I've been nothing but unfailingly polite to you, and I'm getting tired that you repeatedly resort to name-calling and insults when you encounter an opposing opinion.

> But DeepMind is Google and rather dumb comment, no offense. How old are you?

Why are you getting all worked up? That's not an insult to Google, simply pointing out that their own organically grown corporate org (including Brain) was not adequate to do the cutting-edge research they felt they needed.

> AI is not a secretive area. So if Nvidia had something we would know. On the lack of patents puts them in a very weak position. Especially with SDC.

I disagree. Think about this the other way; if some company was quietly plugging away with large AI advances and deciding not to publish them, how would you even know? My evaluation of Nvidia's technology is based on their public presentations and the products that have already been released—products that every single AI practitioner on the planet buy and use, plus the 150+ car ecosystem partners that have decided to go with Nvidia's driving platform [1].

People who are far more deeply enmeshed in this technology than you or I have voted with their feet and decided to build their core competency for the next 5+ years on Nvidia's platform, while Waymo has maybe 2-3 major automotive partners?

> Google lead in AI is much, much larger than any of Apple. Heck Apple market share is about 14% and Google with Android has over 80% market share.

Bottom line, I definitely agree that Google is an AI leader, but I do not believe that the AI future will be run on TPUs, for the simple reason that chipmaking is a risky, expensive endeavour, and Nvidia has much more expertise than Google does in that regard, while having access to a larger partnership, ecosystem, and its own set of data and engineering.

Put it this way, the actual chipmaking stack is more important than the data stack when it comes to making chips. Just think about it—let's say you've run your thousands of NNs to benchmark the workloads on the TPU, and it turns out that CPU-TPU and TPU-TPU bandwidth is the real bottleneck. What do you do as Google? They have no expertise in building interconnects and scaling them, while Nvidia does.

Data only gets you so far, you still need to be able to do the semiconductor engineering + create partnerships, and in that regard Nvidia is light-years ahead.

To belabor the point, if the goal is to make chips, then being good at chipmaking is very important, and Nvidia is closer to Google in data, than Google is to Nvidia in chipmaking.

I will bet you that 2 years down the line, Nvidia will have abandoned its own TPU project and all major players just buying Nvidia chips, both for inferencing and training.

This is exactly the role Intel plays today in CPUs, and it's both natural and reasonable, and the largest reason is because of structural market factors, which you have never even responded to.

Google's cloud is a fraction of the size of AWS and Azure's — that means Nvidia makes far more money from Voltas than Google will ever save on the TPUs, and plough that right back into additional R&D. Business people demand a positive ROI. Where will the positive ROI from a TPU come from?

Google is and will be an AI leader. But it will certainly not be doing its own chips.

[1] https://www.nvidia.com/en-us/self-driving-cars/partners/