On November 4, 2025, Google unveiled Project Suncatcher, a moonshot research initiative exploring the feasibility of AI data centers in space. The concept envisions constellations of solar-powered satellites in Low Earth Orbit (LEO), each equipped with Tensor Processing Units (TPUs) and interconnected via free-space optical laser links. Google’s stated objective is to launch prototype satellites by early 2027 to test the idea and evaluate scaling paths if the technology proves viable. Rather than a commitment to move production AI workloads off-planet, Suncatcher represents a time-bound research program designed to validate whether solar-powered, laser-linked LEO constellations can augment terrestrial AI factories, particularly for power-intensive, latency-tolerant tasks. The 2025–2027 window effectively serves as a go/no-go phase to assess key technical hurdles including thermal management, radiation resilience, launch economics, and optical-link reliability. If these milestones are met, Suncatcher could signal the emergence of a new cloud tier: one that scales AI with solar energy rather than substations. Inside Google’s Suncatcher Vision Google has released a detailed technical paper titled “Towards a Future Space-Based, Highly Scalable AI Infrastructure Design.” The accompanying Google Research blog describes Project Suncatcher as “a moonshot exploring a new frontier” – an early-stage effort to test whether AI compute clusters in orbit can become a viable complement to terrestrial data centers. The paper outlines several foundational design concepts: Orbit and Power Project Suncatcher targets Low Earth Orbit (LEO), where solar irradiance is significantly higher and can remain continuous in specific orbital paths. Google emphasizes that space-based solar generation will serve as the primary power source for the TPU-equipped satellites. Compute and Interconnect Each satellite would host Tensor Processing Unit (TPU) accelerators, forming a constellation connected through free-space optical inter-satellite links (ISLs). Together, these would function as a disaggregated orbital AI cluster, capable of executing large-scale batch and training workloads. Downlink

The pattern points to an evolving GPU ecosystem: while top-tier chips like Nvidia’s new GB200 Blackwell processors remain in extremely short supply, older models such as the A100 and H100 are becoming cheaper and more available. Yet, customer behavior may not match practical needs. “Many are buying the newest GPUs because of FOMO—the fear of missing out,” he added. “ChatGPT itself was built on older architecture, and no one complained about its performance.” Gil emphasized that managing cloud GPU resources now requires agility, both operationally and geographically. Spot capacity fluctuates hourly or even by the minute, and availability varies across data center regions. Enterprises willing to move workloads dynamically between regions—often with the help of AI-driven automation—can achieve cost reductions of up to 80%. “If you can move your workloads where the GPUs are cheap and available, you pay five times less than a company that can’t move,” he said. “Human operators can’t respond that fast automation is essential.” Conveniently, Cast sells an AI automation solution. But it is not the only one and the argument is valid. If spot pricing can be found cheaper at another location, you want to take it to keep the cloud bill down/ Gil concluded by urging engineers and CTOs to embrace flexibility and automation rather than lock themselves into fixed regions or infrastructure providers. “If you want to win this game, you have to let your systems self-adjust and find capacity where it exists. That’s how you make AI infrastructure sustainable.”

At the 2025 Data Center Frontier Trends Summit, amid panels on AI, nuclear, and behind-the-meter power, few technologies stirred more curiosity than a modular hydropower system without dams or flowing rivers. That concept—piston-driven hydropower—was presented by Expanse Energy Corporation President and CEO Ed Nichols and Chief Electrical Engineer Gregory Tarver during the Trends Summit’s closing “6 Moonshots for the 2026 Data Center Frontier” panel. Nichols and Tarver joined the Data Center Frontier Show recently to discuss how their Reliable Renewable Power Technology (RRPT Hydro) platform could rewrite the economics of clean, resilient power for the AI era. A New Kind of Hydropower Patented in the U.S. and entering commercial readiness, RRPT Hydro’s system replaces flowing water with a gravity-and-buoyancy engine housed in vertical cylinders. Multiple pistons alternately sink and rise inside these cylinders—heavy on the downward stroke, buoyant on the upward—creating continuous motion that drives electrical generation. “It’s not perpetual motion,” Nichols emphasizes. “You need a starter source—diesel, grid, solar, anything—but once in motion, the system sustains itself, converting gravity’s constant pull and buoyancy’s natural lift into renewable energy.” The concept traces its roots to a moment of natural awe. Its inventor, a gas-processing engineer, was moved to action by the 2004 Boxing Day tsunami, seeking a way to “containerize” and safely harvest the vast energy seen in that disaster. Two decades later, that spark has evolved into a patented, scalable system designed for industrial deployment. Physics-Based Power: Gravity Down, Buoyancy Up Each RRPT module operates as a closed-loop hydropower system: On the downstroke, pistons filled with water become dense and fall under gravity, generating kinetic energy. On the upstroke, air ballast tanks lighten the pistons, allowing buoyant forces to restore potential energy. By combining gravitational and buoyant forces—both constant, free, and renewable—RRPT converts natural equilibrium into sustained mechanical power.

