In 2025, AI coding tools have become a significant component of engineering workflows, with major companies like Microsoft and Google reporting that 30% of their generated code is AI-driven. This trend is prompting developers to explore the benefits and challenges of AI tools, which can enhance productivity but also increase bugs and bottlenecks in other development processes. There is a growing consensus that an end-to-end AI development tool stack is necessary to address these challenges, with layers including AI coding, code review, testing, refactoring, and documentation tools. These tools, while improving speed and efficiency, also require careful integration to avoid exacerbating existing issues. The integration of AI tools varies among companies, with some adopting comprehensive stacks while others opt for selective or minimal implementations based on their specific needs and resources. The adoption of AI tools is seen as transformative, akin to the impact of platforms like GitHub, necessitating a reevaluation of development practices to fully leverage their potential.