This is a complex topic and I think Gelman (the linked poster) is either misinterpreting and/or confused by Kaufman and Glǎveanu (the article he's discussing). Just for some context, I agree with Gelman and KG both in their main arguments.
There's different issues colliding in the open science movement. One is what you're referring to, the fact that scarcity is gone. Combined with the overcrowding and hypercompetitiveness of science, you not only have what you are referring to, a decrease in verdicality, you also have a decrease in signal-to-noise in general. So the nonsense increases, but so do the traditional signals to quality. So nonsense appears in high-profile journals, and very quality work appears in low-profile journals or even as "unpublished" pieces.
The other problem though, what my colleagues refer to as the "science police", is an increasing tendency for certain groups to argue that a certain set of practices are not only good, but necessary for "good" science, and by implication, everything that does not is "bad" science, in a black-and-white kind of way.
For one thing, not all problems are with replication. Nonsense can replicate well, and very important legitimate phenomena can be difficult to replicate. If something is really not replicable at all, that's a problem, but replicability per se is only one part of scientific progress, and it comes in degrees with various causes.
It's also much more difficult to determine what is replicable sometimes than it might seem on the surface. Replicate what? What's important to replicate? How? Sometimes this is clear, but other times it is not.
Also, when you really delve into it, there's not really a good rationale for what, exactly, are the important ingredients for open science, or why. For example, is it really necessary to have preregistered studies? What's to keep someone from preregistering but then silently declining to publish null results? Or to "preregister" something they've already collected? If an important unanalyzed pre-existing dataset becomes available, is that "tainted" because it wasn't preregistered? Is it important to preregister, or just to make the data openly available? Is it better to use modeling to identify anomalies in studies, or to rely on preregistration? These issues aren't always clear.
I think there's a sense sometimes that the open science movement is not only trying to dismantle a broken system run by an established elite, but to replace it dogmatically with a new system run by a new elite, with its own imperfect rules. Already I've seen misuses of open science guidelines used to bully and discredit legitimate work (for example, by suggesting that someone is hiding something by not sharing data, when the data contains protected healthcare information and would be accessible to them anyway if they would just go through proper channels). This is tricky to discuss, as you might imagine, so it comes out in pieces like KG's piece. Gelman is asking "why not publish everything", which is responding (I think) to something different from what KG are responding to. Maybe I'm misreading KG, but I think they might also argue "why not publish everything"; they just have a different group they're addressing when they would say that.