SoftBank, DigitalBridge, and Stargate: The Next Phase of OpenAI’s Infrastructure Strategy

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SoftBank, DigitalBridge, and Stargate: The Next Phase of OpenAI’s Infrastructure Strategy

OpenAI framed Stargate as an AI infrastructure platform; a mechanism to secure long-duration, frontier-scale compute across both training and inference by coordinating capital, land, power, and supply chain with major partners. When OpenAI announced Stargate in January 2025, the headline commitment was explicit: an intention to invest up to $500 billion over four to five […]

The strategic backdrop in 2025 was straightforward. OpenAI’s model roadmap—larger models, more agents, expanded multimodality, and rising enterprise workloads—was driving a compute curve increasingly difficult to satisfy through conventional cloud procurement alone. Stargate emerged as a form of “control plane” for:

Capacity ownership and priority access, rather than simply renting GPUs.
Power-first site selection, encompassing grid interconnects, generation, water access, and permitting.
A broader partner ecosystem beyond Microsoft, while still maintaining a working relationship with Microsoft for cloud capacity where appropriate.

2025 Progress: From Launch to Portfolio Buildout

January 2025: Stargate Launches as a National-Scale Initiative

OpenAI publicly launched Project Stargate on Jan. 21, 2025, positioning it as a national-scale AI infrastructure initiative. At this early stage, the work was less about construction and more about establishing governance, aligning partners, and shaping a public narrative in which compute was framed as “industrial policy meets real estate meets energy,” rather than simply an exercise in buying more GPUs.

July 2025: Oracle Partnership Anchors a 4.5-GW Capacity Step

On July 22, 2025, OpenAI announced that Stargate had advanced through a partnership with Oracle to develop 4.5 gigawatts of additional U.S. data center capacity. The scale of the commitment marked a clear transition from conceptual ambition to site- and megawatt-level planning.

A figure of this magnitude reshaped the narrative. At 4.5 GW, Stargate forced alignment across transformers, transmission upgrades, switchgear, long-lead cooling equipment, and—critically—financing structures capable of absorbing multi-year construction timelines and staged ramp schedules.

September 2025: Five New Sites Define a Clearer U.S. Footprint

On Sept. 23, 2025, OpenAI, Oracle, and SoftBank Group announced five new U.S. Stargate data center sites, describing a program nearing 7 GW of planned capacity and more than $400 billion in projected investment over the next three years.

Subsequent reporting clarified the site mix: three Oracle-led developments, including Shackelford County, Texas, and Doña Ana County, New Mexico, alongside two additional sites tied to SoftBank and its affiliates, including Lordstown, Ohio, and Milam County, Texas.

OpenAI later confirmed that the initially referenced “Midwest” site would be located in Wisconsin, developed by Oracle in partnership with Vantage Data Centers.

By this point, Stargate was clearly evolving from a single flagship build into a portfolio strategy: multiple campuses, multiple utility territories, diversified construction schedules, and, implicitly, diversified political and regulatory risk.

October 2025: Supply Chain Alignment and Campus Expansion

Samsung and SK join (Oct. 1, 2025)
OpenAI announced that Samsung and SK hynix had joined Stargate-related efforts, framed around scaling advanced memory production and broader AI infrastructure enablement.

The announcement underscored a defining 2025 constraint: high-bandwidth memory had become one of the primary governors of effective GPU deployment. Participation in Stargate was as much about long-term capacity assurance across the HBM supply chain as it was about signaling alignment with OpenAI’s infrastructure ambitions.

Wisconsin campus (Oct. 22, 2025)
OpenAI, Oracle, and Vantage confirmed plans for a Stargate campus in Wisconsin, explicitly tying the site to the earlier 4.5-GW expansion announced in July.

Michigan campus (Oct. 30, 2025)
OpenAI announced a Stargate campus in Saline Township, Michigan, stating that the overall program now exceeded 8 GW in planned capacity and $450 billion in projected investment across previously announced U.S. sites.

The Michigan project quickly became a case study in “AI infrastructure meets local process.” Coverage noted that regulators approved power delivery—reported at roughly 1.4 GW—in a manner that triggered public backlash over transparency and community impact, highlighting the role of local governance in shaping AI infrastructure timelines.

