AI is a new G

I returned from MWC 2026 with an uneasy feeling.

The telecommunications industry has long been defined by its generational leaps—each "G" marking a profound shift in capabilities, use cases, and societal impact.

2G brought reliable digital voice and SMS, enabling mass mobile communication. 3G introduced mobile data and picture messaging, laying the foundation for internet on the go. 4G powered the explosion of social media, apps, and always-on connectivity. 5G delivered massive bandwidth, fueling high-definition video streaming.

These evolutions followed a predictable cadence governed by 3GPP standards, with operators methodically upgrading infrastructure, spectrum, and devices in multi-year cycles. Parallel to this, the network itself transformed through virtualization: from SDN separating control and data planes, to disaggregating hardware from software, and evolving VNFs (Virtual Network Functions) into cloud-native CNFs (Cloud-native Network Functions). These shifts improved flexibility, scalability, and cost efficiency but remained incremental within the familiar "G" framework.

AI is entering telecom in silos—AI-RAN for spectrum and energy optimization, agentic AI in OSS for autonomous operations and predictive assurance, customer service copilots for intent-based support—delivering proven cost savings (e.g., 25-40% OPEX reductions in network ops, up to 35% energy efficiency). Yet these domain-specific wins rarely connect into a unified, end-to-end intelligence layer. Data stays fragmented across RAN, core, edge, OSS/BSS, leading to duplicated efforts, incomplete visibility, and "agent sprawl" risks. Industry sources highlight how silos impede multi-agent ecosystems and true autonomous networks.

This misconception manifests in several ways:

Viewing AI as incremental tech add-ons — Operators often pursue isolated pilots (e.g., AI-RAN trials, genAI copilots, or agentic OSS agents) expecting quick wins without addressing deeper structural issues.

Underplaying organizational and cultural complexity — AI demands far more than engineering upgrades. It requires breaking down legacy silos (RAN/IT/OSS/BSS), fostering cross-functional agility, upskilling thousands in ML ops/data governance, and driving cultural shifts to trust agentic systems. Cultural resistance, job security fears, and fragmented skills often stall progress, with many projects failing to move beyond pilots (only ~30% of genAI use cases reach production in some analyses). Organizational challenges—including change management and silo-breaking—as top barriers, yet leadership frequently delegates AI to a separate function rather than owning it as a CEO imperative.

Misjudging the scale of change needed — Unlike past "G" evolutions (hardware/spectrum-driven, standardized via 3GPP), AI is a software-defined, data-hungry, adaptive intelligence layer that reshapes workflows, decision-making, operating models, and even business identity (from connectivity provider to intelligent platform). Treating it as "just tech" ignores the need for unified data fabrics, intent-based orchestration, governed multi-agent ecosystems, and radical process redesign—efforts that can take years, not quarters, and demand massive internal rewiring.

New vendors (hyperscalers, specialized AI-RAN players, agentic platforms) disrupt legacy supplier models, while operating models evolve toward intent-driven, cloud-native, agent-orchestrated environments requiring cross-functional agility and new skills. Massive CAPEX uncertainty surrounds compute (GPUs, accelerators), high-bandwidth memory, power, and cooling—often in the hundreds of billions globally—amid unclear ROI timelines and risks like underutilization. AI excels at cost management through optimization, but revenue-generating services (e.g., enterprise AI platforms, GPUaaS, network APIs for AI workloads, personalized offerings) remain nascent for most operators. This imbalance—cost wins without broad revenue upside, vendor shifts, and compute investment risks—demands an AI strategy that starts with organization and operational models, not technology.

This underestimation risks turning AI from a greenfield opportunity into added complexity: persistent silos, agent sprawl, duplicated investments, and missed revenue potential. Proven cost optimizations are real, but without holistic transformation, operators may achieve efficiency gains while remaining commoditized pipes in an AI-driven world. Warning to operators: AI is not "plug-and-play." Underestimating its demands—starting with organization, leadership alignment, operating model redesign, and cultural renewal before heavy technology scaling—will lead to stalled initiatives, wasted CAPEX (especially on compute/infra), and competitive disadvantage. Frontrunners recognize AI as a radical reinvention requiring bold, enterprise-wide commitment; the rest risk being left behind as the intelligence generation unfolds.

