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  • 800V AI Rack Power Is Being Built in Layers: Navitas GaN, Microchip Control

    800V AI Rack Power Is Being Built in Layers: Navitas GaN, Microchip Control

    TL;DR · 30-second read

    The Short Version

    Microchip and Navitas, two chip companies, have published a blueprint for a power board that feeds the chips running artificial intelligence. It takes electricity at 800 volts, several times what a home outlet supplies, and steps it down in a single move to the 6 volts those chips use.

    The board is about a fifth thinner than a phone. At its best, it aims to lose just 4% of the power along the way.

    Artificial intelligence buildings are straining for electricity. This fix is being assembled from more than one company’s parts.

    Microchip Technology and Navitas Semiconductor have jointly released an 800V DC-to-6V DC reference design for AI data center rack power, ET Datacenters reported on October 10, 2026. The design pairs Microchip’s dsPIC33AK digital signal controllers and TA100 security chip with 16 of Navitas’ GaNFast gallium nitride transistors. It targets up to 96% peak efficiency at full load and a power density of 2,100 watts per cubic inch, and the companies say it aligns with the Open Compute Project’s 800V DC standard.

    The release arrives four days after Navitas, in an 8-K filed October 6, reported closing its acquisition of Claros, Inc., a developer of integrated voltage regulators that handle the final step of delivering power to AI processors.

    Executive Summary

    A reference design is an engineering blueprint: working hardware, software and documentation that other companies can copy or adapt instead of starting from scratch. This one tackles a specific problem in AI racks: converting high-voltage 800V DC power down to the low voltage that sits near the GPU board. It does so in a single conversion stage, where conventional designs use two.

    The design matters for two reasons. First, it puts concrete targets on the 800V transition: 96% peak efficiency, 2,100 W/in³ and a board the companies describe as about 20% thinner than a mobile phone. Second, it divides the work among specialist suppliers. Navitas supplies the power switches. Microchip supplies the controller that times them and the chip that secures the firmware. As hyperscalers and power-system makers move toward 800V racks, that layered structure points to a supply chain with more than one chipmaker in each power board.

    The targets are design goals, not field results. The companies have not named customers, production timelines or pricing.

    Three Jobs, Two Companies: How the Board Divides the Work

    Converting 800 volts down to 6 volts in one step requires three distinct kinds of silicon, and this design takes them from two suppliers. The first job is switching. The power delivery board uses 16 Navitas NV6034 GaN transistors, each rated at 650 volts and 17 milliohms of resistance, in a stacked half-bridge arrangement. That arrangement spreads the high input voltage across devices individually rated below it. Gallium nitride is favored here because it can switch on and off very quickly with little loss, and the design runs at 1 MHz: one million switching cycles a second.

    The second job is control. At 1 MHz, each switching cycle lasts one microsecond, and the controller has to decide every cycle how long each transistor stays on. Microchip’s dsPIC33AK256MPS306 family brings a 200 MHz 32-bit core with a double-precision floating-point unit, pulse-width modulators with 78-picosecond resolution, and 12-bit analog-to-digital converters sampling at up to 40 million samples per second. Put plainly, the controller can adjust timing in steps of roughly one twelve-thousandth of a single cycle while measuring voltage and current fast enough to react within it. The third job is trust. Microchip’s TA100 provides a hardware root of trust for secure boot, authenticated firmware updates and key management.

    This division is what makes the transition multi-vendor in practice. GaN expertise, real-time digital control and hardware security are separate engineering disciplines. A reference design that pre-validates one combination of them lowers the cost for power-shelf makers and hyperscaler hardware teams to adopt that combination. The companies say the package reduces design risk and shortens development cycles. Because the design aligns with the Open Compute Project’s 800V DC standard, an open specification that rack buyers use to compare and interchange suppliers, it also sits inside a framework built for multiple vendors rather than one. This is a single data point, not proof of an industry pattern. Still, it shows how the parts list for 800V rack power is taking shape.

    A Grid-to-xPU Strategy That Still Partners at the Control Layer

    The timing is notable. Navitas’ October 6 filing frames its strategy as covering power delivery from the grid to the processor. The company describes its GaN and high-voltage silicon carbide devices as enabling the new 800V DC architecture, and the newly acquired Claros technology as the last step to the chip. Navitas told investors it expects Claros to add at least $3.5 billion to its identified 2030 serviceable available market, more than doubling the total to over $8 billion. That total combines the existing $3.5 billion GaN and silicon carbide opportunity with about $1 billion from new JFET technology.

    Yet in this design, the control and security silicon comes from Microchip. That does not contradict Navitas’ strategy. Claros’ integrated voltage regulators, as the filing describes them, address a different stage of the power path. What the pairing does show is that even a supplier assembling an end-to-end portfolio is working with partners where another company has an established controller and security line. For buyers, that is mildly reassuring: the 800V transition is not funneling every component through one chipmaker. For Navitas, the partnership is a channel. A Microchip-backed reference design can put GaNFast devices in front of engineers who already standardize on Microchip controllers.

    The serviceable-market figures deserve to be read as what they are: Navitas’ estimate of the opportunity its products could address by 2030. They are not revenue, backlog or contracted demand.

    One Stage Instead of Two: The Efficiency Argument

    The core technical claim is that converting 800V DC directly to 6V DC folds two conversion stages into one converter, improving end-to-end efficiency. The logic is straightforward. Every conversion stage loses some energy as heat, so removing a stage removes a source of loss. Distributing power at 800 volts helps for a related reason: higher voltage means less current for the same power, which cuts resistive losses and the amount of copper needed in the rack.

    The 96% figure is a peak target at full load, and the release discloses no efficiency curve. Converters typically perform differently at partial load, where AI racks may spend much of their time, so operators will want measured data across the operating range. The physical design may matter as much as the efficiency. A board about 20% thinner than a phone can sit close to the GPU board, which the companies say improves transient performance. Transient performance is the converter’s ability to respond when processor demand jumps suddenly. Shorter distances between converter and chip mean less voltage drop and faster response, a growing concern as Navitas’ own CEO letter describes processors needing thousands of amps delivered with high precision.

    Security Moves Into the Power Path

    Including a dedicated security chip in a DC/DC converter design reflects a quieter shift. Rack power hardware now carries firmware that controls how electricity reaches some of the most expensive processors in a data center, and that firmware can be updated. The TA100’s secure boot and authenticated updates are meant to ensure only trusted code runs on the converter. The dsPIC33AK family’s support for the post-quantum algorithms recommended in the Commercial National Security Algorithm Suite 2.0 extends that protection against future code-breaking methods.

    For data center security teams, this places power conversion alongside servers and network gear as equipment with a firmware supply chain to manage. That is a practical consequence of the layered design, and it gives procurement teams one more vendor relationship to qualify.

    Background

    Navitas Semiconductor, listed on Nasdaq as NVTS, develops power semiconductors based on gallium nitride and high-voltage and ultra-high-voltage silicon carbide. Under its Navitas 2.0 strategy, the company has refocused on AI infrastructure. On October 6, 2026, it closed its acquisition of Claros, whose integrated voltage regulator technology moves power conversion to within millimeters of the processor. Navitas describes the combined portfolio as covering the full path from grid to processor and has scheduled an October 20 webinar to present it.

    Microchip Technology is a long-established supplier of microcontrollers and digital signal controllers, the chips that run real-time control loops in power supplies, motors and industrial equipment, along with dedicated security devices. The joint design reflects a broader move in AI data centers toward 800V DC rack power, driven by rack power demands that conventional lower-voltage distribution struggles to serve efficiently.

    Sources

    Source: Microchip, Navitas partner on 800V DC power for AI data centers, ET Datacenters: the companies’ 800V DC-to-6V DC reference design for AI rack power.

    Primary sources: Navitas Semiconductor Form 8-K, filed October 6, 2026; Exhibit 99.1: Navitas Closes Acquisition of Claros, Advancing AI Infrastructure with Grid-to-xPU Power Delivery; Exhibit 99.2: A Letter from Chris Allexandre, President and CEO, Navitas 2.0: Breaking the AI Infrastructure Power Wall.

  • NOARK’s 75-Day Pitch Bets AI Data Center Power Gear Sells on Schedule, Not Price

    NOARK’s 75-Day Pitch Bets AI Data Center Power Gear Sells on Schedule, Not Price

    TL;DR · 30-second read

    The Short Version

    • Artificial intelligence is driving a boom in giant computer warehouses, and each one needs heavy electrical equipment before it can switch on.
    • NOARK Electric, a maker of that equipment, says it got one North American order from purchase to job site in about 75 days, roughly two and a half months.
    • Its sales pitch is about speed and meeting local safety standards, not about being cheaper.
    • It has not named the customer or said what that order contained, so it is one example, not proof.

    NOARK Electric said in an Oct. 10, 2026 release distributed via PR Newswire that it showcased its data center power infrastructure lineup at Yotta 2026 in Las Vegas. The equipment spans medium-voltage gas-insulated switchgear, low-voltage switchboards and busway, and air and molded-case circuit breakers, which the company is marketing under a “global-to-local” model that pairs global engineering and manufacturing with North American assembly, compliance work and service.

    The release’s one hard data point: on a recent, unnamed North American project, NOARK says coordinated engineering, manufacturing, logistics and local fulfillment took the order from purchase to on-site delivery in approximately 75 days.

