This paper provides a comprehensive examination of the transformation in computer science and engineering during the 2010-2026 period through the lenses of generative artificial intelligence, secure AI systems, and scalable computing infrastructures. First, it addresses the growing computational demands of a data-intensive world, the limitations of hardware scaling, and software-centered optimization approaches, while also discussing interdisciplinary impacts across healthcare, energy, finance, and defense. It then evaluates large language models, multimodal systems, Edge AI, lightweight model architectures, and explainable AI within the broader generative AI and foundation model framework, highlighting model reliability and hallucination as core limitations of contemporary AI systems. In the security-oriented part, the chapter examines adversarial attacks, data/model poisoning, the intersection of AI and cybersecurity, regulatory frameworks, and algorithmic justice. Finally, it discusses the technical and strategic dimensions of modern computing infrastructures through GPU/TPU-accelerated systems, cloud and hybrid architectures, HPC clusters, Slurm-based resource management, edge-cloud task distribution, and energy-efficient computing. Overall, the chapter demonstrates that the development of reliable, explainable, sustainable, and scalable computing systems has become one of the central trajectories of computer science in the age of AI.