Shares in two companies building small, factory-made nuclear reactors jumped on Thursday. Oklo rose about 13% and NuScale about 10%.
The trigger was a vote in the US House of Representatives on how to power the huge computer warehouses behind artificial intelligence. The bill does not require anyone to use nuclear power.
That gap matters. Oklo had just arranged to sell up to $1 billion of new stock to fund its reactors. A higher share price means it can raise that money while handing out fewer new shares.
Shares of small modular reactor developers Oklo and NuScale Power rose sharply on Thursday after the U.S. House voted on legislation addressing electricity supply for data centers. Yahoo Finance and 24/7 Wall St. reported Oklo up 13% and NuScale up 10%. TechStock² put the gains at 11% and 9% and reported that the measure stops short of ordering the use of nuclear power. Other nuclear names, including NANO Nuclear Energy and fuel supplier Centrus Energy, also rose.
The rally came one week after Oklo disclosed, in an 8-K filed September 11, an equity distribution agreement with ten banks. The agreement lets it sell up to $1 billion of Class A common stock “at the market,” meaning gradually and at prevailing share prices.
Executive Summary
The House vote gave nuclear equities a policy signal, not a purchase order. The bill as reported does not compel data centers or utilities to buy nuclear power. It does not fund a reactor. It does not change how quickly the Nuclear Regulatory Commission licenses one. Even so, investors marked up the whole small-reactor group in a single session. That shows how much of these companies’ value rests on expectations about Washington’s direction rather than on operating assets. Neither Oklo nor NuScale yet runs a commercial reactor in the United States.
For Oklo, the timing turns a sentiment move into a financing variable. Its new at-the-market program sells shares at whatever the market is paying. A higher price lets the company raise the same dollars while issuing fewer shares, which means less dilution for existing holders. The company can also choose to raise more. For AI data-center developers hoping small reactors will eventually supply firm power, a well-funded developer matters. The capital to build is the first thing between a reactor design and a delivered megawatt.
A Rally on a Signal, Not a Mandate
The central fact of Thursday’s trade is the mismatch between the policy and the price move. The House measure concerns power for data centers, the warehouse-scale computing sites whose electricity demand has climbed with AI training and inference. The measure reportedly does not order that any of that power come from nuclear plants. Investors bid the sector up anyway. The best reading is that the market is pricing direction: Congress is treating data-center power as a legislative priority, and nuclear is the carbon-free, always-on source most often named alongside it.
That is a reasonable thing to be optimistic about, but it is not a revenue event. Small modular reactors (SMRs) are nuclear units far smaller than conventional plants, designed to be built in factories and installed in modules. They still have to clear licensing, secure fuel, finance construction and sign binding power contracts before they produce anything a data center can buy. A vote that addresses none of those steps directly moved the stocks by roughly a tenth. That tells readers more about how these valuations are built than about how soon reactors will run.
Why the Jump Matters Most to Oklo’s Share Sales
Oklo’s September 11 8-K sets out the mechanism. Under the equity distribution agreement, ten banks, including Goldman Sachs, BofA Securities, Citigroup, J.P. Morgan and Morgan Stanley, can sell Class A shares for Oklo in ordinary market trades, block trades or negotiated deals. Sales happen “at market prices prevailing at the time of sale,” up to $1 billion in gross proceeds. Oklo sets a daily maximum and a minimum acceptable price, and pays commissions of up to 1.5%. On the full $1 billion, that would be up to $15 million.
Because the price floats, the share price directly sets the cost of this capital to existing owners. Take any fixed dollar amount raised at a price 13% higher: it requires about 11.5% fewer new shares than it would have the day before (1 divided by 1.13). A policy-driven rally therefore does something concrete for Oklo that it does not do for a company without an open program. It cuts the dilution attached to funding reactor development, or lets Oklo raise more for the same dilution. The 8-K describes the shares as issued off a shelf registration declared effective in December 2025. That means Oklo can act on the higher price without a fresh registration process.
Two caveats keep this honest. Oklo has not said whether it has sold any shares under the program or at what prices. And a price gain built on a signal can reverse as quickly as it came. The benefit is real only for sales made while the higher price holds. Existing shareholders are the other affected party. Every share sold dilutes them, whatever the price.
A Basket Move, Not Company News
The breadth of the rally points to sector sentiment rather than company-specific developments. NuScale filed no material report with the SEC in the days around the vote. NANO Nuclear Energy’s most recent 8-K, filed September 17, reports only routine results from its September 15 annual meeting: six directors re-elected and Withum ratified as auditor. Nothing in that filing concerns the House bill, customers or reactors. When developers at different stages, and a fuel supplier, rise together on the same day, the market is repricing the theme, not the individual businesses.
For data-center developers and hyperscalers, the largest cloud operators, the practical lesson is to separate equity momentum from procurement reality. The gating items for nuclear-supplied AI capacity have not moved: licensed designs, fuel supply, construction financing and signed offtake agreements. Offtake agreements are long-term contracts to buy a plant’s output. A stronger share price can help with one of these, financing, and only for companies positioned to tap it.
Background
Oklo went public in 2024 through a merger with AltC Acquisition Corp., a blank-check company co-founded by Sam Altman. It is developing the Aurora, a compact fast reactor. Its first combined license application was denied by the Nuclear Regulatory Commission in 2022, and it has since been working back through licensing. NuScale Power’s design was the first small modular reactor design certified by the NRC. Its flagship Carbon Free Power Project with Utah municipal utilities was cancelled in 2023 after subscriptions fell short.
Both companies have positioned themselves as future suppliers of firm, carbon-free electricity to data centers, whose power needs have grown quickly with AI workloads. Both remain pre-revenue on commercial reactor sales. That leaves their share prices sensitive to policy news, to financing conditions and to any sign that large power buyers will commit.
Nvidia, the company whose chips run most of today’s artificial intelligence systems, says it will sell twice as many chips next year as it does this year.
That sounds like it means twice as many data centers, twice the electricity and twice the cooling. It may not. Nvidia’s chips include the one inside the Nintendo Switch 2, plus chips for laptops and robots, and the company does not say how many of each it sells.
The businesses that must supply the power and cooling need that breakdown before they can plan.
Nvidia chief executive Jensen Huang said on Thursday, September 17, that the company will double the number of chips it sells next year, CNBC reported. “I expect Nvidia to sell twice as many chips as this next year as we do this year,” Huang said at a summit on AI safety with King Charles III in Scotland, attended also by representatives of Google DeepMind, OpenAI and Anthropic.
The remark follows Nvidia’s forecast of 70% growth for the fiscal year ending January 2028, a figure CNBC put at about $673 billion. Nvidia does not disclose how many chips it sells in total; last fall Huang said the company had shipped 6 million Blackwell GPUs in four quarters.
Executive Summary
Huang’s forecast is a headline-friendly number: twice as many chips in a single year. For the companies that build and supply AI data centers, it reads as a demand signal for everything a chip needs to run: electricity, cooling, high-bandwidth memory and the networking that links processors together.
The number is less precise than it sounds. Nvidia sells far more than data center graphics processing units (GPUs, the accelerators that train and run AI models). Its lineup includes central processors, network switch chips, optical networking chips, laptop chips, Jetson modules for robots and cars, and the processor in Nintendo’s Switch 2. A doubling of total units says nothing on its own about how many high-power data center accelerators are in the mix.
That matters because infrastructure is planned in megawatts and racks, not in chip counts. The direction of Huang’s forecast is clear; the load it places on grids and cooling plants is not yet specified.
A Unit Count Is Not a Load Forecast
A data center’s electricity and cooling needs are set by the accelerators and servers installed in it. A Blackwell or Rubin GPU sits in a liquid-cooled rack drawing power continuously; a Switch 2 processor sits in a handheld console on battery. Both count as one chip in a total unit figure. Doubling a blended count could therefore mean data center shipments doubled, or rose less while consumer and embedded lines rose more, or any combination in between.
