6 ms·

Jevon's paradox would imply that there's good reason to think that demand for shovels will increase. AI doesn't seem to be one of those things where society as

by elihu 2y ago

Jevon's paradox would imply that there's good reason to think that demand for shovels will increase. AI doesn't seem to be one of those things where society as a whole will say, "we have enough of that; we don't need any more". (Many individual people are already saying that, but they aren't the people buying the GPUs for this in the first place. Steam engines weren't universally popular either when they were introduced to society.)

int_19h 2y ago
The other thing is that if this pushes the envelope further on what AI models can do given a certain hardware budget, this might actually change minds. The pushback against generative AI today is that much of it is deployed in ways that are ultimately useless and annoying at best, and that in turn is because the capabilities of those models are vastly oversold (including internally in companies that ship products with them). But if a smarter model can actually e.g. reliably organize my email, that's a very different story.
- llm_trw 2y ago
  An rag model can already sort your email. Its just that it costs too much to do that for the hoi polloi who think everything digital should be free forever.
  - int_19h 2y ago
    It can do it if you're okay with hallucinations. Which is generally not the case.
jcgrillo 2y ago
> AI doesn't seem to be one of those things where society as a whole will say, "we have enough of that; we don't need any more". Really? Has anyone made a useful, commercially successful product with it yet?
- mrbungie 2y ago
  Not even Microsoft Copilot 365 had a successful launch. The same happened with Apple Intelligence. People talk like the end user demand part of the equation is really solved when invoking an Econ 101 magical interpretation of the Law of Supply and Demand or Jevons Padadox.
  - dmix 2y ago
    Apple Intelligence has barely released what they announced is coming Deep integration into iOS won't be tacked on in a rush to market addition to OS.
    - jcgrillo 2y ago
      I sure hope not, because what they've released so far is worse than useless. The "notifications summaries" in particular are hilariously bad. It's the same problem with Google's "AI Overview"--wrong or misleading enough that you simply can't trust any of it. But to be clear, none of these things are commercial products. They're gimmicks. Google is an ads company, they make their money selling ads. Apple is a computer company, they make their money selling computers. These "AI products" are a circus sideshow.
- roenxi 2y ago
  Yeah, lots of them. I never thought I'd be paying for a search subscription but after a few months of using ChatGPT I expect to be paying for the privilege from now on. Maybe not paying OpenAI, but someone. There isn't much of a moat there, so there are going to be many companies basically on-selling GPU time. And even if for some weird reason there is no commercially successful AI-specific product it is causing shockwaves in how work is done, most people I know who are effective have worked it into their workflow somehow.
- trhway 2y ago
  >Really? Has anyone made a useful, commercially successful product with it yet? Aren't millions or even tens of millions students using ChatGPT for example? To me that sounds like a commercial success (and looks comparable with the usage of Google Search - a money printing machine for more almost 30 years now - in the first years) And enterprise-wise - heard recently a VP complaining about entering expenses. As we don't have secretaries anymore in the civilized world, that means "Agents AI" is going to have a blast. (i'm long on NVDA and wondering is it enough blood on the streets to buy more :)
puppymaster 2y ago
I also dont get how this is bearish for NVDA. Before this, small to mid companies would give up on finetuning their own model because openai is just so much better and cheaper. Now deepseek SOTA model gives them much better quality baseline model to train on. Wouldn't more people want to RAG on top of deepseek? or some startups accountant would run the numbers and figures we can just inference the shit out of deepseek locally and in the long run we still come out ahead of using oenai api. Either way that means a lot more NVDA hardware being sold. You still need CUDAs as rocm is still not there yet. In fact NVDA needs to churn out more CUDAs than ever.
- ip26 2y ago
  Not sure if I believe it’s bearish, but the PC made computers cheaper and demand exploded, yet wasn’t very good for IBM.
  - woah 2y ago
    It could have been great for IBM if they had done things differently
    - dralley 2y ago
      IBM probably couldn't have done things differently given the antitrust scrutiny they were under. And that was likely the best outcome for the industry anyway.
      - protocolture 2y ago
        Agree completely. I would hate to have seen what would have happened if IBM could legally prevent the clean room reimplementation of their bios.
- theptip 2y ago
  The bear case is something like “investors are going to call BS on multi-billion training DC investments”. That represents most of their short-run demand. Not sure what is supposed to happen to the inference demand but I guess that could be modeled as more of a long-run thing, as inference is going to be very coin-operated (companies need it to be net profitable now) whereas training is more of a build now profit later game.
fabfoe 2y ago
Apparently it’s just Jevons, not possessive.
wisty 2y ago
Yes, a global market of 5 big LLM (ChatGTP, Llama, Claude, Mistral, Qwen ... any other big ones?) is not exactly good for Nvidia. If every well funded start-up can have a shot, then they buy more GPUs and the big players will need to buy even more to stay noticeably ahead.
flashman 2y ago
Jevons was talking about coal as an input to commercial processes, for which there were other alternatives that competed on price (e.g. manual/animal labour). Whatever the process, it generated a return, it had utility, and it had scale. I argue it doesn't apply to generative AI because its outputs are mostly no good, have no utility, or are good but only in limited commercial contexts. In the first case, a machine that produces garbage faster and cheaper doesn't mean demand for the garbage will increase. And in the second case, there aren't enough buyers for high-quality computer-generated pictures of toilets to meaningfully boost demand for Nvidia's products.
- hnaccount_rng 2y ago
  I recently had a discussion with a higher ranked executive and his take on AI changed my outlook a bit. For him the value of ChatGPT:tm: wasn't so much the speed up in any particular task (like presentation generation or so). It's a replacement for consultants. Yes, the value of those only exists mostly if your internal team is too stubborn to change its opinion. But that seems to be the norm. And the value (those) consultants add is not that high in the first place! They don't have the internal knowledge of _why_ things are fucked up _your particular way_ anyways. That part your team has to contribute anyhow. So the value add shrinks to "throw ideas over the wall and see what sticks". And LLMs are excellent at that. Yes, that doesn't replace a highly technical consultant that does the actual implementation. Yes, that doesn't give you a good solution. But it probably gives you 5 starting points for a solution before you even finish googling which consultancy to pick (and then waiting for approval and hoping for a goodish team). And that's a story that I can map to reality (not that I like this new bit of information about reality..) If we accept that story about LLM value, then I think NVIDIA is fine. That generated value is far greater than any amount of energy you can burn on inferring prompts and the only effect will be that the compute-for-training to compute-for-inference ratio decreases further
  - cryptonym 2y ago
    "Throw ideas and see what sticks" sounds very entry-level. Maybe it saves time it would take for one of your team to read first two chapters of a book on the topic. That exec was hiring consultant and no longer is, in meaningful proportion, thanks to LLM?
mlyle 2y ago
When you get more marginal product from an input, it's expected you buy more of that input. But at some point, if the marginal product gets high enough, the world needs not as many, because money spent on other inputs/factors pays off more. This is a classic problem with extrapolation. Making people more efficient through the use of AI will tend to increase employment... until it doesn't and employment goes off a cliff. Getting more work done per unit of GPU will increase demand for GPUs ... until it doesn't, and GPU demand goes off the cliff. It's always hard to tell where that cliff is, though.
- jjk166 2y ago
  If there's one thing I doubt the world will have a glut of anytime soon, it's intelligence.

