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  • TAR’s $1B Series A Prices Off-Grid Power for AI Data Centers as Its Own Category

    TAR’s $1B Series A Prices Off-Grid Power for AI Data Centers as Its Own Category

    TL;DR · 30-second read

    The Short Version

    TAR, a Texas company started only this year, has raised $120 million from investors who now value it at $1 billion.

    It builds private power plants for the giant computer warehouses behind artificial intelligence tools. The plants use renewable energy and huge batteries, and never plug into the public electricity grid.

    Why it matters: these warehouses use so much electricity that many wait years to be connected. TAR says its approach lets them get power without competing with nearby homes and businesses for it.

    Austin-based TAR has raised $120 million in Series A funding at a $1 billion post-money valuation, Pulse 2.0 reported on September 11, 2026. Spark Capital led the round. TAR was founded in 2026 by Pat Becker and Leonhard (Lenny) Soenke. It builds self-contained, modular power systems that combine renewable generation with battery storage and run independently of the electric grid, aimed at AI data centers at gigawatt scale.

    The company says the money will expand its Austin headquarters and its San Francisco engineering office. It will also grow manufacturing and logistics operations in West Texas and speed up deployments already underway. Those deployments include a utility-scale project with one of the largest neoclouds, a dedicated project campus, and TAR Terminal One, a manufacturing and logistics center in West Texas.

    Executive Summary

    TAR’s raise is one of the clearest signs yet that investors see off-grid power for AI as a business category in its own right. It is no longer just a temporary fix for data centers waiting on a utility hookup. A company founded this year has reached unicorn status on its first institutional round by promising dedicated renewable-plus-battery generation that it designs, builds and operates itself.

    The thesis rests on two claims. The first is that power, not chips, is now the binding constraint on AI computing. The second is that the fastest way to supply it is to own the whole project lifecycle and automate construction. TAR pairs energy veterans from Hut 8, AES and Vistra with robotics engineers from Zipline, GrayMatter Robotics and Lucid Motors, which suggests an attempt to treat power plant construction partly as a manufacturing problem.

    What the company has not yet made public matters as much as the valuation. TAR has not disclosed installed megawatts, its customer’s name, contract terms or project timelines. The central engineering question for any off-grid system serving round-the-clock computing is how it maintains reliable supply, and TAR has not yet explained its approach.

    Off-Grid Power Gets Its Own Venture Price Tag

    For much of the data center boom, on-site generation has been framed as a bridge. It energizes a campus while the project waits in a utility’s interconnection queue, the line of projects awaiting approval to connect to the grid. A $1 billion post-money valuation for a company founded in 2026 suggests Spark Capital sees something more durable: a standalone category in which generation is built and run as a permanent, dedicated supply for computing.

    The arithmetic is instructive. A $120 million raise at $1 billion post-money implies a pre-money valuation of roughly $880 million. TAR reached that figure without publicly disclosing completed megawatts, contracted revenue or a named customer. At that level, investors are paying for team, speed and position in a market where, as Spark’s Will Reed put it, “power is becoming the main bottleneck to scaling compute.” It is a bet on the size of the problem as much as on a track record.

    The deal also points to a shift in where AI infrastructure capital goes. Energy assets have traditionally been funded by utilities, infrastructure funds and project lenders, who price them for steady, bond-like returns. A venture-style valuation for a power developer implies that some investors now treat the speed of energy delivery, and not only its cost, as a source of technology-like upside.

    Owning the Whole Stack: Faster Delivery, Concentrated Risk

    TAR’s strategy is vertical integration. It handles site selection, detailed engineering, procurement, logistics, civil construction, commissioning and ongoing operations itself, rather than coordinating a chain of outside contractors. The reasoning is that schedules tend to slip at the handoffs between firms. Co-founder Pat Becker said gigawatt-scale deployments (a gigawatt is a thousand megawatts, comparable to the output of a large power plant) “in tight time windows necessitate owning the full stack end-to-end.”

    The company’s purpose-built deployment automation stack is meant to install generation faster, at larger scale and with less field labor than conventional construction. If it works, the limit on how fast power can be built shifts away from the supply of skilled field crews and toward factory and logistics throughput. TAR Terminal One in West Texas appears designed to supply that throughput.

    The trade-off is concentration of risk. A contractor ecosystem spreads execution risk across many firms, while an integrated developer carries all of it. TAR must scale engineering, robotics, manufacturing, logistics and field operations at the same time. That is capital-intensive and operationally demanding. TAR has not yet disclosed completed projects that show its automation claims working at scale.

    The Reliability Question for Renewables Plus Batteries

    AI data centers run around the clock, and their customers expect very high uptime. A grid connection offers a backstop when on-site generation falls short, and an off-grid system has no such backstop. TAR describes its systems as combining renewable generation with battery storage and operating independently of the grid. The design therefore has to cover periods of weak sun or wind entirely on its own.

    That problem can be solved in several ways: oversizing generation, adding longer-duration storage, or including firm backup generation. Each adds cost, equipment and land. How TAR balances these choices will largely decide whether its power competes on price and reliability with grid electricity or with gas-fired on-site plants, the other common off-grid option.

    TAR’s community argument does resonate. Its systems do not draw on shared grid capacity, so the company says data center projects can add generation without competing with surrounding communities. That matters in siting debates, where the effect of very large facilities on local electricity supply and prices is a recurring concern.

    Who Stands to Gain if the Model Works

    Neoclouds are natural early customers. These are cloud providers that specialize in renting out GPU computing for AI, as opposed to the broad hyperscale clouds. They compete heavily on how quickly they can bring capacity online, which makes long grid timelines especially costly for them. TAR says it is already executing a utility-scale deployment with one of the largest neoclouds, a sign that its offer has found at least one buyer with urgent demand.

    For utilities and grid operators, the effects are mixed. Off-grid campuses ease pressure on congested transmission lines, but they also mean large new electricity users that never become utility customers. Suppliers of batteries and generation equipment, along with West Texas manufacturing and logistics workforces, stand to benefit from any sustained buildout. TAR’s competition includes other on-site power developers and the traditional engineering and construction firms whose multi-contractor model it is explicitly positioning against.

    Background

    AI data centers use far more electricity than traditional facilities, and in many regions connecting a new large user to the grid means joining a long interconnection queue and waiting for new transmission and generation. That has pushed developers toward “behind-the-meter” power: generation built on or next to the site that serves the data center directly. Such projects have often been treated as temporary bridges until a grid connection arrives.

    TAR, founded in 2026 in Austin, is pursuing the off-grid approach as a permanent model. It uses modular renewable generation and battery storage and manages projects end to end. Its lead investor, Spark Capital, is a venture capital firm. The company’s leadership draws on experience at energy and infrastructure companies including Hut 8, AES and Vistra, as well as robotics and hardware companies Zipline, GrayMatter Robotics and Lucid Motors.

    Sources

    Source: TAR Raises $120 Million Series A At $1 Billion Valuation To Build Off-Grid Power For AI Data Centers (Pulse 2.0), covering TAR’s Spark Capital-led round to expand off-grid renewable and battery power systems for AI data centers.

  • Fermi Founder Seeks Independent Strategic Review Before October 30 Vote

    Fermi Founder Seeks Independent Strategic Review Before October 30 Vote

    TL;DR · 30-second read

    The Short Version

    Fermi is a young company trying to build a huge private power and computing campus to run artificial intelligence systems. Its founder, Toby Neugebauer, was removed as chief executive in April but still owns about a fifth of the company.

    He has now formally asked the board to hire an outside bank to test whether selling the company, or bringing in a big partner, would be worth more than going it alone. Shareholders meet October 30.

    The stock has dropped about a fifth since April, a sign investors doubt the giant project will get built on schedule.

    Toby Neugebauer, co-founder and largest shareholder of Fermi Inc. (Nasdaq: FRMI), stepped up pressure on the company’s board ahead of its October 30 annual meeting. Neugebauer’s group, the Fermi Founder Parties, holds approximately 22% of shares outstanding. In a statement issued September 11 via PR Newswire, the group said it sent the board a letter on September 9 and a presentation on September 10. On September 10 it also submitted a shareholder proposal under SEC Rule 14a-8. The proposal asks the board to retain an independent, nationally recognized investment bank to evaluate extraordinary transactions, including a full-value sale or a majority recapitalization with a strategic partner, and to report the results to shareholders.

    Neugebauer also asked the board to voluntarily put two non-binding questions to shareholders. One would restore pre-IPO voting standards in place of a 70% supermajority adopted since April. The other would raise the company’s 2.5% ownership limit to 9.8%. The statement said Fermi shares hit an intraday low of $5.26 on September 10, down roughly 20% since Neugebauer was removed as CEO on April 17.

    Executive Summary

    The founder of an AI power-campus developer is using the formal shareholder-proposal process to force a question onto the agenda: is Fermi worth more as a standalone company or in a transaction? Neugebauer is careful to say he has never asked the board to sell. His proposal explicitly leaves the existing standalone plan in place and asks only that alternatives be tested against it by an independent banker. He also stated that his group is not soliciting proxies.

    The fight matters beyond one company. Fermi went public in October 2025 pitching Project Matador as a private utility, with its own power, transmission and water on a single scalable campus for AI compute. Neugebauer’s critique rests on specific gaps. The one disclosed tenant lease, 222 megawatts with TensorWave, is under 5% of Fermi’s stated 4.8 gigawatts of near-term power opportunity. As of September 10, he says, supporting guarantees and project financing for that lease had not been confirmed. And the company still has no permanent outside CEO.

    For investors in gigawatt-scale energy and data center developments, the episode illustrates how quickly confidence can erode. When projects are mostly still potential, the market reacts to leadership turmoil, unconfirmed financing and governance rules that limit shareholder influence.

    A Modest Ask With Pointed Timing

    Rule 14a-8 is the SEC mechanism that lets eligible shareholders place a proposal in a company’s proxy materials, the documents shareholders receive before voting. Such proposals are typically advisory. Even if one passes, the board is not legally required to act, though ignoring a strong vote carries reputational cost. The proposal is also narrowly drawn: hire an independent bank, test a sale or majority recapitalization against the standalone plan, and report back. A recapitalization means bringing in new capital that would change who controls the company. It is hard to argue against gathering that information in principle, which is likely the point.

