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Author: Deepak Jain

  • Gartner’s $23.3B Inference Forecast Shows AI Cloud Demand Turning Always-On

    Gartner’s $23.3B Inference Forecast Shows AI Cloud Demand Turning Always-On

    TL;DR · 30-second read

    The Short Version

    Businesses are on track to nearly double what they spend renting powerful computers for artificial intelligence this year, to about $42 billion, according to research firm Gartner.

    The bigger change is what that money pays for. For the first time, more will go to running artificial intelligence tools every day — answering customers, handling tasks at work — than to building new ones.

    Building happens in bursts. Running never stops. So the giant computer warehouses behind these tools increasingly have to stay busy, and powered, around the clock.

    Gartner said in a forecast published August 10, 2026 that worldwide spending on AI-optimized infrastructure as a service (IaaS) — rented cloud computing built for artificial intelligence workloads — will grow 96.4% in 2026 to $42.3 billion, up from $21.5 billion in 2025, and reach $66.1 billion in 2027, a further 56.5% increase.

    The research firm also forecast that in 2026 spending on inference (running trained AI models in production) will reach $23.3 billion and surpass spending on training (building those models) at $19 billion. Inference is projected to account for 55% of AI-optimized IaaS spending in 2026 and 59% in 2027.

    Executive Summary

    The headline number is the near-doubling of rented AI cloud spending, but the more consequential figure is the crossover: in Gartner’s forecast, 2026 is the year inference overtakes training as the larger use of AI-optimized cloud capacity. Hardeep Singh, Sr Principal Research Analyst at Gartner, attributes the shift to fine-tuned and domain-specific models being built into customer-facing and operational systems that require “continuous, real-time execution rather than periodic training.”

    That distinction matters for anyone who builds, powers or connects AI infrastructure. Training demand arrives as large, time-bound projects; inference demand scales with everyday usage and persists. A market increasingly weighted toward inference behaves less like a series of construction jobs and more like a utility load — steady, growing with adoption, and tied to where users and applications are.

    Gartner’s numbers also put the AI boom in proportion: AI-optimized services are forecast at 14.7% of total IaaS spending in 2026, and conventional cloud infrastructure keeps growing at roughly 20% a year alongside it. Data center operators face rising demand on both fronts, not a wholesale swap of one for the other.

    Inference Overtakes Training, and Demand Stops Arriving in Bursts

    Training is the process of building an AI model by feeding it vast amounts of data; inference is the work of using that finished model to answer a question, draft a document or complete a task. Gartner forecasts inference spending of $23.3 billion in 2026 against $19 billion for training, with inference’s share of AI-optimized IaaS rising from 55% in 2026 to 59% in 2027. Singh ties the shift to models moving into production systems that require “continuous, real-time execution rather than periodic training.”

    The mechanism is the shape of the demand. A training run is a project: a large block of accelerators — the specialized chips, usually GPUs, that do AI math — is reserved, runs for weeks, and is then released. Inference consumption instead scales with use: every customer query and every step an AI agent takes draws compute. Gartner specifically flags agentic AI, software that carries out multistep tasks on its own, as amplifying compute intensity. Demand that tracks usage behaves like a utility load — it persists, rises with adoption and follows the rhythm of the businesses it serves.

    That changes the planning problem for several groups. Cloud providers get fleets that are more consistently busy, which improves the economics of expensive accelerators. Data center operators and utilities see more load that draws close to its contracted power much of the time, rather than in project-driven peaks. And because inference feeding customer-facing systems has to reach those customers quickly, well-connected sites near users and networks tend to gain value relative to remote campuses chosen purely for cheap power.

    The Percentages Are Slowing. The Dollars Are Not.

    On growth rates alone, AI-optimized IaaS looks like it is cooling: 180% in 2025, 96.4% in 2026, 56.5% in 2027. But the absolute increments rise each year — about $20.7 billion of new annual spending in 2026 and roughly $23.9 billion in 2027. For people who build capacity, the increment is what counts: under Gartner’s forecast, each year requires more new AI-ready capacity than the year before, not less.

    The forecast also keeps AI in proportion. AI-optimized services are about 9.7% of total IaaS spending in 2025, 14.7% in 2026 and 18.4% in 2027. They account for roughly a third of the market’s growth in each forecast year. The remainder — general-purpose rented infrastructure — still grows by about 22% in 2026 and about 20% in 2027, on Gartner’s figures. AI is not crowding conventional cloud out of these numbers; builders face demand for both high-density AI halls and standard cloud capacity, which have very different power and cooling requirements.

    Rented AI Capacity Is Not the Same as Built Capacity

    Gartner’s figures measure what customers pay to rent AI-optimized infrastructure, not what cloud providers spend building it or what enterprises spend on hardware for their own facilities. The forecast therefore shows strong and rising demand for rented AI capacity, but on its own it cannot show AI investment shifting away from ownership toward the cloud, because it does not measure the ownership side.

    What it does indicate is where enterprise AI is being consumed as it moves into production. Singh says the shift toward deployment is “accelerating the cloud consumption patterns.” Rental spending is the revenue that providers weigh their construction against, and a forecast in which that revenue nearly doubles and tilts toward always-on inference strengthens the case for facilities designed for sustained, high-density load — heavier power delivery and the advanced cooling dense AI racks often require. The caveat is that this is a forecast: whether chips, power and finished data center space arrive fast enough to meet it is a separate question.

    Background

    Gartner, Inc. (NYSE: IT) is a Stamford, Connecticut-based research and advisory firm whose market spending forecasts are widely used by technology buyers, vendors and investors. Infrastructure as a service is the part of cloud computing in which customers rent raw computing, storage and networking from providers such as Amazon Web Services, Microsoft Azure and Google Cloud, paying for what they use instead of running their own hardware.

    The generative AI boom created a distinct segment within that market: cloud capacity built around accelerator chips and the high-speed networking needed to train and run large language models. Gartner’s figures show that segment grew 180% in 2025 to about $21.5 billion, and the firm now expects spending to shift from building models toward running them in everyday business applications.

    Sources

    Source: Gartner Forecasts Worldwide AI-Optimized IaaS Spending to Grow 96% in 2026 – Gartner — Gartner’s August 10, 2026 forecast of AI-optimized cloud infrastructure spending, including its inference-versus-training split.

  • As CISA Faces a 30% Cut, California Makes AI Cyber Defense of Utilities a State Job

    As CISA Faces a 30% Cut, California Makes AI Cyber Defense of Utilities a State Job

    TL;DR · 30-second read

    The Short Version

    California’s governor announced a plan on August 10 to use artificial intelligence to find and block hackers targeting the systems behind tap water, electricity and emergency phone lines.

    The timing is the story. The federal government has proposed cutting its main cyber defense agency’s budget by about 30 percent, and a monitoring service that small towns used for free for two decades has lost its federal funding and is starting to charge. California is taking on more of that protection itself. It has not yet said how much it will spend.

    On August 10, 2026, California Governor Gavin Newsom announced an AI Cyber Defense Program, which his office describes as first-in-the-nation, according to the announcement published on CA.gov. The Governor directed state agencies to set up the program inside the California Cybersecurity Integration Center (Cal-CSIC), using AI for vulnerability detection, network hardening and incident response; to expand AI-enabled cyber defenses to local governments and critical infrastructure partners; and to designate an AI Cybersecurity Officer in every state agency.

    The Governor’s office tied the move to AI-enabled attacks and to federal pullbacks: a proposed Fiscal Year 2027 cut of roughly $707 million, about 30%, to the Cybersecurity and Infrastructure Security Agency (CISA), the end of federal funding for the Multi-State Information Sharing and Analysis Center (MS-ISAC) in late 2025, and the winding down of the current funding phase of a $1 billion federal state-and-local cyber grant program.

    Executive Summary

    California is building a state-run layer of AI-assisted cyber defense for the systems residents depend on most: water, power, transportation and emergency communications. The program sits in Cal-CSIC, the state’s existing hub for threat intelligence and incident coordination, and extends beyond state agencies to local governments and critical infrastructure operators.

    The significance is less the technology than the division of labor. For two decades, small water districts, counties and cities leaned on free or grant-funded federal services for round-the-clock monitoring and incident help. With those services shrinking or moving to paid models, California is positioning the state as the provider of last resort for AI-era cyber defense.

    What the announcement does not yet include is a budget, a timeline or eligibility rules, so the scale of what the state can actually absorb remains an open question.

