The Price Of RAN Security In Europe: 40B Euros... And More

Seven of Europe's largest operators commissioned GSMA Intelligence to price the removal of designated high-risk vendors from their networks, and the number came back this week. Under the European Commission's proposed Cybersecurity Act 2, stripping out equipment from suppliers such as Huawei would cost the region's operators between thirty and forty billion euros in direct costs, split across mobile at up to twenty two billion, fixed at around five billion, and transport at up to twelve billion. The figure that matters more, though, is the second one. GSMAi finds that the same measure would push mobile equipment prices up by roughly twenty four percent, because forcing designated vendors out of the market shrinks the field of suppliers, and a smaller field charges more. Deutsche Telekom, Fastweb plus Vodafone, Meo, Orange, Telefonica, United Group and Vodafone Group supplied the underlying data.

I have spent part of my career making the opposite bet. The entire economic premise of Open RAN, the work I led at Telefonica and have written about for years, is that opening the interfaces widens the supplier base, and a wider base drives the cost of the radio down. You can read my longer assessment of where that project actually stands on the state of Open RAN, but the direction of the argument was never in doubt: more suppliers, more competition, lower unit cost. What the Cybersecurity Act 2 proposal does is run that logic in reverse. It removes suppliers, it narrows competition, and the price goes up. The twenty four percent is not a side effect anyone chose. It is arithmetic. You cannot both mandate a smaller supplier pool and expect the survivors to hold their prices.

What makes this more than a European regulatory footnote is where it occurs. It lands on top of a repricing that is already under way for an entirely different reason. I have been arguing for some weeks that the AI build-out is bidding up the exact components a modern radio depends on, high bandwidth memory, advanced substrates, power semiconductors, because AI factories and the RAN supply chain now compete for the same silicon. The consequence I keep pointing to is that even an operator which never buys a single GPU still pays more for its radios, because the AI boom has repriced the inputs. Now set the GSMAi finding beside that. You have two independent forces, one driven by AI demand and one driven by security regulation, both pushing the unit cost of the radio in the same direction, at the precise moment operators are being told to find capital for AI.

Beware of stacked figures though, so let me be precise. Do not add the two numbers. The forty billion is a one-time direct cost of replacement. The twenty four percent is a forward price trajectory on new equipment. They measure different things over different horizons, and stacking them produces a headline number no operator will ever see on an invoice, which is the same error I flag when vendors add their individual RAN energy savings into a total no network has ever achieved. The direction is clear, though.The radio is getting more expensive from two sides at once, and only one of those sides is optional.

This sharpens a position I have held on the location question. If the radio is being repriced upward by both AI demand and regulation, the case for distributing expensive compute out to every cell site gets weaker, not stronger. Costly, repriced silicon is exactly the kind of asset you concentrate where you can amortise it, at the central office and the switching centre, not the kind you scatter across tens of thousands of sites on the promise of a latency budget that most workloads do not need. I made that argument on its own terms when I wrote that operators are leaning in on AI Grid location and set out the fabric versus location considerations underneath it. The cost side now reinforces the physics side. Scarcity concentrates capital.

There is a monetisation discipline here as well, and it is the one I set out when I argued for separating revenue from cost avoidance. High-risk vendor removal is not a modernisation and it is not a growth programme. It is a defensive cost, imposed from outside, that produces no new revenue line and buys no new capability. An operator can, of course, use a forced swap-out as the occasion to modernise, and some will. But the spend itself belongs on the cost side of the ledger, named honestly, and not folded into an AI or transformation story where it does not belong. The moment a swap-out driven by security regulation starts appearing in a slide about AI readiness, someone has mixed the flows again.

At last, this is RAN. While going forward these elements will become more programmatic, it is still unclear whether a 2G, 3G or 4G radio from a high risk vendor actually represents a security risk. The highest compromise risk is in the Core, the OSS and ancillary systems. It is easier to install IMEI catchers and antenna spoofs than to hack into a deployed live system.

Europe may well decide the security case is worth forty billion euros. That is a legitimate political choice. What the choice is not is free, and it is not neutral for competition. A framework designed to reduce dependence on one class of vendor achieves it by reducing dependence on vendors generally, which is to say by making the market smaller and the equipment dearer. Operators should book that outcome for exactly what it is: a defensive, imposed, largely non-recoverable cost, landing on an equipment line that the AI build-out was already repricing without any help from regulators. It is certainly not random that the leading RAN vendors are predominantly europeans, with emerging Korean and Japanese options.

The political decision would transfer value from networks to vendors. It is unclear whether that is sustainable if the pool of vendors does not increase substantially.

Verizon Earning: From Copper to Fibre to Edge

 

Verizon disclosed on its second quarter earnings call last week a dark fibre agreement with Google worth well in excess of a billion dollars, and chief executive Dan Schulman was explicit that it is the first of several, with further deals expected by year end worth multiple billions of dollars in revenue over the coming years. The headlines went to the number and to the counterparty. The more instructive detail came later in the same call, where Schulman described retrofitting thousands of central offices, the copper now being decommissioned, into edge data centres for low latency AI inferencing. One operator, one earnings call, two of the arguments I have been making all month, and they are not the same argument.

Let's start with the fibre. This is not cost avoidance dressed as growth, which is the trap I wrote about when Google Cloud called agentic AI a sixty billion dollar opportunity and every figure underneath the headline turned out to be a saved operating cost. It is also not the speculative new revenue that strategy decks reach for. It is my second money flow, AI creating fresh demand for what the operator already sells, landed on the income statement as contracted revenue. Schulman called it incremental, long duration, high quality, and drawn from some of the most demanding infrastructure customers in the world. He is right to be pleased. Route and real estate are exactly the assets an operator holds that a hyperscaler cannot conjure at will, and the AI build out is short of both.

