Turing's halting problem and why we cannot fully predict AI

The essay builds its argument on a distinction Turing drew between analyzing individual programs and predicting all programs universally. The author concedes that for a specific program you can analyze its behavior and set stopping conditions. What the halting problem rules out, he says, is a universal predictor covering arbitrary programs. From this he draws a practical conclusion for AI safety: design and verification can only cover the range of behavior developers can anticipate, so unforeseen situations should be assumed rather than treated as a failure of imagination.

The second half turns to analogy. Even in human relationships, the author notes, you cannot fully predict what a trusted person will do, and no relationship is completely risk-free. Yet people still seek connections, live together, cooperate and build societies. He poses the same question for AI: if its behavior cannot be fully predicted and all dangers cannot be removed in advance, can we build trust with it the way we do with people, or do trust and safety need to be conceived differently for machines? The essay ends on that open question rather than an answer.