The QTS data center in Cambois, North East England
When the UK announced its AI Opportunities Action Plan in January – a major plan for deploying the technology across society – Prime Minister Keir Starmer said the strategy would make the country an “AI superpower”.
One of the key pillars of this plan was to rapidly build data centers capable of meeting the enormous computing needs of AI adoption. This would be driven by “AI growth zones” – designated areas with relaxed building permits and improved access to electricity.
Almost a year later, and Nvidia, MicrosoftAnd Google All of them have committed billions of dollars to AI infrastructure in the country. Four AI growth zones have been unveiled, and homegrown startups such as Nscale have emerged as major players in this space.
But critics point to severely limited access to energy via the national grid and slow construction as signs that the country risks falling even further behind its global rivals in the AI race.
“Ambition and execution are not yet aligned,” Ben Pritchard, CEO of data center power provider AVK, told CNBC.
“Growth has been largely held back by constraints on electricity availability. In particular, grid constraints have slowed the pace of development and mean the UK is not yet building infrastructure fast enough to keep pace with global competition.”
Delays in connection to the grid
The expansion of AI infrastructure in the UK is still in its early stages, with AI growth zones currently in the early stages of their development.
A site in Oxfordshire, the first announced in February, has yet to begin construction and is still considering proposals for delivery partners. Ground preparation work for a building in north-east England, announced in September, has begun, with construction officially due to begin in early 2026.
Two further sites in North and South Wales opened in November. The former is looking for an investment partner, which the Department of Science, Technology and Innovation (DSIT) expects to confirm to CNBC in the coming months. The latter consists of a collection of sites, some of which are already operational and others that require additional construction work, DSIT said.
The UK government announced in July that it was targeting a core group of AI growth zones that would cover at least 500 megawatts of demand by 2030, with at least one growing to more than a gigawatt by then.
However, the biggest challenge in achieving these ambitions is the UK’s limited network capacity, Pritchard said.
“Developers are anticipating grid connection delays of eight to 10 years, and the volume of outstanding connection requests, particularly around London, is unprecedented,” he told CNBC.
AI workloads are also “dramatically increasing energy demands” as businesses and consumers begin to use the technology, putting additional pressure on an overburdened energy system, Pritchard added. “They are no longer isolated risks, but are actively slowing or blocking development across the country.”
The open call for applications for the AI Growth Zones initiative has resulted in landowners with poles or power cables running across their land applying for designation, said Spencer Lamb of Kao Data.
“This resulted in the national grid being flooded with power grid applications from speculative sources” with no realistic prospect of success, he told CNBC.
Lay the foundation
The National Energy System Operator (Neso) – the UK public agency responsible for managing the national electricity grid – has taken steps to improve the situation.
Earlier this month, the company announced plans to prioritize hundreds of projects to provide faster access to the power grid. Neso declined to comment on whether AI infrastructure projects were among the prioritized projects when asked by CNBC, but said a significant portion were data centers.
There have also been big pledges of money from tech giants, many of which were unveiled by the UK government in September.
Microsoft, Nvidia, Google, OpenAI, CoreWeave and others announced billions of dollars in AI investments during US President Donald Trump’s state visit, which included plans to deploy the latest chips in the country and open new data centers.
Homegrown startup Nscale, which provides access to AI computers and builds data centers, also announced contracts to deploy tens of thousands of Nvidia chips in an AI factory just outside London by early 2027.
The Nvidia GB10 Grace Blackwell superchip will be on display at the company’s GTC conference in San Jose, California on March 19, 2025.
Max A. Cherney | Reuters
“Investment from large private players has laid important foundations,” Puneet Gupta, general manager for the UK and Ireland at data infrastructure company NetApp, told CNBC. “Momentum is also growing around national research supercomputers and plans for new computing capacity, with commitments to build AI ‘gigafactories’ in the UK.”
But the “real test” will be how quickly these plans can be translated into usable computing power for UK organizations, Gupta said.
Avoiding a “sugar rush” of the AI infrastructure
The long-term success of expanding the country’s AI infrastructure requires investment in the “full stack,” including data pipelines, storage, energy sourcing, security, talent and capabilities, Stuart Abbott, managing director of UK and Irish AI infrastructure company VAST Data, told CNBC.
“If the UK wants this to last and not become a year-long sugar rush, it needs to treat AI infrastructure like economic infrastructure.”
Stuart Abbott
Managing Director for UK and Ireland at AI infrastructure company VAST Data
That means “developing an operational structure that enables real-world institutions to safely deploy AI at scale,” he added. “If the UK wants this to last and not become a year-long sugar rush, it needs to treat AI infrastructure like economic infrastructure.”
The challenges are significant. The value of data center deals in Europe pales in comparison to the amounts flowing into projects in the US. The UK also currently has the most expensive energy in Europe, around 75% higher than before Russia’s invasion of Ukraine, and aging grid infrastructure that can take many years to connect to new locations.
A possible solution for projects that cannot ensure secure access to the national grid is microgrids, AVK’s Pritchard said. Microgrids are self-contained networks of electricity from sources such as motors, renewable energy and batteries.
Pritchard said AVK is currently designing two microgrids for partners building cloud computing, but not AI, in the UK. Construction can take about three years and currently costs about 10% more than energy from the grid.
Co-locating computing power where there is already power, rather than “forcing everything into greenfield sites” – the term for undeveloped sites – is also a way to get AI infrastructure up and running faster, VAST Data’s Abbot said.
The pace of implementation will be crucial, Kao Data’s Lamb told CNBC. “Unless fundamental issues surrounding energy availability and prices, AI copyrights and funding for AI developments are resolved quickly, the UK will miss out on one of the most remarkable economic opportunities of our time and ultimately risk becoming an international AI laggard.”
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