Now look at what Verizon actually sold. Dark fibre is unlit glass. Google puts its own optics on each end, chooses its own wavelengths, runs its own capacity, and owns everything above the physical layer. Verizon is the landlord of the route and nothing more. On the capacity, platform, outcome ladder I set out two weeks ago, this is not even rung one, it is the ground the ladder stands on. That is not a criticism. A contracted, long duration, low churn landlord business against demand this strong is a genuinely good thing to own, and it is more defensible than most of what operators like to call platforms. The discipline is only this: name it correctly. The moment next year's deck describes a dark fibre lease as an AI platform business, the margin expectation that travels with the word platform will arrive, and a landlord business will not carry it.

The central office retrofit is the disclosure I want to emphasize. For two years I have argued that AI grid compute belongs first at the central office and the mobile switching office, not at the cell site, because power, cooling, fibre, real estate and security all favour the building the operator already runs. I made the fabric versus location case in early July and watched operators lean into central office siting a few days later. Verizon has now put capital behind it on an earnings call. The copper decommission is what makes it work: retiring the old plant frees the floor space and, more importantly, the power feed and the fibre entrance, which are the two constraints that actually bind at an inference site. I built what was probably the first fully programmable multi access edge platform at Telefonica in 2018, and the lesson from that programme was that the physics was never the obstacle. The obstacle was a paying tenant. Low latency inference is the tenant the central office was always waiting for.

The reason to read the two disclosures together is that they resolve a question people keep posing as a choice. Fabric or location, route or venue, is the AI grid a transport problem or a siting problem. Verizon's answer, in one call, is both, and the call even tells you which is which today. The fabric is the contracted revenue, available now, sold in its rawest form. The location is the capital project, the copper coming out and the racks going in, its revenue still ahead of it. An operator that understood only the first would sell glass to hyperscalers and miss the building. An operator that understood only the second would light up central offices with no anchor tenant, which is precisely the mistake the edge computing industry made for a decade. Verizon is doing both, and the sequencing is correct.

So I would put the deal through the same three questions I put to every operator AI business case. Which money flow is this. It is flow two, defended and grown connectivity revenue, honestly labelled, not flow three in disguise. What binding constraint does the buyer pay to remove. Google is paying for route diversity and a dedicated physical layer it controls end to end, away from shared congestion, which is a real constraint and an operator asset. And where does it sit on the ladder. Rung one for the fibre, with a credible path upward only if the central office retrofit becomes a platform the operator actually operates, rather than a colocation cage it merely rents to the same hyperscalers. The fibre deal is booked. The ladder question is still open, and it will be answered in the buildings, not on the routes.

I wrote a fortnight ago that the operators who will be interesting in 2030 are not the ones with the most GPUs but the ones who can still tell you which of the three flows each dollar came from. Verizon has just given the cleanest demonstration yet of the discipline: a fabric dollar and a location dollar, named separately, on the same call. The test now is whether it keeps them separate all the way up the ladder, or whether the word platform arrives before the platform does.

The Telco AI $60 Billion "Opportunity"

Google Cloud published a piece in RCR Wireless this morning arguing that agentic AI represents a sixty billion dollar opportunity for telecom operators. It is a well constructed argument, the engineering description is accurate, and the case studies are real. It is also, read carefully, an argument about cost avoidance wearing the vocabulary of growth. I want to be precise about this, because eight days ago I published a piece arguing that the industry's central discipline problem is its refusal to separate the two, and this article is the cleanest illustration of the problem I have seen since.

Start with the numbers. The sixty billion figure comes from Appledore Research, and Appledore is explicit about what it is measuring: operational cost savings by 2030. The McKinsey research cited alongside it reports a thirty to seventy percent reduction in troubleshooting tickets and a fifty five to ninety percent reduction in network operations centre costs. Deutsche Telekom's RAN Guardian identified 237,000 network events in early 2026 and compressed major incident handling from hours to about sixty seconds. Bell Canada's AI Ops platform achieved a twenty five percent reduction in customer reported issues and a faster mean time to repair. Vodafone's agents protect millions in annual operating expenditure. Every one of those is a genuine achievement. Not one of them is revenue. The article's own evidence base is, without exception, my first money flow: AI that reduces cost, which I described as real, happening, the largest near term financial impact of AI on operators, and emphatically not a new line of business.

The word doing the concealing is "opportunity". An opportunity, in the way a board hears it, is something you invest in to get money back that you were not getting before. A cost saving is something you invest in to stop spending money you were already spending. The two justify different capital, different organisational patience, and different governance. Operators that hear sixty billion and staff a growth programme will find, three years in, that they have built a very good efficiency programme and told their investors the wrong story about it. That is not a hypothetical failure mode. It is the failure mode the industry has run repeatedly, and the reason I keep insisting the flows be kept apart on the page before they are kept apart in the budget.

There is a second problem with the sixty billion, which is that it is sitting next to a Deloitte figure of a hundred and fifty billion in "total value" and the two are quietly being read as the same kind of number. They are not. One is a cost line, the other is a mixed construct that includes cost, defended revenue and speculative new revenue in a single total. Adding vendor and consultancy figures that measure different things is the same error I flag on RAN energy savings, where individually plausible percentages get stacked into a number no operator has ever achieved. Treat the sixty billion as the honest number, because at least you can tell what it counts.

Now to the architecture, which is where the article is most interesting and most incomplete. Google Cloud's prescription is a fabric of hyper specialised micro agents, billing agents, inventory agents, RAN guardians, communicating through standardised orchestration protocols, validated against a digital twin, bounded by what it calls a deterministic governance framework with explicit decision boundaries and clean handoff to human engineers. This is good engineering. It is also, for at least the sixth time in a month, a description of containment rather than coordination. Every agent in that picture belongs to the operator. Every protocol is internal. Every boundary is a boundary between the operator's machine and the operator's human. Nothing in the design describes what happens when an agent that the operator does not own, and cannot inspect, arrives with a request.

