September 25 2014
by Todd Hixon

The Rise Of The Spoon-Fed Internet

[This post first appeared at on September 23, 2013.]

I’m a long-time iGoogle user, and iGoogle is going away on November 1. iGoogle is Google’s personal homepage: one page on which my favorite news feeds and current information like weather and stock indexes appear. Why did something that is so useful to me disappear?

The answer, of course, is: relatively few people use it.  The official Google blog explains: “We originally launched iGoogle in 2005 before anyone could fully imagine the ways that today’s web and mobile apps would put personalized, real-time information at your fingertips … the need for iGoogle has eroded over time, so we’ll be winding it down.” Google clearly wants a user’s “home” page to be Google+, Google’s answer to Facebook.

What options are out there for jilted iGoogle users? Various independent sites offer an RSS reader and gadgets: ProtoPage is a good example. But ProtoPage seems to be a lightly-invested, infrequently updated site, suggesting that it’s not that strong as a business. MSN offers far less customization and seems relentlessly focused on promoting Microsoft products and celebrities. The major site that comes closest is My.Yahoo. My.Yahoo does much of what iGoogle did, received a recent design refresh, and offers to import iGoogle settings.

In fact, My.Yahoo brings together the major elements of Yahoo’s product spectrum: news, weather, financial information, email, and the Flickr photo sharing site. But Yahoo is struggling: revenue declining, frequent CEO change, and viewed as a Web 1.0 company. This says something interesting about where the web is going, especially with the rise of mobile.

Internet users are receiving an ever-more filtered, spoon-fed information stream. Search does this inherently: it filters the web and delivers information that best matches the user query, his/her inferred buying intent, and what Google’s history with that user implies the user wants to see. Facebook and Twitter add social filtering: the user sees information from or endorsed by friends or people s/he has chosen to follow. Advertisers fuel this system because they pay most when their message displayed to users with the characteristics of their target market and the highest level of inferred buying intent. And mobile amps this phenomenon up by making us view the web through a smaller window: smaller screen and less bandwidth. Hence, information has to be displayed more selectively, giving value to filtering.

The most successful internet companies are the ones that can filter most powerfully and adapt their approach to mobile: Google, Facebook, and Twitter all do this very well. AOL’s turnaround makes sense in this context too: AOL has become a source of highly edited or curated content, which plays well in a filtered, small-screen world.

Meanwhile, Yahoo remains an information smorgasbord. Yahoo gets criticized for lack of focus (more), but I think the real issue is failure to filter: to design and cross reference its products to gather all possible information about the user and exploit it to spoon-feed information, which is what Millennials seem to want, what fits well with mobile, and what advertisers pay for.

So I suspect that the spoon-fed internet is the future, and perhaps the next mega consumer web IPO will be a company that invents and perfects yet another powerful way to filter the information stream to create deep value for users and advertisers. As an investor I will be looking for that team.

My.Yahoo appeals to me, however, because I like to keep my eye on a variety of topics and scan a lot of headlines to find the ones I think are most important, and I don’t want Google or Facebook to tell me what matters. I use Duck-Duck-Go, the search engine that pledges not to track or bubble*, for similar reasons. I hope there are enough people like me to make keep vialbe some information sources for which the user controls the filter.


*Search engines “track” when they keep a record of past searches (plus other information) and use that to determine user interests and buying intent. They “bubble” when they filter search results on the basis of information they have acquired about the user; hence different users who make the same search at the same time will see different results.

Image credit: picturepartners / 123RF Stock Photo


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