[tzhenghao]

> motivate everyone (Intel, AMD, ARM, Google, etc.) to try and tackle the problem by making new chips

Yes, there has been repeated efforts to chip at Nvidia's market share, but there's also a graveyard full of AI accelerator companies that fail to find product market fit due to lack of software toolchain support - and that applies even for older Nvidia GPUs and their compatible toolchains, let alone other players like AMD. This isn't a hit on Nvidia, I'm just saying things move so quickly in the space that even the only-game-in-town is trying to catch up.

Nvidia is also leading by being one or two hardware cycles ahead of their competition. I'm pretty confident AI workloads in enterprise is their next major focus [1]. I think this more than anything else will accelerate AI adoption in enterprise if well executed.

To your point, I think the industry needs to focus more on the toolchains that sit right between the deep learning frameworks (PyTorch, Tensorflow etc.) and hardware vendors (Nvidia, AMD, Intel, ARM, Google TPU etc.) Deep learning compilers will dictate if we allow all AI workloads run on just Nvidia or several other chips.

[1] - https://www.nvidia.com/en-us/data-center/solutions/confident...