Definitely. My (indirect) point was that scientific computing is a much less competitive area than Go and Rust are targeting. And I can imagine a world where NumPy, R, and Julia are all strong projects.
Arguably, lack of commercial support is a bigger problem for mature projects than young projects. Whether or not Julia pulls away users from R probably has more to do with how much R adapts to new directions in statistics than with anything Julia does (assuming that julia passes a minimum threshold of credibility, which it probably doesn't yet for most statisticians.) A lot of my interest in julia comes from a specific simulation that I need to run to prototype a new estimator that would be infeasible in straight R. If R were faster, I wouldn't bother.... (To preempt the obvious reply, I could always write the slow part in C. But if I need to introduce a different language anyway, why not look at other options?)
Arguably, lack of commercial support is a bigger problem for mature projects than young projects. Whether or not Julia pulls away users from R probably has more to do with how much R adapts to new directions in statistics than with anything Julia does (assuming that julia passes a minimum threshold of credibility, which it probably doesn't yet for most statisticians.) A lot of my interest in julia comes from a specific simulation that I need to run to prototype a new estimator that would be infeasible in straight R. If R were faster, I wouldn't bother.... (To preempt the obvious reply, I could always write the slow part in C. But if I need to introduce a different language anyway, why not look at other options?)