Salvatore D'Oro is a research associate professor at Northeastern University's Institute for Intelligent Networked Systems since 2017. He holds a PhD in Computer Engineering from the University of Catania and has completed postdoctoral research and visiting positions at leading institutions including Ohio State University and Université Paris-Sud 11. D'Oro serves on technical program committees for prestigious IEEE publications and conferences including IEEE INFOCOM and IEEE Vehicular Communications Magazine. His research focuses on Open RAN architecture, artificial intelligence applications in radio access networks, network slicing, virtualization techniques for 5G and beyond systems, and physical layer security. His expertise bridges telecommunications infrastructure and advanced computational methodologies.

The EU's digital sovereignty focuses on building its own infrastructure, cloud services, and cybersecurity standards to reduce reliance on non-EU tech.

In 2025, EDF continued to decommission or prepare for the decommissioning of its facilities. ASNR notes that good progress has been made on some projects ...

Pure DC, a British-headquartered firm, is launching a major AI infrastructure project in Seinäjoki, Finland, representing the UK's largest inward investment into the country. Phase 1 involves over €1.5 billion investment for a 110MW AI campus, with potential scaling to 550MW and €7.5 billion subject to regulatory approvals. The fully leased facility features an operational power substation and secured planning permissions. The project promises substantial economic benefits including employment across construction and permanent roles, local tax reinvestment, waste heat repurposing for district heating, and education initiatives, positioning Seinäjoki as a catalyst for broader AI ecosystem development.

SGP.32 is the GSMA's third-generation eSIM specification, purpose-built for headless and constrained IoT devices requiring industrial-scale deployment. Published in 2023, it introduces a novel management architecture that separates profile download from device interfaces, enabling remote SIM provisioning without physical access or user interaction. Unlike its predecessors SGP.02 and SGP.22, SGP.32 addresses critical gaps in industrial IoT standardization by eliminating dependencies on always-on infrastructure, local users, and predictable device locations. The specification defines an eSIM IoT Manager platform capable of asynchronously pushing profile commands and a lightweight IoT Profile Assistant that operates on resource-constrained devices. This advancement is essential for enterprises managing billions of infrastructure, utility, and industrial devices deployed across remote, power-limited environments requiring automatic operator switching based on network conditions or commercial requirements.

Artificial intelligence in customer service represents a strategic approach to enhancing operational efficiency and reducing costs while delivering personalized support at scale. AI-powered customer service tools enable organizations to automate experiences, streamline workflows, and augment agent capabilities, resulting in significant time and cost savings. When implemented effectively, AI fosters authentic human connections with customers by facilitating warm, familiar interactions that strengthen loyalty. Industry research indicates that two-thirds of customer experience organizations recognize AI's potential to enhance human-centered service interactions. As AI technology continues to evolve, intelligent tools are expected to become integral to all service interactions, offering substantial business benefits alongside improved customer satisfaction.

Curity Identity Server provides telecom operators with a scalable identity platform designed to secure subscriber access and monetize APIs across multiple brands and markets. Unlike consumption-based pricing models that escalate with authentication volume, Curity offers flat-rate licensing covering millions of subscribers and billions of logins, stabilizing identity costs regardless of traffic growth. The platform consolidates fragmented identity systems across acquisitions and regional operations while maintaining per-brand customization and centralized policy management. Built specifically for telecom infrastructure demands, it delivers robust API access control with scoped tokens, audit trails, and support for machine-to-machine workflows alongside traditional subscriber authentication, enabling operators to leverage network APIs as distinct revenue streams.

Enterprise AI deployments face a critical structural pricing crisis as token costs plummet while overall spending skyrockets. Despite inference costs dropping approximately 280-fold since 2023—with GPT-4-equivalent performance falling from over $400 to $0.40 per million tokens—companies report year-over-year AI bill increases of 320 percent. The mismatch stems from inadequate usage controls and rapidly scaling agentic workflows that compound costs exponentially. Industry giants like Uber have already exhausted annual budgets mid-year. This unsustainable pricing model, where unit economics improve dramatically while total expenditures surge, indicates fundamental misalignment between vendor pricing strategies and actual enterprise consumption patterns, prompting major providers to repricing initiatives.

Optical transceivers are roughly 60% of networking cost and 45% of networking power; with networking at ~15–18% of total cluster cost, optics alone are ...

In April 2026, an unreleased AI model found a software flaw that had survived 27 years of human review inside OpenBSD, the operating system security professionals choose precisely because it is hardened. Anthropic deemed the model Claude Mythos Preview too dangerous to release to the public.The telecom industry filed the story under cybersecurity and moved on. But that is the wrong reading of what is coming.Mythos did more than embarrass the security profession. It exposed the founding assumption of the mobile generation model. Not the caricature that networks only change once a decade, because they are patched and upgraded constantly, but something subtler and harder to fix.The generational cycle sets the speed at which the industry can revise its assumptions, and the operational doctrine around it sets the speed at which even routine fixes reach the live network.

