Is AI-RAN the future of telco?

 AI-RAN has emerged recently as an interesting evolution of telecoms networks. The Radio Access Network (RAN) has been undergoing a transformation over the last 10 years, from a vertical, proprietary highly concentrated market segment to a disaggregated, virtualized, cloud native ecosystem.

Product of the maturation of a number of technologies, including telco cloudification, RAN virtualization and open RAN and lately AI/ML, AI-RAN has been positioned as a means to disaggregate and open up further the RAN infrastructure.

This latest development has to be examined from an economic standpoint. RAN accounts roughly for 80% of a telco deployment (excluding licenses, real estate...) costs. 80% of these costs are roughly attributable to the radios themselves and their electronics. The market is dominated by few vendors and telecom operators are exposed to substantial supply chain risks and reduced purchasing power.

The AI RAN alliance was created in 2024 to accelerate its adoption. It is led by network operators (T-Mobile, Softbank, Boost Mobile, KT, LG Uplus, SK Telecom...) telecom and IT vendors (Nvidia, arm, Nokia, Ericsson Samsung, Microsoft, Amdocs, Mavenir, Pure Storage, Fujitsu, Dell, HPE, Kyocera, NEC, Qualcomm, Red Hat, Supermicro, Toyota...).

If you are familiar with this blog, you already know of the evolution from RAN to cloud RAN and Open RAN, and more recently the forays into RAN intelligence with the early implementations of near and non real time RAN Intelligence Controller (RIC). 

AI-RAN goes one step further in proposing that the specialized electronics and software traditionally embedded in RAN radios be deployed on high compute, GPU based commercial off the shelf servers and that these GPUs manage the complex RAN computation (beamforming management, spectrum and power optimization, waveform management...) and double as a general high compute environment for AI/ML applications that would benefit from deployment in the RAN (video surveillance, scene, object, biometrics recognition, augmented / virtual reality, real time digital twins...). It is very similar to the edge computing early market space.

The potential success of AI-RAN relies on a number of techno / economic assumptions:

For Operators:

• It is desirable to be able to deploy RAN management, analytics, optimization, prediction, automation algorithms in a multivendor environment that will provide deterministic, programmable results.
• Network operators will be able and willing to actively configure, manage and tune RAN parameters.
• Deployment of AI-RAN infrastructure will be profitable (combination of compute costs being offloaded by cost reduction by optimization and new services opportunities).
• AI-RAN power consumption, density, capacity, performance will exceed traditional architectures in time.
• Network Operator will be able to accurately predict demand and deploy infrastructure in time and in the right locations to capture it.
• Network Operators will be able to budget the CAPEX / OPEX associated with this investment before revenue materialization.
• An ecosystem of vendors will develop that will reduce supply chain risks

For vendors:

• RAN vendors will open their infrastructure and permit third parties to deploy AI applications.
• RAN vendors will let operators and third parties program the RAN infrastructure.
• There is sufficient market traction to productize AI-RAN.
• The rate of development of AI and GPU technologies will outpace traditional architecture.
• The cost of roadmap disruption and increased competition will be outweighed by the new revenues or is the cost to survive.
• AI-RAN represents an opportunity for new vendors to emerge and focus on very specific aspects of the market demand without having to develop full stack solutions.

For customers:

• There will be a market and demand for AI as a Service whereas enterprises and verticals will want to use a telco infrastructure that will provide unique computing and connectivity benefits over on-premise or public cloud solutions.
• There are AI/ML services that (will) necessitate high performance computing environments, with guaranteed, programmable connectivity with a cost profile that is better mutualized through a multi tenant environment
• Telcom operators are the best positioned to understand and satisfy the needs of this market
• Security, privacy, residency, performance, reliability will be at least equivalent to on premise or cloud with a cost / performance benefit. 

