5 ms·

There's at least the possibility that they intentionally degrade the models as time passes. We can't really verify that we're getting what we're paying for all

There's at least the possibility that they intentionally degrade the models as time passes. We can't really verify that we're getting what we're paying for all of the time. All the more reason to invest in local inference.

taytus 5mo ago
At current prices, and considering these OS Models' performance, investing in local inference sounds like a bad idea.
- matheusmoreira 5mo ago
  Current prices are insane but at this point I'm starting to feel like it's an existential issue. I'm not a US citizen. At any point the USA could come up with some arbitrary export controls. Not having a computer capable of running at least Qwen is starting to actually seem risky to me. At least it's going to be usable as a very high end gaming PC.
  - awakeasleep 5mo ago
    Why would you buy and build everything before the low probability catastrophe strikes, though? You don’t get any benefit from switching early and you pay a big opportunity cost.
    - inigyou 5mo ago
      because as soon as it strikes computer hardware will be completely unavailable to buy?
      - CamperBob2 5mo ago
        Also, there's a nontrivial learning curve involved in running your own inference server, once you move past the casual-goofing-around-with-llama-server stage. If you care about not being a sharecropper on Sam's or Dario's plantation, you should consider learning the ropes. Even if you don't put these skills to immediate use in your day job. I didn't appreciate this until I started down that road myself.
        matheusmoreira 5mo ago
        > If you care about not being a sharecropper on Sam's or Dario's plantation Couldn't have put it better myself. That's what all this comes down to. Owning the hardware, owning the inference. Not perpetually renting them out on a meter like in the dystopian future they're envisioning.
        inigyou 5mo ago
        You also have the option to not use AI
        CamperBob2 5mo ago
        Not in the future, not if you want to get paid.
        matheusmoreira 5mo ago
        Yeah but the truth is I don't want to go back to the pre-LLM world. I've been programming alone for over ten years. Having a coding buddy to talk to, collaborate with or just bounce ideas off of quite literally changed my life. I don't want to go back to solo programming, and my projects aren't exactly swimming in a sea of active contributors.
    - Lapel2742 5mo ago
      > low probability catastrophe There is also a low probability that someone enters peace negotiations solely to threaten the negotiators with death, yet here we are. With these guys it is: Better safe than sorry.
    - OtomotO 5mo ago
      Because you will not be the only one struggling to get the hardware in the "unlikely" case the POTUS blurts out another fart.
  - alfiedotwtf 5mo ago
    > At any point the USA could come up with some arbitrary export controls lol his already happened with Fable!
- jrm4 5mo ago
  At current "proprietary inference company behavior," investing in local inference sounds like the exceedingly far more rational option. Long term predictability ought to far outweigh a few more cycles of performance.
manyatoms 5mo ago
Unless what you're getting is really explicitly spelled out in a contract, you should flatly assume that they're doing whatever they like whenever they like.
- OtomotO 5mo ago
  Even if it's in the contract, but can't be verified.
inigyou 5mo ago
What if the new model is exactly as good as the last model on launch day but better than the last model was on the new model's launch day because it was degraded? Every single time?
- no-name-here 5mo ago
  There are lots of benchmarks to compare the absolute values of different models on the same scale (as opposed to vibes (my apologies for the shorthand), etc.).
- matheusmoreira 5mo ago
  The thought has definitely crossed my mind. I don't think it's true because there's definitely an improvement when new models are released. Maybe the truth is the newest models aren't actually as impressive as we thought. Maybe our perception of progress is being manipulated via months of gradual, silent and unverifiable degradation.
- foo42 5mo ago
  Makes me think of [shepherd tones](Shepard tone - Wikipedia https://share.google/xooRbF7wIIhcsTt2J https://share.google/xooRbF7wIIhcsTt2J) which sounds like they're rising in pitch indefinitely
  - inigyou 5mo ago
    why are you linking to Wikipedia in invalid markdown format, which wouldn't work on HN even if it was valid, to a site called share dot google?
LPisGood 5mo ago
People talk about this a lot. What I have never seen is a discussion of methods they might employ to degrade the models. Let’s say I’m a bad faith LLM operator, and I want to degrade my model so the next release looks better and people want to switch to the more expensive one. How would I do that?
- maybe_pablo 5mo ago
  Weight quantization, n-expert capping, routing to smaller model, context window truncation, aggressive sampling constraints, lossy speculative decoding and probably more.
  - alfiedotwtf 5mo ago
    I'm pretty sure you could do n-expert capping on any MoE model with only a handful lines of changes to ik_llama.cpp, but yeah... my bet is the have various quantisations and run the lower ones at peak (along with different system prompts i.e we're GPU-bound right now. Get to the point with less chatter)
  - trollbridge 5mo ago
    I can't prove any of it, but it sure feels like that happens sometimes on Anthropic's platform. I don't seem to get any of this with GPT-5.5 or GPT-5.5-Pro (not that I use 5.5-Pro enough to know for sure, but when I do use it, it never seems nerfed).
- Tepix 5mo ago
  Use quantisation.
- nessex 5mo ago
  They would quantize the model. That'd make it cheaper to run, and have slightly worse output but it would still generate outputs with a similar feel, derived from a compressed version of the same knowledge base etc. They wouldn't even need to do this uniformly, quantized versions of the model could be routed only a subset of the requests. They could do this to nerf the old model, or more likely just to give themselves more hardware to run the new one on by handling more requests on less hardware. Or to handle increased request volume as traffic ramps up faster than hardware can be provisioned. Playing with local models at various quants, the degradation can be hard to spot. Sometimes it's only noticeable in aggregate. And even then, you never really know if you just got unlucky with a bad response due to RNG. I've had Opus 4.6 fall into some weirdly incoherent loops that I rarely see from even Sonnet, that felt like the kind of thing I got frequently with Qwen3.5 9B on local. And the above applies... Was that just bad RNG? Or was my request to Opus routed to some lower quality variant? There's no great way for me to tell for any given request, nor any way to guarantee Anthropic _didn't_ do that.

