GREENING AI: HOW THE UK CAN POWER THE ARTIFICIAL-INTELLIGENCE ERA

The following information was released by the Tony Blair Institute for Global Change:

By multiple experts (3)

Chapter 1

Executive

Summary

The United Kingdom’s progress in the years ahead will be defined by its ability to lead two transformative technological shifts: the artificial-intelligence revolution and the energy transition. These developments are deeply interconnected, each powering the potential and necessity of the other.

Significant breakthroughs in generative AI in recent years have led to an acceleration of the focus on its potential as a general-purpose technology. Huge amounts of private capital have been poured into AI development, with Microsoft, Google and Meta announcing compute infrastructure investments of historic proportions.

Data centres and supercomputers have become the factories of the future, storing information and providing the processing capabilities critical to the progress of AI. The magnitude of this is vast: 25,000 Nvidia graphics processing units were used to train GPT-4 over the course of 100 days. The cost of this one model alone was more than $100 million and the energy used was significant.

As the pace of AI accelerates and investment continues, energy consumption will rise. Globally, the International Energy Agency has projected[_] that demand from data centres could double by 2026, while some estimate that data centres could account for 4.5 per cent of energy consumption by 2030.[_]

In the UK, data centres could represent up to 6 per cent of UK electricity demand by 2030, up from around 1 per cent today an increase in energy demand from 4.8k gigawatt hours (GWh) in 2023 to 19.6k GWh in 2030.

Some of this demand will be difficult to predict, as the efficiency of AI will improve. But there is little doubt that the AI era will increase the need for power.

The significant energy demand of AI has raised concerns over its climate footprint and impact on the pace of the energy transition. While these are understandable concerns, there is another way of looking at the issue: countries and governments can harness the investment in and powers of AI to accelerate clean technology and the energy transition.

Alongside a growing demand for power there is a growing demand for this power to be clean. Many of the large AI hyperscalers also have ambitious climate strategies. Their pursuit of 24/7 clean-power matching and net-zero emissions has resulted in a boom in clean-energy investment.

The incentive to lead in AI is therefore creating an incentive to lead in clean energy. As laid out in a recent paper by the Tony Blair Institute for Global Change, Greening AI: A Policy Agenda for the Artificial Intelligence and Energy Revolutions, countries that see the interconnected nature of these revolutions will power the future.

The United States is the best example of a country that is poised to do this. Not only is it home to significant capital and almost all the major players in AI, but it is also more energy self-sufficient than most of its peers and has a thriving clean-technology innovation ecosystem.

As an example, companies such as Google are becoming some of the biggest investors in clean-technology innovation and projects worldwide. By 2022, the company had invested $3.5 billion in renewable energy globally. In recent times it has accelerated its efforts, building a first-of-its-kind geothermal power project in the US state of Nevada, while also using the power of AI to help with discovery in nuclear fusion[_] and materials for battery technology.[_] In May, Microsoft announced that it will purchase 10.5 gigawatts of clean power from Brookfield Renewable Partners, the largest-ever such deal announced.

But the US need not be the only leader. The UK is well-placed to drive the AI and clean-energy agendas. It has ambitious AI and clean-energy strategies as well as an advanced grid. Focus should now be on integrating these strategies to make the power system a platform for investment in compute and use the AI transition to accelerate the net-zero transition.

However, the UK is now only 12th on upcoming data-centre builds, which are the data centres that are more likely to run AI workloads.[_] A core bottleneck is access to power. Data-centre operators currently applying for more power are being told they could face a wait of five to seven years. This issue is not confined to data centres solar, wind and battery providers are facing delays[_] to get much-needed power onto the grid too.

For the UK, data centres provide critical infrastructure for the future, but without fundamental changes to the planning system to accelerate green-energy expansion, Britain’s capability in AI will be curtailed. The government needs to show strong leadership in both areas to encourage the building of data centres and development of clean-energy projects, making it easy and low in cost to make the right types of investment in the right locations.

The UK needs the right strategy to get this moving. Countries that act now will not only become AI superpowers, but also clean-tech powers, controlling two of the most critical technologies of our time.

For the UK to position itself as an AI and clean-tech superpower, it needs to:

Develop better-integrated strategic planning that brings AI considerations into the national Strategic Spatial Energy Plan and economic planning to more strategically identify sites for AI-infrastructure development.