The digital twin makes the gap unusually visible. A twin is a high fidelity replica of your own network, and it is exactly the right tool for testing a configuration change before you ship it. It is useless for the case that actually matters commercially, because you cannot build a twin of the counterparty. When an enterprise's AI agent negotiates for a guaranteed slice, the operator's agent is not reasoning about a system it can simulate. It is reasoning about an intent it must infer, an authority it must verify, and a commitment it must be able to audit afterwards. That is not a simulation problem. It is a problem of shared topology, shared ontology, explicit authority boundaries and durable audit trails, which is the meta model of the agentic plane I have been arguing for since the spring, and which a runtime does not supply no matter how good the runtime is.

I should say plainly that a hyperscaler making this argument is not a criticism of the hyperscaler. Google Cloud is selling a stack that does what it says it does, and the operators quoted are getting real results from it. My argument is with how operators will read it. There is a version of the next two years in which the industry retools its operations beautifully, takes out a very large amount of cost, calls the result an AI business, and arrives in 2030 with the same revenue line and a smaller headcount. That would not be a failure of technology. It would be a failure to name what was bought.

So the test I would apply to this article, and to every agentic AI business case that lands on a telco investment committee this quarter, is the one I set out eight days ago. Which flow is this, cost, defence, or new revenue? If the supporting evidence is entirely tickets, incidents and NOC headcount, the answer is cost, and the paper should say so in its first sentence rather than its appendix. What binding constraint does an external buyer pay to remove? If nobody outside the company pays anything, there is no buyer, and the word opportunity is unearned. And where on the capacity, platform, outcome ladder does this sit? An operator that automates its own operations has not stepped onto the ladder at all, because the ladder is about what you sell, and nobody is buying your NOC.

Sixty billion dollars of avoided cost is worth having. It is worth a serious programme, serious money and serious executive attention. It is worth all of that as what it is. The operators that will be interesting in 2030 are not the ones that saved the most. They are the ones that could still tell you, at the end of it, which of the three flows each dollar came from.

AI monetization for operators: separating revenue from cost avoidance

Every operator earnings call now features AI prominently. Listen closely, however, and most of what is described as "AI monetization" is nothing of the sort. It is cost avoidance cosplaying a revenue costume.

This distinction matters because the two require different investment logic, different organizational capabilities, and different patience horizons. Operators that blur them will misallocate capital. Operators that separate them have a chance at building genuine new B2B revenue lines — narrower than the hype suggests, but investable.

Three money flows, not one

AI touches operator economics through three distinct channels, and the discipline starts with refusing to aggregate them.

1. AI that reduces cost. Autonomous network operations, agentic customer care, energy optimization, predictive maintenance. This is real, it is happening, and it is the largest near-term financial impact of AI on operators. It is also not revenue. A dollar of opex avoided is valuable, but it does not create a new line of business, and it does not justify the "operators as AI companies" narrative. It justifies a leaner operator.

2. AI that defends existing revenue. Enterprises deploying AI workloads have new connectivity requirements: deterministic performance, low latency to inference endpoints, secure private connectivity to GPU capacity, data-gravity-aware networking. Operators that serve these requirements protect and modestly grow their core B2B connectivity business. This is differentiated connectivity for the AI era — important, defensible, but fundamentally an evolution of what operators already sell.

3. AI that creates new revenue. This is the category everyone wants to talk about and the one that deserves the most scrutiny. It exists, but it is narrower than most strategy decks admit.

The four credible new revenue lines

Having spent the last two years working on AI infrastructure with operators and vendors on both sides of the Atlantic, I see four B2B revenue opportunities that survive contact with commercial reality. They are not equal — they differ in demand maturity, margin profile and time horizon, and they should be funded accordingly.

Sovereign AI capacity — GPU-as-a-service and AI factories — is the most immediate and the most misunderstood. Demand is real and policy-driven, concentrated in regulated sectors; the margin profile is low-to-mid, because the business is capex-heavy and carries utilization risk; and the revenue is available now. The demand side is genuine: governments, healthcare systems, defense, financial services and public administrations in Europe increasingly cannot — or will not — run inference on US hyperscaler infrastructure under foreign jurisdiction. Operators hold assets that map remarkably well to this demand: national data center footprints, energy contracts, security clearances, sovereign trust, and enterprise sales relationships.

Telefónica's recent national rollout of edge-based GPU-as-a-service in Spain is instructive. The underlying edge platform was architected years earlier — I led the team that built and productized it — and for years the business case was marginal on enterprise use cases alone. What changed was not the technology. It was the arrival of sovereign AI demand, which finally gave the infrastructure a paying anchor tenant profile. The lesson generalizes: edge and distributed compute investments become fundable when sovereignty is the demand driver, not the garnish.

The caution: this is a capex-intensive, utilization-sensitive business competing against hyperscalers with structurally lower unit costs. Operators win where sovereignty, data residency and proximity are binding constraints — and lose everywhere else. The addressable market is the regulated slice of national demand, not "the AI market."

Edge inference is real but earlier than its promoters claim. Demand exists where latency or data gravity bind; margins are mid-range; and the horizon is two to five years before this becomes a broad product line. The use cases that pay today are those where physics or data gravity make centralized inference impossible: industrial vision, real-time media production, autonomous operations in ports and factories. I have seen these work commercially. But the buyer set is narrow, and each engagement still resembles a system integration project more than a product sale. This becomes a scalable product line when agentic AI workloads distribute themselves across infrastructure tiers — the architecture I have described elsewhere as the AI Grid. That shift is underway, not arrived.

Data and trust services are the sleeper. Deepfake detection on voice calls, branded and verified calling, identity assurance for AI agents, provenance services. These are small revenue lines today, but they are high-margin, they monetize immediately, they sit directly on operator trust assets that hyperscalers cannot replicate, and demand grows with every AI-enabled fraud headline. For a B2B operator, this category has the best margin-to-capex ratio of the four.

