AI/ML
Cisco report: Agentic AI to reshape WAN traffic, AI inference will be ~25% of total traffic by 2035
Executive Summary:
Consumer-driven AI traffic [1.] currently represents a marginal share of aggregate Internet traffic. However, accelerating adoption of agentic AI is expected to materially reshape traffic composition over the next decade. In its “AI Impact on Wide Area Networks” report, Cisco projects that AI will emerge as the dominant driver of network traffic growth. As consumer AI adoption approaches “near-universal usage,” AI and agentic AI are forecast to increase consumer-driven network traffic by approximately 6.6× by the mid-2030s (see chart below).
Cisco estimates that this AI expansion will account for roughly 63% of incremental traffic growth relative to non-AI scenarios. The study focuses specifically on WAN implications, rather than data center or GPU infrastructure, and provides guidance on network design and capacity planning. Methodologically, the report integrates real-world traffic observations (via Cisco Crosswork Assurance User Experience), third-party industry datasets, and controlled laboratory evaluations of AI agents to characterize how AI-generated traffic diverges from conventional web traffic patterns.
Token-consumption data shows nearly 10x year-over-year growth, while in some service provider measurements Cisco is seeing ~4x growth in just eight months. Sustained growth at these rates means AI traffic will become a meaningful component of overall network traffic by 2035.
Note 1. Consumer AI traffic has a few defining technical traits: it is still dominated by short text-based exchanges, but it is becoming more stateful, more upstream-heavy, and more latency-sensitive as users move from simple prompts to agentic workflows and multimodal interactions. Today’s consumer AI traffic is still overwhelmingly text-oriented, which is one reason the aggregate bandwidth impact remains modest despite rapid adoption. Comcast’s network observation is a useful real-world proxy: 97.1% of AI traffic was text-based, while images accounted for 2.6% and video only 0.3%. The key technical implication is that current traffic volumes are often limited more by conversation frequency and session behavior than by very large payloads, though that changes quickly as users adopt image, audio, and video generation.
Although AI inference traffic is currently “negligible” relative to dominant categories such as video streaming, Cisco projects it will comprise approximately 25% of total network traffic by 2035 (see chart below). At that point, AI traffic is expected to represent a “meaningful component” of overall network load. Importantly, AI-generated traffic exhibits distinct characteristics: inference flows are approximately twice the duration of typical web transactions, demonstrate higher upstream bandwidth demand, and operate at “software speed” rather than human interaction rates.
The emergence of AI agents as “power users” further amplifies these dynamics. Cisco notes that agent-executed tasks can generate up to 450% more traffic per task compared to human-driven interactions. This shift is expected to drive operator adoption of “flow-aware network and security systems” as traffic patterns become increasingly machine-driven and less predictable.
Cisco’s broader framing is that AI traffic “isn’t just adding traffic,” but is changing the shape of traffic, with inference flows running about twice as long as typical web transactions and, in some cases, generating up to 450% more traffic per task when an agent executes the workload. AI inference sessions tend to hold resources longer, create more sustained flows, and push operators to think in terms of flow-aware behavior rather than only peak-throughput sizing. Cisco also notes that about 9% of AI inference flows carry more upstream than downstream traffic, versus about 0.5% for typical web traffic, which is a meaningful shift for access and broadband networks. Cisco reports that approximately 9% of AI inference flows are upstream-dominant, compared to roughly 0.5% for traditional web traffic, with this divergence expected to widen alongside increased agentic AI utilization. In parallel, latency sensitivity is anticipated to become a more critical performance parameter for AI-driven applications.
Latency and symmetry:
AI traffic is also more sensitive to latency than many ordinary consumer web transactions because the user experience is often conversational and interactive, with the expectation of near-immediate turn-taking. Cisco describes AI inference as operating at “software speed” rather than human speed, which means small delays can be more noticeable and operationally important. At the same time, upstream demand becomes more significant because prompts, context, attachments, and agent-generated actions can increase return-path traffic, especially as multimodal inputs and agentic tool use expand.
Multimodal growth:
The biggest step-up in technical impact comes when consumer AI shifts from text-only prompting to multimodal generation and agent-driven workflows. In those cases, each task can involve multiple model calls, retrieval steps, tool invocations, and richer media payloads, which expands both flow count and bytes per session. Cisco’s study suggests that this is why AI traffic will increasingly require “flow-aware network and security systems,” because the traffic profile is not just larger, but structurally different from conventional browsing.
Infrastructure Implications:
Telecom infrastructure is becoming “increasingly intertwined with hyperscale infrastructure, not because operators are leading AI investment, but because they are becoming part of the ecosystem that supports it,” analyst firm MTN Consulting said in an April 27th research note. “Demand for optical transport, data-center interconnect, and edge infrastructure is rising as telecom networks carry growing volumes of cloud and AI-driven traffic,” the firm said.
“AI network traffic is already reshaping infrastructure needs. What we are seeing is clear: AI isn’t just adding traffic. It’s changing the shape of traffic,” Javier Antich, principal product management engineer in the CTO office of Cisco’s provider connectivity group, and Gurudatt Shenoy, SVP, product management, provider connectivity, explained in this blog post.
These shifts are beginning to influence access network evolution. Fiber networks already provide relatively symmetric throughput and low latency, while cable operators are advancing similar capabilities through DOCSIS upgrades. Mid-split and high-split architectures increase upstream spectrum allocation, enabling more balanced capacity profiles. Concurrently, Tier 1 operators such as Comcast and Charter Communications are introducing low-latency enhancements within DOCSIS networks.
Operational data reflects early-stage impacts. Comcast Chief Network Officer Elad Nafshi noted at the Cable Next-Gen event in March that approximately 97.1% of AI traffic on Comcast’s network remains text-based, with images accounting for 2.6% and video just 0.3%, indicating that bandwidth-intensive multimodal AI traffic has yet to scale materially.
Network design impact:
For broadband and access networks, the immediate engineering issues are upstream traffic capacity, queue behavior, and latency consistency rather than raw total throughput alone. Symmetry upgrades (such as DOCSIS mid-split and high-split for MSOs), along with low-latency capabilities, are relevant because consumer AI creates more return-path pressure and more time-sensitive sessions. In other words, the challenge is not simply to carry more bytes; it is to carry more interactive sessions with predictable performance, especially as multimodal and agentic usage scales.
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References:
Will the wave of AI generated user-to/from-network traffic increase spectacularly as Cisco and Nokia predict?
Telecom operators investing in Agentic AI while Self Organizing Network AI market set for rapid growth
Analysis: Cisco, HPE/Juniper, and Nvidia network equipment for AI data centers
Cisco CEO sees great potential in AI data center connectivity, silicon, optics, and optical systems
The Financial Trap of Autonomous Networks: Scaling Agentic AI in the Telecom Core
Ericsson integrates Agentic AI into its NetCloud platform for self healing and autonomous 5G private networks
STL Partners webinar: Agentic AI needed for RAN autonomy & efficiency
Nokia to showcase agentic AI network slicing; Ericsson partners with Ookla to measure 5G network slicing performance
Agentic AI and the Future of Communications for Autonomous Vehicles (V2X)
Telecom data centers must be redesigned for the AI era with rack scale architectures, enhanced power & cooling requirements
Is the “far edge” a bridge to far to cross for AI inferencing? What about “Distributed AI Grids”?