Where Do Network Operators Go From Here? A View Ahead of MWC 2026

With Mobile World Congress just around the corner in Barcelona, the telecom sector finds itself at another inflection point. The headlines are familiar: ongoing layoffs across major operators, C-level reshuffles, persistent ARPU erosion, and debt structures that constrain organic investment. Vendors are already talking up 6G roadmaps while AI dominates conversations—both for aggressive OPEX reduction and tentative new revenue paths. Yet the near-term reality feels more evolutionary than revolutionary.

The recent wave of workforce reductions is not, in my view, primarily an AI story—at least not yet. It reflects the long tail of a structural shift that began over a decade ago: the gradual but relentless transition from proprietary telco platforms to cloud-native architectures. We are finally seeing the full operational benefits of user/control-plane separation, hardware/software disaggregation, widespread network virtualization, and centralized policy orchestration. These changes deliver greater automation, elastic scaling, and dramatically shorter development and validation cycles. The outcome is clear: managing a modern mobile network no longer requires the headcount levels of the previous era. Painful as the adjustment is, it is the inevitable consequence of borrowing proven cloud-native principles. Cost discipline is essential, but it is not a growth strategy. The more pressing question is how operators convert more reliable, elastic, and automated networks into sustainable revenue expansion.

Private Networks: Successes Exist, but They Remain Hard-Won

Private cellular networks continue to polarize opinion. Some portray them as a commercial disappointment; others point to hundreds of documented use cases. The reality sits firmly in between. Genuine deployments delivering positive returns do exist, particularly in verticals with high-value connectivity requirements and tolerance for tailored solutions. Energy (smart grids and remote monitoring), healthcare (indoor coverage in hospitals and clinics), large venues (stadiums and event spaces), mining (autonomous haulage and safety systems), and ports (crane automation and terminal logistics) stand out as segments where demand is tangible and economics can work. The common thread in successful cases is not technology alone but deployment philosophy: cloud-native designs that run on commodity hardware, leverage centralized intelligence, and minimize site-specific customization. When executed this way, private networks become scalable and margin-accretive rather than bespoke projects that drain resources. Operators who treat private 5G as an extension of their public edge and orchestration capabilities—rather than isolated silos—are better positioned to capture repeatable value.

Data: The Next Realistic Monetization Frontier

Beyond connectivity and private networks, operators sit on an underutilized asset: vast quantities of network-derived and network-transported data. Until recently most of this information has been siloed for internal analytics, dashboards, and regulatory reporting. That picture is beginning to change. Monetization remains nascent compared with the advertising-driven models of social platforms, yet the opportunity is material. API gateways that expose selected network and user context (location aggregates, mobility patterns, congestion signals, roaming events) represent only the surface layer. Consider a few practical illustrations:

• Ride-hailing platforms could benefit from near-real-time insight into clusters of international roamers converging in a city district—an indicator of an upcoming conference, trade show, or major event. Pre-positioning drivers becomes more efficient, improving service levels and reducing wait times.
• eSIM and travel-focused virtual operators could package value-added bundles—discounted car rentals, hotel reservations, restaurant bookings, or attraction tickets—targeted at detected travelers arriving in high-demand locations.
• Navigation services (Google Maps, Waze, and equivalents) could gain from telco-sourced, fine-grained congestion and flow data that augments probe-vehicle inputs, especially in areas with sparse device coverage or during atypical events. Privacy and regulatory compliance are non-negotiable hurdles, as are competitive dynamics with hyperscalers and data aggregators. Success will depend on responsible data handling, anonymization at scale, clear value propositions for enterprise partners, and commercial models that avoid commoditization. Operators that can evolve from pure connectivity providers toward curated data intermediaries—leveraging their unique position across physical infrastructure, subscriber scale, and real-time network telemetry—stand to capture incremental revenue without requiring entirely new network builds. As we head to MWC 2026, the conversation will likely revolve around AI acceleration, 6G timelines, and edge monetization. Beneath the buzz, though, the fundamentals remain: disciplined cost management, selective private-network wins, and thoughtful exploration of data opportunities. What are you seeing in your markets? Are private networks crossing the chasm in specific verticals? And where do you place data monetization on the priority list for the next 18–24 months? I welcome your perspectives in the comments.