    Executive Summary

    NOARK Electric is positioning itself to win electrical-equipment orders from AI-era data center developers by competing on delivery time and regulatory readiness. The company describes a four-step model, “Engineer. Source. Qualify. Deliver,” in which global R&D and factories supply capacity while a North American team handles application engineering, UL and IEEE compliance, localized assembly, fulfillment and after-sales support.

    The announcement matters because electrical gear sits on the critical path of every data center build: a facility cannot power its servers until switchgear and distribution equipment are installed and commissioned. NOARK’s release frames customer priorities as speed to power, reliability and scalability, and never mentions price, a clear signal that it believes schedule certainty is now the deciding factor in these procurements.

    The supporting evidence is thin but specific: a single 75-day order-to-delivery example, plus product-level claims about where assembly and supply are localized. How far that example generalizes depends on details NOARK has not yet disclosed.

    Price Never Appears in NOARK’s Pitch

    NOARK builds its case around three customer priorities (speed to power, reliable performance and scalable infrastructure), and price is not among them. The only hard number the company offers is a schedule: roughly 75 days from order to on-site delivery on a recent North American project. That choice of evidence is the strategy. A supplier competing on cost would lead with cost. NOARK leads with time.

    The mechanism is straightforward. Switchgear, switchboards and circuit breakers sit on the critical path of a data center build. Until they are installed and commissioned, the building cannot take power from the utility or distribute it to servers and cooling. On an AI campus, where the computing hardware is itself a large capital outlay, every week that hardware waits on electrical equipment is a week of deferred revenue. In that setting a developer can rationally accept a higher equipment price in exchange for a firmer delivery date. NOARK’s own framing, that “power availability, equipment lead times and deployment certainty are increasingly central to project planning,” is a bet that buyers now weigh the trade-off this way.

    The people this targets are developers and EPC (engineering, procurement and construction) contractors who own the schedule, and the engineering firms that write equipment specifications. The limits are just as clear. The 75-day figure describes one project. NOARK has not said what equipment it covered, at what voltage class or quantity, or whether it was a standard catalog item or a custom-engineered lineup. As evidence, it illustrates the pitch. It does not establish a repeatable lead time.

    Compliance Is Where a Challenger Loses Time

    For a supplier that is not already on an engineer’s approved list, the slowest step can be qualification, which means getting products accepted against the codes and standards a project must meet. NOARK addresses this directly. It says its local engineering and compliance team integrates UL (Underwriters Laboratories, the North American safety certification body) and IEEE (Institute of Electrical and Electronics Engineers) requirements into product qualification and project execution, to “reduce qualification uncertainty.”

    That is the second half of the lead-time argument. A fast factory helps little if a product spends months in submittal review or needs field modifications to pass inspection. By promising compliance “from the start,” NOARK is aiming at the hidden schedule risk of switching suppliers, not only at shipping time.

    The specific claim is narrower than the framing. The release says a product platform shown at Yotta was “developed to meet applicable IEEE requirements.” It does not list which products carry UL listings, or for which applications. For specifiers, that product-by-product detail determines whether the compliance advantage holds on a given job.

    A Hybrid Supply Chain Built for Multi-Phase Campuses

    NOARK describes a layered supply model. Low-voltage switchboards and busway (prefabricated enclosed conductors that carry power along data hall rows in place of cable) draw on U.S.-based supply to “shorten supply paths.” Its ACB and MCCB platforms, meaning air circuit breakers and molded-case circuit breakers, are assembled in North America on global designs. The company’s global manufacturing footprint serves as reserve capacity for large, multi-phase deployments.

    That structure fits how AI campuses are built: in phases, with repeated, standardized electrical blocks. Local assembly can shorten the final leg and make change orders easier to absorb, while global plants can supply volume when a later phase arrives. NOARK also extends its local presence to commissioning, technical assistance and after-sales support. This matters because reliability in a live facility depends on service response as much as on hardware.

    The trade-off is dependency. The release says only “selected equipment” is supplied locally, which suggests much of the content still comes through the global network, so delivery certainty still depends on that network performing. The medium-voltage gas-insulated switchgear, the heaviest item in the lineup, is described in terms of compact footprint and global R&D, not local supply. Buyers weighing the model should map which parts of a lineup take the short path and which take the long one.

    Background

    NOARK Electric makes low- and medium-voltage electrical distribution, protection and control equipment for commercial, industrial, infrastructure and data center applications, and operates in North America and other markets. Its data center lineup covers the main electrical path inside a facility: medium-voltage switchgear that receives and controls incoming power, low-voltage switchboards and busway that distribute it, and circuit breakers that protect individual circuits.

    Yotta is a data center industry event held in Las Vegas. NOARK used the 2026 edition to argue that the rapid AI-driven expansion of North American data centers has made power infrastructure, specifically equipment availability, lead times and code compliance, a deciding factor in how quickly projects move from planning to operation.

    Sources

    Source: NOARK Combines Global Scale with Local Execution for Data Center Power Infrastructure, NOARK Electric’s Oct. 10, 2026 release on the data center power equipment and delivery model it showcased at Yotta 2026.

  • Frore’s 35% Tokens-per-Watt Claim Turns GPU Hotspots Into an AI Power Lever

    Frore’s 35% Tokens-per-Watt Claim Turns GPU Hotspots Into an AI Power Lever

    TL;DR · 30-second read

    The Short Version

    A California company called Frore Systems says it can get up to about a third more work out of the chips that run artificial intelligence, without using more electricity.

    The trick is cooling. These chips get extremely hot in a few tiny spots, and when they overheat they slow themselves down. Frore places small pieces of diamond, which moves heat better than metal, right over those spots.

    Why it matters: electricity is now the scarcest ingredient in building artificial intelligence. Squeezing more from the same power could mean fewer new buildings. Frore has not yet published independent test results or named customers.

    Frore Systems announced LiquidJet Diamond on October 8, 2026, in San Jose, California. The product is a liquid-cooling coldplate (the metal block that sits on a chip and carries heat away into circulating fluid) with diamond wafers built into its base, positioned directly over the hottest regions of a GPU. Frore says the design cools hotspots of up to 770 watts per square centimeter and delivers 30–35% more AI tokens per watt than a conventional skived coldplate.

    The company will demonstrate LiquidJet and LiquidJet Diamond cooling a 2,300-watt NVIDIA Rubin GPU, along with its LiquidJet Nexus integrated coldplate system for half-height compute trays, at the OCP Global Summit in San Jose, October 12–16, 2026.

    Executive Summary

    LiquidJet Diamond extends Frore’s existing LiquidJet coldplate, which uses what the company calls a 3D ultra short-loop multi-stage design customized to each GPU’s power map. Frore says the base product lowers die temperature by 12°C, worth 25% more tokens per watt, and the diamond spreader lowers hotspot temperature a further 10°C, lifting the gain to 35%. Against a skived coldplate, it claims over 150% higher maximum hotspot power density, 75% higher heat transfer efficiency, a GPU running 16–22°C cooler, and a drop-in upgrade path.

    The significance lies in the framing rather than the hardware alone. Frore is arguing that in AI data centers capped by grid connections and transformer capacity, chip temperature is a capacity variable: a cooler chip produces more useful output from the same fixed power budget. If the numbers hold up under customer testing, cooling moves from a facilities cost line to a revenue lever.

    The headline claim of up to 35% higher profitability, however, rests on a chain of assumptions about demand, pricing and component cost that Frore has not detailed, and the performance figures have not yet been independently validated.

    Why a Few Square Millimeters Can Set a Data Center’s Output

    A modern AI accelerator does not heat evenly. Some regions of the die, the slab of silicon doing the computation, run much hotter than others. A chip protects itself by slowing down when its hottest point crosses a temperature limit, a behavior known as thermal throttling, and hotter silicon also leaks more electrical current that does no useful work. Both effects mean the temperature of the worst spot on the chip, not the average temperature of the rack, can determine how much computing each watt buys.

    That is the mechanism behind Frore’s numbers. The base LiquidJet design cuts die temperature by 12°C, which Frore equates to 25% more tokens per watt. Tokens are the chunks of text an AI model reads or writes, so tokens per watt is a rough measure of useful AI output per unit of electricity. Adding diamond, which conducts heat far better than copper, directly over the hotspots cuts hotspot temperature a further 10°C and lifts the claimed gain to 35%. Frore says the diamond version handles hotspot power density more than 150% higher than a skived coldplate, a statement about peak heat concentrated in a small area rather than total heat removed.

    The reason that ratio matters is the constraint Frore itself names: utility interconnection (the queue to connect a site to the grid), transformer capacity and on-site power delivery cap what an AI facility can draw. In a building whose power is fixed, more tokens per watt is effectively added capacity without a new substation. Chief executive Seshu Madhavapeddy makes that case directly, saying that with 35% more tokens from the same resources, hyperscalers “would reach their growth goals faster with fewer AI Factories.” The operators most exposed are those deploying the newest, highest-power GPUs, where hotspot density climbs fastest.

    From Tokens per Watt to Profit Takes Several Assumptions

    The release headline promises up to 35% higher profitability; the body says tokens per watt and revenue rise 35%, “with the increased revenue flowing directly to profit.” Each step in that chain carries conditions. Extra tokens only become revenue if there are buyers for them at the same price. Extra throughput only materializes if the GPU is limited by heat rather than by memory bandwidth, networking or software. And the gain only flows straight to profit if the diamond coldplate costs no more over its life than the part it replaces, a figure Frore has not published.

