Ericsson Says Autonomy Does Not Need Perfect Data - I disagree

Lucky La Riccia, head of cloud software and services for EMEA at Ericsson, told Fierce Network at Ericsson's OSS/BSS Summit in Zurich that operators do not have to wait for clean data before pursuing autonomous networks. In Fierce's report of 22 October, his advice is to understand the limits of the data, put controls around how autonomous systems use it, and accept some imperfection: "Maybe it's okay to have some unstructured data." Ericsson favours a manager-in-the-loop model in which a person can oversee an AI agent, halt or review its actions, and approve them to continue. Autonomy should be pursued domain by domain, starting where business value is highest, and La Riccia said no customer will reach Level 4 across a network within the next year. The operator examples are Telia, which is starting with a common network inventory view for orchestration and provisioning, and Swisscom, which is starting with a cloud-native core and an intelligent automation platform that includes closed-loop automation in the radio access network.

Probabilistic models, deterministic networks

I read the data question differently. AI is a probabilistic process: a model returns the most likely answer with a degree of confidence, not a determined result. A telecom network is operated to deterministic outcomes. A handover completes or it drops, a slice meets its service level or it does not, a configuration change succeeds or it fails. A closed automation loop connects the two, because a probabilistic output triggers a deterministic action on a live network.

Imperfect data widens that gap at every stage. It produces inaccurate readings of network state, weaker predictions built on those readings, and lower confidence in the recommended action. In an automated loop the errors compound, because each action changes the network state that the next prediction reads. The value of the AI falls, a human has to stay in the loop to catch the errors, and the operator ends up further from Level 4. On this reading, manager-in-the-loop is the control that compensates for data quality. It is not a stage on the way to autonomy. It is the same pattern as Deutsche Telekom's RAN Guardian in Deutsche Telekom Extends Agentic AI Beyond RAN: autonomous action on routine events, with a human approving every critical change.

The way to narrow the gap between probabilistic and deterministic outcomes is data. Full-fidelity data, meaning complete counters, traces and state records and not sampled aggregates, lets a model read the network as it is. Long time windows let the operator measure how often predictions were right across seasonal cycles, faults and rare events, and calibrate confidence against outcomes. That measured accuracy is the evidence for deciding which actions can run without approval, so human intervention in the loop falls as the track record builds. Without it, the approval step has no basis for being removed.

Cross-domain loops raise the requirement further, because one agent's output becomes another's input. They need an agreed definition of state, a statement of who is authorised to change what, and a record of what was done, which is the argument in Agentic AI: Autonomous Agents Governance and Enforcement and in DTW Ignite 2026: The API Is Not Enough. Domain-by-domain sequencing is sensible for delivery, and it points to a rule for choosing the first domains: those where the operator already holds full-fidelity data over long windows. A common inventory view such as Telia's is in effect a data quality investment, and it is the kind that moves an operator toward Level 4.

AT&T Pitches AI Grid as Connectivity, Not Compute

AT&T used its third-quarter call on 21 October to describe how it intends to take part in the AI grid, and RCR Wireless's report on 22 October shows that the answer is connectivity. CEO John Stankey said more than 90% of US data centres are within 10 miles of AT&T's fibre network and more than 80% are within 1 mile. The product is managed links across metro networks, business endpoints and cloud on-ramps, which Stankey described as building the "right expressways in and out of their clouds" with end-to-end visibility and performance. RCR reports that AT&T sees itself as a fibre and connectivity provider and not a GPU landlord, and expects managed connectivity to earn higher margins than renting out AI capacity.

Stankey said edge inference, the in-between part of the grid, will take a while to work out, and that robotics and industrial autonomy will bring more demanding requirements and likely another level of investment. The capital behind the stated position is in fibre: AT&T is buying Lumen's consumer fibre assets for $5.75 billion and has signed a $3 billion multi-year fibre supply agreement with Corning.

