Autonomous AI agents are transforming 6G network management by overcoming the limitations of traditional rule-based automation. While 5G introduced Management Data Analytics and intelligent RAN controllers for optimization, current systems rely on predefined procedures requiring manual reconfiguration during network updates or vendor changes. As 6G emerges with increasingly diverse services, denser deployments, and multi-technology coexistence, fragmented management interfaces across radio access, core, and transport domains demand a paradigm shift. AI agents enable autonomous decision-making and end-to-end automation across heterogeneous environments, moving beyond static integration logic to adapt dynamically to evolving network conditions and complex multi-domain operations.

Monthly ARPU has declined from roughly $99 in 2023 to about $66 by early 2026, reflecting both expansion into lower-income geographies and more aggressive ...

An agent gateway serves as a unified data plane for agentic AI systems, functioning as a proxy that manages communication between agents and their tools. As AI agent deployments scale beyond single instances, they create complex distributed systems requiring centralized governance of network traffic. Similar to traditional API gateways, agent gateways provide consistent routing, authentication, security, and observability across multiple protocols including MCP, LLM, and HTTP/gRPC traffic. This unified approach eliminates the need for individual agents to implement redundant authentication, retry logic, and policy enforcement. AgentGateway, an open-source Apache-licensed project hosted by the Linux Foundation's Agentic AI Foundation, exemplifies this solution by enabling multi-protocol traffic management with centralized discovery, role-based access control, audit logging, token budgeting, and intelligent routing across multiple model providers and self-hosted inference servers.

Starlink/LEO satellite broadband could impact AT&T's broadband subscriber growth, to US$33.4 market could expand from US$5.1 billion in 2024 to US$24.6 billion ...

Satellite-based links are of key importance to provide ever-increasing data rates. Free-space optics is a technology that can help satisfy this demand thanks to ...

In 1932, American industrialist Eben Byers died after his jaw physically collapsed from drinking “Radithor”, an over-the-counter energy drink infused with raw radium. In the early 20th century, society was so mesmerized by the superficial “glow” of radiation that brands blindly put thorium and radium into cosmetics, toothpaste, and water. It wasn’t until 1938, when the lethal, cellular-level destruction of radiation became undeniable, that governments stepped in, stripped radioactive elements from consumer shelves, and locked nuclear energy behind strict regulatory walls.Today, we are repeating that exact historical error.

AI-RAN (artificial intelligence—radio access network) is a technology that enables the full integration of AI into the radio access network to realize transformative gains in operational performance, deliver new AI-based services, and unlock monetization opportunities. It enhances connectivity across mobile networks by leveraging AI to improve spectral efficiency, dynamic traffic handling, and real-time responsiveness.

SpaceX appears positioned for a potentially record-breaking initial public offering, though investor enthusiasm faces notable headwinds. The company's artificial intelligence division, led by xAI, projects profitability by 2028, according to former CFO Jonathan Shulkin's communications with investors. However, the firm's most significant revenue generator currently remains its core space operations segment. While SpaceX's diverse business portfolio and ambitious growth trajectory present compelling investment opportunities, market conditions and competitive pressures continue to temper broader investor confidence in the anticipated offering.

SpaceX has filed for a public offering following the publication of its prospectus, addressing investor questions about its strategic direction. Founded by Elon Musk in 2002, the company operates across three segments: space launch with reusable rockets, Starlink satellite internet connectivity, and artificial intelligence. In 2025, SpaceX generated approximately $18.7 billion in revenue, with Starlink contributing $11.4 billion as its primary revenue source. The company pursued this public listing to secure substantial capital for AI sector expansion, with 2025 capital expenditures reaching $12.7 billion. Additionally, SpaceX acquired xAI in February 2026 and consolidated X Corporation, creating an integrated ecosystem of rockets, satellites, AI models, and social media. This integration leverages SpaceX's space infrastructure to support AI training capabilities, establishing synergies between computing resources, data, and orbital assets.

SpaceX's Starlink satellite internet service achieved remarkable growth, reaching 10.3 million subscribers by Q1 2026, doubling year-over-year figures. However, the company's IPO prospectus reveals concerning underlying trends. Average revenue per user declined significantly to $66 monthly from $86 annually and $99 in 2023, reflecting aggressive expansion into price-sensitive markets across Africa, Southeast Asia, and Latin America. While this strategy drove substantial subscriber gains, operating income growth stalled, rising only modestly from $1.03 billion to $1.19 billion despite doubled subscriptions. As Starlink transitions from serving underserved rural areas to competing directly with established terrestrial providers in suburban and urban markets, profitability faces mounting pressure, particularly given higher equipment production costs and intense competitive pricing dynamics.

Managing agentic AI at scale presents unprecedented cost challenges that extend far beyond simple model pricing. Organizations like Uber have experienced dramatic budget overruns, with monthly API costs reaching five hundred to two thousand dollars per engineer as agentic workflows consume five to thirty times more tokens than standard queries. Enterprise AI inference now dominates eighty-five percent of total AI budgets, while the economics of agents differ fundamentally from previous AI pricing models. The true cost encompasses planning, context retrieval, tool calls, state management, output validation, and error handling. This pattern repeats across organizations of varying sizes, with production expenses vastly exceeding pilot projections. Understanding these cost dynamics and implementing effective strategies before budget exhaustion becomes critical for businesses deploying agentic AI in production environments.

