When a consumer installs a satellite dish on a roof, they establish a pristine, uncompromised line of sight to the sky using an active phased-array antenna with massive gain (30+ dBi). That architecture scales. Direct-to-Device does not. When you remove the dedicated roof dish and attempt to close that same 500 km link with an unmodified smartphone, you are forced to rely on an isotropic, omnidirectional antenna yielding roughly 0 dBi of gain and operating at a fraction of a watt of transmit power.Despite the massive satellite apertures currently being deployed to brute-force the uplink, the laws of physics remain unforgiving. Orbital D2D cannot provide meaningful concurrent sector capacity, cannot provide low latency, and, due to a microscopic penetration margin, absolutely cannot provide indoor coverage.Orbital D2D is more like an insurance policy. It is a brilliant, necessary solution for the 0.0001% of edge cases like the stranded hiker, the mid-ocean SOS, and extreme remote telemetry. But for Telcos tasked with delivering gigabits of data to dense urban and suburban populations, where 80% of data is consumed indoors, orbital D2D is practically useless as a core capacity layer.

Telecom autonomy has struggled to evolve under traditional machine learning models because these older systems operate as uninterpretable black boxes. Machine learning excels at deterministic tasks and pattern recognition across massive datasets. However, network engineers managing critical infrastructure require clear explanations before executing system changes.This lack of explainability stalled the progression of autonomous networks. Handing over control of complex actions without understanding the underlying math creates an extremely risky trust gap for Telcos. To resolve this, Nokia and Google Cloud designed the Gemini-powered agents using a “glass box” architecture.Instead of executing an unverified command, the action reasoner agent functions as an advisory layer. It processes the network data and presents a confidence-based recommendation to the human operator. Because these specialized agents can explain their conclusions and reasoning, they elicit greater trust from users. Human engineers retain control and final approval over critical control points before logging and executing fixes. By combining autonomous data analysis with human observability, the platform establishes the required safety to deploy machine-speed operations.

The Linux Foundation announced the creation of the Tokenomics Foundation to establish open standards and best practices for managing AI costs, addressing enterprises' growing struggle with unpredictable artificial intelligence spending. Unlike traditional cloud expenses, AI token costs—representing units of text processed by models—exhibit volatile consumption patterns that finance teams find difficult to forecast and control. Major industry players including Google, Microsoft, IBM, and JPMorgan Chase have pledged support for the initiative. Recent data reveals alarming spending trends, with some companies experiencing token cost increases of up to fifty percent quarterly. The foundation, launching formally in June at FinOps X, aims to bring transparency and accountability to the AI economy through established benchmarks and best practices.

As artificial intelligence becomes increasingly embedded in organizational operations, token usage has emerged as a critical cost consideration for executives and boards. While AI technology's potential is widely acknowledged, scaling deployment effectively remains challenging, with organizations struggling to capture full value. Tokenomics—the discipline of understanding and managing AI token consumption—has become essential as companies transition from experimentation to production-scale implementation. Though per-token costs have declined dramatically since 2022, exponential increases in usage offset these savings, particularly as organizations explore transformative agent-led applications. Managing token efficiency effectively is now crucial for controlling AI expenditures and maximizing return on investment in scaled deployments.

Billing is fully integrated: tokens are metered and charged through the operator's existing BSS using the same mediation infrastructure as today's data plans.

Mavenir has introduced a billing-grade infrastructure platform enabling telecom operators to monetize artificial intelligence token usage directly through their existing Business Support Systems. Telecom providers traditionally face significant integration challenges when attempting to monetize large language model consumption, relying entirely on third-party cloud vendors for token counting. This creates uncontrolled variable costs and prevents usage quota enforcement. Mavenir's Digital Enablement platform addresses this gap by integrating AI token metering directly into operator BSS with regulated-grade precision, mirroring established data plan billing mechanisms. The solution incorporates an intelligent Model Router that strategically routes requests between cost-free internal language models and premium external frontier models based on subscriber tier and task complexity, enabling operators to control costs and achieve commercial viability for AI-delivered services.