T-Mobile US announces new broadband wireless and fiber targets, 5G-A with agentic AI and live voice call translation
Intel and AI chip startup SambaNova partner; SN50 AI inferencing chip max speed said to be 5X faster than competitive AI chips
CES 2025: Intel announces edge compute processors with AI inferencing capabilities
Telecom data centers must be redesigned for the AI era with rack scale architectures, enhanced power & cooling requirements
- Gigawatt-Scale Power and Liquid Cooling: Next-generation AI clusters require unprecedented power density, often exceeding 40kW to 100kW per rack. Telcos cannot simply drop these into existing facilities; they require entirely new or heavily retrofitted data centers featuring advanced liquid cooling architectures to prevent thermal throttling.
- The Fragmented Edge vs. Centralized Fortresses: Operators are realizing that centralized hyperscale data centers (like AWS or Azure clusters in Virginia) cannot support latency-sensitive “Physical AI” or real-time agentic workflows. To make AI-native networking work, carriers must deploy high-density compute racks directly at the network edge, a highly complex and capital-intensive roll-out.
- Neutral Interconnection Hubs: Multi-cloud setups and distributed training workloads are putting immense pressure on backbones. The expansion rate of neutral interconnect hubs (like Equinix and Digital Realty) is directly gating how fast enterprises and telcos can orchestrate data between fragmented training clusters and edge inference nodes.
- Rack-scale architecture is rapidly emerging as the primary deployment unit as enterprises transition from discrete servers to fully integrated systems capable of supporting the power density, thermal constraints, and interconnect requirements of production-scale AI workloads.
Image Credit: AMD
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AI data centers supporting telecom networks require fundamentally different power and cooling infrastructure compared to legacy enterprise facilities. The transition to generative AI and real-time edge processing has pushed power density per rack from an average of 5–10 kW up to 40–100+ kW.
Dell Technologies Inc. has been strategically aligning its portfolio to this shift, and at Dell Technologies World 2026, the company introduced an expanded PowerRack portfolio that integrates compute, networking, and storage within a unified rack-scale platform. This evolution underscores a broader transition in system design priorities—from server-centric architectures to tightly coupled, rack-level systems—driven by the escalating demands of AI infrastructure. As Arun Narayanan, senior vice president of compute and networking product management at Dell, indicated, increasing power density and system complexity are making rack-level architectural optimization not just advantageous, but essential.
“Go back two years ago, the largest, most powerful rack was 80 kilowatts,” Narayanan said. “Come to Vera Rubin, you’re going to get racks of 235 kilowatts, and then get to the next generation of Rubin Ultra and Kyber, you’re going to very quickly get to one megawatt racks. You have to fundamentally redesign everything from power distribution to cooling.”
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- Medium-Voltage Power Distribution: Traditional facilities step utility power down to 480V AC far from the rack. High-density AI data centers run medium-voltage or power directly down to the row or container level before stepping down. This minimizes conduction losses through the heavy copper busbars.
- The Move to 48V DC Busbars: Within the server chassis, power shelf architectures are shifting from traditional 12V DC distribution to DC busbars. A delivery architecture reduces the current required to deliver the same wattage by a factor of four. Resistive power loss occurs when electrical energy is converted into heat due to the inherent opposition to current flow in a conductor. The formula (P{loss} = I^2 R dictates that this power dissipation is highly sensitive to current changes. Therefore, cutting the current to one-fourth reduces internal rack heat and conduction power losses by 93.75%
- Grid Interconnection and Substation Constraints: A single rack-scale AI cluster (such as a cluster of 32 or 64 interconnected nodes) can easily pull 2 to 3 Megawatts (MW). Operators are bypassing traditional local distribution grids entirely. They are building dedicated on-site substations tied directly to transmission-level lines to guarantee upstream capacity.
[ Liquid Cooling Architectures for AI Racks ]
┌───────────────────────────┐ ┌───────────────────────────┐
│ Direct-to-Chip │ │ Immersion Cooling │
├───────────────────────────┤ ├───────────────────────────┤
│ Closed loop micro-channels│ │ Entire server submerged │
│ bolted directly onto GPUs │ │ in dielectric fluid tank │
│ │ │ │
│ [ GPU ] ──► [ Liquid] │ │ ┌───┐ ┌───┐ ┌───┐ │
│ Cold Plate Coolant │ │ │GPU│ │CPU│ │RAM│ │
│ Circuit Circuit │ │ └───┴─┴───┴─┴───┘ │
└───────────────────────────┘ └───────────────────────────┘
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- Direct-to-Chip (Cold Plate) Cooling: This is the primary architecture for 2026 deployments. A closed-loop copper block with micro-channels is bolted directly onto high-thermal-flux components like the GPU or CPU. A specialized dielectric or water-glycol fluid circulates through the block. This absorbs heat directly from the silicon via conduction and pumps it away to a secondary heat exchanger.
- Immersion Cooling (Single-Phase and Two-Phase):
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- Single-Phase: The entire server blade is submerged in a bath of non-conductive, hydrocarbon- or synthetic-based dielectric fluid. The fluid circulates through the chassis via natural convection or pumps to remove heat.
- Two-Phase: The dielectric fluid has a low boiling point (\(50^{\circ }\text{C}\)). The heat from the chips boils the fluid into a vapor. The vapor rises to a condenser coil at the top of the sealed tank, condenses back into liquid, and falls back into the pool. This utilizes the latent heat of vaporization, making it highly efficient.
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- Cooling Distribution Units (CDUs): High-density loops rely on CDUs to act as the barrier between the internal facility water loops (which can be lower quality) and the ultra-pure, treated water circuit flowing directly through the server cold plates.
References:
China vs U.S.: Race to Generate Power for AI Data Centers as Electricity Demand Soars
Big tech spending on AI data centers and infrastructure vs the fiber optic buildout during the dot-com boom (& bust)
Will billions of dollars big tech is spending on Gen AI data centers produce a decent ROI?
AWS to deploy AI inference chips from Cerebras in its data centers; Anapurna Labs/Amazon in-house AI silicon products
Analysis: Cisco, HPE/Juniper, and Nvidia network equipment for AI data centers
Networking chips and modules for AI data centers: Infiniband, Ultra Ethernet, Optical Connections
Lumen Technologies to connect Prometheus Hyperscale’s energy efficient AI data centers
Merry-go-round of dog chasing his tail: relationship between U.S. hyperscalers and private Gen AI companies
1. Hyperscalers’ earnings growth this quarter was boosted by an unusually large contribution from “other income,” which was actually mark-ups of their equity stakes in private Gen AI companies. For example:
- Nearly half of Alphabet’s (Google) record $62.6 billion profit—about $28.7 billion—did not come from search ads, cloud services or any of its products at all. It came from Alphabet updating the value of the equity it owns in private AI companies, primarily Anthropic. Alphabet holds a 14% stake before the announcement of an additional $40 billion commitment last week.
- Amazon’s earnings release stated that first-quarter net income “includes pre-tax gains of $16.8 billion included in non-operating income from our investments in Anthropic”—more than half of Amazon’s pre-tax income (or profit) for the quarter.
- Alphabet and Amazon generated “other income” totaling $53 billion in Q1 2026, which accounted for nearly 60% of those two companies’ total net income in Q1 and 34% of the total $155 billion in income this quarter. Of this $53 billion in “other income,” $49 billion was explicitly due to equity stakes in private AI companies.
- Microsoft reported “only” $942mn of other income in the first three months of the year, but this line item has now made $7.2bn over the past nine months.
