4 ms·

I am no-where near as concerned by this as I was a year ago, when I was expecting the axe to fall at any moment before the Chinese labs achieved some sort of es

by sho 5mo ago

I am no-where near as concerned by this as I was a year ago, when I was expecting the axe to fall at any moment before the Chinese labs achieved some sort of escape velocity. I now think it's too late, all the cats are out of all the bags, there's no moat except maybe a temporal one of a few months, the genie is out of the bottle. There is no secret sauce the US labs have that the Chinese ones don't, or won't have soon enough. Deepseek 4 and Kimi 2.5 are not quite Claude 4.5/GPT5.5 but there's no fundamental principle missing - they are strong evidence that there's no real advantage the "frontier" labs possess that isn't related to scale, which they will gain in time (if they even need to). The RL post-training techniques that work are widely known and easily copied. All Deepseek is really lacking is data, which they're getting - and the harder Anthropic/the USG makes it to access claude in china, the more of that precious data they'll get! I used to sort of entertain the "fast take-off breakaway" scenario as being plausible but not really anymore. The only genuine moat the frontier labs have is their product take-up, which isn't nothing, far from it, but it's not some unbreakable technological wall. Too late guys - it might have been too late for quite some time.

BrtByte 5mo ago
I agree the genie is out of the bottle technologically. I'm less convinced that means access stops being politically and economically important. The bottle may be gone but the best lamps are still expensive
- jorvi 5mo ago
  Virtually no one is going to pay for the best performing lamp if the next best lamp does 90% as good for an order of magnitude cheaper. I will say, as pointed out by others, DeepSeek and other Chinese providers still lack a bit in the tooling that Claude has, but they'll get there.
  - BrtByte 5mo ago
    If the second-best lamp is 90% as good and 10x cheaper, most people will use the second-best lamp...
    - avazhi 5mo ago
      That’s what he said?
    - deleted 5mo ago
      [deleted]
  - baq 5mo ago
    And yet it seems that 90% are happily paying for the marginal 10% capability and saturate datacenters.
    - lugu 5mo ago
      That is called marketing.
      - baq 5mo ago
        not necessarily. it might just as well be 'time is money'.
    - lmm 5mo ago
      Happy to pay for? Or happy to spend other people's money on?
      - baq 5mo ago
        somebody is happy to spend that money
  - Paradigma11 5mo ago
    That presumes that there is a linear scale that measures performance. This can be tested: https://en.wikipedia.org/wiki/Rasch_model https://en.wikipedia.org/wiki/Rasch_model Even assuming this holds, what utility you gain by the best models depend completely by your workload. If you have tasks that require performance 10 and DeepSeek has 9, you will gladly pay for SotA models.
- trollbridge 5mo ago
  But a “good enough” lamp just got a lot cheaper. The cost of tokens on DeepSeek V4 Pro is so low I don’t even think about and currently am trying to figure out useful things for as many agents simultaneously running as I can. What would have cost $150 less than a year ago now costs 35¢. Likewise Qwen 3.6 absolutely blows me away and that’s on a 35b 6-bit model on a local 5090. Same thing, busy trying to find stuff to do to keep it busy 24/7. I can still find some niches for Opus 4.7 but being able to attack problems and not worry about consumption is a game changer.
gpt5 5mo ago
I wish it was true. I would gladly use a GPT 5.2 high model equivalent for coding (6 months old) if it was offered cheaper by Deepseek or Kimi. And I'm sure that's an extremely prevalent opinion by the millions of Claude and Codex users who are bothered by the costs. However, they just don't perform that well in practice. That's the real issue. You can actually see it when you move away from open benchmarks. Deep seek 3.2 is 4% on Arc-AGI 2 [1], while GPT 5.2 high is 52% and GPT 5.5 pro high is 84.6%. That's the real reason why nobody is using these models for serious work. It's incredibly frustrating. In addition, I already feel the pain myself on the model restriction. I'll asking my codex 5.5 agent to crawl a website - BOOM, cybersecurity warning on my account. I'll ask it to fix SSH on my local network - another warning. I'm worried about the day my account would be randomly banned and I cannot create a new one. OpenAI already asks you to perform full identification in order to eliminate these warnings - probably exactly for that - so that if they ban you, it's permanent. [1] https://arcprize.org/leaderboard https://arcprize.org/leaderboard
- sho 5mo ago