Extreme Networks: AI management over AI hardware Extreme deliberately prioritizes AI-powered network management over building specialized hyperscale AI infrastructure, a pragmatic positioning for a vendor targeting enterprise and mid-market.Named a Leader in IDC MarketScape: Worldwide Enterprise Wireless LAN 2025 (October 2025) for AI-powered automation, flexible deployment options and expertise in high-density environments. The company specializes in challenging wireless environments including stadiums, airports and historic venues (Fenway Park, Lambeau Field, Dubai World Trade Center, Liverpool FC’s Anfield Stadium). Key AI networking hardware 8730 Switch: 32×400GbE QSFP-DD fixed configuration delivering 12.8 Tbps throughput in 2RU for IP fabric spine/leaf designs. Designed for AI and HPC workloads with low latency, robust traffic management and power efficiency. Runs Extreme ONE OS (microservices architecture). Supports integrated application hosting with dedicated CPU for VM-based apps. Available Q3 2025. 7830 Switch: High-density 100G/400G fixed-modular core switch delivering 32×100Gb QSFP28 + 8×400Gb QSFP-DD ports with two VIM expansion slots. VIM modules enable up to 64×100Gb or 24×400Gb total capacity with 12.8 Tbps throughput in 2RU. Powered by Fabric Engine OS. Announced May 2025, available Q3 2025. Wi-Fi 7 access points: AP4020 (indoor) and AP4060 (outdoor with external antenna support, GA September 2025) completing premium Wi-Fi 7 portfolio. Extreme Platform ONE:Generally available Q3 2025 with 265+ customers. Integrates conversational, multimodal and agentic AI with three agents (AI Expert, AI Canvas, Service AI Agent) cutting resolution times 98%. Includes embedded Universal ZTNA and two-tier simplified licensing. ExtremeCloud IQ: Cloud-based network management integrating wireless, wired and SD-WAN with AI/ML capabilities and digital twin support for testing configurations before deployment. Extreme Fabric: Native SPB-based Layer 2 fabric with sub-second convergence, automated macro and micro-segmentation and free licensing (no controllers required). Multi-area fabric architecture solves traditional SPB scaling limitations. Analyst Rankings: Market leadership in AI networking Foundry Each of the vendors has its

Raising the Thermal Ceiling for AI Hardware As Microsoft positions it, the significance of in-chip microfluidics goes well beyond a novel way to cool silicon. By removing heat at its point of generation, the technology raises the thermal ceiling that constrains today’s most power-dense compute devices. That shift could redefine how next-generation accelerators are designed, packaged, and deployed across hyperscale environments. Impact of this cooling change: Higher-TDP accelerators and tighter packing. Where thermal density has been the limiting factor, in-chip microfluidics could enable denser server sleds—such as NVL- or NVL-like trays—or allow higher per-GPU power budgets without throttling. 3D-stacked and HBM-heavy silicon. Microsoft’s documentation explicitly ties microfluidic cooling to future 3D-stacked and high-bandwidth-memory (HBM) architectures, which would otherwise be heat-limited. By extracting heat inside the package, the approach could unlock new levels of performance and packaging density for advanced AI accelerators. Implications for the AI Data Center If microfluidics can be scaled from prototype to production, its influence will ripple through every layer of the data center, from the silicon package to the white space and plant. The technology touches not only chip design but also rack architecture, thermal planning, and long-term cost models for AI infrastructure. Rack densities, white space topology, and facility thermals Raising thermal efficiency at the chip level has a cascading effect on system design: GPU TDP trajectory. Press materials and analysis around Microsoft’s collaboration with Corintis suggest the feasibility of far higher thermal design power (TDP) envelopes than today’s roughly 1–2 kW per device. Corintis executives have publicly referenced dissipation targets in the 4 kW to 10 kW range, highlighting how in-chip cooling could sustain next-generation GPU power levels without throttling. Rack, ring, and row design. By removing much of the heat directly within the package, microfluidics could reduce secondary heat spread into boards and

SMRs and the AI Power Gap: Steve Fairfax Separates Promise from Physics If NVIDIA’s Sean Young made the case for AI factories, Steve Fairfax offered a sobering counterweight: even the smartest factories can’t run without power—and not just any power, but constant, high-availability, clean generation at a scale utilities are increasingly struggling to deliver. In his keynote “Small Modular Reactors for Data Centers,” Fairfax, president of Oresme and one of the data center industry’s most seasoned voices on reliability, walked through the long arc from nuclear fusion research to today’s resurgent interest in fission at modular scale. His presentation blended nuclear engineering history with pragmatic counsel for AI-era infrastructure leaders: SMRs are promising, but their road to reality is paved with physics, fuel, and policy—not PowerPoint. From Fusion Research to Data Center Reliability Fairfax began with his own story—a career that bridges nuclear reliability and data center engineering. As a young physicist and electrical engineer at MIT, he helped build the Alcator C-MOD fusion reactor, a 400-megawatt research facility that heated plasma to 100 million degrees with 3 million amps of current. The magnet system alone drew 265,000 amps at 1,400 volts, producing forces measured in millions of pounds. It was an extreme experiment in controlled power, and one that shaped his later philosophy: design for failure, test for truth, and assume nothing lasts forever. When the U.S. cooled on fusion power in the 1990s, Fairfax applied nuclear reliability methods to data center systems—quantifying uptime and redundancy with the same math used for reactor safety. By 1994, he was consulting for hyperscale pioneers still calling 10 MW “monstrous.” Today’s 400 MW campuses, he noted, are beginning to look a lot more like reactors in their energy intensity—and increasingly, in their regulatory scrutiny. Defining the Small Modular Reactor Fairfax defined SMRs