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Nokia and Ericsson are taking different routes toward AI-RAN, while T-Mobile US offers a real-world view of how operators are setting strategies

Nokia and Ericsson broadly agree on the destination for the radio access network (RAN): more intelligence embedded throughout the RAN, greater automation, improved use of scarce spectrum and, ultimately, infrastructure capable of supporting increasingly AI-centric traffic and services. Where they are beginning to diverge is on how operators should get there and, more specifically, how much the underlying RAN compute architecture needs to change.

Ericsson’s near-term proposition centers on embedding AI directly into existing RAN software and purpose-built hardware. Nokia is making a broader architectural bet around AI-accelerated computing, shared infrastructure and a programmable RAN platform designed to evolve toward AI-native 6G. The distinction is not absolute; Ericsson supports cloud and accelerated-compute architectures, while Nokia offers evolutionary paths for its installed base, but the center of gravity is increasingly different.

T-Mobile US makes the comparison particularly relevant. The operator is working with Ericsson on AI-native RAN software deployed against live commercial traffic while also working with Nokia and NVIDIA on GPU-accelerated AI-RAN infrastructure capable of running radio and AI workloads together. In effect, T-Mobile US is simultaneously testing two visions of where AI creates value in the RAN.

Ericsson: Put AI into the RAN operators already have

Ericsson’s approach begins with a straightforward operator proposition: improve network performance without requiring a wholesale change in infrastructure.

Its AI in RAN software suite embeds models directly into basebands and radios, using existing AI-ready Ericsson hardware rather than requiring operators to deploy GPUs at specific sites. Ericsson’s broader portfolio distributes intelligence across radios, RAN Compute, Cloud RAN and rApps, with its purpose-built silicon supporting AI inference close to the radio functions it is optimizing.

Ericsson and T-Mobile US moved AI-native Scheduler with Link Adaptation into large-scale trials on a live 5G Advanced network. The neural-network-based scheduler runs on Ericsson hardware and predicts changing radio conditions in real time. Ericsson reported close to a 10% improvement in spectral efficiency and up to a 15% increase in downlink throughput compared with legacy rule-based scheduling.

That is important because it makes the AI-RAN business case legible in familiar operator terms. Spectrum is expensive, capacity is valuable, and software that produces more throughput from already-deployed spectrum and infrastructure can be evaluated against a relatively conventional return-on-investment model.

The strategy also reflects the practical reality of the 5G investment cycle. Operators have already spent heavily on radios, basebands, spectrum and modernization. Ericsson’s message is that AI-RAN does not have to begin with another major infrastructure replacement. Its software can instead increase the intelligence — and potentially extend the economic value — of hardware already in the network.

That does not mean Ericsson is rejecting accelerated compute or Cloud RAN. Ericsson and T-Mobile have also demonstrated Cloud RAN software running on NVIDIA infrastructure. Rather, Ericsson’s immediate commercial emphasis is on applying the right AI model at the right point in the existing network architecture, with purpose-built silicon handling inference where it makes economic and technical sense.

Nokia: AI-RAN as an architectural transition

Nokia starts from a more expansive premise: AI is not simply another software capability to be added to the RAN; over time, it changes what the RAN itself needs to become.

Its AI-RAN proposition combines its anyRAN software with AI-accelerated computing and offers three deployment paths. Operators can add an AI-accelerated plug-in card to existing AirScale basebands, deploy dedicated accelerated AI-RAN nodes or move to cloud-native AI-RAN running on commercial off-the-shelf servers. All three sit on a common software foundation and roadmap toward 6G.

That range of options communicates to operators that AI-RAN requires an immediate move to a fully GPU-based network. But the architectural direction is explicit: RAN functions and AI workloads increasingly run on a shared compute foundation, creating infrastructure that can improve radio performance while also providing compute for AI applications.

In discussion with RCRTech, Nokia Vice President and Head of AI-RAN and Cloud RAN Aji Ed characterized the immediate value proposition around spectrum. “We bring twice the network capacity, twice the spectral efficiency compared to what we have today,” he said, describing a roadmap in which increasingly sophisticated algorithms and compute architectures drive progressive efficiency gains.

Nokia said it has already demonstrated more than 20% efficiency gains from AI-driven radio innovations and sees advanced AI models pushing spectral efficiency toward more than 2x current levels. Crucially, however, Nokia pairs that performance claim with a change in development cadence: a software-defined RAN that can continue improving through new models and algorithms rather than waiting for traditional hardware refresh cycles.

Ed described that shift as a move toward an architecture that “runs at the software speed, not just dependent on the silicon speed.”

The longer-term ambition goes beyond AI for RAN. Nokia sees accelerated infrastructure supporting AI and RAN — radio and AI workloads sharing compute — and eventually enabling distributed AI inference at the network edge. Its work with NVIDIA and T-Mobile US has already demonstrated concurrent AI and RAN processing on shared accelerated infrastructure in T-Mobile’s lab environment.

T-Mobile US points to an operator-led synthesis

T-Mobile’s strategy suggests that operators may not view the emerging vendor approaches as mutually exclusive.

In an interview with RCRTech, T-Mobile Chief Network Officer Ankur Kapoor repeatedly framed AI around customer outcomes rather than technology deployment. “No automation is good enough unless it’s really impacting customer experience,” he said. “If it’s not delivering a differentiated customer experience, it’s only operational benefits.”

That framing helps explain why T-Mobile US can pursue Ericsson’s incremental software model and Nokia’s more architectural AI-RAN proposition at the same time.

Ericsson offers something operators can measure now: AI changing scheduling and radio optimization on infrastructure already carrying commercial traffic. Nokia’s proposition points toward what comes next: turning the cell site into part of a distributed compute architecture capable of processing both connectivity and AI workloads.

Kapoor’s own description of the future closely tracks that broader trajectory. T-Mobile US, he said, is trying to bring intelligence into cell sites that historically have simply “moved bits and bytes,” enabling them to support radio and AI workloads simultaneously.

“I’m not going to say that every tower is going to become a robot, but every tower is going to become an actual intelligent fabric,” Kapoor said. That local intelligence can feed T-Mo’s existing self-organizing network and Dynamic CX platforms, allowing distributed compute and centralized automation to work together.

The emerging Nokia-Ericsson divergence, then, is less about whether AI belongs in the RAN than where operators should begin and how quickly the compute architecture should change.

Ericsson is showing how AI-RAN can create incremental, quantifiable value inside today’s network. Nokia is positioning AI-RAN as the beginning of a deeper architectural transition in which connectivity, accelerated computing and distributed AI increasingly converge.

For operators, the answer may ultimately be both. The near-term requirement is better spectrum utilization, lower cost and improved customer experience. The longer-term opportunity is to turn widely distributed RAN infrastructure into an intelligent compute fabric. T-Mobile’s work with both vendors suggests the path to AI-native 6G will likely require operators to capture the first set of benefits without losing sight of the second.