Tell Me a Story: Julie Rubicon


In this series, I highlight stories that have inspired, delighted, or intrigued me. I hope they can do the same for you. 


On March 15, 2016, Robin Sloan posted “Julie Rubicon” on Facebook. 

It was, he said, a story entrusted to him by an anonymous source. Sloan asked recipients of his email newsletter to help him discover who had written the piece. Readers embraced the challenge, suggesting a number of eminent writers. The mystery of its origin gave the story an air of dangerous intrigue, and, despite its farfetched premise—or maybe because of it—a prickling sense that maybe, just maybe, it could be real. 

Though Robin Sloan himself is the author, the story retains that mystery and magic for me. I hope that you can find the same sense of wonder in it.

“Julie Rubicon” is both story and performance art. A story about Facebook written on Facebook; a story about viral sharing that is itself virally shared. 

The narrator, an unnamed Facebook analyst, and fellow analyst Julie Rubicon discover a dangerous secret lurking within Facebook’s data stores. When that secret takes a personal turn, they must face the enormity of the algorithm’s power and choose how to react in the face of destiny.

Read it here: Julie Rubicon


Title: Julie Rubicon

Author: Robin Sloan

Form: Short Story

Platform: Facebook Notes

Since its publication in 2016, the story has only gained in relevance. More people use Facebook daily. More people get their news from Facebook—news that is curated to give them viewpoints that they agree with, to keep them satisfied instead of challenging them, to keep them coming back for more. The algorithms that decide what you see decide what you think.

The more skeptical among Facebook’s users may decide to fact check the news they see. How? They’ll “Google it.” And how does Google decide what the facts are? Another algorithm, another layer of curation. Algorithms push us closer to people who think like us, talk like us, agree with us, and open gulfs between different clusters of people who used to mingle at the edges of opinion where debate is spirited and fruitful.

Do you know how it works?

Most people don’t. Mark Zuckerberg’s appearance in front of the Senate Intelligence Committee confirmed that even those in charge of legislating big data use have little idea of how it works. Even as algorithms take on more and more prominence in our lives, we keep the lids on their little black boxes.

“Here’s an exercise: The next time you hear someone talking about algorithms, replace the term with ‘God’ and ask yourself if the meaning changes,” Ian Bogost wrote in The Cathedral of Computation, published in The Atlantic in 2015. The algorithms tell us what to read, what to think, what to buy. They tell us what is true. If we don’t understand how they work, their predictions become prophecy.

We become reactive instead of proactive, and when we react, the prophecy fulfills itself. 

We click on a slightly biased news article since that’s what is in our feed. The algorithm confirms its hypothesis that this news is what we want to see, and next time it shows us something a little more radical, a little more specifically tailored for people who believe what we do.

Julie runs to escape the spike, and her disappearance causes the very thing that she was afraid of. 

What can we do?

Within “Julie Rubicon,” Sloan gives us the secret to living with the algorithms that shape our lives.

Julie and the narrator come face to face with the edict issued by the algorithm. Julie runs, powerless to contradict the fate that Enchilada has drawn for her in a line chart. The narrator doesn’t give up. Underneath the hood, Enchilada is a counter. “Each one counts separately.” Armed with the knowledge of how the technology works, the narrator can take charge of the phenomenon, creating the spike that Julie feared to save her from an alternate cause, beating Enchilada at its own game.

Here’s the secret: Enchilada isn’t even playing.

Algorithms are tools. What they mean and how much power they have is up to us, to you and I as consumers of the output that they produce. We can take them as truth or we can take them as suggestion, as one input in the suite of tools that we use to empower our decisions.

You’ve been given a hammer. You can use it to connect two boards with a screw, sure, but your project will turn out a lot better if you use the hammer for the nails and find a screwdriver to use on the screws.

To know how to use an algorithm’s output, you have to understand its limitations.

You start by opening up that black box and looking inside.