[TeMPOraL]

I see your point better now, thanks for clarifying.

> When their primary financial incentive is user engagement to sell ads, I have a hard time believing that the latter doesn't influence the use of my data as much as the former.

I don't agree with it in case of Google. I may be mistaken, but my impression was that they sort of separate their products into two groups - the ad-related are earning the money, and then the money is spent on funding something else (like GMail), with little direct connection between the two groups. That is, products like GMail certainly help Google earn more money on ads, but are not themselves optimized for ad-related purposes.

> I think that machine learning has tremendous things to offer society, but I'm not sure I'm super excited about a company using smartish machines to pull meaning and context from my emails so that we can save some percentage of users 10 seconds when they reply to something.

I think you're seriously underestimating the potential for productivity gains here. Mobile use case is perfect for replying to e-mails, but mobile experience totally sucks. This service, if it works as advertised, will be probably saving not 10 seconds, but something like one minute per e-mail. That + friction reduction have enabling properties that make people do things they didn't before (e.g. I often don't reply to e-mails on a phone only because it's too slow, opting to browse Facebook instead), and aggregated that can help liberate a lot of time.