The Future of AI in Global Data Centres

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The Impact of Artificial Intelligence on Global Data Centres

In a recent article that breaks down the current trends for global data centres, CBRE, a global commercial real estate services and investment firm, concludes that the market is becoming increasingly tightened by a ‘worldwide shortage of available power’. ‘Large corporations are finding it increasingly difficult to find enough data center capacity’ and, the article warns, ‘Low supply, construction delays and power challenges are impacting all markets’. Subsequently, the impact of these obstacles causes an increase in price. 

As the presence of artificial intelligence (AI) in the workplace rapidly increases, becoming as prolific in our real lives as in the work of science fiction, it will be necessary to expand the capacity of data centres to accommodate this newly burgeoning market. The CBRE articles continues: ‘The rapid growth of artificial intelligence […] is expected to drive continued strong data center demand. This will spur innovations in data center design and technology as operators aim to deliver the capacity that meets the increased power density requirements of high-performance computing.’

In this article, we will break down how this looks in the current digital climate, unpack the impact on data centres globally, and discuss what this means for the immediate future.

Types of AI Systems

As it stands, there are two primary types of AI or Machine Learning (ML) systems that we expect to exert differing, pressures on the data centre market: training engines and inference engines. These equate to different stages of the AI process: in brief, in the training phase, AI systems are fed datasets in order to learn everything required to analyse this data; this will then produce a model which is used within the inference phase to make assessments or predictions which can subsequently be translated into actionable results.

Most forms of training engine require huge amounts of computational power, and hence data centre capacity. In most cases, the physical location of the training engines is not important, so decisions on where to base such systems can be based on cost, frequently placed where electricity and operating costs are the cheapest. 

Inference engines, on the other hand, can be sensitive to distance, especially when they are operating in ‘real-time’ environments which require low network latency. Examples of these are manufacturing systems used to control production lines or industrial process control systems which take inputs from Internet of Things (IoT) devices such as sensors and cameras. These systems can be much smaller than training engines, but also need to be much closer to the action, thus suiting a distributed data centre topology.

AI and Data Centre Capacity

Despite the different requirements by each stage of AI, what is becoming increasingly clear is that the overall data centre capacity needed to host such engines is expected to undergo a huge acceleration in growth. Mark Ganzi, the CEO of DigitalBridge (one of the world’s largest investors in digital infrastructure) is quoted as predicting a near-tripling in the gigawatts (GWs) required: ‘Today, public cloud, which really has been building and leasing space to the data center marketplace over the last 10 years, is about 13GW. Ultimately, to drive AI and to get networks to where we think they will, we believe the opportunity is close to 38GW.’

Given this surge, the preparations made by major data centre colocation organisations are already observable. The Financial Times has estimated that the Blackstone Real Estate Income Trust (Breit) plans to inject $8bn into building new data centres, directly due to the increase in AI workloads. Similarly, Brookfield Infrastructure Partners has announced that it will own and operate one of the largest global hyperscale data center platforms in the world. Again, CEO Udhay Mathialagan attributed this decision to the impact of AI: ‘We’re definitely noticing a bit of an uptick with hyperscalers in particular and others looking at applications like AI and seeking to secure supply.’

Hyperscalers, large cloud service providers that provide computing and storage at an enterprise scale, have contributed a significant metric to the accommodations being made for the future of AI. Analysts from TD Cowen have observed that hyperscalers have already begun to pre-lease capacity 2-3 years in advance of facility delivery, an increase from the 12-18-month pre-leasing window witnessed last year. On this discrepancy, they explained: ‘In 2022, leasing prices increased due to the increased cost of building data centers. Now they are higher simply due to limited supply and high demand.’

It is worth noting that most of the growth over the next two years is expected to be in building very large sites for training engines. The wider network of smaller, distributed inference engines is likely to follow at a slightly slower pace. The data centre industry is still debating with itself how and when that will happen.

Key Takeaways

To summarise, in an industry that is already growing rapidly and materialising a great impact on our digital climate, AI stands to cement its position even further by innovating demand for data centre capacity worldwide. With this comes two prescient considerations: how the UK and Europe can capitalise in this expanding market, and how we can all work together to mitigate sustainability risks.

Regarding the former, the most immediate action the UK can take is to emulate the standard currently being set by the US. Adrian Josef, the chief data and AI officer at BT, declared in a parliamentary meeting of the Science and Technology Committee that the global tech industry is in an AI ‘arms race’. Specifically, this so-called ‘race’ pits us against the ‘Big Tech Companies’ of the US and potentially China: ‘There’s a very real risk, that unless we begin to leverage and invest and encourage our startup communities to leverage the great academic institutions that we’ve got to ensure that we have public and private sector all working together […] we in the UK could be left behind.’ At a recent conference, Jaap Zuiderveld of nVidia pointed out that the US is way ahead of the UK and Europe in building out capacity for AI, issuing a wake-up call for the data centre industry.

As countries attempt to stay ahead in this race, it is essential that we, globally, are acknowledging the environmental impact of AI, as well as the societal benefits. AI hardware frequently requires much more power than standard servers, due mostly to their use of Graphics Processing Units (GPUs). This creates a demand for higher energy density, which will necessitate the adoption of innovations such as liquid cooling in order to make energy usage more efficient—a subject we will consider in a future article. 

We can Help with your Data Centre Strategy 

Cambridge Management Consulting is currently working with clients on their AI and data centre strategy. There is momentum building in all sectors which, for most companies, means that there are opportunities that need to be understood and developed. The Cambridge MC team, led by Duncan Clubb, can help in what is a very fast-paced changing technology environment.

Please use the form below to get in touch with Duncan and enquire about our data centre services.

About Cambridge Management Consulting

Cambridge Management Consulting (Cambridge MC) is an international consulting firm that helps companies of all sizes have a better impact on the world. Founded in Cambridge, UK, initially to help the start-up community, Cambridge MC has grown to over 150 consultants working on projects in 20 countries.

Our capabilities focus on supporting the private and public sector with their people, process and digital technology challenges.

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