    The timing is deliberate. Neugebauer paused an earlier proxy contest, meaning a campaign to win shareholder votes for his own positions or nominees. He now says he expected the board to finalize counterparty agreements his team had negotiated, on the terms agreed, and that execution has stagnated instead. Rather than restart a full contest, he is using lower-cost tools: a proposal, a public letter, and a request that the board voluntarily add advisory questions. That keeps pressure on without the expense and escalation of a formal vote-solicitation campaign, while positioning him to argue later that the board refused reasonable requests.

    The board’s side of the story does not appear in Neugebauer’s statement. The company has previously said it completed all five objectives of its 90-day plan, as of August 13. Shareholders will need its response to weigh the founder’s characterization of those milestones.

    222 Megawatts Against a 4.8-Gigawatt Story

    The core economic tension is scale. A megawatt is a unit of power capacity. A gigawatt is 1,000 megawatts, roughly the output of a large power plant. Fermi’s stated near-term power opportunity is 4.8 gigawatts, and its disclosed TensorWave lease is 222 megawatts, about 4.6% of that figure. For a company valued on the promise of a multi-gigawatt campus, the gap between contracted demand and stated potential is what investors are pricing.

    Financing is the second pressure point. Neugebauer says that as of the morning of September 10, Fermi had not confirmed the guaranteed agreement it previously said would support the TensorWave lease. He also says it had not confirmed that project financing was secured. In data center development, a signed lease is only as valuable as the tenant’s credit, or the guarantee standing behind it. That credit support is often what unlocks construction loans. Until those pieces are confirmed, the lease reads as a milestone rather than de-risked revenue.

    The stock-price figures in the statement also deserve a careful reading. Shares are down roughly 20% since April 17, 35% since July 2, and 20% since August 13. Taken together, those numbers imply shares traded higher in early July than on the day Neugebauer was removed. They also imply shares were near April levels by mid-August. The decline has not been a straight line from the leadership change, which complicates attributing it to any single board decision.

    Governance Rules as a Valuation Lever

    Two of Neugebauer’s requests concern who can own and influence Fermi. The first targets voting standards adopted since April. Amending bylaws now requires a 70% supermajority, and electing directors requires a majority of all outstanding shares. He wants to restore the standards discussed before the IPO: a majority of shares outstanding for bylaw changes, and a plurality of votes cast for directors. A plurality vote means the candidates with the most votes win, even without an outright majority. Higher thresholds make shareholder-initiated change harder to achieve, particularly when turnout is incomplete. Boards commonly defend such provisions as protection against a single large holder gaining effective control without paying other shareholders a premium. With a 22% holder in a dispute with the board, that argument is plausible here. Fermi’s own stated rationale is not included in the release.

    The second request concerns Fermi’s real estate investment trust (REIT) ownership limit. REITs are tax-advantaged property companies, and they must avoid concentrated ownership under federal tax rules. For that reason their charters typically cap how much any one holder can own. Neugebauer notes that peers such as Digital Realty, Equinix and Prologis use 9.8%, while Fermi’s general limit is 2.5%. A lower cap restricts how large a position any single institution can build. That can thin out the pool of large, long-term buyers, and some investors view that as a drag on valuation. Raising it would widen the potential shareholder base. Whether that would change the stock’s trajectory more than delivering contracts and financing would is an open question.

    Leadership Uncertainty and Counterparty Confidence

    Neugebauer argues that counterparties considering committing billions to Project Matador want to know who is running the company. After a four-month search, Fermi named board member Lee McIntire as CEO. Neugebauer contends that the choice showed no outside candidate would take the role, and says the company itself described McIntire as a temporary solution. The first point is his inference rather than an established fact; searches end with internal appointments for many reasons. The underlying concern is still reasonable. Large tenants, lenders and equipment suppliers weigh management continuity when signing long-dated commitments.

    Neugebauer’s own claims warrant scrutiny too. The contention that Fermi is a one-of-a-kind asset, and significantly undervalued, is an argument, not a demonstrated valuation. He says counterparties had agreed to terms while he led the company, but those terms have not been made public. His request that the board release all written communications with him since the IPO could clarify the record for both sides. His stake also gives him an obvious interest in a higher share price. That interest aligns with other shareholders on value, though not necessarily on control.

    Background

    Fermi Inc., doing business as Fermi America, went public on Nasdaq under the ticker FRMI on October 1, 2025. It was capitalized around Project Matador, which the company describes as a private utility campus. The campus would supply its own power, transmission and water for AI computing at scale, with the stated aim of avoiding added burden on public infrastructure and local ratepayers. The company is organized with a real estate investment trust ownership structure. It reports a near-term power opportunity of 4.8 gigawatts and a disclosed 222-megawatt lease with TensorWave.

    Co-founder Toby Neugebauer was removed as CEO on April 17, 2026. Since then, according to Neugebauer, the board adopted a 70% supermajority to amend bylaws and a majority-of-outstanding-shares standard to elect directors. On July 2 it removed the option of a dual-path strategic process, and on August 13 it announced completion of its 90-day plan. Neugebauer launched and later paused a proxy contest. He has also publicly criticized a $375 million convertible note offering, which he tied to former CFO Miles Everson’s resignation from the board. Board member Lee McIntire has since been named CEO.

    Sources

    Source: Fermi Founder Parties File Formal Proposal for Independent Strategic Review of Extraordinary Transactions, Press Board on Restoring Texas-Style Governance Ahead of October 30 Annual Meeting, a statement from Toby Neugebauer and affiliated entities on their shareholder proposal and governance requests to Fermi’s board.

    Primary sources: Fermi Founder Parties File Formal Proposal for Independent Strategic Review of Extraordinary Transactions, Press Board on Restoring Texas-Style Governance Ahead of October 30 Annual Meeting (PR Newswire, September 11, 2026).

  • Applied Digital’s Harwood, ND AI Data Center (Polaris Forge 2): Site, Power, Cost

    Applied Digital’s Harwood, ND AI Data Center (Polaris Forge 2): Site, Power, Cost

    TL;DR · 30-second read

    The Short Version

    Two buildings north of Fargo, North Dakota are nearly finished, and together they are bigger than the largest building in the state, an Amazon warehouse two miles away. About 1,800 workers are on site.

    The buildings will hold computers that run artificial intelligence, and they will draw about as much electricity as a small city. The local council approved the project in September 2025, and part of it is supposed to be running by the end of this year.

    The company building it, Applied Digital, has not said which businesses will rent the space.

    InForum reported that Applied Digital’s Polaris Forge 2 campus in Harwood, North Dakota is nearing completion, with roughly 1,800 workers finishing two buildings of 907,000 square feet each. The Harwood City Council approved the $3 billion project on September 10, 2025. Part of the campus is on pace to become operational by the end of 2026, with the remainder opening in early 2027, according to Nick Phillips, Applied Digital’s executive vice president of public affairs and real estate acquisition.

    The site is a 280-megawatt facility drawing power from Cass County Electric, and Applied Digital is paying $112 million for the substation and transmission upgrades that serve it. The company has not disclosed which companies will occupy the buildings. It expects 200 to 250 permanent employees once the campus opens, on 400 acres of which about 300 have been graded.

    Executive Summary

    Harwood is Applied Digital’s third North Dakota project, after Jamestown and the Polaris Forge 1 campus at Ellendale, with a fourth under construction near Center. Ellendale, announced in late 2024, is partially operational. The pattern across all four is the same wager: build large blocks of power-dense capacity in a cold, cheap-power state and race to energize them while demand for artificial intelligence compute outruns supply.

    What makes Harwood worth studying is the calendar. From a municipal approval on September 10, 2025 to first operational capacity at the end of 2026 is roughly fifteen months for a 280-megawatt campus. In a market where developers routinely quote three to five years between site control and delivered power, that interval is the entire product. “Time to power” is the industry’s shorthand for how long a tenant waits between signing and switching on machines, and it has become the variable that decides which sites win contracts.

    The unresolved half of the story is commercial rather than physical. Steel, generators, cooling loops and a substation are visible and verifiable. Leases are not. Applied Digital has not named a tenant for Harwood, has not disclosed how much of the 280 megawatts is contracted, and has not detailed how the $3 billion build is financed. Those omissions do not undermine the construction progress, but they determine whether the speed converts into revenue.

    Fifteen Months From Vote to Volts

    The construction detail in the account is mostly a lesson in sequencing. Eight generator rooms in each building hold twelve units apiece, which is 96 generators per building and 192 across the campus, a level of on-site backup that signals a design intended for tenants who will not tolerate interruption. Roughly 86,000 electrical connections and several miles of copper cable have to be terminated and tested before any of it carries load. Workers wear helmets with augmented reality systems that check whether components are installed in the right place and whether crews are tracking to schedule, which is less a gadget than an admission that verification, not fabrication, is the thing most likely to slip.

    Phillips describes the split inside each building as about 25% data halls and 75% supporting systems. That ratio is the clearest single indicator of how the economics have shifted. In a conventional enterprise facility, the computer room dominates the floor plan. In a building designed for dense artificial intelligence racks, the electrical rooms, switchgear, uninterruptible power supply gear and cooling plant swallow three-quarters of the envelope. Applied Digital is not really building a warehouse for servers. It is building a power conversion and heat rejection plant that happens to have servers in the middle of it.

    The arithmetic underlines the point. If 280 megawatts is measured at the computers, spread across roughly 453,000 square feet of data hall, that is on the order of 600 watts per square foot, an order of magnitude above the densities most existing commercial data centers were designed for. Applied Digital has not specified whether the 280-megawatt figure is the load delivered to equipment or the total drawn from the utility, and the distinction changes that number materially. Either way, the campus is firmly in the density range that only recently became buildable at scale.

    The $112 Million Sentence

    The most consequential figure in the account is not the $3 billion headline but the $112 million Applied Digital says it will pay for the substation and transmission upgrades. That is roughly $400,000 per megawatt of connection, and it is the developer, not the utility’s other customers, footing the bill. Grid connection cost allocation is the live political question in nearly every jurisdiction now absorbing large computing loads, because the alternative structure, in which network upgrades are socialized across the rate base, is what makes residents worry about their bills. Harwood drew protesters on exactly that concern.

    Applied Digital’s counter-claim is that its facilities do not raise electricity bills, and Phillips cited more than $46 million in savings for customers across North Dakota and South Dakota since the Ellendale project began. The underlying mechanism is plausible in principle: a very large, very steady customer spreads a utility’s fixed costs across more kilowatt-hours, which can lower the per-unit cost everyone pays. But the claim as stated is not yet verifiable by a reader. It does not identify which utility’s customers, over which period, measured against which counterfactual, or whether the figure is an avoided increase rather than a reduction. Those are answerable questions, and regulated utilities generally have to answer them in rate proceedings.