In 2020, the US government made a bet. To approve the merger of T-Mobile and Sprint, regulators needed to pretend the country would still have four wireless carriers, so they picked a successor. Dish Network, a satellite TV company run by Charlie Ergen, a former professional poker player who had spent two decades quietly hoarding spectrum licenses, would inherit Boost Mobile and its 9 million subscribers, buy divested spectrum, and build a brand new nationwide 5G network to challenge Verizon, AT&T, and T-Mobile.Ergen built it. Between 2020 and 2025, Dish Wireless installed more than 144,000 radios on roughly 24,000 towers, covering over 80% of the US population. Court filings put the total investment at about $46 billion, more than $30 billion on spectrum and more than $16 billion on construction. And it was not a copy of anyone else’s network. Dish built the world’s first nationwide cloud native Open RAN network, with radios from Samsung and Fujitsu, software from Mavenir, and a core running in Amazon’s cloud. Engineers around the world studied it as the future of network construction.But customers never showed up. Boost shrank rather than grew, sliding from 9 million subscribers to 7.53 million by early 2026, while quarterly net additions collapsed to a rounding error. A network sized for tens of millions of users carried a small fraction of that, and the bills kept arriving anyway. Tower rent alone ran to $567.8 million in 2025. By last year, Dish had quietly given up, moving Boost’s traffic onto AT&T’s network under a wholesale deal and, in August 2025, beginning the abandonment of the network it had just finished building.

Plans follow the familiar tiered structure: Basic (10M tokens/month), Standard (50M tokens), Business Unlimited. The operator controls pricing by setting ...

The Tokenomics Foundation has been established to unite enterprises, hyperscalers, and frontier model developers in addressing escalating artificial intelligence costs. As organizations struggle to balance AI expenditure with usage, major technology providers are introducing cost management solutions. Oracle is rolling out limited token bundles to enhance spending predictability, while AWS launched its FinOps Agent to identify cost anomalies. The foundation, operating in partnership with the Linux Foundation, aims to establish clear financial controls for AI consumption. This initiative reflects a broader industry shift, with FinOps teams expanding their traditional cloud cost management responsibilities to encompass AI spending oversight and business value alignment. Organizations increasingly recognize the urgent need for comprehensive financial visibility and governance frameworks in their AI investments.

FinOps X 2026 opened with a chart that did most of the arguing for the week: token consumption climbing from single-digit trillions to tens of trillions in ...

Mavenir's CEO highlights significant cost implications of AI adoption, revealing that transitioning to a usage-based model for large language models tripled software development expenses. Despite initial hesitations, Kohli advocates that telecommunications companies invest in owning GPU infrastructure and deploying open-source models, citing seven to eight-year hardware lifecycles and modest performance gaps with frontier models. Financial analysis suggests potential savings of twelve million dollars annually through strategic infrastructure investment. Additionally, Kohli raises critical data security concerns, asserting that public language models may be harvesting proprietary developer information for training purposes, underscoring the importance of maintaining in-house AI capabilities for protecting sensitive corporate knowledge and intellectual property.

Yup, you can be a trillion-dollar company; you can have the cheapest plans on the market; you can hire the best engineers in the world. But if you don’t have enough spectrum, everything is an uphill battle. Spectrum is the land your network is built on. Everything else, such as radios, software, and marketing, is what you build on top of it, and no amount of construction skill compensates for a small lot.The United States is about to prove this twice. Once with fresh performance data that shows exactly how spectrum, not equipment, decides who wins today. And once with what’s coming with the mother of all spectrum auctions, 160 MHz of prime mid-band, the rules for which the FCC votes on July 22, 2026, which could redraw the American wireless map for the next decade.

On July 1, 2026, the Wall Street Journal dropped a story that moved $130 billion of market value in an afternoon: SpaceX had quietly shown investors a prototype handset during its IPO roadshow. Slimmer than an iPhone, with a proprietary operating system, a Qualcomm Snapdragon chip and xAI’s Grok woven into its core. Elon Musk’s response took four words, “utterly false”, and the market’s response took about 7% of SPCX shares, erasing more than $50 billion of Musk’s net worth and, for a day, his title as the world’s first trillionaire.

A Token Policy architecture functions as a hybrid system, moving beyond the traditional network layers. It requires a convergence of functions that bridge the transport layer, where the 5G core operates, with the application layer, where token-based requests are actually generated.Because the network must now “read” the intent of the traffic, we are essentially building a cross-layer gateway that sits between the user equipment and the service destination. The long-term viability of this approach depends on whether the 3GPP decides to standardize these functions.

The question is not if, but how. Mapping the strategic paths for SpaceX’s entry into the terrestrial mobile space, and why, while difficult, it is far from impossible for Musk.

Sam Altman has proposed reimagining artificial intelligence as a metered utility similar to electricity, where users pay monthly bills based on token consumption rather than subscription tiers. Under this model, light users would pay minimal amounts while heavy users incur substantial costs, with the ultimate goal of achieving AI abundance where intelligence becomes "too cheap to meter." However, critics raise concerns about potential gatekeeping and forced minimum charges, while real-world evidence—such as Uber exhausting its 2026 AI budget in just four months—suggests the industry remains far from achieving affordable abundance. Drawing parallels to failed 1950s predictions about nuclear power becoming too cheap to meter, questions persist about whether Altman's vision represents inevitable pricing evolution or merely consolidates intelligence access behind expensive paywalls, particularly without sufficient infrastructure investment.