Achieving GPU-friendly protocols in privacy-preserving machine learning has several potential implications. Firstly, it can greatly enhance the scalability and speed of computations, allowing for larger datasets and more complex models to be processed efficiently. This can lead to improved accuracy and faster decision-making processes in machine learning tasks while maintaining data privacy. Additionally, GPU-friendly protocols enable seamless integration with existing hardware infrastructure, making it easier for organizations to adopt privacy-preserving techniques without significant overhead costs or resource constraints.