    It is worth being precise about what each side is arguing, because they are not arguing about the same thing. The savings claim is retrospective and concerns Ellendale. The objection at Harwood is prospective and concerns what happens when a second large load lands on a different distribution system. A past result under one set of contracts does not settle the future question, and neither does a protest settle whether the mechanism works. The $112 million commitment is the strongest evidence Applied Digital has offered that it intends to carry its own interconnection costs, which is the part of the dispute that can actually be documented.

    Air, Not Water, and Why North Dakota Rewards That Choice

    Cooling is where siting logic becomes visible. Branden Cummins, the company’s civil structure architecture superintendent, described a closed loop that circulates fluid through six data halls in each building, carries it to the roof, and uses large fans to shed the heat before recirculating it. Because the loop is sealed, Applied Digital says water consumption will be comparable to a household. That is a meaningful claim in an industry now facing real scrutiny over evaporative cooling, which chills equipment by boiling away water and can consume millions of gallons a year at this scale.

    The trade is not free. Rejecting heat to air rather than to evaporating water costs electricity for fans, and the penalty grows with ambient temperature. In a Texas or Arizona summer, that penalty is severe enough that many operators accept the water consumption instead. In eastern North Dakota, where cold hours dominate the year, the fan energy penalty is small and the water savings are close to pure gain. The closed loop is not primarily an environmental gesture; it is the design that the climate makes cheapest, and the environmental benefit follows from the geography.

    Noise and setback receive the same treatment. Silencing equipment and sound barriers on the generator plant, with the nearest home about half a mile away and the town of Harwood more than a mile off, reflect a siting choice made before mitigation was needed. Developers increasingly buy distance rather than engineer around its absence, because generator testing and rooftop fan arrays are the two complaints most likely to follow a facility through its operating life.

    A Builder Without a Named Tenant

    Applied Digital is a merchant developer, meaning it builds capacity and then leases it, as opposed to a hyperscaler building for its own use. That model turns on a single question: how much of the capacity is committed before it is energized. The company has not disclosed which companies will occupy Harwood. Until it does, an observer cannot distinguish between a campus built against signed leases and one built on conviction about demand.

    The local economics are more legible. Roughly 1,800 construction workers will give way to 200 to 250 permanent employees, a ratio close to eight to one that is typical of the sector and worth stating plainly: data centers are enormous construction projects and modest employers. The durable local benefit is tax base rather than payroll. Polaris Forge 1 at Ellendale paid about $4.3 million in property tax for 2026, Harwood is receiving no tax incentives or abatements according to the company, and Phillips estimated Applied Digital would become the largest single taxpayer in the state by 2029, while acknowledging that Harwood’s own assessment is not yet estimated because the building is not finished.

    The absence of abatements is the notable item. Large data centers are frequently recruited with property tax exemptions that hollow out the fiscal case communities were sold, and a project that forgoes them changes the arithmetic for the county. It also suggests the developer’s binding constraint was schedule rather than subsidy. When capacity delivered in 2026 is worth materially more than the same capacity delivered in 2029, a year of negotiation over abatements costs more than the abatements are worth.

    Background

    Applied Digital is a Texas-based developer that builds and leases data center capacity rather than operating computing services itself, a model the industry calls merchant development. Its North Dakota expansion began with a facility in Jamestown, followed by the Polaris Forge 1 campus at Ellendale announced in late 2024 and now partially operational, the Harwood project approved in September 2025, and a fourth site under construction near Center, about 40 miles northwest of Bismarck.

    The expansion tracks a broader shift in where computing gets built. Training and running large artificial intelligence models requires racks that consume several times the power of conventional servers, which has turned electrical capacity, rather than fiber or floor space, into the binding constraint on the industry. Developers have responded by moving to regions with available generation, cold climates that reduce cooling costs, and utilities able to energize large loads quickly. That has brought industrial-scale construction to rural communities with no prior exposure to it, along with recurring local debates over electricity rates, noise, water and tax base that Harwood has seen in full.

    Sources

    Source: An inside look at the Harwood data center as it nears completion — InForum’s report from Applied Digital’s Polaris Forge 2 construction site in Harwood, North Dakota, with detail on schedule, power, cooling, staffing and taxes from company executives.

  • Mombasa’s $1.5B Floating Data Centre Bets on Off-Grid Power

    Mombasa’s $1.5B Floating Data Centre Bets on Off-Grid Power

    TL;DR · 30-second read

    The Short Version

    A Greek energy company and an American turbine maker want to build a $1.5 billion artificial intelligence data centre at the port of Mombasa, Kenya.

    The unusual part: it would generate its own electricity on a barge floating in the harbour, burning imported natural gas, and never plug into Kenya’s national power network at all. That sidesteps blackouts and the long wait to get connected.

    If it is approved and built, East Africa would gain the kind of heavy computing power that today sits mostly in Europe and America. Kenyan regulators have not yet said yes.

    Construction Review reported that Greek multinational Amaco Energy Group has partnered with US energy equipment and services company GE Vernova on Project HERCULES, a proposed $1.5 billion (roughly KSh 194.2 billion) artificial intelligence data centre at the Port of Mombasa, Kenya. Under the arrangement, GE Vernova gas turbines would be integrated into an Amaco power barge, an unmotorised floating platform housing a power plant, that supplies the campus with electricity and cooling.

    The facility is designed to run entirely off-grid on offshore liquefied natural gas, with power scalable from an initial 60 to 100 megawatts up to 1,000 megawatts, and with the option to export surplus electricity to Kenya’s national network near the Mariakani substation. The project sits in the Port of Mombasa and Dongo Kundu ecosystem, is listed as proposed and pending regulatory approval, and is estimated to take about 28 months to build once approved.

    Executive Summary

    Project HERCULES is best understood not as a data centre that happens to have a power plant, but as a power project that has found a customer. The blueprint evolved from an earlier liquefied natural gas-to-power transmission proposal registered in 2024. In its current form, the generation, the cooling and the compute halls are bundled into a single floating architecture moored inside the port, with GE Vernova supplying the turbines and electrification equipment.

    That structure is a direct answer to the constraint that has slowed large computing campuses across much of Africa. Land is available, subsea fibre has landed, and capital is willing. What is scarce is firm, reliable power delivered on a developer’s schedule rather than a utility’s. By declining to connect at all, Amaco converts a multi-year grid question into a procurement and permitting question it can largely control.

    The trade is real but not free. An off-grid campus becomes its own utility: it carries fuel price exposure, it must build its own redundancy because there is no network to fall back on, and it must satisfy Kenyan energy, environmental and port regulators before any of it exists. Nothing has been approved, no customer has been named, and the financing behind the headline number has not been described.

    When the Grid Is the Bottleneck, Bring Your Own

    In mature markets, the queue to connect a large electrical load to the national network, known in the industry as the interconnection queue, has become the single longest item on a data centre schedule. In much of sub-Saharan Africa the problem takes a different shape: the wait can be shorter, but the delivered power is less firm, and a campus full of high-density servers cannot absorb unplanned outages. The Amaco framing is explicit on this point. Bypassing the grid, it argues, removes downtime tied to supply shortages rather than merely hedging against it.

    Inverting the sequence has a commercial logic that goes beyond reliability. A developer who owns generation can commit to an energisation date, and an energisation date is what an anchor tenant is actually buying. The cost is that the developer becomes a power company. Fuel must be contracted and shipped, turbines must be maintained, and the redundancy that a utility connection normally provides has to be built into the platform itself. A campus that is genuinely 100 percent islanded has no external backstop, so its reserve margin is a design choice with a capital cost attached.

    The Barge Is the Product

    An unmotorised power barge is built in a shipyard and towed to its mooring. That matters more than it sounds. Shipyard fabrication trades unpredictable on-site civil works for a controlled construction environment, which is one plausible route to the stated 28-month build. It also makes the asset relocatable in principle, and it places the plant on water, which simplifies the heat rejection problem that dominates dense computing. The release describes direct cooling integrated into the same floating structure, though the specific method is not detailed.

    The capacity range published for the project spans from an initial 60 to 100 megawatts to an eventual 1,000 megawatts. That is more than a tenfold spread, and it should be read as a statement of modular ambition rather than a committed build. The $1.5 billion valuation is not tied in the announcement to any particular point on that range. Anyone sizing the opportunity, whether a lender, a turbine supplier or a prospective tenant, needs to know which phase the figure buys, because the answer determines whether this is a large regional facility or the first slice of something hyperscale.

    Landing Where the Cables Land

    Mombasa’s strategic case rests on geography. It is East Africa’s principal arrival point for international subsea fibre-optic cables, the undersea trunk lines that carry almost all intercontinental internet traffic. Putting compute next to a cable landing station shortens the path between regional users and the machines serving them, and it makes local data residency possible for customers whose regulators or contracts require it. The project also targets industrial synergy with the neighbouring ARISE Coast Integrated Industrial Park inside the 3,000-acre Dongo Kundu Special Economic Zone, which offers a second class of power offtake alongside the servers.

    The demand question is more nuanced than the connectivity story suggests. Large-scale artificial intelligence training is relatively indifferent to latency and gravitates to wherever power is cheapest. What benefits from proximity is inference, the stage where a trained model actually answers queries, along with sovereign and enterprise hosting for governments, banks and telecom operators that want their data inside national borders. Those are credible markets, but they are being contested. The announcement itself notes that other countries, Ethiopia among them, are pursuing the same regional hub position.

    Who Gains, and What Has to Be True

    For GE Vernova, spun out of General Electric in 2024, the appeal is straightforward. Gas turbines have become one of the few technologies that can add firm generation at computing-campus timescales, and a floating deployment extends that market to places where onshore siting is difficult. For Amaco, the repositioning is the more interesting move: a gas-to-power scheme from 2024 has been recast with artificial intelligence demand as its anchor load, which is a considerably more bankable story than selling electrons into a constrained transmission system. The proposed surplus export near the Mariakani substation adds a second revenue line and, just as usefully, gives Kenyan authorities a reason to see the project as a contributor rather than an enclave.

