Demand remains abundant across the data center and AI infrastructure market. Alphabet has raised its capital spending forecast again. OpenAI has unveiled a 3.2-gigawatt project in Georgia. Hut 8 has signed another multibillion-dollar lease in Texas, while BlackRock and its partners have acquired Aligned Data Centers for approximately $40 billion. Oracle, meanwhile, could face a $7 billion collateral requirement in Wisconsin, Meta is paying more to finance a $12 billion Texas project, and proposed developments are drawing resistance across North America and beyond. Together, these developments describe a market moving into a more demanding phase. Land, power and capital remain available, but not on the same terms everywhere. The projects most likely to move are those that combine a credible customer, a durable power path, institutional financing and a development strategy capable of surviving public scrutiny. Hyperscaler Spending Keeps Rising After Google Cloud revenue grew 82% year over year to $24.8 billion in the second quarter, Alphabet increased its expected 2026 capital expenditures to between $195 billion and $205 billion. That was $15 billion above its previous forecast and more than twice the $91.5 billion the company spent in 2025. The cloud growth explains the spending without removing its financial pressure. The largest platforms are committing unprecedented cash to facilities, chips, networks and power systems whose economics will be measured over many years. OpenAI’s Project Camellia in Effingham County, Georgia, extends the scale further. OpenAI says it is designing and developing the campus itself and has contracted with Georgia Power for 3.2 GW to be delivered in phases from 2028 through 2032. Local reporting places the initial investment at at least $20 billion across roughly 1,400 acres. The 25-year power supply agreement may be as important as the campus size. OpenAI says it will cover the infrastructure costs, protect residential

Three Networks Inside the AI Factory Understanding the optical challenge begins with recognizing that an AI cluster contains several distinct networking environments. Gartner divided AI infrastructure into three broad tiers: scale-up, scale-out and scale-across. Each operates over a different distance, carries a different level of traffic and creates a different set of requirements for the interconnect. Scale-up describes the connections within a rack, where operators place as many GPUs as possible inside servers and then pack those servers into the available rack footprint. Gartner estimated that the bandwidth within this environment can be approximately 500 times that of a traditional wide-area network application. Scale-up connections still rely heavily on electrical interfaces because electrical interconnects remain relatively inexpensive and power efficient over short distances. Once the compute capacity of a rack has been exhausted, the cluster must expand into additional racks. This is the scale-out network, where 400G and 800G pluggable optics connect large numbers of GPU systems operating in parallel. Gartner characterized scale-out bandwidth as roughly 50 times the capacity associated with a conventional WAN environment. The third tier, scale-across, emerges when a data center reaches its practical power limit and the AI infrastructure must extend into another facility. Those data centers may be separated by tens or hundreds of kilometers, requiring coherent optical technology capable of carrying extremely high-capacity signals over longer distances. Scale-across networks can represent approximately 14 times traditional WAN bandwidth, according to Gartner. Taken together, the three tiers illustrate why optics has become inseparable from the AI infrastructure discussion. Network capacity must expand inside the rack, across rows of racks and increasingly between separate data centers—all without consuming an untenable share of the power budget or introducing failures that leave GPUs idle. When One Link Slows the Whole Cluster The reliability requirement for AI networks differs

Canada’s expansion as an artificial intelligence infrastructure market received its strongest endorsement yet on July 8, when Meta broke ground on a data center campus representing an investment of more than C$13 billion. The project, located in Sturgeon County north of Edmonton, will be Meta’s first data center in Canada and the 33rd facility in its global portfolio. Planned initially at 1 GW of power capacity, the AI-optimized campus could eventually scale to 1.8 GW, placing it among the largest data center developments under construction anywhere outside the United States. The announcement follows the Canadian federal government’s May launch of consultations on a forthcoming National Electricity Strategy, which identifies AI data centers as a major source of future electricity demand.  Approximately 3,000 construction workers are expected to be on the site at peak activity, while more than 300 permanent employees will operate the campus after completion. Meta is also committing approximately C$60 million to improvements involving local roads, water systems and other community infrastructure. The Meta announcement illustrates a fundamental change in Canadian data center construction. Rather than selecting a building site and applying for an ordinary utility connection, Meta and its partners have spent years coordinating the data center with a purpose-built 932 MW generating station, grid upgrades and long-term natural-gas transportation agreements. The project effectively combines a data center, a power plant and an infrastructure development program into a single construction ecosystem. Construction Has Begun on the Canadian Campus Meta describes the Sturgeon County project as an AI-optimized facility intended to support the computing demands of its core platforms, AI services and connected devices. The company said the buildout will occur in phases rather than delivering the entire gigawatt at once. Alberta’s major-project registry estimates a roughly three-year construction period. That timeline will require a sustained deployment of

