There was a fascinating article in last week’s Economist about the future of space travel,* which looked at how, for the fourth time in 60 years, space is revolutionizing the way people think about the planet.
The first revolution, it said, was an “anywhere” revolution, in which satellites could look wherever they were needed, allowing Cold War enemies to assess one another’s stockpiles and capabilities, and thereby keep the peace.
The second revolution was an “everywhere” one, with pictures of the Earth and a shared global understanding of the need for ecological protection and green politics.
The next revolution was another “anywhere” one, in which GPS and other technologies let you know where you are, wherever you happen to be. Think about the time we save, and the efficiencies and new technologies produced by global positioning.
The fourth revolution we're in now is both “anywhere” and “everywhere,” in which the Earth itself is increasingly transforming into a gigantic set of data that can be used for analysis and new ways of thinking. Examples include cross-referencing radio signals to satellite feeds to predict crop growth, or tracking planes mid-flight, and the meta-data that results from these practices.
Similarly, the way we think about community, and who we are as individuals within it, is also undergoing an anywhere-everywhere revolution right now. Because what we do as individuals anywhere keeps on having an impact on people everywhere. We don’t always like being reminded of this, but sometimes we’re really proud of it. The way we preserve our planet’s resources, the way we stand up for what we believe in. The way we help others.
There's a good reason why one of the basic building blocks of Jewish Federation is “do good everywhere from anywhere.” It’s because in this accelerating technological world, whose boundaries and horizons are rapidly expanding, even to space, we can’t go it alone. We can’t survive as individuals. Anywhere you go, community is everywhere.
*fascinating for the nerds among us, at least
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