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Sold after the price rise from this announcement. They can make the sales projection, but it doesn't mean they'll hit it: 1. Small models are rapidly growing i

by drbscl 1mo ago

Sold after the price rise from this announcement. They can make the sales projection, but it doesn't mean they'll hit it: 1. Small models are rapidly growing in capability, require less compute to train and serve 2. There are more suppliers now, both in China & the US (OpenAI even have their own inferencing hardware now) 3. Memory still constrains how much they can ship in the short term

shubhamjain 1mo ago
Every quarter I see a similar analysis, similar projection. Yet, they keep posting these insane numbers. Everyone knows it’s a bubble, the problem is determining the top. Nvidia is continuously showing the top is far far higher than everyone imagines.
- tuesdaynight 1mo ago
  I don't get why people say Nvidia is a bubble. They are selling products now, not in the future! If AI market collapses (I doubt it will happen), they will still be selling GPUs. They will make less money, but that is expected
  - manquer 1mo ago
    Bubble means inflated not fake, i.e when they make less money their stock will crash and bubble will pop, which is what people buying the stock today are concerned with , is this going to hold
  - order-matters 1mo ago
    the bubble doesnt mean they are worthless only that after a pop the value of stock will drop significantly. it is out of their control if people overvalue the stock, and thats what creates the bubble which will eventually need to pop for self correction - but might trigger a massive oversale bringing the stock below actual value and causing all sorts of problems that will challenge the solvency of the company (basically challenging their liquid funds vs how much debt that they backed to their stock value). if they survive that then a bounce back is expected and buying while they were low would get you profit again. if they overleveraged themselves during the bubble bc they bought into the hype themselves, then they could face serious financial troubles and be susceptible to getting bought out.
    - tuesdaynight 1mo ago
      But isn't that supposed to happen when you create a hit product? I imagine some people said similar things about Apple when iPhone started selling like water, but I don't think that it was the majority like here
zozbot234 1mo ago
> Small models are rapidly growing in capability, require less compute to train and serve According to Jevons' paradox a reduction in resource requirements (improved resource efficiency for the same payoff) leads to an increase in demand. This stops working when demand for compute is completely exhausted, but we are very far from that. There's even some very silly predictions floating around (see the latest Dwarkesh Patel podcast) that say compute will soon be most of the economy, even dictating market interest rates. Now, that has to be wrong, but the directional outlook is closer to correct than "very small and efficient models mean there will be ~0 demand for HPC-like compute".
- kemiller 1mo ago
  Yeah, I think there's a tendency to underestimate how much demand is still gated behind cost constraints. The market for this is HUGE. The PC era, call it 1975-2005, was one of the greatest wealth creation events in history, was characterized by the cost of the underlying commodity dropping mercilessly for the whole time. Each time it did, the space of problem you could solve with a PC would increase, to the point that by the end, they were both replacing mainframes and powering users who do nothing but chat and post cat pictures. Could there be a correction in the short run? Quite possibly. I think an underestimated last mile problem is just the massive weight of bureaucracy and human process inertia. But in the long run, cheap, efficient intelligence is a new engineering capability that we've just begun to even explore.
  - GiorgioG 1mo ago
    > The market for this is HUGE. Source(s)?
    - formerly_proven 1mo ago
      The backlog of every software team on the planet being anywhere between 1 and 100 years long at human burn rates.
  - minraws 1mo ago
    > how much demand is still gated behind cost constraints. The market for this is HUGE. I think this misses the actual limits here. The problem isn't demand it's, "how much people are willing to spend on it". Cheap AI has to be served on cheap compute, and if inference gets cheap enough to unlock massive usage numbers, by definition it also doesn't require anywhere near as much infrastructure per unit of demand. Take DeepSeek serving ~100T tokens/day, depending on workload and utilization, you're potentially talking about only a few thousand last-gen GPUs. With current-gen GPUs maybe closer to ~1,000, and with Rubin even fewer I will be damned if I could get my hands on one. That's the part I think people are missing when they extrapolate token demand into enormous infrastructure or AI revenue. Yes usage will explode. But if the cost per unit collapses, the revenue doesn't necessarily go up with it. You can't simultaneously argue that intelligence becomes so cheap that everyone uses enormous amounts of it, while also assuming customers will somehow spend trillions of dollars a year consuming it. There is no obvious $1T customer-facing AI revenue number at the end of this rainbow in the short/medium term. The average person isn't going to spend anything remotely comparable to what they spend on a car every year for