December 2025: SoftBank Strengthens Its Role Through DigitalBridge

On Dec. 28, 2025, SoftBank announced its acquisition of DigitalBridge, bringing approximately $108 billion in digital infrastructure assets; including data centers, fiber networks, and edge platforms—under its control.

The move marked a shift in SoftBank’s role within Project Stargate, from primarily a financial participant to a stakeholder with direct exposure to infrastructure ownership and operations, broadening and deepening its alignment with OpenAI.

International Expansion: UAE First, Argentina Explored

In May 2025, OpenAI announced Stargate UAE, describing a 1-gigawatt compute cluster to be built by G42 and operated by OpenAI and Oracle, with Cisco and NVIDIA named among key participants.

In October 2025, Reuters reported that OpenAI had signed a letter of intent with Sur Energy to explore “Stargate Argentina,” a proposed $25 billion project targeting up to 500 MW. The announcement framed Argentina as Stargate’s first potential foothold in Latin America.

The Less Glamorous—but Decisive—Work of 2025

Financing: Why Bankability, Not GPUs, Became the Constraint

At Stargate’s scale, progress rarely takes the form of ribbon cuttings. More often, it appears as debt facilities, equity partnerships, and contract structures (such as take-or-pay power agreements and long-term leases) that determine whether projects can move from paper to steel.

By late 2025, reporting pointed to financing volatility around specific campuses. Coverage by the Financial Times and Reuters, for example, described uncertainty surrounding Oracle’s roughly $10 billion Michigan project after talks with a key backer reportedly stalled. The episode illustrated a core reality of Stargate’s approach: even with massive top-line commitments, each campus must independently clear underwriting and risk-allocation hurdles.

Grid Interconnection and Permitting: The Quiet Schedule Drivers

The Michigan experience also underscored a broader 2025 reality. Grid interconnection timelines, utility approvals, and local buy-in increasingly functioned as gating items for AI infrastructure delivery. “AI urgency” alone does not compress public utility oversight, regulatory review, or community process: factors that can materially shape schedules regardless of capital availability or demand signals.

What 2025 Progress Meant for OpenAI—Beyond the Stargate Program Itself

Compute optionality beyond Microsoft.
Stargate gave OpenAI meaningful leverage. The company could add capacity through Oracle- and SoftBank-led developments while continuing to rely on incumbent cloud relationships, most notably Microsoft, where they remained strategically or operationally advantageous.

A shift from cloud customer to infrastructure orchestrator.
Across 2025 announcements, OpenAI was consistently positioned not as a tenant, but as the operational lead and architectural coordinator of a multi-partner industrial buildout spanning sites, power procurement, supply chain alignment, and complex financing structures.

A playbook for the AI factory era.
The cadence of 2025 vis a vis gigawatt-scale capacity commitments, multi-site portfolios, and early international replication made Stargate resemble a standardized rollout model rather than a one-off mega-campus. The project increasingly looked like a template for how frontier AI infrastructure will be deployed at scale.

The 2025 Bottom Line

By the end of 2025, OpenAI had moved Stargate from announcement to mapped portfolio: multiple U.S. sites across Texas, New Mexico, Ohio, Wisconsin, and Michigan, alongside early international nodes in the UAE and exploratory efforts in Argentina.

The clearest indicators of progress were not completed facilities, but gigawatt-level capacity commitments, named partners, identified sites, and the start of the complex utility, financing, and public-process work that will ultimately determine whether deliveries between 2026 and 2028 arrive on schedule.

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China to Sustain Energy Storage Leadership, WoodMac Projects

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Venezuela to turn over 30-50 million bbl of oil to US; Washington seizes additional tankers

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Goldman conference wrap: Northern boss says ‘we’re at the bottom of the cycle’

