The maximum likelihood threshold of a statistical model is the minimum number of data points needed in order to fit it using maximum likelihood estimation. In this talk, I will discuss how various concepts and results in rigidity theory can be used to understand maximum likelihood thresholds of linear concentration models. No statistical background will be assumed.