According to the post, the prediction accuracy of Multi-issue bargaining model described in the post is high enough that the technique dramatically improved negotiation tactics in the following areas:
Negotiating harder: The new agents held longer conversations with humans, in turn accepting deals less quickly. While people can sometimes walk away with no deal, the model in this experiment negotiates until it achieves a successful outcome.
Intelligent maneuvers: There were cases where agents initially feigned interest in a valueless item, only to later “compromise” by conceding it — an effective negotiating tactic that people use regularly. This behavior was not programmed by the researchers but was discovered by the bot as a method for trying to achieve its goals.
Producing novel sentences: Although neural models are prone to repeating sentences from training data, this work showed the models are capable of generalizing when necessary.
To be honest, NYT is inaccurate about many things. Some of them knowingly and some maybe unknowingly. Though this doesn't concern the ongoing discussion topic, I thought to mention it since you are explicitly advising people to read NYT.
Found this interesting Machine Learning styleguide from Google. It describes how they apply ML to various products as well as best practices they have discovered in the process. A little long but extremely insightful read if you build or are thinking of building products which use ML.