Ericsson Says the Telco Edge Was Too Early

Ericsson has put a name to the failure of the last edge computing cycle. Joe Constantine, the company's Americas chief strategy and technology officer, told Fierce Network in a piece published by Diana Goovaerts on 21 August that mobile edge computing was early rather than wrong, and his summary of what went wrong is short. "Ten years ago, MEC was a supply side concept." What has changed, in his account, is that a demand driver has arrived. "We have AI inferencing. It's the growth opportunity, an application that MEC did not have at the time. So, if you look at this, we believe that the industry, the technology and the market is vastly different today from 10 years ago." He supports the traffic case with Ericsson's own forecasts, that global mobile traffic will triple between 2023 and 2029 with AI as the key driver, and that uplink traffic will grow 10 times by 2035. He argues the network has changed too, from best effort connectivity to 5G built for time-critical communication and capable of 15 millisecond latency and "five nines of reliability." Fierce notes in the same piece that TM Forum's chief executive has told it that operators should not bet the farm on the edge.

The diagnosis is correct and it is unusual to hear it from a vendor

Supply side concept is the right post-mortem, and it is the shortest accurate description of that decade anyone has offered on the record. Operators built edge capacity because they had property, power and a latency story, then went looking for someone who wanted it. The platforms worked. The buyers did not appear. When I wrote about a fully programmable multi-access edge platform in November 2018, the services I could name were faster file uploads, console games without a console, and editing a document without downloading it. Those were real improvements to existing experiences. None of them was a business that an enterprise procurement department was going to sign for. Inference is a materially better answer than that, because it is a workload with a measurable cost that somebody is already paying somewhere else. That is a genuine change in the argument and it should be credited before anything else is said about it.

15 milliseconds is a central office number

The interesting discipline in Constantine's case is that his own numbers settle the location question, and they settle it away from the radio. A 15 millisecond budget is a loose one. It is comfortably met from a metro exchange or a mobile switching office serving hundreds of sites, and it does not require compute at the tower. That matters because the edge conversation still routinely conflates two very different capital programmes. Putting accelerators in a few hundred central offices is a brownfield project on estate that is already zoned, powered, cooled, fibred and physically secured. Putting them at tens of thousands of cell sites is a different business with a different power bill, a different maintenance model and a different landlord. I have argued on the AI Grid that deployment starts at central offices and mobile switching offices for exactly these reasons, and the 2018 platform was built into the central office for the same ones. Nothing in the Ericsson case contradicts that. If 15 milliseconds is the requirement, the requirement is an exchange.

The economic claim is asserted rather than costed

"Routing all this traffic to a centralized cloud isn't just only slow, it's economically not even sustainable" is the load-bearing sentence of the whole argument, and it arrives without a number attached. There is no published cost per inference at a telco edge site set against the same inference in a hyperscale region, at comparable utilisation, including the operator's power, cooling, refresh and remote-hands costs. Until that comparison exists, the economic case for distribution rests on the intuition that moving bits is expensive and moving them less must therefore be cheaper. That intuition ignores utilisation, which is what actually decides the economics of accelerators. A central region runs its fleet hot across many customers and time zones. A metro edge site runs a smaller fleet against local demand that peaks and troughs. The traffic forecasts are also Ericsson's own, published in its Mobility Report, and a 10 times uplink projection reaching to 2035 is a forecast rather than an observation. It should be read as one.

Physical AI names a use case, not a counterparty

Asked what actually requires the edge, Constantine points to robotics and physical AI. "Any system that moves, so drones, vehicles, humanoids, robots talking to other robots, all these will become an autonomous system that needs a network to think with it." His example is an autonomous car whose onboard sensors can see what is in front of it but not around the corner, with network sensing supplying the rest. The technical case is sound. The commercial case has the same shape as the one that failed. A drone fleet, a warehouse robot estate and a vehicle platform are owned by companies that are not the operator, and the thing being sold to them is not raw compute, which they can buy cheaper elsewhere, but contextual awareness that the network holds and they do not.

Selling that is a coordination problem before it is a latency problem. Robots talking to other robots across an ownership boundary need an agreed model of what is being asserted, whose authority stands behind it, and what record both sides will accept afterwards. That is the gap I argued no runtime supplies, and that NGMN has now enumerated at length for coordination inside a single operator's own domains, which is the easier version of the same problem. An operator can install the accelerators in its exchanges this year. It cannot unilaterally produce the model that lets a vehicle manufacturer's autonomy stack trust and pay for what the network says about the road. The compute is the part of this that money can buy quickly, which is why it is the part vendors describe in detail.