    The temperature figures also mix measures. The 12°C reduction refers to die temperature and the additional 10°C to hotspot temperature; their sum, 22°C, matches the top of the 16–22°C range Frore quotes for a cooler GPU, and the 30–35% tokens-per-watt range suggests results vary by chip and workload. “Up to” marks 35% as the best case. The baseline matters too: “traditional skived coldplates” covers a wide range of designs, and the size of any improvement depends on which one is being compared.

    None of this makes the direction implausible. Cooler silicon generally runs faster and wastes less power, and hotspot-targeted cooling is a logical response to rising chip power. The open question is magnitude under real data center conditions, which is what customer qualification testing exists to establish.

    The Harder Claim Is Manufacturing at Hyperscale

    Frore positions LiquidJet between two incumbent approaches. Skiving, in which thin fins are carved from a copper block, is mature and cheap but, Frore argues, cannot deliver the precision today’s GPUs demand. Electrochemical additive manufacturing (ECAM), a form of 3D metal printing, offers design freedom but, in Frore’s telling, cannot meet hyperscaler quality, reliability, volume or timelines. Frore says it combines semiconductor-fabrication precision with high-volume manufacturing, with production operations in Taiwan.

    Size is part of the pitch. Frore cites die areas ranging from 1,488 mm² for NVIDIA Blackwell Ultra to 46,225 mm² for the Cerebras Wafer-Scale Engine, roughly 30 times larger, and claims its process is the only one able to support the power, performance and size requirements of such chips. Its LiquidJet Nexus system goes further, integrating multiple coldplates and eliminating hoses, connectors and manifolds to enable half-height (½U) compute trays, which would mean fewer potential leak points and denser servers.

    If those claims survive qualification, the pressure falls on suppliers of skived coldplates and on 3D-printing approaches competing for the same high-end sockets. The “seamless drop-in upgrade” claim is commercially important because it lowers switching costs, but hyperscale buyers typically qualify cooling hardware over long cycles, and a component that sits directly on a multi-thousand-dollar GPU faces a high bar for reliability evidence.

    Background

    Frore Systems built its reputation on AirJet, a solid-state active air-cooling chip used in consumer, industrial and Internet-of-Things devices, including a concept ultra-slim notebook unveiled with Lenovo. The company, which describes itself as an AI industry unicorn, has since moved into data center liquid cooling with LiquidJet and LiquidJet Nexus, and earlier published a report, “Chip to Chiller,” arguing that heat costs AI facilities a large share of their compute capacity.

    The broader market context is a shift from air cooling to direct-to-chip liquid cooling as AI accelerators have climbed into the thousands of watts per chip. With grid interconnection and transformer capacity now limiting how fast AI data centers can grow, suppliers across the thermal stack are increasingly pitching their products on output per watt rather than on cooling alone. The OCP Global Summit, run by the Open Compute Project industry consortium, is a key venue where hyperscalers evaluate such hardware.

    Sources

    Source: LiquidJet Diamond Coldplates can increase AI Factory profitability by up to 35% — Frore Systems’ announcement of its diamond-spreader liquid-cooling coldplate for AI GPUs, distributed via PR Newswire.

  • At 40 Minutes per Trailer, Oracle’s AI Gas Stopgap Has a Ceiling

    At 40 Minutes per Trailer, Oracle’s AI Gas Stopgap Has a Ceiling

    TL;DR · 30-second read

    The Short Version

    Oracle, the business-software giant, is building huge computer warehouses for artificial intelligence faster than the gas pipelines meant to power them can be finished. So it is filling trucks with natural gas and driving the fuel to the sites instead.

    It works, but the gas costs roughly four times its normal price. It also does not go far. Running even a small slice of one planned site in New Mexico would need a fresh truckload about every 40 minutes, day and night.

    The lesson is that building artificial intelligence now depends on pipes, permits and trucks, not just computer chips.

    Bloomberg reported that Oracle has been trucking compressed natural gas (CNG) directly to several data center sites so that on-site generators can run before permanent gas pipelines are ready. According to people familiar with the work, truck deliveries kept a data center outside Salt Lake City moving for more than a year while Oracle waited for a pipeline. The same approach is supporting initial work at a campus Oracle is building for OpenAI in Shackelford County, Texas. Oracle is now considering it for Project Jupiter in New Mexico, where a delayed pipeline threatens the timeline of a site with an ultimate footprint of 2.45 gigawatts.

    Certarus, a unit of Superior Plus, supplied the Utah deliveries, and VoltaGrid supplies Texas. Oracle publicly thanked VoltaGrid for “finding cost-effective power solutions to ensure our customers can access OCI capacity on schedule,” referring to Oracle Cloud Infrastructure. Last month Oracle sent the New Mexico project’s developer a force majeure notice. Oracle shares closed down 5.5% at $135.69, their steepest one-day drop since July 16.

    Executive Summary

    The announcement-free headline here is a supply chain story. Many large AI data centers have turned to on-site gas-fired power plants because grid connections take too long. Those plants still need fuel, and fuel normally arrives by pipeline, which can take months to design and build and can be held up by regulators. Oracle is filling that gap with “virtual pipelines”: fleets of trucks hauling compressed gas from a pipeline tap to the site, where it is decompressed and burned in generators.

    The approach buys time, but it has limits. Jack Weixel of East Daley Analytics puts the delivered cost at roughly four times the price of gas at a major pipeline hub once labor, specialized equipment and truck fuel are included. Ellie Holbrook of SemiAnalysis estimates that if Project Jupiter ran just 100 megawatts on trucked gas, which is about 4% of its planned capacity, each large trailer would supply only about 40 minutes of electricity. Trucked gas can energize the first phases of a campus. The date the whole campus comes online is still set by the pipeline.

    For the industry, the episode adds a new item to the list of things that can delay a data center. Developers already track turbines, transformers and interconnection queues. Gas logistics now belongs on that list too, along with the regulators who approve pipeline routes.

    The Trailer Math Sets the Ceiling

    Holbrook’s figures are the clearest way to see how far a virtual pipeline can stretch. At a 100-megawatt load, one large CNG trailer lasts about 40 minutes. A day has 1,440 minutes, so running that load around the clock means roughly 36 trailer loads every day, or one arrival every 40 minutes with no gaps for weather, traffic or a missed fill. Each load is a full cycle. Gas is drawn from a pipeline, compressed into the trailer, driven to the site (sometimes for several hours), then decompressed into the generators. A trip that long ties up several trailers on the road for every one unloading, so the fleet behind a steady stream of arrivals is much larger than the arrival count.

    That 100 megawatts is only about 4% of Project Jupiter’s planned 2.45 gigawatts. As a rough illustration, if fuel demand scaled in a straight line with load, the full footprint would need close to 900 trailer loads a day. No one has proposed doing that, and the arithmetic shows why. Trucked gas is a bridge sized for early phases, such as commissioning, initial server halls and construction power. It cannot replace a pipeline at campus scale.

    The consequence for people planning AI capacity is straightforward. A virtual pipeline can move the date a site first produces revenue. It does not move the date the site reaches full capacity. That second date still depends on Energy Transfer’s rerouted pipeline, which has slipped from this summer to next year.

    Fuel Logistics Joins Turbines and Grid Queues on the Critical Path

    On-site gas generation became popular because it let developers skip the years-long wait to connect to the electric grid. The Oracle sites show that the workaround brings its own bottleneck. A gas plant without a gas pipeline is idle equipment, and pipelines face many of the same constraints as power lines. In New Mexico, state regulators rejected Energy Transfer’s proposed route, forcing a reroute that pushed back the in-service date. The approval delay simply moved from the power line to the pipe.

    This has practical effects on how sites are chosen and planned. When fuel may need to arrive by road for a year or more, as it did in Utah, factors that rarely appeared in data center site studies start to matter. These include the distance to a pipeline tap with available compression, road access able to handle constant heavy-truck traffic, and on-site space for decompression equipment and trailer staging. Developers comparing sites need to weigh the logistics of the interim fuel supply alongside land, water and fiber.

    The suppliers stand to benefit. Until now, CNG trucking has mostly served remote mining and oil drilling operations far from the grid. Certarus and VoltaGrid are now marketing to data center builders as a transitional power source. Certarus said longer timelines for pipeline and grid connections “have created a growing need for rapidly deployable and scalable energy solutions.” Whether these services can scale is a separate question. The number of trucks, compressors and trailers available, along with the drivers to move them, sets a physical limit on how many large campuses can rely on trucked gas at the same time.

    Paying Four Times for Speed

    Oracle described VoltaGrid’s service as “cost-effective,” while East Daley’s estimate puts trucked gas at about four times hub prices. The two claims can both hold, because they measure different things. Per unit of fuel, trucked CNG is expensive. Compared with leaving finished data halls empty, missing delivery dates for customers such as OpenAI, or facing contractual exposure, a costly fuel bill can still be the cheaper choice. Oracle has not disclosed the numbers that would show which effect is larger.

    Those numbers matter because of Oracle’s financial position. Its free cash flow is negative and is expected to stay that way until more of its AI data centers are completed, so each month a site comes online earlier brings revenue forward. The force majeure notice points to the same pressure from another direction. A force majeure clause excuses a party from certain obligations when events beyond its control occur, and Oracle’s notice may shield it from some payments to the New Mexico developer if delays continue. The trucks and the notice are two responses to the same pipeline delay. One keeps work moving, and the other limits the cost if work stalls.

