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nVidia will fail right after reaching an 8 trillion valuation and supplying 80% of the world's hardware! Trust me guys, it's over!

doctorwho42 2mo ago
I think this is a great example of the disconnect people have in these types of conversations. You can both become a company that supplies 80% of the world with your type of product, and then still have your stock go down in value. All it takes is over evaluation by the stock market. Then a course correction from unsustained growth on growth (second order). So even if you continually replace YoY 80% of the world's hardware on a rotating business, but you don't increase market share or increase demand (aka growth)... Your business looks stagnant to the stock market, and there isn't really anything you can do about it. The best you can do is track inflation +/- 2%. And that's why a lot of older established companies were dividend stocks. You don't expect to growth anymore, but that's not where the value is anymore... The value is in the reliable sales that will happen after infinitum because your company controls a majority share of the business... And that's ok! Unfortunately, silicon valley has created a philosophy of 'you gotta expand into new fields or your on the decline' - aka neo-monopolization
acdha 2mo ago
Nobody is saying they’re outright failing, but that they’re not going to be printing money the way they have been recently. Think about Intel circa 2010: most of their competitors like POWER or MIPS were marginalized, they owned the desktop and server markets with a bit of competition from AMD well contained, and their biggest desktop competitor (Apple) had just switched. A lot of analyst predictions … did not match what happened next. The same was true of Cisco a decade earlier. Both companies are still there but they don’t set the terms in their market segments. I’m not predicting Nvidia will MBA themselves to death in the near future but I think there’s a tendency to overstate how profitable companies will stay. The more money Nvidia makes, the more motivated their competitors will be to get a piece of that market and the more customers will be looking for alternatives like the push into TPUs which the article discussed. The current administration is definitely corrupt enough that you could imagine an anti-competitive deal of some sort but I don’t think there’s a way for even that to change matters because key competitors are well-connected American companies willing to play that game, too.
- coredog64 2mo ago
  > The more money Nvidia makes, the more motivated their competitors will be to get a piece of that market and the more customers will be looking for alternatives like the push into TPUs which the article discussed. Your margin is my opportunity - Jeff Bezos
  - bigyabai 2mo ago
    But people have been saying this about CUDA for twenty years, and we are not any closer to a replacement GPGPU paradigm today. The root comment in this thread was about Nvidia hedging their bet on lost AI market share. They recognize that a reduced pace in training and inference competition will undercut their business, but CUDA isn't a one-trick pony for LLMs alone. TPUs are - you can't even reuse the same architecture for training and inference, they're separate ASICs unlike CUDA cores/ALUs. Veterans of crypto mining will tell you that the ASICs lost in the end, as Nvidia was evolving their hardware faster than the ASIC manufacturers could iterate. When the crypto acceleration landscape diversified away from ETH/BTC into altcoins, Nvidia was still there making money hand-over-fist from mining hardware. I guess you could argue that robotics, world models or computer vision won't be a trillion-dollar market. But Nvidia is positioned to be the first mover in all of these markets, and none of their competitors are even coming close to the integrated stack that they sell consumers.
    - coredog64 2mo ago
      > you can't even reuse the same architecture for training and inference AWS begs to differ. They originally split between `Trainium` and `Inferentia` but now support both with `Trainium`
      - bigyabai 2mo ago
        I stand corrected, only Google's post-Ironwood TPUs have the split as well. Nonetheless, TPU architectures are still a systolic array, and have their own limitations for scalability and flexibility. CUDA is no silver bullet, but it satisfies the demands of the edge and research customers very well.

doctorwho42 2mo ago

I think this is a great example of the disconnect people have in these types of conversations. You can both become a company that supplies 80% of the world with your type of product, and then still have your stock go down in value. All it takes is over evaluation by the stock market. Then a course correction from unsustained growth on growth (second order). So even if you continually replace YoY 80% of the world's hardware on a rotating business, but you don't increase market share or increase demand (aka growth)... Your business looks stagnant to the stock market, and there isn't really anything you can do about it. The best you can do is track inflation +/- 2%. And that's why a lot of older established companies were dividend stocks. You don't expect to growth anymore, but that's not where the value is anymore... The value is in the reliable sales that will happen after infinitum because your company controls a majority share of the business... And that's ok! Unfortunately, silicon valley has created a philosophy of 'you gotta expand into new fields or your on the decline' - aka neo-monopolization

acdha 2mo ago

Nobody is saying they’re outright failing, but that they’re not going to be printing money the way they have been recently. Think about Intel circa 2010: most of their competitors like POWER or MIPS were marginalized, they owned the desktop and server markets with a bit of competition from AMD well contained, and their biggest desktop competitor (Apple) had just switched. A lot of analyst predictions … did not match what happened next. The same was true of Cisco a decade earlier. Both companies are still there but they don’t set the terms in their market segments. I’m not predicting Nvidia will MBA themselves to death in the near future but I think there’s a tendency to overstate how profitable companies will stay. The more money Nvidia makes, the more motivated their competitors will be to get a piece of that market and the more customers will be looking for alternatives like the push into TPUs which the article discussed. The current administration is definitely corrupt enough that you could imagine an anti-competitive deal of some sort but I don’t think there’s a way for even that to change matters because key competitors are well-connected American companies willing to play that game, too.

coredog64 2mo ago
> The more money Nvidia makes, the more motivated their competitors will be to get a piece of that market and the more customers will be looking for alternatives like the push into TPUs which the article discussed. Your margin is my opportunity - Jeff Bezos
- bigyabai 2mo ago
  But people have been saying this about CUDA for twenty years, and we are not any closer to a replacement GPGPU paradigm today. The root comment in this thread was about Nvidia hedging their bet on lost AI market share. They recognize that a reduced pace in training and inference competition will undercut their business, but CUDA isn't a one-trick pony for LLMs alone. TPUs are - you can't even reuse the same architecture for training and inference, they're separate ASICs unlike CUDA cores/ALUs. Veterans of crypto mining will tell you that the ASICs lost in the end, as Nvidia was evolving their hardware faster than the ASIC manufacturers could iterate. When the crypto acceleration landscape diversified away from ETH/BTC into altcoins, Nvidia was still there making money hand-over-fist from mining hardware. I guess you could argue that robotics, world models or computer vision won't be a trillion-dollar market. But Nvidia is positioned to be the first mover in all of these markets, and none of their competitors are even coming close to the integrated stack that they sell consumers.
  - coredog64 2mo ago
    > you can't even reuse the same architecture for training and inference AWS begs to differ. They originally split between `Trainium` and `Inferentia` but now support both with `Trainium`
    - bigyabai 2mo ago
      I stand corrected, only Google's post-Ironwood TPUs have the split as well. Nonetheless, TPU architectures are still a systolic array, and have their own limitations for scalability and flexibility. CUDA is no silver bullet, but it satisfies the demands of the edge and research customers very well.