Create a 21st-century energy grid by reforming the planning process, to ensure that decisions can be made at speed, and by regulating for rapid grid expansion and allowing more expansive use of innovative grid-enhancing technologies.

Create the conditions to attract investment into clean-technology projects by expanding power-purchase agreements, considering electricity-market reforms and ensuring that regulation permits new types of clean technologies to come on stream.

Encourage strategic siting and use of data centres by reforming the electricity market and considering the use of the planning system or the connections queue to prioritise certain projects.

Support RandD and startups to accelerate progress by expanding energy-efficient-AI RandD projects and working with academia and industry to expedite the translation of research into deployed solutions.

Chapter 2

Understanding

Data-Centre

Siting

and

Energy

Use

The data-centre business has evolved significantly over time due to advancements in technology and changes in business requirements.

In the early days of computing, data centres were primarily on-premises facilities owned and operated by large enterprises, supporting basic computing and storage needs, custom-built to enable specific organisational needs. The main priority for siting during this period was proximity to corporate headquarters to facilitate easy access and control. Most often, data centres were housed within the headquarters themselves.

The rise of the internet and the expansion of global business operations led to an increase in demand for data-centre capacity. This period saw the emergence of more standardised data-centre designs housed in custom-made buildings, which allowed businesses to scale their operations more easily. The infrastructure began to evolve with modular designs, increased focus on networking, and the introduction of server racks and mass storage systems. Power density started to increase as the need for more powerful computing resources grew. As the internet expanded, data centres began to move closer to network hubs and major cities to reduce latency and improve data-transmission speeds. Proximity to fibre-optic networks became critical, and there was a growing need for facilities in multiple geographic locations to support redundancy and disaster recovery.

The emergence of cloud computing dramatically transformed the data-centre business. Companies like Amazon, Google, Microsoft and Oracle began to offer cloud services, leading to the construction of massive, hyperscale data centres. These facilities were designed to handle vast amounts of data and computing power, resulting in higher power demand. With growing energy demand, energy efficiency also became a critical factor, leading to innovations in cooling, power management and the use of renewable energy. This also meant that the location of data centres became even more strategic. Factors like access to renewable energy, cooler climates, political stability, and proximity to major population centres and undersea-cable landings started to play a significant role in site selection. Additionally, the rise of edge computing led to the development of smaller, distributed data centres closer to end users to reduce latency for real-time applications like financial transactions and gaming.

In the UK, this has meant most data centres are located near London, either to the west in Slough or the south in Croydon. The UK is currently ranked third globally when it comes to the number of cloud data centres in the world.

With the rapid expansion of AI, the data-centre industry is changing further. By 2028, more than half of data centres will be dedicated to AI, with their development being designed specifically for AI workloads. Leading tech companies have announced more than $250 billion in investment in chips, compute and data centres, and big tech firms are spending $10 billion a quarter to build AI data centres.

AI-specific data centres will need to be capable of providing larger amounts of power and removing it when it turns into waste heat. Ten years ago, nearly all data centres used fewer than 10 megawatts (MW) of power. Today, large data centres can require 100 MW of power or more equivalent to powering more than 230,000 homes. While the industry continues to innovate on chip efficiency, AI-specific chips are consuming more absolute power. For instance, while Nvidia‘s new GB200 NVL72 AI superchip can train and run AI models more efficiently, it consumes much more power in an absolute sense, using 120 kilowatts (kW) per rack (compared to 5 to 10 kW of power for a rack in a typical non-AI-specific data centre). Even if future chips are more computationally efficient (as they likely will be), they will still consume much larger amounts of power.[_] Some projections suggests that AI training facilities capable of accommodating 2 GW to 5 GW of power demand are feasible by 2030.[_]

This growing demand for power is combined with an increasing focus on sustainability, with a push towards carbon neutrality. Innovations in energy storage, such as battery technology, and the integration of AI and machine learning for predictive maintenance and energy optimisation are becoming more common. Data centres are also exploring more efficient cooling methods and are increasingly relying on renewable energy sources. This means that regions with abundant renewable energy, like the Nordic countries, are becoming preferred locations.

Additionally, geopolitical factors, data-sovereignty laws, proximity to 5G networks, and local regulatory environments and incentives such as tax breaks are influencing where new data centres are being built. For instance, Ireland attracts a large number of data centres partially due to an attractive tax regime. With increased pressure on grids around the world and long connections queues, the speed of access is also increasingly becoming a key consideration.

