Does Every AI Have the Same Blind Spot?

*Testing whether N'Ko invisibility is a universal property of language models, or a quirk of one.* Qwen3-8B, an 8-billion-parameter model trained on trillions of tokens, processed N'Ko text with measurably less activation than English at every single layer. More dead neurons. Less information being distributed. Flatter circuits. The model wasn't failing because N'Ko is difficult. It was failing because it had barely seen the script in training. The technical name for this is an activation deficit. When a layer processes unfamiliar input, fewer neurons fire. The ones that do fire produce weaker signals. The result is a flatter, sparser activation profile across all 4,096 hidden dimensions. You can measure this with four numbers: L2 norm (how loudly is the layer speaking?), Shannon entropy (how spread out is the information?), sparsity (what fraction of neurons are essentially turned off?), and kurtosis (how specialized are the active circuits?). For N'Ko, all four metrics pointed the same direction. The model was running on reduced capacity. It was processing N'Ko text with the cognitive equivalent of one hand behind its back. Experiment A asks a direct question: is N'Ko's invisibility specific to Qwen, or is it a structural property of models trained on data where N'Ko barely exists?