One of the most eye-popping figures of the AI boom – a proposed $100 billion Nvidia commitment to OpenAI and as much as 10 gigawatts of compute for the company’s Stargate AI infrastructure buildout – is no longer on the table. And that partial retreat tells the data center industry something important. According to multiple reports surfacing at the end of January, Nvidia has paused and re-scoped its previously discussed, non-binding investment framework with OpenAI, shifting from an unprecedented capital-plus-infrastructure commitment to a much smaller (though still massive) equity investment. What was once framed as a potential $100 billion alignment is now being discussed in the $20-30 billion range, as part of OpenAI’s broader effort to raise as much as $100 billion at a valuation approaching $830 billion. For data center operators, infrastructure developers, and power providers, the recalibration matters less for the headline number and more for what it reveals about risk discipline, competitive dynamics, and the limits of vertical circularity in AI infrastructure finance. From Moonshot to Measured Capital The original September 2025 memorandum reportedly contemplated not just capital, but direct alignment on compute delivery: a structure that would have tightly coupled Nvidia’s balance sheet with OpenAI’s AI-factory roadmap. By late January, however, sources indicated Nvidia executives had grown uneasy with both the scale and the structure of the deal. Speaking in Taipei on January 31, Nvidia CEO Jensen Huang pushed back on reports of friction, calling them “nonsense” and confirming Nvidia would “absolutely” participate in OpenAI’s current fundraising round. But Huang was also explicit on what had changed: the investment would be “nothing like” $100 billion, even if it ultimately becomes the largest single investment Nvidia has ever made. That nuance matters. Nvidia is not walking away from OpenAI. But it is drawing a clearer boundary around

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 Onsite Engineer – Critical FacilitiesCharleston, SC This is NOT a traveling position. Having degreed engineers seems to be all the rage these days. I can also use this type of candidate in following cities: Ashburn, VA; Moncks Corner, SC; Binghamton, NY; Dallas, TX or Indianapolis, IN. Our client is an engineering design and commissioning company that is a subject matter expert in the data center space. This role will be onsite at a customer’s data center. They will provide onsite 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, sustainable design and LEED 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 Engineer Ashburn, VA This traveling position is also available in: New York, NY; White Plains, NY;  Richmond, VA; Montvale, NJ; Charlotte, NC; Atlanta, GA; Hampton, GA; New Albany, OH; Cedar Rapids, IA; Phoenix, AZ; Salt Lake City, UT; Dallas, TX; Kansas City, MO; Omaha, NE; Chesterton, IN or Chicago, IL. *** ALSO looking for a LEAD EE and ME CxA Agents and CxA PMs *** Our client is an engineering design and commissioning company that has a national footprint and specializes in MEP critical facilities design. They provide design, commissioning, consulting and management expertise in the critical facilities space. They

The AI conversation has been dominated by model announcements, benchmark races, and the rapid evolution of large language models. But in enterprise environments, the harder problem isn’t building smarter models. It’s making them work reliably with real-world data. On the latest episode of the Data Center Frontier Show Podcast, Sailesh Krishnamurthy, VP of Engineering for Databases at Google Cloud, pulled back the curtain on the infrastructure layer where many ambitious AI initiatives succeed, or quietly fail. Krishnamurthy operates at the intersection of databases, search, and AI systems. His perspective underscores a growing reality across enterprise IT: AI success increasingly depends on how organizations manage, integrate, and govern data across operational systems, not just how powerful their models are. The Disconnect Between LLMs and Reality Enterprises today face a fundamental challenge: connecting LLMs to real-time operational data. Search systems handle documents and unstructured information well. Operational databases manage transactions, customer data, and financial records with precision. But combining the two remains difficult. Krishnamurthy described the problem as universal. “Inside enterprises, knowledge workers are often searching documents while separately querying operational systems,” he said. “But combining unstructured information with operational database data is still hard to do.” Externally, customers encounter the opposite issue. Portals expose personal data but struggle to incorporate broader contextual information. “You get a narrow view of your own data,” he explained, “but combining that with unstructured information that might answer your real question is still challenging.” The result: AI systems often operate with incomplete context. Vector Search Moves Into the Database Vector search has emerged as a bridge between structured and unstructured worlds. But its evolution over the past three years has changed how enterprises deploy it. Early use cases focused on semantic search, i.e. finding meaning rather than exact keyword matches. Bug tracking systems, for example, began

