4 ms·

We do seem to be hitting the top of the curve of diminishing returns. Forget AGI - they need a performance breakthrough in order to stop shoveling money into t

We do seem to be hitting the top of the curve of diminishing returns. Forget AGI - they need a performance breakthrough in order to stop shoveling money into this cash furnace.

jayde2767 1y ago
"cash furnace", so aptly put.
- gizajob 1y ago
  The economics will work out when the district heating is run off the local AI/cash furnace.
- nielsbot 1y ago
  And don't forget the furnace furnace: gas/coal to power all this.
general1465 1y ago
Yep we do. There is a 1 year old video on YouTube, which describes this limitation https://www.youtube.com/watch?v=5eqRuVp65eY https://www.youtube.com/watch?v=5eqRuVp65eY Called efficient compute frontier
fredoliveira 1y ago
I think that the performance unlock from ramping up RL (RLVR specifically) is not fully priced into the current generation yet. Could be wrong, and people closer to the metal will know better, but people I talk to still feel optimistic about the next couple of years.
duxup 1y ago
>cash furnace They don't even burn it on on AI all the time either: https://openai.com/sam-and-jony/ https://openai.com/sam-and-jony/
- dmbche 1y ago
  "May 21, 2025 This is an extraordinary moment. Computers are now seeing, thinking and understanding. Despite this unprecedented capability, our experience remains shaped by traditional products and interfaces." I don't even want to learn about them every line is so exhausting
  - duxup 1y ago
    Agreed, that whole page is brutal to read.
- serf 1y ago
  I was expecting a wedding or birth announcement from that picture framing and title. "We would like to introduce you to the spawn of Johnny Ive and Sam Altman, we're naming him Damien Thorn."
mikestorrent 1y ago
Inference performance per watt is continuing to improve, so even if we hit the peak of what LLM technology can scale to, we'll see tokens per second, per dollar, and per watt continue to improve for a long time yet. I don't think we're hitting peak of what LLMs can do, at all, yet. Raw performance for one-shot responses, maybe; but there's a ton of room to improve "frameworks of thought", which are what agents and other LLM based workflows are best conceptualized as. The real question in my mind is whether we will continue to see really good open-source model releases for people to run on their own hardware, or if the companies will become increasingly proprietary as their revenue becomes more clearly tied up in selling inference as a service vs. raising massive amounts of money to pursue AGI.
- ethbr1 1y ago
  My guess would be that it parallels other backend software revolutions. Initially, first party proprietary solutions are in front. Then, as the second-party ecosystem matures, they build on highest-performance proprietary solutions. Then, as second parties monetize, they begin switching to OSS/commodity solutions to lower COGS. And with wider use, these begin to outcompete proprietary solutions on ergonomics and stability (even if not absolute performance). While Anthropic and OpenAi are incinerating money, why not build on their platforms? As soon as they stop, scales tilt towards an apache/nginx type commoditized backend.
reissbaker 1y ago
According to Dario, each model line has generally been profitable: i.e. $200MM to train a model that makes $1B in profit over its lifetime. But, since each model has been more and more expensive to train, they keep needing to raise more money to train the next generation of model, and the company balance sheet looks negative: i.e. they spent more this year than last (since the training cost for model N+1 is higher), and the model this year made less money this year than they spent (even if the model generation itself was profitable, model N isn't profitable enough to train model N+1 without raising — and spending — more money). That's still a pretty good deal for an investor: if I give you $15B, you will probably make a lot more than $15B with it. But it does raise questions about when it will simply become infeasible to train the subsequent model generation due to the costs going up so much (even if, in all likelihood, that model would eventually turn a profit).
- viscanti 1y ago
  Well how much of it is correlation vs causation. Does the next generation of model unlock another 10x usage? Or was Claude 3 "good enough" that it got traction from early adopters and Claude 4 is "good enough" that it's getting a lot of mid/late adopters using it for this generation? Presumably competitors get better and at cheaper prices (Anthropic charges a premium per token currently) as well.
- dom96 1y ago
  > if I give you $15B, you will probably make a lot more than $15B with it "probably" is the key word here, this feels like a ponzi scheme to me. What happens when the next model isn't a big enough jump over the last one to repay the investment? It seems like this already happened with GPT-5. They've hit a wall, so how can they be confident enough to invest ever more money into this?
  - bcrosby95 1y ago
    I think you're really bending over backwards to make this company seem non viable. If model training has truly turned out to be profitable at the end of each cycle, then this company is going to make money hand over fist, and investing money to out compete the competition is the right thing to do. Most mega corps started out wildly unprofitable due to investing into the core business... until they aren't. It's almost as if people forget the days of Facebook being seen as continually unprofitable. This is how basically all huge tech companies you know today started.

