This research started in 2016, and is now slowing down. My active project is Meta Medium.
With enough data we all get averaged. Walk into a hipster coffee shop in Brooklyn or a pop up doggy store in Soho and you can almost see the optimizing algorithms working around you. A quest for an efficiency singularity which will not stop until we’re all smooth, rounded individuals dancing in a perfect harmony of sameness.
Each of these places is architecturally sound. It has everything it needs to have. Everyone smiles, it is usable, you can plug your laptop, and you will be offered a miniature bottle of water when you walk in. No cash is required, and it is all very tidy and bright. Every unnecessary step is removed, every corner is lit, and everything is scripted. The entire operation is running on a checklist, and every item is monitored. The efficiency machine is there to deal with any anomaly. The system is designed to remove any trace of friction.
Frictionless shopping is defined as:
“the method of using data, technology and devices to integrate buying opportunities as seamlessly as possible into the everyday activities of shoppers. The goal is to reduce the amount of time and hassle involved in the steps between desire for a product and receiving it.”
The original intent of removing friction is anchored in design of usable experiences. When we didn’t know how to design software (or had constant feedback loops) we had to listen to what people said, and often they said that our design was not friendly enough – for example completing a certain task took too many steps. So we set as a goal the removal of those extra screens, as not to tax our users with extra clicks on the way to their desired outcome. But what algorithmic retail does is reduce friction on the path to desire. And what is a desire if not the quest to be unique, and interesting. As Sianne Ngai articulates in ‘Our Aesthetic Categories’ interesting is always in relation to something else. And as such it is a social system. If we create one average aesthetic–one form of interesting-for the entire segment, we are just a smoke and mirror distraction until the next moment. Because ‘the objects or persons we find interesting are never stable or permanent’. It is a social interaction because ‘at any moment, we feel, they may become too interesting for us, or we too interesting for them’.
Our interconnectedness generates data, which is then used to serve advertising. This creates a dissonance which we seldom think about. A machine has a view of yourself which is different than the view you have of yourself.
Data about our recreational life is sometimes referred to as the new oil, but I want to take a deeper stance, and side with Shoshana Zuboff on the claim that ‘human experience’ is the raw material (and not the data itself). It is not what the data says, but the fact that the data exists (The Age of Surveillance Capitalism).
If before you could go out and choose between door 1, door 2 or door 3, you are now in a situation where someone could build a house to hold door number 1, just based on the possibility that you choose it. The problem with it is that you didn’t commit to that door, or you might have not even known about the existence of such an option, and that you might show up in the next moment not at all interested.
We are not some average segment of people around our age group, circle of friends or favorite band. There is more to us than our segmented preferences, but that makes little sense to this neurotic algorithm.
Generations of behavioral economists tell us that we are not rational when we make decisions, we don’t follow discrete rules. We follow our biases, and respond to our implicit views of the world. What we want is conviction, the intent and the interesting. The aesthetic that stands in contrast to the average. No one wants to look like everyone in their segment group. We get upset when we see someone wearing the same shirt as do, not realizing that it is average thinking, fed into an algorithm that is reducing our optionality to choose a shirt, and funnel our diversity of choice.
Even if we don’t know it – and we often don’t – we all want change, newness and the next articulation on last year’s version of me. That is why when we look at old pictures we lament on how dorky we looked wearing those jeans. But change is tough, it requires grit, the ability to go against the grain and feel the friction. Algorithms only speak the language of efficiency, and will never understand this desire for grit – because machine thrive on rules, and seeks the average.
Our desires are antithetical to the machine. The faster we will correct the dissonance between the segmented online self and our own desires, the more interesting and diverse we all become. We are more nuanced than the segment we’re placed in, we should use algorithms and not be used by them.