The data center industry’s race for power has become a contest against time. Developers are pursuing new generating capacity, transmission upgrades, utility interconnections, and increasingly elaborate behind-the-meter strategies to satisfy an AI infrastructure market whose demand keeps outrunning delivery schedules. Yet another possibility is emerging alongside the construction boom: Some of the power needed to expand AI computing may already be available inside operational facilities, committed to customers but not consistently consumed. That is the proposition behind Hammerhead AI, a Redwood City, California-based company developing software to coordinate electrical infrastructure, cooling systems, and computing workloads. Rather than treating a data center’s power allocation as a fixed ceiling that can only be raised through construction, Hammerhead argues that more compute can be accommodated by managing existing resources dynamically. In a new episode of the Data Center Frontier Show, Hammerhead AI founder and CEO Rahul Kar discussed the company’s approach with DCF Editor-in-Chief Matt Vincent, examining how reinforcement learning and workload orchestration could convert otherwise idle electrical capacity into revenue-producing AI infrastructure. The idea arrives at a consequential moment for the colocation market. DCF’s analysis of CBRE’s first-half 2026 data center market report found North American primary-market vacancy at just 1.4%, even as new construction reached record levels. Power commitments, delivery timelines, and access to ready infrastructure continue to determine where and when additional compute can be deployed. Hammerhead’s central argument is that the industry should examine how much of its already allocated capacity can be made productive before assuming that every increment of computing demand requires another increment of physical infrastructure. The commercial opportunity is substantial. Whether it can be realized without undermining the reliability expectations of mission-critical facilities is the harder question. A Power Shortage, or a Utilization Problem? Kar begins with a distinction between the electricity infrastructure available to

Each month Data Center Frontier, in partnership with Pkaza, posts some of the hottest data center career opportunities in the market. Here’s a look at some of the latest data center jobs posted on the Data Center Frontier jobs board, powered by Pkaza Critical Facilities Recruiting. Looking for Data Center Candidates? Check out Pkaza’s Active Candidate / Featured Candidate Hotlist  Lead Mechanical Engineer – Data Center Design Dallas, TX / Remote This position is also available in: Denver, CO; Indianapolis, IN; Cedar Rapids, IA; Austin, TX; White Plains, NY; New York, NY; Richmond, VA; Ashburn, VA; Charlotte, NC; Atlanta, GA; Phoenix, AZ; Salt Lake City, UT; Kansas City, MO; Chicago, IL; Los Angeles, CA; Seattle, WA or San Jose, CA. Our client is a leading engineering design and commissioning company that is a subject matter expert in the data center space. They will provide design coordination and construction administration, consulting and management support for the data center / mission critical facilities space with the mindset to provide reliability, energy efficiency, and sustainable design expertise when providing these consulting services for enterprise, colocation and hyperscale companies. This career-growth minded opportunity offers exciting projects with leading-edge technology and innovation as well as competitive salaries and benefits. Electrical Commissioning Agent – Data Centers Phoenix, AZ (limited travel) Non-traveling CxA positions available in: Indianapolis, IN; Cedar Rapids, IA; Atlanta, GA and Columbus, OH. Traveling CxA based near any major airport, otherwise traveling to: New York, NY; White Plains, NY; Montvale, NJ; Richmond, VA; Boydton, VA; York, SC; Charlotte, NC; Salt Lake City, UT; Kansas City, MO; Chesterton, IN or Chicago, IL. *** Also looking for a lead EE and ME CxA agents and CxA PMs *** This opportunity is with a leading EPC company of data center design / build / commissioning solutions. This company provides a

Modular Air-Assisted Cooling Targets Existing Data Centers One of the most instructive systems examined during the tour was Cooler Master’s 300 kW Air-Assisted Liquid Cooling (AALC) CDU, a modular liquid-to-air design intended for data centers without an existing facility water distribution system. Rather than transferring server heat into a building’s chilled-water or facility-water loop, the AALC system circulates coolant through the IT equipment and rejects the captured heat through fan-driven radiators. The distinction matters for operators seeking to introduce liquid-cooled AI racks into facilities that were not built with dedicated liquid cooling infrastructure. Although air-assisted systems still require sufficient facility airflow and heat rejection capacity, they offer an alternative to extending a new water distribution network throughout the data hall. The approach is already finding applications in the field. As DCF recently reported, Corvex deployed liquid-cooled NVIDIA Blackwell infrastructure inside an existing air-cooled data center, using Lenovo Neptune technology to avoid a conventional facility-wide liquid cooling conversion. Cooler Master’s 300 kW design separates the pumping equipment from its radiator assemblies. During the tour, a company representative explained that the system can be configured with a central pump rack and either one or two radiator racks, each providing a nominal 150 kW of cooling capacity. This modular arrangement allows cooling capacity to be matched more closely to the installation, rather than requiring every deployment to use the full 300 kW configuration. Serviceability is a major feature of the design. The system uses redundant pumps in a 2+1 arrangement, with two pumps supporting normal operation and a third providing standby capacity. Pump modules are designed for replacement without interrupting cooling operations. Other field-replaceable components include fans, filters, expansion tanks, and power-related assemblies. The product also incorporates redundant filtration and monitoring capabilities intended to help operators identify faults and maintain cooling performance. The

