Polynomial time algorithms offer manageable performance increases with input size, while exponential time algorithms experience drastic growth, making them impractical for large datasets. The discussion highlights Strassen's matrix multiplication as an example of a polynomial improvement that significantly reduces computational complexity. In contrast, NP-complete problems, such as the traveling salesman problem, illustrate the stark difference in feasibility between polynomial and exponential solutions in real-world applications. Even a modest input size can lead to exponential algorithms becoming unmanageable, emphasizing the importance of choosing efficient algorithms. Understanding these differences is crucial for optimizing performance in various fields, including logistics and software development.