The assault of artificial intelligence on telecommunications networks is no longer a forecast but a concrete accelerator. Ericsson has confirmed that demand for AI services and workloads is pushing operators to step on the gas for 5G Standalone (SA) and Fixed Wireless Access (FWA), two technologies set to reshape enterprise and industrial connectivity in the coming years.

Unlike non-standalone implementations that still rely on a 4G core, 5G SA introduces a fully new architecture with a cloud-native core and network slicing capabilities. This allows an operator to carve out dedicated network slices with guaranteed latency and bandwidth for specific applications. FWA, for its part, uses the cellular network to deliver fixed broadband links without running fiber, bringing fiber-like speeds to areas hard to wire.

What does AI have to do with it? Real-time inference services, distributed training, data pipelines between sensors and models: all these activities devour bandwidth and cannot tolerate jitter. An LLM running on-premise in a factory, for instance, may need ultra-low-latency links to computer vision systems or a reliable backhaul to exchange model updates with a central node. 5G SA, through network slicing, can provide exactly that: a software-defined “express lane” that isolates AI traffic from the rest of the network, reducing variability.

On the FWA side, the benefit is equally tangible. Companies wanting to keep data and inference on-site, without cloud dependency, often hit the wall of inadequate connectivity at production or remote sites. FWA can bridge that gap, offering the bandwidth needed to synchronize models, download updates, or handle hybrid workloads without laying fiber. For those evaluating on-premise deployment, it’s a piece of the puzzle that reduces the trade-off between data control and access to remote resources.

Of course, Ericsson has every incentive to paint a rosy picture, but market signals are consistent: operators are indeed increasing investments in SA and FWA, and enterprise connectivity requests for AI workloads are growing. Open questions remain: spectrum, multi-vendor network slicing maturity, and the security of such a distributed network. Still, anyone designing architectures for self-hosted LLMs today would do well to watch this evolution: connectivity is no longer a mere accessory but an enabling element, and 5G may become the backbone of an increasingly distributed AI.