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It will work with utilities and public commissions to set the rates it pays high enough to cover data center electricity costs (including build-outs, additions, and active use). “Our goal is straightforward: To ensure that the electricity cost of serving our data centers is not passed on to residential customers,” Smith emphasized. For example, the company is supporting a new rate structure Wisconsin that would charge a class of “very large customers,” including data centers, the true cost of the electricity required to serve them. It will collaborate “early, closely, and transparently” with local utilities to add electricity and supporting infrastructure to existing grids when needed. For instance, Microsoft has contracted with the Midcontinent Independent System Operator (MISO) to add 7.9GW of new electricity generation to the grid, “more than double our current consumption,” Smith noted. It will pursue ways to make data centers more efficient. For example, it is already experimenting with AI to improve planning, extract more electricity from existing infrastructure, improve system resilience, and speed development of new infrastructure and technologies (like nuclear energy). It will advocate for state and national public policies that ensure electricity access that is affordable, reliable, and sustainable in neighboring communities. Microsoft previously established priorities for electricity policy advocacy, Smith noted, but “progress has been uneven. This needs to change.” Microsoft is similarly committed when it comes to data center water use, promising four actions: Reducing the overall amount of water its data centers use, initially improving it by 40% by 2030. The company is exploring innovations in cooling, including closed-loop systems that recirculate cooling liquids. It will collaborate with local utilities to map out water, wastewater, and pressure needs, and will “fully fund” infrastructure required for growth. For instance, in Quincy, Washington, Microsoft helped construct a water reuse utility that recirculates

Can retired naval power plants solve the data center power crunch?

HGP’s plan includes a revenue share with the government, and the company would create a decommissioning fund, according to Bloomberg. The alternative? After a lengthy decommissioning process, the reactors are shipped to a remote storage facility in Washington state together dust along with dozens of other retired nuclear reactors. So the carrier itself isn’t going to be turned into a data center, but its power plants are being proposed for a data center on land. And even with the lengthening decommissioning process, that’s still faster than building a nuclear power plant from scratch. Don’t hold your breath, says Kristen Vosmaer, managing director, JLL Work Dynamics Data Center team. The idea of converting USS Nimitz’s nuclear reactors to power AI data centers sounds compelling but faces insurmountable obstacles, he argues. “Naval reactors use weapons-grade uranium that civilian entities cannot legally possess, and the Nuclear Regulatory Commission has no pathway to license such facilities. Even setting aside the fuel issue, these military-designed systems would require complete reconstruction to meet civilian safety standards, eliminating any cost advantages over purpose-built nuclear plants,” Vosmaer said. The maritime concept itself, however, does have some merit, said Vosmaer. “Ocean cooling can reduce energy consumption compared to land-based data centers, and floating platforms offer positioning flexibility that fixed facilities cannot match,” Vosmaer said.

What exactly is an AI factory?

Others, however, seem to use the word to mean something smaller than a data center, referring more to the servers, software, and other systems used to run AI. For example, the AWS AI Factory is a combination of hardware and software that runs on-premises but is managed by AWS and comes with AWS services such as Bedrock, networking, storage and databases, and security. At Lenovo, AI factories appear to be packaged servers designed to be used for AI. “We’re looking at the architecture being a fixed number of racks, all working together as one design,” said Scott Tease, vice president and general manager of AI and high-performance computing at Lenovo’s infrastructure solutions group. That number of racks? Anything from a single rack to hundreds, he told Computerworld. Each rack is a little bigger than a refrigerator, comes fully assembled, and is often fully preconfigured for the customer’s use case. “Once it arrives at the customer site, we’ll have service personnel connect power and networking,” Tease said. For others, the AI factory concept is more about the software.

Meta establishes Meta Compute to lead AI infrastructure buildout

At that scale, infrastructure constraints are becoming a binding limit on AI expansion, influencing decisions like where new data centers can be built and how they are interconnected. The announcement follows Meta’s recent landmark agreements with Vistra, TerraPower, and Oklo aimed at supporting access to up to 6.6 gigawatts of nuclear energy to fuel its Ohio and Pennsylvania data center clusters. Implications for hyperscale networking Analysts say Meta’s approach indicates how hyperscalers are increasingly treating networking and interconnect strategy as first-order concerns in the AI race. Tulika Sheel, senior vice president at Kadence International, said that Meta’s initiative signals that hyperscale networking will need to evolve rapidly to handle massive internal data flows with high bandwidth and ultra-low latency. “As data centers grow in size and GPU density, pressure on networking and optical supply chains will intensify, driving demand for more advanced interconnects and faster fiber,” Sheel added. Others pointed to the potential architectural shifts from this. “Meta is using Disaggregated Scheduled Fabric and Non-Scheduled Fabric, along with new 51 Tbps switches and Ethernet for Scale-Up Networking, which is intensifying pressure on switch silicon, optical modules, and open rack standards,” said Biswajeet Mahapatra, principal analyst at Forrester. “This shift is forcing the ecosystem to deliver faster optical interconnects and greater fiber capacity, as Meta targets significant backbone growth and more specialized short-reach and coherent optical technologies to support cluster expansion.” The network is no longer a secondary pipe but a primary constraint. Next-generation connectivity, Sheel said, is becoming as critical as access to compute itself, as hyperscalers look to avoid network bottlenecks in large-scale AI deployments.

