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Yes but how much of that compute shortage is from demand that is subsidized? We’ve seen companies like Uber drastically cut how much they are willing to spend o

Yes but how much of that compute shortage is from demand that is subsidized? We’ve seen companies like Uber drastically cut how much they are willing to spend on AI because they are paying actual usage costs, while at the same time OpenAI and Anthropic increase the limits on their fixed cost plans for individuals meaning people not paying usage costs are using it more and more… doesn’t this show that the compute shortage is because OpenAI and Anthropic are paying for it, not their customers? And the moment OpenAI and Anthropic stop paying for it, demand will collapse.

rayiner 3mo ago
The compute demand is not fake. Non-coding industries have barely begun to deploy this technology. In the legal sector, I’ve been a tech pessimist my entire career, because it was uniformly quite bad. I’ve spent the last few months demoing legal tools backed by frontier models, and we’re definitely going to buy one of them. They’re real and they work and they address a bunch of needs.
- reticulates 3mo ago
  You’re talking across the issue. The demand is real because it is cheap. The demand is being generated by OpenAI and Anthropic selling inference below cost on fixed price plans. If everyone was paying the actual costs then demand would fall through the floor. The legal tools you’re looking at use barely any compute. They’re not driving the compute demand. You can validate this by asking how much they are spending on API usage. A company spending $100,000 per month on a frontier model via an API is the equivalent of… 10 or so OpenAI and Anthropic fixed price plan customers. Are these legal tools spending hundreds of millions per year on the frontier models?
  - dash2 3mo ago
    Compute would have to be very expensive not to be cheaper than a junior lawyer.
  - rayiner 3mo ago
    My point is that you’re projecting forward into the future where providers need to raise prices, but overlooking the demand that will arise when the rest of the economy starts using AI.
    - reticulates 3mo ago
      The entire economy could be entirely powered by AI and use less compute than is being used today. You're projecting the amount of compute used by coding while subsidized onto other industries, but that doesn't translate. Speak to some of these legal technology companies and ask them 2 questions: 1. How much is your AI spend on coding? 2. How much is your AI spend on AI within your product? The answer to #1 will dwarf #2 by orders of magnitude. And that's now, when these companies are still finding their feet, using the most expensive frontier models for their product that are likely overkill (as the product matures, they'll find the right mix of cost vs. capability, whether that's lower cost models from frontier labs, or open weight models). Put simply, compute does not scale with economic value. A task you bill $500/hour for could be done in 2 seconds by an LLM vs. a task a developer bills $500/day for could take an LLM 10 minutes. Same dollar value, huge disparity in compute. The only use-cases for AI that are comparable to coding on compute are image and video generation. Unless we end up with an economy primarily made up of companies producing code, image and video, there's literally no way compute needs can keep growing without subsidies. I think it is hard to overstate just how much "work" is being done by Claude Code and Codex because it is "free" at the point of use. There are millions of newly minted developers prompting Claude and Codex to generate trillions of lines of code every single day because it has no marginal cost, not because it is driving any economic value. And as soon as they're exposed to the real cost, when economic value becomes a factor, they're going to stop doing it (as we're already seeing with companies like Uber). Through your YC connection and legal work, you have access to a lot of very