One interesting point about VPNs is raised by fully a third of capacity-hungry enterprises: SD-WAN is the cheapest and easiest way to increase capacity to remote sites. Yes, service reliability of broadband Internet access for these sites is highly variable, so enterprises say they need to pilot test in a target area to determine whether even business-broadband Internet is reliable enough, but if it is, high capacity is both available and cheap. Clearly data center networking is taking the prime position in enterprise network planning, even without any contribution from AI. Will AI contribute? Enterprises generally believe that self-hosted AI will indeed require more network bandwidth, but again think this will be largely confined to the data center. AI, they say, has a broader and less predictable appetite for data, and business applications involving the data that’s subject to governance, or that’s already data-center hosted, are likely to be hosted proximate to the data. That was true for traditional software, and it’s likely just as true for AI. Yes, but…today, three times as many enterprises say that they’d use AI needs simply to boost justification for capacity expansion as think they currently need it. AI hype has entered, and perhaps even dominates, capital network project justifications. These capacity trends don’t impact enterprises alone, they also reshape the equipment space. Only 9% of enterprises say they have invested in white-box devices to build capacity and data center configuration flexibility, but the number that say they would evaluate them in 2026 is double that. This may be what’s behind Cisco’s decision to push its new G300 chip. AI’s role in capital project justifications may also be why Cisco positions the G300 so aggressively as an AI facilitator. Make no mistake, though; this is really all about capacity and QoE, even for AI.

JLL’s North America Data Center Report Year-End 2025 makes a clear argument that the sector is no longer merely expanding but has shifted into a phase of industrial-scale acceleration driven by hyperscalers, AI platforms, and capital markets that increasingly treat digital infrastructure as core, bond-like collateral. The report’s central thesis is straightforward. Structural demand has overwhelmed traditional real estate cycles. JLL supports that claim with a set of reinforcing signals: Vacancy remains pinned near zero. Most new supply is pre-leased years ahead. Rents continue to climb. Debt markets remain highly liquid. Investors are engineering new financial structures to sustain growth. Author Andrew Batson notes that JLL’s Data Center Solutions team significantly expanded its methodology for this edition, incorporating substantially more hyperscale and owner-occupied capacity along with more than 40 additional markets. The subtitle — “The data center sector shifts into hyperdrive” — serves as an apt one-line summary of the report’s posture. The methodological change is not cosmetic. By incorporating hyper-owned infrastructure, total market size increases, vacancy compresses, and historical time series shift accordingly. JLL is explicit that these revisions reflect improved visibility into the market rather than a change in underlying fundamentals; and, if anything, suggest prior reports understated the sector’s true scale. The Market in Three Words: Tight, Pre-Leased, Relentless The report’s key highlights page serves as an executive brief for investors, offering a concise snapshot of market conditions that remain historically constrained. Vacancy stands at just 1%, unchanged year over year, while 92% of capacity currently under construction is already pre-leased. At the same time, geographic diversification continues to accelerate, with 64% of new builds now occurring in so-called frontier markets. JLL also notes that Texas, when viewed as a unified market, could surpass Northern Virginia as the top data center market by 2030, even as capital

The data center industry has spent the past two years obsessing over power constraints, AI density, and supply chain pressure. But according to longtime mission critical leader Dennis Cronin, the sector’s most consequential bottleneck may be far more human. In a recent episode of the Data Center Frontier Show Podcast, Cronin — a founding member of 7×24 Exchange International and board member of the Mission Critical Global Alliance (MCGA) — delivered a stark message: the workforce “talent cliff” the industry keeps discussing as a future risk is already impacting operations today. A Million-Job Gap Emerging Cronin’s assessment reframes the workforce conversation from a routine labor shortage to what he describes as a structural and demographic challenge. Based on recent analysis of open roles, he estimates the industry is currently short between 467,000 and 498,000 workers across core operational positions including facilities managers, operations engineers, electricians, generator technicians, and HVAC specialists. Layer in emerging roles tied to AI infrastructure, sustainability, and cyber-physical security, and the potential demand rises to roughly one million jobs. “The coming talent cliff is not coming,” Cronin said. “It’s here, here and now.” With data center capacity expanding at roughly 30% annually, the workforce pipeline is not keeping pace with physical buildout. The Five-Year Experience Trap One of the industry’s most persistent self-inflicted wounds, Cronin argues, is the widespread requirement for five years of experience in roles that are effectively entry level. The result is a closed-loop hiring dynamic: New workers can’t get hired without experience They can’t gain experience without being hired Operators end up poaching from each other Workers may benefit from the resulting 10–20% salary jumps, but the overall talent pool remains stagnant. “It’s not helping us grow the industry,” Cronin said. In a market defined by rapid expansion and increasing system complexity, that