What to watch

3 things. First, where the accelerators physically land. If operator edge AI deployments in the next 18 months are announced at central offices and metro sites, the location argument is settled and the cell site edge can be retired from the conversation. Second, a published cost per inference or cost per token at a telco edge site against a hyperscale baseline, from anybody, on the record. That single figure decides whether distributed inference is an economic position or a latency preference. Third, the first contract in which an enterprise pays an operator for network context rather than for compute or connectivity, because that is the class of deal the 2010s never produced and it is the only one that would show the demand side has actually changed.

The philosophical problem with agentic AI

Jensen Huang’s address at GTC gave me a lot to think about. So much so that I decided to drive to Sana Cruz for a taste of the ocean. I had to wait 30 minutes to get the table I wanted, just by the beach, in the sun but with a little shade… as I mistype table on my iPad, I am thankful for the autocorrect to sanitize my  somewhat boozy prose, while mostly appreciating the elegantly subtle blue underlying of the word batle, prompting me to consider “is that really what you meant to write, or do you meant table”?

I like that. I like that more than the blue pencil with the little star that insistently offers an AI assisted rewrite. Oh, sure, I am not a native English writer, so my grammar is somewhat tainted by the other 3 languages I might think in at any point in time. If I compound St Patrick and this weekend’s VI nations rugby results for France, you will understand if my writing is not the usual corporate polish. 

Having said that, I was at GTC for the first time, I listen to Jensen’s performance and I was left enlightened and a bit worried. By now, the headline and the sound bite out there must be the $1 Trillion line of sight on chip revenues for Nvidia over the next couple of years. Obviously, it is an extraordinary number. Unfathomable. Impossible to imagine for most of us. Almost impossible to think that we, collectively would spend 125$ ( at 8 billion people) of Nvidia stuff over the next couple of years. Surely that’s impossible. 

Unless this is not about need, but about demand.  Unless that demand is accelerated, compounded, exponentially nurtured beyond its natural curve. 

Essentially, what I retained from the presentation was that the larger the model, the more the interactions, the larger the demand, the faster and more the tokens have to be created to satisfy it. (I am sure AI could rewrite this sentence more elegantly, but screw it). The measurement unit becomes token per Watt,as it is a limiting factor for a given data center and tokens per second as it is the limiting factor for a given service. Jensen even alluded to the fact that they will factor in token per month grants in engineering packages as it becomes a productivity factor. 

The thesis for the 1T$ revenue relies on demand exploding and the emergence of low latency, high I/O token market. Low latency, high I/O is understandable. Multimodal, video models, requiring real time inferencing from vehicles, robots and generally physical AI will drive it. The demand explosion, though, even factoring in the integration of compute and AI in to its, devices, edges… if we look at adoption curves and industrial capacity is decades away,  not in 2 years. Unless…

Unless we are not the demand. Us, consumers, enterprises, industries, governments… Agentic AI and Clawdbot are just showing how, beyond automation, agency becomes a compounding factor. Agents, that you create, for specific purpose are understandable, useful controllable. 

Agents, that interpret your intent, create other agents to enact their interpretation, have access to your digital life, credit card, HR, accounts receivables, invoices, orders, security cameras, GPS movements better be accountable, auditable, controllable. Agents that create fleets of agents to parcel out their workload is where I have doubts. The d’explosion in demand relies on the hypothesis that we will let agents create agents consume tokens to satisfy our needs.

No doubt, we will have agents to control, audit, police agents, but it feels wrong to delegate tasks just because you can or for the concept of efficiency.

This is where the the philosophical debate clashes with the economic model. I learned that hard times create hard men. Hard men create easy times. Easy times create easy men. Easy men create hard times. We might have evolved from this adage, but I feel that, being a kinetic, rather than a literal learner, I’ve learned from trying. I’ve learned from friction. To this day, I write on my notebook with a pen. I don’t forget anything I write. I forget most of what I type. It feels to me that friction is an integral part of the learning experience. More, it is an integral part of the human experience. The taste for effort, trying the hard things, failing is not only what most mankind experience on a daily basis, it is also, at least for me a great  condition to happiness. I am infinitely happier labouring and succeeding than an automated, frictionless, efficient experience. Even with a better result.