Network APIs are the line whose trajectory has changed most in the past two years. The strategic logic has always been sound — AI agents will need to programmatically request network resources, quality on demand, location, verification — and the commercial signals are finally following: revenues are growing, aggregation initiatives have consolidated distribution, and enterprise visibility is rising with every agentic deployment that needs verified identity or guaranteed quality. It remains the earliest-stage of the four, and the AI agent wave — rather than developer evangelism — is what gives it genuine demand pull. I would invest now to be positioned, while sizing near-term revenue expectations with discipline; the inflection is likely in the second half of the decade.

The monetization ladder

Across all four lines, there is a ladder that determines margin and defensibility:

Sell capacity → sell platform → sell outcomes. Capacity here includes every consumption-metered unit: GPU hours, tokens, gigabits.

Selling raw capacity — GPU hours, token-metered inference, connectivity — is rung one: necessary, low-margin, commoditizing from day one. Tokens deserve a specific caution here: metering in tokens rather than GPU-hours changes the billing unit, not the business. An operator selling tokens against someone else's models and someone else's stack is still selling capacity, at prices that will be set by the most efficient infrastructure provider in the market. Selling a platform — inference-as-a-service with orchestration, security, compliance tooling — is rung two, where margins improve and switching costs appear. Selling outcomes — a fraud-detection rate, a production workflow, a compliant AI deployment for a hospital group — is rung three, where the economics finally resemble a services business worth building.

Operators historically stall at rung one. The reasons are organizational, not technological: product management that thinks in network elements rather than buyer problems, sales forces compensated on connectivity, and business cases that demand payback before the platform layer has time to mature. The operators that climb the ladder will be those that treat AI monetization as a product management and go-to-market transformation, not an infrastructure deployment.

What the buyer actually pays for

A final discipline. In every commercially successful case I have worked on, the enterprise buyer was not paying for "AI." They were paying for a constraint to be removed: data that could not leave the country, latency that broke the use case, a fraud pattern that was costing millions, a compliance requirement that blocked deployment. Price the constraint, not the technology. The moment an operator's AI proposition cannot name the constraint it removes, it is a science project.

Three questions before approving any operator AI business case

1. Which of the three money flows is this — cost, defense, or new revenue? If the answer mixes them, send it back.
2. What binding constraint does the buyer pay to remove, and why is an operator structurally better placed to remove it than a hyperscaler or an integrator? Sovereignty, proximity and trust are acceptable answers. "We have a network" is not.
3. Where does this sit on the capacity–platform–outcome ladder, and what is the credible path up? Rung-one economics with rung-three ambitions is where operator AI investments go to die.

The AI B2B opportunity for operators is real. It is also smaller, slower and more demanding of commercial discipline than the current narrative suggests. The winners will not be the operators with the most GPUs. They will be the ones that can tell the difference between a cost saving, a defended revenue and a new business — and fund each accordingly.

Operators Lean In On AI Grid Location

Earlier this week I argued that the AI Grid debate needs to move on from where you place a GPU to whether geographically dispersed compute can behave as a single fabric. I stand by that. But a story that has been building across the press this week is a useful reminder that the location question, the one I have been answering the same way for two years, is now being settled in public by the people who actually own the radio networks. And they are settling it against the tower.

The reporting is consistent. Light Reading describes Nokia and Nvidia's AI-RAN proposition running into telco resistance. Verizon, Vodafone, Orange and, notably for me, Telus have all raised doubts about putting graphics processing units into the radio access network. AT&T's chief technology officer has cast public doubt on the case for AI compute at the far edge. The enthusiasm for GPU-in-the-RAN comes from two operators, T-Mobile US and SoftBank, and almost no one else. Much of the rest of the industry is looking at Intel's newer CPUs for its open RAN rollouts rather than filling cell sites with accelerators.

I want to be precise about what this does and does not prove. It does not prove that AI in the RAN is a bad idea. Applying machine learning to scheduling, link adaptation and energy management inside the baseband is real, it is shipping, and Ericsson's AI-in-RAN software subscription is a reasonable way to bring it into existing hardware. What the operators are rejecting is narrower and more specific. They are rejecting the proposition that the cell site should become a general-purpose AI inference venue, stuffed with GPUs, monetised by hosting third-party workloads at the edge of the network. That is the proposition I have said for two years does not survive contact with power, cooling, space, security and, above all, the absence of a monetisation model.

My position has been that AI Grid deployment begins at the central office and the mobile switching office, not the cell site, because every physical and commercial constraint favours the aggregation point over the tower. The reasoning was never controversial to anyone who has stood in both kinds of building. A central office has power feeds, environmental control, physical security and fibre already in place. A cell site has a cabinet, a limited power budget and a landlord. When Verizon, Vodafone, Orange and Telus decline to put GPUs at the far edge, they are not making a new argument. They are confirming an old one, and they are confirming it with capital allocation decisions rather than conference slides, which is the only confirmation that counts.

There is a workstream reason this caught my eye. Telus appearing on the skeptics' list is consistent with what I see in the market: operators that are serious about autonomous operations are also the ones being disciplined about where AI compute physically lands. Those two forms of discipline are related. An operator that thinks clearly about the economics of edge inference tends to think clearly about the economics of everything else in the network.

The AI-RAN enthusiasm gap also matters for how we read vendor claims. When a technology has two vocal operator champions and a longer list of vocal operator skeptics, that is the signature of a capability that has been field-validated in specific conditions but not commercially validated across the market. I have made this distinction before and it applies cleanly here. SoftBank's agentic AI-RAN demonstrations and T-Mobile's Nvidia-backed edge trials are real engineering. They are not yet evidence that the model generalises to operators with different cost structures, different energy prices and different enterprise demand. Treat a two-operator enthusiasm as a pilot signal, not a market verdict.

So where does this leave the fabric argument I made last week? Exactly where I left it, and stronger. The operators are removing the least defensible node from the AI Grid, the cell site as inference host, which clears the ground for the argument that actually matters. Once you accept that heavy inference will not live at the tower, the interesting question becomes how you knit central offices, regional data centres and a small number of genuinely latency-bound edge sites into one addressable pool. The industry spent this week deciding where the compute will not go. That is progress. The harder decision, who owns the fabric that arbitrates across the places it will go, is still open, though.

