Cooling’s New Reality: It’s Not Air vs. Liquid Anymore. It’s Architecture.

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Cooling’s New Reality: It’s Not Air vs. Liquid Anymore. It’s Architecture.

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 processor cooling capability by 40% or reduce pumping power by more than 10X. That pumping-power claim is not a footnote. In AI-era liquid-cooled designs, it’s increasingly part of the economic and architectural equation.

“We designed this technology with real data center constraints in mind,” said Christopher Roper, principal investigator at HRL.

At the core is a cooling block architecture that uses an engineered 3D-printed manifold to distribute coolant through hundreds of short flow paths directly over the processor, addressing fundamental issues of conventional designs such as long channels, friction losses, and uneven coolant delivery. HRL’s disclosed metrics include:

Thermal interface resistance: 8.2 °C/kW
Pressure drop: below 1 psi per cooling block
Pumping power: less than 1% of rack IT power (block-level)
Hot-loop capability: coolant inlet temperatures up to 70°C

The company also claims that the approach can remove 40% more heat load compared to state-of-the-art cooling blocks under equivalent pumping power, supports heat flux up to 400 W/cm², and is scalable to higher powers. HRL even ties the performance envelope to future GPU roadmaps, stating the approach can help meet NVIDIA’s anticipated Rubin and Feynman GPU cooling needs.

Crucially, HRL’s “why it matters” pitch extends beyond silicon. By enabling ultra-low thermal resistance at the processor level, the approach can support hotter coolant loops, which in turn can make dry air coolers more viable, reducing reliance on evaporative systems and improving compatibility with water-constrained climates.

“As a private company owned jointly by Boeing and GM,” HRL positioned the work as deployable and partner-ready, while explicitly stating it is seeking development partners.

The takeaway? Single-phase liquid cooling isn’t done evolving. If the performance claims hold up in broader deployments, the “single-phase vs. two-phase” decision may be less about theoretical limits and more about how far innovations like manifold geometry can push practical limits.

2) OEMs Aren’t Just Shipping Chillers, They’re Buying Liquid Cooling IP

If HRL is about the physics, Johnson Controls’ news is about strategy. On Feb. 18, 2026, the company said it had signed an agreement to acquire Alloy Enterprises, a Boston-based firm founded in 2020 focused on a proprietary thermal management platform for high-performance data centers and other mission-critical industrial applications.

Johnson Controls says Alloy’s advanced direct liquid cooling components can enable:

Those numbers land squarely in the same territory HRL is emphasizing: cooling efficiency and pumping power, not just peak capacity.

“This acquisition is about enabling our customers to stay ahead of fast-changing compute demands by adding another core technology that enables us to optimize the overall thermal management architecture of a data center,” said Lei Schlitz, Vice President and President, Global Products & Solutions, for JCI. “It will also strengthen our core technology capabilities that can scale across the Johnson Controls portfolio and reinforces our long-term commitment to lead more broadly in advanced thermal management solutions for mission critical applications.”

The company also used the announcement to remind the market that it’s building a broad thermal toolbox, citing offerings including its:

YDAM magnetic bearing chiller delivering 3.5 MW of cooling and a 20% capacity density increase versus competing solutions.
YK-HT two-stage economized centrifugal chiller, almost 30% smaller than alternatives and requiring up to 60% fewer dry coolers.
Silent-Aire Coolant Distribution Unit (CDU) platform with cooling capacities from 500 kW to over 10 MW.
YHAU absorption chillers, designed to recover waste heat and deliver additional cooling more than 90% more efficiently than electrical cooling.

Alloy’s CEO Alison Forsyth framed the deal as a scaling moment:

“We’re excited to join Johnson Controls and accelerate the impact of our unique technology… We look forward to this new chapter and continuing to scale with one of the world’s most respected and experienced leaders in thermal management innovation.”

The transaction is expected to close in fiscal Q3, subject to regulatory approvals and closing conditions. Financial terms were not disclosed.

The bigger signal is to discern how cooling IP, especially for liquid cooling component architecture, is now strategic enough to acquire. That puts liquid cooling on the same playing field as power distribution and electrical gear as a domain where differentiation is increasingly tied to proprietary capability, not just integrator packaging.

3) Chillers Re-Examined for Reliability in the Real World

At the facility layer, two announcements make an unambiguous case that regardless of how “hot” future coolant loops get, real-world conditions still demand robust heat rejection and fast recovery.