The people who must translate Huang’s number into physical capacity — utilities fielding interconnection requests (applications to connect new load to the grid), developers securing sites, cooling-equipment makers, and memory suppliers allocating production — plan against deliveries of data center systems. Huang’s statement does not give them that figure. It confirms that demand is rising; it does not tell them by how much in the category that consumes the power.
That is the practical risk on both sides. Suppliers who read “twice the chips” as “twice the megawatts” could overbuild if the growth skews toward low-power parts. Suppliers who discount the statement could fall short if the growth is concentrated in data center accelerators. Either error is expensive in a business where transformers, cooling plants and memory lines take long lead times to add.
The Revenue Forecast Tells a Different Story Than the Unit Forecast
Nvidia’s own dollar guidance offers a cross-check. The company expects 70% growth in the fiscal year ending January 2028, which CNBC put at about $673 billion. That fiscal year broadly overlaps calendar 2027, the “next year” Huang appears to be describing.
If the two forecasts cover comparable periods, units growing 100% while revenue grows 70% implies average revenue per chip falls. One reading is a mix shift toward lower-priced parts; another is that the two figures simply measure different things over slightly different windows. Either way, the gap between them is a reason not to treat the unit figure as a proxy for data center buildout. The revenue figure, heavily weighted to data center products, is the better, though still indirect, gauge of the infrastructure load ahead.
What Power, Cooling and Networking Suppliers Can Still Take From It
The statement is not empty. Huang tied it to broad demand — “almost every single country that we’re in, people want to invest in AI” — and it extends a run of forecasts that, CNBC noted, pushed analyst expectations up and point to continued growth over the next six quarters. Nvidia’s list of product lines also includes switch chips and optical networking chips, so growth in AI clusters carries a networking and fiber component alongside compute.
For power equipment makers, cooling vendors, memory producers and data center landlords, the sensible reading is directional: demand for AI capacity continues to grow, and at a pace Nvidia expects to exceed this year’s. The sizing — how many megawatts, how many racks, how much memory — still depends on figures Nvidia has not broken out.
Background
Nvidia began as a graphics-chip company and became the dominant supplier of the accelerators used to train and run AI models. Its data center GPU families, Blackwell and now Rubin, are deployed in dense, often liquid-cooled racks whose power and cooling needs have reshaped how data centers are designed and where they are built.
The company also sells a wide range of other semiconductors, from networking and optical chips that link AI clusters together to processors for laptops, robots, cars and Nintendo’s Switch 2. That breadth is why a company-wide chip count and a data center buildout forecast are not the same thing.
Eaton, a long-established maker of the equipment that moves electricity safely through buildings, says its best years are still ahead.
The reason is data centers: the giant warehouses of computers behind apps, online search and chatbots. Each one needs enormous amounts of electricity switched, routed and backed up in case the power fails, and Eaton sells much of that equipment.
The companies supplying the wiring behind the artificial intelligence boom can profit no matter which tech giant wins. The big open question is how long the building spree lasts.
Yahoo Finance reported that Eaton Corporation (NYSE: ETN), the power management company, sees its best years ahead, with demand from data centers powering its growth. The outlook ties Eaton’s trajectory directly to the electrical infrastructure needed by the facilities that run cloud computing and artificial intelligence (AI) workloads.
Executive Summary
Eaton is signaling confidence that data center construction will drive growth beyond anything in its recent history. The company makes the electrical backbone of these facilities: switchgear that routes and protects high-voltage power, uninterruptible power supplies (UPS) that keep servers running through outages, and power distribution units that deliver electricity to server racks.
The outlook matters because it points to where AI spending ultimately lands. Chips and cloud platforms get most of the attention, but every new data center hall needs power equipment before a single server can be switched on. That puts electrical suppliers among the most direct beneficiaries of the buildout, whichever chipmaker or cloud provider comes out ahead.
A forward-looking claim is not a result, though. A “best years ahead” view depends on data center spending staying strong, on Eaton’s ability to manufacture enough equipment, and on customers securing the grid power they need to finish their projects.
Selling Picks and Shovels for the AI Power Crunch
A data center is, at its core, a building that turns electricity into computation. Power arrives from the utility at high voltage, then passes through transformers, switchgear, backup systems and distribution equipment before it reaches the servers. Each stage is a purchase, and Eaton competes in many of them.
This is the classic picks-and-shovels position. Eaton does not need to predict which AI model, chip architecture or cloud provider will dominate. It needs data centers to keep getting built, and it needs those facilities to keep drawing more power. The industry trend toward denser server racks, where more computing and more electricity are packed into the same floor space, tends to raise the amount of electrical equipment each facility requires.
That makes the claim structurally credible. Unlike speculative software revenue, electrical infrastructure is a physical requirement: a facility cannot operate without it, and none of it can be skipped to save money.
Why Electrical Gear Holds Its Value
Power distribution equipment is often engineered to order, has to meet strict safety and reliability standards, and must be qualified by operators before it goes into a live facility. Large data center operators tend to standardize designs across many sites, so a supplier that wins a place in a reference design can see repeat orders as the design is rolled out.
Those features give incumbents such as Eaton real advantages over new entrants. They also help explain why established rivals, including Schneider Electric, Vertiv, ABB and Siemens, are chasing the same demand. The market is large enough to lift several suppliers, but competition over pricing, delivery slots and new designs will likely sharpen as manufacturing capacity catches up.
For data center developers, the practical result has been a supplier’s market for critical electrical equipment. Buyers who reserve manufacturing capacity early are generally better placed than those who order once a site is ready to build.
The Risks Beneath a Bullish Outlook
The most obvious risk is concentration. If much of Eaton’s growth depends on a few very large buyers of data center capacity, a slowdown in their capital spending would show up quickly in new orders. Infrastructure booms have historically been followed by periods of digestion, when customers pause to absorb capacity they have already built.
A less obvious risk is power itself. In many markets, data centers are limited less by equipment than by how quickly utilities can connect them to the grid. If projects stall while waiting for electricity, orders for the gear that distributes that electricity can slip too. Eaton’s fortunes are therefore tied partly to utility and grid timelines it does not control.
Execution matters as well. Meeting surging demand requires factory expansion, skilled labor and reliable component supply. Growth that outpaces manufacturing capacity can lead to delivery delays that push customers toward competitors.
What It Means for Buyers and Investors
For data center operators and developers, a supplier this confident in demand is a signal to lock in procurement early and to qualify more than one vendor for critical equipment. Lead times and pricing are likely to remain a negotiation shaped by how tight the market stays.
For investors, Eaton’s outlook reinforces the view that power infrastructure is one of the more direct ways to gain exposure to the AI buildout. The durability of that thesis rests on the questions below: how much demand is actually contracted, and how long it lasts.
Background
Eaton Corporation is a long-established industrial company that has repositioned itself around power management. Its electrical business makes the equipment that carries electricity from the utility connection to the point of use, including switchgear, uninterruptible power supplies, power distribution units and related controls. The company also serves the aerospace, vehicle and industrial markets, and it trades on the New York Stock Exchange as ETN.
Data centers have become one of the most important markets for electrical equipment suppliers. The spread of cloud computing, and more recently the rapid growth of AI, has driven large technology companies and data center developers to build bigger, more power-hungry facilities. That has strained both equipment supply chains and utility grids, turning electrical infrastructure from a background purchase into a strategic bottleneck.
Texas cannot connect every giant computer warehouse that wants to plug into its power grid. So the agency running the grid has started rationing, deciding who gets electricity first by paperwork and start dates rather than by who asks loudest.
One company, Cipher, cleared an early hurdle for enough power to run a large city. Its shares jumped about nine percent.
A filing from rival Core Scientific shows the catch. Sites switched on before March 2022 skip the line. Everything newer waits, even after the concrete is poured.
Yahoo Finance reported that shares of Cipher Digital rose roughly 9% after the company received conditional status from the Electric Reliability Council of Texas (ERCOT), the operator of the Texas power grid, for 3.2 gigawatts of capacity in the state, with Core Scientific up about 4% and IREN slightly higher on the same read-across.
