US frontier models from OpenAI, Anthropic, and Google carry the same opacity risks as Chinese models — none of these black boxes reveal their internal biases or training data, and all can hallucinate or give wrong information.
CJ notes that users cannot see into US models like ChatGPT, Claude, or Gemini any more than they can see into Chinese models — the black-box problem is universal, not specific to any country of origin. ✦ AI generated
CJ · Syntax · 2026-07-30 · original ↗
starts at this moment · 20:11
And the same risk goes for using models from the big US AI labs, right? Chad GBT, Claw, 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 20:11
20:11over again and maybe check to see, are we always getting that same vulnerable response when we use that same persona? But that's really the inherent risk of using something that you cannot look inside of. And the same risk goes for using models from the big US AI labs, right? Chad GBT, Claw, Gemini. We can't see into those models. We don't know what inherent biases they have. We don't
20:33know 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
20:53locally, 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 you've had or cleared up some
21:12misconceptions. [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, please share this video with them. Now, if this kind of thing interests you, I have several other deep dive videos on