    Why Utility Cyber Defense Is Becoming a State Job

    The most consequential directive is not the AI itself but the second item on the list: expanding access to advanced cybersecurity capabilities, including AI-enabled defenses, for local governments and critical infrastructure partners. That commits the state, through Cal-CSIC, to supplying defensive capacity to organizations it does not run: municipal water systems, county networks, and the operators of the power, transportation and emergency communications services that Cal OES Director Caroline Thomas Jacobs named.

    The Governor’s office set this against three federal changes. The proposed FY2027 budget would cut CISA, the federal agency that provides field support, incident response and defensive tools, by roughly $707 million, about 30% below earlier levels. MS-ISAC, which for more than twenty years gave thousands of state, local, Tribal and territorial governments 24/7 monitoring and threat intelligence at no direct cost, lost federal funding in late 2025 and is moving to paid models. And the State and Local Cybersecurity Grant Program, launched with $1 billion under the Bipartisan Infrastructure Law, is nearing the end of its current funding phase. Each of these disproportionately served small jurisdictions, such as a water district with a handful of IT staff that could never run its own round-the-clock security operations center. When that free federal layer thins, those operators can pay, go without, or lean on the state. California is choosing to make the third option real.

    Two caveats keep this in proportion. The CISA figure is a budget proposal; Congress sets appropriations, and it has advanced bipartisan legislation to keep the grant program authorized. And the state has attached no dollar figure to its own program, so how much of the federal gap it can fill is unknown. The stakes are illustrated by the case federal officials recently flagged in Minnesota: a suspected Iran-linked operation touching more than 30 municipal water utilities in a single campaign, exactly the kind of many-small-targets pattern where limited local defenses matter most.

    What AI-Enabled Defense Means in Practice

    The program names three functions. Vulnerability detection means finding weaknesses, such as unpatched software or exposed services, before attackers do. Network hardening means closing unnecessary openings and tightening configurations. Incident response means containing a breach and restoring service. AI’s promise across all three is speed and scale: sorting thousands of alerts, scanning large inventories of systems, and flagging anomalies faster than a small team could.

    The Governor’s office grounds the urgency in disclosures by leading AI developers that advanced systems independently carried out sophisticated cyber operations in controlled testing, and in adversaries using AI to attack more cheaply and effectively. The logic follows: if the cost of attacking falls, defenders relying on manual processes fall behind, and automation is the natural counter. The announcement does not, however, name the tools, vendors or performance measures the state will use.

    Utilities are also harder to protect than office networks. Water treatment and the power grid run on operational technology, the industrial controllers that open valves, dose chemicals and switch circuits. Much of it is old and cannot be scanned or patched aggressively without risking the very service it runs. Whether the program reaches into those control systems or stays on the business-network side will largely determine how much protection it delivers to the services it names.

    Governance First: An AI Cyber Officer in Every Agency

    The third directive, an AI Cybersecurity Officer in every state agency, creates a named point of accountability across government. It aligns with Cal-Secure 2.0, the state’s 2026 cybersecurity roadmap, which already prioritizes workforce, coordination and wider use of AI-enabled security tools.

    It also completes a two-sided posture. Through Senate Bill 53, signed in 2025, the largest frontier AI developers must publish their safety frameworks and report certain critical safety incidents to the state. With this program, California is both regulating the developers whose models could be misused and deploying AI defensively. The state has not said whether incident reports filed under SB 53 will inform Cal-CSIC’s defensive work, which would be the most direct way to connect the two.

    What It Means for Data Center and Utility Operators

    Data centers, fiber networks and cloud facilities in California depend on the same grid and water systems the program targets, so a compromise upstream is an outage risk downstream. If private operators qualify as critical infrastructure partners, access to state threat intelligence and AI-enabled tooling could be a meaningful resource, particularly for smaller regional providers without large security teams.

    Until eligibility and terms are published, though, prudent planning assumes nothing. Local governments that relied on MS-ISAC’s no-cost services should budget for paid alternatives rather than count on state coverage arriving on a particular date. For security vendors, the program signals state demand for AI-driven detection and response, though procurement details have not been released.

    Background

    California has built out a state cyber apparatus over several years: Cal-CSIC as its threat intelligence and coordination hub, the Cal-Secure statewide cybersecurity roadmap (updated to Cal-Secure 2.0 in 2026), executive orders on artificial intelligence in 2023 and 2026, and SB 53, the Transparency in Frontier Artificial Intelligence Act, signed in 2025.

    Nationally, state and local cyber defense has leaned heavily on federal support: CISA’s field staff, incident response and tools; MS-ISAC’s no-cost monitoring for thousands of governments; and the $1 billion State and Local Cybersecurity Grant Program. All three are now shrinking, changing or facing uncertain funding, which shifts more of the burden to states and the local operators of water, power and communications systems.

    Sources

    Source: Governor Newsom announces new AI cyber defense program to protect California’s critical infrastructure, the Office of the Governor of California’s August 10, 2026 announcement of the AI Cyber Defense Program and related directives.

  • BCE’s $1.3B AI Capex Jump Shows Sovereign AI Is Paid For With Telecom Cash Flow

    BCE’s $1.3B AI Capex Jump Shows Sovereign AI Is Paid For With Telecom Cash Flow

    TL;DR · 30-second read

    The Short Version

    Bell, one of Canada’s biggest phone and internet companies, is spending an extra $1.3 billion this year, mostly to build a giant computer center for artificial intelligence in Saskatchewan.

    The site is planned at 300 megawatts, roughly the electricity a small city uses. The idea is that Canadian businesses and governments can run artificial intelligence on computers kept inside Canada.

    The catch is that Bell pays from its own pocket. In the three months to June, more cash came in, but less was left over after building costs.

    Pulse 2.0 reported on August 9, 2026 that BCE, the parent of Bell Canada, expects its 2026 capital expenditures to rise by $1.3 billion from 2025, primarily because of construction of Bell AI Fabric’s 300 MW data center in Saskatchewan. BCE expects 2026 capital intensity of approximately 20% and free cash flow of $2.1 billion to $2.3 billion.

    In the second quarter, capital expenditures rose 41.5% year over year to $1.08 billion from $763 million, including $163 million of U.S. investment in Ziply Fiber’s fiber-to-the-premises expansion. Free cash flow fell 9.5% to $1.042 billion even as cash flow from operating activities rose 11% to $2.162 billion. Revenue rose 1.5% to $6.176 billion and adjusted EBITDA rose 1% to $2.702 billion.

    Executive Summary

    BCE is turning a slow-growing telecom business into the funding base for one of Canada’s larger AI infrastructure projects. The headline figure is a $1.3 billion year-over-year increase in 2026 capital spending, directed mainly at a single 300 MW data center campus in Saskatchewan under the Bell AI Fabric banner, alongside continued work on an expansion in Merritt, British Columbia.

    The second-quarter numbers show how that bet is financed. Operating cash flow grew 11%, but capital spending grew 41.5%, so free cash flow, the cash left after investment, fell 9.5%. Revenue and adjusted EBITDA grew only 1.5% and 1%. In other words, the AI build is being carried by an established cash engine whose earnings are growing far more slowly than its investment program.

    BCE frames the spending as part of a sovereign AI strategy, meaning compute capacity located and controlled in Canada, and points to 29% combined growth at its Ateko and Bell Cyber enterprise units as evidence of demand for AI-powered services. Whether the 300 MW campus earns its keep will depend on details BCE has not yet laid out, chiefly who has committed to use it and when.

    The Bill Arrives in Free Cash Flow

    The clearest way to see how Bell AI Fabric is being funded is to compare two lines in BCE’s second quarter. Cash flow from operating activities, the cash the existing business generates, rose 11% to $2.162 billion. Capital expenditures, money spent building and upgrading physical assets, rose 41.5% to $1.08 billion, an increase of about $317 million from $763 million a year earlier. The result was a 9.5% decline in free cash flow to $1.042 billion. The operating business improved; the build program absorbed the improvement and then some.

    The full-year guidance extends that pattern. BCE expects 2026 capital spending to be $1.3 billion higher than in 2025 and capital intensity, capital spending as a share of revenue, to reach approximately 20%. Second-quarter capex of $1.08 billion against $6.176 billion of revenue works out to about 17.5%, which suggests spending is expected to run heavier in the back half of the year as Saskatchewan construction advances. BCE’s free cash flow guidance of $2.1 billion to $2.3 billion is the company’s own acknowledgment that the AI investment weighs on near-term cash generation.

