AI in Telecom Operations

Telcos, Welcome to Your New Customers: AI Agents The iPhone marked a before and after in telecom. Networks engineered for voice collapsed under video demand. Operators spent billions on spectrum, radios, and fibre backhaul, but ARPU sank from $22.39 in 2009 to $13.56 by 2019 and another 20 percent by 2023. The value was captured by Apple, Google, and digital platforms, not the carriers who carried the load. A second shock is arriving with AI agents. These are not IoT devices with dumb SIMs but autonomous pieces of software, often cloud-based, that authenticate, negotiate, and transact thousands of times per second. Their arrival reshapes every part of the telco business. Networks shift from managing downstream video streams to orchestrating upstream biometric data, inference payloads, and relentless bursts of signalling. Edge compute becomes the new backbone, replacing CDNs as the critical layer of performance. Operations and BSS no longer revolve around monthly bundles but around real-time billing, event-based charging, and automatic SLA credits. The customer journey breaks apart: the “user” is no longer a human who can be persuaded by advertising or loyalty points, but an algorithm that selects providers based only on latency, trust, and price. Commercial logic pivots from ARPU to RPI, revenue per thousand verified interactions, with identity and determinism becoming the true products. Even the ecosystem map shifts: just as Apple and Google seized the interface in the smartphone era, hyperscalers are already racing to build agent marketplaces. SoftBank has announced plans to deploy one billion AI agents across its companies, and forecasts put the telecom opportunity at $188 billion by 2034. Nobody willl invite Telcos to the party. We will need to claim our role this time, or once again build the infrastructure while someone else takes the economics. Full analysis here: https://lnkd.in/gvkTKqzx

Nvidia just invested $1 Billion in Nokia. For AI networking. This isn't just another tech deal. This is the future of telecom being written. 💰 WHAT HAPPENED: The Deal: → Nvidia: $1B into Nokia → Focus: AI-powered network infrastructure → Signal: AI + Telecom convergence is REAL Why it matters: Biggest validation yet that edge AI needs telecom networks. 🤔 WHY THIS MAKES SENSE: Nvidia's need: → Dominates AI chips ($2T valuation) → But AI must move from cloud to EDGE → Edge = telecom networks → Nvidia doesn't do telecom Nokia's assets: → O-RAN technology leader → 5G/6G infrastructure → Global operator relationships Together: → Nvidia GPUs at cell towers → Real-time edge intelligence → $100B+ market unlocked 🚀 WHAT THIS ENABLES: 1. AI-Powered Networks → Self-optimizing in real-time → 40-50% efficiency gains → Zero-touch operations 2. Edge AI at Scale → AI processing at 100K+ cell sites → <10ms latency → Autonomous vehicles, robotics, AR/VR 3. 6G Foundation → AI-native architecture from day 1 → Being built NOW for 2030 launch 📊 THE BIGGER RACE: Partnerships forming: → Nvidia + Nokia ✅ → AWS + Ericsson → Google + Samsung → Microsoft + ??? The pattern: Hyperscalers + Telecom vendors = New normal Why NOW: → O-RAN deployments accelerating → AI workloads moving to edge → 6G standards starting → Enterprise private networks exploding 💡 INDUSTRY IMPACT: Operators: ✅ Better network optimization ✅ Edge computing platform ✅ New revenue (AI inference) ⚠️ Risk: Becoming "dumb pipes" Nokia: ✅ $1B + Nvidia partnership ✅ AI credibility boost ⚠️ Risk: Execution challenges Nvidia: ✅ 100K+ new edge locations ✅ Beyond data centers ⚠️ Risk: Telecom is slow/complex Competitors (Ericsson, Huawei, Samsung): 🚨 Need hyperscaler partnerships NOW 🚨 Can't compete on AI chips alone 🎯 THE 3 BIG SHIFTS: 1. Cell Towers = AI Nodes → Every site becomes edge compute → Mainstream by 2026-2028 2. Telecom = Platform → Not selling connectivity → Selling "AI inference as a service" 3. 6G = Different Game → Chip makers + cloud + AI companies involved → Not just traditional telecom vendors ⚠️ THE UNCOMFORTABLE QUESTION: If Nvidia gets deep into networks... Learns the business... Has the AI chips... The operator relationships... Could they bypass operators entirely? Nokia got $1B today. But did operators just let Nvidia inside the castle? THE BOTTOM LINE: This $1B isn't about networking equipment. It's about control of the AI edge infrastructure. The companies that control where AI runs Will control the next $1 Trillion market. Nvidia just made their move. Who's next? Your take? → 💪 Smart move by both companies? → 🚨 Threat to traditional telecom? → 🤔 Too early to tell? Drop your thoughts 👇 Join my Free 5G/6G Learning Free whatsapp Channel : https://lnkd.in/gerTY-kr ♻️ Repost this to help your network get started ➕ Follow Nitin Gupta for more

