  Seismic advances in generative AI algorithms for imagery, text, and other
data types has led to the temptation to use synthetic data to train
next-generation models. Repeating this process creates an autophagous
(self-consuming) loop whose properties are poorly understood. We conduct a
thorough analytical and empirical analysis using state-of-the-art generative
image models of three families of autophagous loops that differ in how fixed or
fresh real training data is available through the generations of training and
in whether the samples from previous generation models have been biased to
trade off data quality versus diversity. Our primary conclusion across all
scenarios is that without enough fresh real data in each generation of an
autophagous loop, future generative models are doomed to have their quality
(precision) or diversity (recall) progressively decrease. We term this
condition Model Autophagy Disorder (MAD), making analogy to mad cow disease.