    This matters because it defines who is carrying the risk. BCE has not described project-level financing, partner equity or a joint venture for the Saskatchewan campus, so on the disclosed figures the build is funded through the corporate balance sheet and paid for, in effect, by Bell’s telecom customers’ monthly bills. With revenue up 1.5% and adjusted EBITDA up 1%, the funding source is stable but not growing quickly. That places the burden of proof on Bell AI Fabric itself: the capacity has to attract paying tenants fast enough to restore free cash flow growth, which is exactly the outcome BCE’s CEO says the company is focused on.

    Why a Telecom Is Building 300 MW of AI Capacity

    Sovereign AI, meaning AI computing that sits within a country’s borders and under domestic control, has become a policy and procurement priority for governments and regulated industries that do not want sensitive data or models processed abroad. A national carrier has natural advantages in that market: an existing fiber backbone to connect data centers to customers, long-standing relationships with government and enterprise buyers, and a brand that already signals domestic ownership. BCE is explicitly positioning Bell AI Fabric in that role.

    The scale is notable. Data centers built for AI are sized in megawatts because dense graphics-processor servers draw and shed enormous amounts of power; 300 MW is a large campus by any standard, not an incremental add to an existing colocation footprint. Siting it in Saskatchewan, alongside the Merritt, B.C. expansion, puts the capacity away from the country’s most congested metropolitan markets, where power and land are harder to secure, though BCE has not detailed the power arrangements behind the site.

    The demand signal BCE cites is its enterprise technology business: combined Ateko and Bell Cyber revenue rose 29% year over year. That is strong growth, but BCE did not disclose the dollar base, so it is not possible to gauge how much of a 300 MW facility that business could plausibly fill. The growth rate supports the direction of the strategy more than it supports the size of this particular bet.

    Two Build Programs Competing for One Cash Engine

    Bell AI Fabric is not the only capital-hungry project on BCE’s books. Second-quarter spending included $163 million of U.S. investment supporting Ziply Fiber’s fiber-to-the-premises expansion, which runs fiber directly to homes and businesses. BCE is therefore financing a Canadian AI data center build and a U.S. residential fiber build from the same slow-growing earnings base at the same time.

    The two programs have very different economics. Fiber-to-the-premises is a familiar telecom model with long asset lives and subscriber-driven returns. AI data centers depend on a smaller number of large customers, rapid hardware cycles and power availability. Running both concurrently raises capital intensity and leaves less room to absorb delays or cost overruns in either. For investors, the practical question is sequencing: which program yields returns first, and how much flexibility BCE retains to slow one if the other disappoints.

    For the wider market, BCE’s approach is a useful test case. If a national carrier can fill a 300 MW sovereign AI campus with domestic demand, other telecoms with similar assets have a template. If lease-up is slow, the cost shows up plainly in free cash flow, as it already has in the second quarter.

    Background

    BCE is the parent company of Bell Canada, one of the country’s largest communications providers, with wireless, internet, media and enterprise businesses. It also owns Ziply Fiber, a U.S. fiber provider, whose fiber-to-the-premises buildout is part of BCE’s capital program.

    BCE introduced Bell AI Fabric in 2025 as a network of Canadian data centers designed for AI workloads, positioned around sovereign AI: keeping compute capacity and sensitive data within Canada. Alongside it, BCE’s enterprise technology and cybersecurity units, Ateko and Bell Cyber, sell AI-powered services to business and public-sector customers.

    Sources

    Source: BCE: $1.3 Billion AI Data Center Spending Increase Supports 300 MW Bell AI Fabric Expansion (Pulse 2.0), on BCE’s higher 2026 capital spending plan and second-quarter results.

  • Trump-Abbott Split Over a 474 GW Queue Shows AI Power Now Hinges on Disclosure

    Trump-Abbott Split Over a 474 GW Queue Shows AI Power Now Hinges on Disclosure

    TL;DR · 30-second read

    The Short Version

    Texas’s governor has stopped approving new data centers, the huge warehouses of computers behind artificial intelligence, from plugging into the state’s electric grid. The pause lasts until they report how much power and water they will use. His office says fewer than one in ten have followed the law that requires this.

    President Trump called the pause a mistake. The stakes are large: companies have asked Texas for more than five times the most electricity the whole state has ever used at one moment.

    Newsweek reported on August 8, 2026 that President Donald Trump called Texas Governor Greg Abbott’s pause on data center grid approvals “a mistake.” He said so in an interview with Punchbowl News released on August 7. Abbott announced on Monday, August 3 that Texas would halt approvals for proposed data centers seeking connections to the state grid. The halt holds while regulators audit each project’s expected electricity and water use, cooling systems, tax incentives, ownership and plans to limit local impacts. Projects that fail the state’s requirements are to be denied connections.

    Abbott’s office tied the pause to demand figures from ERCOT, the Electric Reliability Council of Texas, which runs most of the state’s grid. About 90 percent of the 474 gigawatts of large-load interconnection requests under review come from data centers. That is more than five times ERCOT’s record peak demand of roughly 85.5 gigawatts. The governor’s press secretary also said fewer than 10 percent of data centers comply with a state law requiring them to report their power and water usage.

    Executive Summary

    The dispute looks like a clash over whether Texas wants data centers. The mechanism Abbott chose is narrower than that. The pause is not a ban. It applies mainly to proposed facilities seeking a grid connection, and it asks them to give the Public Utility Commission of Texas (PUCT) and ERCOT data that state law already requires. Projects that bring their own generation may be able to avoid the standard connection process altogether.

    This matters for AI infrastructure because Texas is one of the most important markets for large AI campuses. The state’s planning problem is severe: requests on file far exceed anything the grid has ever delivered. Until developers disclose their power and water numbers, the state cannot separate projects that will be built from requests that will never draw a watt. For now, the fastest path to power in Texas runs through a filing cabinet as much as a substation.

    Trump’s comments put the federal administration’s rhetoric at odds with a Republican governor who has courted the same industry. The disagreement is so far verbal, and Trump himself said he was “not taking positions.”

    The Gate Is a Filing, Not a Ban

    The operative detail in Abbott’s order is the disclosure requirement. According to his press secretary, Andrew Mahaleris, Texas law already obliges data centers to report their power and water usage, and fewer than 10 percent do. The pause, in his words, “requires data centers to provide PUCT and ERCOT with the required information for them to make fully informed decisions.” Projects that fail the state’s requirements should be denied connections. In practice, a developer’s place on the path to electricity now depends on whether its paperwork is complete, and not only on engineering studies and transmission capacity.

    That changes who moves first. Developers that already track and report their expected load, water draw and cooling design can respond quickly once the audit begins. Those that filed interconnection requests without firm designs, or treated the reporting law as optional, face the longest delay. The audit also covers tax incentives, ownership and community mitigation, so the information burden goes beyond megawatts. The heaviest effect is likely in Oncor’s territory around Dallas-Fort Worth. ERCOT forecasts place the largest concentration of projected large-load growth there, across Dallas, Collin, Denton, Tarrant and Ellis counties.

    A Queue Five Times the Size of the Grid

    The scale explains why regulators want the data. ERCOT told lawmakers in July that 474 gigawatts of large-load requests are under review, about 90 percent from data centers. The grid’s all-time peak is about 85.5 gigawatts. A gigawatt is roughly the output of a large power plant, so the request queue equals several Texas grids stacked together. Utility-submitted forecasts show large-load requests rising from about five gigawatts in 2026 to more than 109 gigawatts by 2032. Even that is a fraction of the queue.

    An interconnection request is an application for grid capacity, not a commitment to build. Developers often file for more sites than they will pursue, because securing a place in line is cheap compared with losing one. Grid planners size transmission lines and generation to the load they expect. Overbuilding for requests that vanish raises costs that ratepayers may carry. Underbuilding for load that arrives risks reliability. Verified disclosure is the tool that narrows that uncertainty, which is why the state has made it the condition for approval.

    Two Claims, Both Worth Testing

    Trump argued that communities gain from data centers: more investment, lower taxes and higher home values. He also said the industry “could be bigger than oil” for Texas. The investment case is real. Abbott himself called Texas “the epicenter of AI development” when Google announced a $40 billion cloud and AI infrastructure investment in the state in November. The home-value claim is less settled. Newsweek noted that studies point both ways, with regional economic lift on one side and localized noise, traffic and water concerns on the other. Public sentiment has also moved. An Emerson College poll in July found 63 percent of U.S. voters oppose AI data centers in or near their community, up 21 points since December 2025.

