His contention is the same as mine – that we have always had too much data to contend with, and so arguments that claim that we currently face numerous problems because of an attention deficit are starting from a false premise. You should read the whole post, but here is one key section of Boyd’s argument:

In the final analysis, I am saying there is no ‘answer’ to those that say we are overloaded, that we are being driven mad by or enslaved to the tools we are experimenting with, or that there is some attention calculus that trumps all other value systems.

Instead, I suggest we continue experimenting, cooking up new ways to represent and experience the flow of information, our friends’ thoughts, recommendations, and whims, and the mess that is boiling in the huge cauldron we call the web.

I suggest we just haven’t experimented enough with ways to render information in more usable ways, and once we start to do so, it will like take 10 years (the 10,000 hour rule again) before anyone demonstrates real mastery of the techniques involved.

There is no “answer” since they are asking a false question, one that hides preconceived premises and biases. Starting out with the assumption that we have moved past our abilities to cope with the stream of information, and therefore something has to give, is a bias.

I think that this is correct. However, the issue of attention economics is still valid – the question of how we allocate limited attention to overwhelming amounts of available data is critical. And as Tom Peters suggests, there are significant advantages that accrue to those that are able to intelligently process larger amounts of data:

The biggest change that we face now is not the fact that there is too much information that we have to process, but rather the tools that are available to help us – we are much more able now to outsource aspects of data collection and processing. And fortunately, there are some very smart people thinking about how we can best take advantage of the available tools. In addition to the blog by Boyd, those of Harold Jarche and Venessa Miemis have many good ideas on this topic. So does George Siemens – who teaches a class which involves intentionally overwhelming the students with data. Their success in the course is then dependent upon how well they are able to build and use a data-gathering and -processing network.

I like the prescription to win by outreading the competition. But I’m still working out the best way to do it. If anyone has any further suggestions or tips, I’d love to hear them.