As data-centre workloads become increasingly AI-specific, the future of data centres will follow a hybrid model. Edge computing facilities will be located near major cities to handle low-latency tasks, while hyperscale data centres, which power large-scale computing and AI, can be situated in more remote locations optimised for energy efficiency and cost. Colocation data centres, data-centre facilities that rent out rack space to third parties for their servers, will continue to serve as critical hubs within this hybrid model, bridging the gap between edge and hyperscale facilities. They will provide businesses with the flexibility to locate near urban centres for low-latency requirements and in more remote areas where energy costs are lower, ensuring a balanced approach to data management. Edge data centres will process data close to users, reducing network load and ensuring quick responses, while hyperscale facilities will manage extensive data storage, complex computations and AI training. This approach allows for both rapid, localised processing and the efficient handling of massive workloads, ensuring the seamless delivery of diverse digital services. Together, edge, hyperscale and colocation data centres will form a complementary network, meeting the growing demands of real-time applications and large-scale computing.

This more decentralised, hybrid approach means data centres could serve important functions beyond just that of delivering critical digital services. For instance, data centres at the edge could be integrated into new housing-development plans, providing lower cost energy and heating to local communities through waste-heat recovery efforts something a number of Nordic countries are already piloting. Hyperscale data centres could be more deeply integrated into the grid, becoming more demand responsive and active in helping balance the grid. In regions with easy renewable-energy access, data centres could be directly integrated with vertical-farming efforts, leveraging waste heat, sharing infrastructure and reducing operational costs.

Data centres can become more than just the sum of their parts, but doing so requires joined-up thinking across sectors, and smarter and more strategic planning and investment strategies.

Chapter 3

AI’s

Energy

Requirements

and

How

the

UK

Stacks

Up

As energy access becomes an increasing bottleneck in AI-data-centre development, understanding the specific energy requirements of AI is crucial to identifying the appeal of a location for investment in AI data centres.

First, rapid access to abundant and uninterrupted power is essential. This means having the requisite grid infrastructure in place to allow for fast connections, as well as consistent access to power to limit interruptions in real-time operations and allow for continuous learning.

Second, energy should ideally be low in cost. Given the vast amount of energy that data centres consume and the fact that electricity costs constitute a significant portion of the operating expenses, AI-data-centre providers are cost sensitive. High electricity costs can erode profit margins, particularly for colocation data centres. This means that AI-data-centre providers are looking for low-cost locations.

Together, the need for secure and reliable low-cost power requires as much insulation as possible from geopolitical risks or weather disturbances that lead to price volatility or supply interruptions. The primary solution to this is strong access to domestic sources of power, and ideally baseload power sources such as nuclear or geothermal, or solar combined with batteries in areas with high solar outputs.

Finally, the demand for this power is increasingly shifting to clean sources. Many AI companies, particularly hyperscalers, have made commitments to dramatically reduce their carbon emissions. Despite differences in approaches,[_] Google, Microsoft and Amazon have pledged to power their data centres with 100 per cent renewable energy by 2030 and Oracle has committed to 100 per cent renewable energy for its data centres globally by 2025. Google and Microsoft have also signed the 24/7 Carbon-Free Energy Compact,[_] aiming to secure round-the-clock matched renewable energy.

The countries that can provide rapid access to clean, stable, low-cost energy are the contenders to become real AI superpowers.

The UK is part of the way there. Compared to many countries, the UK has a relatively low-carbon grid and the government’s ambitious plans to decarbonise the grid by 2030 and accelerate the development of low-carbon technologies puts the UK in a prime position.

However, the system remains plagued by a historic lack of anticipatory investment in the electricity grid and long planning processes, leading to a long connections queue for both renewable-energy projects on one side and data centres on the other. The UK also has higher, and increasingly more variable, energy costs compared to many competitors. UK industrial-power costs are high compared to the US and other advanced economies in 2022, industrial-electricity costs[_] were 18.55 pence per kilowatt hour (kWh) in the UK, compared to 7.21 pence per kWh in the US and 11.12 pence per kWh in France.

With ambitious clean-energy and AI strategies, the UK is a real contender to be a leader in the AI and energy nexus. But it will require a more radical plan of action that recognises the connection between these agendas.

