AI Native Telco Vision - The future telco is envisioned as an AI native telco, leveraging AI for operations, customer experience, and innovation acceleration [1][2] - Telecom networks are transitioning from connectivity providers to intelligence fabric providers, embedding compute-based intelligence [12] - AI is defining the future, and the future moves on telecom networks, requiring hundreds of billions of real-time connections [6][11] Market Adoption and Partnerships - 90% of the top 50 telcos are working with NVIDIA, and 97% of 450 telecom professionals surveyed are already using or planning to use AI [3] - Eighteen telcos worldwide are generating revenue through tokens, indicating a shift from traditional ARPU models [19] - NVIDIA is releasing its first AI blueprint for network operations, enabling network engineers to reconfigure parameters through agentic flow [31][32] Key Building Blocks for AI Native Telcos - A homogeneous, scalable, accelerated infrastructure is essential for any workload, built upon hardware and a disaggregated software layer [16][17][18] - Autonomous operations for networks, customers, and employees are enabled by large language models and agentic flows, leading to benefits like a 2x ROI and 22% capital savings [29][30] - A network capable of last-mile connectivity, supporting both 5G and AI native 6G, is crucial for distributing intelligence [40] - Digital twins enable simulation and visualization of network operations, facilitating the fusion of network operations AI across all parts of the network [63][64] 6G and AI RAN - 6G standards are being formed with a focus on AI, aiming to improve spectral efficiency through integrated sensing and semantic communications [44][46][47] - The AI RAN Alliance has grown to 75 member companies, demonstrating rapid movement through new partnerships [54] - AI RAN (AI fused with the Radio Access Network) is gaining pace, with examples of telcos fusing AI and RAN to deliver services like education and healthcare [53][56]

AI Native Wireless Networks Overview - NVIDIA AI Aerial is revolutionizing wireless systems by enabling neural networks to learn from data, adapt in real-time, and scale with complexity [1] - This approach boosts spectral efficiency, lowers costs, and improves connectivity in wireless networks [1] Key Steps & Technologies - Building AI native wireless networks involves three key steps: training, simulation, and deployment, all unified by NVIDIA AI Aerial [2] - NVIDIA uses Shauna and Aerial radio frameworks to train neural network models, replacing traditional 5G channel estimation algorithms [2] - The trained model is deployed on a live 5G network at NVIDIA using Aerial RAN computer [3] - NVIDIA provides the Shona research kit on NVIDIA Jetson, enabling developers to create a working AI model in a live 5G network with minimal code [5] Performance & Results - AI implementation nearly doubles the cell throughput [3] - AI achieves 100% higher throughput on a live 5G network [4] - The AI model continuously improves over time through a feedback loop between the physical and virtual worlds [4] Future Implications - Improved AI models will boost capacity, unlock new applications, increase efficiency, and extend coverage [5] - NVIDIA encourages participation in the 6G developer program to accelerate AI native wireless research [5]