My pleasure. I'm always glad to see a discussion take a turn for the better rather than just sliding off the rails. Unfortunately, it seems as though the value of known-inaccurate simplified models is often enough poorly understood that some people treat them as "Haha, your model doesn't happen in real life, therefore Economics Refuted!" and others treat them as though their results can actually be trusted when talking about the real-world situations that they are intended to help analyze.
In this case, perfect information is obviously not happening(if nothing else, you'd be crowned God-Emperor of HR for all eternity if you actually found a way of objectively ranking an employee's expertise with enough precision to justify the difference between their salary and the category average down to the last dollar, or even the nearest $10k in a lot of cases); but it does seem like a pretty decent example of how a situation goes from being substantially not-'free-market'(information is both imperfect and asymmetric, with Google knowing all the salaries and each employee knowing only their salary) to one that is markedly closer to 'free market'(Google knows all the salaries, each employee knows at least a fair number of salaries; and is negotiating from a position of much better price information).
I admit that my initial post was pretty snippy; I get annoyed at the cries of "SOCIALISM!!!", especially now that the Cold War is over, all the 'communist' states have either collapsed or turned into crony-capitalist states of various flavors; and the closest thing you can find to 'socialism' is capitalist countries with comparatively cushy social safety nets; and whoever the AC was pushed my buttons.
That specific annoyance aside, though, I'm actually rather fascinated by how useful(across a wide variety of disciplines) models that we know are false can be, despite their falsehood. They are wrong; but by being wrong in well defined ways that are amenable to (relatively) simple analysis they can be such a good jumping off point for examining the real world and figuring out how it must be different in order to produce the results you see.