Enterprises exploring generative AI are finding that model training, inference, and data movement place heavier demands on networks, creating new challenges around latency, cost, and compliance for technology leaders., and Verizon Business is aiming to address these shifts in its network services.
As part of a new agreement with AWS, Verizon Business plans to add long-haul fibre routes that link key AWS data centres. The links are meant to give organisations more stable paths for AI workloads that depend on live data, shared training jobs, and cloud-based tools.
The move fits into a larger effort to handle rising network demand. Training advanced models already requires large computing resources, but the shift toward real-time decision-making is expected to raise demand more. McKinsey expects most AI workloads to move toward live inference by the end of the decade, which will increase the need for faster connectivity, more edge capacity, and stronger security controls. The changes will shape how organisations design systems, plan budgets, and manage data.
To support this trend, Verizon Business has released Verizon AI Connect, a suite of network services for AI workloads. Instead of building everything from scratch, the company is reworking parts of its network, including 5G coverage, fibre lines, edge computing, and cooling systems for large-scale processing. Cloud providers like Google Cloud and Meta are already drawing on this extra capacity.
The value goes beyond faster cables. A bank that needs to spot fraud in real time, for example, could cut down on false alerts if it moves large datasets between systems more quickly. Faster networks can also shorten training cycles and help teams test ideas without harming other business apps that share the same network.
Partnerships show how this space is expanding. NVIDIA is working with Verizon on GPU-based platforms that can live inside private 5G networks, giving customers more control over workloads that cannot leave their site. Vultr is teaming up with Verizon on GPU cloud access so customers can run training and inference on demand. Google Cloud is working with Verizon on AI systems that spot network issues before they disrupt service. Verizonβs work with Meta will also reach deeper into network infrastructure to support the companyβs AI needs.
Past projects between Verizon and AWS show that results do not depend on a single tool. Real performance gains happen when network teams, data teams, and developers work together to plan routes, set priorities, and monitor use. Many early projects slow down when teams skip basics like tracking data, training staff, or aligning with existing security controls.
Some obstacles remain. Organisations need clearer insight into traffic in many network paths. They must weigh the cost of moving large datasets between cloud regions and confirm that routing decisions meet compliance rules. AI workloads can also spike without warning, which can make planning harder. Security leaders will need to confirm that their monitoring and encryption tools still work when volume increases.
Interest in this area points to a bigger trend: companies are pushing AI closer to users and devices to reduce delays and improve stability. That shift is driving closer work between network teams, cloud architects, and security groups, especially as compliance rules change.
As AI adoption grows, the structure of the network underneath it will shape efficiency, user experience, and long-term cost. In sectors where even small delays matter, stronger network performance may become a competitive edge.
(Photo by Leon Bredella)
See also: Verizon is building a 6G alliance with telecomsβ biggest players
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