I’m also not the first to point out that many of the worst examples of Facebook’s influence around the world — the way it has facilitated genocide in Myanmar and Sri Lanka, led to deadly riots in India and Nigeria, and helped destabilize democracies around the world through the spread of disinformation and false news — are a direct result of the way its products are designed to maximize engagement.
To its credit, Facebook is trying to change some of this. A couple of weeks ago, Mr. Zuckerberg published a long note about the company’s efforts to reduce the spread of “borderline” content that almost but doesn’t quite break its rules.
But it’s not clear yet that Facebook can meaningfully change the engagement patterns on its apps — which are, after all, built on giving users more of what they want, even if what they want is hateful or misleading — without unraveling its business.
It’s also not clear, from the reporting my colleagues have done over the last few months, that the company’s leaders all agree that these product issues are problems at all. Some executives seem to believe that the real issues are image and perception, and that most of what’s needed to correct them is a big, new hearts-and-minds campaign on Capitol Hill and a revamped media strategy.
So the big questions for Facebook now are: Can the company first acknowledge that it has product problems, in addition to political and perception problems? If it can agree on the product problems, how much money is Mr. Zuckerberg willing to lose in order to fix them? And how much damage will the company absorb in the meantime?
In other tech news of the week:
■ The most valuable company in the world is now … Microsoft? My colleague Steve Lohr explained how this happened: It’s a story of a company’s succeeding by embracing cloud computing, cutting some losing bets and loosening up its notoriously tight grip on its products. There is, it turns out, a benefit to being boring.
■ I’m obsessed with the article by Li Yuan, our China tech columnist, about the booming Chinese industry of data-tagging. It’s a good preview of the kinds of jobs that might be created by artificial intelligence — in this case, cleaning up and labeling data and images so that they can be processed and fed into machine learning systems — even as other categories of work disappear.