“The G200 chip was for the scale out, because what’s happening now is these models are getting bigger where they don’t just fit within a single data center. You don’t have enough power to just pull into a single data center,” Patel said. “So now you need to have data centers that might be hundreds of kilometers apart, that operate like an ultra-cluster that are coherent. And so that requires a completely different chip architecture to make sure that you have capabilities like deep buffering and so on and so forth… You need to make sure that these data centers can be scaled across physical boundaries.”  “In addition, we are reaching the physical limits of copper and optics, and coherent optics especially are going to be extremely important as we go start building out this data center infrastructure. So that’s an area that you’re starting to see a tremendous amount of progress being made,” Patel said. The second constraint is the AI trust deficit, Patel said. “We currently need to make sure that these systems are trusted by the people that are using them, because if you don’t trust these systems, you’ll never use them,” Patel said. “This is the first time that security is actually becoming a prerequisite for adoption. In the past, you always ask the question whether you want to be secure, or you want to be productive. And those were kind of needs that offset each other,” Patel said. “We need to make sure that we trust not just using AI for cyber defense, but we trust AI itself,” Patel said. The third constraint is the notion of a data gap. AI models get trained on human-generated data that’s publicly available on the Internet, but “we’re running out,” Patel said. “And what you’re starting to see happen

Physical AI: A Reusable Robotics Stack for Data Center Operations This is where the recent collaboration between Multiply Labs and NVIDIA becomes relevant, even though the application is biomanufacturing rather than data centers. Multiply Labs has outlined a robotics approach built on three core elements: Digital twins using NVIDIA Isaac Sim to model hardware and validate changes in simulation before deployment. Foundation-model-based skill learning via NVIDIA Isaac GR00T, enabling robots to generalize tasks rather than rely on brittle, hard-coded behaviors. Perception pipelines including FoundationPose and FoundationStereo, that convert expert demonstrations into structured training data. Taken together, this represents a reusable blueprint for data center robotics. Applying the Lesson to Data Center Environments The same physical-AI techniques now being applied in lab and manufacturing environments map cleanly onto the realities of data center operations, particularly where safety, uptime, and variability intersect. Digital-twin-first deployment Before a robot ever enters a live data hall, it needs to be trained in simulation. That means modeling aisle geometry, obstacles, rack layouts, reflective surfaces, and lighting variation; along with “what if” scenarios such as blocked aisles, emergency egress conditions, ladders left in place, or spill events. Simulation-first workflows make it possible to validate behavior and edge cases before introducing any new system into a production environment. Skill learning beats hard-coded rules Data centers appear structured, but in practice they are full of variability: temporary cabling, staged parts, mixed-vendor racks, and countless human exceptions. Foundation-model approaches to manipulation are designed to generalize across that messiness far better than traditional rule-based automation, which tends to break when conditions drift even slightly from the expected state. Imitation learning captures tribal knowledge Many operational tasks rely on tacit expertise developed over years in the field, such as how to manage stiff patch cords, visually confirm latch engagement, or stage a

Designing for What Comes After the Current AI Cycle Applied Digital’s design philosophy starts with a premise many developers still resist: today’s density assumptions may not hold. “We’re designing for maximum flexibility for the future—higher density power, lower density power, higher voltage delivery, and more floor space,” Cummins said. “It’s counterintuitive because densities are going up, but we don’t know what comes next.” That choice – to allocate more floor space even as rack densities climb – signals a long-view approach. Facilities are engineered to accommodate shifts in voltage, cooling topology, and customer requirements without forcing wholesale retrofits. Higher-voltage delivery, mixed cooling configurations, and adaptable data halls are baked in from the start. The goal is not to predict the future perfectly, Cummins stressed, but to avoid painting infrastructure into a corner. Supply Chain as Competitive Advantage If flexibility is the design thesis, supply chain control is the execution weapon. “It’s a huge advantage that we locked in our MEP supply chain 18 to 24 months ago,” Cummins said. “It’s a tight environment, and more timelines are going to get missed in 2026 because of it.” Applied Digital moved early to secure long-lead mechanical, electrical, and plumbing components; well before demand pressure fully rippled through transformers, switchgear, chillers, generators, and breakers. That foresight now underpins the company’s ability to make credible delivery commitments while competitors confront procurement bottlenecks. Cummins was blunt: many delays won’t stem from poor planning, but from simple unavailability. From 100 MW to 700 MW Without Losing Control The past year marked a structural pivot for Applied Digital. What began as a single, 100-megawatt “field of dreams” facility in North Dakota has become more than 700 MW under construction, with expansion still ahead. “A hundred megawatts used to be considered scale,” Cummins said. “Now we’re at 700