  I 100% agree with you, but I've been convinced over the last year that it's a time and scale issue, not anything fundamental. The Chinese models right now are in a weird spot. Compared to the frontiers, both their pre and post training is woeful - tiny, resource constrained in every dimension including human, slow. I'd compare it to OpenAI 5 years ago except I think even then OpenAI had way more! But they "cheat" quite a lot in distillation and very benchmark-focussed RL and that's where you get this superficial quality in the leaderboards that doesn't match up when you go off-script. Arc is a great example in that it really belies an "inferior soul" at the heart of it all. What gives me great hope though is that those same scaling laws that Altman and others have been hyping forever will absolutely kick in for the Chinese labs just as they did for the US ones, and I don't think anything can stop that process now. So they will catch up. It won't be tomorrow, but it's not going to be 10 years either. 3-5 would be my reasonably educated guess. And the final risk, that China itself might try to restrict availability of the tsunami of GPU or other AI hardware it will inevitably produce - well, I just can't really imagine a country that has been configuring itself for the last 40 years as a single purpose export machine deciding that actually, no, it doesn't want to export something. About the model restrictions - absolutely. I've been trying to do security research on my own software and the frontier models immediately get suspicious. I've been playing with the local ones much more this year basically because of this. They have deficiencies, for sure - they feel very "hollow" compared to the major labs. But I've talked to a lot of people, and the consensus is pretty clear - just a matter of time.
shevy-java 5mo ago
> There is no secret sauce the US labs have that the Chinese ones don't, or won't have soon enough This is not just about mainland China though. The current US government is extremely selfish and self-centered. Other countries really need to consider for their own long-term situation here.
hbarka 5mo ago
Harness engineering is a moat. There’s user loyalty and reliance on the chassis that Claude is on, for example, just like there’s more market share by MacOS+WindowsOS over Linux Open Source.
- ElFitz 5mo ago
  I thought so too. But 1) people use other models with that same harness. 2) I moved on from Claude Code and all the features I cared for up and running in less than a couple days. Without even looking for available plugins or extensions.
- kasey_junk 5mo ago
  I regularly switch between codex and Claude in the same sessions. I’d throw in other models if I could. Data governance and enterprise sales is a moat. The harnesses aren’t.
- thepasch 5mo ago
  > Harness engineering is a moat. I mean, if that’s the case, then Anthropic themselves are currently actively filling in that moat with nice, solid, walkable dirt. Claude Code may have been a moat 6 months ago but these days you’ll want to replace the “m” with a “bl”.
- PunchyHamster 5mo ago
  The industry on tooling have been very much moving in direction of "plug the AI of your choosing" for a while now, and given how much Anthropic fights the 3rd party tools they are definitely afraid to be left in the dust. > just like there’s more market share by MacOS+WindowsOS over Linux Open Source. It's hard to change OS. It's not hard to jump from one AI tool to another
- saberience 5mo ago
  It's absolutely NOT a moat. Making a harness is the EASY part. If you had said "marketing is a moat" then yes, I would say you were right. But creating a harness equal to or better than Claude Code is trivial. The CC harness is actually shit. There are tons of open-source harnesses than work better than CC while using Opus via OpenRouter.
ElFitz 5mo ago
> The only genuine moat the frontier labs have is their product take-up And even then, their is no stickiness. For most use cases there isn’t much value in one frontier model over the other. Just have to look at the people flocking from one to the other for whatever reason.
- dotancohen 5mo ago
  The large AI houses arguably ensure that model switching be a natural action for their clients, by switching the default model of their flagship offerings every few months. Such is the price of progress.
- baq 5mo ago
  I’m flocking from GPT to opus every week for the past 3 months and always come back. The point isn’t that gpt is better, it’s that it is so much better for my work it isn’t even sticky, it’s reinforced concrete. I use opus 1% of the time because it writes better and it’s sticky there. Yes I’ll switch approximately immediately if opus or Gemini (which I use more than opus!) is better for what I do, but at this point frontier model tokens are not fungible.
  - ElFitz 5mo ago
    There will always be dataset and training quirks, and the provider’s own biases and focus, granting one model an edge over the others in some specific domain.
    - baq 5mo ago
      Yup and that’s where the moats are.
nojs 5mo ago
What about access to GPUs and memory? This is becoming a pretty major bottleneck.
- asdff 5mo ago
  Everyone is expecting them to invade Taiwan, but why not merely extort Taiwan?
  - littleparrot 5mo ago
    You mean by contributing to RAMpocalypse the mainland incentives the west to build own fabs, making Taiwan expendable for us someday?
    - zozbot234 5mo ago
      Mainland China is growing its own RAM manufacturing capacity. They are too tiny to make a real dent into the RAMpocalypse yet but this can potentially change.
    - asdff 5mo ago
      West has been incentivized to build their own fabs for years but still fumbles that effort. All the billions spent hardening the south china sea and taiwans chip manufacturing from the future chinese invasion would have probably paid for a lot of manufacturing capacity stateside.
