As far as I've heard, the hopes for Cloud TPUs in general are that people will think it's an amazing service worth paying for.
The hopes for TFRC are more complicated, as you might imagine when a company is giving away free stuff, but one of them, I'd put in the "long-term benefit altruism" category: It's quite hard to do some kinds of machine learning research in academia, because it can be fantastically expensive. We just blew $5k of google cloud credits in a week, and managed only 4 complete training runs of Inception / Imagenet. This was for one conference paper submission. Having a situation where academia can't do research that is relevant to Google (or Facebook, or Microsoft) is really bad from a long-term perspective, and one thing TFRC might do is help make sure that advances in deep learning continue at a rapid pace.
From watching the rate at which my deep learning colleagues get swallowed up by industry, I think it's a very valid concern, and I'm very supportive of all of the industry efforts we're seeing to try to address this. It's good for the entire research ecosystem.
(There are undoubtedly many other reasons, such as those noted below by minimaxir, but this is the one I personally feel the pain of.) Disclaimer: I get paid by Google part-time to do work related to this, but this is not any kind of official statement. The pain-of-ML-in-academia bit is purely from my hat as a CS professor.
Not everything has to be a data play in the big picture.
Before anything else, the format and schema of the customer data would have to be analyzed and converted to data structures that match Google's internal models. While I imagine a computer could do it, I certainly would want a human to verify that the analysis makes sense.
Assuming this has been done, they are then at the mercy of the customer as far as whether the data is accurate, whether it is complete, how often it is updated, etc.
At the end of the day, I don't see how they would build a reliable business around arbitrary data structures which they have no control over. Information you can't trust is pretty useless.
Edit: They would also have to understand how the data was selected. Looking at a series of data points, you would wonder if all these are from Arizona, or all from the year 1976, or all from color blind individuals. Without understanding such limitations, making any sort of deduction from a dataset will just lead you the wrong way.
> how they would build a reliable business around arbitrary data structures which they have no control over
Google Search? The entire web could be described exactly like that.
Another point here is that Google Search isn't an authoritative source of information, it is up to the end user to inspect the returned links and decide if they can trust that site. This is something that I would not try to automate to the point that I could ask users for money in exchange, and if it can't be automated it doesn't seem like a great fit for Google.
†: Parses unreliably at best, Turing-complete at worst. (aka javascript if you didn't catch that)
Nah, the user's mood may be ruined if the search results are junk, but ruining the the model you're trying to build has vastly more costly consequences. I don't think the tech is there (just yet) to have some code simply ingest whatever comes its way, chew it up and use it well; required xkcd (today's!): https://xkcd.com/1838/
"Share the benefits of machine learning with the world"
It doesn't say who shares what :)
Not everything they do is about short-term data plays.
If the data can be used to improve the model it seems like data could be used to damage it, in theory.