The move landed days after Core Scientific furnished an 8-K to the Securities and Exchange Commission on September 10, 2026, disclosing the ERCOT power status of each of its existing and proposed Texas data centers site by site: 371 megawatts at Denton, 600 megawatts at Pecos and 431 megawatts planned at Hunt. That filing is the clearest public description yet of how ERCOT is now sorting large data center loads — and of how much of a miner’s announced capacity is conditional rather than secured.
Executive Summary
The news event is a share price move, but the substance underneath it is regulatory. ERCOT is running an allocation exercise it calls Batch Zero, which assigns large new electrical loads to defined pathways and only releases a final allocation and load ramp when the process concludes. Conditional status inside that process is now the gating item between a developer’s announced gigawatts and energized, billable capacity.
Core Scientific’s 8-K sets out how the sorting works in practice. Denton’s existing 297 megawatts was conditionally approved as “Base Load, Pathway (a)” — longstanding existing load — purely because it energized before March 25, 2022, and is exempt from Batch Zero altogether. A further 74 megawatts at Denton is also exempt, because ERCOT had already incorporated it into the 2025 Regional Transmission Plan study cases. Pecos, by contrast, sits inside Batch Zero: 300 megawatts as “Base Load, Pathway (b)” for load energized after March 25, 2022, and another 300 megawatts as “Studied Load.” Hunt’s planned 431 megawatts qualified under Pathway (e) for demonstrated maturity, which the company earned by completing interconnection studies, clearing a 431-megawatt stability study, posting collateral, ordering long-lead equipment and starting construction.
For anyone buying, financing or underwriting Texas compute capacity, that hierarchy is the story. Two identical 300-megawatt blocks at the same site carry different regulatory certainty because of when they switched on. Cipher’s 3.2 gigawatts of conditional status is a real milestone; it is not the same thing as a final allocation, and the market repriced three companies on the distinction in a single session.
The Scarce Asset in Texas Isn’t a Megawatt. It’s a Date.
Core Scientific’s filing is unusually legible about a mechanism the industry usually discusses in the abstract. At Denton, 297 megawatts of existing power was conditionally approved as “Base Load, Pathway (a)” on one qualification: it was energized prior to March 25, 2022. That single fact takes it outside the Batch Zero allocation process entirely. At Pecos, 300 megawatts of existing power — physically comparable capacity, already operating — was conditionally approved as “Base Load, Pathway (b)” because it energized after that cutoff and before July 10, 2026. It sits inside Batch Zero, and the company states plainly that it will not receive its final allocation and load ramp until the process concludes.
The same asymmetry shows up in a quieter form on Denton’s additional 74 megawatts, which is exempt not because of an energization date but because ERCOT had already validated it inside the 2025 Regional Transmission Plan study cases. Presence in the grid operator’s own planning model functions as an entitlement. This is what makes interconnection paperwork the scarce input: it cannot be bought with capital on a short timeline, it cannot be manufactured, and it is not fungible with megawatts sourced elsewhere.
Run the arithmetic across the three Texas sites and the exposure becomes concrete. Of 1,402 megawatts described in the filing, 371 megawatts — just over a quarter — is outside Batch Zero and effectively settled in status terms. The remaining 1,031 megawatts is conditional and awaiting final allocation. Anyone underwriting Texas capacity, whether a lender sizing a facility, a GPU cloud tenant signing a multi-year lease or an investor modelling ramp, now has to ask which bucket a given block sits in, because the headline gigawatt figure no longer distinguishes them.
Pathway (e) Turns the Queue Into a Capital Test
Hunt is the instructive case, because it shows the alternative route for capacity that has no date advantage. Its planned 431 megawatts was conditionally approved as “Base Load, Advancing Large Load, Pathway (e)” — demonstrated maturity and advancement through the interconnection process. Core Scientific earned that by doing five specific things: completing interconnection studies with its utility partner, obtaining ERCOT approval of a 431-megawatt stability study, posting financial collateral, ordering long-lead equipment, and commencing construction.
That is a queue that rewards irreversible spend. Long-lead equipment orders — transformers, switchgear, generation and cooling plant — are placed years ahead and are difficult to cancel cleanly. Posting collateral ties up cash. Starting construction commits site capital before final allocation is known. The effect is to sort applicants by balance sheet and by willingness to accept sequencing risk, which advantages incumbent operators with existing land, utility relationships and financing access, and disadvantages speculative filings that exist mainly to hold a position.
The policy logic is defensible: a queue clogged with phantom load helps nobody, and requiring proof of advancement filters it. The commercial consequence is that developers must now fund a substantial portion of a project before they know what load ramp they will be granted. That is a materially different risk profile from the pattern of the past several years, and it is a cost that eventually shows up in the price of Texas capacity.
Why Three Companies Repriced on One Company’s Filing
Cipher’s 9% move, Core Scientific’s 4% and IREN’s smaller gain describe a market re-rating regulatory position rather than operating performance. Nothing about hash rate, bitcoin price or a new customer contract changed that day. What changed was information about who is likely to be allowed to energize large load in ERCOT, and the read-across was applied to peers with Texas portfolios.
The spread between those moves is worth reading carefully rather than dramatically. A one-day share price reaction is not a valuation, and conditional approval is several steps short of revenue. The chain runs: conditional status, then final allocation and load ramp, then energization, then a signed tenant, then billable capacity. Core Scientific’s own history illustrates the far end of that chain — its February 2025 expansion with CoreWeave added 70 megawatts of contracted power at Denton, took the site to 260 megawatts of critical IT load, brought CoreWeave’s contracted total across six locations to 590 megawatts, and carried an expected $10.2 billion of revenue over 12-year contract terms against $104 million of company capital expenditure. Note that critical IT load and electrical power are different measures, the former excluding cooling and electrical overhead, so the 260-megawatt figure and the filing’s 297 megawatts are not directly comparable.
There is a neat coincidence in that pairing: Denton, the site carrying the deepest contractual commitment, is also the site with the cleanest queue position. That is the combination the market is now hunting for, and it is rarer than announced gigawatt totals suggest. Capacity that is both contracted and exempt from allocation risk is the genuinely scarce product; capacity that is conditional and uncontracted is an option on a process whose conclusion has not been dated publicly.
Background
Bitcoin miners built large, power-hungry sites in Texas over the past decade because the state combines abundant generation, an independent grid and a market that rewards flexible load. After the 2024 halving cut mining rewards, several of those operators pivoted their footprints toward hosting artificial intelligence and high-performance computing, where tenants pay long-term contracted rates rather than exposing the operator to commodity price swings. Core Scientific’s February 2025 Denton expansion with CoreWeave was an early template: $1.2 billion of investment, 70 additional megawatts of contracted power, and a stated intent to run 900 megawatts of its contracted capacity as HPC hosting against 400 megawatts of mining.
The constraint on that pivot has shifted from capital to connection. As requests for hundreds of megawatts per site multiplied, ERCOT moved to formalise how large loads are approved and sequenced, creating the pathway definitions and the Batch Zero allocation process that Core Scientific’s September 10 filing describes. The practical effect is that a developer’s position in that process — its energization dates, its completed studies, its posted collateral, its presence in the grid operator’s transmission planning model — now determines how much of its announced capacity it can actually energize, and when.
A startup called Blue Energy asked federal nuclear regulators for permission to build a power plant on the Texas coast that runs on both natural gas and small nuclear reactors.
The electricity would feed a computing site built by Crusoe for artificial intelligence — buildings full of computers that need power around the clock.
Here is the catch: the gas part gets built first, by around 2030. The reactors come later, in the 2030s.
So the plant sold as nuclear will spend its early years running mostly on gas.
EnergyTech reported that North Carolina-based startup Blue Energy has submitted its first construction permit application to the U.S. Nuclear Regulatory Commission for a hybrid gas-and-nuclear generating station at the Port of Victoria, Texas. The project would pair a GE Vernova 7HA.02 gas turbine with the GE Vernova-Hitachi BWRX-300 small modular reactor, building toward a 2.5-gigawatt plant by the early 2030s that would serve an AI data center — an “AI factory” — developed by Crusoe, along with other loads.