For much of the past decade, data center growth could be measured in incremental gains: another efficiency point here, another capacity tranche there. That era is over. According to a cascade of recent research from Dell’Oro Group, the AI investment cycle has crossed into a new phase, one defined less by experimentation and more by industrial-scale execution. Across servers, networks, power, and cooling, Dell’Oro’s latest data points to a market being reshaped end-to-end by AI workloads which are pulling forward capital spending, redefining bill-of-material assumptions, and forcing architectural transitions that are rapidly becoming non-negotiable. Capex Becomes the Signal The clearest indicator of the shift is spending. Dell’Oro reported that worldwide data center capital expenditures rose 59 percent year-over-year in 3Q 2025, marking the eighth consecutive quarter of double-digit growth. Importantly, this is no longer a narrow, training-centric surge. “The Top 4 US cloud service providers—Amazon, Google, Meta, and Microsoft—continue to raise data center capex expectations for 2025, supported by increased investments in both AI and general-purpose infrastructure,” said Baron Fung, Senior Research Director at Dell’Oro Group. He added that Oracle is on track to double its data center capex as it expands capacity for the Stargate project. “What is notable this cycle is not just the pace of spending, but the expanding scope of investment,” Fung said. Hyperscalers are now scaling accelerated compute, general-purpose servers, and the supporting infrastructure required to deploy AI at production scale, while simultaneously applying tighter discipline around asset lifecycles and depreciation to preserve cash flow. The result is a capex environment that looks less speculative and more structural, with investment signals extending well into 2026. Accelerators Redefine the Hardware Stack At the component level, the AI effect is even more pronounced. Dell’Oro found that global data center server and storage component revenue jumped 40 percent

Finding Water by Eliminating Waste: Leakage as a Hidden Demand Driver ION Water and Meta frame leakage not as a marginal efficiency issue, but as one of the largest and least visible sources of water demand. According to the release, more than half of the water paid for at some properties can be lost to “invisible leaks,” including running toilets, aging water heaters, and faulty fixtures that go undetected for extended periods. ION’s platform is designed to surface that hidden demand. By monitoring water consumption at the unit level, the system flags anomalies in real time and directs maintenance teams to specific fixtures, rather than entire buildings. The company says this approach can reduce leak-driven water waste by as much as 60%. This represents an important evolution in how hyperscalers defend and contextualize their water footprints: Instead of focusing solely on their own direct WUE metrics, operators are investing in demand reduction within the same watershed where their data centers operate. That shift reframes the narrative from simply managing active water consumption to actively helping stabilize stressed local water systems. The Accounting Shift: Volumetric Water Benefits (VWB) The release explicitly positions the project as a model for Volumetric Water Benefits (VWB) initiatives, projects intended to deliver measurable environmental gains while also producing operational and financial benefits for underserved communities. This framing aligns with a broader stewardship accounting movement promoted by organizations such as the World Resources Institute, which has developed Volumetric Water Benefit Accounting (VWBA) as a standardized method for quantifying and valuing watershed-scale benefits. Meta is explicit that the project supports its water-positive commitment tied to its Temple, Texas data center community. The company has set a 2030 goal to restore more water than it consumes across its global operations and has increasingly emphasized “water stewardship in our data center

If you’re trying to understand where the hyperscalers really are in the AI buildout, beyond the glossy campus renders and “superintelligence” rhetoric, this week’s earnings calls from Microsoft and Meta offered a more grounded view. Both companies are spending at a scale the data center industry has never had to absorb at once. Both are navigating the same hard constraints: power, capacity, supply chain, silicon allocation, and time-to-build.  But the market’s reaction split decisively, and that divergence tells its own story about what investors will tolerate in 2026. To wit: Massive capex is acceptable when the return narrative is already visible in the P&L…and far less so when the payoff is still being described as “early innings.” Microsoft: AI Demand Is Real. So Is the Cost Microsoft’s fiscal Q2 2026 results reinforced the core fact that has been driving North American hyperscale development for two years: Cloud + AI growth is still accelerating, and Azure remains one of the primary runways. Microsoft said Q2 total revenue rose to $81.3 billion, while Microsoft Cloud revenue reached $51.5 billion, up 26% (constant currency 24%). Intelligent Cloud revenue hit $32.9 billion, up 29%, and Azure and other cloud services revenue grew 39%. That’s the demand signal. The supply signal is more complicated. On the call and in follow-on reporting, Microsoft’s leadership framed the moment as a deliberate capacity build into persistent AI adoption. Yet the bill for that build is now impossible to ignore: Reuters reported Microsoft’s capital spending totaled $37.5 billion in the quarter, up nearly 66% year-over-year, with roughly two-thirds going toward computing chips. That “chips first” allocation matters for the data center ecosystem. It implies a procurement and deployment reality that many developers and colo operators have been living: the short pole is not only power and buildings; it’s GPU