It's not just type inference that makes programming in ML so nice. Also important are: (0) Parametricity: Type variables are never case-analyzed. As a result,

It's not just type inference that makes programming in ML so nice. Also important are: (0) Parametricity: Type variables are never case-analyzed. As a result, types convey useful information about what functions may or may not do. Haskellers call this “free theorems”. Java's `instanceof`, C++'s `sizeof` and template specialization, and GHC's `TypeFamilies` are blatant violations of parametricity that make types less informative. (1) A clear distinction between data (sums of products) and operations (functions). In a general-purpose language, operations will always be somewhat ill-behaved: they may raise exceptions, fail to terminate, etc. Data tends to be better behaved, and algebraic laws can be stated about it, which hold even in the presence of effectful and/or non-terminating functions. This makes ML superior to Haskell, not to mention object-oriented languages. (2) A module system that enforces abstraction boundaries between subsystems of a larger system. ML's opaque signature ascription (which has no counterpart in Haskell or object-oriented languages) ensures that the internal representation of abstract types is only visible in the module where they're implemented. In a well architected ML program, trying to violate another module's internal invariants is a type error!