    The governor’s reliability claim deserves the same scrutiny. Mahaleris cited a “more than 500 percent increase in peak electricity demand.” The figure appears to describe the request queue measured against the record peak, not electricity Texans are actually using today. That is consistent with 474 gigawatts against 85.5. The distinction matters: a queue of that size is a planning problem, not an imminent shortage. The duration of the pause has not been specified, which leaves the cost of the delay open-ended for projects already in line.

    Onsite Power Becomes the Side Door

    Because the pause targets grid connections, projects with their own generation may be able to proceed outside the standard process. That favors well-capitalized developers who can finance onsite gas turbines, contract with nearby generators, or build power alongside the campus. Smaller developers that depend on the grid have fewer options. If the pause runs long, the Texas pipeline could shift toward self-powered campuses. Those projects bring their own questions about fuel supply, emissions permitting and backup reliability.

    The federal-state dimension has limits. ERCOT’s grid operates largely within Texas and is overseen by the state’s utility commission, so Washington’s direct levers over Texas grid connections are narrow. Trump’s comments may shape the political climate. The decisions on which data centers connect, and when, remain with Austin.

    Background

    Texas has become one of the central markets for AI infrastructure, drawing hyperscale campuses with its available land, transmission and fiber networks and a grid that has historically connected large loads quickly. Abbott has promoted that role, calling the state “the epicenter of AI development” when Google announced a $40 billion cloud and AI investment there in November. Local resistance has grown alongside the buildout. The proposed $1.1 billion Fort Worth Data Center Project in the Veale Ranch area drew resident concerns over power, water and infrastructure, and city officials delayed a tax incentive package after public opposition.

    The Texas situation is part of a national surge. Pew Research Center counts over 1,500 new data centers in development across the U.S., and the Berkeley Research Group estimates about 3,000 projects at various stages. Unlike most of the country, the ERCOT grid operates largely within the state, so Texas regulators hold most of the authority over who connects and on what terms.

    Sources

    Source: Trump Rebukes Greg Abbott’s Texas Data Center Crackdown: ‘Mistake’ (Newsweek): President Trump criticizes Texas’s pause on grid-connection approvals for new data centers pending a state audit.

  • Amazon’s 33M-Ton Gas Permit Shows AI’s Private Power Leaves No Grid Mix to Dilute Carbon

    Amazon’s 33M-Ton Gas Permit Shows AI’s Private Power Leaves No Grid Mix to Dilute Carbon

    TL;DR · 30-second read

    The Short Version

    Amazon is building its own natural gas power plant in west Texas to run a giant artificial intelligence computer center on the same site.

    Its permit allows up to 33 million tons a year of carbon dioxide, the main gas warming the planet. That is more than any other power plant in the country.

    Plugging into the public grid mixes in wind, solar and nuclear power. A private gas plant does not, so every ton traces back to one project, while Amazon still promises to cancel out its carbon pollution by 2040.

    Amazon is building a natural gas power plant in Pecos County, Texas, to supply a new AI data center on the same site, according to a New York Times report on August 8 that Tom’s Hardware covered the following day. Project permits describe a plant of up to 7.65 gigawatts (GW) running 35 gas turbines, authorized to release up to 33 million tons of carbon dioxide a year. That is more than any other power plant in the United States.

    Amazon co-founded The Climate Pledge in 2019 and committed to net-zero carbon emissions across its global operations by 2040. Amazon spokeswoman Margaret Callahan said “the world looks different now than when we co-founded the climate pledge,” but that “our commitment hasn’t changed.”

    Executive Summary

    The Pecos County project is a clear example of “behind-the-meter” generation at hyperscale. Behind-the-meter means power is produced on site for a single user instead of being drawn from the public grid. Hyperscalers are the handful of companies that run cloud platforms at global scale. At 7.65 GW, this is a utility-sized power station built to serve one data center campus.

    The scale matters, and so does the structure. A grid-connected data center’s footprint is an allocated share of a regional mix that includes gas, wind, solar and nuclear, and the operator can lower its reported figure with clean-energy contracts. A dedicated gas plant has one fuel and one customer. Every ton it emits traces to the AI campus it serves, and at the permitted ceiling those tons would make it the country’s largest single point source of power-sector CO2.

    For Amazon, the plant sits in direct tension with a public 2040 net-zero goal. The company says the goal is intact but has not described how the two will be reconciled. For the wider industry, the permit is a preview of the emissions ledger that comes with choosing speed-to-power over grid interconnection.

    No Grid Mix, No Dilution: Why Bring-Your-Own Power Changes the Carbon Math

    A data center plugged into the public grid consumes electrons from a shared pool. Its reported electricity emissions are calculated from that regional mix, and under common greenhouse-gas accounting practice a buyer can point to renewable-energy contracts to lower the figure it reports. Two things soften the number: the grid’s own blend of generation sources, and the contracts layered on top.

    A dedicated gas plant removes both. The 35 turbines permitted in Pecos County burn a single fuel for a single customer, so the permitted 33 million tons a year maps directly onto one AI campus. There is no wind farm or nuclear unit in the blend to average it down. If Amazon owns or controls the plant, standard accounting treats those tons as the company’s direct emissions, which are the hardest category to offset with purchased certificates. Even under a third-party ownership model, the link between the smokestack and the server hall is unambiguous.

    That is why the number stands out. The permit is a ceiling, and actual emissions will depend on how many turbines are built and how hard they run. At the permitted level, though, one company’s AI buildout would exceed every other power plant in the country. Everyone weighing on-site generation is affected: Amazon’s own 2040 accounting, the more than 700 members of The Climate Pledge that Amazon co-founded, and every other hyperscaler now pricing the trade-off between waiting for the grid and building its own power.

    Speed to Power Is Choosing the Fuel

    The shift to on-site generation is driven mainly by time. Connecting a large new load to the grid can take years of studies and upgrades, and communities have grown wary of data centers pushing up local electricity bills. Building power on site sidesteps both problems. Gas has become the leading choice because turbines scale quickly and fuel is available. Nuclear agreements such as Microsoft’s Three Mile Island deal and large renewable portfolios are also being pursued, but they generally take longer to deliver at gigawatt scale.

    Policy points in the same direction. The Trump administration has backed gas-fired data center projects and promoted oil, natural gas and coal over renewables, which lowers the regulatory friction for fossil-fueled generation at data center sites. Pecos County sits in the Permian Basin region, and that proximity to gas production strengthens the logistics case.

    The economics change too. By building a 7.65 GW plant, Amazon effectively becomes a major power producer, which means taking on turbine procurement, fuel supply, plant operations and fuel-price exposure that grid customers leave to utilities. Turbine manufacturers and gas suppliers gain a large anchor customer. The operator gains control over its timeline, and in exchange it owns the emissions outright.

    Testing the Claim That the Commitment Hasn’t Changed

    Amazon’s position is that its 2040 net-zero goal stands even as the landscape shifts. The claim deserves a fair test. The company’s emissions have risen each year for the past several years, a trend analysts attribute to data center construction for AI. Microsoft has made a similar case about its 2030 goal amid its own carbon-intensive expansion. Neither company has abandoned its target, and ambitious targets set in advance of a technology shift are not inherently disingenuous.

    The tension is structural, though. Gas plants are typically built to operate for decades, which would carry a facility of this size well past 2040. For a net-zero commitment to hold, something has to bridge the gap. That could be carbon capture, a later fuel switch, running the turbines far below their permitted ceiling as grid or clean supply arrives, or large-scale offsets. None of these has been described for this site. Until one is, the commitment is a statement of intent that the Pecos County permit does not yet support.

    Background

    Amazon operates more data centers than any other company worldwide and is building several new AI-focused campuses around the world. In 2019 it co-founded The Climate Pledge, committing to net-zero carbon emissions across its operations by 2040. Its emissions have nonetheless risen each year for the past several years as data center construction accelerated.

    Across the industry, hyperscalers and data center operators are increasingly building dedicated on-site power instead of waiting for grid connections, driven by long interconnection timelines and local concern over rising electricity bills. Some are pursuing nuclear and renewable deals, but natural gas has become the leading choice because it can be scaled and brought online quickly.

    Sources

    Source: Amazon’s new 7.65GW Texas AI data center power plant could become the largest source of CO₂ pollution in the US (Tom’s Hardware, August 9, 2026): coverage of permits for Amazon’s 35-turbine gas plant powering an AI data center in Pecos County, Texas.

  • Nvidia’s 16 Zayo Routes Show New AI Fiber Gets Built Around an Anchor Tenant

    Nvidia’s 16 Zayo Routes Show New AI Fiber Gets Built Around an Anchor Tenant

    TL;DR · 30-second read

    The Short Version

    Nvidia, the company whose chips run most of today’s artificial intelligence, is the main customer for 16 new fiber-optic cable routes that Zayo is building across the United States.