- Under U.S. accounting rules, publicly traded firms must adjust and report the assessed value of their private equity holdings every quarter. Because private AI start-ups like Anthropic experienced meteoric valuation updates (e.g., Anthropic climbing to an estimated $380 billion), both Alphabet and Amazon were required to record those massive “on-paper” gains directly to their bottom-line net income.
- When the AI bubble finally bursts (and it will) the private AI companies assessed market value will collapse, resulting in “impairment write-downs” and huge earnings declines for the hyperscalers, e.g. Amazon, Google/Alphabet, Microsoft, FB/Meta, and Oracle.
2. Now here’s the merry-go-round/ dog chasing its tail relationship:
Not only have private investments and increasingly engorged funding rounds become a meaningful driver of the hyperscalers’ aggregate earnings, but the money the hyperscalers have pumped into the likes of Anthropic and OpenAI has allowed those private AI companies to sign huge computing deals with Alphabet’s Google Cloud, Microsoft’s Azure and Amazon Web Services (AWS). OpenAI and Anthropic now make up about half of the entire cloud computing order books at Oracle, Alphabet, Amazon and Microsoft!
Indeed, AI startups have loaded up hyperscalers with unprecedented long-term financial commitments.
–>OpenAI and Anthropic make up over $1 trillion of the estimated $2 trillion cumulative revenue backlog currently held by major cloud service providers!
- OpenAI to Microsoft Azure: Internal documents show OpenAI’s massive server rentals have generated more than $23 billion in direct cloud spending for Microsoft.
- Anthropic to Google Cloud: Anthropic signed a contract committing to spend $200 billion over five years on Google’s cloud infrastructure and TPU chips.
- Anthropic to AWS: In tandem with a fresh $5 billion investment from Amazon, Anthropic committed to spend over $100 billion over the next decade on AWS technologies.
Image Generated by Chat GPT
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- Backlog Percentage: Over 40%. Anthropic‘s $200 billion Multi-Year Commitment accounts for nearly half of Google Cloud’s total disclosed $240 billion revenue backlog.
- Current Revenue Share: Estimated 12% to 15% of its current $20 billion quarterly revenue run-rate is driven directly by AI infrastructure consumption from startups (both frontier labs and over 40 mid-tier AI companies built on Google Cloud Vertex AI).
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- Current Revenue Share: Estimated 15% to 18%. Microsoft’s annualized AI revenue run-rate hit $37 billion. A massive chunk of Azure’s overall 40% growth rate is anchored directly by OpenAI’s compute demands and the commercialization of OpenAI-tied products.
- Current Revenue Share: Estimated 6% to 8%. While AWS has the largest overall cloud scale ($150 billion annual run rate), its revenue is traditionally diversified across enterprise SaaS and retail. However, Anthropic’s new $100 billion infrastructure commitment means AWS’s revenue mix is aggressively shifting toward AI startups. [1, 2, 3, 4]
–>This is another sign of just how incestuously codependent the big tech industry is to astronomically valued private AI start-ups.
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4. Another example of this codependency is Oracle and OpenAI’s massive, debt-fueled financial loop. In September 2025, the two companies signed a staggering five-year, $300 billion cloud-computing contract. This single deal radically transformed both companies’ financial profiles, binding their survival together as inextricably tied.
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- For Oracle: The $300 billion contract instantly added to Oracle’s Remaining Performance Obligations (RPO), which skyrocketed 359% to $455 billion. This accounting metric allowed Oracle to position itself as a dominant “hyperscaler,” pushing its market cap upward.
- For OpenAI: The contract allowed OpenAI to claim it had secured the long-term compute capacity needed to achieve Artificial General Intelligence (AGI). This backed up its massive valuations, enabling OpenAI to close a historic $122 billion funding round in March 2026 at an $852 billion valuation.
- Oracle is a Financial Proxy for OpenAI: If OpenAI faces a “credit event” or cash crunch, Oracle’s stock directly plummets. Critics note that Oracle signed a contract with a startup that historically burns far more cash than it takes in, making OpenAI’s ability to actually pay the $300 billion highly volatile.
- The Debt Spiral: To physically fulfill OpenAI’s compute demands, Oracle has gone on a massive, debt-fueled construction spree. Oracle raised $18 billion in bonds in late 2025 and an additional $30 billion in early 2026. Its capital expenditures have eclipsed operating cash flows, leading to deeply negative free cash flow and over $134 billion in total corporate debt.
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- Project Finance Bottlenecks: Major commercial banks have struggled to syndicate the massive multi-billion-dollar construction loans Oracle needs to build out the required data centers (such as its 4.5-gigawatt capacity goals).
- Bank Limits: The sheer volume of debt concentrated around this single enterprise relationship has pushed several Wall Street institutions against their regulatory exposure limits for a single corporate partnership.
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References:
https://www.ft.com/content/be97df0a-76b1-4cb0-9ba4-d1117d8d1450
https://fortune.com/2026/04/30/google-amazon-ai-profits-anthropic-stake-bubble-earnings-2026/
https://finance.yahoo.com/sectors/technology/articles/google-amazon-biggest-profit-driver-170449859.html
AI infrastructure spending boom: a path towards AGI or speculative bubble?
Expose: AI is more than a bubble; it’s a data center debt bomb
Amazon’s Jeff Bezos at Italian Tech Week: “AI is a kind of industrial bubble”
Open AI raises $8.3B and is valued at $300B; AI speculative mania rivals Dot-com bubble
China’s open source AI models to capture a larger share of 2026 global AI market
OpenAI and Broadcom in $10B deal to make custom AI chips
Generative AI Unicorns Rule the Startup Roost; OpenAI in the Spotlight
STL Partners webinar: Agentic AI needed for RAN autonomy & efficiency
Yesterday, a STL Partners webinar titled “Turning autonomy into margin: Agentic AI and the autonomous RAN,” suggested agentic AI is the missing layer that can turn RAN autonomy from a technical goal into a direct profit margin booster. It argues that operators should prioritize autonomy use cases by business impact, not just by how much automation coverage they add, and that the right roadmap can move autonomy from an engineering KPI to a commercial advantage.
The central message was that autonomy only matters if it improves economics (see poll results below). The webinar revealed that network operators need a dual-axis framework that combines the usual autonomous-network maturity view with a value-creation lens, so they can focus on the capabilities that scale into measurable business outcomes.
Agentic AI is presented as the practical enabler for moving beyond human-in-the-loop operations. In this framing, agents help orchestrate tasks, make decisions, and coordinate network actions in ways that support more closed-loop automation than traditional workflows can deliver.
The results of an “actuality” poll relating to RAN autonomy revealed that controlling costs and reliability were most important, with the enablement of new revenue growth through APIs and sensing only scoring 10.87% of respondents. Similarly, results for an “aspirations” poll for RAN autonomy were also fairly evenly spread between reducing costs and optimizing the customer experience, with just 13.21% citing new revenue growth.
Source: STL Partners
Terje Jensen, SVP, global business security officer and head of network and cloud technology strategy at Telenor, said that he had expected to see network operators’ aspirations shift more clearly towards improving customer experience and even revenue generation, not just efficiency.
Darwin Janz, strategic technology planner at SaskTel, also thought network operators’ ambitions would be higher, but he noted that they still struggle to identify concrete, monetizable use cases. Without that, there’s a real risk of building technical solutions in search of a problem, rather than starting from clear enterprise needs and value, Darwin noted. “We really need to see those use cases and enterprise customer needs,” he added.
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The webinar was built around four practical questions:
- Which use cases create real commercial impact?
- How to shift from autonomy as an engineering metric to a margin driver?