AI Grid: Fabric vs Location Considerations

The debate about where AI compute belongs in a telecom network has been framed as a location question from the start. Do you put the GPUs at the cell site, the central office, the regional data centre, or the hyperscale campus? I have argued consistently that the honest answer begins at the central office and the mobile switching office, because power, cooling, fibre, physical security and latency sufficiency all favour those sites over the tower. That position has not changed. But two announcements from Asia this week suggest the more consequential question is no longer where the compute sits. It is whether the compute behaves as one pool regardless of where it sits.

NTT Docomo disclosed a nationwide testbed it calls GPU over APN. It pools graphics processing units spread across eight locations in five Japanese cities and presents them to a workload as a single platform, connected over the all-photonics network that NTT Group has been building under its IOWN programme. Docomo describes it as the realisation of its AI-Centric ICT Platform concept, part of what the group now labels AIOWN, its AI-native infrastructure. Strip away the acronyms and the claim is precise and significant: distributed GPUs, addressed as if co-located, over deterministic optical transport.

KT made the point from the other direction. Its new chief executive committed 18 trillion won, roughly 11.7 billion dollars, over three years, including 3.26 billion for one gigawatt of AI data centre capacity and a plan to connect that centralised infrastructure with edge sites serving low-latency workloads such as autonomous vehicles and industrial robotics. One operator is making dispersed compute act centralised. The other is extending centralised compute out to the edge. Both are describing the same thing from opposite ends, which is a compute fabric rather than a compute site.

This matters because it decouples two decisions the industry keeps conflating. Where you place a GPU is a question about power, land and cost. Where you run a workload is a question about latency, data gravity and sovereignty. As long as placement and execution are the same decision, every AI deployment becomes a real estate argument. Once a photonic fabric can make placement invisible to the workload, the two decisions separate. Training and heavy batch inference go where power and space are cheap. Latency-bound inference lands close to the user. The fabric arbitrates between them. This does not contradict the case for the central office, it absorbs it: the central office still wins for latency-bound edge inference, but that win is now a node in a graph rather than an isolated site.

I have some history with this problem. In 2018 I wrote about building at Telefonica what was probably the industry's first fully programmable multi-access edge computing platform, and the hardest part was never the compute. It was making distributed compute addressable, governable and billable as a coherent resource rather than a scatter of isolated sites. The technology around it has moved on considerably, but the unsolved problem is the same one Docomo is now attacking with photonics.

A practitioner's caution is in order. A fabric that makes national-scale GPUs behave as one pool is a testbed today, not a product. Docomo demonstrated it in a lab-grade programme. KT's edge connection is a plan, not a live deployment. Deterministic optical transport carrying commercial service level agreements under contended traffic is a materially harder thing than a controlled demonstration, and I would treat "as if co-located" the way I treat vendor energy savings figures: directionally real, quantitatively unproven at scale. The distance between a testbed that works and a fabric that carries production workloads is precisely the distance Open RAN spent five years crossing.

Still, the framing is the takeaway. The AI Grid conversation needs to move from siting to fabric. The operators that win will be the ones who can treat geographically dispersed compute as a single addressable resource, orchestrate workloads across it against real constraints, and price and settle access to it. That is a transport, orchestration and settlement problem before it is a property problem. The question is no longer which building holds the GPUs. It is who owns the fabric that makes the buildings irrelevant.

The Agent Runtime Is Not the Agent Model

DTW Ignite in Copenhagen made one thing clear: the vendor community has decided that the path to autonomous networks runs through agent runtimes. NVIDIA introduced NemoClaw blueprints and the OpenShell secure runtime to give long-running agents policy guardrails and sandboxed access to telecom systems. AdaptKey is piloting security-hardened agents for self-healing 5G operations. ServiceNow is bringing Project Arc to the NOC, orchestrating incident response from alert to work order. NTT DATA is building anomaly agents that escalate to research agents for telemetry analysis. Synthetic data rounds out the stack, a pragmatic answer to the fact that more than half of operators say their most valuable network data is too sensitive to use.

This is genuine progress and I do not want to minimize it. Containment, auditability and policy enforcement are necessary conditions for letting agents touch production networks. An agent that cannot be sandboxed cannot be trusted, and an agent whose actions cannot be audited cannot be certified. The runtime layer has to be built.

Containment is not coordination

But look carefully at what these announcements govern: individual agents, operating within a single operator's domain, executing workflows that a human has scoped in advance. This is vertical governance. It answers the question of whether an agent is allowed to perform an action. It does not answer the question that autonomous networks will actually pose at scale: when two agents are each permitted to act, and their permitted actions conflict, who decides?

Consider a scenario that is closer than most operators think. An enterprise logistics agent requests guaranteed throughput for a fleet of delivery robots. Simultaneously, a network energy agent, operating under its own perfectly valid mandate, is shutting down capacity in the same cluster to meet a sustainability target. Both agents are sandboxed. Both are auditable. Both are compliant with their policies. The runtime layer sees two well-behaved agents. The network sees a contradiction.

This is the problem I described in my previous post on network APIs. APIs were designed for developer access, not for agent-to-agent negotiation. Runtimes inherit the same blind spot. They secure the execution of each agent without providing any shared representation of the agentic plane itself.

What the meta-model requires

For agents to negotiate rather than collide, the industry needs a meta-model of the agentic plane: a topology of which agents exist and where they sit, an ontology so that an enterprise agent and a network agent mean the same thing by capacity, latency or priority, explicit authority boundaries defining what each agent may commit on behalf of its principal, shared state models so that negotiations reference the same view of the network, and audit trails that span negotiations rather than individual actions. None of the DTW announcements address this layer. They cannot, because it is not a product any single vendor can ship. It is a model the industry must agree on, the way it once agreed on network information models for OSS.

