  Generating synthetic data through generative models is gaining interest in
the ML community and beyond. In the past, synthetic data was often regarded as
a means to private data release, but a surge of recent papers explore how its
potential reaches much further than this -- from creating more fair data to
data augmentation, and from simulation to text generated by ChatGPT. In this
perspective we explore whether, and how, synthetic data may become a dominant
force in the machine learning world, promising a future where datasets can be
tailored to individual needs. Just as importantly, we discuss which fundamental
challenges the community needs to overcome for wider relevance and application
of synthetic data -- the most important of which is quantifying how much we can
trust any finding or prediction drawn from synthetic data.
