The internet will fail? Absurd but part true
Sunday, February 28, 2010 at 8:15AM 
This is a delicious look back at the complete #fail, in Twitter vernacular, of Clifford Stroll’s prediction made in 1995 that the internet will fail. In the same way that John Gruber delights in his “claim chowder” pieces (where he publishes claims about technology made by others for the express purpose of showing how misguided they were) this is pretty easy stuff do to - benefit of hindsight etc etc.
However, what is perhaps more interesting is how right he is in many areas. Take this:
“Consider today’s online world. The Usenet, a worldwide bulletin board, allows anyone to post messages across the nation. Your word gets out, leapfrogging editors and publishers. Every voice can be heard cheaply and instantly. The result? Every voice is heard. The cacophany more closely resembles citizens band radio, complete with handles, harrasment, and anonymous threats. When most everyone shouts, few listen.”
About this, of course, he is 100% accurate. And it’s got worse since then. But he missed the $167.5bn solution: Google, and more broadly, the power of accurate filtering of the signal to noise ratio. These tools have proved adept at allowing users to connect with the info, news, friends that they seek without being suffocated by the Usenet dross.
It is for this reason that services like Twitter are proving so successful because they allow people to create groups of influence who they listen to, and further help to distil the important from the vacuous.
And it is Twitter’s restrictive simplicity that has helped make this so effective. By contrast Buzz may also be useful here but its current user experience design actually contributes to noise by allowing much-followed sites like Mashable to overwhelm everything else in one’s inbox with the number of comments (and currently no way to collapse threads).
Anyway, the point is that the future utility of the web is as much about filtration & collation tools providing the hyper-personal information stream (to paraphrase Marissa Mayer & Jeff Jarvis) as the continuing proliferation of available data.

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