Now that we’ve blogged about our thematic investing approach at Foundry Group, we thought we’d spend some time explaining some of the themes we are excited about. Yesterday we wrote about our interest in next-generation human-computer-interaction applications and technologies, and today we are going to talk about another one of our active themes.
One of the areas we are deeply interested in is what we (and many others) call the implicit web. While it may be imperfect as an umbrella term, it is easier for us to say (and still respect ourselves) than something like Web 3.0, which we sincerely hope does not ever enjoy the ubiquity (and subsequent meaninglessness) that the Web 2.0 moniker attained.
As often happens, our interest in the implicit web evolved out of another, earlier theme we’d been working on, RSS, which led to our prior investments in FeedBurner (now part of Google), NewsGator and Technorati. As we explored the world of RSS, we began blogging and subscribing to hundreds of blogs and mainstream-media news feeds. When we added this new deluge of content to the information onslaught we were already experiencing from email and other information sources, it quickly became apparent to us that the computing tools we used every day would need to evolve to help us cope with the familiar (yet ever increasing) problem of information overload. Around the same time we added feed-reading to our growing list of required daily activities, we were also beginning to spend time maintaining our (stove-piped) social networks in LinkedIn, Facebook and elsewhere.
While there is huge value to be gained through the judicious use of these new tools and technologies, the overhead required to manage them can be onerous. We could all gain even more leverage from these new technologies if our computing environment had even the most basic understanding of how the people, places, things and actions in these different tools were related to one another.
We think of the technologies that fall under the implicit web theme as a next-generation set of applications, tools and infrastructure that stitch together a long list of interrelated and overlapping ideas: the academic and theoretical ideas behind the Semantic Web, the utility of social networks and social media, crowd sourcing/wisdom-of-crowds, folksonomy, user attention data, advanced search and content analysis tools, lifestream analysis and numerous others.
When combined, these technologies offer the promise of a more unified computing environment that spans the applications where a user consumes and creates information (email clients, web browsers, RSS readers, etc) and is aware of the user’s preferences, interests and interpersonal relationships without requiring a ton of heavy lifting on the user’s part to get useful work done.
Naturally, there are many possible embodiments and applications of these ideas. For example, consider messaging and social networks — why do we have to explicitly program multiple sites with our social network data again and again? Wouldn’t an analysis of a user’s actual messaging traffic across twitter and their various IM and email accounts provide a better, more empirical view of their true social network than the one that was explicitly input into LinkedIn that may be more aspirational than actual?
As another example, suppose a user reads every last word of Brad’s posts about technology and entrepreneurship, but always skips over his posts about the show 24 and running marathons. And suppose that same user not only reads all of Fred Wilson’s posts about venture capital but also takes the time to read each of his posts about music and always downloads the mp3s that Fred posts to his blog. Perhaps our compute infrastructure (which includes all the web apps and client apps and computers and devices a person uses) should help the aforementioned user by placing Brad’s technology and entrepreneurship posts and Fred’s VC and music posts at the top of his to-do list, put Brad’s running and TV posts at the bottom of the list, and download Fred’s recommended songs while also suggesting additional related bands and noting upcoming nearby performances of those musicians.
Tools and applications like this are certainly possible today — some aspects may be fiendishly difficult to implement, while others “simply” require a novel combination of tools and a UI that will appear obvious in hindsight after they have spurred mass adoption.
While much of this may sound theoretical, we have already put our money where our mouths (brains?) are. In our portfolio, Lijit is our initial investment in the implicit web theme. Lijit produces search tools and statistics for bloggers and publishers that provide incredibly rich and highly targeted search results based not only on the content that appears on a blogger or publisher’s local site, but also extends out to the broader network of content related to the publisher — it searches their photos and tags on sites like del.icio.us and flickr, and also searches content found and produced by people who appear in that publisher’s blogroll and social network. In effect, Lijit produces a custom search engine that includes the universe of content related to a specific person or publisher, providing highly relevant results by taking a person-centric and relationship-aware view of the web.
Of course, we are actively looking for other investment opportunities that fit within this theme, so if you are working on something interesting, we’d love to hear about it.