an AI service. Even businesses have budgets now, huge demand doesn't matter if the willingness to pay isn't there. The only path I can see to numbers like that is AI consuming existing business domains, even then it's very thin. Say SaaS + legal + consulting + BPO + various other service industries collectively represent something like $10-20T globally. Even if AI eventually replaces an enormous portion of that, it's probably not doing so at the same price. Why would customers switch otherwise? Either the AI product has to be dramatically better, which is difficult for mature workflows, or dramatically cheaper which is much more plausible. If it replaces $10-20T of existing services at roughly 1/10th or 1/100th (more likely) the cost, then you're looking at maybe a ~$1T AI revenue opportunity after replacing an absurdly large fraction of the existing service economy. Who are now unemployed and can't pay for shit. And that's before competition. I think it's crazy to assume AI companies won't compete aggressively on price. As capabilities diffuse, smaller models catch up, inference hits pareto frontier the open-source alternatives have already improved and caught up, margins on routine intelligence should compress "hard" (emphasis on "hard"). We've already seen how difficult adoption can be even when the technology looks impressive on paper. Cheap here means 100x cheaper for 10x more demand that's a net 10x loss before any software or hardware optimizations. So yes, I completely agree that cheap intelligence can bring an enormous amount of new usage. "I just don't think usage means revenue." (you can plaster it on a wall if you want to, "usage doesn't mean revenue", if you want to find that out I have foss software bridge to sell) The PC analogy actually reinforces this if you really think about it. Compute became "vastly more useful" while the cost per unit of compute collapsed. Society captured enormous value, but all computer companies are literal failing giants without the AI hype. Value got caught by people who provided productionization. Now if people expect AI to self productize itself I am happy to tell your try it. We all saw how OpenAI fell behind Anthropic because they thought that would work... Google couldn't productize the search, instead they sold the eye balls and web-real-estate. Maybe that's the AI business model, but that's not $1T worth given you need to unglue people from other stuff. Unless we get something approaching genuine ASI producing so much additional economic value that entirely new trillions, I don't see a path to $1-2T in direct AI revenue from customers. The market simply can't absorb that level of spending. Demand can be effectively infinite at the right price. But I think people are delusional on HN and SF if they think that number is in Trillions like the investments seem to suggest. I am not saying Nvidia will fall tomorrow but someone will have to pull the breaks before this car goes to hell.
SV_BubbleTime 1mo ago
>Small models are rapidly growing in capability, require less compute to train and serve Must be very clear that China’s undercut strategy, which is a well-known and studied tactic that they’ve used for a long time, it is absolutely dominating this point. Right now you can LLM, code, make songs, images, and esp video on gaming hardware in your PC that would’ve been absolutely datacenter shit last year. So the question will be does the scaling continue to benefit efficiency or ability? If ability (needs datacenter storage and performance), how much better can the code get? How much more realistic in the images videos get? There are definitely strides to be made everywhere, but man, just like the bottleneck wasn’t coding, I’m not sure the creation bottleneck is rendering.
keeda 1mo ago
Counterpoints: 1. Depending on the data source you look at, about 50 - 60% of people use AI at work but only for 5 - 15% of work hours. That leaves about 2x (from users) times 7 - 20x (from work hours) for growth. Furthermore agentic usage is much more token-intensive than regular prompts, that's another unknown multiple that will get applied. Small models will make a dent for sure, but even they need to run on hardware. It's not clear how much their lower resource requirements will cancel out the scope for growth, but I think it will take time for that dynamic to play out; people are only just starting to ease up on tokenmaxxing. Anthropic revenues would be the canary in the coalmine, and thankfully they'll be IPO'ing soon. 2. All the relevant fabs (mainly, TSMC) are extremely capacity-constrained, so who actually gets the chips depends on who has the best vendor relationships... and who can pay the most for them. Even Apple, famed for its supply chain mastery, is having trouble these days. I would assume TSMC will try to keep all its customers happy but will prioritize supplying the customer that will pay it the most money, and these days that's Nvidia. Simply because that's where ~all the AI boom money is flowing. Heck, you could even imagine some form of revenue share to keep the spice errr chips flowing... 3. Memory constraints affect all vendors, they will just pass those costs on to customers, like Nvidia with its recent 15% price bump. Notably the bump was announced BEFORE the earnings; I wonder if the effects of that was reflected in these projections. Nvidia is in the same position with acquiring chip supply that Google is with acquiring search traffic: monopoly profits shared with suppliers make it very hard for other companies to compete.

