The point is that AMD is doing the legwork to ensure that AI models can run on their chips. While they could settle for inference workloads (port llama to AMD). It is unlikely that many teams will widely adopt their silicon unless they can be used in the end-end ML stack. Many pure OSS efforts have tried and failed to make AMD work for this use case.
As a chip maker - they will also have some undersold, QA, or otherwise wasted parts available for these training efforts - so the capex is likely less severe for them compared to a random startup betting on AMD.
As a chip maker - they will also have some undersold, QA, or otherwise wasted parts available for these training efforts - so the capex is likely less severe for them compared to a random startup betting on AMD.