taytus 5mo ago

At current prices, and considering these OS Models' performance, investing in local inference sounds like a bad idea.

matheusmoreira 5mo ago
Current prices are insane but at this point I'm starting to feel like it's an existential issue. I'm not a US citizen. At any point the USA could come up with some arbitrary export controls. Not having a computer capable of running at least Qwen is starting to actually seem risky to me. At least it's going to be usable as a very high end gaming PC.
- awakeasleep 5mo ago
  Why would you buy and build everything before the low probability catastrophe strikes, though? You don’t get any benefit from switching early and you pay a big opportunity cost.
  - inigyou 5mo ago
    because as soon as it strikes computer hardware will be completely unavailable to buy?
    - CamperBob2 5mo ago
      Also, there's a nontrivial learning curve involved in running your own inference server, once you move past the casual-goofing-around-with-llama-server stage. If you care about not being a sharecropper on Sam's or Dario's plantation, you should consider learning the ropes. Even if you don't put these skills to immediate use in your day job. I didn't appreciate this until I started down that road myself.
      - matheusmoreira 5mo ago
        > If you care about not being a sharecropper on Sam's or Dario's plantation Couldn't have put it better myself. That's what all this comes down to. Owning the hardware, owning the inference. Not perpetually renting them out on a meter like in the dystopian future they're envisioning.
        inigyou 5mo ago
        You also have the option to not use AI
        CamperBob2 5mo ago
        Not in the future, not if you want to get paid.
        matheusmoreira 5mo ago
        Yeah but the truth is I don't want to go back to the pre-LLM world. I've been programming alone for over ten years. Having a coding buddy to talk to, collaborate with or just bounce ideas off of quite literally changed my life. I don't want to go back to solo programming, and my projects aren't exactly swimming in a sea of active contributors.
  - Lapel2742 5mo ago
    > low probability catastrophe There is also a low probability that someone enters peace negotiations solely to threaten the negotiators with death, yet here we are. With these guys it is: Better safe than sorry.
  - OtomotO 5mo ago
    Because you will not be the only one struggling to get the hardware in the "unlikely" case the POTUS blurts out another fart.
- alfiedotwtf 5mo ago
  > At any point the USA could come up with some arbitrary export controls lol his already happened with Fable!
jrm4 5mo ago
At current "proprietary inference company behavior," investing in local inference sounds like the exceedingly far more rational option. Long term predictability ought to far outweigh a few more cycles of performance.