Valar then supplied the most visible connection between the criticality program and data center technology. After reaching criticality, Valar advanced Ward 250 to approximately 10 kilowatts of thermal output and conducted a separate demonstration in which power from the reactor was used to run Nvidia Blackwell-based computing hardware. On July 1, Valar and Nvidia also announced that they were exploring a small Utah data center using closed-loop cooling and behind-the-meter advanced nuclear generation. The demonstration load was microscopic beside a hyperscale campus that may require hundreds of megawatts. Nvidia described the work as an exploration of how behind-the-meter advanced nuclear systems could support future AI factories, not as an agreement to purchase a specified quantity of electricity. Deployable Energy became the third developer to achieve zero-power criticality when its Unity reactor completed its experiment at Idaho National Laboratory on June 30. DOE announced the result July 1, noting that the three companies had satisfied the administration’s objective of achieving three advanced reactor criticality milestones by July 4. The commercial follow-up came quickly. On July 7, Deployable Energy and energy-infrastructure facilitator GridMarket announced a partnership aimed at data centers, hyperscalers and industrial customers. The agreement includes a committed pilot project and priority access to future Unity capacity. The companies said they were targeting 500 megawatts of annual deployments from 2030 through 2035 and more than 3 gigawatts cumulatively. The companies have not publicly named the pilot host or end customers. Even so, the committed pilot and access provisions put the arrangement ahead of a conventional memorandum of understanding. GridMarket is attempting to assemble sites, customers, technology and capital before commercial Unity units become available. Aalo Atomics completed the fourth criticality experiment on July 4, with DOE announcing the achievement July 6. Aalo-X went from groundbreaking to a sustained chain reaction in

IT teams responsible for data centers, colocation facilities, and test labs face a persistent challenge when it comes to providing affordable, timely maintenance and troubleshooting for their organizations’ IT infrastructure. Facilities aren’t often staffed around the clock, and sending someone on-site to address every hardware failure or issue is slow and expensive, especially for colocation customers that already pay for local staff support. Additionally, when critical infrastructure goes down, organizations can’t afford to endure an outage during the time it takes to get a technician on-site. IT teams typically employ tools to enable remote access, but the most common have significant limitations and drawbacks. Remote desktop protocol (RDP), for instance, provides access, but only while the operating system is running. If the OS crashes or fails to boot, or if a firmware change ends the session, RDP is useless, and IT will need to send a technician for an on-site visit. There are also security concerns, as open software ports make RDP vulnerable to attack. Another tool, physical intelligent platform management interface (IPMI), sits below the OS layer, so it can be used regardless of the state of the OS. However, legacy IPMI implementations have historically been associated with security concerns, particularly when exposed to public or poorly secured networks. Many organizations now restrict IPMI access or supplement it with additional security controls. Some organizations have moved to Distributed Management Task Force (DMTF) Redfish because it provides stronger security. But its security behaviors, such as session timeouts, rate limiting, and lockout policies, are undefined and left to the implementer, so improper implementation poses a significant risk. KVM (keyboard, video, and mouse)-over-IP addresses both the reliability and security concerns of RDP, IPMI, and DMTF Redfish. KVM connects directly to hardware, which gives administrators remote BIOS-level access and full control of a

High risk for new builds While PJM coordinates the wholesale electricity grid across 13 states, including Delaware, Illinois, Indiana, Kentucky, Maryland, etc., and the District of Columbia, not every data center will be affected by this development. The proposal is expected to primarily impact new facilities that will fail to secure dedicated or contracted power supplies. “The data centers most at risk are new ones being built that haven’t signed contracts for their own power source yet, especially smaller or newer companies without deep pockets. Giant companies like Amazon, Google, or Microsoft can more easily afford to build their own backup power, so they’re safer. The riskiest locations are places already packed with data centers, like Northern Virginia and growing areas in Ohio, Pennsylvania, and Maryland, where the local power grid is already stretched thin,” noted Jain.