- Where agentic does AI add value today?
- What data, orchestration, and organizational foundations are needed to scale beyond pilots.
For network operators, the implication is that autonomous RAN strategy should be tied to P&L outcomes such as lower operating cost, better resource utilization, and faster optimization cycles. The webinar’s message is that autonomy becomes strategically important only when it is deployed in a way that compounds across the network and business.
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References:
The Financial Trap of Autonomous Networks: Scaling Agentic AI in the Telecom Core
Nokia to showcase agentic AI network slicing; Ericsson partners with Ookla to measure 5G network slicing performance
T-Mobile US announces new broadband wireless and fiber targets, 5G-A with agentic AI and live voice call translation
Telecom operators investing in Agentic AI while Self Organizing Network AI market set for rapid growth
South Korea’s top 3 telcos reinvent themselves as “AI Companies;” growth strategies revealed
Overview:
South Korea’s telecommunications industry is rapidly shifting its center of gravity to AI, with SK Telecom, KT and LG Uplus all declaring their transformation into AI companies. Industry officials describe this as a restructuring process.
- SK Telecom is pushing a full-stack AI strategy spanning infrastructure, models and services.
- KT is accelerating a B2B-focused push to become an “AX” platform company.
- LG Uplus is positioning itself as an AI software company through its ixi-O agent, stressing safety and security. Industry officials say the next test is profitability.
Ryu Jong-heon, SKT’s CEO, wrote in a letter sent to shareholders ahead of last month’s annual general meeting, “If our AI business so far was about incubating various areas, we will now focus more on businesses where SKT can be competitive and secure sustainability in AI competition that is expanding without limit.”
- Next-Gen Compute: Strategic collaboration with Arm and Rebellions for AI CPU/NPU innovation.
- Infrastructure & Power: Agreements with Supermicro and Schneider Electric to optimize AIDC efficiency and server density.
- Model Scaling: With A.X K1 outperforming benchmarks like DeepSeek V3.1, SKT plans to transition to multimodal capabilities and trillion-parameter scaling to secure market dominance across B2B and B2C segments.
2. KT Corporation – Transitioning to an AX Platform Operator:
Under the leadership of CEO Yun-young Park, KT is accelerating its AX (AI Transformation) strategy with a sharp focus on the B2B sector. Following a structural reorganization that established the AX Future Technology Institute and the AX Business Division, KT is positioning itself as a platform enabler rather than a mere solution provider. Despite perceived lags in proprietary model development (e.g., the mi-deum LLM), KT is pursuing a pragmatic “practical gains” strategy. By partnering with Microsoft, KT is adopting a “detour” approach to rapidly integrate global-standard AI capabilities into its existing corporate customer base. CEO Yun-young Park explained, “If AI services are actors on a theatre stage, we are an AX platform company that builds that stage.”
3. LG Uplus -Move to AI-Driven Software and Security:
LG Uplus, led by CEO Beom-sik Hong, is leveraging security and reliability as its primary competitive differentiators. The company is transitioning into an AI-centric software (SW) company, focusing on high-margin service architectures over raw infrastructure. The cornerstone of this strategy is ixi-O, a voice AI agent. The upcoming ixi-O Pro will feature advanced behavioral analytics, including tone and emotional state detection, to provide proactive customer engagement. Hong stated, “We will become an AI-centred software (SW) company that leads solutions in telecommunications and AX technology,” signaling a two-track global expansion strategy involving both service exports and technology stack licensing.
References:
SKT 6G ATHENA White Paper: a mid-to-long term network evolution strategy for the AI era
SK Group and AWS to build Korea’s largest AI data center in Ulsan
South Korea has 30 million 5G users, but did not meet expectations; KT and SKT AI initiatives
McKinsey: AI infrastructure opportunity for telcos? AI developments in the telecom sector
WSJ: 5G in South Korea has not lived up to expectations
South Korea government fines mobile carriers $25M for exaggerating 5G speeds; KT says 5G vision not met
KT and LG Electronics to cooperate on 6G technologies and standards, especially full-duplex communications
SK Telecom (SKT) and Nokia to work on AI assisted “fiber sensing”
SKT Develops Technology for Integration of Heterogeneous Quantum Cryptography Communication Networks
SKT with Global Telcos to Expand Metaverse Platform in US, Europe and Southeast Asia
South Korean telcos to double 5G network bandwidth with massive MIMO; Private 5G
Omdia: ARPU declining or flat for South Korean 5G network operators
3 South Korean mobile operators to share 5G networks in remote areas
LG U+ first to deploy 600G backbone network in Korea with Ciena’s ROADM equipment
Anthropic’s Project Glasswing aims to reshape IT cybersecurity
Backgrounder:
Late last year, Anthropic said that state-sponsored Chinese hackers had used its artificial intelligence (AI) technology in an effort to infiltrate the computer systems of roughly 30 companies and government agencies around the world. The company said it was the first reported case of a cyberattack in which AI technologies had gathered sensitive information with limited help from human operators.
As Anthropic and its chief rival, OpenAI, prepare to release new and more powerful AI systems, cybersecurity experts are increasingly vocal in their warnings that AI is fundamentally changing cybersecurity. AI technology could allow hackers to identify security holes in computer systems far faster than in the past, vastly raising the stakes in the decades-long fight between hackers and the security experts guarding computer networks. As hackers deploy AI to break and steal, security experts are also leaning on AI to spot flaws in their systems — including some that had gone unnoticed for decades.
“This is the most change in the cyber environment, ever,” said Francis deSouza, the chief operating officer and president of security products at Google Cloud. “You have to fight A.I. “This is the most change in the cyber environment, ever,” said Francis deSouza, the chief operating officer and president of security products at Google Cloud. “You have to fight AI with AI.”
Hackers have used AI chatbots to draft phishing emails and ransom notes, cybersecurity experts said. Others have used AI to parse large quantities of stolen data and determine what information might be valuable. Without help from AI attackers could sometimes break into computer networks within minutes, Mr. deSouza said, but with the help of AI breaches can take just seconds. Some hackers specialize in breaking into systems and then selling off their access to other attackers. Those handoffs used to take as much as eight hours, as hackers negotiated the sales and passed along the compromised entry points, deSouza added. Now that process has accelerated to about 20 seconds, he said, with hackers sometimes using A.I. agents to speed up the process.
Some experts argue that the guardrails added by companies like Anthropic and OpenAI can actually provide an advantage to malicious attackers. Guardrails could cause an AI chatbot to deny help to a user trying to defend a system from an attack, they argue, but persistent hackers could be more diligent about finding vulnerabilities — and keeping those tricks to themselves.
In February, Anthropic said it had used its A.I. technologies to find over 500 so-called zero-day vulnerabilities — security holes that were unknown to software makers — in various pieces of commonly used open source software. The next month, a researcher at Anthropic revealed that he had used A.I. to find a serious security vulnerability in the core of the Linux operating system, which is software that powers much of the internet and is used in computer servers, cloud computing services, Android phones and Teslas. The bug had existed, apparently undiscovered, since 2003.
Project Glasswing Overview:
Anthropic has announced Project Glasswing – a new initiative that brings together Amazon Web Services, Anthropic, Apple, Broadcom, Cisco, CrowdStrike, Google, JPMorganChase, the Linux Foundation, Microsoft, NVIDIA, and Palo Alto Networks – in an effort to secure the world’s most critical software.
