Go Read This: The Verge’s favorite reads from all over the webSee all StoriesPosted Aug 12, 2026 at 4:29 PM UTCDExternal LinkAn algorithmic way to make our algorithms fun again.Lots of smart internet people have been talking about this Lauren Leek essay on the algorithmic flattening of cities and culture and everything else, and it’s as good as advertised. It also offers a shockingly simple way to fix the problem, which is why I spent the morning reading about a machine learning framework known as multi-armed bandits.“[J]ust be a more adventurous consumer” is exactly the conclusion that lets the systems off the hook. The exploration has to be mandated at the level of the machine, because that’s where real change can happen.Follow topics and authors from this story to see more like this in your personalized homepage feed and to receive email updates.David PierceLoading commentsGetting the conversation ready...The Verge DailyA free daily digest of the news that matters most.Email (required)
An algorithmic way to make our algorithms fun again.
Lots of smart internet people have been talking about this Lauren Leek essay on the algorithmic flattening of cities and culture and everything else, and it’s as good as advertised. It also offers a shockingly simple way to fix the problem, which is why I spent the morning reading about a machine learning framework known as multi-armed bandits. > “[J]ust be a more adventurous consumer” is exactly the conclusion that lets the systems off the hook. The exploration has to be mandated at the level of the machine, because that’s where real change can happen. [Link: Temperature Zero for Culture: Why Everything Is Starting to Look the Same | https://laurenleek.substack.com/p/temperature-zero-for-culture-why | Lauren’s data Substack]
Lauren Leek proposes multi-armed bandits ML to mandate algorithmic exploration, countering cultural flattening in recommendation systems. Forced algorithmic diversity becomes competitive edge—preventing monoculture expands engagement and platform market reach.