    Three conditions have to hold. Imported liquefied natural gas must be priced competitively enough that the campus can quote power rates a regional customer will accept, since fuel, not hardware, dominates the operating cost of a gas-fired site. Regulatory approval must cover energy licensing, environmental consent and port anchoring rights simultaneously, and the announcement places the project firmly at the discussion stage with Kenyan authorities and financiers. And the stated future conversion to hydrogen should be treated as design optionality rather than a near-term plan, because it depends on a fuel supply chain that does not yet exist at this scale anywhere.

    Background

    Amaco Energy Group is a Greek multinational energy firm. Its Mombasa plan did not begin as a computing project: the blueprint evolved from an earlier liquefied natural gas-to-power transmission proposal registered in 2024, since recast around artificial intelligence demand as the anchor load. GE Vernova is the energy equipment and services company spun out of General Electric in 2024, whose gas turbine business has become central to a global race to add firm generation quickly enough for data centre construction schedules.

    Kenya has spent a decade positioning itself as East Africa’s digital gateway, and Mombasa is the physical hinge of that claim because the region’s international subsea fibre-optic cables come ashore there. The country is also building out the Dongo Kundu Special Economic Zone, a 3,000-acre industrial area near the port overseen in part through the Kenya Ports Authority. Competition for the regional hub position is active, with Ethiopia among the countries pursuing similar ambitions.

    Sources

    Source: Project HERCULES: Amaco and GE Vernova Partner for $1.5B Off-Grid AI Data Centre in Mombasa — Construction Review’s report on the proposed floating, gas-powered artificial intelligence campus at the Port of Mombasa, including the project factsheet and technical specifications cited above.

  • ABB and BCG Make the Case for DC Power in AI Data Centers

    ABB and BCG Make the Case for DC Power in AI Data Centers

    TL;DR · 30-second read

    The Short Version

    • The Swiss engineering group ABB and the consultancy Boston Consulting Group published a report saying the electricity inside data centers should increasingly flow the way it does in a battery, not the way it does in a wall socket.
    • Computers, solar panels and batteries all run on that kind of power already. Every time electricity is converted from one form to the other, a little is lost as heat, like a phone charger warming up.
    • Artificial intelligence machines use so much power that those small losses now add up to real money and real capacity.
    • ABB sells the equipment, so it has a stake in the argument.

    ABB said on September 9 that it has published The Strategic Case for Hybrid AC/DC Power: Shaping the Transition to the Next Electrical Architecture, a report developed with Boston Consulting Group. The Zurich-headquartered electrification and automation group argues that direct current, or DC, is moving from the periphery to the center of industrial, commercial and digital infrastructure, and that choices made by executives and policymakers over the next two to three years will decide who shapes the transition and who simply inherits it.

    The report singles out AI data centers as one of the most immediate drivers of DC adoption, finding that 800-volt DC distribution is emerging as the defining architecture for next-generation AI infrastructure. It frames the outcome not as a contest between DC and alternating current, or AC, but as a hybrid system: AC remains the backbone of transmission and regional distribution, while DC spreads inside facilities where the major sources and loads already run natively on it.

    Executive Summary

    The argument rests on a piece of electrical plumbing that most people never see. Grids deliver alternating current, in which the flow reverses direction many times a second, because that form is cheap to push over long distances and easy to step up and down in voltage with transformers. Almost everything now growing fastest, though, is natively direct current: solar photovoltaic panels, batteries, electric vehicles, industrial automation gear and the graphics processors inside AI servers. Each handoff between the two forms runs through a converter, and each converter throws off a percentage of the energy as heat.

    ABB and BCG contend that stripping conversion steps out of the chain does three things at once: it raises efficiency, it increases the usable compute capacity behind an existing grid connection, and it simplifies the integration of on-site generation and storage. That middle point is the commercially interesting one. In markets where new grid capacity is the scarce input, an efficiency gain converts directly into more revenue-generating hardware behind the same meter.

    The report also reframes the obstacle. Concerns about DC at scale, its safety and its economics reflect outdated assumptions rather than the capabilities of current equipment, ABB and BCG conclude. What actually slows deployment, they say, is fragmented standards and a shortage of engineers and technicians trained on DC systems. That is a claim about institutions and labor markets rather than about physics, and it points to a slower, less predictable fix.

    Why AI Racks Are Rewriting the Electrical Diagram

    Conventional data center power design has been remarkably stable for two decades. Utility alternating current arrives at a substation, passes through switchgear, an uninterruptible power supply and a power distribution unit, then reaches a server whose internal supply finally converts it to the low-voltage direct current the chips actually consume. Each stage was tolerable when a rack drew a few kilowatts. The report’s contention is that at AI power densities, conventional architectures are reaching practical limits, and the accumulated conversion overhead stops being a rounding error.

    The 800-volt DC figure is the specific technical claim worth understanding. Raising the distribution voltage lets a given amount of power travel through thinner conductors, because power is voltage multiplied by current and it is current that dictates copper cross-section and resistive heating. Delivering high-density racks at low voltage means either enormous busbars or unacceptable losses. Pushing distribution to 800 volts DC and converting close to the load is the same trick the automotive industry adopted for fast-charging electric vehicles, applied to a data hall.

    The practical consequence is that the change does not stop at the rack. It touches switchgear, protection, uninterruptible power supply topology, on-site storage and the interface with the substation. That is a supply-chain shift as much as an engineering one, and it favors vendors who already hold the protection and conversion portfolios.

    The Real Prize Is the Grid Connection

    Read commercially, the strongest line in the release is not about efficiency in the abstract. It is that DC distribution lets operators maximize compute capacity within constrained grid connections. In the markets where AI capacity is being built hardest, the binding constraint is rarely capital or land. It is the megawatts a utility will actually deliver, and when they will deliver them.

    Under that constraint, the economics change character. An efficiency improvement in a power-abundant market is a cost saving measured against the electricity bill. The same improvement behind a fixed, fully subscribed grid connection is a capacity unlock, measured against the revenue of the additional accelerators it lets you energize. Those two framings can differ by an order of magnitude in value, which explains why an argument about conversion losses is landing now rather than a decade ago.

    It also explains the report’s two-to-three-year window. Electrical architecture is set early in a data center’s design and is expensive to revisit. Operators committing to buildings that will be energized late this decade are choosing their topology now, which is precisely when a vendor wants the conversation to happen.

    Standards and Skills Are the Stated Bottleneck

    ABB and BCG place the constraint on adoption in two unglamorous places: fragmented standards and a shortage of DC-specific skills. Both are credible. Direct current has no natural zero crossing, the instant of zero flow that lets a conventional breaker extinguish an arc, which is why DC protection historically demanded different hardware and different design practice. ABB says it launched the industry’s first solid state circuit breaker in 2022, and holds more than 700 DC-related patents.

    That patent position is also why the call for harmonized standards deserves to be read with clear eyes. Common standards genuinely enlarge a market and reduce buyer risk, and buyers should want them. They also tend to reward whoever arrives with the deepest existing portfolio in the standardized technology. Nothing about that is improper, and ABB is not alone in it, but a customer evaluating this report should treat it as a well-argued position paper from an interested party rather than as neutral market research.

    The skills point may be the more stubborn of the two. Standards can be negotiated on a schedule. A workforce of electricians, commissioning engineers and facility staff fluent in high-voltage DC protection is built over years, and its scarcity would constrain even operators fully convinced by the argument.

    What Is Substantiated and What Is Asserted

    The quantified evidence in the announcement is drawn from marine propulsion, where ABB says a DC-based onboard vessel power system achieved fuel savings of up to 27 percent. That is a real, specific result, and it is not a data center result. Ships run isolated power systems with variable-speed loads and their own economics; the number establishes that ABB has long-running DC field experience, not that a data hall should expect a comparable gain.

    For the data center case itself, the announcement offers direction rather than magnitude. It says 800-volt DC is emerging as the defining architecture and that efficiency and usable capacity improve, without published percentages, capital-cost comparisons or named deployments in the summary. The conclusion that scale, safety and economic objections are now outdated is likewise stated as a finding, with the supporting analysis sitting in the full report published on ABB’s site.

    None of that undercuts the thesis, which aligns with where high-density computing and on-site storage are visibly heading. It does mean the announcement is best treated as a framing document. The buyer’s question is not whether DC distribution helps, but by how much, at what installed cost premium, and with which protection and service ecosystem behind it.

    Background

    The choice between alternating and direct current was settled commercially in the late nineteenth century, when transformers made it cheap to step alternating voltage up for long-distance transmission and back down for use. Direct current never disappeared, though. It survived in specialist niches such as long submarine transmission links, traction systems and shipboard power, and it returned quietly through the back door as solar panels, batteries and electronics proliferated, each of them a direct current device tethered to an alternating current grid through a converter.

    ABB sits on both sides of that history as one of the largest electrification and automation suppliers globally, selling switchgear, drives, protection and power distribution equipment into utilities, industry and data centers. The AI buildout has made its data center segment strategically prominent, because the constraint on new compute has shifted from chips and capital toward electrical delivery. This report, produced with Boston Consulting Group, is the company’s argument that the resulting architecture will look meaningfully different from the one the industry has built for the past twenty years.

    Sources

    Source: New ABB and BCG report highlights growing role of direct current technologies in future power systems — ABB’s September 9, 2026 announcement of a report developed with Boston Consulting Group on the shift toward hybrid AC/DC electrical architectures, including 800 VDC distribution for AI data centers. The full report is published at abb.com.

  • Hut 8 Reprices Mining Power as AI Data Center Collateral

    Hut 8 Reprices Mining Power as AI Data Center Collateral

    TL;DR · 30-second read

    The Short Version

    • Hut 8 used to make most of its money running rooms full of computers that create bitcoin. It is now renting that electricity and space to artificial intelligence companies instead.
    • It has signed a 15-year lease worth $9.8 billion for a site in south Texas. The chipmaker Nvidia is the tenant; the AI firm Anthropic is the end user.
    • Hut 8 has also lined up $7.5 billion in borrowing to build it. The shares jumped 19 percent in a day.
    • Why it matters: electricity, not computers, is now the scarce ingredient in artificial intelligence — and whoever already has it can charge a premium.

    Hut 8 Corp. (Nasdaq: HUT) is converting its bitcoin mining power base into leased artificial-intelligence capacity, anchored by a 15-year lease at its Beacon Point campus in Nueces County, Texas with a base term valued at US$9.8 billion. The site is to be leased by Nvidia under Nvidia’s very large computing agreement with Anthropic, and the campus is planned for 1,000 megawatts of total capacity. Dealroom reported that Hut 8 secured $7.5 billion in financing as the pivot accelerated; the lease terms and a 19.0% single-session share move were reported by Simply Wall St on September 7, 2026.