At MWC Barcelona this year, we launched the GSMA Open-Telco LLM Benchmarks to unite a community tackling the unique challenges of telecom AI. The first results were clear: out-of-the-box AI models simply aren’t fit for telco-specific needs. Now, with version 2.0, this effort has evolved into a thriving, open-source collaboration. The findings point to a hybrid architecture as the most effective path forward - combining the broad reasoning of foundation models with the precision of specialised components. In addition to providing clear direction for AI in telecom, what’s really exciting is the unprecedented level of industry collaboration. Operators including AT&T, China Telecom Global, Deutsche Telekom, du, KDDI Corporation, KPN, Liberty Global, Orange, Telefónica, Turkcell, Swisscom, and Vodafone are joined by research and technology partners - Adaptive AI, Datumo, Huawei GTS, Hugging Face, The Linux Foundation, Khalifa University, NetoAI, Universitat Pompeu Fabra - Barcelona (UPF), The University of Texas at Dallas and Queen's University - to build a shared ecosystem for experimentation, validation, and learning. Read more in our latest blog: https://lnkd.in/eTDH5PBX

🚦 **Reflections from NVIDIA GTC Washington, D.C 2025.** Last week’s GTC made one thing clear; AI-native infrastructure is evolving fast, and telecom is being invited to the table. But amid the excitement, it’s worth taking a balanced look at what’s real today versus what’s aspirational. 📡 Telecom in the Spotlight - **Nokia and NVIDIA** announced work on *AI-native 6G RAN nodes* using the Aerial/ARC-Pro platform, a promising signal of how compute and connectivity are converging. - Huang emphasized that *telecom is the nervous system of the economy*, calling for greater technology independence and domestic innovation. - Panels on “AI for Telecommunications” showcased prototypes of intelligent RAN optimization, edge analytics, and network planning powered by machine learning. ⚖️ Signals vs. Substance - **Early days**: Many of these initiatives are still in the *proof-of-concept* phase. Integrating AI models into live RAN environments will require years of testing, spectrum-policy clarity, and vendor alignment. - **Cost and complexity**: Embedding GPUs and AI accelerators into network nodes could shift the economics of telecom infrastructure, it’s a good idea, but not a trivial retrofit. Also, we have been there before with the whole MEC concept (which failed). - **Governance**: As sovereign-tech conversations grow louder, telcos will need to navigate new compliance, data-sovereignty, and security frameworks before large-scale deployment. 💭 My Take AI-enabled wireless is an exciting frontier, it promises smarter, more adaptive networks. .....But for now, the prudent path is **experimentation with guardrails**: pilot at the edge, validate the economics, and align architecture standards before scaling. If you’re in telecom or enterprise network architecture, this is a space to watch closely and approach "thoughtfully". #NVIDIAGTC #Telecom #AI #6G #RAN #EdgeComputing #NetworkTransformation

🚀 How Machine Learning Helps Telecom Networks Self-Optimize What if your network could predict traffic surges and adjust its own resources before users even notice a slowdown? With AI and machine learning, that’s exactly what’s happening in telecom today. Let’s break down how it works: 1️⃣ Data Collection: The Foundation Telecom operators continuously gather network data across: ✔ Different regions ✔ Cities & neighborhoods ✔ Individual cell towers This helps track traffic flow and identify normal usage patterns. 2️⃣ Detecting Anomalies in Real Time ML models compare live data against historical trends. A sudden spike in usage? → Could be a major event, festival, or unexpected demand. → The system flags it before performance drops. 3️⃣ Smart, Automated Adjustments Once an anomaly is detected, the system recommends (or even automates) actions like: 📶 Adding bandwidth ⚙️ Optimizing software resources 🔧 Tweaking network settings 4️⃣ Continuous Learning = Smarter Networks The system learns from every event: ✔ Were predictions accurate? ✔ Did adjustments work? ✔ How can it improve next time? The result? A proactive network that: ✅ Prevents congestion ✅ Enhances user experience ✅ Optimizes costs & efficiency Key Takeaways 🔹 ML turns raw data into actionable insights 🔹 AI-driven recommendations reduce downtime 🔹 Self-improving systems = future-proof networks To learn about AI & 5G, visit - https://lnkd.in/eT-ZZyrP #AI #Telecom #MachineLearning #Networks #Innovation #Tech

