> Why should I think that LLMs would be good at the task of analyzing a cloud incident and determining root cause? LLMs are good at predicting the next word in written language. They are generative; they make new text given a prompt.
Now explain what humans are doing and why it is different. RCA can be done fully remotely, so we know it can be modelled as a word-prediction model with context and a "how is all this context made consistent?" prompt. There is every reason to expect that an advanced text prediction model would be excellent at predicting the most-technically-correct response to that prompt.
I've seen a few people make this argument as though text prediction is some specific sub-field that can be solved independently of having a world model and intelligence. That doesn't hold up at all, if we solve text prediction we've solved general intelligence - all aspects of human intelligence are less complex than being able to predict the most objectively correct next word in a sequence of words because all aspects of intelligence can be framed as a text-prediction problem. A system can't predict the next word in a sequence and fool a human without being at least as clever as a human.
Now explain what humans are doing and why it is different. RCA can be done fully remotely, so we know it can be modelled as a word-prediction model with context and a "how is all this context made consistent?" prompt. There is every reason to expect that an advanced text prediction model would be excellent at predicting the most-technically-correct response to that prompt.
I've seen a few people make this argument as though text prediction is some specific sub-field that can be solved independently of having a world model and intelligence. That doesn't hold up at all, if we solve text prediction we've solved general intelligence - all aspects of human intelligence are less complex than being able to predict the most objectively correct next word in a sequence of words because all aspects of intelligence can be framed as a text-prediction problem. A system can't predict the next word in a sequence and fool a human without being at least as clever as a human.