At Reliance's Annual General Meeting, Jio shared a detailed proposal with India’s space regulator, IN-SPACe, outlining a massive orbital infrastructure project. The plan details the deployment of a proprietary Low Earth Orbit satellite constellation comprising approximately 1,650 satellites. Positioned at an operational altitude of approximately 650 kilometers, the network is designed to target two distinct service models: high-throughput rural fixed broadband and Direct-to-Device connectivity, which allows standard, off-the-shelf smartphones to connect directly to satellites without specialized satellite dishes or hardware modifications.

Global mobile network data traffic grew by 22% between the first quarter of 2025 and the first quarter of 2026. That is a steady number, but it shows growth is slowing. Compare that to 2019, when the explosion of mobile video caused traffic to spike by 80% year-on-year. The data shows that the predicted AI data tsunami on the access network is absent.

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.

By anchoring machine identities directly at the network or hardware level, operators can definitively authenticate automated assets and enforce strict security boundaries. Recent initiatives by operators like SK Telecom and StarHub demonstrate this emerging use case, moving beyond high-level AI experimentation to build concrete frameworks that assign verifiable identities to autonomous systems, enabling audit, monitoring, and secure machine-to-machine operations.

Saturn Cloud and OpenNebula Systems have announced a strategic partnership to deliver comprehensive AI token factory capabilities to organizations managing infrastructure through OpenNebula's platform. This integration combines OpenNebula's robust GPU virtualization, multi-tenant orchestration, and infrastructure management with Saturn Cloud's fine-tuning, model serving, and managed inference services. The unified solution enables AI teams to fine-tune open models, deploy OpenAI-compatible endpoints, and meter token usage within branded environments without requiring in-house development. The platform supports advanced features including multi-GPU DeepSpeed training, distributed training frameworks, and managed development environments, addressing the application layer gap in sovereign AI infrastructure deployment across neoclouds and enterprise data centers.

Genesis represents an innovative agentic AI framework designed to accelerate cellular research and development for next-generation 6G networks. The framework addresses critical limitations of large language models in radio access network development by converting high-level intents—such as specification requirements, network anomalies, or research hypotheses—into validated solutions through over-the-air experiments. Built on composable primitives including agents, skills, and hooks, Genesis features a persistent knowledge base that serves as both ground truth and repository for all generated artifacts, enabling capabilities to compound across iterations. The framework encompasses six automated pipelines handling synthesis, testing, hardening, optimization, discovery, and security operations. By anchoring each agentic step in autonomous observations and validated tests executed across heterogeneous cellular infrastructure and testbeds, Genesis substantially reduces the months of manual engineering work traditionally required per development iteration.

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.

NVIDIA and Red Hat OpenShift have partnered to develop advanced AI-Radio Access Network (AI-RAN) solutions that leverage artificial intelligence to optimize telecommunications infrastructure. This collaborative approach integrates NVIDIA's powerful GPU computing capabilities with Red Hat's containerized Kubernetes platform, enabling telecommunications providers to deploy intelligent, efficient network systems. The solution enhances network performance through AI-driven optimization, improves resource allocation, and reduces operational costs. By combining NVIDIA's hardware acceleration with OpenShift's enterprise-grade container orchestration, organizations can build scalable, secure AI-RAN deployments that address modern network demands while maintaining flexibility and reliability in increasingly complex telecommunications environments.

The Linux Foundation has announced the creation of the Tokenomics Foundation to address enterprises' growing challenge of managing artificial intelligence costs. As organizations deploy multi-agentic AI systems with opaque, token-based pricing models, CIOs struggle to benchmark providers and measure return on investment. The new vendor-neutral foundation will develop open standards, benchmarks, and best practices for AI cost management through collaboration with the FinOps Foundation. Together, they will expand the FinOps Open Cost and Usage Specification to normalize AI token consumption data across platforms and providers. The initiative has already secured support from major technology vendors and enterprises including Google Cloud, Microsoft, IBM, and JPMorganChase, positioning it as a significant step toward standardizing AI financial governance.