Chapter 4

The

Keys

to

Success

To become a leader in the interlinking fields of AI and energy requires the UK to act now, developing a strategy that integrates policy across government functions and creates the right conditions for investment. The Biden administration’s recent assembly of the Task Force on AI Datacenter Infrastructure demonstrates that the US is investing heavily on this front, ensuring cross-government efforts are linked to advance data-centre-development operations in line with economic, national-security and environmental goals.[_] The UK needs to invest in a similar strategy grounded in the UK context in order to remain at the forefront of future development.

Integrate Strategic Planning

As laid out in TBI’s Greening AI: A Policy Agenda for the Artificial Intelligence and Energy Revolutions, the starting point to becoming a leader in AI is better coordination and strategic planning on a national level.

As outlined above, the concentration of the UK’s data centres in west London and Slough was born out of the historic need to be close to the cities and industrial hubs they serve as well as the fibre-optic cables that run from the M4 corridor across the Atlantic to the US. However, the grid and generation capacity in this area is constrained, causing problems with a long connections queue and a buildup in demand in an area with limited clean-energy supply.

But with the development of more diverse data-centre workloads, there are significant opportunities to enable more strategic placement of AI data centres across the UK.

For instance, encouraging more data centres to be built in areas where renewable power is plentiful and less grid infrastructure is required, such as Scotland or the North East of England, would help support the reliability and efficiency of the UK grid. These locations also have cooler climates that reduce cooling demand for operations.

But enabling infrastructure with the right strategic planning driving it must be in place.

The UK’s new Strategic Spatial Energy Plan (SSEP), to be prepared by the new National Energy System Operator (NESO), will be critical to doing this. As outlined in Powering the Future of Britain: How to Deliver a Decade of Electrification, this plan offers an opportunity for enhanced organisation and coordination of the UK energy system to speed up delivery and reduce costs.

This plan should also include AI infrastructure in its remit. In practice, this would mean integrating decisions on energy supply, grid infrastructure and communications infrastructure, such as 5G.

For instance, it could identify sites for small modular reactors, for instance on old nuclear or coal sites, where data centres could buy energy from them, encouraging development of low-cost and reliable energy that meets developers’ requirements. It could also mean encouraging strategic siting of data centres in areas where waste heat from operations could power local heat networks.

Tools powered by data and AI, such as digital twins, can enable this. By integrating data about the physical world from several sources, a digital twin can be used to test and model different scenarios and understand complex whole-system challenges. The government could integrate information about grid, generation, communications infrastructure, population centres and land-use to identify strategic sites for development.

This requires data centres and AI-growth demands to be integrated not only into the SSEP, but also into other planning efforts across government. Data centres should be considered as a cross-functional, utility-like resource in any planning-development effort; they could even be considered the central point of potential special economic zones, with a focus on building cluster and wider agglomeration in other critical industries such as biotech. Joined-up thinking and planning across departments and ministries is essential to tap into the wider opportunities they offer. Connecting spatial energy plans directly to municipal planning efforts, including district heating and housing development planning, would be a critical first step in doing this.

Create a 21st-Century Electricity Grid

Once the strategic plans are in place, building out new and strengthening existing grid infrastructure needs to happen quickly and reliably.

Currently, it takes more than a decade to build a new transmission line in the UK. New wind farms take 12 years from inception to connection to the grid, and six years can be spent in planning. The result is long wait times for companies both renewable generators on one side and data centres on the other to connect to the electricity grid and high investment costs because of the uncertainty. The connection dates currently being assigned are in the mid-2030s, making the UK a less attractive place to invest in new data centres and clean-power capacity.

This is another place where the planning system needs reform.

TBI has previously set out necessary changes to the Nationally Significant Infrastructure Project (NSIP) regime in Building the Future of Britain: A New Model for National-Infrastructure Planning that would cut the time it takes to obtain consent by 80 per cent and make projects of national importance happen. Similar reforms are needed for infrastructure that does not fit within the NSIP process, such as the distribution network. This would ensure decisions can be made quickly on expanding smaller-scale generation and grid infrastructure.

More straightforward planning consent is also required for data centres themselves. The new government has already announced its intention to include data centres in the NSIP regime, which, combined with radical reform of the NSIP regime, would also ensure rapid development of data centres.

Alongside planning reform, wider reform is needed to rapidly build the grid capacity the country needs to connect renewable energy projects with electricity demand, like that of data centres. The regulatory system around the grid must be set up for the transmission network operator and distribution network operators to invest in expansion and optimisation of the grid and to be empowered to rapidly and effectively connect new projects.

