Is your [Dogon's] reference to "your model" a reference to 'my [shanen's] preferred financial model' (obliquely referenced in the original question) or a reference to Vladimir_Nesov's comment?

In the first case, my "preferred financial model" would involve cost recovery for services shared. An interesting example came up earlier in this discussion in relation to recognizing consistency in comments. One solution approach could involve sentiment analysis. In brief, if you change your sentiment back and forth as regards some topic, then that would indicate negative "consistency", whereas if your sentiment towards the same topic is unchanged, then it indicates positive consistency. (If your sentiment changes rarely, then it indicates learning?) So in the context of my preferred (or fantasy) financial model, the question becomes "Are enough people willing to pay for that feature?"

Now things get more complicated and interesting in this case, because there are several ways to implement the feature in question. My hypothesis is that the solution would use a deep neural network trained to recognize sentiments. The tricky part is whether we yet know how to create such a neural network that can take a specific topic as an input. As far as I know, right now such a neural network needs to be trained for a specific domain, and the domain has to be narrowly defined. But for the sake of linking it to my financial model, I'm going to risk extending the hypothesis that way.

Now we get to an interesting branch point in the implementation of this feature for measuring consistency. Where do we do the calculations? As my financial model works, it would depend on which approach the users of the feature wanted to donate money for. I'm going to split it into three projects that could be funded:

1. Developing the deep neural network to analyze sentiments towards input topics. This is basically a prerequisite project and unless enough people are willing to fund this project the feature is DOA.
2. Analyzing the data with the neural network on the LW (LessWrong) side. In this version of consistency measurement there would be a lot of calculation on the LW side testing sentiments against topics, so there would be both a development project and a significant ongoing cost project. Both parts of this double project would need sufficient donor support to use this approach.
3. Analyzing the data with the neural network on the users' side. In this version of consistency measurement, the tedious calculations could be removed from LW's servers. The trained neural network would be downloaded and each person would calculate (and optionally share) the consistency metric using the data of that person's own comments. The cost of the development project should be similar, but there wouldn't need to be donors for a major ongoing cost project. (I would actually favor this version and be more likely to donate money for this version due to privacy considerations.)

(If there are enough donors, then both 2 and 3 could be supported. However, deciding which one to implement first could be determined by which project proposal attracts enough donors first.)

In the second case, I'm afraid I don't understand what part of Vladimir_Nesov's comment was about a "model". And you weren't talking to me, anyway. And I should also apologize for my longish and misdirected response?