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r delivery costs and sustained profitability otherwise the portfolio risks becoming a low priority adjunct to the core networks business earnings call quotes from ceo narvinger with the development of ai many new applications are going to be dependent on mobile connectivity networks will need to be more reliable and resilient with ai models optimized for use cases leveraging ericsson s purpose built silicon we will help customers get more out of their spectrum assets without increasing power consumption we will continue to invest and make sure we stay at the forefront of this development at the same time we remain realistic about the market the ran market is broadly flat and component costs are increasing in the wake of the ai boom we ve been able to mitigate this with tactical actions such as product substitution and upselling but these efforts will not be enough long term we are also taking structural action including portfolio optimization raising prices supply chain measures and efficiency improvements many customer contracts are multi year so structural mitigations will take time to flow through we will not be immune to higher component costs but protecting the value of our technology and profitability of our mobile networks business remains our top priority the area where we have more work to do of course is in the enterprise segment said narvinger in a q a on the q3 earnings call we will dissolve the business as such but continue with the portfolio and solutions we are moving neutral host and private 5g to networks where we can get the better r d muscle and then also accelerate towards profitability satellite will be a fantastic addition to terrestrial networks what will be important is to have seamless connectivity between outdoor and indoor that s going to be one of the challenges how to take the outdoor coverage indoors that seamless high performing experience is going to be very very important that s also one of the reasons we are consolidating our r d efforts in the indoor segment moving our enterprise wireless business into networks references https www ericsson com en press releases 2026 10 ericsson announces simplification of group structure and changes to the executive team https rcrwireless com 20261015 private networks ericsson dissolves private 5g focus on indoor rd for satellite https rcrwireless com 20261015 network infrastructure ericsson retools in flat market nokia s ece divestiture impact on private 5g market disaggregation 4 8 ghz to 4 9 ghz frequency band uses verizon wireless experimental license for testing isac with ericsson ericsson and mediatek demonstrate 3gpp based gnss rtk positioning with sub 30cm accuracy over a commercial 5g network at t s 600 mhz deployment with ericsson turning low band spectrum into coverage and uplink capacity ericsson leads sk telecom ai ran vs nvidia s gpu centric ai ran alliance at t ericsson demonstrate 5g based isac for drone detection at world cup stadium ai ran and agentic ai get real ericsson nokia verizon other operators enter into a new network automation era analysis ericsson s leading role in french intention 6g project ericsson s june 2026 mobility report highlights ai impact on network traffic gsa global private mobile networks exceed 2 000 worldwide ericsson private 5g from verizon business extends beyond u s optus and ericsson achieve 180mhz across 2 3ghz and 3 5ghz bands using carrier aggregation on a live 5g sa network ericsson reports 10 drop in 1st quarter sales targets network growth posted in 5g private networks ericsson private 5g private mobile networks private networks analysis kt s 800 gbps ai dedicated lines for ai data center interconnects posted on october 15 2026 by alan weissberger introduction kt has launched an ai dedicated line service offering 400 gbps and 800 gbps connectivity raising its prior enterprise dedicated line ceiling from roughly 100 gbps to as much as 800 gbps an eightfold increase in available transport capacity the service targets high volume latency sensitive traffic between geographically distributed ai data centers ai dcs including training data movement checkpointing distributed inference and gpu cluster synchronization kt has not publicly disclosed pricing for the 400 gbps or 800 gbps tiers leaving open the key commercial question of how quickly ai operators will trade higher transport cost for reduced training time improved cluster utilization and greater geographic flexibility kt officials looking at ai only line equipment kt importance of ai dc interconnect the announcement reflects a broader transition in ai infrastructure competitive differentiation is no longer confined to gpu acquisition or data center siting but increasingly depends on the optical transport fabric that binds distributed compute assets into an effective system as korean ai dc deployments disperse across regions operators require deterministic capacity low jitter and latency and resilient paths for bulk east west traffic kt positions the new service as a response to exactly that requirement the eightfold bandwidth increase should be understood as added parallel transport capacity not an eightfold reduction in propagation delay more precisely it expands the number of bits that can traverse the circuit per unit time analogous to adding lanes to a highway rather than raising its speed limit for ai workloads that matters because large scale training and inference can generate sustained bursts of traffic dataset ingestion model state synchronization and replication that otherwise create congestion and queueing at inter data center boundaries network architecture and resilience kt says the service will exploit its nationwide optical footprint reported at 840 000 km of fiber cable to engineer shortest path routes among multiple ai dcs and network hubs it also plans dual path designs providing route diversity so that traffic can remain available if a fiber span conduit or node fails the company further states that the lines use an apn based optics centric architecture apn more commonly described in industry usage as an all photonic network minimizes optical to electrical and electrical to optical conversions along the transmission path reducing those conversions can lower processing related latency and simplify the transport path although end to end latency will still be governed substantially by fiber distance route geometry switching and customer edge equipment kt s ai dedicated line is architected around three complementary elements high capacity optical transport route optimization across a large national fiber plant and path diversity for failure resilience the service uses kt s reported 840 000 km nationwide optical cable footprint to connect multiple aidcs and major network hubs over engineered shortest paths reducing transport delay relative to circuitous or congested routing the capacity upgrade from a prior maximum of about 100 gbps to 400 gbps or 800 gbps addresses the dominant traffic pattern in distributed ai large sustained east west flows between training clusters storage tiers and inference sites at 800 gbps a single circuit can carry eight times the nominal bandwidth of the previous 100 gbps ceiling providing substantially more headroom for dataset ingestion checkpoint synchronization model replication and bursty gpu cluster traffic kt states that the offering incorporates an all photonics network apn 1 based optics centric architecture in