The same inherent opacity risk applies to US frontier models — we cannot see inside ChatGPT, Claude, or Gemini either, and they hallucinate and give wrong information.
CJ argues that the black-box problem is not unique to Chinese models; US frontier labs produce models with unknown biases and hallucinations too. ✦ AI generated
CJ · Syntax · 2026-07-30 · original ↗
starts at this moment · 20:32
The same risk goes for using models from the big US AI labs, right? ChatGPT, Claude, Gemini. We can't see into those models. We don't know what inherent biases they have. We don't know what data they have or have not been trained on. And we actually see that sometimes they hallucinate or they give us the wrong information. And we're supposed to be responsible enough to not just accept those bad answers and basically correct the model ourselves, even though we're paying for access to that model from some big company. And so it's not something we can just handwave over and say, well, if you're running it locally, that doesn't mean you're not prone to something bad happening.
verbatim transcript · starts at 20:32
20:32have. We don't know what data they have or have not been trained on. And we actually see that sometimes they hallucinate or they give us the wrong information. And we're supposed to be responsible enough to not just accept those bad answers and basically correct the model ourselves. even though we're paying for access to that model from some big company. And so it's not something we can just handwave over and
20:52say, well, if you're running it locally, that doesn't mean you're not prone to something bad happening, but it is one thing to consider because if you're, let's say, a large enterprise, maybe you want to fine-tune some of these openweight models yourself for use internally. You probably want to have internal checks for what types of biases they have as well. Now, at this point, hopefully I've answered the questions
21:11you've had or cleared up some misconceptions. But if you have any more questions or maybe you think I got something wrong or maybe you have some clarifications that you'd like to give, please throw them down in the comments. And like I mentioned earlier, if you think someone else would benefit from all this info, please share this video with them. Now, if this kind of thing interests you, I have several other deep
21:29dive videos on computing and networking, full stack development, how LLMs work internally, and also how to run local models on your own hardware. So, you can check out these videos somewhere here on the screen. All right, that's all I got. Thank you for watching. I'll see you in the next one.