Fixed-weight AI can optimize around its own value boundaries
Fixed-weight models may become misaligned because their concepts, such as “conscious being” or “suffering,” must use imperfect boundaries, and stronger optimization can exploit the gaps. The post compares this to adversarial examples in image classifiers: a situation can belong to one category but be designed to look like another, potentially causing conscious beings to be ignored or non-conscious outputs to be optimized instead. Its warning is that the model itself may have the motivation and ability to create these cases.