Hilding Elmqvist not only worked on Dymola, he is the original creator of modelica

In fact Dymola was published (including source code) in Hilding Elmqvist's doctoral thesis:

Elmqvist, H. (1978). A Structured Model Language for Large Continuous Systems. [Doctoral Thesis (monograph), Department of Automatic Control]. Department of Automatic Control, Lund Institute of Technology (LTH).

portal: https://portal.research.lu.se/en/publications/a-structured-m...

direct pdf link: https://lucris.lub.lu.se/ws/portalfiles/portal/4602422/85704...

But you can take Fortran seriously?

ASML has sponsored the last couple of JuliaCons and presented there several times https://www.youtube.com/results?search_query=asml+juliacon https://www.youtube.com/watch?v=Nf70byblWEU&t=1347s&ab_chann...

as well as organized a local JuliaCon in Eindhoven themselves

I did see their posts about the Eindhoven one and tried to apply to the relevant positions accordingly but some of the reqs that mentioned Julia explicitly got scrubbed very soon after I saw them.

You mean investment like this? https://info.juliahub.com/juliahub-receives-13-million-strat...

You can imagine what a company like Boeing might be interested in when it comes to a programming language.

Boeing is probably interested in Julia for manufacturing optimization, so their interest is likely in improving the optimization ecosystem around JuMP [1], Optim [2], etc., and compiler improvements related to that.

But we can only guess from the outside, and it's ultimately upto JuliaHub to decide how to spend the money, so I'll cross my fingers and hope that this gets us AoT static compilation sooner!

[1] https://jump.dev/ [2] https://julianlsolvers.github.io/Optim.jl/stable/

13 million is substantial but not even close to the 100 million Modular got. Which really makes me wonder what Modular has been doing with that money, if they're still getting beat in benchmarks like this...

First of all, Mojo is quite new. Secondly, there might not be much CPU performance left on the table for that benchmark, no matter how much money you throw at it.

This is unlikely to be a problem for the vast majority of people using julia as a calculator

The problem is that you won't know when your results are wrong because of low input precision. And when you do know, things will be a bit more complicated.