针对 5G 核心网的全栈能效与 DT 面向电信云的全新架构蓝图，使节能方案规模化部署于整个核心网络德国波恩, Feb. 26, 2026 (GLOBE NEWSWIRE) -- 构建 AI 原生设计移动网络的软件公司 Mavenir 今日证实，其在 Deutsche Telekom AG 两项优化 5G 核心网能耗的战略项目中发挥了核心作用。 首先，在与 Deutsche Telekom 长达数年的合作中，Mavenir 的云原生 5G 核心网软件及具备能源感知能力的自动化功能，对于在实时网络验证中实现高达 65% 的节能起到了关键作用，为欧洲可持续、高性能 5G 核心网运营树立了全新标杆。 依托 Mavenir 首次部署的 5G 软件功能，这款最高能效核心网采用动态软硬件扩缩技术以降低能耗与二氧化碳排放，致力于构建“零比特、零瓦特”的核心网。 这一成果基于 Deutsche Telekom 与合作伙伴共同开发的创新性全栈能效方案。 此外，Mavenir 也是 Deutsche Telekom 向统一云架构 Horizontal TelCo Cloud 转型的关键技术合作伙伴，该架构是实现节能方案规模化部署于 ...

Core Viewpoint - The conflict between AI systems and traditional applications is reshaping the digital landscape, highlighting a power struggle over data control and user interaction methods [1][2][5]. Group 1: Market Dynamics - The recent ban of Doubao Mobile Assistant by major apps indicates a significant shift in the competition between AI agents and native applications [1]. - The Chinese mobile internet advertising market has reached a trillion-level scale, with a substantial portion of revenue relying on user click behavior, which AI assistants threaten by automating tasks like price comparison and booking [2]. - The legal actions, such as Amazon's lawsuit against Perplexity AI for "illegally obtaining user data," underscore the battle for data sovereignty and control over user behavior data [2]. Group 2: Technological Challenges - Current technology standards lag behind, creating a regulatory dilemma where AI agents exploit existing system permissions, such as Android's accessibility services, originally designed for assisting disabled users [3]. - The mismatch of technological tools leads to a "cat-and-mouse game" between developers and platforms, complicating the regulatory landscape [3]. - Differences in data governance across economies force multinational tech companies to adopt regional adaptation strategies, increasing development costs for AI agents [3]. Group 3: Future Development Path - The next phase for AI phones is moving from "AI feature addition" to "AI native design," focusing on building a "cloud-edge collaborative" architecture [3][4]. - On-device AI capabilities will become standard, with advancements in NPU processing power and model miniaturization enabling local execution of large model inference tasks [4]. - Open and standardized interfaces for AI agents are essential, allowing developers to register their services as callable modules, thus maintaining business integrity while integrating into a unified AI framework [4]. Group 4: User Experience and Business Model Innovation - Personalization and situational awareness will be key differentiators for AI agents, enabling them to learn user habits and preferences for tailored services [4]. - The evolution of business models is necessary, as traditional in-app purchases and advertising methods will need to adapt to new mechanisms like "pay-per-task" and "AI service revenue sharing" [5]. - The ultimate goal of AI phones is not to eliminate apps but to transform their role from primary interfaces to backend service providers, creating a seamless and proactive user experience [5][6].