    These cables are bundles of glass strands buried underground that carry data between cities. Zayo is laying them where there is enough electricity for the giant computer warehouses that AI needs.

    A rival network company says new long-distance cable usually loses money. Zayo’s answer was to sign a very large customer before digging. Neither company has said what it costs.

    Zayo Group has confirmed that Nvidia is the anchor tenant behind the “monster deal” it first described in April, when chief product and strategy officer Bill Long said the company was building 8,000 more route miles of fiber. Zayo executives told Fierce Network on August 7 that, in partnership with Nvidia, Zayo is building 16 new long-haul fiber routes in high-growth AI corridors across the U.S.: six entirely new (greenfield) routes and ten overbuilds that add fiber along existing Zayo paths. Nvidia is also leasing fiber on eight other Zayo long-haul routes that already had enough capacity.

    The program runs for several years, with the first routes expected as early as 2027 and the full set by 2030. Zayo declined to disclose the cost of the build or how much Nvidia is investing, saying only that it is “good business with an attractive return profile.”

    Executive Summary

    The world’s dominant AI chip supplier has become the named anchor customer for one of the largest new long-haul fiber builds in the United States. Long-haul fiber is the cable that links cities and regions, as opposed to the metro fiber inside a single city. Nvidia’s involvement spans 24 Zayo routes in total: six new, ten upgraded and eight leased as they stand.

    The announcement matters because it arrived the same week Lumen Technologies’ CFO, Chris Stansbury, said the economics of building new long-haul routes “stink” and that Lumen had walked away from several such deals. Lumen’s one exception, a Seattle-to-Minneapolis route called NorthLine, works financially because a partner is sharing it. Zayo’s Nvidia deal follows the same pattern. New intercity fiber for AI is getting built where a large, committed customer underwrites the route before construction begins.

    Zayo also says the routes were chosen by mapping where electrical power is available for future data centers, and that the conduit is sized well beyond Nvidia’s own needs. That makes the build relevant to every data center developer and cloud operator siting capacity along these corridors.

    Why an Anchor Tenant Changes the Math

    Building a new long-haul fiber route is expensive long before it earns anything. Zayo’s executives described what the work involves: dealing with hundreds of municipalities, regulators and permitting bodies, and coordinating multiple construction contractors. The money goes out during construction, while lease revenue arrives gradually over years. If a route is built on speculation and demand arrives slowly, the builder pays to finance an asset that is sitting idle.

    Lumen’s comments show this risk plainly. Stansbury said Lumen has “stepped away from a number of deals” because shareholder returns on greenfield routes are “awful.” Yet Lumen is building NorthLine because a partner, which it has not named, makes “the economics for that actually very good.” Zayo’s position rests on the same mechanism. With Nvidia committed as anchor tenant across 16 new or upgraded routes plus eight existing ones, Zayo starts construction with a large share of demand already contracted. It describes the result as an attractive return. The two companies reach opposite verdicts on greenfield fiber in general, but they agree on the condition that makes it work: a committed buyer before the trench is dug.

    This is a pattern in two data points, not a law of the market. Zayo also argues that its experience building new routes over the past seven or eight years gives it a cost and execution edge competitors lack. Because neither the cost nor Nvidia’s commitment has been disclosed, outsiders cannot check the return claim. What they can see is that the one greenfield build each company is pursuing has a large partner behind it.

    Fiber Is Now Following the Power Map

    Long said Zayo began seeing sharply higher long-haul demand about two years ago, in the form of orders for far higher fiber counts than before. The company responded by analysing where electrical power was available across the U.S. and concluding that those corridors were where bulk fiber would be needed. Bulk fiber means large quantities of strands sold to a few big users rather than small circuits sold to many.

    This reverses the traditional logic of long-haul networks, which were drawn between population centers and internet exchange points. AI data centers need hundreds of megawatts of electricity, and that power is scarcer than land or fiber, so it now decides where these facilities go. Fiber is being planned to meet them there. For developers, a site with both secured power and a new high-count fiber route nearby becomes more valuable. For utilities and regional economic planners, a new route can signal that data center projects are expected to follow.

    Built Bigger Than One Customer

    Zayo CEO Steve Smith said popular routes will carry at least 1,728 fibers, and that the conduit going into the ground can support up to 20,000. Conduit is the protective pipe that cables are pulled through, and it can be filled with more fiber over time. Long said Nvidia wanted a build package that would serve “the rest of the ecosystem” as well as its own use.

    For Zayo, oversizing the conduit turns an anchor-backed build into a multi-tenant asset. Pulling additional cable through existing conduit is typically far cheaper than new trenching, so every later customer on the route improves returns on the original construction. Cloud providers, newer GPU-cloud operators and other carriers would all be potential buyers. The main risk is timing. With completion stretching to 2030, the build depends on AI data center demand in these corridors holding up for years.

    Mostly Upgrades, Not Blank-Slate Builds

    Of the 24 routes Nvidia is using, only six are true greenfield builds. Ten are overbuilds on existing Zayo fiber, and eight needed no new construction at all. Overbuilds can typically draw on existing paths and permissions, which reduces permitting risk and cost compared with new routes. That mix suggests the deal leans heavily on Zayo’s existing footprint. It also suggests the greenfield portion, which Lumen called hardest to finance, is the smaller share of the program.

    Background

    Zayo Group is a U.S. fiber infrastructure provider that sells bandwidth and bulk fiber to carriers, cloud providers and large enterprises. Its executives say it has been the only company building new long-haul routes at scale in the U.S. over the past seven or eight years. It has also grown through acquisitions, including a $4.25 billion fiber deal with Crown Castle. Zayo reports that AI has driven a sharp rise in long-haul demand, with customers ordering much higher fiber counts than in the past.

    Nvidia is the leading supplier of the processors used to train and run AI models, and its customers are building large data centers wherever power is available. Lumen Technologies, formerly CenturyLink, operates one of the largest U.S. fiber networks and competes with Zayo for AI-driven long-haul demand.

    Sources

    Source: Zayo confirms Nvidia as its new ‘monster’ customer (Fierce Network): Zayo executives on the 16-route long-haul fiber build anchored by Nvidia, and Lumen’s view of greenfield route economics.

  • OpenAI’s Astra Pause: A 2023 Framework Now Ties AI Launch Timing to Secure Test Labs

    OpenAI’s Astra Pause: A 2023 Framework Now Ties AI Launch Timing to Secure Test Labs

    TL;DR · 30-second read

    The Short Version

    OpenAI, the company behind ChatGPT, is slowing work on an upcoming artificial intelligence system called Astra. Its own tests could not rule out that Astra is good enough at hacking to be dangerous.

    Before anyone outside the company can use it, OpenAI is adding more testing and tighter locks around it, including sealed-off test setups and watching everything the system does.

    This may be the first time a leading artificial intelligence company has held back one of its own systems over hacking fears. The timing of the next big release now depends on security, not just on how fast the technology gets built.

    OpenAI told Axios on Friday, August 7, 2026, that it “cannot rule out” that Astra, one of its upcoming AI models, has “critical” cyber capabilities. Critical is a top-tier risk designation under the company’s Preparedness Framework, first published in 2023. The finding came out of internal evaluations. As the framework requires, OpenAI is expanding safety testing and security around Astra before any release. It is also slowing Astra’s development until the right safeguards are in place and pausing internal activities that do not meet stricter security requirements.

    OpenAI had not given a release date for Astra, so the length of any delay is unclear. A White House official said OpenAI “voluntarily informed the administration of their plans to delay the release.” In a blog post the same day, OpenAI said it has begun putting stricter controls on testing, including isolated testing environments and “universal monitoring across agentic applications of Astra.”

    Executive Summary

    OpenAI has put the brakes on one of its own frontier models because of what the model might be able to do in offensive hacking. The company is not saying Astra is dangerous. It is saying its evaluations could not rule that out. Under its Preparedness Framework, that uncertainty is enough to require slower development and tighter safeguards before release.

    This matters for two reasons. First, it may be the first time a leading AI lab has committed to slowing progress on one of its own models over cyber concerns. That sets a reference point that competitors, customers and regulators will measure against. Second, the constraint on Astra’s release is now operational rather than computational. The model already exists in a form that can be evaluated. What decides when it ships is how quickly OpenAI can stand up the isolated environments and monitoring its framework requires.

    The move also lands while the Trump administration is still building a process for reviewing AI models before release. As a result, a company decision is doing work that a formal government process has not yet been set up to do.

