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Hi Tinker

My thoughts are, the GPU is currently the best general purpose chip for a variety of specific problems. This was kind of a lucky break for Nvidia, it just happened that GPUs were the best available option for a number of matrix-based operations.

However, theres plenty of reason to believe that more specific chips (ASICs, FPGAs) can be orders of magnitude better than GPUs for those specific problems.

Its a 'general purpose versus domain-specific' battle. General-purpose by definition will tend to lose those battles.

So the GOOG/AMZN/FB etc of the world don't need to care about NVDA's roadmap. They have specific problems to solve (eg: AI training and inference) and if customers want to solve those problems, they can invest in provisioning custom chips.

The challenge for NVDA is if those datacenter type problems (AI et al) consolidate around a few specific problems that can be solved by specific ASICs (eg: TPUs) which can be commoditised.

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