- wokkel 5mo ago
  It's basically converted sand. Most of that conversion happens in Taiwan at the moment. Which is considered, by China, to be one of their provinces and as a protectorate by the usa. Hence the interest in that region....
- repelsteeltje 5mo ago
  Today's tech echoes 1960-1970 mainframe era: very centralized around a handful of companies controlling "massive cloud compute" in bespoke mainframe-like topology. All of that will all be legacy in a couple of years. Today's B200 clusters are tomorrow's e-waste. Decentralization might happen gradually or abruptly. But to me it's obvious that we'll be thinking of high-tech tensor processors and GPUs the way we thought of individual transistors and tube amplifiers in the 1980s. If AI turns out to be the revolution it purports to be, than the underlying hardware will change much more rapidly than it did with ICs and microprocessors in the late 1970s. Today's hot is tomorrow's junk.
yorwba 5mo ago
All of the reasons in the article also apply to Chinese companies. If a Chinese model becomes good enough to make it significantly easier to hack Chinese government servers, do you think they'll allow random people unfettered access to it? The economic pressures are the same, too. Currently, Chinese models are offered for cheap or in some cases provide weights for free because that's the only way to gain traction. (That closed-weight releases by Baidu, Bytedance, iFlyTek etc. hardly generate any buzz bears that out, as does the fact that when Alibaba does a closed-weight release, someone always gets confused because they associate the Qwen brand with open models.) At some point, their investors are going to want profits, not just user counts. That means higher prices, or no more new models. If there's no secret sauce and all you need is scale, that would actually be kind of the worst-case scenario for catching up to the frontier, since scaling is expensive and the frontier model companies have easier access to capital as well as higher revenues.
- zozbot234 5mo ago
  > If a Chinese model becomes good enough to make it significantly easier to hack Chinese government servers, do you think they'll allow random people unfettered access to it? They aren't trying to become that good, nor do they need to in order to have real positive impact. Models like Mythos are estimated to be humongous even on a datacenter-wide scale, which is actually a big factor in its limited availability at present. It's mostly helpful as a one-of-a-kind proof of concept, to answer the question of whether AI can still plausibly scale by growing capabilities and what happens to alignment concerns when you do that.
  - yorwba 5mo ago
    I expect every company to try to make a model as good as they possibly can, especially now that Mythos has served as a proof of concept to demonstrate that there's lots of interest in AI for cybersecurity. But if they don't try, that hardly assuages concerns about not being able to access the very best models, does it?
scotty79 5mo ago
> There is no secret sauce the US labs have that the Chinese ones don't, or won't have soon enough. Over last year it seems that the only thing US labs are ahead is money spent. At least half of technical innovations if not more came from Chinese labs and was published openly.
- nradov 5mo ago
  Broad and deep capital markets are a real competitive moat for the USA. No other country or economic bloc can quickly deploy huge amounts of capital to new opportunities nearly as fast. China can work around that to an extent with a command economy that focuses resources on national strategic priorities but it's slower and less effective over the long term.
  - wiekke 5mo ago
    Actually this will end up being the greatest disadvantage. Pure spending power doesn’t give you the edge in tech. Creativity, and innovating under constraints leads to success.
    - nradov 5mo ago
      Nah. If that was an actual disadvantage then the USA wouldn't already be the world leader in most technology sectors. Capital is only one of several constraints.
TrackerFF 5mo ago
Which is why, I believe, the big AI companies are starting to focus and roll out vertical products more. They know that the models themselves aren't sticky, people can easily switch between different models with not much hassle. I think the big AI companies are trying to transform into the next Microsoft. Completely capture both enterprise and consumers.
- reeredfdfdf 5mo ago
  "I think the big AI companies are trying to transform into the next Microsoft. Completely capture both enterprise and consumers." That is going to be a failing strategy though. Whatever OpenAI or Anthropic implement, Microsoft and Google can trivially copy and provide to their existing customers that are already deeply invested in their platforms.
moffkalast 5mo ago
I would agree, the only thing Kimi is really missing is stability and harness training, For general chat tasks I consider it mostly on par. Occasionally I'll give the same problem to Kimi, Claude, GPT, Gemini and it's not unusual to see Kimi correctly figure out some kind of weird extra thing that the others missed, like some kind of mentally unstable savant.

