That's what people said about operating systems, and databases, and compilers, and so many other big complicated categories of software that over time became increasingly dominated by OSS
OSS struggles at being relevant when software is non-commodity e.g. office suites. In software domains like databases where the state-of-the-art computer science research is often unpublished, OSS struggles to be relevant at the higher end of the market on technical merits.
When deciding what should be OSS, it is useful to consider the preconditions that have made it successful.
See open weights gaining adoption, OpenAi talking about how 5.6 is cheaper than Fable, people are taking multiple approaches to reduce their token spend, expectations for progress in hardware and algos, and certain Ai leaders talking about how token prices should be 10-100x lower than they are.
Corporations have had many reasons to invest their money in open source software -- custom requirements, marketing / developer mindshare, commoditizing complements -- but as cutting edge LLMs get more and more expensive to train, you'd be hard-pressed to find corporations who will put in that kind of money if they cannot recoup their investments.
If you can not run the training yourself you can not contribute. So open source contribution model does not work. All examples you gave have a fairly low threshold of capital expenditure required to be a contributor (basically a laptop).
Even back in the 90s a person could get a standard, but powerful, PC to do these things. The one exception was 3d graphics which took quite some time to become affordable and even there it was a single one-time expenditure (a workstation) per contributor.
In an open-source LLM model contributors would compete with each other for computing resources for model tweaks and changes. The alternative model is that the contributor pays for the compute, but that increases the bar really high for contributions.