When I worked at Google I made a case for doing protein design/preliminary drug discovery using Google infrastructure and it was well received by the leadership. The leadership at Google is mostly computer scientists who know about, but can't actually do, leading-edge life sciences research, and they want to contribute some amount of Google's resources to advancing the state of the art. That's the only reason exacycle was permitted- because Urs thought we could maybe help save the world with protein design (and it wasn't a good approach because it wasted enormous amounts of power on unbiased sampling of large proteins).

Honestly I don't think Google proper is really a good place for this work to be applied, though. Their attention is easily diverted, they repeatedly fail to commercialize, and most importantly, potential partners are scared Google will steal their data, and replace their business.

I play Go recreationally. I don't think I can use AlphaGo (or its successors) directly, but the published research on AlphaGo has inspired other strong Go AIs. Online Go platforms integrate them to offer AI matches as well as analyze games between humans. I also know that professionally ranked players are adopting things learned from AI into their own play, and a lot of traditional joseki (analogous to chess openings) are being rethought based on insights from AI play.

But more immediately this is an interesting and relevant problem to solve, so it servers as a tool to benchmark and improve AI... and the current theory is that at some point AI will {generate unlimited amount of wealth | lead humanity into the post-scarcity society | solve all human problems by eliminating humans}.