Monetizing AI at the Edge

• Repurposing existing network infrastructure into a low-latency mesh for real-time AI inference
• Understanding why distributed edge AI outperforms centralized data center models enabling more efficient real-time translation, personalization, and generative AI services
• Real world demonstration of reducing network round-trips for better cost efficiency per token while meeting strict service level agreement targets
• Examining how distributed edge inference can generate and deliver individualized ad creatives in real time while maintaining broadcast quality at scale

• Exploring the strategic opportunity for MSOs to capitalize on the trend for federated AI factories by leveraging their networks, distributed aggregation hubs and remote-OLT, to establish micro-AI hubs
• Learning how AI/GPU-as-a-Service models can create new monetization opportunities through real-time AI applications such as network optimization, anomaly detection, cybersecurity, and power management.
• Combining edge-based AI inference with centralized cloud and agentic AI architectures enables secure, sovereign, and commercially scalable AI services for next-generation networks

Technical Paper Presentation: Future Technologies in Focus