For example, every time you shop for a book or a DVD on Amazon.com, Amazon automatically compiles databases of information about what you search for and what you purchase, taking this information as a proxy for recommending to you other items that you may be likely to purchase too. Most of us are familiar enough by now with "Web 2.0," by which users (or, in business terms, patrons) generate content and fill huge information databases for the very services they patronize. "Web 3.0," then, is vaguely characterized as a system in which, based often on prior user-generated information, we can produce computer-generated information that merges with real-time activity in our daily lives.
We seem to value information today as we always have, but with one very significant difference. Today, information means something very different than what it used to.
As this fascinating review explains, there came a point at which it was understood that information could be deployed more effectively were it understood as independent from meaning--what seminal information theorist and applied mathematician Claude Shannon understood as a mathematical abstraction instead:
The enormous success of information theory came from Shannon’s decision to separate information from meaning. His central dogma, “Meaning is irrelevant,” declared that information could be handled with greater freedom if it was treated as a mathematical abstraction independent of meaning.
This understanding still largely informs our means of compiling, distributing, and transferring information today. Our information is largely automated and industrialized, its "democratization" frequently just a democratization of the labor cost of generating information itself.
What each of us no doubt finds in our daily lives, however, is that despite all the information we have, and how easy and cheap it is to get it, we also trust our information less and less. While we continue to place tremendously high value on "information" writ large--information as abstraction--the material value of information is rather low. On any number of political websites, for example, one can read scores of patently false information masquerading as truth. Three people can reach a series of economic conclusions by looking at the same chart. The foods we're supposed to load up on and those we're supposed to stay away from seem to switch off and on every three years. And because information is so deeply and systematically commodified, false advertising and deliberately, meticulously, scientifically misleading information are the norm. This state of things has long since been called "information overload," among other names; but "overload" isn't the only problem. This is what happens when information "evolves" beyond meaning.
Much of this problem has to do with information's response to hypercapitalist demands. Quantifying all normative judgments is cheap and efficient, and quite often very accurate. Just like a processing line of tin cans on a conveyor belt can get filled with tomato soup faster and cheaper than by hand, quantitatively analyzing huge databases of information can mass-produce meaning, or at least a proxy for meaning. Causal relationships can be theorized or brought within a statistically acceptable margin of error. Meaning can come cheap and fast.
Right?
It's worth considering, on the contrary, that what we get from this mode of industrialized processing of mass information isn't actually meaning at all, in any traditional sense of the word. What we get is, simply, more information--a simulacrum of meaning that can be deployed toward innumerable ends, but hardly ever trusted for itself. In this sense, quantification of value, which was always supposed to debunk and render obsolete the unreliable prejudices of subjective judgment, has actually become the most powerful enabler of unrigorous subjectivity. Literally anyone can find "scientific information" to back up a subjective claim without doing the work--and taking the oh-so-expensive time--of applying themselves to reason, skepticism, and careful observation.