Fibre first, compute later

This is consistent with the location argument in Operators Lean In On AI Grid Location: where AI grid compute sits is decided by power, cooling, fibre and security, and the operator's structural advantage is the fibre. It also matches the operator role described in Ericsson Makes Its Case for Edge Inference, where the operator provides infrastructure and assurance to AI providers and does not sell the AI service itself. The 90% and 80% figures measure how close data centres are to AT&T fibre, which is a statement about data centre interconnect demand. They are not a measure of demand for inference at the network edge.

The practical consequence is where AI grid revenue will first be reported. If AT&T's plan holds, the first income arrives as managed network services and cloud connectivity at the metro and data centre layer. Edge compute at central offices becomes a later investment that follows customer commitments, with robotics and industrial workloads as the stated trigger.

Deutsche Telekom Extends Agentic AI Beyond RAN

Deutsche Telekom is extending agentic AI from the radio network to network-wide operations, according to RCR Wireless on 21 October, reporting on DT's AI Investor Day. RAN Guardian, live in Germany since November 2025, uses 3 agents: one scans public and social media and other sources for events, one categorizes events and assesses their network impact, and one remediates ahead of the event through carrier aggregation, uplink adjustments and antenna tilt changes. A cross-domain platform called MINDR is in development, with an initial MVP in Germany by the end of 2026.

Capacity against realised results

Chief network officer Armin Sumesgutner said the agentic approach can analyze and remediate 100,000 events, against about 1,000 handled manually before, with a goal of avoiding 24 million minutes of poor customer experience. RAN Guardian triggered more than 100 remediation actions autonomously in its first month. That is a long way from the 100,000 events of capacity, and RCR does not report actions since. MINDR targets more than 90% automated ticket handling, issue qualification in under 15 minutes, incident resolution in under 1 hour and a 30% productivity improvement.

Level 4 with a human approver

DT states the goal as Level 4 autonomous networks. Group technology officer Arash Ashouriha said that for every critical change there will always be a human in the loop. In the TM Forum framework, Level 3 is conditional autonomy with human oversight, and Level 4 is intent-driven, closed-loop operation with minimal human intervention for routine tasks. Autonomous fixing of low-impact issues with human approval of critical ones fits that wording, but it means the level describes the routine tail and not the changes that carry risk. The approval boundary is therefore the design object: which changes count as critical, who holds the authority to make them, and what record is kept. That is the authority-boundary and audit-trail question in Agentic AI: Autonomous Agents Governance and Enforcement, and the reason I argued in DTW Ignite 2026: The API Is Not Enough that the API layer alone does not supply it.

Ericsson's Mats Karlsson told Fierce Network at the OSS/BSS Summit in Zurich on 20 October that AI automation is not network autonomy and that autonomy levels should not be treated as a numbers game. His example was that a Level 4 investment for a task performed twice a year may never pay back, while automating a task run 1,000 times a day changes the equation. DT's numbers fit that frame, since RAN Guardian earns on event volume. The figures still to be published are the actions executed to date, the share needing human involvement, and the minutes avoided measured against the 24 million goal.

Ericsson and Nokia at odds on AI-RAN silicon

Ericsson CEO Per Narvinger used the Q3 2026 earnings call on 15 October to defend custom ASICs for AI-RAN, according to RCR Wireless on 16 October. Asked by Deutsche Bank's Robert Sanders whether GPUs in the baseband had been ruled out, Narvinger said Ericsson is not philosophically against anything and will pursue what makes business sense. Its software and hardware are disaggregated, so GPUs remain an option, although they would require new hardware at the site. The function Ericsson is selling today, AI-native link adaptation, runs on basebands already deployed. Ericsson reports a 10% spectral efficiency gain and says the function runs on more than 60,000 base stations at 3 operators, including Optus, with AT&T, Bell Canada and T-Mobile reportedly testing it. All figures here come from RCR's account of the call. 