The build is staged. Phase one centers on the gas turbines, which the company says are the quickest to install and commission, targeted for the end of this decade. Phase two would add perhaps five BWRX-300 units, each generating close to 300 megawatts at capacity, with the gas units eventually phased out or reduced. Blue Energy estimates project cost at more than $6 billion, with Société Générale acting as lead financial advisor.
Executive Summary
The filing is a procedural milestone rather than a construction start, and that distinction matters. No small modular reactor has been fully approved or begun construction in the United States. Federal applications of this kind typically take years to move through technical and environmental review before a permit issues. Blue Energy is entering a queue that also contains the Tennessee Valley Authority’s BWRX-300 application at Clinch River — which GE Vernova Hitachi says is approaching a decision — and applications from other developers including X-energy.
What makes the Victoria project analytically interesting is not the reactor. It is the sequencing. A 2.5-gigawatt site whose first phase is combustion turbines and whose second phase is five reactors is, for the first several years of its operating life, a gas plant with a nuclear option attached. If roughly five units at close to 300 megawatts each eventually arrive, that implies on the order of 1.5 gigawatts of nuclear capacity and the balance from gas — meaning the gas fleet is not a token bridge but a substantial share of the nameplate.
For GE Vernova, the strategic value is fleet economics: the same reactor design, the same licensing work and the same supply chain applied across multiple sites, so that each project is cheaper and faster than the last. For data center developers, the practical value is that a credible nuclear pathway may make a gas-first interconnection easier to defend to customers and communities that have made carbon commitments.
The Permit Is Nuclear. The First Electrons Are Gas.
Read the phasing literally. Blue Energy says phase one focuses on the gas turbines because they are the quickest to install and commission, with a target of the end of this decade. Phase two would add perhaps five BWRX-300 reactors, each producing close to 300 megawatts — a megawatt being roughly the power draw of a few hundred homes, and 300 megawatts being enough for a serious data center campus on its own. The total plant is described as 2.5 gigawatts by the early 2030s. Subtract the nuclear arithmetic from the total and roughly a gigawatt of gas-fired capacity is carrying the site before the reactors show up, and continuing alongside them after.
That is the operational conclusion buried in an otherwise routine regulatory filing. Crusoe’s AI factory, if it energizes on the announced schedule, will be a nuclear-adjacent load long before it is a nuclear-powered one. Nobody has misstated this — the release is explicit that gas comes first and that the gas units would later be “phased out or reduced.” But the framing of hybrid gas-nuclear projects generally, and the headlines they generate, tend to lead with the reactor rather than with the turbine that will actually be spinning in 2030.
Who this affects: any hyperscaler or AI developer signing power agreements against projects like this one needs to price two different assets. The near-term asset is dispatchable gas generation with commodity and emissions exposure. The long-term asset is a first-of-a-kind reactor deployment with schedule and licensing risk. Blending them into one “clean firm power” line item on a sustainability disclosure understates the first and overstates the certainty of the second. The honest version is that gas is buying time for nuclear to clear regulatory review — which is a defensible strategy, just not the same claim.
Fleet Licensing Is the Product GE Vernova Is Actually Selling
Jason Cooper, CEO of GE Vernova Hitachi Nuclear Energy, framed the Blue Energy filing around repeatability: “This is what fleet deployment looks like: projects advancing on common technology, licensing work and supply chain capabilities, allowing us to apply lessons from one project to the next.” That sentence is the commercial thesis of the entire small modular reactor category, compressed.
Conventional nuclear construction in the United States has been defeated less by physics than by one-off engineering. Every plant a bespoke design, every licensing review starting near zero, every supply chain assembled and then dispersed. The BWRX-300 bet is that if the same design goes through the Nuclear Regulatory Commission repeatedly — Clinch River with the Tennessee Valley Authority, now Victoria with Blue Energy — the review gets shorter, the vendors keep their tooling, and the crews carry experience forward. Blue Energy’s Jake Jurewicz echoed the point, saying the team moved from technology selection to construction permitting “in a matter of months.”
The claim is plausible and, so far, unproven at the only point that counts: a completed unit. Nothing in the release establishes that the second application will be reviewed faster than the first, or that fabrication cost falls on unit two. Investors and buyers should watch the Clinch River decision closely, because it is the first real data point on how much of the fleet argument the regulator is prepared to credit.
More Than $6 Billion, and the Project-Finance Test
Blue Energy puts the project above $6 billion and has engaged Société Générale as lead financial advisor. Kim Hill, the bank’s transaction lead, described the mandate as a step toward deploying private capital markets for advanced nuclear “just like it has for wind, solar, and battery technologies.” That comparison is the tell. Wind, solar and storage became bankable because their capital costs are predictable, their construction windows are short, and their revenue is typically locked to long-term contracts before anyone breaks ground.
Advanced nuclear does not yet satisfy the first two conditions in the United States — no unit has been built to prove them. Which is another reason the gas-first phasing is more than an engineering convenience: turbines are a financeable, well-understood asset class with an established construction schedule, and early cash flow from gas generation changes the risk profile a lender sees when it underwrites the reactors behind it. The strategic investor list, which includes Constellation Technology Ventures, VXI, Engine Ventures, At One Ventures and Tamarack Global, reflects venture-stage risk tolerance; a $6 billion project needs a different class of capital entirely.
Why the Load Growth Argument Has Real Force — and Its Limits
Nuclear supplies about 18% of the U.S. utility-scale electricity mix, and the fleet spent years under pressure from operating costs, cheap gas, renewables and, critically, flat demand. Data center growth has reversed the demand assumption, and the industry’s argument is that carbon-free baseload — power that runs continuously rather than when the wind blows — is the natural answer for loads that run at high utilization around the clock.
The counterargument, which the release also records, is that nuclear projects remain expensive, slow and contested on health and safety grounds. Both positions can be evaluated on evidence rather than assertion, and the evidence arrives on a schedule: permit decisions, construction starts, and the first concrete date. Until then, the sharpest thing to say about Victoria is that it is a credible attempt at a genuinely difficult sequencing problem — build generation fast enough for AI load that exists now, while licensing generation clean enough for the commitments that load has already made.
Background
Blue Energy is a North Carolina-based startup pursuing what it describes as a repeatable, rapidly built approach to new nuclear construction — EnergyTech has previously reported the company raised $380 million to advance prefabricated reactor construction. It selected the BWRX-300 design earlier this year for co-location with gas turbines and, by its own account, moved from technology selection to federal construction permitting within months.
The BWRX-300 is a boiling water small modular reactor from GE Vernova Hitachi Nuclear Energy, a joint venture between GE Vernova and Hitachi. It is the design furthest along in the U.S. licensing process, through the Tennessee Valley Authority’s Clinch River project, and has been selected by several developers internationally. The broader context is a U.S. power sector that spent a decade planning around flat demand and is now contending with data center and AI load growth that has made firm, round-the-clock generation scarce and valuable again.
Lam Research does not make computer chips. It makes the machines that make them — room-sized tools that carve circuits into silicon wafers.
The company plans to spend about 1.1 billion dollars, or Rs 10,000 crore, on a manufacturing plant in India.
Why it matters: only a few places in the world build these machines, and a new chip factory can wait a long time for one. Every artificial intelligence data centre sits downstream of that wait. A new place to build the tools is a new way to shorten it.
INDIA New England News reported that Lam Research Corporation, one of the world’s largest makers of semiconductor manufacturing equipment, will invest Rs 10,000 crore — roughly $1.1 billion at current exchange rates — in a semiconductor manufacturing facility in India.
Lam is a wafer fabrication equipment (WFE) supplier rather than a chipmaker: its systems etch and deposit the microscopic layers that make up a finished chip, and they are bought by the logic foundries and memory manufacturers that supply the processors and high-bandwidth memory used in artificial intelligence hardware. The investment figure is the substantive disclosure in the announcement; site, phasing, product mix and start-up date were not specified.