There are also ways to make the most of the existing grid to meet immediate demand. These efforts, such as the adoption of new technologies like dynamic line ratings or high-performance conductors, need be prioritised and expedited.

To do this, Ofgem should be given a similar remit to Ofcom with a focus on grid expansion to ensure that the infrastructure that is needed to power Britain’s future is built in time to flow into the price controls and investment parameters for the grid operators. The Network Innovation Allowance should be reformed to allow for a broader spectrum of innovations in grid-enhancing technologies to be implemented, as recommended in TBI paper Reimagining the UK’s Net-Zero Strategy.

Ofgem and NESO should also accelerate progress on reforming the connections-queue system to better weed out projects that are delayed or will never come to fruition and make the connections queue more dynamic. To help drive accountability and allow for better analysis of the connections queue, better data on renewable-energy and data-centre projects in the connections queue should be made publicly available.

Create the Conditions for Investment

Leading the AI and clean energy nexus will require the UK to become an easy and attractive place to invest. Having the plans, infrastructure and ease of building in place will be crucial for this, but the government could consider further strategies to create the conditions for investment to flow into the UK’s AI and clean-energy sectors.

The hyperscalers have significant funding to put into clean energy and are already deploying it at scale.

Google‘s partnership with clean-energy startup Fervo to develop a geothermal power project in the US state of Nevada is now contributing carbon-free energy to the electricity grid. Microsoft is investing in new approaches that shift backup generators from diesel to zero- and low-carbon alternatives such as green hydrogen and modular nuclear reactors, and is using lithium-ion batteries and a grid-interactive uninterruptible power supply to share energy with the local grid when needed.

Microsoft is also investing in fusion companies to accelerate the development of clean abundant sources of power. In March, Google, Microsoft and Nucor came together to aggregate demand for advanced clean-electricity technology such as next-generation geothermal, advanced nuclear, clean hydrogen and long-duration energy storage to accelerate their commercialisation. The UK should position itself to attract similar investments.

To do this, the UK should review how tools such as power-purchase agreements (PPAs) and the development of off-grid solutions can be expanded, made easier and cost less for developers. Enabling this could drive investment in renewables: for example, Amazon, Apple, Google, Meta and Microsoft alone accounted for over 45 GW of corporate renewable purchases worldwide in 2023 more than half of the global corporate renewables market.[_] Amazon was the largest corporate clean-energy buyer, announcing 8.8 GW of PPAs across 16 countries. The company’s clean-energy portfolio totalled 33.6 GW, which is greater in size than the power-generation fleets of markets such as Belgium and Chile.[_]

The government could also consider whether there are ways to drive investment into grid infrastructure as a part of these deals. For instance, while AI companies support the development of clean energy through the use of renewable-energy credits and offsets, questions remain as to whether these credits are driving new additional clean energy onto the grid.[_] The government should explore ways to incentivise investment in additionality through greener power-generation projects that would otherwise not exist both locally and beyond the domestic grid. This could be done, for example, through designing existing offset programmes that credit additionality investments.

To attract investment into clean-technology projects in the UK, it is imperative that there are strong projects that finance can flow into. AI hyperscalers have the finances to be able to invest in novel technologies that can help reduce the cost of these solutions. As set out in Reimagining the UK’s Net-Zero Strategy, the government should take a strong lead in ensuring that regulation permits new types of clean technologies to come on stream and create the market arrangements that make the UK an attractive place to invest in new technologies. For instance, the UK can create an attractive regulatory environment and identify sites for development of small modular reactors, making it easy for AI hyperscalers to invest. Similarly, if the UK creates the foundational infrastructure for the hydrogen economy, it could attract investment in hydrogen projects that can replace diesel as back-up generators.

Encourage Strategic Siting and Use of Data Centres

In addition to improved strategic planning and infrastructure delivery, the government should take proactive steps to encourage data centres to play a positive role in the energy system and make smarter siting decisions the easier and more attractive option.

For instance, to enable speed and clarity of decision-making, the government could use the SSEP to identify candidate zones of investment where simplified planning regulations would create clusters of investment. These zones could be combined with tax or grant incentives as outlined above.

