In his book Statistical Rethinking (2nd edition), Richard McElreath proposes that "multilevel regression deserves to be the default form of regression". He expands:
Papers that do not use multilevel models should have to justify not using a multilevel approach. Certainly some data and contexts do not need th emultilevel treatment. But most contemporary studies in the social and natural sciences, whether experimental or not, would benefit from it. Perhaps the most important reason is that even well-controled treatments interact with unmeasured aspects of the individuals, groups, or populations studied. (p.15)
The only shortcomings he mentions in the next paragraph are related to its practical implementation:
Fitting and interpreting multilevel models can be considerably harder than fitting and interpreting a traditional regression model
My question is, what would be technical arguments against the use of multilevel modeling by default?