Executive Summary
The headline number is large by the standards of India’s semiconductor programme and unusual in its position in the supply chain. Most of the country’s chip announcements to date have concerned fabrication and assembly-test-packaging plants — the places where chips are made and put into packages. An equipment plant sits one layer higher: it makes the capital tools that every fab, anywhere, has to buy before it can produce anything at all.
That distinction is why the commitment is worth attention beyond India. Advanced chip toolmaking is concentrated in a small number of countries — principally the United States, Japan, the Netherlands, and the Taiwan-Korea manufacturing corridor that absorbs much of the output. Lead times on advanced etch and deposition systems are measured in quarters, not weeks, and they are the practical constraint on how quickly a foundry or memory maker can convert a capital-expenditure announcement into shipping silicon.
For data centre operators, cloud providers and anyone financing artificial intelligence capacity, this is the least visible link in the chain and one of the least elastic. A second significant node of tool manufacturing does not solve that constraint on its own. It does change the geography of it — and geography is the variable that has driven the last four years of semiconductor industrial policy.
The Layer Above the Chip Shortage
Public conversation about semiconductor supply focuses on fabs: who is building one, where, and how many wafers it will start per month. The equipment layer above it gets far less attention despite being the gating item. A fab is, in economic terms, mostly a building full of tools. The cleanroom shell, the power feed and the ultrapure water plant are expensive and slow, but they are construction problems with known solutions. The etch chambers, deposition systems, lithography scanners and metrology tools inside are the scarce, long-lead, technically differentiated part.
Lam Research occupies two of the most important slots in that equipment stack. Etch is the subtractive step — selectively removing material to define features. Deposition is the additive step — laying down films sometimes only a few atoms thick. Both processes are repeated hundreds of times over the life of a single wafer, and both have become more tool-intensive as chipmakers moved from flat transistor designs to three-dimensional ones and from planar memory to stacked memory. More process steps means more tools per unit of output, which means equipment demand grows faster than wafer demand.
That structural point is what makes an equipment-manufacturing investment different from a fab investment. A fab serves its owner’s customers. An equipment plant serves everyone building fabs.
Why AI Capacity Waits on Tools, Not Just Fabs
Here is the mechanism the Rs 10,000 crore commitment plugs into. When a memory manufacturer decides to add high-bandwidth memory capacity — the stacked memory that sits alongside an artificial intelligence accelerator and largely determines how fast it can be fed data — it does not simply turn on a line. It places tool orders, waits out the manufacturer’s backlog, takes delivery, installs, qualifies, and only then ramps. The same sequence applies to a foundry adding advanced logic capacity. The interval between the capital decision and the first qualified wafer is dominated by equipment delivery and installation, not by construction.
That interval is what data centre operators experience, one or two steps removed, as accelerator allocation. An operator signing for capacity today is bidding against everyone else for chips whose production capacity was ordered as tools several quarters earlier. When equipment backlogs lengthen, the effect propagates downstream on a lag: tighter chip allocation, longer hardware delivery windows, and capacity plans that slip into the following year. It is a slow-moving constraint, which is exactly why it is easy to under-weight until it binds.
A roughly $1.1 billion manufacturing facility does not shorten that queue by itself, and nobody should claim it does. What it does is add manufacturing capacity and a new geography to a supply base that is unusually concentrated, in a country that is simultaneously trying to build the fabs that consume the output. The effect that matters for buyers is optionality — more places where tools, subsystems and spares can be built and shipped from, and one less single-region dependency in the layer that gates everything below it. Who benefits, concretely: chipmakers ramping capacity, whose tool delivery schedules set their ramp; and the operators and financiers downstream, whose capacity timelines inherit those schedules whether they track them or not.
What Rs 10,000 Crore Actually Buys
Even-handedly, the announcement establishes scale and intent, not capability. Semiconductor equipment plants span a wide range of activity, and the differences are material. At one end is manufacture of subsystems, modules, chambers and precision components, plus spares and refurbishment — genuinely valuable, high-employment work that improves supply resilience without relocating core technology. At the other is final assembly and test of complete process tools, which carries far more supply-chain weight. The announcement as reported does not say which of these the facility will do, or in what mix over time.
The realistic reading is that a plant of this size is a multi-year, phased build rather than a single switch-on, and that its early output is likelier to be components and subsystems than flagship systems. That is not a criticism; it is how equipment supply chains are normally localised. It does mean the supply-chain effect is gradual and should be judged over years, against disclosed milestones, rather than assumed from the headline figure.
There is also an ecosystem question underneath the capital number. Equipment manufacturing depends on a dense local base of precision machining, vacuum components, ultra-clean fabrication, specialty materials and calibrated metrology. India has been building toward that base, but a plant of this scale tends to pull suppliers with it rather than find them already in place. The supplier development timeline, not the construction timeline, is often what determines when a facility reaches meaningful local content.
India Moves Up the Stack, and Why That Is the Interesting Part
India’s semiconductor strategy has been read, sometimes dismissively, as a late entry into commodity fabrication and packaging. An equipment investment complicates that reading. Toolmaking is higher-margin, more engineering-intensive and considerably harder to replicate than assembly — and it is a market where demand comes from every fab on earth rather than from a domestic customer base that India is still building. A plant serving global equipment demand does not need local fabs to be viable, which removes the sequencing problem that has slowed other parts of the programme.
For the wider industry, the significance is directional rather than immediate. Since 2020, the semiconductor supply chain has been reorganised around resilience — second sources, allied manufacturing, and reduced exposure to any single strait, grid or jurisdiction. Most of that effort has gone into chips themselves. Far less has gone into the tools, which remain the narrowest part of the chain. Adding a manufacturing node in a fourth major jurisdiction is a modest structural change in the right direction, and it is the kind of change that shows up in lead-time statistics years after the ribbon-cutting, if it shows up at all.
The honest caveat is that concentration is not only about where plants sit. Intellectual property, critical subcomponents and the scarcest engineering talent can remain concentrated even as assembly disperses. Diversifying manufacturing footprint and diversifying the supply chain are related but not identical, and the announcement as it stands speaks to the first.
Background
Lam Research Corporation, headquartered in Fremont, California, is one of the small group of companies that supply the wafer fabrication equipment used by every advanced chip factory in the world. It is best known for etch and deposition systems and sells to both logic foundries and memory manufacturers. Its business is inherently cyclical: revenue tracks its customers’ capital spending, which rises and falls with chip demand — and which has been pushed upward in recent years by investment in capacity for artificial intelligence processors and the memory that accompanies them.
India launched a national semiconductor programme earlier this decade, pairing capital incentives with state-level support to attract fabrication, assembly and testing investment. Most announced projects to date have sat in those downstream segments. Equipment manufacturing is a newer and higher-margin part of the chain, and one where global demand does not depend on a domestic customer base — which is what makes an investment at this layer notable in the programme’s trajectory.
Amazon is building a string of data centers across Pennsylvania — large buildings packed with computers that run websites, cloud services and artificial intelligence.
Pennsylvania does not charge its 6 percent sales tax on the computer equipment inside them. State budget writers expect that break to cost close to $2 billion in uncollected taxes by 2031.
Lawmakers in both parties voted this summer to end it. Neither version reached the governor’s desk. The argument is really about who helps pay for the buildings behind your chatbot.
Capital & Main reported on September 16 that Amazon has so far claimed Pennsylvania’s data center sales-and-use tax exemption at two locations, according to the state Department of Revenue, while expanding a portfolio that now includes a permit application for 36 data centers at the 4.5-gigawatt Homer City campus in southwestern Pennsylvania, a 1,300-acre site in Hazle Township, a pending site in Kline Township, and a prospective end-user role at a rezoning-denied site in Montour County. Amazon pledged $20 billion of data center investment in the state in 2025, beginning with projects in Salem Township and Falls Township.
The exemption relieves buyers of the state’s 6% sales and use tax on equipment bought, stored or used at qualifying data centers. Pennsylvania projects it will forgo $188.4 million in fiscal 2026-27 and close to $2 billion by 2031. In late June, the Democratic-controlled House and Republican-controlled Senate each passed a repeal; neither reached Governor Josh Shapiro before he signed the budget on July 12. Shapiro’s August 18 executive order left the exemption intact but conditioned it on a consent order binding operators to new siting, disclosure and energy standards.
