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Comment by mjr00 | Hacker News Reader

> Because if Huang is removed it's very likely they will face challenges finding and reacting the next big use-case for GPUs

Huang has done a fine job managing NVidia, but he's also the poster child for "better lucky than good."

It's not like he led NVidia out of dire straits to pivot into the cryptocurrency/blockchain, and then AI, bubbles. They were making hardware for computer graphics, and that just so happened to be the same foundational hardware for the two biggest technology bubbles of the 21st century, twice in a row.

I find it hard to believe NVidia's incredible success can be attributed exclusively to him. Sometimes you're just in the right place at the right time.

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They’ve been in the right place at the right time too many times.

It’s a bit like when you watch a fantastic FPS player play, they just always seem to be in the right place to catch the other guy off guard.

Huang didn’t know exactly what was coming - but he knew enough and managed to put NV into a position where it could quickly exploit whatever did emerge.

> twice in a row

If I ever hit a home run in baseball, it would be blind luck. If I did it twice, nah, that would be incredibly unlikely, as I have zero talent at any ball sports.

Gates, Jobs, and Musk - each had a home run 3 times. That is so unlikely to be happenstance, ya gotta admit they had talent.

I'm not a fan of Jensen by any means, but he did start the CUDA program some eight years before it was actually useful (the alexnet moment). He called the need for high performance linear algebra for scientific computing, it just took a while for it to find its first killer app.

That was becoming a hot topic at the time prior to DL getting on the map but it was smart to recognize the latent demand for GPGPU. Was still going to lead to solid growth with AI was just not going to be the exponential growth we saw if it didn't happen.

CUDA was a formalization of what happened outside of NV.

https://en.wikipedia.org/wiki/CUDA#History

CUDA was well-used in HPC and other compute-intensive spaces long before neural nets were in style.

If you look at the evolution of GPGPU computing, at least from an HPC perspective, CUDA is almost an obvious step. What’s perplexing is that AMD and Intel let it get so far ahead in the late 2000s and early 2010s.

Meanwhile intel (as but one prominent example) was also researching and manufacturing GPUs for most (all?) of that timeline yet somehow managed to fumble it. Quite often IMO simply not screwing up while already holding all of the necessary cards is a lot harder than it looks.

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