The Markov chain that powers Google's trillion-dollar ranking algorithm only looks at the current state to predict what happens next.

The law of large numbers states that as you run more and more independent trials, the average outcome gets closer and closer to the expected value.

A nuclear chain reaction occurs when a neutron splits a U-235 nucleus, releasing two or three more neutrons; if on average those neutrons go on to split more than one other U-235 nucleus, you get a runaway chain reaction and a nuclear bomb.

Modern large language models operate on tokens (letters, words, or punctuation) rather than just an alphabet, predicting the odds of the next token given a string of prior tokens—but unlike simple Markov chains they use 'attention' to decide which prior tokens matter, allowing context like 'blood' and 'mitochondria' to disambiguate that 'cell' means biology rather than a prison.