    The open question for investors and customers is where the extra cost lands. It could reduce Oracle’s margins in the early phases, be passed through to the tenant, or be absorbed through the developer agreement. None of these arrangements has been made public, and the answer will determine whether trucked gas is a minor bridging expense or a significant cost of speed.

    Background

    Oracle, long known for database and business software, has become a major builder of AI data centers through Oracle Cloud Infrastructure (OCI). It has committed billions of dollars and its reputation to delivering capacity quickly for customers including OpenAI, and its free cash flow is expected to remain negative until more of those facilities are complete. Its projects include a campus for OpenAI in Shackelford County, Texas, a data center near Salt Lake City, and Project Jupiter in New Mexico.

    Power has become one of the main constraints on the AI data center buildout. Many large developments now generate their own electricity with on-site natural gas plants rather than waiting for grid connections. That approach depends on gas pipelines, which take months to design and build and can be delayed by regulators. Compressed natural gas trucking companies such as Certarus and VoltaGrid, which previously served mainly remote mining and drilling operations, are now offering their services to data center developers as a temporary fuel supply.

    Sources

    Source: Oracle Moves Gas by Trucks to Avoid Data Center Power Delays (SupplyChainBrain, via Bloomberg) on Oracle’s use of trucked compressed natural gas to keep data center projects in Utah, Texas and New Mexico on schedule.

  • Lumentum’s Sellout to 2029 Turns AI Network Optics Into a Long-Lead Item

    Lumentum’s Sellout to 2029 Turns AI Network Optics Into a Long-Lead Item

    TL;DR · 30-second read

    The Short Version

    Lumentum, a company most people have never heard of, makes tiny lasers. They turn computer data into flashes of light so it can travel through glass fiber between the thousands of chips inside the huge buildings that run artificial intelligence.

    Its chief executive says its factories are fully booked into early 2029. For some products, about seven in ten of the parts customers want will still be missing by the end of next year.

    The race to build artificial intelligence is usually told as a story about chips. This shows a small, unglamorous part can set the pace too.

    Lumentum Holdings, the San Jose-based maker of lasers and optical components that carry data inside AI data centers, says its optical component production is fully booked into early 2029. Bloomberg reported on October 8, 2026 that the company, which it described as Nvidia-backed, sees its opto-parts capacity sold out to 2029. GuruFocus reported that chief executive Michael Hurlston said roughly 70% of demand for certain products will remain unmet by the end of next year, and about 30% of demand for other products will go unfilled until 2028. GuruFocus put the company’s market value at about $95 billion.

    The comments came two days after Lumentum filed its definitive proxy statement with the SEC on October 6. In that filing, the company called fiscal 2026, the year ended June 27, 2026, a “landmark year.” It reported record shipments of high-speed transceivers, transport products and laser chips, and said it had started production of next-generation 1.6T modules. It also said its optical circuit switch business had passed a $100 million quarterly revenue run rate.

    Executive Summary

    Lumentum is telling the market that it cannot make enough of the lasers and optical modules that link AI accelerators together. Its production is committed into early 2029. For some product lines, most of the demand it sees for next year will go unserved. Those are the parts that convert electrical signals to light and back again, so that thousands of GPUs can behave like one machine.

    The significance for infrastructure builders is timing. AI capacity plans have mostly been framed around securing GPUs, power and buildings. A multi-year sellout at a major laser supplier adds optical interconnect to the list of items that have to be reserved well before a cluster is commissioned. It also matters because Lumentum’s October 6 proxy statement argues that optics will move deeper into the server rack, which would raise the amount of optics each AI system needs.

    What is substantiated is narrower than the headline figure suggests. The company has stated a booking horizon and percentages of unmet demand. It has not disclosed which products are affected, who holds the committed capacity, or whether those commitments are binding orders or allocations.

    Why a Laser Sellout Becomes a Cluster Schedule

    Modern AI training and inference clusters are networks first. Every server in a scale-out cluster, meaning the fabric that connects many servers across a data center hall, reaches its switches through optical transceivers. A transceiver is a plug-in module that turns electrical data into light for the fiber run and back again. Each one depends on laser chips. Lumentum makes both the chips, including EML (electro-absorption modulated) and CW (continuous-wave) lasers grown on indium phosphide wafers, and finished modules such as the 1.6T (1.6 terabits per second) parts it began producing in fiscal 2026. A rack of GPUs that arrives without its optics cannot join the cluster, however many accelerators it holds.

    That is the mechanism behind the long-lead label. If Lumentum’s production is booked into early 2029, a buyer placing a new order with Lumentum today is effectively scheduling delivery more than two years out. Hurlston’s figures sharpen the point. Roughly 70% of demand for certain products will still be unmet by the end of next year, and about 30% of demand for other products will be unfilled until 2028. For hyperscalers, neocloud operators and the developers whose tenants need a working network fabric on a fixed go-live date, optics now has to be secured early, the way developers already secure grid capacity and electrical equipment.

    The claim has limits that matter. Lumentum is one supplier, and it has not said what share of the market its booked capacity represents. Buyers with diversified supply may be able to source modules or lasers elsewhere, if other vendors have room. The defensible conclusion is that optical interconnect has become a dated procurement constraint for anyone depending on Lumentum’s capacity. It is not proof that every AI cluster will be delayed, and it does not mean GPUs are no longer scarce.

    Scale-Up Could Raise the Optics Count Per Rack

    The proxy statement explains where Lumentum thinks demand goes next. Its results to date came from scale-out networks and scale-across networks, which link separate data center sites. The company says it is most excited about scale-up, the tight connections between GPUs inside a single rack or system. Today those links are largely electrical, over copper. Lumentum describes a shift toward optical connectivity within the rack that “has the potential” to drive its performance for years.

    If that shift happens at scale, the number of lasers needed per GPU rises. And it would do so at a moment when Lumentum says its existing capacity is already spoken for. The company also points to co-packaged optics (CPO) and near-packaged optics (NPO), designs that place the optical engine on or next to the switch or processor package. Lumentum says these designs eliminate the need for a digital signal processor (DSP), cutting power use and cost. They also depend on the ultra-high-power lasers Lumentum is developing. The scale-up thesis is the company’s forecast rather than a disclosed order book. Still, it explains why a supplier might describe demand in years rather than quarters.

    Optical Circuit Switches: A Reported Number and a Forecast

    Lumentum’s optical circuit switch (OCS) line is the clearest growth figure in the filing. An optical circuit switch redirects beams of light between fibers without converting the signal to electrical form, which can save power in large AI networks. The company says the platform has passed a $100 million quarterly revenue run rate. That is a reported operating figure.

    The next claim is of a different kind. Lumentum says it expects the product line to generate “revenue in the billions in upcoming years,” citing technological differentiation and a first-mover advantage. That is a management expectation with no stated timeline or customer base attached. Readers should weigh it as guidance, not as booked business. The same distinction applies to the 2029 sellout. A booking horizon is a strong demand signal, but its value to the company depends on the pricing and terms attached to it, and Lumentum has not disclosed either.

    Who Gains and Who Waits

    A supplier with sold-out capacity usually has leverage on price and terms. That can support margins unless long-term agreements locked in earlier pricing. Customers who committed early hold an advantage over those arriving now. In practice, AI operators that reserved optics alongside their GPU allocations are better placed than late entrants or smaller cloud providers. Module assemblers that buy laser chips rather than make them are also exposed to whatever allocation decisions upstream laser suppliers make.

    For data center developers, the practical effect is coordination. A powered, cooled hall delivers no revenue until the tenant’s network is live, and that network now has its own multi-year lead time. Lumentum says it has expanded wafer capacity across its global indium phosphide fabs and qualified both CW and EML process flows. It has not said how much capacity that adds or when the backlog eases.

    Background

    Lumentum was spun off from JDS Uniphase in 2015 and is headquartered in San Jose, California. It makes lasers, optical transceivers, transport products and, more recently, optical circuit switches. Its cloud and networking business supplies the components that move data between servers and switches in large data centers. The company’s fiscal year ends in late June, and fiscal 2026 closed on June 27, 2026.

    Demand for optics has risen with the size of AI clusters. Training a large model means synchronizing thousands of accelerators, and every link between them needs bandwidth. Network speeds have stepped up from 400-gigabit to 800-gigabit and now 1.6-terabit modules. Many of those modules rely on laser chips made from indium phosphide, a semiconductor material produced in specialized fabs that take time and capital to expand.

    Sources

    Source: Lumentum Holdings Inc. Faces Soaring Demand for AI Data Center Components (GuruFocus), which reported CEO Michael Hurlston’s remarks that optical component production is booked into early 2029. Also: Nvidia-Backed Lumentum Sees Opto-Parts Capacity Sold Out to 2029 (Bloomberg).

    Primary sources: Lumentum Holdings Inc., DEF 14A Definitive Proxy Statement, filed October 6, 2026 (SEC EDGAR).

  • Bitdeer’s 67MW Malaysia AI Bet Rests on a 9.5MW Sellout — and No Tenant Yet

    Bitdeer’s 67MW Malaysia AI Bet Rests on a 9.5MW Sellout — and No Tenant Yet

    TL;DR · 30-second read

    The Short Version

    Bitdeer, a company best known for bitcoin mining, is adding a large computer center in Malaysia built for artificial intelligence work. It will draw about as much electricity as a sizable town and is due to be ready by mid-2027.

