Genetic programming (GP) is gaining renewed interest as a method to solve complex problems traditionally tackled by machine learning, especially deep learning. Despite doubts expressed in the comments about GP's effectiveness and current relevance, there may be unique problems GP is well-suited for, such as those involving optimization, automatic programming, and symbolic regression. Some participants suggest exploring the integration of GP with deep learning to enhance model capabilities, drawing a parallel to historical skepticism towards innovations like hidden layers in neural networks. There are still challenges and skepticism regarding GP, with questions surrounding its suitability for modern applications. The conversation emphasizes a need for exploration and a potentially evolving landscape where GP could regain traction alongside deep learning methodologies. Overall, while GP has seen declining interest, its ability to handle certain types of problems could present new opportunities in AI development.