Improving the code review process and ensuring confidence in AI-generated changes will be crucial for the future of software engineering.

The next limiting factor for coding agents will be verification. While models are improving at writing code, real-world applications often lack perfect unit tests, making verification challenging.

The bottleneck for AI development may shift from generating solutions to verifying their accuracy and reliability.

The transformation in software engineering due to AI code generation has intensified the need for better tools for code review, which has always been a bottleneck in large companies.

In the generative AI era, engineers can write code faster than ever, but this is just the beginning; thorough code review and understanding user needs are essential for product quality and completeness.

The rise of AI tools is addressing the software development deficit, allowing for more efficient code generation and management.

As AI takes on more coding tasks, maintaining a balance between automation and preserving core engineering skills is essential for the development of effective programmers.

The best engineers will need to develop a taste for quality code and debugging skills, which are crucial in the age of AI-assisted coding.

When using AI for coding, there is often a gap between the code generated by AI and the user's ability to modify it effectively, highlighting the need for better tools.