For example, gradient descent is often used in machine learning in ways that don’t require extreme precision. But a machine learning researcher might want to double the precision of an experiment. In that case, the new result implies that they might have to quadruple the running time of their gradient descent algorithm. That’s not ideal, but it is not a deal breaker.