There is a familiar pattern here. The industry built firewalls before it built routing protocols for the internet's trust boundaries, and it spent two decades paying for the sequencing. We are building the firewalls of the agentic era first. The operators and standards bodies that formalize the agentic plane meta-model will define how enterprise AI and network AI transact for the next decade. The ones that stop at the runtime will discover that a network full of safely contained agents is not an autonomous network.

DTW Ignite 2026: The API Is Not Enough

I returned from DTW Ignite in Copenhagen with one conviction: the interface between enterprise applications and network infrastructure is about to change in a way the industry has not yet designed for.

Network APIs were never really about autonomous networks. That framing conflates two separate problems. APIs — CAMARA, GSMA Open Gateway, the decades of network exposure work that preceded them — were designed to let developers discover and consume network resources from outside the operator domain. Quality on Demand, location services, device status, number verification: clean REST interfaces exposed through a developer portal so that a programmer writing a B2B application could request a network capability and pay for it. Real progress on a real problem. But the problem was developer access, not network autonomy.

What is coming next is different in kind, not degree.

Enterprise AI agents are beginning to consume network infrastructure directly — not through a developer writing an integration, but autonomously, in real time, as part of executing a business objective. An industrial automation agent that needs guaranteed low-latency connectivity for a robotics fleet. A financial services agent that needs to provision a secure, isolated network path for a time-sensitive transaction. A logistics agent that needs to dynamically reserve bandwidth across multiple carrier domains as a shipment moves between jurisdictions. In none of these cases is there a developer in the loop. The agent has an intent, it needs network resources to fulfil it, and it needs to negotiate those resources with the network — now, at machine speed, without human mediation.

That negotiation cannot happen through a developer portal. It cannot happen through a static API catalogue with a PDF explaining what each endpoint does. The enterprise agent and the network need to speak to each other, and neither CAMARA nor MCP — whatever their respective merits — were designed for that conversation.

The network side of this exchange needs to be represented by network AI agents of its own: agents that can expose available capacity in real time, understand the constraints and commitments already in place, reason over competing demands, and negotiate resource allocation in a way that respects the network's operating boundaries. That is not a developer API. That is an autonomous counterparty.

And for those network agents to function — to negotiate reliably, to be governed, to be audited, to avoid conflicting with each other across RAN, transport, core, and the operational layers of OSS and BSS — they need something the industry is not yet building: a meta-model of the agentic plane itself.

Operators building autonomous networks are doing the right foundational work. Network topology models. Data ontologies. Decision layers. Closed-loop control architectures. These give the automation layer a complete and current picture of the environment it is operating in. But agents operating on that network need an equivalent model of themselves. Every agent with an identity, a capability scope, an authority boundary, a state, a dependency graph, and an audit trail. An abstract topology and ontology of agents, sitting alongside the topology and ontology of the network.

Without that model, what looks like autonomous negotiation between enterprise AI and network AI is actually uncontrolled interaction between systems that cannot see each other. An enterprise agent requesting bandwidth does not know what the network agent is authorised to commit. The network agent does not know what other network agents have already promised. No shared representation, no conflict detection, no governance.

The developer exposure problem is largely solved, or at least well understood. The agent-to-agent negotiation problem has barely been framed. That is the conversation the industry needs to have, and Copenhagen convinced me we are not having it yet.

O-RAN PlugFest Spring 2026: Smaller, Sharper, More Ambitious

The O-RAN ALLIANCE published results from its Global PlugFest Spring 2026 on June 4. Conducted from February to May 2026, it brought together 31 companies and institutions across 9 labs worldwide, co-hosted by 13 operators, OTICs and independent institutions. Those numbers will look like a step backward to anyone tracking PlugFest participation over time — and they are, deliberately.

For comparison: the Fall 2024 PlugFest ran across 28 labs with 115 participants. Spring 2025 had 19 labs and 69 participants. Spring 2026 cut the footprint by more than half again. This is not a loss of momentum. It is a deliberate shift from ecosystem-building to integration hardening. Getting everyone in the tent was the objective of the first five years. Making what exists actually work together is the objective now.

Massive MIMO — finally the main act

The centrepiece of Spring 2026 was multi-vendor end-to-end integration of massive MIMO O-RAN components, including mMIMO beamforming. This matters more than it might appear. Massive MIMO has always been the credibility gap for Open RAN. Traditional vendors have proprietary beamforming pipelines refined over a decade of commercial deployment. High-band, high-capacity urban coverage — stadiums, CBDs, transport hubs — has been essentially off-limits for disaggregated RAN. Demonstrating that multi-vendor mMIMO beamforming can be integrated in a structured, neutral lab environment is the first necessary step toward closing that gap.

AmpliTech's O-RAN CAT-B 64T64R Massive MIMO radio was the only radio of that configuration at the event — the only American-designed and commercialized O-RU at that specification level — and it demonstrated multi-vendor interoperability alongside operators including AT&T, Deutsche Telekom, Korea Telecom, LG Uplus, Orange and Rakuten Mobile. That combination — 64 transmit, 64 receive antennas, multi-vendor integration, neutral lab, named Tier-1 operator validation — is the kind of result that shifts a proof of concept into a procurement conversation.

Commercial-grade performance parity with incumbent vendors is a separate question, and a longer road. But you cannot get there without first doing what was done here.

AI-RAN — directionally consistent, details deferred

Several labs focused on AI-RAN solutions targeting network performance improvements and energy savings. The press release did not publish quantified results — a contrast with Spring 2025, which reported 25–30% intelligent RAN energy savings delivered by rApps on Non-Real-Time RICs. Detailed results will follow in technical session readouts. The direction is not in question. AI-driven energy efficiency via the RIC architecture has been the dominant AI-RAN use case in structured testing for two years running, and the logic is clear: energy is the largest controllable operating cost in RAN, the levers are cell on/off switching and power scaling, and the RIC is the right control point. What the Alliance needs to demonstrate next is that these savings hold at scale, under real traffic conditions, not just in lab scenarios with predictable load profiles.

