It's true that we use tools with uncertainty all the time, in many domains. But crucially that uncertainty is carefully modeled and accounted for.
For example, robots use sensors to make sense of the world around them. These sensors are not 100% accurate, and therefore if the robots rely on these sensors to be correct, they will fail.
So roboticists characterize and calibrate sensors. They attempt to understand how and why they fail, and under what conditions. Then they attempt to cover blind spots by using orthogonal sensing methods. Then they fuse these desperate data into a single belief of the robot's state, which include an estimate of its posterior uncertainty. Accounting for this uncertainty in this way is what keeps planes in the sky, boats afloat, and driverless cars on course.
With LLMs It seems like we are happy to just throw out all this uncertainty modeling and to leave it up to chance. To draw an analogy to robotics, what we should be doing is taking the output from many LLMs, characterizing how wrong they are, and fusing them into a final result, which is provided to the user with a level of confidence attached. Now that is something I can use in an engineering pipeline. That is something that can be used as a foundation to something bigger.