by sho 5mo ago

BrtByte 5mo ago

I agree the genie is out of the bottle technologically. I'm less convinced that means access stops being politically and economically important. The bottle may be gone but the best lamps are still expensive

jorvi 5mo ago
Virtually no one is going to pay for the best performing lamp if the next best lamp does 90% as good for an order of magnitude cheaper. I will say, as pointed out by others, DeepSeek and other Chinese providers still lack a bit in the tooling that Claude has, but they'll get there.
- BrtByte 5mo ago
  If the second-best lamp is 90% as good and 10x cheaper, most people will use the second-best lamp...
  - avazhi 5mo ago
    That’s what he said?
  - deleted 5mo ago
    [deleted]
- baq 5mo ago
  And yet it seems that 90% are happily paying for the marginal 10% capability and saturate datacenters.
  - lugu 5mo ago
    That is called marketing.
    - baq 5mo ago
      not necessarily. it might just as well be 'time is money'.
  - lmm 5mo ago
    Happy to pay for? Or happy to spend other people's money on?
    - baq 5mo ago
      somebody is happy to spend that money
- Paradigma11 5mo ago
  That presumes that there is a linear scale that measures performance. This can be tested: https://en.wikipedia.org/wiki/Rasch_model https://en.wikipedia.org/wiki/Rasch_model Even assuming this holds, what utility you gain by the best models depend completely by your workload. If you have tasks that require performance 10 and DeepSeek has 9, you will gladly pay for SotA models.
trollbridge 5mo ago
But a “good enough” lamp just got a lot cheaper. The cost of tokens on DeepSeek V4 Pro is so low I don’t even think about and currently am trying to figure out useful things for as many agents simultaneously running as I can. What would have cost $150 less than a year ago now costs 35¢. Likewise Qwen 3.6 absolutely blows me away and that’s on a 35b 6-bit model on a local 5090. Same thing, busy trying to find stuff to do to keep it busy 24/7. I can still find some niches for Opus 4.7 but being able to attack problems and not worry about consumption is a game changer.

gpt5 5mo ago

I wish it was true. I would gladly use a GPT 5.2 high model equivalent for coding (6 months old) if it was offered cheaper by Deepseek or Kimi. And I'm sure that's an extremely prevalent opinion by the millions of Claude and Codex users who are bothered by the costs. However, they just don't perform that well in practice. That's the real issue. You can actually see it when you move away from open benchmarks. Deep seek 3.2 is 4% on Arc-AGI 2 [1], while GPT 5.2 high is 52% and GPT 5.5 pro high is 84.6%. That's the real reason why nobody is using these models for serious work. It's incredibly frustrating. In addition, I already feel the pain myself on the model restriction. I'll asking my codex 5.5 agent to crawl a website - BOOM, cybersecurity warning on my account. I'll ask it to fix SSH on my local network - another warning. I'm worried about the day my account would be randomly banned and I cannot create a new one. OpenAI already asks you to perform full identification in order to eliminate these warnings - probably exactly for that - so that if they ban you, it's permanent. [1] https://arcprize.org/leaderboard https://arcprize.org/leaderboard