by drbscl 1mo ago

shubhamjain 1mo ago

Every quarter I see a similar analysis, similar projection. Yet, they keep posting these insane numbers. Everyone knows it’s a bubble, the problem is determining the top. Nvidia is continuously showing the top is far far higher than everyone imagines.

tuesdaynight 1mo ago
I don't get why people say Nvidia is a bubble. They are selling products now, not in the future! If AI market collapses (I doubt it will happen), they will still be selling GPUs. They will make less money, but that is expected
- manquer 1mo ago
  Bubble means inflated not fake, i.e when they make less money their stock will crash and bubble will pop, which is what people buying the stock today are concerned with , is this going to hold
- order-matters 1mo ago
  the bubble doesnt mean they are worthless only that after a pop the value of stock will drop significantly. it is out of their control if people overvalue the stock, and thats what creates the bubble which will eventually need to pop for self correction - but might trigger a massive oversale bringing the stock below actual value and causing all sorts of problems that will challenge the solvency of the company (basically challenging their liquid funds vs how much debt that they backed to their stock value). if they survive that then a bounce back is expected and buying while they were low would get you profit again. if they overleveraged themselves during the bubble bc they bought into the hype themselves, then they could face serious financial troubles and be susceptible to getting bought out.
  - tuesdaynight 1mo ago
    But isn't that supposed to happen when you create a hit product? I imagine some people said similar things about Apple when iPhone started selling like water, but I don't think that it was the majority like here

zozbot234 1mo ago

> Small models are rapidly growing in capability, require less compute to train and serve According to Jevons' paradox a reduction in resource requirements (improved resource efficiency for the same payoff) leads to an increase in demand. This stops working when demand for compute is completely exhausted, but we are very far from that. There's even some very silly predictions floating around (see the latest Dwarkesh Patel podcast) that say compute will soon be most of the economy, even dictating market interest rates. Now, that has to be wrong, but the directional outlook is closer to correct than "very small and efficient models mean there will be ~0 demand for HPC-like compute".

