That's the issue though, this wouldn't be similar to your typical usage. Instead, if they're using your GPU to train neural networks, it'll be running close to or at full capacity.
I realize that you rounded the costs up, but lets just look at the costs of a GPU often used for machine learning - Nvidia GTX 980 TI. According to Nvidia, it draws 250W under load which according to your figures would result in a yearly cost of roughly $344. That's just for the reference card, a typical card that a consumer would purchase would draw even more. You can buy a 980 TI for a little more than $400. That doesn't even begin to look at hardware actually designed for commercial and research applications.
I think that it's possible to find a way of monetizing computer resources, however, I think it has more to do with arbitraging differences in electricity costs. Suchflex's model certainly wouldn't work where I live (electricity costs in NYC are roughly 20 cents per kw-hour) but parts of the US are under 10 cents. I could see a company attempting to profit from these differences by setting up hardware in a cheap state and negotiating a favorable electricity rate. Heavy computation could then be done on these networks for significantly less than it could in New York or California.
In summary, the value of a consumer's unused computer has more to do with their electricity rate than their hardware.