The big difference is that if a competent plumber looks at a sink, it will take them 30 seconds to figure out. If a competent programmer looks at a new codebase with 30000 lines of code, it will take them 6 months to understand it, and then still be missing 80% of the details.
The other difference is that for most modern ice, you get plenty of torque, but only a quarter second after you asked for it due to turbo lag. With an EV, the torque is instant.
one interesting thing is that you will fairly frequently see professional orchestras be out of phase with the conductor, but everything still works out.
Is it? The fundamental reason the war on drugs failed is that drugs have relatively inelastic demand. Someone addicted to meth will find a way to get meth if it costs $10 or $500. Software exploits aren't like that at all. They are a tool used by gangs and governments that are only useful if they cost less than they're worth.
Imo, the solution is to stop using C/C++ for high performance applications. Today's computers are diverse enough that distributing compiled binaries leaves a ton of performance on the table.
The short answer is that changing the type system to allow multiple inheritance (traits) involves solving some really hard and open-ended problems (eg method specificity). Solving this is probably breaking, and requires a ton of work on a problem that might not even be solvable. If I had to guess it will be solved by the language that replaces Julia in 20 years.
Short list of current priorities (in no particular order)
Making it easier to interface with the compiler (this is step 1 on better ability to static compile/debug)
Better garbage collection
More performance
Reducing the memory footprint of small strings
Lowering the overhead of multithreading
It's worth noting that Julia doesn't really have a core team. A lot of the development is done by a pretty large community of developers without any central management.
This is doable in Julia too. DifferentialEquations.jl has python and R bindings. I expect more to follow as Julia starts getting more best in class packages.
Is there anything you think that prevents Julia from being general purpose? The main one I run into is that the garbage collector needs work, but fundamentally I don't think there's much that keeps Julia from being good as a general purpose language.
I think Julia has a pretty good path forward since it is often faster than C++ (especially once you consider language interop overhead), and it is much more pleasant to write than c++. Since Julia and most of the packages are written in Julia (for the most part), Julia makes it really easy for users to become developers. Python and R have much bigger barriers to entry since users will usually hit a binary blob, which makes it much harder to interact with library implimentations.
The hard part is making it produce the correct results even if the user does something like change the definition of +. It's not un-solvable, but it needs a couple hundred hours of work by people who are very busy.