I've found, from working with scientists and non-software specialists at NASA, universities, and various industries, that the big wins come from specialists learning to code as the vanguard, then a few engineers taking over for production. I think that's ever more true with AI assistance, and I'm not sure what is left for product-ization. All I know is it's changing fast.
It's always something I've hated, coming from software and working with experts. I always wanted to be the "software guy" who gets to learn the specialist engineering disciplines. It just felt like swimming upstream and the incumbent specialists always wanted to DIY.
Having said that, some subject-matter experts just don't have the stomach for it. They are so separated from software that they have a block to building even with AI. A good s/w-specialist pair is dynamite there.
> the big wins come from specialists learning to code as the vanguard, then a few engineers taking over for production
The best places to work are ones with high mutual respect between the 'R' and 'D' of R&D. When either camp dismisses the value of the other and everyone stops giving a little to make the handoff easier it just makes everyone miserable.
Yeah what goes unspoken in all these AI discussions is that there's a culture of collaboration that just evaporates when everyone is pushing their own ideas.
There's very little mutual discussion, brainstorming, anything. The slowness inherit in building software before made those discussions almost necessary, now they're post-hoc criticisms (PR "review") rather than fulfilling collaborations.
It's fine from a technical sense, but I do wonder what it does for culture.
"that the big wins come from specialists learning to code "
I used to work at startup that did video encoding. We had tons of computer vision scientists that refused to learn decent C++ coding practices. Their stuff never worked right and they didn't really contribute much. We also had a few scientists who were decent coders and pretty much all progress came from them.
I see the same with some data scientists. They want to do pure data science and refuse to do the coding to actually get their data. So they have to be spoon fed all the time and their progress is super slow.
It's always something I've hated, coming from software and working with experts. I always wanted to be the "software guy" who gets to learn the specialist engineering disciplines. It just felt like swimming upstream and the incumbent specialists always wanted to DIY.
Having said that, some subject-matter experts just don't have the stomach for it. They are so separated from software that they have a block to building even with AI. A good s/w-specialist pair is dynamite there.