The open-source stack is growing up

Four open-source frameworks were deployed at the PlugFest: Open Air Interface, OCUDU, O-RAN SC, and Sylva. Each represents a different layer of the stack, and their simultaneous presence in integration testing is worth noting.

OAI has become the de facto open-source O-CU/O-DU choice in lab settings — widely trusted, well-documented, increasingly operator-deployed in production pilots. O-RAN SC provides the RIC and SMO components from the Alliance's own software community. OCUDU is a combined CU/DU implementation targeting simplified deployment at the edge. And Sylva — the Linux Foundation's cloud-native telco infrastructure framework — is where the container orchestration and lifecycle management work happens. Carrier-grade Kubernetes for RAN workloads, in plain language.

The intersection of Sylva and O-RAN SC is the one to watch. Cloud infrastructure meeting RAN software — not as a research experiment, but as a jointly validated integration — is where operator confidence in virtualized open RAN will be won or lost operationally. It is still early. But it is no longer theoretical.

ISAC — a 6G signal in a 5G event

Initial tests were performed on Integrated Sensing and Communication, or ISAC — using the RAN simultaneously for wireless communications and environmental sensing. Object detection, positioning, radar-like environmental awareness, all from the same radio infrastructure. This is a 6G-era capability appearing in 5G Advanced specifications, and its inclusion in a 2026 PlugFest is a deliberate statement by the Alliance. They are not waiting for a new standards cycle to begin building the interoperability baseline. Given how long it took Open RAN to move from specification to commercial deployment, starting ISAC integration work in 2026 is not premature. It is probably necessary.

Who was there, and who wasn't

The full participant list is a useful read. Operators present included Korea Telecom, LG Uplus, Orange and Rakuten Mobile, with AT&T represented at the governance level through Brian Daly's TSC co-chairmanship. Academic institutions — Virginia Tech's Commonwealth Cyber Initiative, Iowa State University, North Carolina State University, ETRI and Fraunhofer HHI via the i14y Lab — signal that the research-to-commercialisation pipeline is being built at the institutional level, not just the vendor level. Software Radio Systems, the team behind srsRAN, continues to appear — their open-source 5G stack is quietly becoming a reference DU/CU implementation in operator testbeds.

What is absent is equally notable. No traditional vendors. The large incumbents engage with the Alliance at the specification level; they do not typically show up at PlugFest lab level. This is not surprising, but it is a structural tension. The integration maturity of disaggregated RAN will ultimately be measured in Tier-1 operator deployments, and those operators currently run incumbent infrastructure. The path from PlugFest to procurement runs through a comparison the incumbents are not participating in on neutral terms.

What this PlugFest actually means

The O-RAN Alliance has always faced a version of the same challenge: translating credible lab results into commercial deployments at scale. PlugFests prove interoperability in controlled conditions. Operators need it in live networks, under real traffic, with real SLAs and real interference environments. That gap has not closed. But the Spring 2026 agenda — massive MIMO beamforming, AI-RAN energy efficiency, integrated open-source stacks, ISAC first tests — reflects an ecosystem that knows precisely where it needs to focus to close it. That is not a small thing. Three years ago, the conversation was still largely about whether disaggregated RAN could work at all. That question has been answered. The question now is how fast it can reach performance and cost parity with incumbent solutions in high-capacity, high-stakes deployments. Spring 2026 moved that needle. Not dramatically. But in the right direction, on the right problems.

Innovating and Monetizing in Telecom: The Lean Telco

 

As alluded to in my previous posts, I have integrated the Lean Startup methodology, the design thinking framework and the Wardley Map model to create value in a telco environment.

Value is a subjective topic but in a Telco context, my efforts have been aimed at creating sustainable growth strategies. Very simply, sustainable growth comes from sustainable differentiation, which stems from the creation and evolution of technological, commercial and operational characteristics that become difficult, expensive and time consuming to emulate from your competition.

Sustainable growth comes from sustainable cost reduction and revenue growth (Duh!).

Sustainable cost reduction can be achieved through drastic cost structure changes. In 2026 Telco, it can be attained through the implementation of a cloud native architecture and principles, underpinned by strategies of network disaggregation, extensive use of open APIs and open network topologies; control / user plane separation and systematic automation. While these goals are challenging by themselves, particularly in a brownfield legacy telco environment, they are the bare necessary changes for survival. The challenges associated with the organization, skill sets and methodologies to evaluate, test, deploy, purchase and maintain these technologies are even larger.

Every telco is extremely skilled at managing technological and operational risk, through iterative, waterfall evaluation and tests, resulting in deployment of high availability and capacity networks. This methodology has also led to lengthy evaluation periods and deployments. Most vendor will recognize that the sales cycles in telco are over 2 years long and that making any change in a commercial network takes several million of dollars or euros. This has led to an oligopoly where only a handful of specialized vendors are able to sustain economically these drastic processes.

Lately, telcos have been trying to diversify the pool of vendors to increase competition and innovation by promoting open source and open API projects such as open RAN.

While these projects have shown interesting progress, the real cost reduction comes from the change in methodology and processes to take advantage of these more nimble vendors offering.

What I am proposing with Lean Telco is a methodological framework for identifying, evaluating, testing, sourcing, deploying telco products and services that will provide sustainable differentiation with drastically different cost structure than the incumbent versions.

Once you have successfully changed the cost structure of evaluating, buying, deploying and managing telco infrastructure and capacity, you can survive as a high capacity, low overhead provider of connectivity. But if you want to strive and grow, you need to attack the revenue part of the equation. Actually, one would argue should start with growth objectives, and look at cost structure as an optimization challenge.

Growing revenue sustainably, in a telco environment comes from either having more people using your existing services, or using more of them, connect new people or create new services. I have prototyped, tested and launched projects in each of these categories in my role at Telefonica.