From “Backup” to “Bridging” to Behind-the-Meter Power Plants The most important shift is conceptual: these systems are increasingly blurring the boundary between emergency backup and primary power supply. Traditionally, data center electrical architecture has been clearly tiered: UPS (seconds to minutes) to ride through utility disturbances and generator start. Diesel gensets (minutes to hours or days) for extended outages. Utility grid as the primary power source. What’s changing is the rise of bridging power:  generation deployed to energize a site before the permanent grid connection is ready, or before sufficient utility capacity becomes available. Providers such as APR Energy now explicitly market turbine-based solutions to data centers seeking behind-the-meter capacity while awaiting utility build-out. That framing matters because it fundamentally changes expected runtime. A generator that operates for a few hours per year is one regulatory category. A turbine that runs continuously for weeks or months while a campus ramps is something very different; and it is drawing increased scrutiny from regulators who are beginning to treat these installations as material generation assets rather than temporary backup systems. The near-term driver is straightforward. AI workloads are arriving faster than grid infrastructure can keep pace. Data Center Frontier and other industry observers have documented the growing scramble for onsite generation as interconnection queues lengthen and critical equipment lead times expand. Mainstream financial and business media have taken notice. The Financial Times has reported on data centers turning to aeroderivative turbines and diesel fleets to bypass multi-year power delays. Reuters has likewise covered large gas-turbine-centric strategies tied to hyperscale campuses, underscoring how quickly the co-located generation model is moving into the mainstream. At the same time, demand pressure is tightening turbine supply chains. Industry reporting points to extended waits for new units, one reason repurposed engine cores and mobile aeroderivative packages are gaining

By early 2026, the data center cooling conversation has started to sound less like a product catalog and more like a systems engineering summit. The old framing – air cooling versus liquid cooling – still matters, but it increasingly misses the point. AI-era facilities are being defined by thermal constraints that run from chip-level cold plates to facility heat rejection, with critical decisions now shaped by pumping power, fluid selection, reliability under ambient extremes, water availability, and manufacturing throughput. That full-stack shift is written all over a grab bag of recent cooling announcements. On one end of the spectrum we see a Department of Energy-funded breakthrough aimed directly at next-generation GPU heat flux. On the other, it’s OEM product launches built to withstand –20°F to 140°F operating conditions and recover full cooling capacity within minutes of a power interruption. In between we find a major acquisition move for advanced liquid cooling IP, a manufacturing expansion that more than doubles footprint, and the quiet rise of refrigerants and heat-transfer fluids as design-level considerations. What’s emerging is a new reality. Cooling is becoming one of the primary constraints on AI deployment technically, economically, and geographically. The winners will be the players that can integrate the whole stack and scale it. 1) The Chip-level Arms Race: Single-phase Fights for More Runway The most “pure engineering” signal in this news batch comes from HRL Laboratories, which on Feb. 24, 2026 unveiled details of a single-phase direct liquid cooling approach called Low-Chill™. HRL’s framing is pointed: the industry wants higher GPU and rack power densities, but many operators are wary of the cost and operational complexity of two-phase cooling. HRL says Low-Chill was developed under the U.S. Department of Energy’s ARPA-E COOLERCHIPS program, and claims a leap that goes straight at the bottleneck. It can increase

The AI data center boom has been colliding with grid reality for more than two years. This week, the issue moved closer to the policy front lines. The White House is advancing a “ratepayer protection” framework that has gained visibility in recent days, aimed at ensuring large AI data center projects do not shift grid upgrade costs onto residential customers. It’s a signal widely interpreted by industry observers as encouraging hyperscalers to bring dedicated power solutions to the table. The Power Question Moves to Center Stage Washington now appears poised to push the industry toward a structural response to the data center power conundrum. The new federal impetus for major technology companies to shoulder the cost of their own power infrastructure is quickly emerging as one of the most consequential policy developments for the digital infrastructure sector in 2026. If formalized, the initiative would effectively codify a shift already underway which has found hyperscale and AI developers moving aggressively toward behind-the-meter generation and dedicated energy strategies. For an industry already grappling with interconnection delays, utility pushback, and mounting community scrutiny, the signal is unmistakable. The era of relying primarily on shared grid capacity for large AI campuses may be ending. From Market Trend to Policy Direction Large tech firms, including the biggest cloud and AI players, have been under increasing pressure from regulators and utilities concerned about ratepayer exposure and grid reliability. Policymakers are now signaling that future large-load approvals may hinge on whether developers can demonstrate energy self-sufficiency or dedicated supply. The logic is straightforward. AI campuses are arriving at hundreds of megawatts to gigawatt scale. Transmission upgrades are measured in multi-year timelines. Utilities face growing political pressure to protect residential customers. In that context, the emerging federal posture does not create a new trend so much as accelerate