Executive Summary
The substance here is not that a state subsidizes data centers — most do. It is the shape of this particular subsidy. Pennsylvania’s exemption attaches to equipment, and equipment is the part of a modern AI facility whose cost is rising fastest. The nonprofit research group Epoch AI estimates servers account for almost 60% of the cost of owning a one-gigawatt data center, with the remainder split across the building, network gear, energy, land, labor and water. A tax break written against that base grows with every generation of accelerators, whether or not the campus gets bigger or hires anyone else.
That is why a program appropriated at $5 million in 2016 is now projected to reach $517.2 million in fiscal 2030-31 under Shapiro’s own 2026 budget proposal — more than a hundredfold increase. It also explains why the repeal fight crossed party lines in June and why it is unresolved: the exemption has become the single largest state-level lever on data center economics in Pennsylvania, and the governor chose to attach conditions to it rather than remove it.
For anyone underwriting capacity in the PJM footprint, the operational takeaway is that Pennsylvania’s cost question has migrated. The debate that dominated 2024 and 2025 was about electricity price and interconnection. The debate now decided in Harrisburg is about the state revenue base — and it introduces a policy variable into equipment capex that most site-selection models still treat as fixed.
A Subsidy Indexed to Silicon, Not to Square Footage
Most location incentives are written against things that change slowly: assessed property value, payroll, acreage. Pennsylvania’s data center break is written against the 6% sales and use tax on equipment — servers, storage, and the gear that supports them. That distinction is the whole story. Epoch AI’s estimate that servers make up almost 60% of the cost of owning a one-gigawatt facility means the exempt share of a project’s spend is the share dominated by AI accelerators, whose unit prices have climbed faster than any other line in the stack.
The arithmetic follows mechanically. Two campuses of identical size, power draw and headcount will generate very different amounts of forgone state revenue if one is filled with general-purpose cloud servers and the other with AI training racks. The state’s own projections trace that curve: $188.4 million forgone in fiscal 2026-27, growing to a projected $517.2 million in fiscal 2030-31, against a $5 million appropriation when the program passed in 2016 — before generative AI or hyperscale campuses were part of the conversation. Nothing in the statute had to change for the cost to multiply. The compute did.
Who is affected: the state treasury, which is absorbing the growth while legislators field demands to fund public transit and close a structural deficit; developers whose pro formas capitalize a 6% equipment discount that two chambers have already voted to remove; and county tax bases that receive the land and the substation but not the largest slice of the spend. It also means the conventional local-benefit argument — that a campus pays for itself through construction and property tax — is being made about the smallest part of the capital stack.
A Program the State Cannot Meter
There is a real weakness on both sides of this argument, and it is the same weakness. When the incentive was converted from a refund to an exemption in 2021, Pennsylvania lost the ability to track exactly how much revenue it gives up when an operator claims it. The Department of Revenue confirmed Amazon has received the incentive at two locations; it did not put a dollar figure on them.
That should temper the numbers everyone is citing. The $188.4 million and the roughly $2 billion cumulative figure are budget projections, not measured losses — useful, but not audited outcomes. By the same token, claims that the exemption is a modest and self-financing inducement cannot be verified either, because the state has no per-claim ledger to check them against. Amazon’s $20 billion commitment is not a tax base: only the portion spent on qualifying equipment is exempt, and the company has not broken that out. Good policy on either side would start with disclosure, and the current design forecloses it.
Conditioning the Subsidy Instead of Ending It
Shapiro’s August 18 executive order did something more interesting than the repeal bills would have. It kept the exemption but turned it into an enforcement hook: operators claiming it must sign a consent order committing to the standards in the order, which bars state officials from signing nondisclosure agreements with operators, requires demonstrated local community support, and pushes developers toward supplying their own energy so the grid is not strained. “I want to be clear: These are not voluntary standards — these are legally binding requirements in order to operate here in the Commonwealth,” Shapiro said at the signing.
Critics argue the conditions are beside the point. “Shapiro is Pennsylvania’s governor, not Amazon’s, not Meta’s, not Microsoft’s,” said Megan McDonough of Food & Water Watch when the underlying principles were introduced in May, arguing the state should withhold support rather than regulate access to it. The counterargument is that a consent order reaches conduct a repeal never would — siting, disclosure and self-supplied power are not things the tax code addresses. The tension is genuine: repeal is cleaner and cheaper; conditioning is narrower but touches the grid and permitting questions that actually determine whether these projects get built.
The process history is the part that will keep drawing scrutiny. Heatmap reported in April, based on correspondence obtained by a Pennsylvania resident, that Amazon was shown a “feedback draft” of the plan on March 18, months before public release; that the administration had offered “exclusive early access” to fast-tracked permitting subject to a nondisclosure agreement; and that the Department of Community and Economic Development secretary had described the plan to an Amazon representative as “intended to be voluntary.” The executive order now prohibits the NDA practice. Whether pre-consultation with a major investor is ordinary policy development or preferential access is a fair question, and it is fair in both directions — states routinely test regulatory drafts with the regulated, and doing so under an NDA is what changed.
Permitting Risk May Bite Before Tax Risk Does
Look at the pipeline and a different constraint appears. State officials have identified more than 100 projects at varying degrees of certainty. Yet the Hazle Township site was denied locally last year and is under appeal; the Montour County rezoning was denied in February; Kline Township is still pending. A developer told the governor’s chief transformation and opportunity officer by email that Amazon had been “appealed in EVERY project at EVERY turn,” per Heatmap’s reporting. For a hyperscaler, a 6% equipment discount is worth real money, but it does not compound the way a two-year zoning delay does on a site whose power interconnection and construction slots are already booked.
Capital availability is clearly not the binding constraint. In an 8-K filed September 14, Amazon disclosed it had closed a sterling bond sale of £4.242 billion in aggregate public offering price across four tranches maturing 2029 through 2045, netting roughly £4.235 billion. The filing does not state a use of proceeds, and nothing in it ties the raise to Pennsylvania. What it does show is a company funding a global buildout in size at fixed coupons ranging from 5.200% to 6.650% — a cost of capital against which state-level equipment tax treatment is a meaningful but second-order input.
The practical consequence for anyone building, powering or financing this infrastructure: Pennsylvania’s attractiveness is now a function of two variables moving in opposite directions. The exemption is under bipartisan legislative pressure, with both chambers back in session and 11 scheduled session days left this year. Meanwhile the executive order raises the local-consent and self-supplied-power bar, which favors sites with generation already next door — the pattern visible at Salem Township, where Amazon Web Services construction sits adjacent to Talen Energy’s Susquehanna nuclear station. Projects that solved power first are the ones least exposed to whichever way Harrisburg moves.
Background
Pennsylvania created its data center sales and use tax incentive in 2016 with a $5 million appropriation, aiming to attract facilities housing servers and storage equipment. It was restructured from a refund into an exemption in 2021. Over the following two years the state became a focal point of the AI buildout, with officials identifying more than 100 projects at varying degrees of certainty — drawn by PJM interconnection, legacy generation sites including retired coal plants and operating nuclear units, and abundant natural gas.
Amazon announced its $20 billion Pennsylvania commitment in 2025, anchored by AI-serving campuses in Salem Township and Falls Township. Opposition has grown in parallel and across party lines, driven by electricity demand, land use and local control; hundreds of residents rallied at the state Capitol on June 23. The result is a policy fight in which repeal bills, an executive order, campaign finance scrutiny and local zoning appeals are all running at once, in an election year in which Governor Shapiro is seeking reelection.
Flex, a large manufacturer that builds products for other brands, is splitting in two. Its business making power and cooling equipment for data centers, the giant buildings full of computers that run artificial intelligence tools, will become a separate company called Axiom.
On September 15, Flex named the leaders and board members for both companies. Flex’s current boss will run Axiom, and several of Axiom’s directors come from big electrical-equipment makers.
The split still needs approval from shareholders, market regulators and a court in Singapore. Flex expects to finish it in early 2027.