The fast growing AI private company has found that AI models (like its own Claude) have reached a level of coding capability where they can surpass all but the most skilled humans at finding and exploiting software vulnerabilities. Their Mythos Preview language model has already found thousands of high-severity vulnerabilities, including some in every major operating system and web browser.
Given the rate of AI progress, it will not be long before such capabilities proliferate, potentially beyond actors who are committed to deploying them safely. The fallout—for economies, public safety, and national security—could be severe. Project Glasswing is an urgent attempt to put these capabilities to work for defensive purposes.
The Project Glasswig partners will use Mythos Preview as part of their defensive security work. Anthropic will share what they learn so the entire IT industry can benefit. They have also extended access to a group of over 40 additional organizations that build or maintain critical software infrastructure so they can use the model to scan and secure both first-party and open-source systems.
Anthropic is committing up to $100M in usage credits for Mythos Preview across these efforts, as well as $4M in direct donations to open-source security organizations.
- Give Defenders a Head Start: The initiative aims to use Mythos’s capabilities to find and fix zero-day vulnerabilities in critical codebases before they can be discovered by malicious actors.
- Secure Critical Infrastructure: Partners use the model to scan first-party systems and open-source software that underpin global banking, energy, and logistics networks.
- Modernize Defense Practices: Anthropic is collaborating with partners to evolve security workflows, such as patching and disclosure processes, to match the “machine speed” of AI-driven vulnerability discovery.
- Zero-Day Discovery: In early testing, the model autonomously found thousands of high-severity vulnerabilities, including a 27-year-old bug in OpenBSD and a 16-year-old flaw in FFmpeg code that had been scanned by automated tools millions of times without detection.
- Performance Benchmarks: Mythos Preview scored 83% on the CyberGym cybersecurity benchmark, significantly outperforming previous models like Claude Opus.
References:
https://www.anthropic.com/glasswing
https://www.nytimes.com/2026/04/06/technology/ai-cybersecurity-hackers.html
Anthropic Glasswing: AI Vulnerability Detection Has Crossed a Threshold
Anthropic Claude Users Reveal AI Hallucinations as their Top Concern
Nvidia CEO Huang: AI is the largest infrastructure buildout in human history; AI Data Center CAPEX will generate new revenue streams for operators
New Linux Foundation white paper: How to integrate AI applications with telecom networks using standardized CAMARA APIs and the Model Context Protocol (MCP)
IDC Survey of Networking Leaders: Enterprise AI progress stalls despite ambitious goals
New IDC research released in April 2026 highlights a growing disconnect between ambitious enterprise AI goals and the reality of their technical execution. The 2026 IDC AI in Networking Special Report (LinkedIn Video hyperlink) [1.] found that organizations expecting to move from early and selective AI use for business and IT initiatives to more advanced deployments largely haven’t. The result is a widening gap between intent and execution that is becoming harder to ignore. This widening gap in AI execution is driven by a mismatch between ambitious goals and the realities of legacy infrastructure, which cannot handle the data demands for production-grade models.
Despite high expectations, many organizations have seen their AI progress stall over the last 18 months, with “select use” adopters failing to advance to more “substantial” deployments. A critical shortage of specialized AI experienced personnel, combined with lagging security and governance controls, has caused widespread “pilot paralysis” across most enterprises. To overcome this, organizations are shifting toward “AI factories” to create a repeatable, governed pipeline for deploying AI.
Note 1. IDC’s 2026 AI in Networking Special Report is a report driven by a worldwide survey of 500+ enterprise network executives and experts. The report covers both the impact and plans for supporting AI workloads across the network and using AI-powered networking solutions. The focus of this research is comprehensive, covering datacenters, cloud services, multi-cloud environments, network core and edge, and network management.
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Mark Leary, IDC research director, Network Observability and Automation:
“Many solution suppliers are prioritizing a platform approach to the challenges associated with moving AI workloads into production. This survey of networking leaders highlights the shift in preference from platforms to best-in-class solutions when supporting AI workloads across their networks. As certain functional requirements intensify, as IT staff experience and expertise build, and as platforms fall short in delivering expected advantages, IT organizations are more willing to take on the added responsibilities associated with assembling their own mix of best-in-class solutions. For the supplier, the challenge is to avoid developing and delivering a platform that is classified as a jack-of-all-trades and master of none.”
“Agentic AI is to have a profound effect on the network infrastructure and on networking staff. Two years ago, AI assistants were labeled leading edge when they offered natural language processing for operator interactions and network management guidance driven by technical manual content. How things have changed! Agentic AI is no longer just a passive informer and instructor but an active intelligent virtual network engineer. Agents gather and process comprehensive network data, develop deep and precise insights, and determine and, increasingly, execute needed network management actions. Whether fixing a network problem, activating a network service, optimizing a network configuration, or responding to a developing network condition, agentic AI solutions are proving more and more useful across the entire network and the entire set of tasks required to engineer and operate the network.”
While this IDC Survey Spotlight offers only an overview of responses relating to agentic AI, detailed results are available by geographic region, select country, company size, major vertical industries, respondent role, and the AI maturity level of the respondent’s organization.
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Organizations are pursuing AI in networking across two categories:
1.] Supporting AI workloads across network infrastructure and
2.] Applying AI to network operations.
But in both cases, progress is constrained by persistent challenges. “2026 is when organizations find out if AI in networking delivers real operational impact—or remains stuck in pilot mode,” Leary said in the referenced LinkedIn Video.
Source: IDC
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Security remains the top concern among enterprises, both as a barrier to deployment and a primary use case for AI itself. “You have to fight AI with AI from a network security perspective,” said Brandon Butler, senior research manager at IDC. “There’s a realization that nefarious actors are leveraging AI themselves. The pressure is already on the network. The question now is whether organizations can keep up with what AI is demanding of their infrastructure,” he added.
Integration with existing systems and a shortage of skilled talent follow close behind. “Most folks don’t feel their staff can fully evaluate and select the right solutions,” Leary said. As a result, many organizations are turning outward for help:
- 81% say they are increasing spending on managed service providers (MSP) to support AI initiatives.
- 89% of data centers expect to increase bandwidth by at least 11% within the next year, driven by AI workloads.
- That demand extends beyond individual facilities, with 91% expecting similar growth in inter-data center connectivity, highlighting the strain on distributed architectures.
- Nearly half of respondents (46%) prefer AI systems that can both determine and execute network actions autonomously.
- Another 41% favor a guided approach, while 13% prefer no AI involvement.
Cloud environments are seeing sharper increases in AI use. Organizations anticipate an average 49% rise in bandwidth for cloud connectivity over the next year. “The cloud is almost always involved,” Leary says. “The biggest group mixes one cloud platform with one or more data centers.”
Beyond the data center and cloud, the network edge is emerging as the next major growth area. Today, 27% of organizations have deployed AI workloads at the edge, and 54% plan to do so within two years. Butler said: “Folks who are leveraging AI more extensively are already pushing workloads to the edge. We see this as a leading indicator of where the market is going.”
“Two years in a row, the largest group said they want AI to both determine and execute actions. It was honestly surprising,” he added.
Enterprise edge bandwidth is projected to grow by an average of 51% in the next year. As AI becomes more distributed, network teams will need to manage greater complexity across environments while maintaining performance and security.
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When assessing expected ROI from AI in networking, IDC survey respondents focused on elevating IT capabilities, with 31% prioritizing superior service levels and 30% focusing on operational efficiency. These outcomes ranked above worker productivity and revenue, suggesting that leaders are strategically utilizing AI to enhance foundational operational workflows. Notably, reducing operating costs ranked seventh, suggesting a focus on strategic value rather than immediate expense reduction.