A 1988 Data Center Becomes a 20 MW AI Asset The second presentation, Converting a 1990 Enterprise Data Center to 20MW AI Workloads, featured Coe alongside Ari Chernikoff, vice president of development at Legacy Investing, and Dan Amundson, project manager at Gardner Builders. The project centered on 1001 Third Avenue South in downtown Minneapolis, a financial-services computing facility originally constructed in 1988. The building had supported earlier generations of enterprise computing before portions were converted to commercial office space and smaller colocation operations. Although its remaining operating data center load had declined to approximately 500 kilowatts by the time the AI redevelopment began, the property retained substantial infrastructure inherited from its original mission-critical design. Tall ceilings, robust floor loading, raised access floors, existing medium-voltage substations, and established utility connections made the building an unusually strong candidate for conversion. These characteristics, Chernikoff explained, can determine whether an existing building offers a practical path to high-density computing or proves economically unsuitable for redevelopment. Legacy Investing had identified a market opportunity in what Chernikoff called the industry’s “missing middle”: facilities generally ranging from 20 MW to 80 MW, rather than the 100 MW-plus campuses increasingly associated with hyperscale and frontier AI development. The company viewed existing buildings with available power and favorable infrastructure characteristics as potential shortcuts to deployment, particularly where demand for AI inference and other latency-sensitive workloads favored proximity to major urban markets. Following an AI tenant commitment in summer 2024, the development team faced an aggressive initial requirement: Deliver the first 5 MW of capacity by the end of that year. The first phase was ready for service approximately four months after Gardner’s involvement became formalized, followed by another 5 MW in 2025 and an expansion program ultimately reaching 20 MW over roughly 20 months. All of this had to

Musk predicted that more than 200 neocloud companies could face significant financial distress, predicting that only a small number would survive the coming shakeout. He also said SpaceX would not acquire any of the companies, describing such purchases as “pointless.” The chickens are already coming home to roost on the neoclouds. CoreWeave, Nebius, Applied Digital and TeraWulf have reported quarterly losses over the past year, while IREN has failed to turn a profit in its past three quarters. Shares of CoreWeave, Nebius and IREN have fallen more than 30% during October, according to Reuters. Building AI data centers requires substantial upfront investment in GPUs, networking equipment, power systems, cooling infrastructure and facilities. Operators must then generate enough revenue from leasing computing capacity to cover operating expenses, interest payments and equipment replacement.

The global data center industry is approaching a potentially historic test of its ability to turn artificial intelligence investment into operational infrastructure. In a provocative new forecast, semiconductor and AI infrastructure research firm SemiAnalysis predicts that approximately 51.2 gigawatts of net data center IT capacity will be added globally, excluding China, during 2027. That would represent more than double the 25 GW the firm expects to be added in 2026. Published October 19, the report, Datacenters 2027: The Year of Acceleration, +50GW in a Single Year, makes a forceful argument that much of this unprecedented expansion is already visible in the industry’s construction pipeline. Authored by Jeremie Eliahou Ontiveros, Reyk Knuhtsen and Dylan Patel, the analysis draws on SemiAnalysis’ proprietary tracking of construction starts, individual buildings, hyperscale leasing commitments, equipment procurement and project financing. Its researchers identify more than 60 GW of data center capacity under construction. Among the 40 largest campuses they examined, more than 24 GW is reportedly in advanced stages of development, with much of that capacity expected to be delivered in 2027. The forecast is not entirely composed of projects already under construction, however. SemiAnalysis’ capacity chart includes approximately 11.9 GW of 2027 additions associated with projects still classified as planned. The remainder is associated with facilities already operating or under construction, according to the model’s building-status categories. SemiAnalysis argues that the volume and maturity of projects already underway make a dramatic acceleration increasingly likely, despite well-documented constraints involving electricity, construction labor, equipment and capital. But the prediction has also become the subject of a revealing industry debate, with competing forecasts and new corporate disclosures raising questions about how much infrastructure can actually be delivered on schedule. The disagreement is no longer principally about whether AI infrastructure demand is real. It is about whether the industrial