Unplanned downtime costs manufacturers $50B annually. Root causes, prevention strategies, and CMMS-powered approaches to eliminate reactive maintenance.

AgentxGCore presents an innovative agentic AI-native architecture designed to enhance next-generation mobile core networks (6G). The research addresses the limitations of centralized management approaches in current 3GPP implementations by proposing a distributed AI-driven system. Leveraging large language models and intent-based networking principles, AgentxGCore enables continuous network optimization through autonomous agents capable of reasoning and action. The framework extends existing 3GPP architecture across the Beyond Next Generation Core domain, utilizing established APIs to create closed-loop automated management. This advancement significantly reduces operational complexity while improving network orchestration and responsiveness to emerging application demands in increasingly sophisticated telecommunications environments.

This special issue in Applied Sciences focuses on novel approaches for future supply chains and smart logistics, exploring innovative methodologies and technologies that reshape modern supply chain management. The journal maintains an open access policy, ensuring global accessibility of published research without restrictions. Feature papers represent cutting-edge research with significant impact potential, submitted through invitation or editorial recommendation. Editor's Choice articles highlight outstanding contributions selected by scientific editors worldwide. The platform provides comprehensive support for researchers, including submission guidelines, ethical standards, and article processing information, facilitating collaboration among academics and professionals in advancing supply chain and logistics innovation.

While they may not represent the revolution many predicted, autonomous taxi fleets are rolling out in more cities and becoming a promising urban mobility option. A BCG analysis estimates that the global robotaxi fleet could range between 700,000 to 3 million vehicles by 2035 and that fares in some markets will be lower than traditional ride-hailing services.The speed of deployment will be influenced by the costs of entering new markets, the time needed to scale up, consumers’ willingness to embrace robotaxis, and the degree to which they capture share from other transportation modes. To win in this increasingly competitive market, operators will need patience and years of substantial investment. They’ll also need to navigate complex regulatory, operational, and technological challenges.

Service providers are redefining the edge from a network boundary into a platform for distributed applications and enterprise services. Driven by artificial intelligence requirements and the need for low-latency processing, organizations increasingly demand compute and intelligence co-located with connectivity. This transformation presents significant opportunities for service providers to expand beyond traditional offerings. However, deploying edge infrastructure at scale—potentially across thousands of distributed locations—introduces substantial challenges. Modern edge environments must support diverse deployment scenarios, from regional facilities to micro data centers, requiring innovative solutions to optimize performance, ensure data sovereignty, and manage latency-sensitive AI workloads effectively.

MTN Group plans to revolutionize African telecommunications infrastructure by converting its tower network into a distributed AI inference grid. The initiative involves installing GPU hardware at base stations to simultaneously support cellular network operations and edge AI processing. This strategic approach reduces latency by enabling AI workloads to be processed locally rather than routed to distant data centres, improving performance and network efficiency. The deployment represents MTN's commitment to positioning itself as the foundational infrastructure layer for Africa's emerging AI economy, complementing centralized data centre investments with edge computing capabilities.

Broadcom has unveiled new silicon and software platforms designed to seamlessly integrate AI data centre operations with edge network computing infrastructure. Collaborating with partners such as Samsung and leading telecom operators, the company is developing a unified fabric connecting hyperscale data centres to network edges. This initiative addresses the critical gap between centralized AI model training and the demand for low-latency inference across enterprise and consumer applications. By providing specialized hardware solutions, Broadcom enables network operators to monetize their existing fibre and 5G investments through distributed AI services, while simultaneously simplifying the operational complexity of managing compute infrastructure across geographically dispersed locations.

Techco is dead.The definition was always consulting fluff.McKinsey defined Techco as an evolution into “platform-oriented innovators akin to today’s top tech players.” The financial mechanics prove this is a category error. Telcos and tech companies share nothing but the letter “T”. The inherent financial structure leaves no room for debate: telecom runs on 1% R&D; tech runs on 15% or more. We do not write proprietary operating systems or build global developer ecosystems. We manage heavy, long-lived physical infrastructure, power, and heating loads, using standardized vendor hardware.Attempting to secure a software valuation by changing corporate vocabulary does not alter the depreciation cycle of a cell tower or the CapEx of a fiber trench. Techco is simply the newest headstone in the telecom graveyard, buried alongside WAP portals, Telco 2.0, DSP, and the Smartpipe.

Yes, Laser is the ultimate communications medium. Physics dictates that transmitting photons through a vacuum offers near-infinite bandwidth with zero latency constraints. But deploying naked lasers on Earth has historically been an exercise in frustration. Atmospheric turbulence, fog, rain, and physical interference forced the industry to wrap light in glass cables or default to the reliable, albeit slower, medium of radio frequencies.For decades, Free Space Optics, or FSO, was dismissed as a fragile science experiment, and, to some extent, the case was officially closed.Laser communications are back, driven by an explosion of technology developments and critical infrastructure needs that were once far from mainstream. Today, lasers are taking over every single layer of the network topology simultaneously, building a seamless architecture from space down to the terrestrial core. In orbit, optical inter-satellite links are creating massive mesh networks in a vacuum to route data globally.