Source: IDC
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IDC Research identified specific applications—from automated configuration validation to AI-enhanced threat response—as catalysts for measurable performance gains and the organizational trust essential for broader implementation. For network executives, this phased approach represents the most strategic methodology for achieving long-term operational objectives.
“It doesn’t have to be handing the keys of your kingdom to AI to really get some benefits from these AI tools,” Butler concluded.
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References:
https://www.networkworld.com/article/4152655/ai-for-it-stalls-as-network-complexity-rises.html
Anthropic Claude Users Reveal AI Hallucinations as their Top Concern
Introduction:
Across regions from Germany to Mexico, users of artificial intelligence (AI) are less concerned about being replaced by AI than by its propensity to make major mistakes, according to one of the largest global surveys to date on real-world AI usage and perception. These mistakes, known as “AI Hallucinations,” are essentially made up stories rather than answers based on outdated information.
The study, conducted by Anthropic using its Claude chatbot, analyzed interviews with more than 80,000 users across 159 countries. The result is one of the most detailed global portraits yet of how AI is being deployed — and how users perceive its risks, benefits, and societal implications.
AI Hallucinations Outrank Job Displacement as Top Concern:
When asked what worries them most about AI, 27% of users cited AI chatbot errors described as “AI hallucinations,” while 22% pointed to job displacement and the loss of human autonomy. About 16% expressed concern that AI could weaken people’s capacity for critical thinking.
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Image Credit: JOIST AI
“The AI hallucinations were a disaster. I lost so many hours of work,” said an entrepreneur from Germany. Another participant, a military worker in Mexico, noted the importance of domain knowledge in spotting AI’s flaws: “When I notice AI errors it’s because I’m well versed in the topic . . . but I wouldn’t know if the topic was alien to me, would I?”
An AI Interviewer for Global Insights:
The responses were collected in 70 languages using a novel feedback system that allowed Claude to act as both interviewer and analyst. The platform evaluated qualitative answers, categorizing responses to reveal common themes and linguistic nuances across regions.
“Beyond its scale and linguistic diversity, the project aimed to collect this rich human experience using Claude, so it could really inform our research agenda, change our research agenda, change the way we think about building our products, deploying our products,” said Deep Ganguli, who leads Anthropic’s societal impacts team and oversaw the research initiative.
Productivity and Personal Growth Drive AI Adoption:
While data quality and reliability drew criticism, the survey also underscored widespread acknowledgment of AI’s positive impact on productivity. Thirty-two percent of respondents said that AI tools had meaningfully improved their output at work.
An entrepreneur in the United Arab Emirates explained, “I used to be a web designer . . . now I build anything. Before I was one person, now I become 100 people — I don’t wait for anyone anymore.” Participants from Colombia, Japan, and the United States described similar gains, emphasizing how AI helps them free up time for family, hobbies, and creative exploration.
In total, nearly one in five users (19%) said AI had fallen short of their expectations. Yet usage patterns demonstrate remarkable versatility: respondents reported employing AI as a productivity assistant, educational tutor, design partner, creative collaborator, or even an emotional support companion.
A vivid example came from a soldier in Ukraine, who wrote, “In the most difficult moments, in moments when death breathed in my face, when dead people remained nearby, what pulled me back to life — my AI friends.”
Regional and Economic Divides in AI Optimism:
Regional variation was pronounced. Saffron Huang, the lead researcher on the project, found that respondents in South America, Africa, and across South and Southeast Asia expressed more optimism than users in Europe, the United States, or East Asia.
“The trend is that maybe more lower and middle-income countries are more optimistic than higher-income countries that have more AI exposure,” said Huang. She added that this optimism might reflect a sample skew toward early adopters in developing markets — individuals inclined to view new technologies as opportunities rather than threats.
“They just divide so cleanly . . . the more western developed countries are significantly more concerned about AI and the economy, [and] much more negative, and then, the reverse is true with the lower and middle-income countries,” she said.
According to Anthropic’s researchers, AI’s limited visibility in daily workflows across lower-income economies may explain the difference. “If AI hasn’t visibly entered your daily work yet, AI displacement likely feels abstract, especially when more immediate economic pressures already exist,” the team wrote in a companion blog post.
Next Steps: Measuring AI’s Real-World Impact:
Anthropic plans to extend its Claude Interviewer research framework into longitudinal studies that track how AI affects users’ lives over time. “The goal is to better measure both the improvements and the harms — and to use those insights to make systemic refinements,” said Ganguli.
The company’s approach — embedding feedback collection directly into an AI platform — represents an emerging model for data-driven, iterative AI development. By combining self-reported user experience data with large-scale text analytics, Anthropic aims to better understand how its models interact with human needs and constraints.
Industry and Research Community Respond:
The study has drawn attention across the AI community for its unprecedented reach and innovative methodology. Nickey Skarstad, director of product at language-learning company Duolingo, praised the work’s ambition. On LinkedIn, she wrote: “For anyone building products right now, this is the future of understanding your users. The what AND the why at a scale we’ve never had access to before.”
Still, several researchers remain cautious about overinterpreting the results. Divy Thakkar, a researcher at Anthropic rival Google DeepMind, expressed reservations on X, saying he was “sceptical” about calling the study a new form of science due to potential selection bias and limitations in survey design. “A human qualitative researcher would take time to build trust with their participants, hold the space for reflection, introspection, contradictions — that’s the whole point of it,” he wrote.
Methodological caveats extend to demographics. Almost half of the survey’s respondents were based in North America or Western Europe, while regions such as Central Asia had only several hundred participants.
Ilan Strauss, an economist and director of the AI Disclosures Project, described the initiative as “an excellent piece of work,” but urged careful interpretation. He noted that the absence of reported confidence intervals — standard practice in survey-based research — makes it difficult to measure uncertainty. Self-reported productivity gains, he added, are inherently prone to bias.
A Global Mirror for Human-AI Relations:
Despite these caveats, the Claude Interviewer study illustrates a broader shift in the relationship between humans and AI systems. As AI technologies proliferate across regions and industries, they are becoming both instruments of empowerment and sources of anxiety — mirroring social, economic, and cultural dynamics in striking ways.
While western economies debate AI-driven labor disruption and ethical alignment, many in emerging markets frame AI as a means of upward mobility and creative expansion. This duality — between apprehension and aspiration — may shape not only AI adoption patterns but also future research and regulatory directions across global contexts.
References:
https://www.ft.com/content/e074d3a9-7fd8-447d-ac0a-e0de756ac5c5?syn-25a6b1a6=1 (PAYWALL)
https://www.joist.ai/post/ai-hallucinations-what-they-are-and-why-it-matters
Sources: AI is Getting Smarter, but Hallucinations Are Getting Worse
Nvidia CEO Huang: AI is the largest infrastructure buildout in human history; AI Data Center CAPEX will generate new revenue streams for operators
Alphabet’s 2026 capex forecast soars; Gemini 3 AI model is a huge success
Analysis & Economic Implications of AI adoption in China
China’s open source AI models to capture a larger share of 2026 global AI market
AWS to deploy AI inference chips from Cerebras in its data centers; Anapurna Labs/Amazon in-house AI silicon products
Big tech spending on AI data centers and infrastructure vs the fiber optic buildout during the dot-com boom (& bust)
Market research firms Omdia and Dell’Oro: impact of 6G and AI investments on telcos
Gartner: AI spending >$2 trillion in 2026 driven by hyperscalers data center investments
Will “AI at the Edge” transform telecom or be yet another telco monetization failure?