Telecommunications companies are uniquely positioned to capitalize on AI infrastructure by leveraging their sovereign networks and established relationships with regulated enterprises and governments. However, they currently lack a monetization model aligned with how customers consume AI services today. The token economy represents a paradigm shift from GPU-hour billing to consumption-based pricing, where telcos charge for actual inference outputs—tokens generated, requests served, and workflows completed—governed by performance SLAs. This model enables significantly higher revenue per GPU compared to traditional hourly pricing, while improvements in efficiency directly translate to margin expansion rather than price pressure. The Rafay Platform addresses the critical operational gap by providing telcos with a comprehensive solution for delivering token-metered AI services, including multi-tenant isolation, metering, billing, and governance capabilities, without requiring them to build these components independently.

Sure, 6G will deliver necessary operational improvements. It will utilize new 7GHz spectrum, improve radio performance, and lower the cost per bit for operators still recovering from the 5G capex hangover. However, consumers and enterprises do not pay a premium for faster pipes. Connectivity is now a hyper-commoditized utility.If Telcos want to capture value in this new economy, they must stop defining their core business as “connectivity.” Telecom is a distribution business.Networks serve as the last-mile delivery system for the global economy. In the past, the industry distributed voice, SMS, and 8K video. Today, the asset being distributed is intelligence. Hyperscalers are building massive, centralized AI data centers, but that compute power requires a physical delivery mechanism to reach users.To get in the path of the money, telecom operators must pivot to distributing AI agents and semantic compute to the edge.

Over the last eight months, NVIDIA has been aggressively pushing its AI-RAN strategy into the telecom ecosystem. We saw the high-profile operator announcements with T-Mobile, the deep Layer 1 baseband partnership with Nokia, and their massive strategic investment in custom silicon designer Marvell.But all of that momentum had a strict physical boundary where NVIDIA stayed inside the baseband cabinet at the bottom of the tower. Their servers were targeting the Centralized Unit and the Distributed Unit part of Radio systems. The actual Radio Unit, the box clamped to the antenna at the top of the mast, was considered completely off-limits for general-purpose compute or dedicated chipsets.The reason? The RU handles highly deterministic, real-time Layer 1 tasks in a harsh, passively cooled environment. The industry assumption was that a power-hungry GPU could never survive the strict energy, cost, and thermal constraints of the antenna. That space belonged exclusively to the highly efficient, custom ASICs built by Ericsson, Nokia, and Huawei.

China's three major telecom operators—China Mobile, China Unicom, and China Telecom—have launched commercial AI token packages integrated with mobile phone billing systems. These offerings enable users to purchase AI tokens at affordable monthly rates, starting from 5 to 15 yuan, with quotas ranging from 6 to 10 million tokens. Industry sources indicate that government entities and state-owned enterprises comprise the primary customer base. The initiative reflects a significant strategic pivot by telecom operators to capitalize on China's explosive AI consumption growth, which surged from 100 billion daily tokens in early 2024 to over 140 trillion by March 2026, demonstrating unprecedented demand for AI services and establishing new revenue streams in the competitive telecommunications sector.

The AI industry faces escalating operational costs as companies confront unexpectedly high token consumption expenses. Major enterprises including Uber, Microsoft, and Priceline have experienced significant budget overruns despite declining per-token pricing, driven by aggressive AI adoption and increasingly autonomous agent deployment. Organizations that committed to unlimited subscriptions in early 2025 now urgently seek cost visibility and spending controls. This situation has catalyzed a market response, with startups, vendors, and a newly established Linux Foundation standards body developing tools and frameworks to manage AI expenditures. OpenAI's enterprise leadership confirms that customer conversations have shifted from capability assessment to cost optimization and efficiency metrics, mirroring earlier cloud computing industry developments.