AI, edge, and security: Shaping the need for modern infrastructure management

The rapidly evolving IT landscape, driven by artificial intelligence (AI), edge computing, and rising security threats, presents unprecedented challenges in managing compute infrastructure. Traditional management tools struggle to provide the necessary scalability, visibility, and automation to keep up with business demand, leading to inefficiencies and increased business risk. Yet organizations need their IT departments to be strategic business partners that enable innovation and drive growth. To realize that goal, IT leaders should rethink the status quo and free up their teams’ time by adopting a unified approach to managing infrastructure that supports both traditional and AI workloads. It’s a strategy that enables companies to simplify IT operations and improve IT job satisfaction. 5 IT management challenges of the AI era Cisco recently commissioned Forrester Consulting to conduct a Total Economic Impact™ analysis of Cisco Intersight. This IT operations platform provides visibility, control, and automation capabilities for the Cisco Unified Computing System (Cisco UCS), including Cisco converged, hyperconverged, and AI-ready infrastructure solutions across data centers, colocation facilities, and edge environments. Intersight uses a unified policy-driven approach to infrastructure management and integrates with leading operating systems, storage providers, hypervisors, and third-party IT service management and security tools. The Forrester study first uncovered the issues IT groups are facing: Difficulty scaling: Manual, repetitive processes cause lengthy IT compute infrastructure build and deployment times. This challenge is particularly acute for organizations that need to evolve infrastructure to support traditional and AI workloads across data centers and distributed edge environments. Architectural specialization and AI workloads: AI is altering infrastructure requirements, Forrester found. Companies design systems to support specific AI workloads — such as data preparation, model training, and inferencing — and each demands specialized compute, storage, and networking capabilities. Some require custom chip sets and purpose-built infrastructure, such as for edge computing and low-latency applications.

DCF Poll: Analyzing AI Data Center Growth

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Microsoft will invest $80B in AI data centers in fiscal 2025

And Microsoft isn’t the only one that is ramping up its investments into AI-enabled data centers. Rival cloud service providers are all investing in either upgrading or opening new data centers to capture a larger chunk of business from developers and users of large language models (LLMs). In a report published in October 2024, Bloomberg Intelligence estimated that demand for generative AI would push Microsoft, AWS, Google, Oracle, Meta, and Apple would between them devote $200 billion to capex in 2025, up from $110 billion in 2023. Microsoft is one of the biggest spenders, followed closely by Google and AWS, Bloomberg Intelligence said. Its estimate of Microsoft’s capital spending on AI, at $62.4 billion for calendar 2025, is lower than Smith’s claim that the company will invest $80 billion in the fiscal year to June 30, 2025. Both figures, though, are way higher than Microsoft’s 2020 capital expenditure of “just” $17.6 billion. The majority of the increased spending is tied to cloud services and the expansion of AI infrastructure needed to provide compute capacity for OpenAI workloads. Separately, last October Amazon CEO Andy Jassy said his company planned total capex spend of $75 billion in 2024 and even more in 2025, with much of it going to AWS, its cloud computing division.