    The catch: no customer has signed up to rent it yet. Its main proof that people want this is a Malaysian site one-seventh the size, which sold out this summer.

    So it is a bet that customers will come, made while the company pays as much as 13% a year on some of its loans.

    Bitdeer Technologies (Nasdaq: BTDR), through its Bitdeer AI unit, has secured a 67MW AI data center campus in Malaysia, with delivery targeted for the second quarter of 2027, according to MarketScreener, StreetInsider and Crypto Briefing. TheEnergyMag reported that the addition brings Bitdeer’s total to 273.5MW. FourWeekMBA reported that no offtake has been signed. An offtake is a customer’s commitment to take the capacity. Investing.com put the facility at 60MW rather than 67MW. StreetInsider and Stocktwits reported that Bitdeer’s AI infrastructure pipeline tops $10 billion.

    Separately, Stock Titan reported that Bitdeer told readers to disregard a release that had cited a facility target of up to 350MW. MarketScreener reported that alerts on Bitdeer AI’s data center services agreement were withdrawn. The announcement follows a 6-K filed September 29, in which Bitdeer disclosed that its roughly 9.5MW A102 data center in Malaysia had sold out on five-year offtake commitments.

    Executive Summary

    Bitdeer is adding 67MW of AI data center capacity in Malaysia, a country where it already operates. Delivery is targeted for Q2 2027, only a few quarters away, and no customer has committed to the space. The only Malaysian AI contract the company has put on the record is the 9.5MW A102 sellout disclosed in its September 29 filing. The new campus is therefore about seven times larger than the demand Bitdeer has demonstrated in that market.

    The contrast with Bitdeer’s Norwegian project is sharp. At Tydal, the company signed a named tenant at a disclosed price before building, with roughly $1.3 billion of bank-arranged credit support behind the tenant. The Malaysian campus so far has a size and a date, but no tenant, price or financing disclosed. This is a speculative build, meaning one put up ahead of signed demand. It comes as Bitdeer carries collateralized loans at a 13.0% effective interest rate, with $373.5 million of principal falling due within a year of June 30, 2026.

    Two figure discrepancies need clearing up. One is the withdrawn reference to an up-to-350MW target. The other is the 60MW versus 67MW sizing. Until Bitdeer clarifies them, it is hard to say exactly what is being built.

    A 9.5MW Sellout Is the Demand Case for 67MW

    Bitdeer has put only one piece of Malaysian AI demand on the record, and it is small. In its September 29 6-K, the company said Bitdeer AI sold out approximately 9.5MW at its A102 data center in August, with five-year offtake commitments for its AI Cloud business, which sells computing capacity for AI workloads. The new campus, at 67MW, is about seven times that size. It is being added before any customer has committed to it.

    That ratio is the core of the bet. The A102 sellout shows that buyers in Malaysia will sign multi-year commitments for Bitdeer’s capacity. It does not show that buyers exist for seven times as much, on Bitdeer’s timeline and at the price it needs. The calendar is also short. Q2 2027 is two to three quarters away, so Bitdeer has quarters, not years, to sign tenants. If the campus is delivered before it is contracted, Bitdeer carries the cost of a finished facility with no revenue against it until the space fills.

    Different groups are affected in different ways. For AI companies looking for capacity in Southeast Asia, an uncommitted campus with a near-term delivery date gives them room to negotiate. For Bitdeer shareholders, the value of the project depends on when the first tenant signs and on what terms. A further question is how the space will be used. Bitdeer could fill the campus with its own AI Cloud service, as at A102, which means buying its own processors on top of the facility. Or it could lease the space as colocation, where the tenant brings its own hardware. Those two paths carry very different capital requirements.

    Tydal Is the Template Malaysia Has Not Yet Matched

    Bitdeer’s own filings show what a fully contracted AI campus looks like. Under the Tydal, Norway agreement with Volta Tydal AS, described in the September 29 6-K, Bitdeer’s subsidiary will deliver 121 IT MW of contracted critical load, supported by an estimated 133 gross MW. IT MW is the power available to the computers themselves. Gross MW also covers cooling and other building systems. The contract is worth approximately $4.7 billion over a 16-year base term, at an average rate of about $202 per kilowatt per month. Electricity is fully reimbursed by the tenant, and payments rise 3% a year. Bitdeer expects about $2.4 million of annual revenue per IT MW. It also expects a net operating income margin of roughly 90%, meaning the share of revenue left after operating the property.

    The tenant’s obligations are expected to be backed by letters of credit totaling about $1.3 billion, arranged by affiliates of J.P. Morgan and another major financial institution. These are bank guarantees that pay out if the tenant cannot. That backing is subject to customary conditions, and the tenant can terminate without a fee at year 10. So even Bitdeer’s strongest contract has conditions attached. Still, it has a named counterparty, a price, a term and credit support, all disclosed before construction completes. The design-and-construction contract for Tydal is phased to begin in December 2026.

    The Malaysian campus has none of those elements on the record yet. That is not unusual for a project announced early. But it means the 67MW should be read as capacity Bitdeer intends to sell, not capacity it has sold.

    13% Debt Puts a Clock on the Lease-Up

    Bitdeer’s balance sheet makes the timing of tenant signings more important. According to the financial statements filed September 29, the company’s collateralized loans from BIT Group carried a 13.0% effective interest rate in the first half of 2026. Interest expense on those loans was $32.7 million, up from $3.0 million a year earlier. As of June 30, $373.5 million of that principal was due within twelve months, and $142.1 million was due after that. The loans are secured by mining rigs, inventories and data center assets.

    Bitdeer has other obligations as well. It has a $26.0 million bank facility at 10.31% that matures in December 2026. It has $375.0 million of 5.00% convertible notes due 2032, issued in February. It also had roughly 3,669 Bitcoin outstanding under a separate BIT Group Bitcoin loan as of September 29. In August, the company paid about $100 million in cash for roughly 200 acres in Milam County, Texas. On October 8, its board authorized a new $100 million share buyback after fully using a previous $40 million program.

    None of that is unusual for a company expanding quickly. It does mean the Malaysian campus competes for capital with debt repayments, land acquisition and buybacks. Every quarter the campus sits unleased, capital stays committed with nothing coming back. Bitdeer has not said how the campus is being financed.

    One Figure Withdrawn, Another Disputed

    The withdrawn up-to-350MW reference is worth noting in a measured way. Companies correct releases for many reasons, and the figure that stands is the 67MW campus. But Bitdeer has not explained what the 350MW target referred to or whether a larger expansion is still under consideration. For a company that investors increasingly value on its pipeline of planned capacity, that clarification matters.

    The 60MW versus 67MW gap may have a simple explanation. The ratio is close to the IT-versus-gross distinction Bitdeer itself draws at Tydal, where 121 IT MW sits within 133 gross MW. But Bitdeer has not confirmed that this is the reason. It is a question the company can answer quickly. Buyers pricing capacity and investors modeling revenue need to know which number is the sellable one.

    Background

    Bitdeer Technologies Group is incorporated in the Cayman Islands, headquartered in Singapore and listed on the Nasdaq under the ticker BTDR. It built its business on bitcoin mining. Its revenue lines include self-mining, cloud hash rate (selling mining capacity to others), hosting and mining rig sales, plus a growing AI Cloud business. Its financial statements break out figures for Ethiopia, Canada, Malaysia, Norway, Bhutan, the United States and Singapore. The company cites about 3.0 GW of total power capacity across its global site portfolio.

    Like several miners, Bitdeer is repositioning its power-connected sites toward AI computing. In Norway, it is converting its Tydal data center into an AI facility, under a cost-plus construction contract phased to begin in December 2026. In Texas, it bought about 200 acres near its Rockdale facility in August for roughly $100 million in cash, giving it 742MW of existing and pipeline power capacity in Milam County. In Malaysia, its 9.5MW A102 site sold out to AI Cloud customers in August 2026.

    Sources

    Source: Bitdeer Technologies Expands Malaysian AI Data Center Capacity With 67MW Campus (MarketScreener): Bitdeer AI adds a 67MW Malaysian AI data center campus.

    Primary sources: Bitdeer 6-K filed October 8, 2026 (US$100 million share repurchase program); Bitdeer 6-K filed October 2, 2026 (resignation of director); Bitdeer 6-K filed September 29, 2026 (interim report); Exhibit 99.1 to 6-K filed September 29, 2026 (unaudited consolidated financial statements); Exhibit 99.2 to 6-K filed September 29, 2026 (recent developments).

    Further reporting: FourWeekMBA; StreetInsider; Stocktwits; TheEnergyMag; Crypto Briefing; MarketScreener (withdrawn alerts); Stock Titan; Investing.com.

  • Applied Digital’s $1.59B at 7% Ties Its AI Campus Debt to Go-Live Speed

    Applied Digital’s $1.59B at 7% Ties Its AI Campus Debt to Go-Live Speed

    TL;DR · 30-second read

    The Short Version

    Applied Digital builds huge buildings packed with computers that tech giants rent to run artificial intelligence. Its sales more than quadrupled from a year earlier, to $341.9 million in three months.

    It still lost $221 million under standard accounting rules. It also borrowed $1.59 billion at 7% interest, about $111 million a year, to build another one.

    The catch: interest is owed from the day the money is borrowed, but rent only starts once a building is finished and a tenant moves in. How fast that gap closes is the thing to watch.