sho 5mo ago
I 100% agree with you, but I've been convinced over the last year that it's a time and scale issue, not anything fundamental. The Chinese models right now are in a weird spot. Compared to the frontiers, both their pre and post training is woeful - tiny, resource constrained in every dimension including human, slow. I'd compare it to OpenAI 5 years ago except I think even then OpenAI had way more! But they "cheat" quite a lot in distillation and very benchmark-focussed RL and that's where you get this superficial quality in the leaderboards that doesn't match up when you go off-script. Arc is a great example in that it really belies an "inferior soul" at the heart of it all. What gives me great hope though is that those same scaling laws that Altman and others have been hyping forever will absolutely kick in for the Chinese labs just as they did for the US ones, and I don't think anything can stop that process now. So they will catch up. It won't be tomorrow, but it's not going to be 10 years either. 3-5 would be my reasonably educated guess. And the final risk, that China itself might try to restrict availability of the tsunami of GPU or other AI hardware it will inevitably produce - well, I just can't really imagine a country that has been configuring itself for the last 40 years as a single purpose export machine deciding that actually, no, it doesn't want to export something. About the model restrictions - absolutely. I've been trying to do security research on my own software and the frontier models immediately get suspicious. I've been playing with the local ones much more this year basically because of this. They have deficiencies, for sure - they feel very "hollow" compared to the major labs. But I've talked to a lot of people, and the consensus is pretty clear - just a matter of time.

shevy-java 5mo ago

> There is no secret sauce the US labs have that the Chinese ones don't, or won't have soon enough This is not just about mainland China though. The current US government is extremely selfish and self-centered. Other countries really need to consider for their own long-term situation here.

hbarka 5mo ago

Harness engineering is a moat. There’s user loyalty and reliance on the chassis that Claude is on, for example, just like there’s more market share by MacOS+WindowsOS over Linux Open Source.

ElFitz 5mo ago
I thought so too. But 1) people use other models with that same harness. 2) I moved on from Claude Code and all the features I cared for up and running in less than a couple days. Without even looking for available plugins or extensions.
kasey_junk 5mo ago
I regularly switch between codex and Claude in the same sessions. I’d throw in other models if I could. Data governance and enterprise sales is a moat. The harnesses aren’t.
thepasch 5mo ago
> Harness engineering is a moat. I mean, if that’s the case, then Anthropic themselves are currently actively filling in that moat with nice, solid, walkable dirt. Claude Code may have been a moat 6 months ago but these days you’ll want to replace the “m” with a “bl”.
PunchyHamster 5mo ago
The industry on tooling have been very much moving in direction of "plug the AI of your choosing" for a while now, and given how much Anthropic fights the 3rd party tools they are definitely afraid to be left in the dust. > just like there’s more market share by MacOS+WindowsOS over Linux Open Source. It's hard to change OS. It's not hard to jump from one AI tool to another
saberience 5mo ago
It's absolutely NOT a moat. Making a harness is the EASY part. If you had said "marketing is a moat" then yes, I would say you were right. But creating a harness equal to or better than Claude Code is trivial. The CC harness is actually shit. There are tons of open-source harnesses than work better than CC while using Opus via OpenRouter.

ElFitz 5mo ago

> The only genuine moat the frontier labs have is their product take-up And even then, their is no stickiness. For most use cases there isn’t much value in one frontier model over the other. Just have to look at the people flocking from one to the other for whatever reason.

dotancohen 5mo ago
The large AI houses arguably ensure that model switching be a natural action for their clients, by switching the default model of their flagship offerings every few months. Such is the price of progress.
baq 5mo ago
I’m flocking from GPT to opus every week for the past 3 months and always come back. The point isn’t that gpt is better, it’s that it is so much better for my work it isn’t even sticky, it’s reinforced concrete. I use opus 1% of the time because it writes better and it’s sticky there. Yes I’ll switch approximately immediately if opus or Gemini (which I use more than opus!) is better for what I do, but at this point frontier model tokens are not fungible.
- ElFitz 5mo ago
  There will always be dataset and training quirks, and the provider’s own biases and focus, granting one model an edge over the others in some specific domain.
  - baq 5mo ago
    Yup and that’s where the moats are.

nojs 5mo ago

What about access to GPUs and memory? This is becoming a pretty major bottleneck.