successful people working in every industry: ask those 2 questions, how much are they spending on coding with AI vs. how much are they spending on AI in their product? You're going to find that even the most aggressively AI-integrated products are spending pennies on their product's AI usage compared to the dollars they spend on their AI coding. We're at peak compute demand, and it is almost entirely driven by coding, which is subsidized. The real economy isn't like the creative and chaotic make things and see what sticks world of coding. The real economy is boring, routine, regimented, task oriented, you hire people, train them, they do the tasks, you make some money. Most of the economy could be replaced with a few semi-intelligent macros. Yes, AI is coming to every industry, you're right, but it isn't going to explode compute demand, it is going to make these industries more efficient, it is going to reduce costs, not shift them to compute, because $1 of compute can do more than $1,000 of a human in most industries. We can come back to this comment in a couple of years. I bet we'll be using less compute then than we are today.
- sjsdaiuasgdia 3mo ago
  I'll believe you when I stop seeing lawyers getting busted for hallucinated references in their AI-generated filings.
loolhahalmao 3mo ago
"subsidizing" aka making 30% gross margin instead of 90%.
- reticulates 3mo ago
  no, even if we assume their margins are 90% (they are not) they are still losing money because the $200 plans allow for tens of thousands of dollars worth of inference and a huge number of users are milking every cent across multiple accounts. Every “reset” OpenAI and Anthropic do is setting money on fire. If it were true that they’re making money hand over fist they wouldn’t need to raise tens of billions of dollars every few months. https://xcancel.com/i/article/2076078865060151465 https://xcancel.com/i/article/2076078865060151465
  - OGWhales 3mo ago
    > even if we assume their margins are 90% (they are not) How do you know they are not? It will be curious to see the cost of inference for these newly released open weight models and will help give an idea of the actual cost of inference. But for now, I think saying the $200 plans allows for "tens of thousands of dollars worth of inference" provides very little insight when you are measuring the inference cost in API pricing with an unknown margin.
    - reticulates 3mo ago
      We don’t “know” because they haven’t released any numbers but the most optimistic estimates (which many people believe are very very optimistic) put it at 60%: https://newsletter.semianalysis.com/p/anthropic-growth-and-bedrock-mix https://newsletter.semianalysis.com/p/anthropic-growth-and-b... The simple question to ask is, if it is so profitable, where is all the money going? If Anthropic have 90% margins on API usage and API usage is $50bn+ in revenue per year, where is the $45bn going? Why do they need to raise so much cash, constantly?
      - user43928 3mo ago
        I am not knowledgeable about their finances. But I do wonder how a 60% margin would be realistic when Sonnet costs 3-6x more than GLM 5.2 hosted by third party providers.
OleksandrC 3mo ago
Recent Kimi K3 release is supposedly frontier-grade, so would be interesting to see what it really costs to run inference for models of this class, when the weights get released and other independent providers pick it up (then we could reasonably expect competitive pricing with low-ish margins).
mutkach 3mo ago
True, and even selling token price may not be illustrative of what it actually costs them to provide the service. Tokens may be sold at a loss if majority of spenders are running agents 24/7 The industry and users moves on from single chat-based to more and more "agentic" workflows that may generate longer workloads with multiple simultaneous agents (separate agents - separate contexts - separate KV caches). My estimation is based on, say, running a Kimi3 on a 24 B200 GPUs - it is very easy to lose money when selling tokens at "market" prices.