    Applied Digital (Nasdaq: APLD) reported results on October 7, 2026 for its fiscal first quarter of 2027, the three months ended August 31. Revenue was $341.9 million, up 322% from a year earlier. The net loss attributable to common stockholders was $221.0 million, or $0.76 per share. On the company’s adjusted basis, which excludes its majority-owned ChronoScale subsidiary, the loss narrowed to $4.1 million, or $0.01 per share, and adjusted EBITDA (earnings before interest, taxes, depreciation and amortization) was $64.4 million.

    The company said its Polaris Forge 1 campus in North Dakota reached 250 megawatts (MW) of live capacity on October 1. During the quarter it closed $1.59 billion of 7.000% senior secured notes due 2031 to fund a third 150 MW building there and repay a $300 million bridge loan. Barron’s read the results as a sign the company’s data center bet is starting to pay off.

    Executive Summary

    The quarter marks Applied Digital’s transition from a company announcing capacity to one reporting what that capacity earns. Live capacity at Polaris Forge 1 went from 100 MW to 175 MW on July 1 and to 250 MW on October 1. The company expects initial operations at Polaris Forge 2 in Harwood, North Dakota, to bring total delivered critical IT load across its North Dakota campuses to 300 MW by the end of calendar 2026. Critical IT load is the power available to the computing equipment itself, not counting cooling and other building systems.

    The financing came at the same pace. The $1.59 billion note issue, made through subsidiary APLD ComputeCo 3 LLC, adds a fixed interest bill that starts before the building it funds produces any rent. The 210 MW, 15-year lease at Delta Forge 2, which the company valued at about $5.2 billion of base-term contracted revenue, shows the scale of the revenue on offer. The question for the next several quarters is how quickly finished halls convert into recognized lease revenue. This quarter’s results only partly answer it, because the newest 75 MW came online after the period closed.

    Why Go-Live Speed Now Sets the Debt Math

    The $1.59 billion of 7.000% senior secured notes, issued at par and listed among the quarter’s highlights, carry about $111.3 million of interest a year. That works out to roughly $27.8 million a quarter, or about $9.3 million a month, on these notes alone. The proceeds pay for construction of a third, 150 MW high-performance computing building at Polaris Forge 1 and repay a $300 million bridge loan. Simple subtraction leaves roughly $1.29 billion for construction before fees. Interest accrues from the day the notes are issued. The building earns nothing until it is delivered and its lease begins.

    That gap is the mechanism behind this article’s headline. Every month between closing and lease commencement on building three is a month of coupon payments with no matching rent from the asset that secures the debt. Because the notes were issued through a project-level subsidiary, APLD ComputeCo 3 LLC, lenders are underwriting that specific building’s timeline. For a sense of how much revenue rides on timing, the company’s Delta Forge 2 lease implies about $1.65 million per megawatt per year in average base-term revenue ($5.2 billion over 15 years across 210 MW). Applied Digital has not said whether building three is priced similarly.

    The people affected go beyond shareholders. Noteholders depend on the construction schedule. Contractors and equipment suppliers carry the schedule risk. Tenants need the halls ready when their own chip deliveries arrive. Applied Digital’s track record helps here. It hit two 75 MW Ready for Service milestones, on July 1 and October 1, roughly a quarter apart. Ready for Service is the point at which a finished, powered data hall is handed over for use. The company’s next proof point is whether building three follows a similar cadence.

    322% Growth and a $221 Million Loss Describe the Same Quarter

    Barron’s take that the bet is starting to pay off holds on the company’s operating measures. Adjusted EBITDA was $64.4 million, and net operating income was $58.8 million. Net operating income is a real-estate-style measure of property-level income before corporate overhead and financing costs. On the adjusted basis the company was close to break-even, with a $4.1 million loss.

    Under standard accounting (GAAP), the loss attributable to common stockholders was $221.0 million. The roughly $217 million gap between that figure and the adjusted loss is large for a single quarter. Investors will want to examine the company’s reconciliation of the two closely. Part of the gap reflects the exclusion of ChronoScale, Applied Digital’s majority-owned, separately listed accelerated-compute business, which is consolidated in GAAP results but stripped out of the adjusted figures. The 10-Q filed the same day also shows Series E, E-1 and G preferred stock in the capital structure. That matters because any preferred dividends sit ahead of common holders, and the headline loss is measured after them.

    Neither reading is wrong. They answer different questions. The adjusted figures show what the operating data center business is starting to generate. The GAAP figure shows what the whole enterprise, including its financing and its subsidiary, cost shareholders this quarter.

    The Reported Quarter Mostly Predates the Capacity That Matters

    The timing explains why this is only the first part of the test. In the quarter ended August 31, Polaris Forge 1 ran at about 100 MW for June and 175 MW for July and August. The second 75 MW phase of Building 2 did not reach Ready for Service until October 1, a month after the quarter closed. Fiscal Q2, which runs September through November, will include one month at 175 MW and two at 250 MW. Fiscal Q3 is the first quarter that could reflect the full 250 MW. If Polaris Forge 2 starts on schedule, that quarter would also include the start of Harwood.

    Applied Digital has not broken out how much of its $300.4 million in adjusted revenue came from Polaris Forge 1. It also has not said whether the October 1 phase began billing on its Ready for Service date. Revenue per live megawatt, tracked quarter to quarter, is the clearest test of whether delivered capacity is turning into recognized revenue on schedule. Two more quarters of data should make that measurable.

    Power Is Being Secured Ahead of Buildings

    After the quarter, Applied Digital signed a power purchase agreement with Base Electron for capacity and energy from an approximately 1,200 MW natural gas-fired plant that Base Electron is to develop in North Dakota. It also agreed to up to approximately 1 GW of potential power capacity in Finland. Both are long-dated options on future growth, not operating capacity. In the case of Base Electron, the plant has not yet been built.

    This sequencing reflects a real constraint. Grid power is now one of the slowest inputs to an AI campus, and the company argues that low-cost, power-rich regions will hold value better as coastal and metro markets tighten. Chief Executive Wes Cummins framed the strategy around “durable, high-quality, long-term contracts” with investment-grade hyperscalers, meaning the largest cloud and AI platform operators. Securing power ahead of signed tenants creates an obligation to fill that capacity. Each new campus will also need its own financing, likely structured like the ComputeCo 3 notes. The Delta Forge 2 lease shows hyperscaler demand exists. Matching that demand to each new block of power is the ongoing execution test.

    Background

    Applied Digital is a Dallas-based company that designs, builds, owns and operates large data centers purpose-built for high-performance computing, including artificial intelligence training and inference. Its strategy centers on North Dakota, where it operates Polaris Forge 1 and is developing Polaris Forge 2 in Harwood and Polaris Forge 3 in Oliver County. Its stated goal is to lease capacity on long-term contracts to investment-grade hyperscalers, the largest cloud and AI platform operators.

    The company also holds a majority stake in ChronoScale, a separately listed company that runs its legacy cloud services business and is pursuing a 50 MW deployment with Microsoft built on NVIDIA GB300 NVL72 rack-scale systems with liquid cooling. Because ChronoScale is consolidated, Applied Digital reports both GAAP figures and adjusted figures that exclude it. Investing.com’s market wrap placed the results alongside a broader wave of AI-related debt deals, which is the financing context for this buildout.

    Sources

    Source: Applied Digital’s Earnings Show Its Data Center Bet Is Starting to Pay Off (Barron’s), on Applied Digital’s fiscal first-quarter 2027 results; and AI debt deal wave reports; Applied Digital reports – what’s moving markets (Investing.com), a market wrap covering the results.

    Primary sources: Applied Digital Form 10-Q for the quarter ended August 31, 2026 (filed October 7, 2026); Applied Digital Form 8-K (filed October 7, 2026); Applied Digital Reports Fiscal First Quarter 2027 Results (Exhibit 99.1 to 8-K).

  • TeraWulf’s 1 GW Muskie Deal Puts Its 2029 AI Phase on Kentucky Power’s Clock

    TeraWulf’s 1 GW Muskie Deal Puts Its 2029 AI Phase on Kentucky Power’s Clock

    TL;DR · 30-second read

    The Short Version

    TeraWulf, a company that turns old industrial sites into giant computer warehouses for artificial intelligence, has doubled the electricity reserved for its new Kentucky campus, to about what a large nuclear power plant produces.

    It wants the second half switched on in 2029, a year early. But that date is not its to set. State regulators must sign off, and the local utility must finish building the equipment that delivers the power.

    No tenant has been named yet. TeraWulf is also borrowing more to back financial guarantees for the utility, which are meant to protect ordinary electricity customers if the project falls short.

    TeraWulf has doubled contracted power at its Muskie Data Campus in eastern Kentucky from 500 megawatts (MW) to 1 gigawatt (GW). In an 8-K filed October 5, 2026, the company said its subsidiary DB Long Hunts Data LLC executed amended and restated transmission infrastructure and energy service agreements with Kentucky Power, an American Electric Power (AEP) company, on October 1. The amendment moves delivery of the campus’s second 500 MW phase from 2030 to 2029, subject to Kentucky Public Service Commission (PSC) approval and Kentucky Power’s construction schedule. The first 500 MW phase is still targeted to begin ramping in 2028.

    Data Center Dynamics reported that the roughly 285-acre site, bought in May 2026 from Industrial Equity Partners, sits within the 1,000-acre EastPark Industrial Park and is zoned for data center use. It also reported that Core42 and Fluidstack are live customers at TeraWulf’s Lake Mariner campus in New York, and that Anthropic is set to lease capacity at the company’s separate 480 MW Justified Data Campus in Hancock County, Kentucky.