by elihu 2y ago

int_19h 2y ago

The other thing is that if this pushes the envelope further on what AI models can do given a certain hardware budget, this might actually change minds. The pushback against generative AI today is that much of it is deployed in ways that are ultimately useless and annoying at best, and that in turn is because the capabilities of those models are vastly oversold (including internally in companies that ship products with them). But if a smarter model can actually e.g. reliably organize my email, that's a very different story.

llm_trw 2y ago
An rag model can already sort your email. Its just that it costs too much to do that for the hoi polloi who think everything digital should be free forever.
- int_19h 2y ago
  It can do it if you're okay with hallucinations. Which is generally not the case.

jcgrillo 2y ago

> AI doesn't seem to be one of those things where society as a whole will say, "we have enough of that; we don't need any more". Really? Has anyone made a useful, commercially successful product with it yet?

mrbungie 2y ago
Not even Microsoft Copilot 365 had a successful launch. The same happened with Apple Intelligence. People talk like the end user demand part of the equation is really solved when invoking an Econ 101 magical interpretation of the Law of Supply and Demand or Jevons Padadox.
- dmix 2y ago
  Apple Intelligence has barely released what they announced is coming Deep integration into iOS won't be tacked on in a rush to market addition to OS.
  - jcgrillo 2y ago
    I sure hope not, because what they've released so far is worse than useless. The "notifications summaries" in particular are hilariously bad. It's the same problem with Google's "AI Overview"--wrong or misleading enough that you simply can't trust any of it. But to be clear, none of these things are commercial products. They're gimmicks. Google is an ads company, they make their money selling ads. Apple is a computer company, they make their money selling computers. These "AI products" are a circus sideshow.
roenxi 2y ago
Yeah, lots of them. I never thought I'd be paying for a search subscription but after a few months of using ChatGPT I expect to be paying for the privilege from now on. Maybe not paying OpenAI, but someone. There isn't much of a moat there, so there are going to be many companies basically on-selling GPU time. And even if for some weird reason there is no commercially successful AI-specific product it is causing shockwaves in how work is done, most people I know who are effective have worked it into their workflow somehow.
trhway 2y ago
>Really? Has anyone made a useful, commercially successful product with it yet? Aren't millions or even tens of millions students using ChatGPT for example? To me that sounds like a commercial success (and looks comparable with the usage of Google Search - a money printing machine for more almost 30 years now - in the first years) And enterprise-wise - heard recently a VP complaining about entering expenses. As we don't have secretaries anymore in the civilized world, that means "Agents AI" is going to have a blast. (i'm long on NVDA and wondering is it enough blood on the streets to buy more :)

