When we enter the world of refuse and waste, we cross over into a mirror-image economy. In the "normal" world, we pay to acquire things; on the other side of the looking glass, we pay to get rid of them. Junk isn't merely worthless; it has negative value.
A chemical engineer once told me about a recent improvement in a manufacturing process; by fine-tuning a chemical synthesis he had increased the yield of a certain commodity from 98 percent to 99 percent. I congratulated him, but I couldn't help remarking that this seemed like a rather paltry improvement. "Ah, you miss the important point," he said. "The amount of waste goes from 2 percent down to 1 percent. It's cut in half. We save tremendously on disposal costs."
There’s a movement called the circular economy which is about designing services that don’t include throwing things away. There is no “away.”
A non-extractive economy is going to look very different to today’s economy. These points feel opposed somehow but they are part of the same movement:
With CupClub, it’s all about infrastructure.
With the battery-free Game Boy, it’s untethered from infrastructure: once manufactured, no nationwide electricity grid is required to play.
We’ll need better tools to track and measure. There will be new patterns for new types of services. New technologies to build new products. New language. So it’s fascinating seeing the pieces gradually come together.
Miners seek valuable nuggets of ore buried in the earth, but have no control over what is out there or how hard it is to extract the nuggets from their surroundings. ... Similarly, data miners seek to uncover valuable nuggets of information buried within massive amounts of data.
Farmers cultivate the land to maximize their yield. They manipulate the environment to their advantage using irrigation, pest control, crop rotation, fertilizer, and more. Small-scale designed experiments let them determine whether these treatments are effective. Similarly, data farmers manipulate simulation models to their advantage, using large-scale designed experimentation to grow data from their models in a manner that easily lets them extract useful information.