    The transaction reframes Hut 8 from a company whose revenue rose and fell with the bitcoin price into one selling long-duration, contracted access to energized land and grid capacity — the same asset, sold on a different contract.

    Executive Summary

    The headline number is the lease, not the loan. A 15-year base term worth US$9.8 billion averages roughly $650 million a year of contracted rent — the kind of cash-flow profile that lenders underwrite against, and the reason a company still reporting large losses can raise billions. The financing is the consequence; the lease is the cause.

    That is the structural shift worth noting. For most of the last decade, a bitcoin miner’s power portfolio was valued as an input cost — cheap electricity that made hashing marginally profitable. Under an AI lease, the same substation, the same interconnection agreement and the same acreage become collateral: a contracted, investment-grade-adjacent revenue stream that can be pledged. Miners are not being revalued because mining improved. They are being revalued because someone else needs their grid position.

    The corollary is less flattering to the miners. When roughly $7.5 billion of debt sits against a $9.8 billion base term, the counterparty’s credit and the lease’s covenants — not the operator’s ambitions — set the cost of capital and the order of claims on the cash. Hut 8 has traded volatility for visibility, and a measure of control with it.

    The Asset Was Never the Bitcoin. It Was the Interconnect.

    Hut 8 describes itself as an energy infrastructure platform integrating power, digital infrastructure and compute across the United States and Canada. That description has been true for years; what changed is the buyer. Bitcoin mining was an application that could tolerate cheap, interruptible, badly located power because the output was a fungible commodity produced anywhere. AI training and inference cannot — but it will pay handsomely for the one thing miners accumulated by accident: sites that are already connected to the grid at scale.

    Connecting a large load to a transmission network requires an interconnection agreement, and the queue for one — the utility’s study-and-approval process for new large customers — commonly runs for years in the busiest US markets. An operator holding an executed interconnection and an energized substation is holding a scarce option, and the Beacon Point lease is what that option is worth once a hyperscale tenant bids for it. This is why the repricing is spreading across the mining sector rather than being specific to one company: the balance sheet item being revalued is grid position, and several miners hold it.

    The Texas Gulf Coast location matters too. Nueces County sits in a market with abundant generation and industrial land, and Texas has become the default destination for very large new loads. It is also a region where firm power, water availability and summer grid stress are live operational questions rather than settled ones.

    A 15-Year Lease Is a Credit Instrument Before It Is a Building

    A $9.8 billion base term is not revenue in hand; it is the undiscounted sum of contracted rent over 15 years, before any renewal or escalation, and before Hut 8 spends the capital required to deliver the facility. Averaged evenly, that is on the order of $650 million a year. What makes the figure bankable is who is on the other side of it: the campus is to be leased by Nvidia under its computing agreement with Anthropic, which puts a large, well-capitalised technology company in the tenant position rather than a start-up burning venture funding.

    That structure is the point of the whole trade. A tenant of that quality converts a speculative construction project into something closer to a contracted infrastructure asset — the same reason toll roads and fibre routes get financed on the strength of their offtake agreements. It also concentrates risk. The lease reportedly covers a campus planned at 1,000 megawatts, but the disclosed figures do not establish how much of that 1,000 megawatts the $9.8 billion actually contracts. If the lease covers a portion, the remainder is a speculative build that must be leased separately; if it covers the whole campus, then the implied rent per unit of capacity is the number every competing developer will be modelling.

    There is a delivery risk that no counterparty credit can eliminate. Rent on a data center lease typically begins when capacity is delivered, not when the ink dries. Between now and then sit permits, transformers, switchgear, long-lead electrical equipment and construction labour — the same constraints slowing every other campus in the market.

    $7.5 Billion Against $9.8 Billion: Who Sets the Terms Now

    Put the two numbers side by side and the balance of power becomes visible. Roughly $7.5 billion of financing sits against $9.8 billion of base-term rent — meaning a large majority of the contracted cash flow over 15 years is, in effect, spoken for by capital providers before equity holders see anything. Whether that debt sits at the project level or at the parent, what it costs, when it matures, what covenants it carries and whether it comes with warrants or convertible features are the variables that determine how much of this deal’s economics Hut 8 actually keeps. None of that has been laid out publicly.

    This is the sharper version of the story than “miner pivots to AI.” In a mining business, the operator controlled the dials: turn machines off when power prices spike, sell coins when it suits, defer capital expenditure. Under a long-dated lease funded by multi-billion-dollar structured debt, the dials belong to the documents. Delivery dates are contractual. Debt service is fixed. The flexibility that made miners resilient through crypto drawdowns is the specific thing being exchanged for revenue visibility.

    The market’s uncertainty about how that trade lands is unusually wide. Third-party modelling published alongside the news projects Hut 8 reaching roughly $1.7 billion of revenue and $194.2 million of earnings by 2029 — which would require about 74.6% annual revenue growth and roughly a $793 million swing from a current trailing loss of $599.2 million — and derives a fair value of $161.78. More cautious estimates in the same set assume about $1.0 billion of 2029 revenue and land near $65. These are analyst and community model outputs rather than company guidance, and a spread of that magnitude on the same asset base is itself the finding: the disagreement is not about whether the lease exists, but about whether it gets built, financed and repeated.

    Nvidia Is Now in Everyone’s Capital Stack

    Nvidia’s role here is worth isolating, because it is no longer simply the company selling accelerators into these buildings. At Beacon Point it appears as the leaseholder, standing between the landlord and the ultimate AI workload. That is a chip vendor underwriting real estate risk to make sure its own hardware has somewhere to run.

    The breadth of that posture is visible elsewhere in Nvidia’s disclosures. In an 8-K filed September 3, 2026, Nvidia reported that it had entered a definitive agreement on September 2 to acquire Hugging Face, the open-model platform, for a purchase price of approximately $11.9 billion plus an equity-based retention program of up to roughly $1.0 billion, with closing expected in the first half of 2027. The filing also flags a specific vulnerability: government restrictions on open-source models, including models originating in China, could materially affect the platform and Nvidia’s results. A company deploying capital simultaneously into model distribution and into leased data center capacity is building demand for its silicon at both ends of the stack.

    For infrastructure buyers and investors, that has a practical implication. The creditworthiness underpinning a growing share of new US data center capacity increasingly traces back to a small number of very large technology balance sheets. Concentration of that kind is what makes projects financeable today, and it is also the correlation that would matter most if AI capital expenditure plans were ever revised downward.

    Background

    Hut 8 Corp. trades on Nasdaq as HUT and describes itself as an energy infrastructure platform that integrates power, digital infrastructure and compute at scale for energy-intensive uses across the United States and Canada. Historically its revenue came from bitcoin mining — running specialised computers that earn newly issued bitcoin — through its self-mining fleet and American Bitcoin operations. That business is transaction-driven: revenue moves with the bitcoin price, mining difficulty and power costs, which makes forecasting cash flow difficult.

    The wider shift began when AI compute demand collided with a shortage of grid capacity. Miners had spent years assembling sites with executed interconnection agreements and substations already in place, precisely the constraint slowing new AI data centers. Since 2024 several miners have moved to lease that capacity to AI and cloud tenants under multi-year contracts, trading commodity exposure for contracted rent. Hut 8’s Beacon Point lease in Nueces County, Texas is one of the largest examples of that conversion to date.

    Sources

    Source: Why Hut 8 (HUT) Is Up 19.0% After Pivoting Bitcoin Mining Power Into AI Data Centers — Simply Wall St, September 7, 2026, on the Beacon Point lease terms and Hut 8’s shift from mining to contracted AI infrastructure. See also Hut 8 lands $7.5B financing as AI pivot accelerates with Anthropic-linked deal, Dealroom.

    Primary sources: NVIDIA Corporation, Form 8-K filed September 3, 2026 (SEC EDGAR) — disclosing the September 2, 2026 definitive agreement to acquire Hugging Face, Inc. for approximately $11.9 billion plus an equity-based retention program of up to approximately $1.0 billion, with closing expected in the first half of 2027, and setting out related regulatory risks to open-source model distribution.

  • Google’s $3.2B TeraWulf Backstop: Who Really Funds AI Data Centers

    Google’s $3.2B TeraWulf Backstop: Who Really Funds AI Data Centers

    TL;DR · 30-second read

    The Short Version

    Google has promised to cover $3.2 billion of rent payments on a data center it will not own and will not occupy. The site is a retired coal plant near Buffalo, New York, run by a company that used to mine bitcoin.

    Without that promise, lenders would not fund the buildings. With it, construction can proceed. In return, Google holds rights to roughly 14 percent of the company.

    The borrowing stays off Google’s own books. So, largely, would the loss if the project failed.

    Startup Fortune reported that Google has backstopped $3.2 billion in lease obligations tied to TeraWulf’s AI data center campus at Lake Mariner in western New York, adding $1.4 billion in new support on top of earlier commitments. In exchange, warrants held by Google now represent roughly 14% of TeraWulf, up from about 8% months earlier.

    Google is not the tenant. Fluidstack, an AI cloud provider, holds two 10-year leases at the site covering more than 200 megawatts of critical IT load — the power available to servers themselves, excluding cooling and building overhead — expanded by a 160-megawatt building known as CB-5 to a contracted total of 360 megawatts. Those leases have been valued at $3.7 billion over the initial term and up to $8.7 billion if Fluidstack exercises its extension options. TeraWulf, a Maryland-based bitcoin miner, has spent roughly two years converting the former coal plant site outside Buffalo into an AI campus.

    Executive Summary

    The headline number is a guarantee, not a purchase. Google is not buying a data center, not leasing one, and not putting one on its books. It is standing behind someone else’s rent so that a third party’s lenders will fund construction. That single move is the reason CB-5 and the buildings behind it become financeable at all.

    The economics are straightforward once you strip out the jargon. TeraWulf cannot borrow billions against its own credit history; a former crypto miner’s balance sheet does not support project debt at that scale. Fluidstack’s ten-year lease payments can support it — but only if a lender believes those payments will arrive for a decade. Google’s backstop supplies that belief. TeraWulf gets a construction loan, Fluidstack gets capacity, and Google gets warrants, an expanding equity position, and access to compute without a capital expenditure line.

    This matters beyond one campus in New York. It is the same logic operating at far larger scale elsewhere in the sector, and it means the binding constraint on AI infrastructure is increasingly not land, turbines or chips. It is whose credit sits behind the lease.

