Misguided segmentation or a high level architecture problem however, it is not. A specialized product will perform its specialty better than a general purpose product. There's minimal demand for a card that's good at both compute and gaming. These people exist, but are few compared to those who only care about one or the other.
The benefit of splitting GCN into RDNA and CDNA was immediate. Comparing the Radeon VII (GCN 5) vs RX 5700 XT (RDNA 1), you'll see that they're fairly evenly matched on gaming, trading blows with the Radeon VII slightly ahead when averaged. The RX 5700 XT takes a very heavy loss on compute benchmarks. Both are TSMC 7nm, but the RX 5700 XT has fewer shaders (2560 vs 3840), a smaller die (251 vs 311 mm2), and lower power consumption (225 vs 300 w) which shows how much more efficient it is at gaming. That much lower power consumption and noise, and a couple hundred dollars cheaper made it a much more compelling card for gamers.
CDNA cards are apparently missing components needed for gaming, like render output units. Hence, they have no official support for DirectX, OpenGL, or Vulkan. I've never seen anyone get one of these working for gaming. In exchange, their compute performance is so good that a number of companies are buying these cards over Nvidia's, despite the overwhelming CUDA ecosystem. In 2013, a GCN based supercomputer made the top 100. That is the one and only GCN based system to ever make the top 100. Compare to now, where 8 of the top 10 most energy efficient super computers use CDNA accelerators, as does the number 1 fastest supercomputer outright.