Regions in a Position to Scale California (A- overall)California continues to lead in long-term, scenario-based transmission planning. CAISO’s most recent transmission plan identifies $4.8 billion in new projects to accommodate approximately 76 gigawatts of additional capacity by 2039, explicitly accounting for data center growth alongside broader electrification. For data center developers, California’s challenge is less about planning quality and more about execution. Permitting timelines, cost allocation debates, and political scrutiny remain significant hurdles. Plains / Southwest Power Pool (B- overall, A in regional planning)SPP stands out nationally for embracing ultra-high-voltage transmission as a backbone strategy. Its recent Integrated Transmission Plans approve more than $16 billion in new projects, including multiple 765-kV lines, with benefit-cost ratios exceeding 10:1. This approach positions the Plains region as one of the most structurally “AI-ready” grids in North America, particularly for multi-gigawatt campuses supported by wind, natural gas, and emerging nuclear resources. Midwest / MISO (B overall)MISO’s Long-Range Transmission Planning framework aligns closely with federal best practices, co-optimizing generation and transmission over long planning horizons. While challenges remain—particularly around interregional coordination—the Midwest is comparatively well positioned for sustained data center growth. Regions Facing Heightened Risk Texas / ERCOT (D- overall)Texas has approved massive new transmission investments, including 765-kV projects tied to explosive load growth in the Permian Basin. However, the report criticizes ERCOT’s planning for remaining largely siloed and reliability-driven, with limited long-term scenario analysis and narrow benefit assessments. For data centers, ERCOT still offers speed to market, but increasingly with risks tied to congestion, price volatility, and political backlash surrounding grid reliability. Southeast (F overall)The Southeast receives failing grades across all categories, with transmission development remaining fragmented, utility-driven, and largely disconnected from durable regional planning frameworks. As AI data centers increasingly target the region for its land availability and tax incentives, the lack of

Enter the 8MW CDU Era The next evolution arrived just days later. On Jan. 20, DCX announced its second-generation facility-scale unit, the FDU V2AT2, pushing capacity into territory previously unimaginable for single CDU platforms. The system delivers up to 8.15 megawatts of heat transfer capacity with record flow rates designed to support 45°C warm-water cooling, aligning directly with NVIDIA’s roadmap for rack-scale AI systems, including Vera Rubin-class deployments. That temperature target is significant. Warm-water cooling at this level allows many facilities to eliminate traditional chillers for heat rejection, depending on climate and deployment design. Instead of relying on compressor-driven refrigeration, operators can shift toward dry coolers or other simplified heat rejection strategies. The result: • Reduced mechanical complexity• Lower energy consumption• Improved efficiency at scale• New opportunities for heat reuse According to DCX CTO Maciek Szadkowski, the goal is to avoid obsolescence in a single hardware generation: “As the datacenter industry transitions to AI factories, operators need cooling systems that won’t be obsolete in one platform cycle. The FDU V2AT2 replaces multiple legacy CDUs and enables 45°C supply water operation while simplifying cooling topology and significantly reducing both CAPEX and OPEX.” The unit incorporates a high-capacity heat exchanger with a 2°C approach temperature, N+1 redundant pump configuration, integrated water quality control, and diagnostics systems designed for predictive maintenance. In short, this is infrastructure built not for incremental density growth, but for hyperscale AI facilities where megawatts of cooling must scale as predictably as compute capacity. Liquid Cooling Becomes System Architecture The broader industry implication is clear: cooling is no longer an auxiliary mechanical function. It is becoming system architecture. DCX’s broader 2025 performance metrics underscore the speed of this transition. The company reported 600% revenue growth, expanded its workforce fourfold, and shipped or secured contracts covering more than 500 MW

The AI infrastructure boom is often framed around massive hyperscale campuses racing to secure gigawatts of power. But an equally important shift is happening in parallel: AI infrastructure is also becoming more distributed, modular, and sovereign, extending compute far beyond traditional data center hubs. A wave of recent announcements across developers, infrastructure investors, and regional operators shows the market pursuing a dual strategy. On one end, developers are accelerating delivery of hyperscale campuses measured in hundreds of megawatts, and increasingly gigawatts, often located where power availability and energy economics offer structural advantage, and in some cases pairing compute directly with dedicated generation. On the other, providers are building increasingly capable regional and edge facilities designed to bring AI compute closer to users, industrial operations, and national jurisdictions. Taken together, these moves point toward a future in which AI infrastructure is no longer purely centralized, but built around interconnected hub-and-spoke architectures combining energy-advantaged hyperscale cores with rapidly deployable edge capacity. Recent developments across hyperscale developers, edge specialists, infrastructure investors, and regional operators illustrate how quickly this model is taking shape. Sovereign AI Moves Beyond the Core On Feb. 5, 2026, San Francisco-based Armada and European AI infrastructure builder Nscale signed a letter of intent to jointly deploy both large-scale and edge AI infrastructure worldwide. The collaboration targets enterprise and public sector customers seeking sovereign, secure, geographically distributed AI environments. Nscale is building large AI supercomputer clusters globally, offering vertically integrated capabilities spanning power, data centers, compute, and software. Armada specializes in modular deployments through its Galleon data centers and Armada Edge Platform, delivering compute and storage into remote or infrastructure-poor environments. The combined offering addresses a growing challenge: many governments and enterprises want AI capability deployed within their own jurisdictions, even where traditional hyperscale infrastructure does not yet exist. “There is