    Credit Is the Scarce Input, Not Land

    The industry talks about AI capacity in megawatts, but megawatts are a downstream output. A developer with a permitted site, an interconnection position and a signed tenant still cannot pour concrete without several billion dollars of debt, and debt is priced against the weakest promise in the chain. TeraWulf’s site work at Lake Mariner is real; its balance sheet history is a bitcoin miner’s. Those two facts pull in opposite directions when a credit committee sits down.

    A backstop resolves the contradiction. In plain terms, Google has agreed to make the lease payments if the arrangement falls apart, which allows lenders to underwrite the loan against Google’s ability to pay rather than TeraWulf’s. The developer keeps the project, the operating risk and the upside. The hyperscaler takes on a contingent obligation — one that shows up as a commitment rather than as borrowed money — plus warrants, which are contracts giving the right to buy shares later at a fixed price. Those warrants moving Google from roughly 8% to roughly 14% of TeraWulf is the price of the credit enhancement, paid in equity instead of cash.

    For anyone tracking who builds AI capacity over the next three years, this reframes the competitive question. The winners are not necessarily the developers with the best sites. They are the developers who can attach an investment-grade counterparty to their lease stack.

    The Lease Is the Collateral

    Fluidstack’s 360 megawatts of contracted capacity at Lake Mariner is what the debt is actually secured against. A ten-year lease valued at $3.7 billion over its initial term, rising toward $8.7 billion with extensions, is a cash flow stream — and project finance exists to turn predictable cash flow streams into buildings. The tenant’s obligations, not the landlord’s assets, do the heavy lifting.

    That structure has an obvious sensitivity. The loan is sized to a tenant. If the tenant disappears, the campus is worth whatever the next tenant will pay, which in a softer market is not the same number. Google’s guarantee is the shock absorber, and its existence is a fair signal of how lenders assess a decade-long AI cloud commitment on its own: not sufficient without support. That is an observation about credit markets, not a judgment on Fluidstack’s business.

    It also explains why converted industrial sites keep winning. A retired coal plant near Buffalo already has transmission, substation land, water and a grid position that a greenfield site would spend years acquiring. Crypto miners bought many of those sites cheaply in a different cycle, which is why several of them are now landlords to AI tenants rather than operators of hash rate.

    Off the Balance Sheet, Not Out of the System

    TeraWulf’s structure is modest against the largest comparable. Meta’s Hyperion campus in Richland Parish, Louisiana — around 4 million square feet, expected to draw up to 5 gigawatts on completion in 2029 — was financed through a joint venture with funds managed by Blue Owl Capital, with Blue Owl holding 80% and Meta 20%. Morgan Stanley arranged $27 billion of debt and $2.5 billion of equity through a special purpose vehicle, a standalone company created to hold a single project’s assets and borrowings, with Pimco anchoring a bond sale rated A+ by S&P and maturing in 2049. Because Meta does not control the entity holding the debt, the debt does not appear on Meta’s balance sheet; the long lease is recorded as an operating cost instead.

    The aggregate is no longer marginal. A study cited by Forbes found the five largest US hyperscalers have moved more than $1.6 trillion in AI infrastructure debt into footnotes rather than balance sheets, and Moody’s separately estimates roughly $662 billion of hyperscaler lease commitments now sit outside reported debt — with more than $120 billion of that shift occurring in under two years. None of this is improper; lease and consolidation accounting is well-established, and the commitments are disclosed. But an investor reading only reported debt is reading a partial picture of sector leverage.

    Where the First Loss Actually Lands

    The structure relocates risk rather than eliminating it. At Lake Mariner, if the tenant walked, TeraWulf would hold a partially built campus with debt sized to a lease that no longer exists, and Google’s backstop would be the barrier between that debt and default. In the Louisiana model, private credit investors and bondholders hold the majority position and would absorb first losses, while the hyperscaler’s exposure is capped by a minority equity stake and its continuing rent obligation.

    That asymmetry is a feature, not an accident, and both sides enter it knowingly. Private credit funds are paid a spread precisely to hold decade-length demand risk that public equity investors would punish on a hyperscaler’s own books. The judgment being made across the sector is that AI compute demand holds up long enough to service twenty-plus-year paper. If it does, every party in the stack is paid as modeled. If it does not, the losses surface first in private credit vehicles and among warrant-holding partners, where they are less visible and slower to be marked than a write-down at a listed technology company would be.

    For enterprise buyers of AI capacity, there is a practical takeaway. When you sign with a neocloud or a converted-site operator, it is worth understanding who stands behind the building — because the entity on your contract may not be the entity whose credit made the building possible.

    Background

    TeraWulf is a Maryland-based company that built its business mining bitcoin — running warehouses of specialized computers that consume large amounts of electricity. When mining economics tightened, several operators discovered that their most valuable asset was not the hardware but the sites: power-rich industrial land with grid connections already in place. Over roughly two years, TeraWulf has been converting its Lake Mariner property, a retired coal plant site in western New York, into an AI data center campus leased to third parties.

    The financing model around it is newer than the buildout itself. AI campuses now routinely cost more than a mid-sized developer can borrow, so the industry has converged on project finance structures: a long lease from a creditworthy tenant, debt raised against that lease, and a special purpose vehicle or credit guarantee that keeps the borrowing separate from the hyperscaler’s own balance sheet. Meta’s Hyperion campus in Louisiana, financed through a joint venture majority-owned by Blue Owl Capital funds with bonds anchored by Pimco and rated A+ by S&P, is the largest example to date. Google’s TeraWulf backstop is a smaller variant of the same idea.

    Sources

    Source: Google’s $3.2 Billion TeraWulf Backstop Shows How AI Data Centers Get Built — reporting on Google’s lease guarantee for TeraWulf’s Lake Mariner campus, Fluidstack’s 360 megawatts of contracted capacity, and the off-balance-sheet financing structures behind large AI data center projects.

  • NRG Rallies as AI Power Pipeline Reprices Merchant Generation

    NRG Rallies as AI Power Pipeline Reprices Merchant Generation

    TL;DR · 30-second read

    The Short Version

    Artificial intelligence needs enormous amounts of electricity, and there is not enough to go around. That is making the companies that actually generate power unusually valuable.

    Shares of NRG Energy, a large American electricity producer and retailer, climbed sharply after investors focused on its plan to build about 4 gigawatts of new supply for data centers — roughly what four big nuclear reactors put out. One Texas project, sized at 1.2 gigawatts, is earmarked for a major cloud computing company that has not been named.

    The stock is still down about 29 percent this year, so this is a bet, not a victory lap.

    NRG Energy shares rose sharply in trading after bouncing off a fresh 52-week low, with TipRanks reporting that the move was driven by renewed investor attention to a perceived valuation disconnect and to NRG’s 4-gigawatt pipeline of power projects aimed at data centers — including a 1.2 GW build in Texas serving a major cloud operator. A gigawatt is a billion watts; 1.2 GW is the scale of a large conventional power station.

    The rally coincided with broad strength across power and data-center infrastructure names as markets reassessed the long-term revenue potential of supplying electricity to AI-heavy cloud workloads. No specific analyst price-target changes accompanied the move. NRG’s market capitalisation stood at $23.35 billion, with year-to-date price performance of -29.08% and average trading volume of roughly 2.77 million shares.

    Executive Summary

    What moved was not an operating result but a valuation frame. NRG is an independent power producer and competitive retailer — a company that sells electricity at market prices rather than at rates set by a regulator. For most of the past decade, that business was valued like a mature commodity: cash-generative, cyclical, and structurally exposed to flat demand. The AI data-center buildout has broken that assumption, and the market is now testing whether generation capacity should be priced as a scarce asset instead.

    The specific hook is NRG’s 4 GW development pipeline directed at data centers, anchored by a 1.2 GW Texas project tied to a large cloud customer. That combination — a named market, a named scale, and a described contracted anchor — is what separates a generator with an AI story from a generator with an AI slide. It gives investors something closer to a line of sight on high-margin, long-duration revenue than a general appeal to rising load growth.

    The counterweight is the balance sheet. NRG carries a heavy debt load alongside recent revenue pressure and uneven cash flows, and it has committed to returning capital through buybacks and dividends. Those three claims on cash — deleveraging, development capital expenditure, and shareholder returns — compete directly. A stock still down more than 29% year-to-date reflects that unresolved tension as much as it reflects any AI optimism.

    The Scarce Input Is No Longer the Chip

    The bottleneck in AI infrastructure has migrated. Two years ago it was accelerators; today it is increasingly firm, deliverable electricity at a specific location on a specific date. Servers can be shipped. Interconnection — the physical and contractual process of attaching a large load to the grid — cannot be expedited by writing a bigger cheque. Where a hyperscaler (one of the giant cloud operators that build data centres at national scale) can secure power, it can build; where it cannot, capital sits idle.

    That inversion is what re-rates a merchant generator. A company whose fleet was previously valued on forward power curves and capacity payments now holds an option on the fastest-growing industrial load in a generation. NRG’s move off a 52-week low on pipeline news rather than earnings news is the market pricing that optionality, not the underlying quarter. The distinction matters: optionality is real value, but it is value contingent on execution, and it can be repriced downward just as quickly.

    Texas Is the Point, Not an Incidental Detail

    The 1.2 GW project sits in Texas, and geography is doing analytical work here. Texas operates its own grid under ERCOT, largely outside federal interconnection jurisdiction, with an energy-only market design and a historically faster path from project conception to energisation than the multi-year queues that characterise other US regions. For a cloud operator with a delivery deadline, speed to power is worth a premium over marginal price.

    NRG’s incumbency in that market is the differentiator. It already has generation assets, retail customer relationships, and operating experience in the region — which means the data-centre opportunity is an extension of an existing footprint rather than a greenfield bet. That lowers execution risk relative to a pure-play developer, though it does not eliminate the shared industry constraints: turbine manufacturing lead times, gas supply and pipeline capacity, transmission upgrades, and local permitting all sit outside any single company’s control.

    A Pipeline Is a Menu, Not a Contract

    The most important discipline for readers is distinguishing between the two numbers in this story. The 4 GW figure is a development pipeline — projects at varying stages of maturity, some of which will be built, some deferred, some abandoned. The 1.2 GW Texas build is described as anchored by a major cloud customer, which places it in a different category. Conflating the two produces a valuation that assumes 4 GW of contracted, revenue-generating capacity, which is not what a pipeline means.