As my children are about to enter the workforce, I am confronted daily to the question “what is a safe, fulfilling carrer?”. It used to be that medicine, law, engineering guaranteed a safe economic path. Nowadays, it looks like most entry level intellectual effort can easily, efficiently be replaced, and that agentic AI will only accelerate that trend. How are they supposed to master a domain they won’t be able to tinker and stumble? Maybe I am just an old fart and just like calculators and computers did not replace engineers, a higher level of abstraction will necessitate higher levels of intellectual efforts ? But this feels different. 

Particularly if compute keeps accelerating and artificial intelligence surpasses human intelligence, then what? What is the imperative to learn, labour, try, suffer, if is not necessary? Where do you draw the line between agents that help and augment and agents that enable and replace?

Until then, I’ll keep labouring and burdening you with poorly written posts, but somewhat original or at least unique, because they’re mine. I enjoy this table, i waited 30 minutes for because I chose it and waited for it. I am not sure it would have tasted better should my personal AI butler had booked it for me on my way there.

Where Do Network Operators Go From Here? A View Ahead of MWC 2026

With Mobile World Congress just around the corner in Barcelona, the telecom sector finds itself at another inflection point. The headlines are familiar: ongoing layoffs across major operators, C-level reshuffles, persistent ARPU erosion, and debt structures that constrain organic investment. Vendors are already talking up 6G roadmaps while AI dominates conversations—both for aggressive OPEX reduction and tentative new revenue paths. Yet the near-term reality feels more evolutionary than revolutionary.

The recent wave of workforce reductions is not, in my view, primarily an AI story—at least not yet. It reflects the long tail of a structural shift that began over a decade ago: the gradual but relentless transition from proprietary telco platforms to cloud-native architectures. We are finally seeing the full operational benefits of user/control-plane separation, hardware/software disaggregation, widespread network virtualization, and centralized policy orchestration. These changes deliver greater automation, elastic scaling, and dramatically shorter development and validation cycles. The outcome is clear: managing a modern mobile network no longer requires the headcount levels of the previous era. Painful as the adjustment is, it is the inevitable consequence of borrowing proven cloud-native principles. Cost discipline is essential, but it is not a growth strategy. The more pressing question is how operators convert more reliable, elastic, and automated networks into sustainable revenue expansion.

Private Networks: Successes Exist, but They Remain Hard-Won

Private cellular networks continue to polarize opinion. Some portray them as a commercial disappointment; others point to hundreds of documented use cases. The reality sits firmly in between. Genuine deployments delivering positive returns do exist, particularly in verticals with high-value connectivity requirements and tolerance for tailored solutions. Energy (smart grids and remote monitoring), healthcare (indoor coverage in hospitals and clinics), large venues (stadiums and event spaces), mining (autonomous haulage and safety systems), and ports (crane automation and terminal logistics) stand out as segments where demand is tangible and economics can work. The common thread in successful cases is not technology alone but deployment philosophy: cloud-native designs that run on commodity hardware, leverage centralized intelligence, and minimize site-specific customization. When executed this way, private networks become scalable and margin-accretive rather than bespoke projects that drain resources. Operators who treat private 5G as an extension of their public edge and orchestration capabilities—rather than isolated silos—are better positioned to capture repeatable value.

Data: The Next Realistic Monetization Frontier

Beyond connectivity and private networks, operators sit on an underutilized asset: vast quantities of network-derived and network-transported data. Until recently most of this information has been siloed for internal analytics, dashboards, and regulatory reporting. That picture is beginning to change. Monetization remains nascent compared with the advertising-driven models of social platforms, yet the opportunity is material. API gateways that expose selected network and user context (location aggregates, mobility patterns, congestion signals, roaming events) represent only the surface layer. Consider a few practical illustrations:

• Ride-hailing platforms could benefit from near-real-time insight into clusters of international roamers converging in a city district—an indicator of an upcoming conference, trade show, or major event. Pre-positioning drivers becomes more efficient, improving service levels and reducing wait times.
• eSIM and travel-focused virtual operators could package value-added bundles—discounted car rentals, hotel reservations, restaurant bookings, or attraction tickets—targeted at detected travelers arriving in high-demand locations.
• Navigation services (Google Maps, Waze, and equivalents) could gain from telco-sourced, fine-grained congestion and flow data that augments probe-vehicle inputs, especially in areas with sparse device coverage or during atypical events. Privacy and regulatory compliance are non-negotiable hurdles, as are competitive dynamics with hyperscalers and data aggregators. Success will depend on responsible data handling, anonymization at scale, clear value propositions for enterprise partners, and commercial models that avoid commoditization. Operators that can evolve from pure connectivity providers toward curated data intermediaries—leveraging their unique position across physical infrastructure, subscriber scale, and real-time network telemetry—stand to capture incremental revenue without requiring entirely new network builds. As we head to MWC 2026, the conversation will likely revolve around AI acceleration, 6G timelines, and edge monetization. Beneath the buzz, though, the fundamentals remain: disciplined cost management, selective private-network wins, and thoughtful exploration of data opportunities. What are you seeing in your markets? Are private networks crossing the chasm in specific verticals? And where do you place data monetization on the priority list for the next 18–24 months? I welcome your perspectives in the comments.

Physical AI: How Network Operators Could Leverage Edge Computing for Smarter Robotics

As the telecom landscape evolves, one emerging trend that's catching my eye is Physical AI—the integration of advanced AI into physical devices like robots, enabling them to interact intelligently with the real world. With my background in telco-cloud strategy, I'm particularly intrigued by how network operators could position themselves as key enablers in this space. By providing low-latency edge infrastructure, telcos might unlock new revenue streams while supporting innovative applications that blend robotics, computer vision, and conversational AI.

In a recent analysis, I've been exploring how robots equipped with cameras and speakers could benefit from distributed AI processing at the network edge. This setup allows for real-time scene analysis, object detection, facial recognition, and natural language interactions with humans—all without relying solely on centralized clouds that introduce delays or high costs.

What is Physical AI?

Physical AI refers to AI systems embodied in hardware that perceive, reason, and act in physical environments. Unlike traditional AI that's confined to software, this involves robots or devices that use sensors (like cameras) to understand their surroundings and actuators (like speakers) to respond. The key challenge? Processing massive data streams in real time while maintaining privacy, efficiency, and low latency. This is where telco networks shine, with their distributed edge nodes offering compute power closer to the action.

Edge AI Inference: Powering Perception in Robotics

Operators could facilitate edge-based AI inference, where robots offload complex tasks like scene recognition, object identification, and facial analysis to nearby network edges. For instance, a service robot in a retail store uses its camera to scan the environment: edge inference quickly identifies products on shelves, detects customer faces for personalized greetings (with privacy safeguards), or recognizes obstacles to navigate safely. This sub-10ms processing avoids the pitfalls of cloud round-trips, reducing bandwidth usage and enabling seamless, responsive interactions.

Techniques like federated learning could further enhance this, allowing robots to fine-tune models collaboratively across distributed edges without sharing raw data—ideal for maintaining user privacy in sensitive scenarios.

Generative AI for Natural Language Conversations

Pair that with generative AI models running at the edge for conversational capabilities. Robots with speakers could engage in fluid, context-aware dialogues: a healthcare assistant bot recognizes a patient's face, infers emotional state from scene cues, and generates empathetic responses using natural language processing. Or in manufacturing, a collaborative robot converses with workers in real time—"Hand me the red tool"—while using object recognition to confirm and act.

By offering "AI-as-a-Service" at the edge, operators could provide scalable, usage-based access to these capabilities. Enterprises get high-performance AI without massive capex on private infrastructure, while telcos monetize their pervasive networks.

Real-World Opportunities and Examples

Consider verticals ripe for this:

• Retail and hospitality: Robots greeting customers by name (via facial rec), recommending items based on scene analysis, and chatting naturally to assist.
• Healthcare: Companion bots in hospitals using edge inference to monitor patient environments, detect falls, and converse to provide reminders or emotional support.
• Logistics and manufacturing: Autonomous robots navigating warehouses, identifying inventory via objects/scenes, and collaborating verbally with human teams.
• Smart cities: Public service bots patrolling areas, recognizing incidents (e.g., litter or crowds), and interacting with citizens through voice.

These use cases could drive B2B partnerships, where operators bundle connectivity with edge AI compute—potentially adding 10-20% to ARPU through premium services.

Considerations for Carriers

To capitalize, carriers might assess their edge footprints for AI readiness, pilot federated models for privacy, and collaborate with robot vendors or AI platforms. Challenges like energy efficiency and standardization remain, but the rewards in a growing Physical AI market make it worth exploring.