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> get cheaper to run Irrelevant. The model is the moat > most companies don’t care about that. Wrong. They will use the model that gives them an edge. If the

> get cheaper to run Irrelevant. The model is the moat > most companies don’t care about that. Wrong. They will use the model that gives them an edge. If they are using a PhD but their competitors are using Einstein, they will lose. > center a div For sure a common use case, but is bot what the CEO is concerned about with AI.

vidarh 8mo ago
> Wrong. They will use the model that gives them an edge. If they are using a PhD but their competitors are using Einstein, they will lose. For some tasks that matters. But for a lot of tasks, "good enough but cheaper" will win out. I'm sure there will be a market for whichever company has the best model, but just like most companies don't hire many PhD's, most companies won't feel a need for the highest end models either, above a certain level. E.g. with the release of Sonnet 4.6, I switched a lot of my processes from Opus to Sonnet, because Sonnet 4.6 is good enough, and it means I can do more for less. But I'm also experimenting with Kimi, Qwen, Deepseek, and others for a number of tasks, including fine-grained switching and interleaving. E.g. have a cheap but dumb model filter data or take over when a sub-task is simple enough, in order to have the smart model do less, for example.
- intrasight 8mo ago
  Models will get smarter and cheaper. For those that are burned directly into silicon, there will be a market for old models - as the alternative is to dump that silicon in a landfill. For models that run on general-purpose AI hardware, I don't know why the vendors would waste that resource on old models.
  - vidarh 8mo ago
    Who says anything about old models? What we're seeing is that as the frontier models get better, we get cheaper, better small models that leverage the advanced but cost a fraction. At the same time, hardware provides morez cheaper options. Sometimes far faster options too (e.g. Cerebras). In terms of price, I can get 1m output tokens from Deepseek for 40 cents vs. 25 dollars for Opus, and a number of models near the 1-2 dollar mark that are increasingly viable for a larger set of applications. Providers will keep running those cheaper models as long as there's demand.
  - generallyjosh 8mo ago
    Larger models need more hardware resources to run And, depending on effort settings, they do more 'thinking', i.e., use more rounds of inference to generate longer internal chains of thought Both very good reasons to prefer a smaller model, if the small model is good enough for the task
deleted 8mo ago
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janalsncm 8mo ago
> The model is the moat What model? GPT4o certainly isn’t a moat for open ai. They need to keep training better and better models because qwen3, kimi k2.5 etc constantly nipping at their heels. > Wrong. They will use the model that gives them an edge. If they are using a PhD but their competitors are using Einstein, they will lose. It depends on the business. As much as I’d love to engage a PhD or an Einstein in my Verizon customer support call, it isn’t going to net the call center any value to pay for that extra compute.
- intrasight 8mo ago
  It's a moat. Yes, they must keep refilling it, but it's all they have. My PhD vs Einstein analogy was bad. What I mean is stupid vs smart. Nobody is going to pay for a stupid model when they can pay a bit more for smart.
  - xyzsparetimexyz 8mo ago
    But what if all models that are smart enough for the task? Then its about price no?
robotpepi 8mo ago
> If they are using a PhD but their competitors are using god what are these assumptions
- intrasight 8mo ago
  More analogy than assumption. And admittedly a poor analogy.

vidarh 8mo ago

> Wrong. They will use the model that gives them an edge. If they are using a PhD but their competitors are using Einstein, they will lose. For some tasks that matters. But for a lot of tasks, "good enough but cheaper" will win out. I'm sure there will be a market for whichever company has the best model, but just like most companies don't hire many PhD's, most companies won't feel a need for the highest end models either, above a certain level. E.g. with the release of Sonnet 4.6, I switched a lot of my processes from Opus to Sonnet, because Sonnet 4.6 is good enough, and it means I can do more for less. But I'm also experimenting with Kimi, Qwen, Deepseek, and others for a number of tasks, including fine-grained switching and interleaving. E.g. have a cheap but dumb model filter data or take over when a sub-task is simple enough, in order to have the smart model do less, for example.

intrasight 8mo ago
Models will get smarter and cheaper. For those that are burned directly into silicon, there will be a market for old models - as the alternative is to dump that silicon in a landfill. For models that run on general-purpose AI hardware, I don't know why the vendors would waste that resource on old models.
- vidarh 8mo ago
  Who says anything about old models? What we're seeing is that as the frontier models get better, we get cheaper, better small models that leverage the advanced but cost a fraction. At the same time, hardware provides morez cheaper options. Sometimes far faster options too (e.g. Cerebras). In terms of price, I can get 1m output tokens from Deepseek for 40 cents vs. 25 dollars for Opus, and a number of models near the 1-2 dollar mark that are increasingly viable for a larger set of applications. Providers will keep running those cheaper models as long as there's demand.
- generallyjosh 8mo ago
  Larger models need more hardware resources to run And, depending on effort settings, they do more 'thinking', i.e., use more rounds of inference to generate longer internal chains of thought Both very good reasons to prefer a smaller model, if the small model is good enough for the task

deleted 8mo ago

[deleted]

janalsncm 8mo ago

> The model is the moat What model? GPT4o certainly isn’t a moat for open ai. They need to keep training better and better models because qwen3, kimi k2.5 etc constantly nipping at their heels. > Wrong. They will use the model that gives them an edge. If they are using a PhD but their competitors are using Einstein, they will lose. It depends on the business. As much as I’d love to engage a PhD or an Einstein in my Verizon customer support call, it isn’t going to net the call center any value to pay for that extra compute.

intrasight 8mo ago
It's a moat. Yes, they must keep refilling it, but it's all they have. My PhD vs Einstein analogy was bad. What I mean is stupid vs smart. Nobody is going to pay for a stupid model when they can pay a bit more for smart.
- xyzsparetimexyz 8mo ago
  But what if all models that are smart enough for the task? Then its about price no?

robotpepi 8mo ago

> If they are using a PhD but their competitors are using god what are these assumptions

intrasight 8mo ago
More analogy than assumption. And admittedly a poor analogy.