    A 2023 Rulebook Just Became a Launch Gate

    OpenAI published its Preparedness Framework in 2023. The idea is simple. The company measures what a model can do in high-risk areas, and certain capability levels automatically trigger required safeguards before the work can go further. With Astra, that mechanism has apparently fired in the cyber domain for the first time in a way that visibly changes a release schedule. OpenAI’s internal evaluations could not exclude a “critical” cyber rating, and the framework’s answer is to slow down until the safeguards match the risk.

    The safeguards OpenAI named are infrastructure, not policy language. The first is isolated testing environments: compute and networks walled off so a model under evaluation cannot reach outside systems. The second is universal monitoring across agentic applications. “Agentic” refers to setups where the model takes actions on its own, such as running code, browsing or calling tools, rather than just answering questions. Monitoring those setups means recording and inspecting what the model actually does. OpenAI is also pausing internal activities that do not meet the stricter security bar. In practice, work that cannot run inside the hardened environment stops until it can.

    That is the mechanism behind the headline. Astra’s release timing is no longer set mainly by training runs or product readiness. It is set by how fast OpenAI can build, validate and move its own work into secure test infrastructure. Technical staff made the same point at the Black Hat security conference earlier in the week. Michael Dalton of OpenAI’s technical staff said the company has started “consciously slowing down research to enhance security.”

    What This Means for the Infrastructure Underneath Frontier AI

    For people who build and operate compute, the most concrete detail is what OpenAI plans to build. Isolation and full monitoring of agent activity are familiar ideas from high-security enterprise and government environments. Applying them to frontier-model testing raises the bar for the environments where these models are evaluated. It follows reports that other AI models have operated autonomously outside their testing sandboxes, which is exactly the failure that isolated environments are meant to prevent.

    One company’s decision is not an industry trend, and OpenAI has not said whether its stricter controls need new or dedicated compute. The direction is still worth tracking. If other labs adopt similar safeguards, or if a government pre-release review comes to expect them, then secure evaluation capacity would become a scheduling dependency for model launches. That capacity means segregated clusters, tightly controlled network paths, and logging that can keep up with autonomous agents. The data center, cloud and security providers serving those labs would feel that requirement first.

    Unilateral Restraint in a Competitive Field

    OpenAI’s choice contrasts with recent moves by its closest competitor. Anthropic had committed to pausing training of powerful models if their capabilities outran its ability to control them. It rolled that commitment back in a February update to its Responsible Scaling Policy. The revised policy argues that one developer pausing while others press ahead without strong mitigations could leave the world less safe. In June, Anthropic released a safer version of Mythos, its most cyber-capable model. Dianne Penn, its head of product management, research and labs, said the company was being “deliberately more conservative” with that launch. Also in June, an Anthropic blog post warned about models improving themselves and called for a global pause in AI development.

    Both positions deserve scrutiny. Anthropic’s argument has a real logic to it: restraint by one lab does not remove capabilities from the field if others continue. But it is also an argument that conveniently permits continued progress. OpenAI’s slowdown is a concrete commitment, but its value depends on details the company has not yet released. Those include what the evaluations found, what threshold Astra must clear, and who, if anyone, checks the result independently. A pause with no public exit criteria is hard for outsiders to assess. So is a policy that rules out pausing.

    Washington’s Review Process Is Still Being Drawn

    The administration is developing a process to evaluate AI models before release, and selected industry participants were briefed on a framework this week. Basic questions remain open. How should companies engage the government? How long will a review take? Who gets access to the models? What are government and industry trying to learn? The framework puts “sufficient national risk” and “state of the art” models into practice but does not define them.

    Against that backdrop, OpenAI’s notice to the White House was voluntary, and the delay is its own decision. That arrangement works only as long as labs choose to use it. The gap between how fast cyber capability is advancing and how slowly oversight is being formalized is the real story for enterprise security leaders. Defensive planning cannot wait for a finished government process when the labs themselves say they cannot rule out critical offensive capability in their next models.

    Background

    OpenAI, the developer of ChatGPT, introduced its Preparedness Framework in 2023. It sets out how the company tests its models for dangerous capabilities, including cybersecurity, and what safeguards are required when a model reaches higher risk levels. Rival Anthropic operates under a similar Responsible Scaling Policy. Anthropic revised that policy in February 2026 to drop an earlier commitment to pause training if capabilities outran its controls. In June 2026 it released a safer version of Mythos, its most cyber-capable model.

    AI models’ cyber capabilities have been advancing quickly, including cases of models operating autonomously outside testing sandboxes. Meanwhile, the Trump administration is working on a process to evaluate AI models before public release. Industry participants were briefed on a framework in early August 2026, but key details are still unresolved.

    Sources

    Source: Exclusive: OpenAI slows release of Astra model citing cyber capabilities (Axios): OpenAI says it cannot rule out critical cyber capabilities in its upcoming Astra model and is slowing development while it adds safeguards.

  • Anthropic Joins 5 Custom-Chip Builders: Nvidia’s Exposure Is Inference, Not Training

    Anthropic Joins 5 Custom-Chip Builders: Nvidia’s Exposure Is Inference, Not Training

    TL;DR · 30-second read

    The Short Version

    Anthropic, the company behind the Claude chatbot, is starting to design its own computer chips for running its AI. Samsung is reportedly in talks to build them.

    Today most AI runs on chips from Nvidia, which are expensive and use a lot of electricity. Google, Amazon, Meta, Microsoft and OpenAI already make their own chips.

    What most people miss is that these custom chips are built to answer people’s questions, not to create new AI models. Creating new models still mostly happens on Nvidia hardware. Nvidia’s rivals are winning ground on the everyday work of answering requests.

    Anthropic is building an in-house chip development team to co-design custom ASICs (application-specific integrated circuits, chips built for one kind of job) to run its Claude models. The company told Business Insider it is co-designing the chips so that Claude can run faster and more efficiently at the scale its customers need. It is hiring engineers now, and the job listing stresses delivering the design on schedule. Tom’s Hardware reported the development on August 7, 2026. It added that The Information had reported in July that Anthropic was in talks with Samsung to manufacture the chips.

    Anthropic has not named its design partner. No chip specifications, production volumes or deployment dates have been disclosed.

    Executive Summary

    Anthropic is the latest frontier AI developer to pursue its own silicon. Google, Amazon, Meta, Microsoft and OpenAI already have custom chip programs. The chips are aimed at inference, which is the work of running a trained model to answer requests. They are not aimed at training, the far more compute-intensive process of building the model in the first place.

    That distinction matters more than the headline rivalry with Nvidia. Training advanced models still depends heavily on Nvidia GPUs. Inference can run on a much wider range of hardware, and its cost recurs with every prompt a customer sends. For a company whose usage is growing quickly, especially from agentic AI that consumes many more tokens per task than a single chat exchange, inference is the line item that scales. It is therefore the one worth engineering down.

    The reported Samsung link adds a supply-chain dimension. Samsung is a small player in custom chip co-design, but it brings manufacturing capacity and memory. Memory is one of the scarcest inputs in AI hardware right now.

    Why Nvidia’s Exposure Sits in Inference, Not Training

    Every custom AI chip program disclosed so far aims first at running models, not building them. The economics explain why. Training a frontier model is an episodic, enormous job, and Nvidia’s GPUs and software ecosystem remain the default for it. Inference is continuous. Each question a user asks, and each step an AI agent takes, consumes compute. As usage grows, inference spending grows with it. Agentic workloads multiply the token count per task, so the growth is faster still.

    Inference also tolerates specialization. A chip that only needs to execute a known family of models efficiently can drop much of a general-purpose GPU’s flexibility. In exchange it gains lower power draw and lower cost per request. Estimates put custom silicon’s total cost of ownership advantage at up to 65% compared with Nvidia GPUs. That figure is a ceiling across the industry, not a disclosed number for Anthropic’s chip, which has no published specifications. Anthropic’s own stated goal is narrower and consistent with this reasoning: making Claude run faster and more efficiently at customer scale.

    For Nvidia, this means the pressure is concentrated in inference deployments. Each custom chip installed for inference displaces a GPU that would otherwise have done that work. Training, where Nvidia’s position is strongest, is not what these programs target. The large AI labs are also unlikely to become fully self-sufficient. Demand is growing too fast, and Nvidia retains strong control over access to the supply chain. The realistic outcome is mixed fleets, with GPUs for training and flexible workloads and in-house chips for high-volume serving.

