That is not a very charitable characterization of the position of multiple dispatch in Julia. It's not something that's optional: multiple dispatch is essential for the performance that Julia is looking to achieve. If you notice where acceleration DSLs tend to have trouble, you'll notice that it's always at the point where you get beyond built-in float primitives and onto object support. For example, Numba's object mode has the caveat that "code compiled in object mode will often run no faster than Python interpreted code, unless the Numba compiler can take advantage of loop-jitting", where loop jitting is simply the ability to prove that some loop is compatible with moving to the nopython mode.
The reason why Julia is fast is because automatic function specialization to concrete dispatches gives type-grounded functions which allows the complete optimization to occur on high-level looking code (see https://arxiv.org/abs/2109.01950 for type-theoretic proofs). It's basically a combination of (1) define a type system in a way that allows for type-grounded functions and compile-time shape inference (shape as in, byte structure of the structs), (2) define a multiple dispatch system with automatic function specialization on concrete types, (3) have a typed IR which proves and devirtualizes all dispatches before hitting the LLVM JIT. If you simply slap the LLVM JIT on random code, you will not get that performance. But now because multiple dispatch is fundamental to performance in the language, the rest of the "game" for the language is how to design an ergonomic language around this feature and how to teach people to use it effectively as a problem solving tool.
You actually see something similar going on in the world of Jax. With Jax, you need to be able to perform abstract interpretation to the Jax IR. In order for this to be possible with the interpreters Jax has, the functions that are being interpreted need to always have the same computational graph for the same inputs, i.e. they need to be pure functions. This is why Jax is built on functional programming paradigms. It would be similarly uncharitable to say the reason why Jax does not embrace OO is because the developers just love functional programming: the programming paradigm choice clearly falls out of what the tools needs to do.
It remains to be seen if Jax is the tool that makes more people finally embrace functional programming styles, or if enough people see pervasive performance necessary enough to change to the multiple dispatch paradigm of Julia. But what is clear is that tools that are moving away from OOP are not doing so arbitrarily, it's all about whether doing so is beneficial enough to justify the change.
The reason why Julia is fast is because automatic function specialization to concrete dispatches gives type-grounded functions which allows the complete optimization to occur on high-level looking code (see https://arxiv.org/abs/2109.01950 for type-theoretic proofs). It's basically a combination of (1) define a type system in a way that allows for type-grounded functions and compile-time shape inference (shape as in, byte structure of the structs), (2) define a multiple dispatch system with automatic function specialization on concrete types, (3) have a typed IR which proves and devirtualizes all dispatches before hitting the LLVM JIT. If you simply slap the LLVM JIT on random code, you will not get that performance. But now because multiple dispatch is fundamental to performance in the language, the rest of the "game" for the language is how to design an ergonomic language around this feature and how to teach people to use it effectively as a problem solving tool.
You actually see something similar going on in the world of Jax. With Jax, you need to be able to perform abstract interpretation to the Jax IR. In order for this to be possible with the interpreters Jax has, the functions that are being interpreted need to always have the same computational graph for the same inputs, i.e. they need to be pure functions. This is why Jax is built on functional programming paradigms. It would be similarly uncharitable to say the reason why Jax does not embrace OO is because the developers just love functional programming: the programming paradigm choice clearly falls out of what the tools needs to do.
It remains to be seen if Jax is the tool that makes more people finally embrace functional programming styles, or if enough people see pervasive performance necessary enough to change to the multiple dispatch paradigm of Julia. But what is clear is that tools that are moving away from OOP are not doing so arbitrarily, it's all about whether doing so is beneficial enough to justify the change.