    Executive Summary

    TeraWulf now holds a contract for 1 GW of utility power at Muskie, a site it bought five months ago. It is pitching the campus to prospective tenants for artificial intelligence and high-performance computing (HPC), meaning dense clusters of servers for training and running AI models. The deal has two phases of 500 MW each: the first is targeted to begin ramping in 2028, and the second has been pulled forward a year to 2029. Kentucky Power is building a 765-kV/345-kV substation, the extra-high-voltage hub that will feed the site, and TeraWulf says it is evaluating whether the campus could eventually support up to 2 GW.

    The headline number is real and contractual. The accelerated date is not yet in TeraWulf’s hands. Both the company’s press release and its 8-K make 2029 conditional on a state regulator’s approval and on the utility’s construction schedule. The same 8-K discloses a second dependency: TeraWulf intends to upsize its senior secured revolving credit facility and raise more senior secured debt to cover letter-of-credit needs tied to its utility power partnerships, and it notes there is no assurance that financing will be obtained.

    For AI tenants, Muskie offers a credible path to gigawatt-scale power in a market where power is the scarcest input. For investors, the announcement shows where the next phase of execution risk sits: with the regulator, the utility and TeraWulf’s credit capacity, more than with construction of the data halls themselves.

    The 2029 Date Belongs to the Utility and the Regulator

    A data center developer can buy land, pour foundations and order chillers on its own schedule. It cannot energize a campus until the utility has built the substation and transmission connections to deliver the power. At Muskie, that work falls to Kentucky Power, which is developing a 765-kV/345-kV substation tied to the regional transmission network. Lines at 765 kV are the heaviest-duty class in common use on the US grid, built to move very large blocks of power. A substation at that voltage is a major utility construction project in its own right.

    TeraWulf’s filings are precise about what that means for the second phase. Delivery of the second 500 MW in 2029 is “subject to Kentucky Public Service Commission approval and Kentucky Power’s construction schedule.” The press release adds that development will proceed consistent with Governor Beshear’s Executive Order 2026-494 on data center development. The 2029 date is therefore a target the developer has negotiated, not one it controls. The first phase’s 2028 target carries no new conditions in the release, but it rests on the same utility-built infrastructure.

    This matters most to whoever eventually signs a lease for the second 500 MW. An AI company that plans a 2029 deployment on Muskie is effectively planning around a PSC docket and a utility construction calendar. TeraWulf’s CEO, Paul Prager, framed the acceleration as giving the company “greater flexibility to meet prospective customers’ deployment needs.” That is a fair description of the upside. The flexibility runs only as fast as the regulatory and construction steps outside TeraWulf’s control.

    Contracted Power Is the Product Being Sold

    Prager said customer interest in Muskie “has been tremendous, and power availability is central to those conversations.” That ordering is the business model. TeraWulf is securing power first and tenants second, on the view that a signed utility contract for 1 GW is what gets a large AI customer to the table. The company has a track record of converting sites this way: per its own description, it is repurposing parts of its Lake Mariner bitcoin-mining campus in New York for contracted HPC use, and its Justified campus already has Anthropic lined up as a tenant.

    The customer claim for Muskie itself is unquantified, however. The release names no tenant, letter of intent or lease terms for the campus, and “tremendous” interest is not contracted demand. TeraWulf’s own risk factors list its “ability to attract additional customers to lease its HPC data centers” first among the uncertainties. Until a lease is announced, Muskie is a power-backed site with a strong pitch rather than a revenue-generating campus.

    The Collateral Behind the Megawatts

    The part of the October 5 filing most likely to matter to investors sits under a separate heading: a “Financing Strategy Update.” TeraWulf said it intends to increase its existing senior secured revolving credit facility and seek additional senior secured debt “to further support letter of credit needs in connection with the Company’s long-term power development partnerships with utilities.” A letter of credit is a bank’s guarantee that the utility can draw on if the customer fails to pay or walks away. In practice it ties up borrowing capacity before a single server is installed.

    The release frames this as deliberate. TeraWulf describes its Kentucky approach as coordinating with the utility on infrastructure, providing “financial protections for utility investment” and structuring agreements “to benefit existing customers.” That approach has a precedent. In August, the Kentucky PSC approved the separate electric service agreement for the Justified campus in Hawesville, finding its terms adequately protected existing customers and appropriately allocated the costs and risks of serving the project.

    The arrangement shifts risk in a defensible direction. Kentucky Power’s existing ratepayers are shielded from paying for infrastructure a data center might not fully use, and TeraWulf carries that exposure on its balance sheet instead. The trade-off is that doubling Muskie to 1 GW, and evaluating 2 GW, raises the collateral TeraWulf must post. The company says plainly that there can be no assurance the financing will close. Growing the campus depends on the utility’s construction timeline and on the company’s ability to keep posting guarantees.

    Background

    TeraWulf, based in Easton, Maryland, began as a bitcoin miner and has been repositioning itself as a developer and operator of power-backed data centers for AI and high-performance computing. Its Lake Mariner campus in New York, a former coal plant site, already hosts HPC tenants. The company is developing two large Kentucky campuses: Justified, a 480 MW project on a former aluminum smelter site in Hancock County, and Muskie, an approximately 285-acre site in eastern Kentucky’s EastPark Industrial Park that it acquired in May 2026 for phased AI and HPC development.

    For AI data centers, grid power has become the main constraint on how fast capacity can come online, often more than buildings or equipment. Large new loads usually require the utility to build new substations and transmission ties, and state regulators must decide who bears the cost if the customer never uses the capacity. Kentucky’s PSC answered that question for Justified in August by approving an agreement it found protected existing customers. That gives TeraWulf a template, though not a guarantee, for Muskie.

    Sources

    Source: TeraWulf’s Kentucky campus to reach 1GW in 2029 (Data Center Dynamics) on TeraWulf’s amended Kentucky Power agreement for its Muskie Data Campus.

    Primary sources: TeraWulf Form 8-K filed October 5, 2026 (Muskie expansion and financing strategy update); Exhibit 99.1: TeraWulf Expands Contracted Power Capacity at its Muskie Data Campus to 1 GW.

  • Applied Digital’s 1 GW Finland Deal Buys an AI Power Option, Not Yet a Campus

    Applied Digital’s 1 GW Finland Deal Buys an AI Power Option, Not Yet a Campus

    TL;DR · 30-second read

    The Short Version

    Applied Digital, a Dallas company that builds the huge computer warehouses used to run artificial intelligence, has signed a deal for access to a large supply of electricity in Finland. It is the company’s first project outside the United States.

    At full size, the site could use about as much power as one large nuclear reactor produces. But the first electricity is not expected until 2028, and no customer has signed yet.

    The lesson is that in this business, the electricity gets locked up first. The buildings and the paying tenants come later, if they come at all.

    Applied Digital (Nasdaq: APLD) announced on October 6, 2026 that it has entered into an agreement providing access to up to 1 gigawatt (GW) of potential power capacity in Finland, its first development opportunity outside the United States. The company plans a large-scale “AI Factory” campus that could scale to as much as 1 GW, with initial power availability anticipated beginning in 2028.

    Applied Digital said it has begun marketing the site and is in discussions with leading global hyperscale customers, the largest cloud and AI platform operators. It did not name the site’s location, the counterparty to the agreement, its financial terms or any prospective tenant.

    Executive Summary

    The Finland agreement gives Applied Digital a claim on a very large block of future electricity in a cool-climate market, the input that most constrains how fast AI data centers can be built. The company framed the deal as a measured first step abroad, structured to limit its initial exposure, with further investment contingent on customer demand, commercial milestones and regulatory approvals.

    That framing matters as much as the headline number. “Up to 1 GW of potential” capacity, power from 2028 and an unsigned customer base describe an option rather than a project under construction. The announcement establishes where Applied Digital wants to grow next and how it intends to manage the risk; it does not yet establish how much capacity will actually be built, when, or for whom.

    For industry readers, the deal is a test of whether a US developer’s site-selection model, built around abundant power and cold weather in North Dakota, transfers to the Nordics. For investors, it adds long-dated growth optionality while the company’s own guidance keeps near-term attention on its US pipeline.

    What “Up to 1 GW of Potential” Actually Buys

    Read closely, the release qualifies its headline figure at every step. The agreement provides access to “up to” 1 GW of “potential” power capacity; the campus is one “capable of scaling to as much as” 1 GW; and only “initial” power is anticipated, beginning in 2028. The company says future investment and development decisions will be tied to customer demand, commercial milestones and regulatory and development requirements, and that the deal was structured to limit initial exposure. Each of those statements points the same way: Applied Digital has secured the right to pursue a campus, not a commitment to build one at full scale.

    That is a rational way to buy the scarcest input in AI infrastructure. A gigawatt is roughly the output of one large nuclear reactor, and AI campuses of this size are gated less by land or buildings than by how quickly a large, reliable grid connection can be delivered. Securing power years before a tenant signs lets a developer offer hyperscalers a dated path to capacity, which is the thing those customers are shopping for. The trade-off is that the value of the option depends on terms the company has not disclosed, including what it pays to hold the capacity and what happens if no tenant materialises.

    The people most affected are prospective tenants, who gain another large-scale European option with a stated 2028 start, and shareholders, who should treat the 1 GW as a ceiling on what could be built rather than a measure of what has been contracted.