Flex (Nasdaq: FLEX) on September 15, 2026 unveiled Axiom Solutions International, Inc. as the name of its planned independent Cloud and Power Infrastructure company and filed a preliminary Form 10 registration statement with the U.S. Securities and Exchange Commission. A Form 10 is the document a new company files to register its shares for public trading, and it is typical of a spin-off. In a spin-off, a parent hands shares of a business unit directly to its own shareholders. In a companion announcement, also filed as Exhibit 99.1 to an 8-K dated September 15, Flex set out the expected post-separation boards of both companies, named four new directors, and said Amy B. Schwetz, most recently CFO of Flowserve, will join on October 5, 2026 and is expected to become Flex CFO after the split.
Flex expects to complete the separation in the first quarter of calendar 2027. Completion is subject to final Flex board approval, the Form 10 becoming effective, a Flex shareholder vote and approval from the Singapore High Court.
Executive Summary
The September 15 announcements move Flex’s separation plan from concept toward execution. The spin-off business now has a legal name and a registration filing, and both companies have named their leaders. Revathi Advaithi, Flex’s CEO, is expected to lead Axiom. Michael Hartung, Flex’s chief commercial officer, is expected to become CEO of the remaining Flex, which keeps the Regulated Manufacturing Services (RMS) and Integrated Technology Services (ITS) segments.
The appointments show what each company wants to be. Axiom’s board draws heavily on the electrical-equipment industry, including two Eaton veterans and a former head of electrification at ABB. That points toward competing as a power infrastructure supplier for data centers, which Flex describes as the problem of solving ‘power, heat, and scale challenges’ in the AI era. The remaining Flex board is weighted toward industrial operating and finance experience.
For investors and data center buyers, the practical result would be a dedicated power and cooling supplier that can be valued and judged separately. Flex has not yet presented many of the details that would allow that judgment, including a final capital structure and an Axiom finance chief.
The Pure-Play Bet on Data Center Power
Flex has long operated as a diversified contract manufacturer across 30 countries. Its stated reason for separating the Cloud and Power Infrastructure segment is to create focus. The forward-looking statements attached to the 8-K list the anticipated benefits as ‘enhanced strategic focus, financial flexibility, and value creation for shareholders.’ The logic is familiar. Investors who want exposure to the build-out of AI data centers, which need large amounts of electricity delivered and heat removed, may pay more for a business that does only that than for the same business inside a broader manufacturer.
That thesis is plausible, but Flex has not quantified it. Neither announcement puts a number on value creation, and Flex’s risk factors state that the benefits ‘may not be achieved or may take longer to achieve than expected.’ The strength of the pure-play case will depend on Axiom’s disclosed growth, margins and customer mix once they are tested against the market.
A Board Built From Electrical Incumbents
Axiom’s expected board shows clearly where the company intends to compete. Mark Eubanks, CEO of Brink’s, previously ran Eaton’s Electrical Products business, which Flex says had about $6 billion in annual revenue. David Johnson, CFO of Corteva, spent 29 years at Eaton, finishing in finance and operations for its Electrical Sector, and was later CFO of Atkore. Maryrose Sylvester was U.S. head of electrification at ABB. William D. Watkins, former CEO of Seagate Technology, will chair the board. Michael E. Hurlston, CEO of Lumentum, and Charles K. Stevens III, former CFO of General Motors, round it out.
Directors who helped run established electrical-products businesses bring knowledge of pricing, channels and manufacturing scale in that market. A strong board does not by itself win business, though. Whether Axiom can take share from long-established electrical suppliers is still an open question. The remaining Flex board is more of a general industrial group: George R. Oliver (former chair and CEO of Johnson Controls), Patrick J. Ward (former CFO of Cummins), Brian Yoor (former CFO of Abbott Laboratories), plus existing directors from Raytheon International, UnitedHealth Group and STATS ChipPAC. Schwetz’s background as CFO of Flowserve and Peabody Energy fits that profile.
The Governance Link Between the Two Companies
One feature deserves attention from shareholders of both companies. Advaithi is expected to be CEO of Axiom, a director on Axiom’s board and chair of the Flex board at the same time. It is common for a parent’s chief executive to move to a spun-off company. Chairing the former parent while running the new company is less routine, and it raises reasonable questions about how the two boards will handle any commercial dealings or competing interests between the companies after separation.
Flex has signaled that it is aware of the issue. It plans to appoint a Lead Independent Director of the Flex board, effective at separation. Flex has not named that person or defined the role’s authority, so investors cannot yet judge how strong a counterweight it will be.
What Still Has to Happen
The separation has several conditions. Flex is incorporated in Singapore, and the 8-K references the Singapore Companies Act. That is why the Singapore High Court must approve the transaction in addition to Flex shareholders. Flex says it will file a proxy statement on Schedule 14A to seek the shareholder vote. The Form 10 must also be declared effective, and the Flex board must give final approval. Flex expects the spin-off to be tax-free for U.S. federal income tax purposes and lists the possibility that it will not qualify as a risk.
The 8-K also discloses the pay for incoming directors Oliver and Eubanks: $90,000 in annual cash, a pro-rated share of the current restricted stock unit award, and an annual stock award worth $235,000. Oliver and Eubanks join the Flex board on September 24, 2026. Eubanks moves to Axiom’s board at separation.
Background
Flex is an Austin, Texas-based manufacturer, listed on Nasdaq and registered in Singapore, that designs, builds and manages products for other brands across 30 countries. It reports through segments that include Regulated Manufacturing Services, Integrated Technology Services and Cloud and Power Infrastructure. The last of these supplies power, cooling and IT infrastructure products that Flex says help customers speed up data center deployment.
Demand for AI computing has made electricity delivery and heat removal major constraints for data center operators. That has drawn investor attention to companies that sell power and cooling equipment. Flex’s plan to spin off Cloud and Power Infrastructure as Axiom would create a stand-alone public company focused on that market. The remaining Flex would continue as a diversified manufacturing and technology services company.
Digital Realty, one of the world’s biggest landlords for the warehouse-sized buildings that run the internet, is teaming up with a partner to build them in Türkiye (Turkey).
These buildings, called data centers, hold the computers behind online banking, streaming, shopping and artificial intelligence tools. Building them inside a country makes apps faster for local users. It also helps companies follow local laws about where personal information must be stored.
The companies have not yet said who the partner is, how much they will spend, or when the first building will open.
MarketScreener reported that Digital Realty Trust (NYSE: DLR), the US-based data center real estate investment trust, has formed a joint venture to develop data centers in Türkiye. The move would take one of the world’s largest data center operators into a country that sits between Europe, the Middle East and Central Asia.
The partner’s identity, the planned capacity, the size of the investment, the ownership split and the construction timeline have not been made public.
Executive Summary
Digital Realty is setting up a joint venture, a jointly owned company shared with a partner, to build data centers in Türkiye. For a company whose footprint is concentrated in North America, Europe and selected emerging markets, this is a new country entry rather than an expansion of an existing campus.
The strategic logic is easy to see. Türkiye has a large domestic economy, a strong financial sector, and laws that push some personal and regulated data to be stored inside the country. Istanbul also sits on land and subsea fiber routes that link Europe with the Middle East. Those factors make local capacity attractive to cloud providers, banks, telecom carriers and, increasingly, companies deploying artificial intelligence.
What the announcement does not yet establish is scale. Without figures on megawatts (the unit used to measure how much electricity a data center can supply to its servers), investment or anchor customers, it is too early to say whether this will be a modest colocation foothold or a hyperscale-grade platform built for large cloud and AI deployments.
A Bridge Market, Not Just a New Flag
Türkiye’s appeal to data center operators rests on two things: domestic demand and geography. The country has a population of roughly 85 million people and a sizeable banking, retail and telecom sector, all of which generate data that must be stored and processed somewhere. Serving those users from inside the country cuts latency, the delay between a user’s action and a server’s response, which matters for payments, gaming, streaming and real-time applications.