Carrier’s AquaEdge 30CF: “Perform When it Matters Most”

On Feb. 26, 2026, Carrier introduced the AquaEdge® 30CF air-cooled centrifugal chiller, designed to help operators maintain continuous performance and protect uptime “under real-world operating conditions.” Carrier positioned the product as part of Carrier QuantumLeap™, its portfolio of integrated thermal management solutions for data centers.

“As data centers evolve, operators need confidence that their cooling systems will perform when it matters most,” said Christian Senu, Vice President, Data Centers, Carrier. “The AquaEdge® 30CF was engineered with our customers in mind to protect uptime through reliable operation across a range of ambient conditions and respond quickly if the unexpected occurs.”

Carrier’s key differentiators are very specific:

Operating range: –20°F to 140°F
Recovery: restore 100% cooling capacity in under three minutes after a power interruption
Capacity: more than 3 MW of cooling (depending on ambient conditions)
Architecture: proprietary two-stage, back-to-back centrifugal compressor with magnetic bearing technology
Oil-free platform derived from the AquaEdge® 19MV water-cooled centrifugal chiller

Carrier also emphasized its “expanded global chiller manufacturing capacity” as a lever to reduce deployment and supply chain risk, an increasingly important angle as campuses attempt to replicate deployments across multiple markets simultaneously.

Modine / Airedale TurboChill 3+MW: the hybrid case

Meanwhile, on Jan. 22, 2026, Airedale by Modine™ announced the TurboChill™ 3+MW, calling it a hybrid chiller engineered to manage rising AI workloads by maximizing free cooling when conditions allow while still deploying mechanical cooling for peaks and reliability.

Modine’s Art Laszlo, Group Vice President of Global Data Centers, directly addressed the industry speculation that chillers may become unnecessary as chips tolerate higher temperatures.

“There is speculation that chillers may no longer be required as next-generation chips are designed to operate at higher temperatures,” Laszlo said. “However, customers continue to demand proven cooling and reliability to protect their investments… Thermal architectures with dry coolers as the only form of heat rejection are not practical in many regions where varying ambient and recirculation conditions will still require refrigerant-based cooling for reliable data center operations.”

Modine’s “why hybrid” argument includes:

heat waves and ambient extremes
parasitic system losses and local recirculation
mixed-density facilities (some racks hot-loop, others still needing traditional returns)
global deployments across climates where “dry only” is insufficient

Taken together, Carrier and Modine are arguing that the future is not “chiller-less.” It’s more conditional, as in more free cooling where possible, more liquid at the rack, but with refrigerant-based capacity still serving as the reliability backstop.

4) Immersion Keeps Pressing its Case, Now with “Platform” Language

On Jan. 15, 2026, Infinium launched Infinium Edge™, described as an advanced infrastructure platform designed to enable high-density AI and HPC workloads through immersion cooling. The centerpiece is Infinium Edge Immersion Fluids™, custom-engineered dielectric fluids meant to remove heat “directly at the source.”

Infinium’s messaging was blunt. The company stated that “cooling has emerged as one of the primary constraints to data center performance, efficiency, siting and scale.”

“Cooling has become one of the defining constraints on deploying state-of-the-art AI compute systems,” said Robert Schuetzle, CEO of Infinium. “Infinium Edge leverages our expertise in advanced chemistry and industrial-scale manufacturing to deliver an immersion cooling platform that advances next-generation AI infrastructure.”

The company contrasted typical density bands:

Conventional air: ~10–20 kW/rack
Direct-to-chip: ~40–80 kW/rack
Immersion: higher still, positioned as a path beyond those limits

It also drew a line between its synthetic approach and “commodity” petroleum-derived immersion liquids, arguing that cleaner synthetic products avoid residual contaminants that can limit reliability and long-term performance.

The significance here is as much rhetorical as technical. Immersion is increasingly being sold not as a niche cooling method, but as a platform that unites chemistry + manufacturing + deployment model.

5) The Supply Chain Reality: Cooling is Also a Manufacturing Problem

If the AI era is compressing deployment schedules, then cooling has to scale in two dimensions: technology and throughput. On Feb. 17, 2026, Boyd announced a major expansion of its design and manufacturing facility in Juarez, Mexico to increase production for AI infrastructure, hyperscale, and colocation data centers.