puppymaster 2y ago

I also dont get how this is bearish for NVDA. Before this, small to mid companies would give up on finetuning their own model because openai is just so much better and cheaper. Now deepseek SOTA model gives them much better quality baseline model to train on. Wouldn't more people want to RAG on top of deepseek? or some startups accountant would run the numbers and figures we can just inference the shit out of deepseek locally and in the long run we still come out ahead of using oenai api. Either way that means a lot more NVDA hardware being sold. You still need CUDAs as rocm is still not there yet. In fact NVDA needs to churn out more CUDAs than ever.

ip26 2y ago
Not sure if I believe it’s bearish, but the PC made computers cheaper and demand exploded, yet wasn’t very good for IBM.
- woah 2y ago
  It could have been great for IBM if they had done things differently
  - dralley 2y ago
    IBM probably couldn't have done things differently given the antitrust scrutiny they were under. And that was likely the best outcome for the industry anyway.
    - protocolture 2y ago
      Agree completely. I would hate to have seen what would have happened if IBM could legally prevent the clean room reimplementation of their bios.
theptip 2y ago
The bear case is something like “investors are going to call BS on multi-billion training DC investments”. That represents most of their short-run demand. Not sure what is supposed to happen to the inference demand but I guess that could be modeled as more of a long-run thing, as inference is going to be very coin-operated (companies need it to be net profitable now) whereas training is more of a build now profit later game.

fabfoe 2y ago

Apparently it’s just Jevons, not possessive.

wisty 2y ago

Yes, a global market of 5 big LLM (ChatGTP, Llama, Claude, Mistral, Qwen ... any other big ones?) is not exactly good for Nvidia. If every well funded start-up can have a shot, then they buy more GPUs and the big players will need to buy even more to stay noticeably ahead.

flashman 2y ago

Jevons was talking about coal as an input to commercial processes, for which there were other alternatives that competed on price (e.g. manual/animal labour). Whatever the process, it generated a return, it had utility, and it had scale. I argue it doesn't apply to generative AI because its outputs are mostly no good, have no utility, or are good but only in limited commercial contexts. In the first case, a machine that produces garbage faster and cheaper doesn't mean demand for the garbage will increase. And in the second case, there aren't enough buyers for high-quality computer-generated pictures of toilets to meaningfully boost demand for Nvidia's products.

hnaccount_rng 2y ago
I recently had a discussion with a higher ranked executive and his take on AI changed my outlook a bit. For him the value of ChatGPT:tm: wasn't so much the speed up in any particular task (like presentation generation or so). It's a replacement for consultants. Yes, the value of those only exists mostly if your internal team is too stubborn to change its opinion. But that seems to be the norm. And the value (those) consultants add is not that high in the first place! They don't have the internal knowledge of _why_ things are fucked up _your particular way_ anyways. That part your team has to contribute anyhow. So the value add shrinks to "throw ideas over the wall and see what sticks". And LLMs are excellent at that. Yes, that doesn't replace a highly technical consultant that does the actual implementation. Yes, that doesn't give you a good solution. But it probably gives you 5 starting points for a solution before you even finish googling which consultancy to pick (and then waiting for approval and hoping for a goodish team). And that's a story that I can map to reality (not that I like this new bit of information about reality..) If we accept that story about LLM value, then I think NVIDIA is fine. That generated value is far greater than any amount of energy you can burn on inferring prompts and the only effect will be that the compute-for-training to compute-for-inference ratio decreases further
- cryptonym 2y ago
  "Throw ideas and see what sticks" sounds very entry-level. Maybe it saves time it would take for one of your team to read first two chapters of a book on the topic. That exec was hiring consultant and no longer is, in meaningful proportion, thanks to LLM?

mlyle 2y ago

When you get more marginal product from an input, it's expected you buy more of that input. But at some point, if the marginal product gets high enough, the world needs not as many, because money spent on other inputs/factors pays off more. This is a classic problem with extrapolation. Making people more efficient through the use of AI will tend to increase employment... until it doesn't and employment goes off a cliff. Getting more work done per unit of GPU will increase demand for GPUs ... until it doesn't, and GPU demand goes off the cliff. It's always hard to tell where that cliff is, though.

jjk166 2y ago
If there's one thing I doubt the world will have a glut of anytime soon, it's intelligence.