Probably even more closed than ever. They tend to become more and more restrictive with every new hardware generation. I wonder where their promised open source announcement they preannounced before.
The large majority of researchers and business getting into NVidia products doesn't seem to find it that relevant, rather what tools, GPU programming languages and hardware they are able to put their hands on.
It does impact businesses. It doesn't prevent adoption for e.g. deep learning, but I haven't seen e.g. GPU-based databases reach broad adoption, or many other places where MIMD/SIMD would reduce costs or improve performance. Using classical hardware is clearly cheaper than the business risk and engineering time of relying on a proprietary, closed hardware solution.
I'm at the edge, where my workloads don't require GPU, but could benefit from it. This sort of thing factors into decision-making. I dabble in GPU, but never beyond prototypes, for those reasons.
I think this is one of the reasons why these devices haven't reached wide-spread marketshare. Most computers sold have an integrated chipset. People buying NVidia GPUs are researchers (who don't care), deep learning applications (who don't have a choice), and gamers. There have been predictions for two decades that GPU-style SIMD and MIMD architectures would displace the centrality of the GPU.
Technically, it makes sense. If I type a list comprehension in Python, it would run at higher speed and lower power on a SIMD or MIMD platform.
I think the reason that hasn't happened is because x86 and x64 are open and widely-supported. NVidia is a walled garden, and is only practical for markets NVidia explicitly targets.
This story plays out over and over. Business people push for closed. Eventually, open comes along, and wipes it out. Sometimes, as with x86 or the iPhone, that leads to increased profits. Sometimes, as with Wikipedia, that kills businesses.