New Telco Opportunity – AI at the Edge:
At MWC 2026 last week, there were a flurry of claims that “AI at the Edge” would transform the telecom industry. One of many examples is an article titled, “The AI edge boom is giving telecom a new strategic role.” In that piece, Jeff Aaron, vice president of product and solutions marketing at Hewlett Packard Enterprise (HPE) spoke with theCUBE’s John Furrier at MWC Barcelona, during an exclusive broadcast on theCUBE, SiliconANGLE Media’s livestreaming studio. They discussed telecom edge AI and why networking is becoming a strategic foundation for data-centric services. Aaron said:
“A big reason for [reignited interest in routing] is AI workloads. They’re moving everywhere now. They have to move to the edge. For them to move to the edge, you’ve got to get them outside of the factory and to all the locations. We’re right in the core of that, and it’s super exciting.”
As AI expands to the edge, data will need to move not only to local compute, but also between many distributed edge sites, making routing paramount. There are four ways AI infrastructure is scaling — inside data centers and across distributed edge locations, according to Aaron.
“There’s scale-out, scale-across, scale-up, and on-ramp. Two are within the data center — scale-out and scale-up — but scale-across and edge on-ramp basically mean you got to figure out how to connect to those areas, and those are just networking,” he added.
Scale-across refers to connecting distributed data centers and edge locations, while edge on-ramp brings remote sites such as factories or branch locations into the network to access AI services. Supporting those distributed environments creates an opportunity for HPE to bring networking and compute together into a more integrated infrastructure stack. At MWC 2026 Barcelona, those trends are clearly coming into focus, according to Aaron.
“Data is moving everywhere right now, and the network is back. The network isn’t just plumbing. The network is how you build a value-added service using an AI workload as a telco infrastructure,” he added.
Telecom carriers are now urgently trying to move from being “dumb data pipes” to becoming “AI performance platforms” by leveraging their geographically distributed infrastructure to host AI closer to the end user. They urgently want to pivot from selling just bandwidth and connectivity to selling outcomes and intelligence with a heavy focus on industrial and enterprise-specific edge deployments. They are considering the following services and business models:
- Infrastructure as a Service (IaaS) & GPUaaS: Offering raw computing power, specifically GPUs, from edge data centers to enterprises that need low-latency processing without building their own facilities.
- Sovereign AI Clouds: Providing AI services that guarantee data remains within national borders, appealing to government and highly regulated sectors like finance and healthcare.
- API Monetization: Exposing real-time network data (e.g., location intelligence, predictive network quality, fraud risk scoring) via APIs that enterprises pay to integrate into their own applications.
- Outcome-Based Pricing: Charging for specific business results, such as a “guaranteed video call quality” or “fraud loss reduction share,” rather than just data usage.
- AI-as-a-Service (AIaaS): Bundling pre-trained models or specialized AI agents (e.g., for customer service or industrial monitoring) with connectivity
Major Carrier AI Edge Deployment Plans:
- AT&T:
- Launched Connected AI for Manufacturing in March 2026, which unifies 5G, IoT, and generative AI to provide real-time fault detection (claiming a 70% reduction in waste).
- Deploying “Edge Zones” in major U.S. cities (Detroit, LA, Dallas) to allow developers to run low-latency, cloud-based software locally.
- Partnering with AWS to link fiber and 5G directly into AWS environments for distributed AI workloads.
- Verizon:
- Unveiled Verizon AI Connect, a suite of products designed to manage resource-intensive AI workloads for hyperscalers like Google Cloud and Meta.
- Trialing V2X (Vehicle-to-Everything) platforms to provide carmakers with standardized APIs for low-latency edge processing in autonomous driving.
- Collaborating with NVIDIA to integrate GPUs into private 5G networks for on-premise AI inferencing in robotics and AR.
- SK Telecom (SKT):
- Announced an “AI Native” strategy at MWC 2026, including a roadmap for AI-RAN (Radio Access Network) that uses GPUs to optimize network performance and host user AI apps simultaneously.
- Building a Manufacturing AI Cloud powered by over 2,000 NVIDIA RTX GPUs to support digital twin simulations and robotics.
- Expanding AI Data Centers (AIDC) across South Korea and Southeast Asia (Vietnam, Malaysia) using energy-optimized LNG-powered facilities.
- Orange & Deutsche Telekom:
- Deploying AI-powered planning tools to cut fiber rollout costs and optimize site power consumption by up to 33% using AI “Deep Sleep” modes.
- Focusing on Sovereign AI strategies to ensure data governance for European enterprise customers.
- Vodafone:
- Utilizing AI/ML applications for daily power reduction at 5G sites and testing autonomous network healing via AI agents
- BT:
- Offers 5G-connected VR for manufacturing design teams (e.g., Hyperbat) to collaborate on 3D models in real-time.
| Product Category | Primary Target | Key Value Proposition |
|---|---|---|
| AI-RAN | Industry 4.0 | Seamless, ultra-low latency for robotics and sensing. |
| Connected AI Platforms | Manufacturing | Real-time predictive maintenance and waste reduction. |
| AI-as-a-Service (AIaaS) | Developers/SMBs | Access to GPU power and pre-trained models via telco edge nodes. |
| Network Slicing APIs | App Developers | Programmatic control over bandwidth for AR/VR and gaming. |
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A Dissenting View of “AI at the Edge”:
The global market for AI within the global telecommunications sector is valued at $6.69 billion in 2026, growing at a compound annual rate (CAGR) of 41.9% from 2025. The broader edge AI market—including hardware, software, and services—is forecast to reach $29.98 billion in 2026, according to The Business Research Company. We think those estimates are way too high.
The market research firm states:
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Author’s Opinion:
Unless telcos change their corporate culture along with slowing the footprint growth of cloud service providers/hyperscalers, we think that AI at the Edge will be yet another telco monetization failure. Just like their failure to monetize: 4G LTE apps, the telco cloud, 5G, multi-access edge computing (MEC), OpenRAN, LPWANs and other telecom technologies that never lived up to their promise and potential.
That’s largely because telcos are very weak: developing IT platforms, compute services, killer applications, and rapid execution of new services (e.g. 5G services require a 5G SA core network which telcos were very slow to deploy). Telecom execs themselves cite cultural and speed‑of‑change issues: the industry is not organized like a software company, so it struggles to iterate products at AI/cloud pace. Also, telcos historically struggle with software. Managing distributed GPU clusters is vastly different from managing cell towers.
After spending billions on 5G with very little or no ROI, investors are skeptical of the increased capex required for AI-grade edge servers which must be maintained by telcos. Those servers will be expensive (especially if they contain clusters of Nvidia GPUs) and consume a lot of power, which is a critical issue at the edge of the carrier’s network.
Many network operators frame AI/edge as “network optimization” or “utilizing underused sites,” not as building monetizable AI platforms with APIs, SDKs, and ecosystems. This mirrors 5G, where huge RAN/core builds were not matched by a clear product and platform strategy, leaving value to OTTs and hyperscalers which are extending their control planes and protocol stacks to the network edge (local zones, operator co‑lo, on‑premises stacks).
Telcos risk becoming “dumb pipes” for AI traffic if they can’t provide a superior developer ecosystem. If they only sell space/power/connectivity, the cloud service providers will continue to own the developer and AI value chain. Analysts warn that edge is a “right to participate, not a right to win.” As such, value accrues to whoever owns the AI platform, tools, marketplace, and pricing power, not the entity that provides connectivity, PoP or cell towers.