Telecom Sector Update: October 2025 - Rapid Transformation: The global telecom industry is experiencing a dynamic shift, with AI, automation, and cloud-native networks driving innovation and operational efficiency. The move to 5G and even early steps towards 6G are enabling new business models, especially with private networks for enterprises and advanced IoT deployments. - Market Headlines: Telecom companies worldwide are reporting revenue growth (4.3% to $1.14 trillion globally), with India standing out for network expansion and rural connectivity efforts. Notably, India has reached 75% of its "100% telecom saturation" mission, consolidating leadership through massive investments in infrastructure. - Financial Trends: Operators are under pressure to raise mobile tariffs as investment in network technology outpaces revenue in highly competitive markets. Yet, telecom stocks remain attractive due to their stable, recurring income bolstered by fiber and 5G rollouts. - Leading Indicators:     - Subscriber Base: India remains the world's second-largest telecom market with over 1.2 billion subscribers, and nearly 996 million broadband users as of September 2025.   - Data Trends: Monthly data usage per user leads globally, powered by surging demands for video, gaming, AR/VR, and AI-driven services.   - Network Expansion: Accelerated rollout of 4G densification, fiberization for 5G backhaul, and new broadband growth in tier-2/3 towns are significant.   - Policy Developments: New cybersecurity rules, spectrum auctions, and Digital India policy pushes are shaping the regulatory landscape. - Tech and Business Evolution:     - AI Adoption: Over half of telecom companies have implemented AI at scale, with another 37% actively scaling up. Generative AI is cited as a long-term growth engine by 65% of Indian CXOs.   - Cloud and Edge: Cloud-native networks are the new normal, boosting agility, service assurance, and digital transformation for enterprise customers.   - Sustainability: Green networks and sustainable business practices are coming to the forefront, as the sector aligns with global environmental goals. - Risks & Outlook: Key risks for 2025 include regulatory shifts, cybersecurity threats, and adapting to new business models and spectrum management. Market analysts expect telecom's robust performance to continue fueling a bull run in Indian equities. Conclusion:   The telecom sector is at a crossroads—technology, investment, and sustainability are shaping its future. Markets like India, Turkey, Europe, and North America stand out for innovation and growth. Forward-looking indicators such as rural adoption, ARPU increases, swift 5G rollout, fiber penetration, and strategic AI deployment will point the way ahead. #TelecomTrends #5G #6G #AIinTelecom #DigitalIndia #TelecomNews #IndustryInsights #Connectivity #NetworkInnovation

I just built my first OpenClaw project! I needed to get my hands on it and build an agent with it - and I did - it's called NetClaw — an AI network engineering agent that operates at CCIE-level depth across routing, switching, security, QoS, MPLS, IPv6, multicast, wireless, and more. 30 skills. NetClaw is built entirely on OpenClaw using what I'd call the "tools as skills" architecture. Each skill is a structured knowledge document that teaches the agent how a network engineer thinks — not just what commands to run, but when to run them, what to look for in the output, and what to do next based on what it finds. The agent connects to live network devices through pyATS skills which abstract the MCP server, executes real show commands, parses real output, and makes real engineering decisions.                  I'm writing this post while simultaneously talking to NetClaw in the VibeOps Forum Slack workspace. I asked it to analyze routing tables and interface states from a live device, generate a Draw.io topology diagram, and create an  image of the network state — all through a Slack message. It did all three. No portal. No ticket. No context-switching. Just a conversation with an engineer that never sleeps.                                                                                    Here's what strikes me about this:                                          We've been automating the wrong layer. For years, network automation has focused on pushing configs and collecting data. Template engines. YAML files. CI/CD pipelines for network changes. All valuable. But they automate the execution — not the reasoning. NetClaw automates the reasoning. It doesn't just run show ip ospf neighbor - it knows to check hello/dead timer mismatches, area ID conflicts, MTU issues causing EXSTART stuck states, and passive interface misconfigurations. It follows the same OSI-model troubleshooting methodology that a CCIE would use.                                                                  Skills are composable. The topology discovery skill feeds into the diagram skill. The security audit skill references the compliance skill. The troubleshooting skill pulls from health checks, routing analysis, and log      inspection. This isn't a monolithic runbook — it's a network of knowledge that the agent traverses based on context.                                                              The conversation is the interface. There's something profound about being in a Slack channel, asking a question in plain English, and getting back a security audit with findings categorized as Critical, High, Medium, and Low complete with CVE benchmark references and specific remediation commands. The barrier between "I wonder if..." and  "here's the answer" has collapsed to the speed of thought. https://lnkd.in/eF-xgM8i

