Original article Total Telecom:Read More
Interview
As the adoption of AI continues to surge globally, the data centre interconnect (DCI) networks underpinning this transformation are undergoing rapid change. A recent global survey from US-based vendor Ciena has highlighted that data centre bandwidth demands are set to increase by at least sixfold over the next five years, driven largely by AI workloads.
Total Telecom sat down with Jürgen Hatheier, Chief Technology Officer for Ciena’s EMEA and Asia Pacific regions, to discuss the survey’s findings and how network operators are moving quickly to facilitate surging demand.
“Everybody got the headlines for building all the compute and the data centres[…] but if there’s no network connecting all the good innovations that are happening there, then it would be a car without gas,” said Hatheier.
AI’s pressure on the network
Ubiquitous AI usage could present numerous challenges to network operators, not only in terms of sheer data traffic but also in complexity.
“Historically, network traffic has grown at a rate of 20–30% per year,” said Hatheier. “AI is set to accelerate this growth significantly, meaning operators are rethinking their architectures and planning for how they can meet this demand sustainably.”
Ciena’s survey, which polled over 1,300 data centre decision-makers across 13 countries, found that AI workloads have now overtaken traditional cloud computing and big data analytics as the primary driver of increased DCI infrastructure demand.
But what is it about generative AI that puts such strain on the network? The answer lies in the technology’s core architecture, where training AI models, inference, and feedback loops generate constant movement of data between compute clusters. This creates far more intense networking demands than traditional cloud workloads, which tend to be more transactional or batch-based.
“It is simply the nature of AI,” Hatheier explained. “How it trains, how it interacts with other machines, how it is so hungry for data, and how it generates new data […] That makes a big difference on the expected impact on the network.”
AI traffic also differs significantly from traditional workloads. Hatheier described AI traffic as “unpredictable and exciting,” requiring networks to be flexible and dynamic, often handling large bursts of data with low latency. For example, an AI system may be required to automatically change a traffic light from red to green, or analyse a video for real-time security purposes, both of which will require a network that can guarantee a high quality of service.
“You do not know when you will require that data,” he emphasised. “Networks need to be built with these type of use cases in mind.”
The study highlighted that to meet this huge surge in demand, 43% of new data centre facilities will be exclusively dedicated to AI workloads, which reflects not only an increase in the amount of data being processed, but also a key change in how data centres are structured and interconnected.
Distributed computing
Another key feature emerging from the rise of AI is the growing shift toward distributed computing. As requirements for AI compute continue to grow, the training of large language models (LLMs) is becoming increasingly distributed across different data centres around the world, instead of being conducted in a single location. According to the survey, 81% of respondents believe LLM training will rely on some level of distributed infrastructure, making reliable interconnection between these data centres even more critical.
“There could be a training job that runs in Vienna, in Texas, and in London,” Hatheier said. “It’s the same kind of workload, but you require connectivity on land and subsea to create the kind of network that is able to transfer a high amount of traffic between sites.”
Increasing sustainability as networks grow
As AI drives up compute demand, sustainability has become a core concern for data centre operators. AI workloads are highly energy-intensive, and with growing scrutiny on emissions and energy use, operators are under pressure to expand capacity without expanding their environmental footprint.
Data centres consume significant amounts of energy, to both power servers and to keep them cool. As AI workloads increase, so does the demand for compute, and with it, the strain on power and space. This makes sustainability more important than ever, with operators looking for smarter, more energy-efficient ways to scale their infrastructure without compromising performance. But, as the bandwidth demands of AI intensify, so do the physical limitations within data centres, namely power consumption and space efficiency.
One practical solution, Hatheier explained, is the use of pluggable optics. These are small, USB-sized devices that slot directly into the back of a server or switch, providing a point-to-point connection . This allows networks to move information quickly through fibre optic cables, reducing the strain on the primarily connectivity infrastructure. Because these optics can be plugged in without needing extra racks or bulky equipment, they help data centres expand their capacity while saving space and energy.
Looking ahead
Hatheier believes that the future of AI hasn’t even fully begun yet. “As a society, we are at the brink of starting some massive value creation with AI. And I’m not talking about squeezing a couple of bits out of a network or running some operation better. I’m talking about real creation of value.”
To create this global innovation, the ecosystem must ensure the right infrastructure is in place, and so the networking industry’s biggest hurdle over the coming years will be to provide all the connectivity required, whether that be subsea or terrestrial.
“We want to make sure that everybody gets the benefit, right?”
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