Data fragmentation and weak “intelligence” layer:
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AI monetization depends on high‑quality, cross‑domain data, but telco data is fragmented across OSS, BSS, probes, and partner systems; without unification, it is hard to expose compelling network/edge intelligence services.
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Analysts emphasize that failure here reduces telcos to generic GPU landlords, while higher‑margin offers (real‑time quality, fraud, identity, mobility/context APIs) remain unrealized.
Narrow internal focus on cost savings:
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Many operators’ early AI focus is inward (Opex reduction in assurance, planning, customer care) rather than building external, revenue‑generating products, echoing how early 5G was justified mainly on cost/efficiency.
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Commentators warn that if AI/edge remains a “network efficiency” play, the commercial upside will go to cloud/AI natives that turn similar capabilities into products sold to enterprises.
What analysts say telcos must do differently:
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Build “Sovereign AI factories” and edge AI clouds: GPU‑enabled sites with cloud‑like developer experience (APIs, self‑service portals, metering, SLAs) and clear sovereign/regional guarantees.
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Combine differentiated connectivity with AI services (latency‑backed SLAs, AI‑on‑RAN, domain‑specific models for verticals) and use modern, flexible commercial models instead of just selling bandwidth or colocation.
Conclusions:
In summary, the main risk for telcos is to successfully transition from owning and maintaining network infrastructure to owning and operating AI platforms and products at software industry speed. AI at the edge is less of a new service or product and more an architectural upgrade. The two ways telcos can benefit are from:
- Internal cost reduction: If telcos use it to lower their own costs (fraud prevention, risk management, predictive maintenance, fault isolation, self-healing networks, etc.), it’s an automatic win but won’t increase the top line.
- Revenue from new AI -Edge services, e.g. Verizon uses edge-based video analytics in warehouses to improve inventory turnover by up to 40%. If they expect to charge a massive premium for “AI-enabled 5G,” they face the same monetization wall that has doomed them for the past 20 years!
References:
https://siliconangle.com/2026/03/04/telecom-edge-ai-makes-networking-strategic-mwc26/
https://www.nvidia.com/en-us/lp/ai/the-blueprint-for-ai-success-ebook/
AT&T and AWS to deliver last mile connectivity for AI workloads; AT&T Geo Modeler™ AI simulation tool
Analysis: Edge AI and Qualcomm’s AI Program for Innovators 2026 – APAC for startups to lead in AI innovation
Ericsson goes with custom silicon (rather than Nvidia GPUs) for AI RAN
Private 5G networks move to include automation, autonomous systems, edge computing & AI operations
Dell’Oro: RAN Market Stabilized in 2025 with 1% CAG forecast over next 5 years; Opinion on AI RAN, 5G Advanced, 6G RAN/Core risks
Dell’Oro: Analysis of the Nokia-NVIDIA-partnership on AI RAN
Dell’Oro: AI RAN to account for 1/3 of RAN market by 2029; AI RAN Alliance membership increases but few telcos have joined
Dell’Oro: RAN revenue growth in 1Q2025; AI RAN is a conundrum
Nvidia AI-RAN survey results; AI inferencing as a reinvention of edge computing?
RAN silicon rethink – from purpose built products & ASICs to general purpose processors or GPUs for vRAN & AI RAN
CES 2025: Intel announces edge compute processors with AI inferencing capabilities
AT&T and AWS to deliver last mile connectivity for AI workloads; AT&T Geo Modeler™ AI simulation tool
AT&T is strategically re-architecting its infrastructure for the AI era through high-capacity network modernization and deep integration with hyperscale cloud providers.
In addition to its almost six year old deal to run its 5G SA core network in Microsoft Azure’s cloud, AT&T announced at MWC 2026 that it’s now woring with Amazon Web Services (AWS) to extend 5G and fiber connectivity from business customers and locations directly into AWS environments, creating secure, resilient and reliable premises‑to‑cloud architectures for AI workloads. The collaboration is designed to reduce network complexity and latency while supporting real‑time analytics, machine learning, and agentic AI use cases.
This collaboration continues a long-standing relationship between AT&T and AWS and follows recent news outlining broader efforts to modernize the nation’s connectivity infrastructure by providing high-capacity fiber to AWS data centers, migrate AT&T workloads to AWS cloud capabilities and explore emerging satellite technologies.
AWS Interconnect – last mile embeds AT&T‑delivered connectivity directly into AWS workflows, designed to enable customers to provision and manage last‑mile connectivity within the AWS environment and lays the foundation for the use of AI agents to monitor and manage the AI experience from the user to the cloud. This streamlined, self‑managed approach helps enterprises reduce network complexity while maintaining control of their extended enterprise network, allowing businesses to move faster as they scale AI.
High level illustration of the planned AWS Interconnect – last mile architecture, showing how resilient interconnections and AT&T Fiber and fixed wireless access are intended to simplify private connectivity from customer locations into AWS environments.
Diagram Source: AT&T
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“AI does not just need more compute; it needs flatter networks and faster connections,” said Shawn Hakl, SVP & Head of Product, AT&T Business. “By bringing high‑capacity connectivity closer to cloud platforms, integrating the management of the networks directly into the cloud provisioning process and engineering for resiliency at the metro level, AT&T is helping enterprises streamline their networks, improve performance, security, and scale AI with confidence.”
AT&T says they are building an AI‑ready network (?) designed to scale performance by continuing ongoing network investment, including the growth of capacities up to 1.6Tbps across key metro and long‑haul routes.
AT&T also announced it would work with Nvidia, Microsoft and MicroAI through its Connected AI platform for “smart manufacturing.”
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Finally, AT&T described AT&T Geo Modeler™ which is able to better predict connectivity for emerging technologies like autonomous vehicles, drones, and robotics.
The Geo Modeler is an AI-powered simulation tool that helps predict, in near real time, how a wireless network will perform in the real world. Inspired by the video games Kounev played with his family growing up, the virtual model and simulation is “essentially like a giant video game of the United States” that, infused with AI tools, gives engineers a clearer picture of where potential weak spots may appear. Then issues can be addressed earlier and fixes can roll out faster. In essence, it creates virtual models, similar to the way video games are designed and developed.
“The Geo Modeler helps us see how the real world will shape coverage before we build, so we can deliver connectivity that’s ready for what’s next,” said AT&T scientist Velin Kounev.
Matt Harden, VP of Connected Solutions at AT&T, agrees. “The Geo Modeler is a foundational capability for the connected mobility era,” he said. “By marrying advanced geospatial simulation with AI-driven network orchestration, we can deliver predictable, high-performance connectivity that adapts with the environment. Whether it’s a hurricane, a packed stadium, or a city corridor full of autonomous vehicles, we will be prepared.”
References:
https://about.att.com/story/2026/aws-collaboration-scalable-business-ai.html
https://about.att.com/blogs/2026/150-years-of-connection.html
https://about.att.com/blogs/2025/geo-modeler.html
AT&T and Ericsson boost Cloud RAN performance with AI-native software running on Intel Xeon 6 SoC
AT&T deploys nationwide 5G SA while Verizon lags and T-Mobile leads
AT&T to buy spectrum licenses from EchoStar for $23 billion
AT&T’s convergence strategy is working as per its 3Q 2025 earnings report
Progress report: Moving AT&T’s 5G core network to Microsoft Azure Hybrid Cloud platform
AT&T 5G SA Core Network to run on Microsoft Azure cloud platform