As the market develops, new assumptions are added every day. The AI-RAN alliance has defined three general groups to create the framework to validate them: 

1. AI for RAN: AI to improve RAN performance. This group focuses on how to program and optimize the RAN with AI. The expectations is that this work will drastically reduce the cost of RAN, while allowing sophisticated spectrum, radio waves and traffic manipulations for specific use cases.
2. AI and RAN: Architecture to run AI and RAN on the same infrastructure. This group must find the multitenant architecture allowing the system to develop into a platform able to host a variety of AI workloads concurrently with the RAN. 
3. AI on RAN: AI applications to run on RAN infrastructure. This is the most ambitious and speculative group, defining the requirements on the RAN to support the AI workloads that will be defined

As for Telco Edge Computing, and RAN intelligence, while the technological challenges appear formidable, the commercial and strategic implications are likely to dictate whether AI RAN will succeed. Telecom operators are pushing for its implementation, to increase control over spending, and user experience of the RAN, while possibly developing new revenue with the diffusion of AIaaS. Traditional RAN vendors see the nascent technology as further threat to their capacity to sell programmable networks as black boxes, configured, sold and operated by them. New vendors see the opportunity to step into the RAN market and carve out market share at the expense of legacy vendors.

MWC 25 thoughts

 Back from Mobile World Congress 2025!

I am so thankful I get to meet my friends, clients, ex colleagues year after year and to witness how our industry is moving first hand.

2025 was probably my 23rd congress or so and I always find it invaluable for many reasons. 

Innovation from the East

What stood up for me this year was how much innovation is coming from Asian companies, while most Western companies seem to be focusing on cost control. 

The feeling was pervasive throughout the show and the GLOMO awards winners showed Huawei, ZTE, China Mobile, SK, Singtel… investing in discovering and solving problems that many in Western markets dismiss as futuristic or outside their comfort zone. In mature markets, where price attrition is the rule, differentiation is key.

On a related topic, being Canadian, I can’t help thinking that many companies and regulators who looked at the banning of some Chinese vendors from their markets due to security preoccupations are now finding themselves in the situation to evaluate whether American suppliers do not also represent a risk in the future. 

Without delving into politics, I saw and heard many initiatives to enhance security, privacy, sovereignty, either in the cloud or the supply chain categories. 

Open telco APIs

Open APIs and the progress of telco networks APIs is encouraging, but while it is a good idea, it feels late and lacking in comparison with webscalers tooling and offering to discover, consume, and manage network functions on demand. Much work remains to be done in my opinion to enhance the aaS portion of the offering, particularly if slicing APIs are to be offered. 

Open RAN & RIC

Open RAN threat has successfully accelerated cloud and virtualized RAN adoption. Samsung started the trend and Ericsson’s deployment at AT&T has crystalized the mMIMo +CU+DU+non RT RIC from a main vendor and small cells + rApps from others as a viable option. Vodafone’s RAN refresh should see maybe more players into the mix as Mavenir and Nokia are struggling to gain meaningful market share. 

The Juniper / HPE acquisition drama, together with the Broadcom / VMware commercial strategy seem to have killed the idea of an independent Non RT RIC vendor. Near RT RIC, remains in my mind a flawed proposition as host of 3rd party xApps, and as an expensive gadget for anything else than narrow use cases. 

AI

AI of course, was the belle of the ball at MWC. Everyone had a twist, a demo, a model, an agent but few were able to demonstrate utility beyond automated time series regression as predictions or LLM based natural language processing as nauseam…

Some were convincingly starting to show Small Models that were tailored to their technology, topology and network with promising results. It is still early but it feels that this is where the opportunity lies. The creation and curation of a dataset that can be used to plan, manage, maintain, predict the state of one’s network, with bespoke algorithms seems more desirable than the wholesale vague large and poorly trained models. 

Telco Cloud and Edge computing is having a bit of a moment with AI and GPU aaS strategies being enacted.

All in all, many are trying to develop an AI strategy, and while we are still far from the AI-Native Telco Network, there is some progress and some interesting ventures amidst the noise.