GPUs are generally considered better than CPUs for AI workloads due to their parallel processing capabilities, which are well-suited for tasks such as training and running AI models. GPUs can handle massively parallel tasks, such as matrix multiplications, which are common in AI and deep learning workloads. They provide the raw computational power required for processing largely identical or unstructured data, making them the preferred option for training AI models in most cases. On the other hand, CPUs are less adept at tasks that require millions of identical operations and are better suited for algorithm-intensive tasks that don't support parallel processing. While both CPUs and GPUs offer distinct advantages for AI projects and are more suited to specific use cases, GPUs are generally preferred for AI workloads due to their parallel processing capabilities and efficiency in handling specialized computations.