rayiner 3mo ago

The compute demand is not fake. Non-coding industries have barely begun to deploy this technology. In the legal sector, I’ve been a tech pessimist my entire career, because it was uniformly quite bad. I’ve spent the last few months demoing legal tools backed by frontier models, and we’re definitely going to buy one of them. They’re real and they work and they address a bunch of needs.

reticulates 3mo ago
You’re talking across the issue. The demand is real because it is cheap. The demand is being generated by OpenAI and Anthropic selling inference below cost on fixed price plans. If everyone was paying the actual costs then demand would fall through the floor. The legal tools you’re looking at use barely any compute. They’re not driving the compute demand. You can validate this by asking how much they are spending on API usage. A company spending $100,000 per month on a frontier model via an API is the equivalent of… 10 or so OpenAI and Anthropic fixed price plan customers. Are these legal tools spending hundreds of millions per year on the frontier models?
- dash2 3mo ago
  Compute would have to be very expensive not to be cheaper than a junior lawyer.
- rayiner 3mo ago
  My point is that you’re projecting forward into the future where providers need to raise prices, but overlooking the demand that will arise when the rest of the economy starts using AI.
  - reticulates 3mo ago
    The entire economy could be entirely powered by AI and use less compute than is being used today. You're projecting the amount of compute used by coding while subsidized onto other industries, but that doesn't translate. Speak to some of these legal technology companies and ask them 2 questions: 1. How much is your AI spend on coding? 2. How much is your AI spend on AI within your product? The answer to #1 will dwarf #2 by orders of magnitude. And that's now, when these companies are still finding their feet, using the most expensive frontier models for their product that are likely overkill (as the product matures, they'll find the right mix of cost vs. capability, whether that's lower cost models from frontier labs, or open weight models). Put simply, compute does not scale with economic value. A task you bill $500/hour for could be done in 2 seconds by an LLM vs. a task a developer bills $500/day for could take an LLM 10 minutes. Same dollar value, huge disparity in compute. The only use-cases for AI that are comparable to coding on compute are image and video generation. Unless we end up with an economy primarily made up of companies producing code, image and video, there's literally no way compute needs can keep growing without subsidies. I think it is hard to overstate just how much "work" is being done by Claude Code and Codex because it is "free" at the point of use. There are millions of newly minted developers prompting Claude and Codex to generate trillions of lines of code every single day because it has no marginal cost, not because it is driving any economic value. And as soon as they're exposed to the real cost, when economic value becomes a factor, they're going to stop doing it (as we're already seeing with companies like Uber). Through your YC connection and legal work, you have access to a lot of very successful people working in every industry: ask those 2 questions, how much are they spending on coding with AI vs. how much are they spending on AI in their product? You're going to find that even the most aggressively AI-integrated products are spending pennies on their product's AI usage compared to the dollars they spend on their AI coding. We're at peak compute demand, and it is almost entirely driven by coding, which is subsidized. The real economy isn't like the creative and chaotic make things and see what sticks world of coding. The real economy is boring, routine, regimented, task oriented, you hire people, train them, they do the tasks, you make some money. Most of the economy could be replaced with a few semi-intelligent macros. Yes, AI is coming to every industry, you're right, but it isn't going to explode compute demand, it is going to make these industries more efficient, it is going to reduce costs, not shift them to compute, because $1 of compute can do more than $1,000 of a human in most industries. We can come back to this comment in a couple of years. I bet we'll be using less compute then than we are today.
sjsdaiuasgdia 3mo ago
I'll believe you when I stop seeing lawyers getting busted for hallucinated references in their AI-generated filings.

loolhahalmao 3mo ago

"subsidizing" aka making 30% gross margin instead of 90%.

reticulates 3mo ago
no, even if we assume their margins are 90% (they are not) they are still losing money because the $200 plans allow for tens of thousands of dollars worth of inference and a huge number of users are milking every cent across multiple accounts. Every “reset” OpenAI and Anthropic do is setting money on fire. If it were true that they’re making money hand over fist they wouldn’t need to raise tens of billions of dollars every few months. https://xcancel.com/i/article/2076078865060151465 https://xcancel.com/i/article/2076078865060151465
- OGWhales 3mo ago
  > even if we assume their margins are 90% (they are not) How do you know they are not? It will be curious to see the cost of inference for these newly released open weight models and will help give an idea of the actual cost of inference. But for now, I think saying the $200 plans allows for "tens of thousands of dollars worth of inference" provides very little insight when you are measuring the inference cost in API pricing with an unknown margin.
  - reticulates 3mo ago
    We don’t “know” because they haven’t released any numbers but the most optimistic estimates (which many people believe are very very optimistic) put it at 60%: https://newsletter.semianalysis.com/p/anthropic-growth-and-bedrock-mix https://newsletter.semianalysis.com/p/anthropic-growth-and-b... The simple question to ask is, if it is so profitable, where is all the money going? If Anthropic have 90% margins on API usage and API usage is $50bn+ in revenue per year, where is the $45bn going? Why do they need to raise so much cash, constantly?
    - user43928 3mo ago
      I am not knowledgeable about their finances. But I do wonder how a 60% margin would be realistic when Sonnet costs 3-6x more than GLM 5.2 hosted by third party providers.

OleksandrC 3mo ago

Recent Kimi K3 release is supposedly frontier-grade, so would be interesting to see what it really costs to run inference for models of this class, when the weights get released and other independent providers pick it up (then we could reasonably expect competitive pricing with low-ish margins).

mutkach 3mo ago

True, and even selling token price may not be illustrative of what it actually costs them to provide the service. Tokens may be sold at a loss if majority of spenders are running agents 24/7 The industry and users moves on from single chat-based to more and more "agentic" workflows that may generate longer workloads with multiple simultaneous agents (separate agents - separate contexts - separate KV caches). My estimation is based on, say, running a Kimi3 on a 24 B200 GPUs - it is very easy to lose money when selling tokens at "market" prices.