We’ve seen this story before. 2015: “Self-driving cars in 2 years” 2016: “Radiologists obsolete in 5 years” 2024: Reality check. Now fast forward to Telco & Networking: “AI will run networks autonomously.” “Zero-touch operations will eliminate NOCs.” “Self-healing networks will remove human intervention.” Let’s be precise. In telecom environments—especially large-scale DC fabrics, MPLS cores, and multi-vendor ecosystems—complexity doesn’t disappear. It compounds. AI will transform operations—but not by replacing engineers. It will augment decision-making, reduce MTTR, and surface anomalies faster than humans can detect. What actually works today: • AI-assisted fault correlation (noise reduction across millions of events) • Configuration drift detection and compliance enforcement • Predictive capacity and hardware failure insights • Intelligent automation pipelines (closed-loop, but supervised) What doesn’t (yet): • Fully autonomous networks without human guardrails • Black-box AI making production-impacting decisions • One-size-fits-all models across heterogeneous telco stacks The takeaway for Telco leaders: 👉 Don’t chase hype cycles—engineer for reality 👉 Focus on incremental AI adoption with measurable outcomes 👉 Build observability + data pipelines first, AI later 👉 Keep humans in the loop—especially for critical control planes AI in networking is not a replacement strategy. It’s a force multiplier for operational excellence. #Telco #Networking #AI #NetOps #Automation #NOC #Observability

Delighted to see our work on Large Language Models for 6G Networks published in Nature Reviews Electrical Engineering! 🌐📡 As we move toward 6G, the next generation of wireless communication, networks will no longer just carry data. They will understand intent, make decisions, and adapt intelligently. Our paper explores how large language models (LLMs) , the same technology behind today’s powerful AI, can fundamentally transform wireless networks from static systems into context‑aware, self‑optimizing, intent‑driven platforms. Many current AI tools are good at specific tasks. But LLMs bring something new: the ability to reason, adapt, and generalize across different scenarios, just like advanced human communication does. When integrated into 6G, Networks can interpret user needs and environmental context in real time. Operations like resource allocation, signal control, and service delivery become smarter and more efficient. Intelligent decisions can be made not only in the cloud, but directly at the network edge closer to users. Huge thanks to my co‑authors and collaborators on this journey. Looking forward to pushing the frontier of intelligent wireless systems together! 👉 Published here: Nature Reviews Electrical Engineering — “Large language models in 6G from standard to on‑device networks”: https://lnkd.in/dJ9vepqu #6G #AI #LLM #Telecom #Innovation #Networking #Research #FutureTech

Earlier this week, I shared some of the exciting announcements #Cisco made at #CiscoLiveEMEA. Let's get into the next level of detail on #G300 Cisco is one of the few flagship companies capable of building 100T-class silicon—but what truly sets us apart is how we’re doing it. We're focused not just on speeds and feeds, but on #efficiency, #programmability, and achieving #lower_TCO (total cost of ownership). How are we leading the way? It comes down to 2️⃣ key innovations. The first differentiator is what we call #Intelligent_Collective_Networking —three tightly integrated features that deliver efficient AI infrastructure:   ✅ Fully Shared Packet Buffer:  Our buffer isn’t just bigger—it’s shared across the entire system, ensuring data flows smoothly and efficiently, even under heavy demand.   ✅ Collective Hardware Agents:  Our hardware agents work together to map network traffic, congestion, and failures in real time, enabling the network to automatically adapt and optimize—no more relying on static configurations or slow software convergence.   ✅ Advanced Hardware Telemetry Engines:  These engines continuously analyze network conditions, advertise real-time changes, and react instantly. This boosts performance and enables seamless infrastructure orchestration.   What does this mean in practice?   ✅ Achieve 🚀 33% higher link utilization 🚀 Support more GPUs on the same network lowering TCO for your AI infrastructure. ✅ Experience a 🚀 28% improvement in JCT (job completion times) 🚀 Compared to other packet spray load balancing solutions Run more jobs with the same number of GPUs and maximize your investment.   Stay tuned for my next post, where I’ll dive into the second key differentiator in #G300. Thanks to the leadership and engineering teams! Jeetu Patel, Martin Lund, Nick Kucharewski Mohammad Issa, Marco Croci, Ofer Iny Eli Stein Kevin Wollenweber Gurudatt Shenoy Will Eatherton Rakesh Dubey Vijay Tapaskar Murali Gandluru, Manish Mukherjee Ran Senderovitz Arun Arunkumar Anthony Torza Keith Ring Siobhan Mullen Stoffregen Emily Griffin Nadav Chachmon Sujal Das Ramesh Sivakolundu (He/Him) Yaron Agami Rakesh Kumar Scott Miles and so many more... (Link to my previous posts about the announcement in the comments below)