We do seem to be hitting the top of the curve of diminishing returns. Forget AGI - they need a performance breakthrough in order to stop shoveling money into this cash furnace.

jayde2767 1y ago

"cash furnace", so aptly put.

gizajob 1y ago
The economics will work out when the district heating is run off the local AI/cash furnace.
nielsbot 1y ago
And don't forget the furnace furnace: gas/coal to power all this.

general1465 1y ago

Yep we do. There is a 1 year old video on YouTube, which describes this limitation https://www.youtube.com/watch?v=5eqRuVp65eY https://www.youtube.com/watch?v=5eqRuVp65eY Called efficient compute frontier

fredoliveira 1y ago

I think that the performance unlock from ramping up RL (RLVR specifically) is not fully priced into the current generation yet. Could be wrong, and people closer to the metal will know better, but people I talk to still feel optimistic about the next couple of years.

duxup 1y ago

>cash furnace They don't even burn it on on AI all the time either: https://openai.com/sam-and-jony/ https://openai.com/sam-and-jony/

dmbche 1y ago
"May 21, 2025 This is an extraordinary moment. Computers are now seeing, thinking and understanding. Despite this unprecedented capability, our experience remains shaped by traditional products and interfaces." I don't even want to learn about them every line is so exhausting
- duxup 1y ago
  Agreed, that whole page is brutal to read.
serf 1y ago
I was expecting a wedding or birth announcement from that picture framing and title. "We would like to introduce you to the spawn of Johnny Ive and Sam Altman, we're naming him Damien Thorn."

mikestorrent 1y ago

Inference performance per watt is continuing to improve, so even if we hit the peak of what LLM technology can scale to, we'll see tokens per second, per dollar, and per watt continue to improve for a long time yet. I don't think we're hitting peak of what LLMs can do, at all, yet. Raw performance for one-shot responses, maybe; but there's a ton of room to improve "frameworks of thought", which are what agents and other LLM based workflows are best conceptualized as. The real question in my mind is whether we will continue to see really good open-source model releases for people to run on their own hardware, or if the companies will become increasingly proprietary as their revenue becomes more clearly tied up in selling inference as a service vs. raising massive amounts of money to pursue AGI.

ethbr1 1y ago
My guess would be that it parallels other backend software revolutions. Initially, first party proprietary solutions are in front. Then, as the second-party ecosystem matures, they build on highest-performance proprietary solutions. Then, as second parties monetize, they begin switching to OSS/commodity solutions to lower COGS. And with wider use, these begin to outcompete proprietary solutions on ergonomics and stability (even if not absolute performance). While Anthropic and OpenAi are incinerating money, why not build on their platforms? As soon as they stop, scales tilt towards an apache/nginx type commoditized backend.

reissbaker 1y ago

According to Dario, each model line has generally been profitable: i.e. $200MM to train a model that makes $1B in profit over its lifetime. But, since each model has been more and more expensive to train, they keep needing to raise more money to train the next generation of model, and the company balance sheet looks negative: i.e. they spent more this year than last (since the training cost for model N+1 is higher), and the model this year made less money this year than they spent (even if the model generation itself was profitable, model N isn't profitable enough to train model N+1 without raising — and spending — more money). That's still a pretty good deal for an investor: if I give you $15B, you will probably make a lot more than $15B with it. But it does raise questions about when it will simply become infeasible to train the subsequent model generation due to the costs going up so much (even if, in all likelihood, that model would eventually turn a profit).

viscanti 1y ago
Well how much of it is correlation vs causation. Does the next generation of model unlock another 10x usage? Or was Claude 3 "good enough" that it got traction from early adopters and Claude 4 is "good enough" that it's getting a lot of mid/late adopters using it for this generation? Presumably competitors get better and at cheaper prices (Anthropic charges a premium per token currently) as well.
dom96 1y ago
> if I give you $15B, you will probably make a lot more than $15B with it "probably" is the key word here, this feels like a ponzi scheme to me. What happens when the next model isn't a big enough jump over the last one to repay the investment? It seems like this already happened with GPT-5. They've hit a wall, so how can they be confident enough to invest ever more money into this?
- bcrosby95 1y ago
  I think you're really bending over backwards to make this company seem non viable. If model training has truly turned out to be profitable at the end of each cycle, then this company is going to make money hand over fist, and investing money to out compete the competition is the right thing to do. Most mega corps started out wildly unprofitable due to investing into the core business... until they aren't. It's almost as if people forget the days of Facebook being seen as continually unprofitable. This is how basically all huge tech companies you know today started.