It all began in the South African gold mines in the 1950s. An engineer named Danie Krige was frustrated by the inaccuracy of reserve estimates. The manual methods of the time were subjective, inconsistent and led to costly mis‑judgements. The problem was simple yet crucial: how could one estimate the grade of millions of tonnes of ore based on only a few hundred drill holes? 🔹 The solution came from France: Georges Matheron, a French mathematician, was struck by Krige’s empirical work. In 1962 he formalized the mathematical theory behind Krige’s observations, creating what we now know as kriging. The kriging revolution: Unbiased estimates with minimal variance Quantification of uncertainty through the kriging variance A rigorous statistical method replacing specialists’ “gut feeling” A mathematical foundation based on variograms and spatial correlation Transformational impact: Kriging was not merely an incremental improvement-it was a paradigm shift. For the first time in the history of mining, there was a method that not only estimated grades but also quantified the confidence placed in those estimates. 🔹 Concurrently, another revolution: In 1965, Helmut Lerchs and Ingo Grossmann solved another fundamental problem: what is the optimal pit? Their algorithm, based on graph theory, provided for the first time a mathematically optimal solution for delineating open pits. Before: manual pit design based on experience. After: mathematical optimization ensuring maximum economic value. The perfect marriage: Kriging provided reliable grade estimates. The Lerchs–Grossmann algorithm optimized economic recovery. Result: scientific planning in place of intuition. A historical note: the Lerchs–Grossmann algorithm only became widespread twenty years later, once computers were powerful enough. The Whittle 3D software of the 1980s was the milestone that democratized pit optimization. Why does this matter today? These two pillars-reliable estimation and mathematical optimization-laid the foundation of modern mining. Without them, we would not have the basis to integrate machine learning, stochastic simulation and advanced optimization, as we see today. The journey continues: 1960s: Kriging + Lerchs–Grossmann 1980s: Stochastic simulation 2000s: Optimization under uncertainty 2020s: Machine learning + geostatistics From South Africa to the world, from intuition to science, from deterministic to probabilistic methods.