an apn traffic is carried over end to end optical wavelength paths with minimal intermediate optical to electrical and electrical to optical conversion ntt which originated the apn concept within its iown program describes it as a means of achieving high capacity low power consumption and low latency through photonic wavelength networking note 1 apn originated as a core technology area of ntt s iown innovative optical and wireless network initiative as per the reference below ntt defines it as a network that extends photonics from devices through the network using optical wavelength paths and photonic technologies to deliver high capacity low latency and low power consumption for ai data center interconnect apn is relevant because distributed training and inference generate large latency sensitive flows between gpu clusters storage systems and remote sites keeping those flows in the optical domain can reduce per hop processing overhead and support more predictable transport performance for kt s use case the practical benefit is reduced per hop processing and conversion overhead fewer electro optic conversions can reduce latency and simplify the transport chain between customer facing equipment and the long haul optical layer kt specifically says the architecture minimizes conversions between optical and electrical signals during transmission thereby strengthening ultra low latency performance resilience is addressed through diversified or dual path routing rather than relying on a single physical route between data centers kt says it can provision alternate paths so that service can continue if a fiber span conduit or network node fails this is particularly important for ai workloads where an interruption in inter site connectivity can stall distributed training interrupt checkpointing or degrade inference availability kt also positions the service as customizable rather than a fixed point to point product it says transmission routes and equipment will be designed according to each customer s data center locations and network environment allowing the operator to optimize for distance capacity protection requirements and the customer s existing interconnect topology architectural implications capacity layer 400g and 800g dedicated wavelengths provide a major step up from legacy 100g enterprise circuits aligning the service with ai cluster interconnect requirements latency layer shortest path route engineering and apn based optical transport are intended to reduce delay although physical fiber distance remains a fundamental constraint availability layer dual path routing provides protection against localized failures and supports continuity for latency and availability sensitive ai operations service layer customer specific route and equipment design allows kt to adapt the offering to heterogeneous aidc footprints and enterprise network environments the important caveat is that kt s public description does not specify the precise apn implementation protection switching mechanism wavelength granularity or service level latency targets those details will determine how the offering performs against competing dark fiber wavelength and managed data center interconnect services the following table summarizes kt s new ai dc interconnect service design element kt s stated approach ai workload relevance circuit capacity 400 gbps and 800 gbps options up from about 100 gbps supports bulk data transfer and high utilization gpu cluster interconnection route engineering shortest path design across a nationwide fiber network reduces transport delay and supports latency sensitive distributed ai operations resilience diversified dual path routing preserves connectivity through localized fiber or node failures transport architecture apn based optical centric design seeks to reduce optical electrical conversions and associated latency target customers aidc operators and enterprises building large scale ai infrastructure aligns the offer with ai training clusters inference platforms and gpu infrastructure builders market context 800g becomes mainstream kt s move is consistent with a rapid global migration toward 800g class optical interconnect trendforce estimates that optical transceiver modules operating at 800 gbps and above will rise from 19 5 of global shipments in 2024 to more than 60 by 2026 becoming standard components in ai oriented data centers that trajectory indicates that kt is not merely offering a premium enterprise circuit it is aligning its b2b portfolio with the dominant capacity class now being adopted for ai clusters the competitive frontier is also moving beyond 800g the optical communications industry continues to demonstrate single wavelength transmission beyond 1 2 tbps in next generation equipment signaling that 1 6t class data center interconnect will become the next capacity milestone commercial and strategic implications for kt the service extends its role from national connectivity provider to ai infrastructure enabler the operator says it will tailor routes and equipment to each customer s data center location and network environment then expand adoption among aidc operators and companies constructing large scale gpu infrastructure the strategic significance is twofold first high capacity dedicated optical connectivity can become a differentiator for attracting ai workloads to korean data center locations particularly where customers require predictable performance rather than best effort public internet transport second the offer reinforces the growing view that ai infrastructure competitiveness now spans compute power real estate and the high capacity optical network that connects them kt s own framing makes that linkage explicit as ai dcs and gpu infrastructure expand ultra fast ultra low latency networks become more important end quotes jeon myung joon head of kt s enterprise service division said as aidc and gpu infrastructure expand the importance of ultra high speed and ultra low latency networks will increase we will support customers to quickly and flexibly build large scale ai transition ax infrastructure through ai only lines the more ai data centers and graphics processing unit infrastructure expand the more important ultra fast ultra low latency networks will become a kt official said through ai dedicated lines kt will help customers build and scale large scale ai transformation infrastructure quickly and flexibly our infrastructure strengths including nationwide optical cable coverage of 840 000 kilometers and diversified routes allow us to design optimal shortest and dual paths between multiple data centers or sites minimizing latency and ensuring stability the company said as quoted by the seoul economic daily references https www mk co kr en it 12175603 https biz chosun com en en it 2026 10 14 xz35kipb55cqxblq22gezbbmfe https en sedaily com technology 2026 10 14 kt launches ai dedicated lines with speeds up to 800gbps https www lightreading com network technology kt rolls out dedicated ai line hitting 800 gig capacity ntt s iown is finally evolving to an all photonics network apn physics based ai for enterprise ot lumen to acquire alkira to accelerate its push into multi cloud and data center interconnect services kt and lg electronics to cooperate on 6g technologies and standards especially full duplex communications south korean startup rebellions to use open source software for carriers to quickly build ai stacks with its ai inferencing chips posted in da...