Best of 2019: How AI Killed Google’s Social Network

[February 4, 2019] Facebook turns 15 today, after announcing last week a record profit and 30% revenue growth. Also today, “you will no longer be able to create new Google+ profiles, pages, communities or events,” in anticipation of the complete shutdown in April of Google’s social network, its bet-the-company challenge to Facebook.

Both Google and Facebook have proved many business mantras wrong, not the least of which is the one about “first-mover advantages.” In business, timing is everything. There is no first-mover advantage just as there is no late-mover advantage (and there are no “business laws,” regardless of what countless books, articles, and lectures tell you).

When Google was launched on September 4, 1998, it had to compete with a handful of other search engines. Google vanquished all of them because instead of “organizing the world’s information” (in the words of its stated mission), it opted for automated self-organization. Google built its “search” business (what used to be called “information retrieval”) by closely tracking cross-references (i.e., links between web pages) as they were happening and correlating relevance with quantity of cross-references (i.e., popularity of pages as judged by how many other pages linked to them). In contrast, the dominant player at the time, Yahoo, followed the traditional library model by attempting to build a card-catalog (ontologies) of all the information on the web. Automated classification (i.e., Google) won.

Similarly, Facebook wasn’t the first social network. The early days of the web saw SixDegrees.com and LiveJournal and, in 2002, Friendster reached 3 million users in just a few months. MySpace launched in 2003 and 2 years later reached 25 million users. These early movers conditioned consumers to the idea (and possible benefits) of social networking and helped encourage increased investment in broadband connections. They also provided Facebook with a long list of technical and business mistakes to avoid.

There was also a shining example of a successful web-born company–Google–for Facebook to emulate. Like Google, it attracted clever engineers to build a smart and scalable infrastructure and, like Google, it established a successful and sustainable business model by re-inventing advertising. Facebook, however, went much further than its role model in responding to rising competition by either buying competitors or successfully copying them.

It also led Google to launch Google+, its most spectacular failure to date. The major culprit was the misleading concept of a “social signal.” Driven by the rise of Facebook (and Twitter), the conventional wisdom around 2010 was that the data Google was collecting, the data that was behind the success of its search engine, was missing out the “social” dimension of finding and discovering information. People on the web (and on Facebook and Twitter) were increasingly relying on getting relevant information from the members of their social networks, reducing their use of Google Search.

When Larry Page took over as Google CEO in 2011, adding a “social signal” to its search engine—and trying to beat Facebook at its own game—became his primary mission. In his first week as CEO in April 2011, Page sent a company-wide memo tying 25% of every employee’s bonus to Google’s success in social. Google introduced its answer to the Facebook “like” button, the Google “+1” recommendations, which, according to Danny Sullivan, the most astute Google watcher at the time, could “become an important new signal for Google to use as part of its overall ranking algorithm, during a time when it desperately needs new signals.”

The complete competitive answer to Facebook, Google+, was launched in June 2011, as “one of the most ambitious bets in the company’s history,” and a “response to the disruption of Web 2.0 and the emergence of the social web,” per Eric Schmidt and Jonathan Rosenberg in How Google Works (2014). But in January 2012, ComScore estimated that users averaged 3.3 minutes on the site compared to 7.5 hours on Facebook. And it was all downhill from there. Why?

A part of the problem was that Google tried very hard to show the world it’s not just copying Facebook but improving on it. Facebook’s simple approach to creating a social network was perceived to be too simple as it designated as “friends” (and still does) everybody in your network from your grandmother to someone you never met in person who has worked with you on a time-limited work-related project. Google’s clever answer was “circles,” allowing you to classify “friends” into specific and meaningful sub-networks. This, of course, went against Google’s early great hunch that user (or librarian) classification does not work on the web because it does not “scale.” So what looked like a much-needed correction to Facebook ultimately failed. Trained well by Google to expect and enjoy automated classification, users did not want to play librarians.

More important, I guess that even the relatively small number of active participants in Google+ (90 million by the end of 2011) was enough for Google to discover pretty quickly that the belief that “Making use of social signals gives Google a valuable new signal closely tied with individuals and known accounts that it could use” was simply a mirage. “Social signals” did not improve search results. In addition, 2012 brought about the Deep Learning (what we now call “AI”) revolution that changed everything at Google, especially how it engineered its search algorithm.

Sophisticated statistical classification—finding hidden correlations in huge amounts of data and using them to put seemingly unrelated entities into common buckets—was the foundation of Google’s initial success. In 2012, a specific approach to this type of statistical analysis of vast quantities of data, variously called “machine learning,” “deep learning,” and “artificial intelligence (AI),” burst out of obscure academic papers and precincts and became the buzzword of the day.

Two major milestones marked the emergence of what I prefer to call “statistics on steroids”: In June 2012, Google’s Jeff Dean and Stanford’s Andrew Ng reported an experiment in which they showed a deep learning neural network 10 million unlabeled images randomly taken from YouTube videos, and “to our amusement, one of our artificial neurons learned to respond strongly to pictures of… cats.” And in October of the same year, a deep learning neural network achieved an error rate of only 16% in the ImageNet Large Scale Visual Recognition Challenge, a significant improvement over the 25% error rate achieved by the best entry the year before. “AI” was off to the races.

The impact of the statistics on steroids revolution was such that even Google’s most sacred cow, its search algorithm, had to—after some resistance—incorporate the new, automated, scalable, not-user-dependent, “AI” signal, an improved way to statistically analyze the much bigger pile of data Google now collects. “RankBrain has moved in, a machine-learning artificial intelligence that Google’s been using to process a ‘very large fraction’ of search results per day,” observed Danny Sullivan in October 2015.

AI killed Google+.

Good for Google. Analysts expect Google’s parent Alphabet to report earnings today after the market close of $11.08 per share and adjusted revenue of $31.3 billion. These results would represent year-over-year growth rates of 14% and 21%, respectively.

Update: Alphabet’s Q4 2018 revenues were up 22% at $39.3 billion, and earnings per share were $12.77, up 31.6%.

Originally published on Forbes.com