Narvinger put a 10% spectrum efficiency gain at about $800 million, against the $8 billion SpaceX was reported to have paid for spectrum the week before. RCR does not say what base the $800 million is calculated on, so I treat it as an illustration, not a measurement. The comparison does clarify the nature of the benefit. Spectral efficiency is capacity gained on spectrum already licensed, which is cost avoidance, the category I separated from new revenue in AI monetization for operators: separating revenue from cost avoidance. On a single cell the value arrives when a capacity upgrade is deferred, and Vish Nandlall's RCR Analyst Angle of 13 October put that at about $18,000 in present value for a cell at 91% load with a $200,000 upgrade deferred by about 1 year. An average gain across 60,000 sites says less than how many of those sites sit near an upgrade trigger.

Light Reading reported on 12 October that Nokia is building its AI-RAN on Nvidia GPUs and has stopped new custom silicon, with operators testing this quarter and commercial availability in early 2027. Ericsson's answer is that value can be delivered now on silicon operators already own. Both positions have an economic logic, and neither has been tested against the other in a published cost comparison. This is the gap between field-validated and commercially validated that I described in Is AI-RAN the future of telco?. A footprint of 60,000 sites is a field result for a software function on existing ASICs. It does not show that a GPU baseband earns its premium. Where accelerators are needed, the position in Non-RT RIC, AI-RAN, and the AI Grid applies: deployment starts where power, cooling and fibre are already in place, which points to the central office before the cell site.

Component costs add to the comparison. RCR reports that Ericsson beat Q3 forecasts on 7% organic sales growth, helped by customers buying ahead of expected AI-related price rises, and warned that AI-driven component costs will pressure margins through 2027. 

As you know if you follow this blog, I am skeptical of the cost-benefit of putting GPUs in a base station. The costs are high and growing, both form a capex and an energy perspective, while the benefits are entirely speculative. Would Nokia have taken the same architectural decision without NVIDIA's 1B$ investment? Unlikely. Between data centers, AI-RAN, sovereign AI, and AI factories, NVIDIA plays on a lot of different fields in telecom. They can afford for some of these explorations to backfire. Can Nokia afford to fail the next generation of radio architecture by betting it all on GPU?

Ericsson Makes Its Case for Edge Inference

Ericsson published a blog on 7 October, written by Benedek Kovacs, Wolfgang John and Mischa Dohler, arguing that where AI inference runs is becoming a strategic decision for enterprises and that communications service providers are well placed to answer it. Ericsson draws on an F5 report for the shift toward enterprises running some inference themselves. The blog states that service providers operate about 100,000 distributed network data centres, which could support over 100 GW of new AI compute over time. It names 3 advantages for the telco edge: access to live network context such as radio conditions and device location, stable low latency for physical AI, and confidential computing that keeps data private from the operator and from co-located workloads. It adds that a Swedish regulatory sandbox assessed the confidential computing approach as compliant with current data protection rules, subject to user-controlled attestation and key management.

The 100 GW figure divided by 100,000 sites implies an average of about 1 MW per site, which is my arithmetic and not Ericsson's. The blog gives it as an eventual ceiling with no timeline and no split between sites that have the power, cooling and fibre today and those that would need a rebuild. Those 3 constraints are why I placed AI Grid deployment at central offices and mobile switching offices rather than at the cell site in Operators Lean In On AI Grid Location. A count of 100,000 sites says little about how many of them can host accelerated compute.

Ericsson's own performance chart points the same way. At 30 tokens per second it shows roughly 7.0 billion parameters running on a device and roughly 30.8 billion at an edge site, which is the range of small and mid-size models, not frontier models. The workload Ericsson builds the latency case on is physical AI: robotics models of 10 billion to 100 billion parameters, control loops of 10 Hz to 20 Hz and responses under 100 ms. Those are real requirements, and they define a narrower market than general enterprise inference.

The operator role Ericsson describes

The more notable statement is about 6G. Ericsson expects specialised AI providers to deliver most early edge AI services on top of telco infrastructure, with the service provider's value in orchestration, network API exposure and end-to-end assurance. That is a vendor describing the operator as the infrastructure and assurance layer, not the seller of the AI service, and it is a shift from the position I covered in Ericsson Says the Telco Edge Was Too Early. Operators that accept that role should price edge sites on power and connectivity economics at the central office and metro layer, and should treat network context as a paid input that third-party AI providers consume through APIs.