The ambitious $500 billion Stargate AI infrastructure project is moving significantly slower than anticipated, with SoftBank Group CFO Yoshimitsu Goto publicly acknowledging the delays during the company’s Q1 2025 earnings call.
“It’s taking a little longer than our initial timeline,” Goto said during the call, describing the project as proceeding “slower than usual.”
The delays come seven months after President Trump announced the transformational data center initiative with great fanfare, as the project struggles with complex site selection challenges and stakeholder negotiations that mirror obstacles enterprise IT leaders face when scaling AI infrastructure.
What’s driving the Stargate delays?
The root causes of these delays are familiar to any CIO who has managed large-scale infrastructure projects.
“There are a lot of options, and it’s been taking time to select a good site,” Goto said during the earnings call. “And there are a lot of stakeholders. To build consensus, we need to have a lot of discussions and also technical issues and construction issues.”
Despite acknowledging the slower-than-expected progress, Goto maintained confidence in the overall timeline. “We want to deliberately spend time to build the first model successfully,” he said. “So, eventually — or looking back, you may think that it was taking more than or longer than expected, but we don’t have to worry about that.”
The CFO emphasized that SoftBank remains committed to its original target of $346 billion (JPY 500 billion) over 4 years for the Stargate project, noting that major sites have been selected in the US and preparations are taking place simultaneously across multiple fronts.
Requests for comment to Stargate partners Nvidia, OpenAI, and Oracle remain unanswered.
Infrastructure reality check for CIOs
These challenges offer important lessons for enterprise IT leaders facing similar AI infrastructure decisions. Sanchit Vir Gogia, chief analyst and CEO at Greyhound Research, said that Goto’s confirmation of delays “reflects a challenge CIOs see repeatedly” in partner onboarding delays, service activation slips, and revised delivery commitments from cloud and datacenter providers.
Oishi Mazumder, senior analyst at Everest Group, noted that “SoftBank’s Stargate delays show that AI infrastructure is not constrained by compute or capital, but by land, energy, and stakeholder alignment.”
The analyst emphasized that CIOs must treat AI infrastructure “as a cross-functional transformation, not an IT upgrade, demanding long-term, ecosystem-wide planning.”
“Scaling AI infrastructure depends less on the technical readiness of servers or GPUs and more on the orchestration of distributed stakeholders — utilities, regulators, construction partners, hardware suppliers, and service providers — each with their own cadence and constraints,” Gogia said.
The scale of infrastructure investment required compounds these coordination challenges. Goldman Sachs Research estimates that about $720 billion of grid spending through 2030 may be needed to support AI datacenter growth. McKinsey research suggests that companies must strike a balance between deploying capital quickly and doing so prudently, tackling projects in stages rather than attempting massive upfront deployments.
Mazumder warned that “even phased AI infrastructure plans can stall without early coordination” and advised that “enterprises should expect multi-year rollout horizons and must front-load cross-functional alignment, treating AI infra as a capital project, not a conventional IT upgrade.”
Planning for modular AI deployment
Given the lessons from Stargate’s delays, analysts recommend a pragmatic approach to AI infrastructure planning. Rather than waiting for mega-projects to mature, Mazumder emphasized that “enterprise AI adoption will be gradual, not instant and CIOs must pivot to modular, hybrid strategies with phased infrastructure buildouts.”
The solution is planning for modular scaling by deploying workloads in hybrid and multi-cloud environments so progress can continue even when key sites or services lag. Gogia warned that “Stargate illustrates the risk of hinging downstream business commitments on a single flagship facility.”
For CIOs, the key lesson is to integrate external readiness into planning assumptions, create coordination checkpoints with all providers, and avoid committing to go-live dates that assume perfect alignment.
As Gogia said, “This is less about projects stalling and more about resequencing delivery to align with ecosystem availability.” The 70,000+ enterprises already using Arm-based chips demonstrate that viable alternatives exist for organizations seeking immediate infrastructure improvements while larger projects mature.
Source:: Network World