But not everyone is that optimistic. According to Gartner, space-based data centers won’t be useful for decades, so companies should focus on expanding capacity down here on Earth. “I honestly think the idea with the current landscape of putting data centers in space is ridiculous,” OpenAI CEO Sam Altman told The Indian Express in February. Current satellite computing can’t easily scale to data centers, agrees Holger Mueller, an analyst at Constellation Research. “Weight is still the restriction,” he says. “It’s the equivalent of you buying a tablet or small laptop to travel across Latin America versus putting in a data center in the Amazon. Different power requirements, investment, totally different setup.” Then there are issues like damaged solar panels from meteorite storms and satellite debris, he adds. “You would have to pay for operational redundancy, which is further investment.” “Data centers will be built where they are affordable,” he says. “I don’t see space happening soon. Remember the Microsoft submerged one? Crickets…” But he agrees that solar power is nice, though the sun is only visible from one side of the planet at any given time. And space is cold, he says. Cooling down in outer space In fact, space is very cold. Close to absolute zero cold. But vacuum is also a great insulator, and there’s no air to move the heat around. “You can’t convect heat away,” says Richard Bonner, CTO at Accelsius, a liquid cooling company. Bonner has worked on NASA research projects about the challenge of cooling in space and is very familiar with the problem. A small proportion of the heat might be turned back into useful electricity, but that’s not really a solution, he says, because computer chips don’t get quite that hot. Instead, heat is radiated. When an object warms up, it generates

The rapid global buildout of AI infrastructure is colliding with a new constraint that hyperscalers cannot solve with capital or GPUs: local opposition. In the first months of 2026, community resistance has already begun reshaping the development pipeline. A February analysis by Sightline Climate estimates that 30–50 percent of the data center capacity expected to come online in 2026 may not be delivered on schedule, reflecting a growing set of constraints that now include power availability, permitting challenges, and increasingly organized local opposition. The financial stakes are already substantial. Recent reporting indicates that tens of billions of dollars in planned data center development have been delayed or halted amid community pushback, including an estimated $98 billion worth of projects delayed or blocked in a single quarter of 2025, according to research cited by Data Center Watch. What had been framed throughout 2024 and 2025 as an inevitable expansion of hyperscale campuses, gigawatt-scale power agreements, and AI “factory” clusters is now encountering a different kind of gatekeeper: the communities expected to host the infrastructure. The shift is already visible in project outcomes. Across the United States, multiple projects were canceled, blocked, or fundamentally reshaped in the opening months of 2026 due to organized local opposition. Reporting from The Guardian found that 26 data center projects were canceled in December and January, compared with just one cancellation in October, suggesting that community resistance campaigns are increasingly capable of stopping projects before construction begins. At the same time, local governments are responding to community pressure with moratoriums, zoning restrictions, and permitting delays that can stall projects long enough to jeopardize financing or push developers to seek more favorable jurisdictions. While opposition to data center development is not new, the scale, coordination, and success rate of these efforts suggest a structural shift in how

The data center industry’s explosive growth in the AI era is transforming how projects are conceived, financed, and built. What was once a real estate-driven business has become something far more complex: an engineering and infrastructure challenge defined by power availability, network topology, and local politics. That was one of the key themes in this recent episode of the Data Center Frontier Show podcast, where Editor-in-Chief Matt Vincent spoke with Michael Siteman, President of Prodigious Proclivities and a longtime leader and board member within 7×24 Exchange International. Drawing on decades of experience spanning brokerage, development, connectivity strategy, and infrastructure advisory, Siteman offered a field-level view of how the industry is adapting to the demands of AI-driven infrastructure. “The business used to be a pure real estate play,” Siteman said. “Now it’s a systems engineering problem. It’s power, network topology, the real estate itself, and political risk—all of these factors that have to work together.” Site Selection Becomes Systems Engineering For much of the early data center era, location decisions revolved around traditional real estate considerations: available buildings, proximity to customers, and nearby fiber connectivity. That logic has fundamentally changed. “Years ago, the question was: Is there a building? Are there carriers nearby?” Siteman recalled. “Now it’s completely different. Power availability, network topology, community acceptance—these are the variables that define whether a site works.” Utilities themselves have become gatekeepers in the process. “You go to a utility and ask if there’s power,” he explained. “They might say, ‘We might have power, but you have to pay us to study whether we actually have power.’” In many regions experiencing rapid digital infrastructure expansion, the answer increasingly comes back the same: there simply isn’t enough grid capacity available. Power Becomes the Project In the gigawatt-scale era of AI infrastructure, power strategy has moved