manyatoms 5mo ago

Unless what you're getting is really explicitly spelled out in a contract, you should flatly assume that they're doing whatever they like whenever they like.

OtomotO 5mo ago
Even if it's in the contract, but can't be verified.

inigyou 5mo ago

What if the new model is exactly as good as the last model on launch day but better than the last model was on the new model's launch day because it was degraded? Every single time?

no-name-here 5mo ago
There are lots of benchmarks to compare the absolute values of different models on the same scale (as opposed to vibes (my apologies for the shorthand), etc.).
matheusmoreira 5mo ago
The thought has definitely crossed my mind. I don't think it's true because there's definitely an improvement when new models are released. Maybe the truth is the newest models aren't actually as impressive as we thought. Maybe our perception of progress is being manipulated via months of gradual, silent and unverifiable degradation.
foo42 5mo ago
Makes me think of [shepherd tones](Shepard tone - Wikipedia https://share.google/xooRbF7wIIhcsTt2J https://share.google/xooRbF7wIIhcsTt2J) which sounds like they're rising in pitch indefinitely
- inigyou 5mo ago
  why are you linking to Wikipedia in invalid markdown format, which wouldn't work on HN even if it was valid, to a site called share dot google?

LPisGood 5mo ago

People talk about this a lot. What I have never seen is a discussion of methods they might employ to degrade the models. Let’s say I’m a bad faith LLM operator, and I want to degrade my model so the next release looks better and people want to switch to the more expensive one. How would I do that?

maybe_pablo 5mo ago
Weight quantization, n-expert capping, routing to smaller model, context window truncation, aggressive sampling constraints, lossy speculative decoding and probably more.
- alfiedotwtf 5mo ago
  I'm pretty sure you could do n-expert capping on any MoE model with only a handful lines of changes to ik_llama.cpp, but yeah... my bet is the have various quantisations and run the lower ones at peak (along with different system prompts i.e we're GPU-bound right now. Get to the point with less chatter)
- trollbridge 5mo ago
  I can't prove any of it, but it sure feels like that happens sometimes on Anthropic's platform. I don't seem to get any of this with GPT-5.5 or GPT-5.5-Pro (not that I use 5.5-Pro enough to know for sure, but when I do use it, it never seems nerfed).
Tepix 5mo ago
Use quantisation.
nessex 5mo ago
They would quantize the model. That'd make it cheaper to run, and have slightly worse output but it would still generate outputs with a similar feel, derived from a compressed version of the same knowledge base etc. They wouldn't even need to do this uniformly, quantized versions of the model could be routed only a subset of the requests. They could do this to nerf the old model, or more likely just to give themselves more hardware to run the new one on by handling more requests on less hardware. Or to handle increased request volume as traffic ramps up faster than hardware can be provisioned. Playing with local models at various quants, the degradation can be hard to spot. Sometimes it's only noticeable in aggregate. And even then, you never really know if you just got unlucky with a bad response due to RNG. I've had Opus 4.6 fall into some weirdly incoherent loops that I rarely see from even Sonnet, that felt like the kind of thing I got frequently with Qwen3.5 9B on local. And the above applies... Was that just bad RNG? Or was my request to Opus routed to some lower quality variant? There's no great way for me to tell for any given request, nor any way to guarantee Anthropic _didn't_ do that.