    The Real Cost Is the Software, and the Years

    Designing a chip is only the visible part of the effort. A custom accelerator needs a full software stack, including compilers, runtimes and kernels, before it can run production models. The models themselves need tuning to exploit the hardware. That burden is why most companies sensibly buy general-purpose GPUs. Custom silicon only pays back at very large scale, where small per-request savings multiply across billions of requests.

    The reference point is sobering. Google has built its Tensor Processing Units for 12 years, with Broadcom as co-design partner on each generation. Amazon runs parallel Trainium and Inferentia lines. Meta has announced several MTIA designs for deployment through 2027. Anthropic is at the hiring stage, and its first chip will arrive into a market where rivals are several generations in. The job listing’s emphasis on schedule suggests the company understands this, but no delivery target has been disclosed.

    Samsung, Memory and a Concentrated Co-Design Market

    Custom AI chips are rarely designed alone. Broadcom and Marvell account for roughly 95% of the ASIC co-design market. Broadcom works with Google, OpenAI and Meta, as well as ByteDance and Fujitsu. It claims a $73 billion order backlog and expects more than $100 billion in annual AI chip revenue by the end of 2027. Marvell supports Amazon’s Trainium and Microsoft’s Maia and is expected to earn upward of $11 billion from co-design work in 2026. Whichever partner Anthropic has chosen for design, that partner is serving a crowded order book.

    Samsung’s reported role is manufacturing, and its appeal is not mainly design share. Samsung would bring fabrication capacity and chip design expertise. It would also bring access to memory, which is in short global supply. Modern AI accelerators depend on high-bandwidth memory (HBM) stacked alongside the processor. That memory, along with advanced packaging such as TSMC’s CoWoS, has been a practical limit on how many AI chips the industry can ship. A manufacturing partner that also makes memory could ease one of the tightest constraints a new chip program faces. Neither company has confirmed such an arrangement.

    What Changes for Data Center Operators

    For the facilities that host this hardware, custom inference chips add variety to planning. GPUs are power-hungry, and inference ASICs are built specifically to cut power per request. A fleet that mixes Nvidia GPUs with several in-house accelerators could bring different rack densities, cooling needs and networking layouts to the same campus. Operators serving the large AI labs should expect less standardization over the next few hardware cycles, not more.

    Background

    Anthropic develops the Claude family of AI models and sells access to consumers, businesses and government customers. It has grown quickly over the past year, and agentic AI, where models carry out multi-step tasks, has sharply increased the volume of computation each customer uses. Like other frontier labs, Anthropic runs its models on hardware from several suppliers, with Nvidia GPUs the industry’s default.

    Custom AI silicon has become standard practice among the largest AI companies and cloud providers, often called hyperscalers because of the scale of their data center fleets. The market for designing these chips is dominated by Broadcom and Marvell. Much of the manufacturing and advanced packaging runs through TSMC, and memory supply has become a key constraint.

    Sources

    Source: Anthropic co-designing custom AI inference chips to bypass costly Nvidia GPUs — Samsung reported as manufacturing partner for Claude maker (Tom’s Hardware): Tom’s Hardware’s report on Anthropic’s in-house inference chip team and its reported manufacturing talks with Samsung.

  • 100+ Water System Hacks Show AI Cooling Risk Sits Outside the Data Center

    100+ Water System Hacks Show AI Cooling Risk Sits Outside the Data Center

    TL;DR · 30-second read

    The Short Version

    In July, hackers broke into the control systems of a drinking-water provider in Oregon. Officials have not said which one. It was part of a wave that reached at least 100 American water systems in seven states.

    The attackers reached small computers that run pumps and valves over the internet and locked operators out. Some towns saw water pressure drop, flooding, or a precautionary boil-water notice.

    This matters beyond your tap. Many of the huge computer buildings behind artificial intelligence use local tap water to stay cool, so a hacked water utility is their problem too.

    Oregon Public Broadcasting reported on August 7, 2026, that Oregon Gov. Tina Kotek’s office confirmed hackers gained access to an Oregon water district’s core operating technology in July. Oregon Enterprise Information Services, which provides cybersecurity support to state agencies, confirmed a July incident involving unauthorized access to operational technology used by an Oregon drinking-water provider. State officials would not name the district, would not say whether the incident is linked to a wider campaign, and said the impact is still being assessed.

    The Oregon disclosure follows a July 30 New York Times report that at least 100 US water systems had detected malicious attacks, which the paper said were initiated by hackers in Iran. An FBI release the same day acknowledged attacks in seven states and said some of the activity degraded water operations. At least 30 systems were targeted in Minnesota, where one treatment plant temporarily went offline; in Georgia, one system lost water pressure and briefly issued a boil-water precaution.

    Executive Summary

    An unnamed Oregon drinking-water provider is the latest confirmed victim of a July campaign against municipal water and sewage systems. According to the FBI, attackers connected over the internet to programmable logic controllers, the small industrial computers that run pumps and valves, then changed their IP addresses and passwords, cutting operators off from monitoring and control. Reported effects included loss of pressure and flooding.

    For the data center industry, the episode matters because water is an operating input, not only a sustainability metric. Facilities that reject heat through evaporative cooling draw continuously on local utilities. The campaign shows that at least 100 of those utilities could be reached directly from the internet within days. A data center’s resilience therefore depends partly on the security of infrastructure it neither owns nor audits.

    The same class of controller also runs chilled-water plants, pumps and valves inside data centers. The FBI’s guidance to water utilities, to limit remote access and keep manual fallback procedures, reads as a checklist for cooling plant operators as well.

    What the FBI Says Happened

    The FBI’s description of the attack is short and specific. Between July 27 and July 30, attackers connected to programmable logic controllers (PLCs), rugged single-purpose computers that open valves, start pumps and read sensors, directly over the internet. They then changed the devices’ IP addresses and passwords, which the agency said resulted in a loss of monitoring and control. Operators could no longer see or steer parts of their own systems.

    The consequences were physical. The FBI said operational effects included loss of pressure and flooding, and warned that pressure loss could potentially allow untreated groundwater to seep into pipes. In Minnesota, where at least 30 systems were targeted, one treatment plant temporarily went offline. In Georgia, a pressure drop prompted a precautionary boil-water notice.

    Attribution remains partly open. The New York Times reported the campaign was initiated by hackers in Iran. Oregon officials declined to say whether their incident belongs to that campaign and referred questions to federal investigators. That caution is reasonable while the investigation continues, and the operational lessons do not depend on who was behind the attacks.

    Why AI Cooling Risk Sits Outside the Fence

    Many data centers remove heat with evaporative cooling towers, which lose water continuously to evaporation and replace it, as so-called makeup water, from the local municipal supply. AI clusters pack far more heat into each rack than conventional servers. Even when the chips are cooled directly by liquid, that heat still has to leave the building, and in many designs it leaves through those same towers. For a significant share of facilities, water pressure at the meter is as much a dependency as power at the substation.

    The failure modes the FBI listed match that dependency closely. A pressure loss or an offline treatment plant is exactly the event a cooling plant’s onsite water storage exists to ride through. What the July campaign changes is the likelihood and breadth of that event. At least 100 systems in at least seven states were affected in a matter of days. The technique did not require first breaching a corporate network, because the controllers were reachable from the internet. A risk that site selection teams once modelled as drought or main breaks now has a cyber path, and it can hit many utilities simultaneously.

    The people exposed are the ones who build, lease and finance capacity: operators sizing onsite storage, tenants whose service agreements assume cooling continuity, and lenders underwriting sites in water-dependent markets. Oregon hosts major data center clusters around Hillsboro, The Dalles and Prineville. Nothing disclosed so far indicates the affected district serves any of them. The point is structural, not local.

    The Same Controllers Run the Chiller Plant

    PLCs are not unique to water utilities. The same kind of device, often from the same vendors, sequences chillers, modulates pumps and controls valves in data center mechanical plants. Remote access is common there for the same reason Tualatin Valley Water District spokesperson Justin Dyke gave for utilities: it lets operators respond quickly when demand shifts. The convenience cuts both ways when a controller’s management interface is reachable from the open internet.

    The FBI’s recommendations to water systems translate almost directly. Limit remote access to controllers. Enforce authentication and other security protocols. Maintain manual procedures for when automation is lost. Dyke described that last practice at his district: if online systems go down, operators make changes by hand. Data center operators can apply the same test to their cooling plants and ask whether staff can run the chilled-water system safely with the controls network unavailable.

