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I hear your argument, but short of major algorithmic breakthroughs I am not convinced the global demand for GPUs will drop any time soon. Of course I could easi

I hear your argument, but short of major algorithmic breakthroughs I am not convinced the global demand for GPUs will drop any time soon. Of course I could easily be wrong, but regardless I think the most predictable cause for a drop in the NVIDIA price would be that the CHIPS act/recent decisions by the CCP leads a Chinese firm to bring to market a CUDA compatible and reliable GPU at a fraction of the cost. It should be remembered that NVIDIA's /current/ value is based on their being locked out of their second largest market (China) with no investor expectation of that changing in the future. Given the current geopolitical landscape, in the hypothetical case where a Chinese firm markets such a chip we should expect that US firms would be prohibited from purchasing them, while it's less clear that Europeans or Saudis would be. Even so, if NVIDIA were not to lower their prices at all, US firms would be at a tremendous cost disadvantage while their competitors would no longer have one with respect to compute. All hypothetical, of course, but to me that's the most convincing bear case I've heard for NVIDIA.

coryrc 9mo ago
Not that locked out: https://www.cnbc.com/2025/12/31/160-million-export-controlled-nvidia-gpus-allegedly-smuggled-to-china.html https://www.cnbc.com/2025/12/31/160-million-export-controlle...
- deleted 9mo ago
  [deleted]
- agentcoops 8mo ago
  Yeah, that's the bull case for sure. Chinese firms might not accept training setbacks even given CCP regulations that they dogfood X homegrown chip.
iLoveOncall 9mo ago
> short of major algorithmic breakthroughs I am not convinced the global demand for GPUs will drop any time soon Or, you know, when LLMs don't pay off.
- selfhoster11 9mo ago
  They already are paying off. The nature of LLMs means that they will require expensive, fast hardware that's a large capex.
  - kortilla 9mo ago
    They aren’t yet because the big providers that paid for all of this GPU capacity aren’t profitable yet. They continually leap frog each other and shift around customers which indicates that the current capacity is already higher than what is required for what people actually pay for.
    - MrDarcy 9mo ago
      Google, Amazon, and Microsoft aren’t profitable?
      - notyourwork 9mo ago
        I assume the reference was AI use cases are not profitable. Those companies are subsidizing and OpenAI/grok are burning money.
        onion2k 9mo ago
        Google, Amazon, and Microsoft do a lot of things that aren't profitable in themselves. There is no reason to believe a company will kill a product line just because it makes a loss. There are plenty of other reasons to keep it running.
        notyourwork 8mo ago
        I didn't imply anything about what big-tech would do.
        lossyalgo 9mo ago
        Yeah but OpenAI is adding ads this year for the free versions, which I'm guessing is most of their users. They are probably hedging on taking a big slice of Google's advertising monopoly-pie (which is why Google is also now all-in on forcing Gemini opt-out on every product they own, they can see the writing on the wall).
reppap 9mo ago
People will want more GPUs but will they be able to fund them? At what points does the venture capital and loans run out? People will not keep pouring hundreds of billions into this if the returns don't start coming.
- gadflyinyoureye 9mo ago
  Money will be interesting the next few years. There is a real chance that the Japanese carry trade will close soon the BoJ seeing rates move up to 4%. This means liquidity will drain from the US markets back into Japan. On the US side there is going to be a lot of inflation between money printing, refund checks, amortization changes and a possible war footing. Who knows?
tracker1 9mo ago
Doesn't even necessarily need to be CUDA compatible... there's OpenCL and Vulkan as well, and likely China will throw enough resources at the problem to bring various libraries into closer alignment to ease of use/development. I do think China is still 3-5 years from being really competitive, but still even if they hit 40-50% of NVidia, depending on pricing and energy costs, it could still make significant inroads with legal pressure/bans, etc.
- bigyabai 9mo ago
  > there's OpenCL and Vulkan as well OpenCL is chronically undermaintained & undersupported, and Vulkan only covers a small subset of what CUDA does so far. Neither has the full support of the tech industry (though both are supported by Nvidia, ironically). It feels like nobody in the industry wants to beat Nvidia badly enough, yet. Apple and AMD are trying to supplement raster hardware with inference silicon; both of them are afraid to implement a holistic compute architecture a-la CUDA. Intel is reinventing the wheel with OneAPI, Microsoft is doing the same with ONNX, Google ships generic software and withholds their bespoke hardware, and Meta is asleep at the wheel. All of them hate each other, none of them trust Khronos anymore, and the value of a CUDA replacement has ballooned to the point that greed might be their only motivator. I've wanted a proper, industry-spanning CUDA competitor since high school. I'm beginning to realize it probably won't happen within my lifetime.
  - zozbot234 9mo ago
    The modern successor to OpenCL is SYCL and there's been some limited convergence with Vulkan Compute (they're still based on distinct programming models and even SPIR-V varieties under the hood, but the distance is narrowing somewhat).
    - pjmlp 8mo ago
      Which is basically Intel for practical purposes.
  - Balinares 9mo ago
laughing_man 9mo ago
I suspect major algorithmic breakthroughs would accelerate the demand for GPUs instead of making it fall off, since the cost to apply LLMs would go down.
- nroets 9mo ago
  Some changes to the algorithms and implementations will allow cheaper commodity hardware to be used.
  - Rover222 9mo ago
    There will always be an incentive to scale data centers. Better algorithms just mean more bang per gpu, not that “well, that’s enough now, we’ve done it”.
- MaxBarraclough 8mo ago
  Sounds like the Jevons paradox. From https://en.wiktionary.org/wiki/Jevons_paradox https://en.wiktionary.org/wiki/Jevons_paradox : > The proposition that technological progress that increases the efficiency with which a resource is used tends to increase (rather than decrease) the rate of consumption of that resource. See also Wikipedia: https://en.wikipedia.org/wiki/Jevons_paradox https://en.wikipedia.org/wiki/Jevons_paradox
kelseyfrog 8mo ago
Algorithmic breakthroughs (increases in efficiency) risk Jevons Paradox. More efficient processes make deploying them even more cost effective and increases demand.

