Tech

Shira Ovide is a Bloomberg Gadfly columnist covering technology. She previously was a reporter for the Wall Street Journal.

Let's talk about a scourge of modern times. There is so much stuff to watch, read, listen to, buy, eat or learn about. The world is available at our fingertips at any moment. It feels glorious but also horribly, paralyzingly overwhelming.

Should I wade into Spotify's sea of every song ever recorded or give up and listen to my downloaded copy of Adele's "Hello" for the 47,000th time? Psychologist Barry Schwartz called this the "paradox of choice" in his 2004 book of the same name. Like many ideas that come out of TED Talks, it is too simplistic to say more choices are counterproductive, but I think we've all experienced the feeling.

Naturally, technology companies have some ideas about how to help people discover things and select among the flood of options -- and make money in the process. And even they are recognizing the limits of technology in helping people stay informed and entertained.

Computerized recommendations were among the original big ideas of the internet age. Google web search is essentially the use of computers to sift through the morass of web links to surface the most compelling options. Netflix, Amazon and Spotify suggest entertainment or products based on what you have shown interest in before, or what its computer models conclude will fit your taste.

Favorite Pastimes
Television dominates how people spend their leisure hours, but the average daily time spent on the internet is surging globally
Source: Zenith
Note: Figures for 2017 to 2019 are projections.

It turns out computers are incredibly effective at guiding us. About 80 percent of the music videos people watch on YouTube are the result of computerized suggestions, the chief financial officer of Google parent company Alphabet said at the recent Code conference. (When I finish watching the "Hello" video on YouTube, it automatically starts playing Adele's weepy "Someone Like You.")

Of course there is a downside to the power of the algorithms. Sometimes computers are dumb. I don't know why Amazon keeps nudging me to buy glass cleaner. And picking things based on your tastes means you may never break out of your comfort zone and listen to a song that you couldn't imagine you would like. The same is true with computer-aided social network feeds like Facebook. If your friends are like you, their suggestions for what to read or how to understand world events may keep you in a "filter bubble" of your own making.

Now, even tech companies that preach the gospel of the algorithm are trying a human touch. If you're deciding between two outfits to wear, you can now send a photo of yourself to Amazon, and "fashion specialists" will tell you which one looks best. Snapchat's "Discover" section is essentially a modernized version of a newspaper front page. Apple has a selection of "Editors' Choice" apps, and it trumpets Apple Music song recommendations made by people in addition to machines. Facebook has said a priority for this year is offering people information they don't know they were interested in. 

Computers Rule
Netflix with its computerized entertainment recommendations has quadrupled its web video subscribers since 2011
Source: Bloomberg

As algorithms guide more of our lives, I increasingly find myself reverting back to old-fashioned methods of sifting through choices. When I was shopping for air conditioners last year, I leaned on Consumer Reports and other professional recommendations. I read traditional book reviews and ask friends what books they've enjoyed recently. Thanks for the suggestions, computers. But I'll let the mere mortals have a turn now.

A version of this column originally appeared in Bloomberg's Fully Charged technology newsletter. You can sign up here.

This column does not necessarily reflect the opinion of Bloomberg LP and its owners.

To contact the author of this story:
Shira Ovide in New York at sovide@bloomberg.net

To contact the editor responsible for this story:
Daniel Niemi at dniemi1@bloomberg.net