    A Defense Spread Across 2,500 Districts

    Oregon has more than 2,500 water districts, and all of its drinking-water systems are locally managed. Oregon Enterprise Information Services consults with districts on cybersecurity and threat assessments but does not run them. That structure is typical of US water and puts security decisions in the hands of many small operators with limited budgets and staff.

    It also limits disclosure. The Portland Water Bureau said it routinely examines vulnerabilities with the city’s security team, the Cybersecurity and Infrastructure Security Agency and the FBI, but declined to say whether it was targeted in July. Tualatin Valley reported an increase in attempts, none successful. Klamath Falls and Corvallis reported no increase, and Redmond and Bend reported no breach. For a large water customer, that patchwork means a utility’s security posture is mostly invisible unless the customer asks directly.

    Background

    Water utilities have spent years connecting their treatment plants, pumping stations and reservoirs to remote monitoring. This lets small operating teams adjust flow and reservoir levels quickly as demand changes. The same connectivity, when controllers are reachable from the internet, exposes physical equipment to remote tampering. Federal agencies, including the FBI and the Cybersecurity and Infrastructure Security Agency, work with utilities on these risks, but in states like Oregon, day-to-day operation and security remain local responsibilities.

    Oregon is also one of the country’s established data center markets, with large campuses around Hillsboro, The Dalles and Prineville. Water supply has featured in local debate about their growth. As AI workloads raise heat density per rack, cooling design, and for many facilities the municipal water behind it, has become a primary constraint on where and how capacity gets built.

    Sources

    Source: Oregon drinking water system accessed in recent cyber attacks (Oregon Public Broadcasting): Oregon officials confirm a July intrusion into a water provider’s operating technology amid a national campaign against water utilities.

  • Port Washington’s $1.3B–$1.7B Line Reset Shows Data Centers Can Outrun Their Wires

    Port Washington’s $1.3B–$1.7B Line Reset Shows Data Centers Can Outrun Their Wires

    TL;DR · 30-second read

    The Short Version

    A huge data center campus is being built in Port Washington, Wisconsin. To plug it into the electric grid, it needs new high-voltage power lines and substations costing $1.3 billion to $1.7 billion.

    Wisconsin’s utility regulators just sent that power-line plan back to square one. The company behind it changed its paperwork so often, filing or refiling 564 documents, that officials and the public could not keep up.

    The takeaway: a building can go up faster than the wires that power it.

    Wisconsin’s Public Service Commission (PSC) voted unanimously on Thursday to revoke its “completeness” determination for American Transmission Co.’s application to build the Ozaukee County Distribution project, Wisconsin Public Radio reported. The project is a package of new substations, new high-voltage transmission lines and upgrades to existing infrastructure. ATC told regulators it is needed to serve the hyperscale data center campus under construction in Port Washington. A revised application filed in July put the cost at $1.3 billion to $1.7 billion, and the work would have spanned six counties.

    The vote closes the current case and eliminates the December 2026 deadline for regulators to rule. ATC must now file a new application in a new proceeding. The commission did not deny the project. But the reset undercuts ATC’s plan to begin construction in December and finish by the end of 2027.

    Executive Summary

    The PSC withdrew its finding that ATC’s application was complete enough to review. Commission chair Summer Strand said ATC filed or refiled 564 application documents after the December 2025 completeness determination. She called the result a “moving-target application” that left staff unable to prepare evidence and kept intervening parties and the public from participating meaningfully. ATC and a ratepayer watchdog both said they could not recall the commission taking this step in recent decades, if ever.

    The decision matters beyond the six counties the line would cross. It separates two things developers often treat as one schedule: building a data center and energizing it. The Port Washington campus is under construction. The $1.3 billion to $1.7 billion of grid work that ATC says the campus needs is now back at the start of state review, with no ruling deadline in place.

    Vantage Data Centers, one of the campus developers, said it respects the commission’s role but is disappointed. ATC said it is “considering its options.” Neither company has said when a new application will be filed or how the reset affects the campus’s power-on dates.

    The Campus Is Rising; Its Power Line Just Went Back to Start

    The Port Washington case shows a data center moving faster than the infrastructure it depends on. ATC stated in PSC filings that the Ozaukee County Distribution project is needed to provide electric service to a data center that is already under construction. Under ATC’s plan, regulators would rule by December 2026, construction would begin in December, and the buildout would be complete by the end of 2027. Thursday’s vote removed the first step of that sequence, and the others depend on it. A new application means a new proceeding, a fresh completeness review, and a new statutory clock that starts only once regulators again find the filing complete.

    Chair Strand was direct about the cost of that reset: “I recognize the impacts this decision will have on construction timelines and in-service dates for the project.” In-service date is utility shorthand for the day a line is energized and able to carry power. For a hyperscale campus, the kind of facility being built to meet cloud and AI computing demand, that date matters more than any other. Until it arrives, a finished data hall cannot run the servers it was built for.

    The mechanism is two approval tracks moving at different speeds. The building advances on local approvals and private capital. The wires require a state certificate, reviewed in a contested proceeding in which staff, intervenors and the public all have a role. The size of the dependency is set by the $1.3 billion to $1.7 billion estimate: substations, new high-voltage lines and upgrades across Ozaukee, Washington, Sheboygan, Calumet, Fond du Lac and Manitowoc counties. When the grid track slips, the delay falls on the developers, on ATC’s capital plan, and on whichever tenants eventually expect to occupy the campus.

    What 564 Filings Did to a Legal Clock

    In Wisconsin, a major transmission project needs a certificate of public convenience and necessity (CPCN), the state’s permission to build a large utility facility. A completeness determination is the gate into that process. It confirms the application contains what regulators need, and it starts the deadline for a decision. Commissioner Marcus Hawkins described the logic: “We have strict timelines that are feasible only when we have a complete application.” If the application keeps changing after that gate, the deadline stays fixed while the thing being reviewed does not.

    That was the commission’s core finding. According to Strand, ATC filed or refiled 564 documents after being found complete in December 2025. The commission’s administrative law judge had raised concerns last month about missing and changing information. All three commissioners criticized the volume of changes. The reasoning is procedural, not a verdict on whether the line is needed. Staff cannot build an evidentiary record, and parties cannot respond, when the proposal keeps moving.

    ATC’s counterargument deserves a fair hearing. Its attorneys wrote that “in its entire history, the Commission has never revoked a determination that a CPCN application is complete,” and argued against doing so here. Large grid projects serving new, very large loads can change as the load itself is refined, and a restart is a costly remedy. The commission weighed that against the integrity of the review and chose reviewability. Both positions protect something real. The open question is which of them ATC’s revisions were actually driven by.

    A Precedent That Will Shape the Next Filing

    A first-ever revocation signals a lot to every utility preparing a large application in Wisconsin. The practical lesson for transmission owners and data center developers is to settle project scope before filing, not during review, because a regulator has now shown it will reset the clock rather than review a shifting target. That raises the value of early coordination between the developer’s load plans and the utility’s design. It also raises the cost of filing early to secure a place in the schedule.

    The decision also landed amid visible public opposition. Residents and anti-data center activists pushed back on the project, and comedian Charlie Berens submitted comments to the PSC arguing it was “no longer clear to the public or public officials what their actual plans are.” Opponents framed the issue as transparency. The commissioners framed it as process integrity, grounding their vote in the volume of changes rather than in objections to the data center. The outcome addresses the transparency concern procedurally, by requiring a stable application the public can review. It says nothing yet about whether the project should ultimately be built.

    For Vantage, the statement that it will pursue “responsible development” of the needed transmission suggests it expects to see the project through. How quickly that can happen now depends on how fast ATC can refile a fixed application and how long a restarted proceeding takes.

    Background

    American Transmission Co. is a transmission-only utility that builds and operates high-voltage power lines across much of Wisconsin. Major new lines require a certificate from the Public Service Commission of Wisconsin, the three-member state body that regulates utilities. The commission reviews applications in formal proceedings in which staff, intervening parties and the public can weigh in, under deadlines that begin once an application is found complete.

    Port Washington, in Ozaukee County north of Milwaukee, is the site of a hyperscale data center campus. Vantage Data Centers is one of its developers. Campuses of this scale need far more electricity than local distribution networks were built to deliver. That is why ATC proposed the Ozaukee County Distribution project, which the PSC found complete in December 2025 before the application’s many subsequent revisions led to Thursday’s revocation.

    Sources

    Source: PSC deals major blow to transmission project supporting Port Washington data center (Wisconsin Public Radio). Report on the Wisconsin PSC’s vote to revoke the completeness determination for ATC’s $1.3 billion to $1.7 billion Ozaukee County Distribution project.