Geography adds a second layer. Istanbul straddles Europe and Asia, and operators have long pitched it as a potential interconnection point for traffic moving between Europe, the Caucasus and the Middle East. Whether that promise turns into real traffic depends on carriers, internet exchanges and cloud on-ramps choosing to meet inside the new facilities. Digital Realty’s business model has leaned heavily on exactly this kind of interconnection density, so the value of a Türkiye entry will depend on its ability to attract networks, not just tenants.
Why Build With a Partner
Digital Realty has used joint ventures before when entering markets where local knowledge matters, including Brazil and India. The pattern is familiar across the industry. A local partner can bring land, relationships with power utilities and regulators, and familiarity with permitting, while the global operator brings design standards, a customer roster of multinational cloud and enterprise clients, and access to capital.
A joint venture also shares financial risk. Large data center campuses require heavy up-front spending long before rent arrives, and a partner that contributes equity reduces the capital Digital Realty must commit from its own balance sheet. The trade-off is control and economics: the parent books only a share of the returns, and governance terms such as who decides on expansion, pricing and exit can shape how the venture performs. None of those terms have been disclosed.
Power, Fiber and Fault Lines
Every modern data center project now lives or dies on electricity. AI servers draw far more power per rack than traditional enterprise equipment, and they often require liquid cooling rather than air. A Türkiye campus aimed at AI workloads would need both a large, reliable grid connection and a path to add more capacity over time. The availability and price of that power, and how much of it can be sourced from renewables to meet customer sustainability targets, will be central to its competitiveness.
Two further risks are specific to the market. Türkiye has experienced high inflation and sharp currency swings in recent years, which complicates long-term pricing; operators in such markets commonly seek contracts indexed to or denominated in hard currencies, but local customers may resist. The country is also seismically active, so site selection, structural engineering and geographic redundancy between facilities will be part of any serious customer’s due diligence.
Sovereignty Rules Could Decide the AI Question
Data sovereignty, the principle that data is subject to the laws of the country where it is collected or stored, is a strong driver of in-country capacity. Türkiye’s personal data protection law restricts how personal information can leave the country, and the rules governing cross-border transfers were revised in 2024. Sector regulations, particularly in banking, have long required certain systems to be kept inside Türkiye.
Those rules create steady demand from regulated industries regardless of AI. The open question is whether large-scale AI training and inference follows. Training frontier models tends to concentrate in places with abundant, cheap power, while inference, the day-to-day running of AI applications for users, benefits from being close to those users and inside their legal jurisdiction. A Türkiye platform is better positioned for the second category, and its success with AI is likely to hinge on whether global cloud providers and local enterprises choose to run AI services in-country rather than serving Turkish users from European hubs.
Background
Digital Realty Trust, founded in 2004 and headquartered in Austin, Texas, is one of the largest data center owners in the world. Its 2020 acquisition of Interxion significantly expanded its European footprint, and it has entered other markets through joint ventures and acquisitions, including Brazil, India and South Africa. The company earns revenue by leasing data center space, power and interconnection to cloud providers, network carriers and enterprises.
Türkiye’s data center market has historically centered on Istanbul, serving domestic banks, telecom operators and enterprises. Demand for in-country capacity has been supported by the country’s personal data protection law and sector rules that require certain data and systems to remain in Türkiye, alongside growing use of cloud services and, more recently, artificial intelligence.
President Trump says resistance to the giant computer warehouses that power artificial intelligence is a ‘hoax’ and part of a ‘sick conspiracy.’ He made the remarks during a phone call with the head of Nvidia, the company whose chips run most of these facilities.
Why it matters to you: these buildings use as much electricity as a small city, and many neighbors worry about higher power bills, water use and noise. Those local fights are usually decided by town and state officials, not the president, so the remarks raise the temperature without settling the argument.
President Donald Trump called opposition to data centers a ‘hoax’ and said there is a ‘SICK conspiracy’ against artificial intelligence and data centers, according to reports from the Associated Press and CNBC. CNBC reported that the remarks came during a phone call with Nvidia CEO Jensen Huang at the All-In Summit, a technology and investing conference. The AP reported that the president also described AI risks as a ‘hoax.’
Executive Summary
The president has publicly placed the White House on the side of the AI build-out in one of the most active local policy fights in the United States: whether communities should accept very large data centers, the warehouse-scale buildings full of servers that train and run AI models. Opponents in many areas have raised concerns about strain on the electricity grid, water consumption, noise, land use and whether ordinary households end up paying for grid upgrades through their utility bills.
The remarks matter less for what they change legally than for what they signal. Most data center approvals run through county zoning boards, state utility regulators and local water authorities, and a presidential statement does not alter those processes. But framing the opposition as a conspiracy, delivered alongside the CEO of the world’s most important AI chip supplier, raises the political stakes for developers, utilities and local officials at a moment when several states are writing tougher rules for large power users.
A National Voice in a Local Fight
Data center siting is decided close to the ground. A developer typically needs a zoning approval from a county or municipality, an agreement with the local utility to connect to the grid, and in many cases water permits and tax-incentive deals negotiated with state or local economic development agencies. None of those decisions sits with the White House, which means the president’s remarks function primarily as political signaling rather than policy.
That signaling still has consequences. It tells developers the administration views the build-out as a national priority, and it may encourage federal agencies to favor faster permitting where they do have authority, such as on federal land or for projects requiring federal environmental review. It also risks nationalizing what have often been nonpartisan local debates, where opponents and supporters alike include residents across the political spectrum.
Testing the Claims on Both Sides
The concerns raised by local opponents are, for the most part, measurable. How much power a facility will draw, what share of grid upgrade costs it will pay, how much water its cooling system consumes, and how much tax revenue it generates after incentives can all be documented and checked. Describing those concerns collectively as a ‘hoax’ does not engage with the specifics, and a community weighing a project that would add a large new electricity load has legitimate reasons to ask those questions.
The same scrutiny applies to opposition claims. Water use, for example, varies widely depending on cooling design; some facilities use closed-loop or air-based systems that consume far less than evaporative cooling, and blanket claims about data centers draining aquifers do not hold for every project. Likewise, whether opposition in a given place is grassroots or organized is an empirical question. It can be answered with evidence such as funding disclosures, shared messaging or coordinated filings, and neither the ‘conspiracy’ label nor the ‘concerned residents’ label settles it on its own.
What It Means for Nvidia and the Builders
Nvidia’s growth depends on customers being able to build and power the facilities that house its chips. Local permitting delays and grid connection backlogs, the long waitlists utilities keep for large new power users, are among the practical limits on how fast AI capacity can come online. Public support from the White House is a tailwind for the sector’s political standing, but it does not add transmission lines, generating capacity or zoning approvals.
For developers, the risk is that polarization makes local approvals harder rather than easier. Officials who might have negotiated conditions such as community benefit agreements, dedicated power supply or water limits may face more pressure to take a side. The companies most likely to fare well are those that can show, project by project, that they are paying for their own grid impact and meeting local environmental conditions.
The Electricity Bill Question
The most politically durable concern is cost to ordinary ratepayers. When a utility builds new substations, transmission or generation to serve a large facility, regulators must decide how those costs are divided between the new customer and existing households and businesses. Several states have moved toward requiring very large power users to commit to long-term contracts or cover more of the upfront grid investment.
That is where the debate is likely to be decided, in utility commission proceedings rather than in speeches. The industry’s strongest argument is not that opposition is illegitimate but that well-structured tariffs can make data centers pay their own way. Whether that holds up will depend on the details states adopt.
Background
The rapid growth of artificial intelligence has triggered a construction boom in large data centers across the United States, driven by technology companies racing to secure computing capacity. These facilities require substantial and continuous electricity, and in many regions their projected demand has pushed utilities to plan new generation and transmission for the first time in years.
That boom has produced local resistance in a growing number of communities, where residents and officials have questioned grid impacts, water use, noise and tax incentives. State legislatures and utility regulators have responded with new rules on how large power users connect to the grid and share costs. Nvidia, as the leading supplier of AI chips, sits at the center of the build-out, and the administration has repeatedly framed AI infrastructure as a national economic and strategic priority.
