Actually this started before Lattner. Richard Wei kicked this off with his DLVM thesis.
Either way, Swift's functional design around a type system has some compiler implications towards solving larger problems that Julia probably will struggle with as it moves forward beyond the goal of just being a faster Python.
Essentially Swift is in Lattner's words "Syntactic sugar for LLVM"... meaning it was designed from a compilers point of view to provide an ontology for code that a compiler can analyze, interpret, optimize, and enable deeper functionality. Swift backed that ontology with a functional type system that forms a set proofs of the relationships and meaning of your code.
If you take a language like C++, the "atoms" of the code like a Float32, are just hard coded intrinsics in the compiler. As such, they have no ontological meaning in relationship to a Float16, an Int32, or an Array of Strings. In Swift, a float is built from a set of proofs as equatable, hashable, Numeric, FloatingPointNumber, and etc. As related to Julia or Python, a static language built around these proofs, enable compiler's to provide "co-pilot" development assistance, guarantees at runtime, code validation.
Why is this important? With deeper knowledge, a compiler can optimize things in ways that weren't previously possible, extract graphs, and automate code execution in heterogeneous computing evironments.
Either way, Swift's functional design around a type system has some compiler implications towards solving larger problems that Julia probably will struggle with as it moves forward beyond the goal of just being a faster Python.