AoT compiled binaries and libraries are fundamental building blocks for distributing software and running it quickly without latency. Julia itself calls AoT bi

AoT compiled binaries and libraries are fundamental building blocks for distributing software and running it quickly without latency. Julia itself calls AoT binaries, for example when calling Linear Algebra functions [1]. Chris Rackauckas's team had to leverage AoT compilation (i.e. system images) to reduce TTFX on DifferentialEquations.jl [2]. PyTorch and Tensorflow run without TTFX because they use AoT compiled binaries. What's more, most end users don't have to compile anything on their computer--they can simply download the binaries and libraries. For small scripts, yes you can simply distribute the package and have the user recompile on the fly. But imagine if your operating system had to recompile every time you turned the computer off. Or if an autonomous vehicle had to recompile an unseen patch of code while in motion. [1] https://docs.julialang.org/en/v1/stdlib/LinearAlgebra/#Standard-functions https://docs.julialang.org/en/v1/stdlib/LinearAlgebra/#Stand... [2] https://sciml.ai/news/2022/09/21/compile_time/ https://sciml.ai/news/2022/09/21/compile_time/