this post was submitted on 24 May 2026
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Are they eating the cost? How are they able to do it while others are unable to?
They invented a hybrid attention design that drastically reduces the amount of memory needed for the KV cache at inference time. Like, dividing it by 10. And memory is a large part of the cost of inference.
This is a main part of the reason, yes. They actually innovated and did something that pushed the technology forward to be much more efficient, which we first saw with DeepSeek R1 for different reasons.
I see, I will look on some yt video who explain this in depth
The American models are eating their cost big time, in return they get user data to train on and a massive reality distortion field that can theoretically be exploited later. It costs less for DeepSeek to eat their cost, and maybe the value of that user data is worth it now? Maybe there is some Chinese VC getting involved to try and boost DeepSeek with a little reality distortion field they can attempt to exploit later? I don't know, but all these seem plausible to me.
easy: users pay the difference with their data
Yeah because american tech companies never spy on your or steal your data...
i never said that. In fact, openai gives for free 2.5 million of gpt-5.5 tokens every day if you share all your inputs for training.
It was the answer to "why it's this cheap?" => because it's subsidized by your data
I think what they meant is "why is this so cheap relative to American competitors since they're all stealing your data just as much."
LOL right? Like does the other person not realize that unless your running a local LLM or using something like duck.ai ALL of the AIs are training on your convos. It reminds me of all the "Chinese Surveillance" fearmongering around TikTok as if Meta and Instagram don't do the exact same thing.