coryrc 9mo ago

Not that locked out: https://www.cnbc.com/2025/12/31/160-million-export-controlled-nvidia-gpus-allegedly-smuggled-to-china.html https://www.cnbc.com/2025/12/31/160-million-export-controlle...

deleted 9mo ago
[deleted]
agentcoops 8mo ago
Yeah, that's the bull case for sure. Chinese firms might not accept training setbacks even given CCP regulations that they dogfood X homegrown chip.

iLoveOncall 9mo ago

> short of major algorithmic breakthroughs I am not convinced the global demand for GPUs will drop any time soon Or, you know, when LLMs don't pay off.

selfhoster11 9mo ago
They already are paying off. The nature of LLMs means that they will require expensive, fast hardware that's a large capex.
- kortilla 9mo ago
  They aren’t yet because the big providers that paid for all of this GPU capacity aren’t profitable yet. They continually leap frog each other and shift around customers which indicates that the current capacity is already higher than what is required for what people actually pay for.
  - MrDarcy 9mo ago
    Google, Amazon, and Microsoft aren’t profitable?
    - notyourwork 9mo ago
      I assume the reference was AI use cases are not profitable. Those companies are subsidizing and OpenAI/grok are burning money.
      - onion2k 9mo ago
        Google, Amazon, and Microsoft do a lot of things that aren't profitable in themselves. There is no reason to believe a company will kill a product line just because it makes a loss. There are plenty of other reasons to keep it running.
        notyourwork 8mo ago
        I didn't imply anything about what big-tech would do.
      - lossyalgo 9mo ago
        Yeah but OpenAI is adding ads this year for the free versions, which I'm guessing is most of their users. They are probably hedging on taking a big slice of Google's advertising monopoly-pie (which is why Google is also now all-in on forcing Gemini opt-out on every product they own, they can see the writing on the wall).

reppap 9mo ago

People will want more GPUs but will they be able to fund them? At what points does the venture capital and loans run out? People will not keep pouring hundreds of billions into this if the returns don't start coming.

gadflyinyoureye 9mo ago
Money will be interesting the next few years. There is a real chance that the Japanese carry trade will close soon the BoJ seeing rates move up to 4%. This means liquidity will drain from the US markets back into Japan. On the US side there is going to be a lot of inflation between money printing, refund checks, amortization changes and a possible war footing. Who knows?

tracker1 9mo ago

Doesn't even necessarily need to be CUDA compatible... there's OpenCL and Vulkan as well, and likely China will throw enough resources at the problem to bring various libraries into closer alignment to ease of use/development. I do think China is still 3-5 years from being really competitive, but still even if they hit 40-50% of NVidia, depending on pricing and energy costs, it could still make significant inroads with legal pressure/bans, etc.

bigyabai 9mo ago
> there's OpenCL and Vulkan as well OpenCL is chronically undermaintained & undersupported, and Vulkan only covers a small subset of what CUDA does so far. Neither has the full support of the tech industry (though both are supported by Nvidia, ironically). It feels like nobody in the industry wants to beat Nvidia badly enough, yet. Apple and AMD are trying to supplement raster hardware with inference silicon; both of them are afraid to implement a holistic compute architecture a-la CUDA. Intel is reinventing the wheel with OneAPI, Microsoft is doing the same with ONNX, Google ships generic software and withholds their bespoke hardware, and Meta is asleep at the wheel. All of them hate each other, none of them trust Khronos anymore, and the value of a CUDA replacement has ballooned to the point that greed might be their only motivator. I've wanted a proper, industry-spanning CUDA competitor since high school. I'm beginning to realize it probably won't happen within my lifetime.
- zozbot234 9mo ago
  The modern successor to OpenCL is SYCL and there's been some limited convergence with Vulkan Compute (they're still based on distinct programming models and even SPIR-V varieties under the hood, but the distance is narrowing somewhat).
  - pjmlp 8mo ago
    Which is basically Intel for practical purposes.
- Balinares 9mo ago

laughing_man 9mo ago

I suspect major algorithmic breakthroughs would accelerate the demand for GPUs instead of making it fall off, since the cost to apply LLMs would go down.

nroets 9mo ago
Some changes to the algorithms and implementations will allow cheaper commodity hardware to be used.
- Rover222 9mo ago
  There will always be an incentive to scale data centers. Better algorithms just mean more bang per gpu, not that “well, that’s enough now, we’ve done it”.
MaxBarraclough 8mo ago
Sounds like the Jevons paradox. From https://en.wiktionary.org/wiki/Jevons_paradox https://en.wiktionary.org/wiki/Jevons_paradox : > The proposition that technological progress that increases the efficiency with which a resource is used tends to increase (rather than decrease) the rate of consumption of that resource. See also Wikipedia: https://en.wikipedia.org/wiki/Jevons_paradox https://en.wikipedia.org/wiki/Jevons_paradox

kelseyfrog 8mo ago

Algorithmic breakthroughs (increases in efficiency) risk Jevons Paradox. More efficient processes make deploying them even more cost effective and increases demand.