    This is where the balance sheet re-enters. Multi-gigawatt generation development is capital-intensive, front-loaded, and slow to convert to cash. A company with elevated leverage and uneven cash flows faces a genuine allocation problem: money spent on turbines is money not spent on debt reduction or buybacks. Investors rewarding NRG for growth and rewarding it for capital returns are, at some point, asking for two different companies. How management sequences those priorities — and whether it funds development on-balance-sheet, through project finance, or through partnership structures — will matter more to the outcome than the headline gigawatt count.

    Who Wins, Who Pays

    The clear beneficiaries of this repricing are owners of dispatchable generation — plants that can produce on demand rather than when the wind blows or the sun shines — in markets with load growth and workable interconnection timelines. Their bargaining position with data-centre developers has improved materially, and the contract structures now being negotiated increasingly resemble long-dated industrial offtake agreements rather than spot commodity sales.

    The less-discussed side is who bears the cost. Large new loads on a constrained system put upward pressure on prices for everyone else, and the allocation of transmission and generation build costs between data-centre customers and general ratepayers is an unresolved regulatory question in several states. For enterprise buyers of colocation and cloud capacity, the practical implication is more immediate: power availability is becoming a primary site-selection constraint and a growing share of total cost, and contracts that were once priced casually on a per-kilowatt basis now deserve close reading on escalation, curtailment, and term.

    Background

    NRG Energy is a Houston-headquartered independent power producer and competitive retail electricity provider, operating generation assets and selling power to residential and commercial customers in deregulated US markets, with a substantial presence in Texas. Unlike a regulated utility, which earns a set return on approved investment, a merchant generator’s economics turn on wholesale power prices, plant availability, and the terms it can negotiate with large customers — a model that rewards scarcity and punishes oversupply.

    For much of the past decade, US electricity demand was broadly flat, and the sector was valued accordingly: as a source of cash returns rather than growth. The data-center buildout has reversed that assumption within roughly two years. Grid operators across several regions are now processing interconnection requests measured in gigawatts from single customers, and the constraint on AI capacity expansion has shifted from computing hardware toward power delivery. That shift is what puts companies like NRG — which already own dispatchable generation in load-growth markets — at the centre of an investment story they were not previously part of.

    Sources

    Source: NRG Energy Surges As AI Power Deal Ignites Rally — TipRanks report on NRG Energy’s share move off a 52-week low, its 4 GW data-center power pipeline, and the 1.2 GW Texas project serving a major cloud operator.

  • Marvell’s 5 Million Photonic Chips: Optics Goes Volume

    Marvell’s 5 Million Photonic Chips: Optics Goes Volume

    TL;DR · 30-second read

    The Short Version

    Marvell makes chips that move data using light instead of electricity. The company says it has shipped more than five million of them, built with a manufacturing partner, Tower Semiconductor.

    That matters because the computers behind artificial intelligence are now so large they no longer fit in one room. Moving data between them fast enough has become a bottleneck, and light is the fix.

    Marvell’s most recent quarter set a company revenue record: $2.739 billion, up 37 percent from a year earlier.

    Stocktwits reported on June 18, 2026 that Marvell Technology (NASDAQ: MRVL) said it had shipped more than 5 million coherent photonic integrated circuits to customers worldwide, produced in partnership with specialty foundry Tower Semiconductor, and that Marvell shares rose more than 5% in premarket trading; the same report said the announcement came days before Marvell’s June 22, 2026 entry into the S&P 500, replacing Pool Corporation, and cited Nvidia chief executive Jensen Huang’s earlier description of Marvell as the “next trillion-dollar company.”

    The company framed the shipment as a response to component scarcity in the data center market. “This milestone demonstrates the strength of our collaboration,” said Dr. Radha Nagarajan, Marvell’s Senior Vice President and Chief Technology Officer, Optical Engineering, adding that the two companies would “continue working… to advance next-generation coherent technologies for scale across data center architectures.” Two months later, in results reported to the SEC on August 27, Marvell posted record quarterly revenue of $2.739 billion, up 37% year over year, with Data Center revenue growth accelerating to 46%.

    Executive Summary

    A coherent photonic integrated circuit is a chip that sends and receives information as light rather than as electrical current, encoding data in the phase and amplitude of a laser beam. The technique was developed for long-haul telecom, where signals travel hundreds of kilometres. It has migrated into the data center because AI training and inference clusters have outgrown single buildings, and copper cabling loses too much signal over the distances involved.

    The significance of a five-million-unit figure is not technical but industrial. Performance claims are made with a single working part; volume claims require yield, tested wafer capacity, packaging, and a foundry relationship that holds under load. Marvell attaching a unit count to a photonics announcement, rather than a data rate or a power-per-bit number, is a statement that this product line has left the qualification stage.

    Marvell’s own financial disclosures give that claim a supportive backdrop without confirming its specifics. In the press release filed as an exhibit to its August 27 Form 8-K, Marvell reported record second-quarter fiscal 2027 revenue of $2.739 billion and guided the current quarter to $3.150 billion, plus or minus 5%. Chairman and CEO Matt Murphy pointed to “strong demand in Connectivity and a significant acceleration in our Custom business.” What the release does not do is size the photonics business itself.

    A Unit Count Is a Supply Claim, Not a Speed Claim

    Optical interconnect vendors usually announce firsts: the first 800-gigabit module, the first demonstration at a new modulation scheme. Those announcements tell buyers what is possible. A shipment total tells them something different and, for anyone building capacity, more useful — that parts exist in quantity, that they have been through a customer’s qualification process, and that a supplier is willing to be measured against a number it will have to keep growing.

    That distinction is the heart of the story. For most of the current AI buildout, optics has been discussed as a constraint: lasers, modulators and the specialty processes that produce them have been among the harder items to source, alongside advanced packaging and memory. A supplier publicly counting units in the millions is signalling that at least one part of that constraint is loosening. It also changes the competitive question from who has the best device to who can produce devices reliably at a price that survives volume, which is the question commodities are settled on.

    Buyers should hold the enthusiasm in proportion. The milestone is cumulative and undated — five million units shipped over what period, across how many product generations, and to how many customers are all open. A number that large spread thinly across several years and several generations describes a mature product family; the same number concentrated in recent quarters would describe a genuine volume inflection. Marvell has not said which it is.

    The Foundry Half Deserves Equal Billing

    Silicon photonics is a specialty manufacturing problem before it is a design problem. Building waveguides, modulators and detectors alongside conventional transistors requires process steps that leading-edge logic foundries generally do not offer, which is why the work concentrates in a small number of specialty fabs. Naming Tower Semiconductor in the announcement is therefore not courtesy — it identifies where the physical capacity actually sits.

    This is a real strategic asset and a real dependency at the same time. A designer with a proven, high-yielding photonics platform at a partner fab has a lead that competitors cannot close by hiring engineers alone; process qualification takes years. But the same relationship concentrates risk. If demand for coherent optics keeps compounding at the rate Marvell’s Data Center commentary implies, the binding constraint moves upstream to wafer starts, laser supply and packaging throughput — none of which Marvell controls outright.

    Neither company has described the commercial architecture underneath the partnership: whether capacity is contractually reserved, whether the arrangement is exclusive in either direction, or what expansion is planned. Those terms determine how much of a five-million-unit milestone is a durable moat and how much is a snapshot of a good year.

    What the August Filings Confirm — and What They Leave Out

    Marvell’s fiscal calendar ends in late January, so the quarter reported on August 27 (fiscal Q2 2027) covers the three months to August 1, 2026 — the period immediately following the photonics announcement. The results were strong on their face: revenue of $2.739 billion came in $39.0 million above the midpoint of prior guidance, GAAP net income was $308.0 million ($0.33 per diluted share), non-GAAP net income was $865.9 million ($0.94), and operating cash flow was $605.5 million. The company raised its revenue outlook for both fiscal 2027 and fiscal 2028.

    The gap between $0.33 GAAP and $0.94 non-GAAP earnings is worth explaining rather than glossing over. GAAP figures follow standard accounting rules; Marvell’s non-GAAP measures exclude stock-based compensation, amortisation of intangible assets acquired in past deals, restructuring and similar items. The company itself notes in the release that acquired intangibles “contributed to Marvell’s revenues” and that their amortisation will recur. Both numbers are informative; neither is the whole picture, and the spread between them is unusually wide.

    Crucially, the results tell you nothing directly about photonics. Marvell’s quarterly filings group revenue into Data Center and Communications and Other, and the 10-Q filed August 28 for the quarter ended August 1 also flags geographic revenue concentration, including China — a disclosure that matters for any company whose growth depends on globally distributed AI buildouts. Optical connectivity, custom AI silicon, storage and networking all sit inside the Data Center line. When management says growth accelerated to 46% there and that Custom will accelerate significantly in the second half, it is describing a portfolio, not a product. Anyone using the five-million-unit figure to infer photonics revenue is doing arithmetic the company has not enabled.

    Index Inclusion and Endorsements Are Not Operating Results

    Marvell’s addition to the S&P 500 generates mechanical demand for its shares from index funds, and it reflects that the company already met the index’s size, liquidity and profitability tests. It is a consequence of prior performance, not a cause of future performance, and it says nothing about coherent optics volumes, gross margin or customer concentration. The same holds for a competitor chief executive’s public praise: a warm comment from Nvidia’s Jensen Huang is a read on sentiment within the industry, not a purchase order, a forecast, or a commitment by anyone.

    The more meaningful signal in the same window is the one attached to a filing. Raising the revenue outlook for two consecutive fiscal years in an SEC-furnished release carries legal weight that a milestone press release does not, because guidance is measured against actuals ninety days later. Marvell’s Q3 guide of $3.150 billion, with non-GAAP gross margin of 57.5% to 58.5% — slightly below the 58.9% just delivered — is the kind of detail that rewards attention: it implies a mix shift toward products that carry lower margin as they scale, which is exactly what commoditisation looks like in the numbers.

    For infrastructure buyers, that trade is generally welcome. Falling unit economics on optical interconnect is how link budgets across a campus stop being a scarce resource. For investors, it is the tension to watch across Marvell’s October 6, 2026 Investor Day: revenue growing faster while the margin profile of the growth is not identical to the margin profile of the past.