    Why Finland Fits the North Dakota Playbook

    Chief executive Wes Cummins tied the choice directly to the company’s US model: Finland “stood out because many of the characteristics that have supported our success in North Dakota are also present in the Nordics — a favorable climate, an attractive energy ecosystem, strong connectivity and the ability to develop at meaningful scale.” Each of those factors has an operational meaning. Cold ambient air reduces the energy spent removing heat from dense AI servers; a strong energy ecosystem determines whether large power blocks can be delivered; and fibre connectivity determines whether the site can serve customers beyond its immediate region.

    The release presents this as continuity, not reinvention: securing scalable power and land where AI capacity can be developed “efficiently, responsibly and repeatedly.” That is a reasonable thesis, but it is a claim to be proven. Cummins said as much, describing Finland as the chance “to begin proving that platform can travel.” The company’s own risk disclosures in the release flag what could differ abroad, including sourcing materials and labour internationally and complying with regulations outside the US.

    The 2028 Clock and the Hyperscaler Question

    With initial power not expected until 2028, the commercial work happens now. Applied Digital says it has begun marketing the site and is in discussions with leading global hyperscale customers. Large AI campuses are typically financed around long-term leases with creditworthy tenants, so the timing and size of any signed lease will largely determine whether the Finland project moves from option to construction, and how quickly it scales beyond its first phase.

    The release does not indicate how much of the 1 GW becomes available in 2028 versus later years. That phasing matters: a hyperscaler weighing the site will compare not just the headline ceiling but the megawatts deliverable in each year against competing sites. Until a tenant and a phasing schedule are disclosed, the near-term financial contribution of the Finland site should be treated as zero, consistent with the company’s own emphasis on its US projects.

    Discipline as the Message

    The release spends notable space reassuring investors. Cummins called the deal “a deliberate first step, not a change in our strategy,” and said scale “is not about planting flags in as many markets as possible.” That emphasis is consistent with the risk factors the company lists, which include its ability to raise additional capital for data center construction and to obtain financing for leases. Framing Finland as low-exposure optionality signals that the company does not intend to divert capital from US execution to fund it.

    Whether that framing holds will depend on disclosures still to come: the cost of holding the capacity, any deposits or milestone payments, and how a Finnish build would be financed if a tenant signs.

    Background

    Applied Digital, founded in 2021 and headquartered in Dallas, designs, builds and operates large data centers for high-performance computing, including artificial intelligence, cloud, networking and blockchain workloads. The company markets closed-loop cooling, a system that recirculates coolant rather than continuously consuming water, along with rapid deployment, and describes its build model as an “AI Factory” franchise. Its US development has centred on North Dakota, which the company credits for traits it now seeks in Finland: cold weather, available energy and room to scale. Datacloud named it Best Data Center in the Americas for 2025.

    Across the AI infrastructure market, developers increasingly compete to secure large blocks of electricity years ahead of construction, because access to reliable power at scale has become a principal constraint on how fast AI computing capacity can be added. The Nordics, which the company describes as an emerging European AI market, have drawn growing investment from global technology firms on the strength of their climate, energy and digital networks.

    Sources

    Source: Applied Digital Secures Up to 1 GW of Potential Power Capacity in Finland, Marking First International Development in the Nordics: Applied Digital’s October 6, 2026 announcement of its first development opportunity outside the United States.

  • Everyone Sees Google’s 3.6 GW Deal. AI’s Grid Gain Is the 890 MW of New Nuclear

    Everyone Sees Google’s 3.6 GW Deal. AI’s Grid Gain Is the 890 MW of New Nuclear

    TL;DR · 30-second read

    The Short Version

    Google signed a long-term deal to buy electricity from Constellation Energy, the biggest owner of nuclear power plants in the United States. The total is roughly what three large nuclear reactors produce.

    The surprise: only about a quarter of it is described as new power that does not exist today. The companies have not said whether the rest is new.

    That matters because the giant computer buildings that run artificial intelligence are straining the power grid across a large part of the eastern United States. New plants add electricity for everyone. Buying power that already exists mostly changes who gets it.

    Google has entered a long-term power agreement with Constellation Energy that Reuters reported totals 3.6 gigawatts (GW), a gigawatt being a thousand megawatts, or roughly the output of one large nuclear reactor. Google’s announcement says the agreement will bring 890 megawatts (MW) of new nuclear capacity to the PJM grid, the regional power system covering the Mid-Atlantic and parts of the Midwest. Bloomberg, which reported the talks before the announcement, put the deal’s value at roughly $1 billion or more.

    Executive Summary

    The headline number is 3.6 GW, which makes this one of the larger power agreements a technology company has signed for its data centers. But Google’s own framing singles out a smaller figure: 890 MW of new nuclear capacity on PJM. That is about a quarter of the total. The companies have not described the remaining roughly 2.7 GW as new supply.

    The distinction matters because data centers built for artificial intelligence (AI) are adding electricity demand faster than grid operators are adding generation, and PJM is where that pressure is most visible. New capacity increases the amount of power the grid can deliver to everyone. A contract for output from plants that already run changes who is credited with that power, but does not, on its own, add megawatts to the system.

    So the useful question about this deal is not how large it is, but how much of it is additive. On current disclosures, the answer is 890 MW, with the composition of the rest still unexplained.

    The 890 MW Is the Only Slice Labeled as New Supply

    Subtract the new nuclear capacity from the total and roughly 2.7 GW of the 3.6 GW agreement remains without a public description. If that remainder is drawn from reactors already running and already selling into the grid, it is a reassignment: the same electrons are produced, but a contract now ties their output, or the clean-energy credit attached to it, to Google rather than to the open market. Energy analysts call the difference ‘additionality’, meaning whether a purchase causes new generation to exist that would not otherwise have been built.

    The 890 MW is different in kind. Whether it comes from upgrading existing reactors to produce more power (known as ‘uprates’), restarting idle capacity, or something else, the companies describe it as new, which means it should expand what PJM can actually supply once it comes online. For the utilities, data center developers and households that share that grid, the 890 MW is the part of this deal that eases supply. The rest primarily determines who holds a claim on supply that was already there.

    That is why the 890 MW, not the 3.6 GW, is the operationally meaningful number. It is the portion that shows up as added capacity in grid planning, and it is the portion other large power buyers on PJM will feel.

    Why PJM Is Where Additionality Matters Most

    PJM Interconnection coordinates wholesale electricity for 13 states and Washington, D.C., and its territory includes Northern Virginia, the densest concentration of data centers in the world. PJM runs an auction that pays power plants to commit capacity years in advance, and prices in recent auctions rose sharply as forecasts of data center demand climbed. Meanwhile, new generators wait in an ‘interconnection queue’, the line of projects seeking permission to connect to the grid, which has been slow to clear.

    In that setting, contracts that lock up existing output for one large buyer can tighten the market for everyone else, because the total available supply does not change while one claimant’s share does. Co-location arrangements involving existing nuclear plants in PJM have already drawn regulatory scrutiny over who pays for grid services. A deal anchored in new capacity sidesteps much of that argument, which may be one reason Google’s announcement leads with the 890 MW figure.

    The Case for Contracting Existing Nuclear Output

    Reassignment is not worthless, and it is fair to say so. Long-term contracts give a plant owner price certainty, which can justify the spending needed to extend a reactor’s licensed life or fund the very uprates that create new capacity. A reactor kept online through a contract is supply the grid does not lose. For Google, which has set a goal of running on carbon-free energy around the clock by 2030, contracted nuclear output also supports that target in a way intermittent wind and solar cannot fully match.

    Constellation has used this model before, with Microsoft for the restart of a reactor at the former Three Mile Island site, now called the Crane Clean Energy Center, and with Meta for output from its Clinton plant in Illinois. The pattern suggests technology companies are willing to pay a premium for firm, carbon-free power, and that Constellation can convert that willingness into investment in its fleet. The open question is how much of each contract funds new megawatts versus securing existing ones, and this deal is the clearest case yet where the split is visible in the headline numbers.

    What the $1 Billion Figure Cannot Yet Explain

    Bloomberg’s figure of roughly $1 billion or more has not been broken down publicly. It is unclear whether it represents total contract value, annual spending, or capital tied specifically to the new capacity. Without a contract length or price per megawatt-hour, readers cannot judge whether Google is paying a premium for new supply, a market rate for existing output, or a blend.

    That matters to investors comparing Constellation’s tech-sector contracts, and to other buyers trying to benchmark what firm, carbon-free power now costs on PJM. Until the companies disclose more, the dollar figure is best read as a signal of scale rather than a basis for valuation.

    Background

    Technology companies have become some of the largest buyers of electricity in the United States as they build data centers to train and run AI models. Nuclear power is attractive to them because it runs around the clock without carbon emissions, unlike wind and solar, which vary with the weather. Google has set a goal of matching its electricity use with carbon-free energy every hour of every day by 2030, and has previously signed a deal for future small modular reactors with Kairos Power.

    Constellation Energy, spun off from Exelon in 2022, owns the largest fleet of nuclear plants in the country, much of it inside PJM. It has signed long-term agreements with Microsoft and Meta, making it a central player as AI-driven demand collides with a grid that is slow to add new generation.

    Sources

    Source: Google and Constellation Energy Near $1 Billion Nuclear Power Deal as AI Data Centers Drive Power Demand: Google and Constellation Energy reach a long-term nuclear power agreement as AI data centers drive electricity demand.