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Join our daily and weekly newsletters for the latest updates and exclusive content on industry-leading AI coverage. Learn More Self-driving tractors might be the path to self-driving cars. John Deere has revealed a new line of autonomous machines and tech across agriculture, construction and commercial landscaping. The Moline, Illinois-based John Deere has been in business for 187 years, yet it’s been a regular as a non-tech company showing off technology at the big tech trade show in Las Vegas and is back at CES 2025 with more autonomous tractors and other vehicles. This is not something we usually cover, but John Deere has a lot of data that is interesting in the big picture of tech. The message from the company is that there aren’t enough skilled farm laborers to do the work that its customers need. It’s been a challenge for most of the last two decades, said Jahmy Hindman, CTO at John Deere, in a briefing. Much of the tech will come this fall and after that. He noted that the average farmer in the U.S. is over 58 and works 12 to 18 hours a day to grow food for us. And he said the American Farm Bureau Federation estimates there are roughly 2.4 million farm jobs that need to be filled annually; and the agricultural work force continues to shrink. (This is my hint to the anti-immigration crowd). John Deere’s autonomous 9RX Tractor. Farmers can oversee it using an app. While each of these industries experiences their own set of challenges, a commonality across all is skilled labor availability. In construction, about 80% percent of contractors struggle to find skilled labor. And in commercial landscaping, 86% of landscaping business owners can’t find labor to fill open positions, he said. “They have to figure out how to do

2025 playbook for enterprise AI success, from agents to evals

Join our daily and weekly newsletters for the latest updates and exclusive content on industry-leading AI coverage. Learn More 2025 is poised to be a pivotal year for enterprise AI. The past year has seen rapid innovation, and this year will see the same. This has made it more critical than ever to revisit your AI strategy to stay competitive and create value for your customers. From scaling AI agents to optimizing costs, here are the five critical areas enterprises should prioritize for their AI strategy this year. 1. Agents: the next generation of automation AI agents are no longer theoretical. In 2025, they’re indispensable tools for enterprises looking to streamline operations and enhance customer interactions. Unlike traditional software, agents powered by large language models (LLMs) can make nuanced decisions, navigate complex multi-step tasks, and integrate seamlessly with tools and APIs. At the start of 2024, agents were not ready for prime time, making frustrating mistakes like hallucinating URLs. They started getting better as frontier large language models themselves improved. “Let me put it this way,” said Sam Witteveen, cofounder of Red Dragon, a company that develops agents for companies, and that recently reviewed the 48 agents it built last year. “Interestingly, the ones that we built at the start of the year, a lot of those worked way better at the end of the year just because the models got better.” Witteveen shared this in the video podcast we filmed to discuss these five big trends in detail. Models are getting better and hallucinating less, and they’re also being trained to do agentic tasks. Another feature that the model providers are researching is a way to use the LLM as a judge, and as models get cheaper (something we’ll cover below), companies can use three or more models to

OpenAI’s red teaming innovations define new essentials for security leaders in the AI era

Join our daily and weekly newsletters for the latest updates and exclusive content on industry-leading AI coverage. Learn More OpenAI has taken a more aggressive approach to red teaming than its AI competitors, demonstrating its security teams’ advanced capabilities in two areas: multi-step reinforcement and external red teaming. OpenAI recently released two papers that set a new competitive standard for improving the quality, reliability and safety of AI models in these two techniques and more. The first paper, “OpenAI’s Approach to External Red Teaming for AI Models and Systems,” reports that specialized teams outside the company have proven effective in uncovering vulnerabilities that might otherwise have made it into a released model because in-house testing techniques may have missed them. In the second paper, “Diverse and Effective Red Teaming with Auto-Generated Rewards and Multi-Step Reinforcement Learning,” OpenAI introduces an automated framework that relies on iterative reinforcement learning to generate a broad spectrum of novel, wide-ranging attacks. Going all-in on red teaming pays practical, competitive dividends It’s encouraging to see competitive intensity in red teaming growing among AI companies. When Anthropic released its AI red team guidelines in June of last year, it joined AI providers including Google, Microsoft, Nvidia, OpenAI, and even the U.S.’s National Institute of Standards and Technology (NIST), which all had released red teaming frameworks. Investing heavily in red teaming yields tangible benefits for security leaders in any organization. OpenAI’s paper on external red teaming provides a detailed analysis of how the company strives to create specialized external teams that include cybersecurity and subject matter experts. The goal is to see if knowledgeable external teams can defeat models’ security perimeters and find gaps in their security, biases and controls that prompt-based testing couldn’t find. What makes OpenAI’s recent papers noteworthy is how well they define using human-in-the-middle