How AI Will Continue to Impact the Data Centre Industry
AI use cases are continuing to surge, particularly with generative AI (Gen AI) and AI agents and chatbots dominating the landscape. With such a sharp spike in the industry, the data centre industry has been pulled into the global conversation, as AI infrastructure demand also grows.
The data centre hosts and powers these AI systems in order to support enterprise demand. According to a recent survey from Ciena, 53% of data centre experts globally have identified AI workloads as the most significant driver of data centre interconnect (DCI) demand over the next two to three years.
Now, Ciena suggests that new AI use cases are expected to surpass cloud computing (51%) and big data analytics (44%) workloads during this time.
In considering this, we hear from Jürgen Hatheier, Ciena’s International Chief Technology Officer.
“The rapid expansion of AI is reshaping the data centre networking infrastructure landscape,” he explains. “Hyperscalers and data centre providers must be able to scale their networks to meet these performance requirements, carving out a clear path to an AI-driven future.”
AI workloads depend on the data centre
Jürgen suggests that there will be numerous challenges for the data centre industry to tackle, particularly as hyperscalers and network operators seek to manage increased network loads.
“The amount of training required to build trustworthy, high-quality AI is prompting network operators and hyperscalers to investigate how their infrastructure will evolve to meet demand,” he explains.
“In fact, on average, data centre experts globally expect that 43% of the new data centres their company is planning to build will be dedicated to handling AI workloads.”
Ciena’s report found that 81% of respondents believe LLM training will take place over some level of distributed data center facilities. This requires DCI solutions to be connected to each other.
Likewise, the survey asked data centre experts about the key factors shaping where AI inference will be deployed.
- AI resource utilisation over time is the top priority (63%)
- Reducing latency by placing inference compute closer to users at the edge (56%)
- Data sovereignty requirements (54%)
- Offering strategic locations for key customers (54%)
“This means we can expect the AI ecosystem of tomorrow to be a network of interconnected data centres, all with unique roles to play,” Jürgen highlights.
“Edge data centres, for instance, will handle inferencing as well as offer strategic locations to improve performance of latency-sensitive applications, such as security applications using facial recognition.”
Power spikes inevitably lead to sustainability concerns
With AI requiring more power and energy in order to run, data centres are having to wrestle with environmental challenges.
Ciena’s survey suggests that, in order to support the increased demands on DCI, network and data centre operators will put more focus on increasing the performance and efficiency of their interconnected assets.
When asked what the needed performance of fibre-optic capacity for DCI would be, the survey found that 87% of data centre experts surveyed believe they will need 800 Gb/s per wavelength, or higher.
“These concerns underscore the importance of solutions that help minimise power consumption and physical footprint, such as high-capacity pluggable optics,” Jürgen says.
“The International Energy Agency (IEA) predicted that data centre electricity demand will more than double globally from 2022 to 2026 because of traffic driven by AI applications – and it’s a subject that’s being taken seriously by the entire industry.”
- Interconnectivity, efficiency and sustainability are some of the most critical areas that will need attention
While it’s clear that AI will continue to transform the data centre industry in the near future, Jürgen says it’s important to recognise there is no ‘one-size-fits-all’ architecture for data centre operators.
“How data centre operators tackle these challenges will vary. Ultimately, cloud providers and data centre operators will need to adopt custom network strategies tailored to their specific business needs and customers,” he explains.
“Over the coming months and years, we will see different network architectures and expansion strategies that fit specific business models, and which best cater to their customers. Regardless of the approach taken, the key will be high performance DCI connectivity.
“Operators must ensure their overall network infrastructure is ready for an AI-centric future – and if the research is anything to go by, they’re already considering the architecture necessary to make the mass adoption of AI a success.”
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