The same black-box risk applies to US frontier models too — we cannot see into ChatGPT, Claude, or Gemini either, and they hallucinate and give wrong information.
CJ balances the discussion by noting that the inability to see inside models applies equally to US AI labs — ChatGPT, Claude, and Gemini are also black boxes with unknown biases that hallucinate, and users must be responsible about verifying outputs. ✦ AI generated
CJ · Syntax · 2026-07-30 · original ↗
starts at this moment · 20:26
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.
verbatim transcript · starts at 20:26
20:26ChatGPT, 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
20:48that model from some big company. And so, it's not something we can just hand-wave over and say, 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 open-weight models yourself for use internally, you probably want to have internal checks
21:06for what types of biases they have as well. Now, at this point, hopefully I've answered the question you've had or cleared up some misconceptions, [music] 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,
21:24please share this video with them. Now, if this kind of thing interests you, I have several other deep dive 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 and I'll see you in the