This is quite a different situation to traditional scientific computing.
No because of JIT compilation he would write code faster than Python by default. Now to truly rival optimized C++ code one has to do the tricks mentioned in this post like optimizing memory access, SIMD and maximizing instruction parallelism.
The key point is you are better off by default and can do some ugly stuff in the critical parts of the code while still using the same language.
But yeah if you're writing a loop or something else where the majority of work is actually being done by python itself, then it's going to typically be much much slower than the equivalent julia code.
This is not possible by definition, or is a misunderstanding of where and how performance occurs. If this is possible, then it is just as easy to perform worse if the beginner steps to either side of the happy path or if their problem doesn't fit the preconceived optimizations and is therefore no longer a "language" but some kind of "library". I think Julia should be seen as a library and not a language because a language is not comparable in this way that Julia likes to handwave away as magic.