Silicon as a Data Center Design Tool Custom silicon also allows hyperscale operators to shape the physical characteristics of the infrastructure around it. Traditional GPU platforms often arrive with fixed power envelopes and thermal constraints. But internally designed accelerators allow companies like Meta to tailor chips to the rack-level power and cooling budgets of their own data center architecture. That flexibility becomes increasingly important as AI infrastructure pushes power densities far beyond traditional enterprise deployments. Custom accelerators like MTIA can be engineered to fit within the liquid-to-chip cooling frameworks now emerging in hyperscale AI racks. These systems circulate coolant directly across cold plates attached to processors, removing heat far more efficiently than air cooling and enabling higher compute densities. For operators running thousands of racks across multiple campuses, small improvements in performance-per-watt can translate into enormous reductions in total power demand. Software-Defined Power One of the subtler advantages of custom silicon lies in how it interacts with data center power systems. By controlling chip-level power management features such as power capping and workload throttling, operators can fine-tune how servers consume electricity inside each rack. This creates opportunities to safely run racks closer to their electrical limits without triggering breaker trips or thermal overloads. In practice, that means data center operators can extract more useful compute from the same electrical infrastructure. At hyperscale, where campuses may draw hundreds of megawatts, these efficiencies have a direct impact on capital planning and grid interconnection requirements. The Interconnect Layer AI accelerators do not operate in isolation. Their effectiveness depends heavily on how they connect to memory, storage, and other compute nodes across the cluster. Industry analysts expect next-generation inference platforms to rely increasingly on high-speed interconnect technologies such as CXL (Compute Express Link) and advanced networking fabrics to support disaggregated memory architectures and low-latency

PJM Interconnection is moving to rewrite how behind-the-meter power is treated across its grid, signaling a major shift as AI-scale data centers push electricity demand into territory the current regulatory framework was never designed to handle. For years, PJM’s retail behind-the-meter generation rules allowed customers with onsite generation to “net” their load, reducing the amount of demand counted for transmission and other grid-related charges. The framework dates back to 2004, when behind-the-meter generation was typically associated with smaller industrial facilities or campus-style energy systems. PJM now argues that those assumptions no longer hold. The arrival of very large co-located loads, particularly hyperscale and AI data centers seeking hundreds of megawatts of power on accelerated timelines, has exposed gaps in how the system accounts for and plans around those facilities. In February 2026, PJM asked the Federal Energy Regulatory Commission to approve a tariff rewrite that would sharply limit how new large loads can rely on legacy netting rules. The move reflects a broader challenge facing grid operators as the rapid expansion of AI infrastructure begins to collide with planning frameworks built for a far slower era of demand growth. The proposal follows directly from a December 18, 2025 order from FERC finding that PJM’s existing tariff was “unjust and unreasonable” because it lacked clear rates, terms, and conditions governing co-location arrangements between large loads and generating facilities. Rather than prohibiting co-location, the commission directed PJM to create transparent rules allowing data centers and other large consumers to pair with generation while still protecting system reliability and other ratepayers. In essence, FERC told PJM not to shut the door on these arrangements, but to stop improvising and build a formal framework capable of supporting them. Why Behind-the-Meter Power Matters Behind-the-meter arrangements have become one of the most attractive strategies for hyperscale

Power is rapidly becoming the defining constraint on the next phase of data center growth. Across the industry, developers and hyperscalers are discovering that the biggest obstacle to deploying AI infrastructure is no longer capital, land, or connectivity. It’s electricity. In major markets from Northern Virginia to Texas, grid interconnection timelines are stretching out for years as utilities struggle to keep pace with a surge in large-load requests from AI-driven infrastructure. A new industry analysis from Bloom Energy reinforces that emerging reality. The company’s 2026 Data Center Power Report finds that electricity availability has moved from a planning consideration to a defining boundary on data center expansion, transforming site selection, power strategies, and the design of next-generation AI campuses. Based on surveys of hyperscalers, colocation providers, utilities, and equipment suppliers conducted through 2025, the report concludes that the determinants of data center growth are changing in the AI era. Across the industry, the result is a structural shift in how data centers are planned, financed, and powered. Industry executives interviewed for the report say the shift is already visible in real-world development decisions. “We’re seeing a geographic shift as certain regions become more power-friendly and therefore more attractive for data center construction,” said a hyperscaler energy executive quoted in the report, noting that developers are increasingly prioritizing markets where large blocks of electricity can be secured quickly and predictably. AI Load Is Accelerating Faster Than the Grid Bloom’s analysis suggests that U.S. data center IT load could grow from roughly 80 gigawatts in 2025 to about 150 gigawatts by 2028, effectively doubling within three years as AI training clusters and inference infrastructure expand. That surge is already showing up in grid planning models. The Electric Reliability Council of Texas (ERCOT), which oversees the Texas power market, now forecasts that statewide