1. Having more people use your existing products is difficult for telcos, because those products (residential and enterprise mobility, internet, telephony, TV...) are poorly differentiated, since they rely on the same technology from the same vendors. As a result most telcos end up trying to deploy first (5G, SDWAN...) or to claim a performance advantage, usually derived from a superior spectrum or infrastructure investment. The only real differentiation ends up being pricing. This is very expensive and not sustainable.
2. Having your customers using more of your service does not necessarily lead to more revenue, as bundles and unlimited plans are periodically rolled out to counter internet hyperscalers offering who rely on a different cost structure and revenue model. Again, since these services are mostly the same from one operator to another, differentiation comes from bundling and pricing. This is not sustainable.
3. Connecting new people / clients is a worthy endeavour, but the last unconnected live mostly in rural, low density areas and selling services to new corporate clients usually mean competing against public cloud offering that are more cost effective and flexible than what most telcos can offer. There are possibility of growth there, but it requires breaking out from the current telco technological framework and a willingness to assemble new value chains.
4. Creating new services is certainly where there is the most value, if we look at the growth of telephony over internet, video streaming services, social media and social messaging, SDWAN, cloud security, SASE, AIaaS... it is also the area with the most uncertainty and risk.

Telcos are not well equipped to manage the risk and uncertainty inherent in the discovery and creation of new services. The methodologies, organization and processes they use is to deliver with absolute certainty a product or utility with zero default to a mass market without variation. This model works well for mature, disciplined technology and vendors, not at all for exploration and innovation.

Too often, some Telcos build an extremely detailed plan, with contingencies. They budget it, staff it, resource it to execute it within a given timeframe, only to discover that the client didn't really want / need / value what was proposed (cf. push to talk, IMS/VoLTE, RCS, private networks...).

Just like in Lean Startup, the methodology I propose allows the progressive liberation of resource and funds as commercial uncertainty is shed by direct client interaction, testing and feedback. In a typical telco environment, the client interaction is at the very end of the process, here we are going to intersperse it throughout the development process to allow pivots, or early termination if the hypothesis are not met.

Trained, mentored and helped by many, I have adapted a few methodologies to enable Telcos to identify, validate, and deploy new services in an agile and cost effective fashion. I call it the Lean Telco Methodology.

How do I create a Lean Telco?

I use Wadley Maps for situational awareness and create a topographical representation of the current environment, which in my area of interest range from AI (sovereign, factories, grid, agentic, physical...), telco network cloud, orchestration, cloud native distribution and orchestration (K8, micro services) and hybrid cloud / edge computing (telco private stacks, AWS outpost, MS Azure, AI grid...). This is not a map until we apply the level of maturity (Genesis / handmade, Custom / solution, Product, Utility) to each of them, as well as their direction and barriers on the horizontal axis. On the vertical axis, instead of using Wardley's traditional visibility method, I use technology stacks such as access, transport, core network, OSS /BSS, orchestration... The purpose of the map is not to be precise or even right, it is to share and compare understanding of the environment, the players, their direction, velocity and the barriers. This visualization enables a level of shared understanding necessary to strategic discussions and gameplay around permutations and what-if? scenarios.

Once identified priorities and areas of risks / opportunities to investigate, I use the Lean UX framework and Lean Startup methodology to systematically identify potential current problems needing solving, unmet customer needs, unsatisfactory experiences and potential new products / services that customer wouldn't even know or have an opinion about. A series of workshop is usually best to crystalize the ideas. Once identified, they need to be refined into customer centric objectives. Design thinking frameworks help, but contrary to popular belief, customer centricity is not necessarily going to ask prospective customers about what they think. Most wouldn't have any idea about what to do with 6G, physical AI or a token exchange if you asked them. This is where lean UX and empathetic composite models are useful.

Each idea is reviewed by a jury and graded, the jury defines which ideas can make it to the next stage. The ideas are shaped and staffed as independent projects, with dedicated resource, budget and time box. Each project lead has the overall responsibility for moving the project to the next phase and to deliver the results of the current phase to justify additional resource and budget for the next one.

At a high level, the phases are:

• Ideation - ($5k-10k /1 - 3 months) -the idea is shaped into a project, with central opportunities, areas of innovation, right to play for the company, sustainable differentiating factor, commercial high level opportunity and cost / timing for the next phase.
• Prototyping - ($20k - 50k / 2 - 6 months) - In this phase a prototype is built, that might or might not incorporate any development or use of technology for the target invention. The idea is just to emulate the resolution of the problem and put it into customers hands as early as possible to identify whether the objectives, assumptions are framed properly and whether the client would value the resolution.
• Beta - ($300k - $600k / 3 - 6 months) -  once the central problems are identified, and we know the client values their resolution, it is time to create a MVP to prove that it is technically, commercially, organizationally possible to solve that problem and that the value created exceeds the costs.
• Product - ($1m - $3m / 3 - 6 months) - In this phase, once proven that the solution is possible, it is necessary to prove that the solution will scale and will be deployable with a mature operational and commercial model.
• Growth - (TBD) This is the phase where the project needs to be commercially and economically sustainable.

Each phase require client interactions, in the form of actual tests in conditions as close as possible to commercial network. Within each phase, we decompose the project into customer centric objectives. Each objective into hypothesis. Each hypothesis into series of experiments that will validate or invalidate the hypothesis. It helps to set clear expectations and success criteria for each of these.Wardley maps helps again, within each phase understanding what tasks, experiments are more suited for pioneers, settlers or town planners and indeed whether the project lead can adopt this mental posture in this phase or whether someone else needs to take the lead.

The result is a portfolio of new revenue making projects, that are systematically validated by customer feedback, capacity and propensity to pay; together with a robust operational and commercial model. Each project is periodically reviewed and graded, all projects must pass a gate review before the next phase and liberation of funds, which allow a nimble, measured, progressive investment plan, as risks and uncertainty decrease throughout the life of the project.