    Background

    Marvell Technology is a Santa Clara-based supplier of data infrastructure semiconductors — the silicon behind networking, storage, custom accelerators and optical connectivity rather than the general-purpose processors most consumers recognise. Its optical position was substantially built through the 2021 acquisition of Inphi, which brought the digital signal processing expertise that turns raw optical signals into usable data links. The company’s fiscal year ends in late January, so its fiscal 2027 second quarter closed on August 1, 2026.

    Tower Semiconductor is a specialty foundry that manufactures analog, radio-frequency and silicon photonics devices on processes distinct from leading-edge digital logic. Silicon photonics has spent two decades moving from telecom long-haul networks toward shorter reaches, and the AI buildout accelerated that migration sharply: as training clusters spread across multiple halls and campuses, the interconnect between them became a design constraint on how large a single computing fabric can be. That is the market both companies are addressing.

    Sources

    Source: MRVL Stock Rises Premarket: Marvell Ships Over 5M Photonic Chips Ahead Of S&P 500 Entry — report on Marvell’s coherent photonic integrated circuit shipment milestone with Tower Semiconductor and the company’s S&P 500 addition.

    Primary sources: Marvell Technology, Form 10-Q for the quarter ended August 1, 2026, filed August 28, 2026; Marvell Technology, Form 8-K dated August 27, 2026; Exhibit 99.1 — “Marvell Technology, Inc. Reports Second Quarter of Fiscal Year 2027 Financial Results,” August 27, 2026.

  • Flex Bets $4.4B That Power Conversion Is AI’s Bottleneck

    Flex Bets $4.4B That Power Conversion Is AI’s Bottleneck

    TL;DR · 30-second read

    The Short Version

    Flex, a company that builds and assembles products for other big brands, is paying $4.4 billion in cash for EPC Power, a California maker of equipment that converts electricity into the exact form computers can use.

    Why care? The machines that run artificial intelligence swallow enormous amounts of electricity, and the unglamorous boxes that deliver it cleanly have become the hard part to get. Flex is betting the shortage is in the electrical plumbing, not the chips.

    The deal is expected to be completed by the end of 2026.

    Flex Ltd. (NASDAQ: FLEX) has agreed to acquire EPC Power Corp., a Poway, California designer and manufacturer of power conversion systems, for $4.4 billion in cash. The parties signed a Stock Purchase Agreement on September 3, 2026, under which Flex subsidiary ACS Acquisitions, Inc. buys all equity in EPC Power from Charge Parent, LLC, the holding vehicle for controlling shareholders Goldman Sachs Alternatives and Cleanhill Partners. Flex disclosed the agreement in an 8-K filed with the SEC on September 4, and is a party to the contract solely to guarantee its subsidiary’s obligations.

    Flex’s announcement puts EPC Power’s calendar 2026 revenue at approximately $800 million, with roughly 40% organic growth expected in 2027 and EBITDA margin expanding by double-digit percentage points to approximately 30% that year. EPC Power says it has more than 15 GW deployed across 62 countries, and that annual U.S. manufacturing capacity will surpass 30 GW in 2027. The transaction is expected to close in the fourth quarter of 2026, subject to Hart-Scott-Rodino antitrust clearance and other customary conditions, after which EPC Power joins Flex’s Cloud and Power Infrastructure segment.

    Executive Summary

    This is a contract manufacturer paying a technology-company price. Flex has built its business designing and building hardware for other brands; EPC Power builds the equipment that sits between the grid and the server rack — rectifiers, DC-DC converters and grid-forming inverters, with solid-state transformers described in Flex’s announcement as planned development. Flex’s stated thesis, in CEO Revathi Advaithi’s words, is that “a generational shift in power architecture is underway,” and that owning the conversion layer lets Flex sell data center infrastructure as an integrated system rather than as parts.

    The financial architecture matters as much as the strategic one. The 8-K describes a “locked box” structure in which EPC Power’s enterprise value was fixed as of June 30, 2026, with contractual protections against value leakage until closing — a mechanism common in private equity exits that gives the seller price certainty. Flex simultaneously signed a commitment letter with Citigroup Global Markets, Bank of America and BofA Securities for a senior unsecured 364-day bridge facility of up to $4.4 billion, a backstop it expects to replace with a permanent mix of debt and equity.

    The timing is the sharpest detail. Flex states in both the 8-K and its announcement that EPC Power will land inside the Cloud and Power Infrastructure business, which Flex plans to separate into an independent public company in the first calendar quarter of 2027 — weeks after the acquisition is expected to close. Flex is, in effect, buying a $4.4 billion asset and then handing it to shareholders in a new vehicle.

    The Bottleneck Moved Downstream of the Chip

    For three years the constraint on AI buildouts was accelerators. The constraint discussed in this transaction is different: getting utility power into a building fast, and then converting it into something a rack of GPUs can drink without destabilizing the neighborhood grid. AI training and inference clusters draw power in large, fast swings — thousands of processors ramping together — which is closer to an industrial arc furnace than to the steady hum of a traditional colocation hall. EPC Power’s marketing centers on exactly this problem, and Flex is paying for the claim that solving it is a durable business rather than a moment.

    The technical hook is the shift toward 800-volt DC distribution inside the data center. Conventional facilities step voltage down repeatedly and convert between alternating and direct current several times before power reaches a chip, losing a slice at each stage; higher-voltage DC architectures cut the number of conversions and the copper needed to carry the current. Flex’s announcement says EPC Power’s platform is engineered for these architectures with capabilities across rectifiers and DC-DC conversion. The grid-forming piece is complementary: most inverters follow the grid, matching a frequency someone else sets, while grid-forming units can establish voltage and frequency themselves, which is what lets a site run on batteries through a disturbance or energize before the utility interconnection is fully mature.

    What $4.4 Billion Buys, Priced Against Flex’s Own Forecast

    Work the disclosed numbers and the price becomes legible. Against approximately $800 million of calendar 2026 revenue, $4.4 billion is roughly 5.5 times sales. Apply the stated 40% organic growth and 2027 revenue lands near $1.1 billion; at the stated approximately 30% EBITDA margin, that implies something in the neighborhood of $330 million of 2027 EBITDA, or about 13 times forward earnings before interest, taxes, depreciation and amortization. Those multiples are derived arithmetic from Flex’s own projections, not company-stated figures, and they hold only if both projections land.

    The margin guidance contains its own admission. If EBITDA margin expands by “double-digit percentage points” to reach approximately 30%, the 2026 margin is by definition 20% or below. Flex is paying today for a profitability structure that does not yet exist, on the expectation that mix shift toward data center product and scale in U.S. manufacturing delivers it. That is a defensible bet in a supply-constrained market — pricing power tends to follow scarcity — but it is a bet, and it is the single assumption most worth revisiting when the combined business reports actual results.

    Buying an Asset You Plan to Give Away

    Flex has told investors it will separate its Cloud and Power Infrastructure portfolio into an independent public company in the first quarter of 2027. EPC Power is being bought into that segment in the fourth quarter of 2026. The sequencing is deliberate: it makes the entity that goes public materially larger, faster-growing and more differentiated than it would otherwise be, and it moves the story of that company from “builds infrastructure hardware to order” toward “owns the power conversion layer.”

    It also raises a capital-structure question that the filings flag rather than answer. Flex says it is evaluating financing alternatives and expects a combination of debt and equity, with committed financing from Citi and Bank of America standing behind it. Which balance sheet ultimately carries that debt — Flex’s or the new company’s — shapes both entities. Flex’s own forward-looking statement language lists the ability to achieve anticipated capital structures, credit ratings and financing in connection with the spin-off among its risks, which is an unusually direct acknowledgment that the two transactions are financially entangled. There is also a calendar risk: the purchase agreement’s outside date is December 31, 2026, subject to two automatic three-month extensions, meaning a slow antitrust review could push closing past the intended separation date.

    Domestic Manufacturing as the Second Product

    EPC Power’s pitch is not only technical. Flex describes U.S.-based engineering and manufacturing, and EPC Power’s own announcement says it expanded its domestic manufacturing footprint nearly tenfold under Goldman Sachs Alternatives and Cleanhill Partners, which first invested in 2021. In a market where electrical equipment lead times are a scheduling input and where tariff exposure and procurement rules increasingly favor domestic content, capacity located in the United States is a commercial asset in its own right, distinct from the merits of the hardware.

    For buyers, the practical consequence is consolidation of the supply chain. A hyperscaler or colocation developer that previously bought conversion equipment, cooling and IT integration from separate vendors can, in principle, buy a larger integrated block from one supplier — faster to specify, but a heavier dependency. For other electronics manufacturing services firms, the transaction sets a marker: this asset was priced as differentiated intellectual property, not as assembly capacity, and the pool of independent power conversion specialists available to buy is not deep. For established power-equipment vendors, a well-capitalized new competitor now sits inside a manufacturing organization with a footprint spanning 30 countries.

    Background

    Flex Ltd. is one of the largest electronics manufacturing partners in the world, designing and building products for other brands across a footprint spanning 30 countries. In recent years it has pushed beyond assembly into data center power, cooling and IT infrastructure, organizing that work in a Cloud and Power Infrastructure segment that it has told investors it intends to separate into a standalone public company in the first quarter of 2027. Acquiring a power conversion specialist into that segment shortly before separation changes what the new company will look like on its first day of trading.

    EPC Power was founded in 2010 in California and spent most of its life supplying inverters to the utility-scale battery storage market. Cleanhill Partners invested in 2021 on the thesis that power conversion would become critical as renewables, grid modernization and digital infrastructure converged; Goldman Sachs Alternatives later joined as a controlling shareholder. AI data centers then arrived as a second, larger market for the same core competence, because a building drawing power in large, rapid swings needs the same conversion and grid-stabilization hardware that a battery plant does — which is the convergence Flex is paying for.

    Sources

    Source: EPC Power Announces Sale to Flex for $4.4 Billion — the seller’s September 3, 2026 announcement of its definitive agreement to be acquired by Flex.

    Primary sources: Flex Ltd. Form 8-K filed September 4, 2026, describing the Stock Purchase Agreement, the locked box mechanism, the closing conditions and outside date, and the $4.4 billion bridge facility commitment letter; Exhibit 99.1 — Flex to Acquire EPC Power, Adding Leading Power Conversion Capabilities for AI Data Centers and Grid Applications, containing the revenue, growth and EBITDA margin expectations and the deployed and manufacturing capacity figures; and Exhibit 99.2 — Flex investor materials, setting out the forward-looking statement risks tied to the transaction and the planned spin-off.