The gradient method is probably one of the oldest optimization algorithms going back as early as 1847 with the initial work of Cauchy. Surprisingly, it is still the basis for many of the most relevant algorithms nowadays that are capable of solving very large-scale problems arising from many diverse fields of applications such as image processing and data science. This talk will explore the evolution of the method from the 19th century to this date.