoption slows, demand for high-density infrastructure could disappoint relative to current growth projections.
- Overbuilding risk: Significant capital flowing into AI data center development could create localized oversupply, particularly in markets with aggressive speculative builds ahead of secured tenant demand.
- Regulatory and permitting delays: Power interconnection processes, environmental reviews, and local permitting can extend timelines by years, creating opportunity cost and financing challenges for development strategies.
- Technology disruption: While cooling physics are constant, breakthrough efficiency improvements in chip design or novel cooling approaches could reduce the competitive moat of current-generation infrastructure.
- Grid capacity constraints: Utility infrastructure limitations and political opposition to new generation capacity could throttle AI deployment faster than data center supply, creating stranded assets with power capacity but insufficient demand.
Key Takeaways: The Inflection Point for AI Infrastructure Investing
The CME outage serves as a market-defining moment for AI infrastructure—the physical manifestation of a constraint that sophisticated investors sensed but the market hadn't priced. The incident converts theoretical concerns about thermal capacity into concrete evidence of systemic vulnerability, accelerating the repricing of data center assets based on cooling resilience.
Key Insight 1
Thermal Capacity Is the Hidden Constraint
While GPU scarcity dominated AI infrastructure narratives, cooling capacity is the actual bottleneck. Legacy facilities designed for 8–15 kW/rack cannot accommodate AI workloads demanding 60–120 kW/rack without prohibitive retrofits. This creates structural advantages for purpose-built AI infrastructure.
Key Insight 2
Redundancy Architecture Matters More Than Assumed
The CME incident exposed how N+1 cooling redundancy—standard for decades—fails under high-density workloads where thermal runaway occurs in minutes. Data centers with N+2 or 2N redundancy configurations trade at measurable valuation premiums reflecting reduced outage risk.
Key Insight 3
Infrastructure Failures Are Now Systemic Events
When an $8 trillion derivatives market halts for 11 hours due to chiller failure, physical infrastructure becomes a financial stability issue, not an IT operations concern. This elevates data center resilience from facilities management to C-suite and board-level strategic priority.
Most Important
The Cooling Resilience Premium Is Structural, Not Cyclical
AI workload growth drives a permanent demand shift toward high-density thermal infrastructure. This isn't a technology cycle or sentiment-driven trend—it reflects fundamental physics where compute requires cooling. Assets with verified thermal capabilities capture sustained premiums through pricing power, occupancy, and valuation multiples.
The $400 billion question for investors: how to capture the value created by this infrastructure constraint without overpaying for AI hype or taking excessive development risk. The answer lies in systematic evaluation of cooling capacity, redundancy architecture, and deployment certainty across data center assets—transforming thermal infrastructure from an operational detail into a first-order investment criterion.
Continue Learning About AI Infrastructure Investing
The CME cooling crisis demonstrates AI infrastructure constraints within the broader compute capacity ecosystem. For comprehensive coverage of data center economics, power infrastructure, and GPU supply dynamics: