The fields of electronic design automation, code development, and software verification are experiencing significant transformations with the integration of machine learning, artificial intelligence, and large language models. Researchers are exploring innovative approaches to improve circuit design and performance prediction, code completion and review, and software testing and verification. Notable advancements include the application of graph neural networks and probabilistic flow models for RFIC performance prediction, the development of frameworks for defect classification and code review automation, and the use of large language models for code generation and testing. The integration of AI-driven automation and formal methods is also improving testing and verification processes, leading to more efficient and reliable software development. Furthermore, the adoption of large language models in electronic design automation is simplifying and automating various aspects of the design-to-manufacturing workflow. Overall, these advancements have the potential to significantly accelerate hardware development, improve software quality, and increase developer productivity. Key research areas include the development of more efficient and automated reasoning systems, the integration of deontic logics with other formal systems, and the application of large language models in software development and testing. As these fields continue to evolve, we can expect to see significant improvements in the efficiency, effectiveness, and reliability of electronic design automation, code development, and software verification.