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Thanks for your detailed comments, and will give your Model of Attraction document a read. Specific to your comments, I am particularly interested in the question of how related content items get linked together. Clearly, in the optimal world, I or others in my trusted network can grab stories of interest, product listings, product reviews, manufacturer's data, pricing information, etc., pull them into our personal content store, and the logical linkages can autonomously be built between the different items pertaining to a context. One example is, “Show me the five items drive a buying decision on a given product.”
Today, this process can work pretty well manually, and occurs all the time on an ad hoc basis (e.g., ask a friend who recently bought a digital camera what they bought and why). It seems with better tools that more of this process can get automated (caveat: I am a heavy skeptic of applications that are overly dependent on AI type of functionality).
Making information more “magnetic,” as your slide presentation alludes to, seems to be the right way to go. Proving this out in the real world is largely (in my opinion) a question of finding the balance between:
1. Manual vs. automated procedures: algorithmic analysis of text within content/links between documents/aggregate maps of a user’s click steps vs. simpler techniques like user-defined key word stamping
2. Personal, inter-personal and global data sources: personal information vs. trusted network sources vs. Google search-able
3. Information abstraction models vs. optimized for a specific run time environment: Always on and accessible anywhere on any device or within any application just makes sense in the age of mobility and in a world where we all have multiple modalities (home/work is a simple once) and truly good information is re-usable, but there is the lingering question of when highest common denominator vs. lowest common denominator practices prevail. My posting on When client applications matter: http://www.oreillynet.com/pub/wlg/3819 speaks to this one somewhat.
Thanks again.
Mark
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