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How a 1913 Russian Math Experiment Became the Foundation of Google and Modern AI

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In January 1913, Russian mathematician Andrey Markov presented a probability model to the Imperial Academy of Sciences in St. Petersburg, derived from analyzing letter sequences in Pushkin's poem Eugene Onegin. His framework, later called a Markov Chain, predicts future states based solely on the current state, ignoring all prior history — a principle known as the memoryless property. Decades later, in 1998, Stanford PhD students Larry Page and Sergey Brin applied this concept to build PageRank, the algorithm that powered Google's search engine by ranking websites according to the probability a random user would land on them. The same mathematical foundation underpins many modern AI and machine learning systems used today. The article, authored with AI assistance and published on DEV Community in August 2026, traces this 113-year journey from a poetry-counting experiment to world-changing technology.

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