asdff 5mo ago
Everyone is expecting them to invade Taiwan, but why not merely extort Taiwan?
- littleparrot 5mo ago
  You mean by contributing to RAMpocalypse the mainland incentives the west to build own fabs, making Taiwan expendable for us someday?
  - zozbot234 5mo ago
    Mainland China is growing its own RAM manufacturing capacity. They are too tiny to make a real dent into the RAMpocalypse yet but this can potentially change.
  - asdff 5mo ago
    West has been incentivized to build their own fabs for years but still fumbles that effort. All the billions spent hardening the south china sea and taiwans chip manufacturing from the future chinese invasion would have probably paid for a lot of manufacturing capacity stateside.
wokkel 5mo ago
It's basically converted sand. Most of that conversion happens in Taiwan at the moment. Which is considered, by China, to be one of their provinces and as a protectorate by the usa. Hence the interest in that region....
repelsteeltje 5mo ago
Today's tech echoes 1960-1970 mainframe era: very centralized around a handful of companies controlling "massive cloud compute" in bespoke mainframe-like topology. All of that will all be legacy in a couple of years. Today's B200 clusters are tomorrow's e-waste. Decentralization might happen gradually or abruptly. But to me it's obvious that we'll be thinking of high-tech tensor processors and GPUs the way we thought of individual transistors and tube amplifiers in the 1980s. If AI turns out to be the revolution it purports to be, than the underlying hardware will change much more rapidly than it did with ICs and microprocessors in the late 1970s. Today's hot is tomorrow's junk.

yorwba 5mo ago

All of the reasons in the article also apply to Chinese companies. If a Chinese model becomes good enough to make it significantly easier to hack Chinese government servers, do you think they'll allow random people unfettered access to it? The economic pressures are the same, too. Currently, Chinese models are offered for cheap or in some cases provide weights for free because that's the only way to gain traction. (That closed-weight releases by Baidu, Bytedance, iFlyTek etc. hardly generate any buzz bears that out, as does the fact that when Alibaba does a closed-weight release, someone always gets confused because they associate the Qwen brand with open models.) At some point, their investors are going to want profits, not just user counts. That means higher prices, or no more new models. If there's no secret sauce and all you need is scale, that would actually be kind of the worst-case scenario for catching up to the frontier, since scaling is expensive and the frontier model companies have easier access to capital as well as higher revenues.

zozbot234 5mo ago
> If a Chinese model becomes good enough to make it significantly easier to hack Chinese government servers, do you think they'll allow random people unfettered access to it? They aren't trying to become that good, nor do they need to in order to have real positive impact. Models like Mythos are estimated to be humongous even on a datacenter-wide scale, which is actually a big factor in its limited availability at present. It's mostly helpful as a one-of-a-kind proof of concept, to answer the question of whether AI can still plausibly scale by growing capabilities and what happens to alignment concerns when you do that.
- yorwba 5mo ago
  I expect every company to try to make a model as good as they possibly can, especially now that Mythos has served as a proof of concept to demonstrate that there's lots of interest in AI for cybersecurity. But if they don't try, that hardly assuages concerns about not being able to access the very best models, does it?

scotty79 5mo ago

> There is no secret sauce the US labs have that the Chinese ones don't, or won't have soon enough. Over last year it seems that the only thing US labs are ahead is money spent. At least half of technical innovations if not more came from Chinese labs and was published openly.

nradov 5mo ago
Broad and deep capital markets are a real competitive moat for the USA. No other country or economic bloc can quickly deploy huge amounts of capital to new opportunities nearly as fast. China can work around that to an extent with a command economy that focuses resources on national strategic priorities but it's slower and less effective over the long term.
- wiekke 5mo ago
  Actually this will end up being the greatest disadvantage. Pure spending power doesn’t give you the edge in tech. Creativity, and innovating under constraints leads to success.
  - nradov 5mo ago
    Nah. If that was an actual disadvantage then the USA wouldn't already be the world leader in most technology sectors. Capital is only one of several constraints.

TrackerFF 5mo ago

Which is why, I believe, the big AI companies are starting to focus and roll out vertical products more. They know that the models themselves aren't sticky, people can easily switch between different models with not much hassle. I think the big AI companies are trying to transform into the next Microsoft. Completely capture both enterprise and consumers.

reeredfdfdf 5mo ago
"I think the big AI companies are trying to transform into the next Microsoft. Completely capture both enterprise and consumers." That is going to be a failing strategy though. Whatever OpenAI or Anthropic implement, Microsoft and Google can trivially copy and provide to their existing customers that are already deeply invested in their platforms.

moffkalast 5mo ago

I would agree, the only thing Kimi is really missing is stability and harness training, For general chat tasks I consider it mostly on par. Occasionally I'll give the same problem to Kimi, Claude, GPT, Gemini and it's not unusual to see Kimi correctly figure out some kind of weird extra thing that the others missed, like some kind of mentally unstable savant.