Edge AI has emerged as a fundamentally different operating environment from cloud-based machine learning, requiring developers to navigate distinct constraints around latency, power consumption, and privacy. Brandon Shibley, Edge AI Solutions Engineering Lead at Qualcomm's Edge Impulse, discussed how the landscape has evolved in 2026, highlighting the shift toward smaller, more efficient models that can run directly on devices rather than relying on cloud connectivity. The conversation examined several key technologies reshaping edge AI development, including the emergence of generative AI at the edge, the proliferation of small language models optimized for device constraints, and cascading model architectures that balance performance with resource limitations. Developers building practical edge AI systems today must grapple with real-world constraints including processing power, battery life, and the need to keep sensitive data local rather than transmitting it to remote servers. MLOps practices and evolving hardware capabilities are becoming increasingly critical as enterprises deploy AI directly on phones, IoT devices, and embedded systems. The panel emphasized that successful edge AI implementation requires rethinking traditional machine learning development workflows and hardware selection strategies tailored to distributed computing environments.