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Join our daily and weekly newsletters for the latest updates and exclusive content on industry-leading AI coverage. Learn More Self-driving tractors might be the path to self-driving cars. John Deere has revealed a new line of autonomous machines and tech across agriculture, construction and commercial landscaping. The Moline, Illinois-based John Deere has been in business for 187 years, yet it’s been a regular as a non-tech company showing off technology at the big tech trade show in Las Vegas and is back at CES 2025 with more autonomous tractors and other vehicles. This is not something we usually cover, but John Deere has a lot of data that is interesting in the big picture of tech. The message from the company is that there aren’t enough skilled farm laborers to do the work that its customers need. It’s been a challenge for most of the last two decades, said Jahmy Hindman, CTO at John Deere, in a briefing. Much of the tech will come this fall and after that. He noted that the average farmer in the U.S. is over 58 and works 12 to 18 hours a day to grow food for us. And he said the American Farm Bureau Federation estimates there are roughly 2.4 million farm jobs that need to be filled annually; and the agricultural work force continues to shrink. (This is my hint to the anti-immigration crowd). John Deere’s autonomous 9RX Tractor. Farmers can oversee it using an app. While each of these industries experiences their own set of challenges, a commonality across all is skilled labor availability. In construction, about 80% percent of contractors struggle to find skilled labor. And in commercial landscaping, 86% of landscaping business owners can’t find labor to fill open positions, he said. “They have to figure out how to do

2025 playbook for enterprise AI success, from agents to evals

Join our daily and weekly newsletters for the latest updates and exclusive content on industry-leading AI coverage. Learn More 2025 is poised to be a pivotal year for enterprise AI. The past year has seen rapid innovation, and this year will see the same. This has made it more critical than ever to revisit your AI strategy to stay competitive and create value for your customers. From scaling AI agents to optimizing costs, here are the five critical areas enterprises should prioritize for their AI strategy this year. 1. Agents: the next generation of automation AI agents are no longer theoretical. In 2025, they’re indispensable tools for enterprises looking to streamline operations and enhance customer interactions. Unlike traditional software, agents powered by large language models (LLMs) can make nuanced decisions, navigate complex multi-step tasks, and integrate seamlessly with tools and APIs. At the start of 2024, agents were not ready for prime time, making frustrating mistakes like hallucinating URLs. They started getting better as frontier large language models themselves improved. “Let me put it this way,” said Sam Witteveen, cofounder of Red Dragon, a company that develops agents for companies, and that recently reviewed the 48 agents it built last year. “Interestingly, the ones that we built at the start of the year, a lot of those worked way better at the end of the year just because the models got better.” Witteveen shared this in the video podcast we filmed to discuss these five big trends in detail. Models are getting better and hallucinating less, and they’re also being trained to do agentic tasks. Another feature that the model providers are researching is a way to use the LLM as a judge, and as models get cheaper (something we’ll cover below), companies can use three or more models to

OpenAI’s red teaming innovations define new essentials for security leaders in the AI era

Join our daily and weekly newsletters for the latest updates and exclusive content on industry-leading AI coverage. Learn More OpenAI has taken a more aggressive approach to red teaming than its AI competitors, demonstrating its security teams’ advanced capabilities in two areas: multi-step reinforcement and external red teaming. OpenAI recently released two papers that set a new competitive standard for improving the quality, reliability and safety of AI models in these two techniques and more. The first paper, “OpenAI’s Approach to External Red Teaming for AI Models and Systems,” reports that specialized teams outside the company have proven effective in uncovering vulnerabilities that might otherwise have made it into a released model because in-house testing techniques may have missed them. In the second paper, “Diverse and Effective Red Teaming with Auto-Generated Rewards and Multi-Step Reinforcement Learning,” OpenAI introduces an automated framework that relies on iterative reinforcement learning to generate a broad spectrum of novel, wide-ranging attacks. Going all-in on red teaming pays practical, competitive dividends It’s encouraging to see competitive intensity in red teaming growing among AI companies. When Anthropic released its AI red team guidelines in June of last year, it joined AI providers including Google, Microsoft, Nvidia, OpenAI, and even the U.S.’s National Institute of Standards and Technology (NIST), which all had released red teaming frameworks. Investing heavily in red teaming yields tangible benefits for security leaders in any organization. OpenAI’s paper on external red teaming provides a detailed analysis of how the company strives to create specialized external teams that include cybersecurity and subject matter experts. The goal is to see if knowledgeable external teams can defeat models’ security perimeters and find gaps in their security, biases and controls that prompt-based testing couldn’t find. What makes OpenAI’s recent papers noteworthy is how well they define using human-in-the-middle

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