The UK should also consider how the electricity market could be reformed to provide lower prices at times or locations with high renewable-energy output. This would help the system become more competitive on costs for industry while helping to balance the grid. The UK is in the process of fully decarbonising its electricity grid using intermittent renewables and the National Grid Electricity System Operator[_] has set out that large electricity users like data centres provide a significant opportunity to reduce investment costs associated with managing constraints on the electricity network.

The UK should review whether the electricity market can be reformed to reduce and better reflect locational energy costs across the country. Analysis by FTI Consulting shows that moving to locational pricing could reduce energy costs across the country, except in the South East where it would stay roughly the same. Scotland would have the lowest wholesale energy prices in Europe under this model, with all other regions having lower prices than Germany, Ireland and the Netherlands. This would reduce the need for grid buildout and make strategic areas of the UK highly cost-competitive places to invest in energy-intensive activities such as data centres.

Innovations are also happening in terms of how data centres use energy, with companies developing promising solutions that could support, rather than strain, the UK’s energy system. The UK could seek to be at the forefront of encouraging and enabling new innovative methods. For instance, companies like Google are taking steps[_] to move non-urgent operations to times of day when the output of renewable-energy sources is higher, or to shift operations between data centres in different locations depending on where renewable-energy output is high. The company Soluna is placing data centres near wind or solar farms to remove the burden of curtailment costs payments from the grid operator to farm operators to reduce generation when supply exceeds demand.[_] In 2023, UK energy-bill payers paid nearly 1 billion in constraint costs.[_] Similarly, other companies like Deep Green[_] are working on integrating data centres with local heat networks. The government recently funded a project seeking to do this in west London as a part of the Green Heat Network Fund.[_]

The ESO and the government should take a proactive approach to assessing how innovative solutions could help augment the energy system and help create the conditions for arrangements to be developed to encourage these types of solutions. The UK should explore providing better temporal signals in electricity prices, enabling more accurate time-of-use tariffs or creating other more bespoke arrangements to attract data centres that can provide these types of services for the grid.

Finally, the government should better utilise the planning system and the connections queue to help prioritise projects that support the broader energy system. For instance, the connections queue could be used to prioritise projects that meet certain criteria as a means to encourage investment in and development of not only more energy-efficient technologies but also of approaches that bring additional renewable energy to the grid. Such criteria could include investments in renewable energy and grid additionality to help reinforce the grid locally,[_] utilising energy storage and energy management to integrate with the grid for energy export, integrated data-centre flexibility requirements in service-level agreements. Longer term data-centre projects with large-scale UK content in their technical teams could be prioritised to encourage more RandD to take place in the UK to boost the value to the UK economy.

Support RandD and Startups

Finally, new solutions are also necessary. Energy efficiency largely is and will continue to be driven by industry. While the amount of computing carried out in Google data centres increased by about 550 per cent between 2010 and 2018, the amount of energy consumed by data centres only grew by 6 per cent[_] during the same period. And given the incentive to drive down costs and create new companies in the UK, an array of new approaches is emerging.

But given the strategic importance of this issue, there are opportunities for the government to help accelerate progress and develop new avenues for efficiency.

Some efforts are already underway. Most notably, the Advanced Research and Invention Agency (ARIA) is providing 42 million to investigate cheaper and less energy-intensive alternatives to current solutions, reducing the costs by a factor of more than 1,000.

The government should review the case for expanding support for AI energy-efficiency projects. For instance, the UK could explore creating a data-centre RandD sandbox, which would enable researchers and startups to test and demonstrate new solutions to improve the efficiency of operations. This could serve a similar function and anchor opportunities for the UK to develop more efficient solutions and become a base for the companies that work out how to do this.

As set out in Reimagining the UK’s Net-Zero Strategy, Britain should also consider how to streamline and improve innovation funding to support startups and innovation in this space, including providing better support for university spinouts. This should include collaborating closely with industry and academia to expedite the translation and commercialisation of foundational research into deployed solutions. This could make the UK the home of innovative startups that can improve the energy efficiency of data-centre operations.

Chapter 5

An

AI

and

Clean-Energy

Superpower?

The UK can become an AI and clean-energy superpower. But it will require an ambitious strategy that identifies opportunities and develops a practical plan to enable the solutions. This strategy must look at these technologies as connected together, they will power the next wave of economic growth and progress. The giants of the modern economy are making historic investments to make this happen. The UK must act at speed to make sure we aren’t left behind.