kemiller 1mo ago
Yeah, I think there's a tendency to underestimate how much demand is still gated behind cost constraints. The market for this is HUGE. The PC era, call it 1975-2005, was one of the greatest wealth creation events in history, was characterized by the cost of the underlying commodity dropping mercilessly for the whole time. Each time it did, the space of problem you could solve with a PC would increase, to the point that by the end, they were both replacing mainframes and powering users who do nothing but chat and post cat pictures. Could there be a correction in the short run? Quite possibly. I think an underestimated last mile problem is just the massive weight of bureaucracy and human process inertia. But in the long run, cheap, efficient intelligence is a new engineering capability that we've just begun to even explore.
- GiorgioG 1mo ago
  > The market for this is HUGE. Source(s)?
  - formerly_proven 1mo ago
    The backlog of every software team on the planet being anywhere between 1 and 100 years long at human burn rates.
- minraws 1mo ago
  > how much demand is still gated behind cost constraints. The market for this is HUGE. I think this misses the actual limits here. The problem isn't demand it's, "how much people are willing to spend on it". Cheap AI has to be served on cheap compute, and if inference gets cheap enough to unlock massive usage numbers, by definition it also doesn't require anywhere near as much infrastructure per unit of demand. Take DeepSeek serving ~100T tokens/day, depending on workload and utilization, you're potentially talking about only a few thousand last-gen GPUs. With current-gen GPUs maybe closer to ~1,000, and with Rubin even fewer I will be damned if I could get my hands on one. That's the part I think people are missing when they extrapolate token demand into enormous infrastructure or AI revenue. Yes usage will explode. But if the cost per unit collapses, the revenue doesn't necessarily go up with it. You can't simultaneously argue that intelligence becomes so cheap that everyone uses enormous amounts of it, while also assuming customers will somehow spend trillions of dollars a year consuming it. There is no obvious $1T customer-facing AI revenue number at the end of this rainbow in the short/medium term. The average person isn't going to spend anything remotely comparable to what they spend on a car every year for an AI service. Even businesses have budgets now, huge demand doesn't matter if the willingness to pay isn't there. The only path I can see to numbers like that is AI consuming existing business domains, even then it's very thin. Say SaaS + legal + consulting + BPO + various other service industries collectively represent something like $10-20T globally. Even if AI eventually replaces an enormous portion of that, it's probably not doing so at the same price. Why would customers switch otherwise? Either the AI product has to be dramatically better, which is difficult for mature workflows, or dramatically cheaper which is much more plausible. If it replaces $10-20T of existing services at roughly 1/10th or 1/100th (more likely) the cost, then you're looking at maybe a ~$1T AI revenue opportunity after replacing an absurdly large fraction of the existing service economy. Who are now unemployed and can't pay for shit. And that's before competition. I think it's crazy to assume AI companies won't compete aggressively on price. As capabilities diffuse, smaller models catch up, inference hits pareto frontier the open-source alternatives have already improved and caught up, margins on routine intelligence should compress "hard" (emphasis on "hard"). We've already seen how difficult adoption can be even when the technology looks impressive on paper. Cheap here means 100x cheaper for 10x more demand that's a net 10x loss before any software or hardware optimizations. So yes, I completely agree that cheap intelligence can bring an enormous amount of new usage. "I just don't think usage means revenue." (you can plaster it on a wall if you want to, "usage doesn't mean revenue", if you want to find that out I have foss software bridge to sell) The PC analogy actually reinforces this if you really think about it. Compute became "vastly more useful" while the cost per unit of compute collapsed. Society captured enormous value, but all computer companies are literal failing giants without the AI hype. Value got caught by people who provided productionization. Now if people expect AI to self productize itself I am happy to tell your try it. We all saw how OpenAI fell behind Anthropic because they thought that would work... Google couldn't productize the search, instead they sold the eye balls and web-real-estate. Maybe that's the AI business model, but that's not $1T worth given you need to unglue people from other stuff. Unless we get something approaching genuine ASI producing so much additional economic value that entirely new trillions, I don't see a path to $1-2T in direct AI revenue from customers. The market simply can't absorb that level of spending. Demand can be effectively infinite at the right price. But I think people are delusional on HN and SF if they think that number is in Trillions like the investments seem to suggest. I am not saying Nvidia will fall tomorrow but someone will have to pull the breaks before this car goes to hell.

SV_BubbleTime 1mo ago

>Small models are rapidly growing in capability, require less compute to train and serve Must be very clear that China’s undercut strategy, which is a well-known and studied tactic that they’ve used for a long time, it is absolutely dominating this point. Right now you can LLM, code, make songs, images, and esp video on gaming hardware in your PC that would’ve been absolutely datacenter shit last year. So the question will be does the scaling continue to benefit efficiency or ability? If ability (needs datacenter storage and performance), how much better can the code get? How much more realistic in the images videos get? There are definitely strides to be made everywhere, but man, just like the bottleneck wasn’t coding, I’m not sure the creation bottleneck is rendering.

keeda 1mo ago

Counterpoints: 1. Depending on the data source you look at, about 50 - 60% of people use AI at work but only for 5 - 15% of work hours. That leaves about 2x (from users) times 7 - 20x (from work hours) for growth. Furthermore agentic usage is much more token-intensive than regular prompts, that's another unknown multiple that will get applied. Small models will make a dent for sure, but even they need to run on hardware. It's not clear how much their lower resource requirements will cancel out the scope for growth, but I think it will take time for that dynamic to play out; people are only just starting to ease up on tokenmaxxing. Anthropic revenues would be the canary in the coalmine, and thankfully they'll be IPO'ing soon. 2. All the relevant fabs (mainly, TSMC) are extremely capacity-constrained, so who actually gets the chips depends on who has the best vendor relationships... and who can pay the most for them. Even Apple, famed for its supply chain mastery, is having trouble these days. I would assume TSMC will try to keep all its customers happy but will prioritize supplying the customer that will pay it the most money, and these days that's Nvidia. Simply because that's where ~all the AI boom money is flowing. Heck, you could even imagine some form of revenue share to keep the spice errr chips flowing... 3. Memory constraints affect all vendors, they will just pass those costs on to customers, like Nvidia with its recent 15% price bump. Notably the bump was announced BEFORE the earnings; I wonder if the effects of that was reflected in these projections. Nvidia is in the same position with acquiring chip supply that Google is with acquiring search traffic: monopoly profits shared with suppliers make it very hard for other companies to compete.