Multiple reasons might apply: - Focusing on one use case might make it difficult to generalize the design later. Lessons learned might not carry over, and the

Multiple reasons might apply: - Focusing on one use case might make it difficult to generalize the design later. Lessons learned might not carry over, and the platform will get anchored into particular design choices. This only gets worse the more the solution gets deployed. - Having a product that can do both makes it simpler to do applications that combine multiple computational loads. Else, the data has to be transferred to another device. This seems unattractive since graphics, ML and massively parallel scientific computations are more similar to each other than to what CPUs are used for. - Development budget gets fragmented, which is a disadvantage since the different use cases might not correspond to markets that are large enough to support each product. - One of the platforms might just die off. Users that use both would be stuck in between and would have to find a solution, and most likely it will be NVidia/AMD again.