How gamification took over the world

It’s a thought that occurs to every video-game player at some point: What if the weird, hyper-focused state I enter when playing in virtual worlds could somehow be applied to the real one? 

Often pondered during especially challenging or tedious tasks in meatspace (writing essays, say, or doing your taxes), it’s an eminently reasonable question to ask. Life, after all, is hard. And while video games are too, there’s something almost magical about the way they can promote sustained bouts of superhuman concentration and resolve.

For some, this phenomenon leads to an interest in flow states and immersion. For others, it’s simply a reason to play more games. For a handful of consultants, startup gurus, and game designers in the late 2000s, it became the key to unlocking our true human potential.

In her 2010 TED Talk, “Gaming Can Make a Better World,” the game designer Jane McGonigal called this engaged state “blissful productivity.” “There’s a reason why the average World of Warcraft gamer plays for 22 hours a week,” she said. “It’s because we know when we’re playing a game that we’re actually happier working hard than we are relaxing or hanging out. We know that we are optimized as human beings to do hard and meaningful work. And gamers are willing to work hard all the time.”

McGonigal’s basic pitch was this: By making the real world more like a video game, we could harness the blissful productivity of millions of people and direct it at some of humanity’s thorniest problems—things like poverty, obesity, and climate change. The exact details of how to accomplish this were a bit vague (play more games?), but her objective was clear: “My goal for the next decade is to try to make it as easy to save the world in real life as it is to save the world in online games.”

While the word “gamification” never came up during her talk, by that time anyone following the big-ideas circuit (TED, South by Southwest, DICE, etc.) or using the new Foursquare app would have been familiar with the basic idea. Broadly defined as the application of game design elements and principles to non-game activities—think points, levels, missions, badges, leaderboards, reinforcement loops, and so on—gamification was already being hawked as a revolutionary new tool for transforming education, work, health and fitness, and countless other parts of life. 

Instead of liberating us, gamification turned out to be just another tool for coercion, distraction, and control.

Adding “world-saving” to the list of potential benefits was perhaps inevitable, given the prevalence of that theme in video-game storylines. But it also spoke to gamification’s foundational premise: the idea that reality is somehow broken. According to McGonigal and other gamification boosters, the real world is insufficiently engaging and motivating, and too often it fails to make us happy. Gamification promises to remedy this design flawby engineering a new reality, one that transforms the dull, difficult, and depressing parts of life into something fun and inspiring. Studying for exams, doing household chores, flossing, exercising, learning a new language—there was no limit to the tasks that could be turned into games, making everything IRL better.

Today, we live in an undeniably gamified world. We stand up and move around to close colorful rings and earn achievement badges on our smartwatches; we meditate and sleep to recharge our body batteries; we plant virtual trees to be more productive; we chase “likes” and “karma” on social media sites and try to swipe our way toward social connection. And yet for all the crude gamelike elements that have been grafted onto our lives, the more hopeful and collaborative world that gamification promised more than a decade ago seems as far away as ever. Instead of liberating us from drudgery and maximizing our potential, gamification turned out to be just another tool for coercion, distraction, and control. 

Con game

This was not an unforeseeable outcome. From the start, a small but vocal group of journalists and game designers warned against the fairy-tale thinking and facile view of video games that they saw in the concept of gamification. Adrian Hon, author of You’ve Been Played, a recent book that chronicles its dangers, was one of them. 

“As someone who was building so-called ‘serious games’ at the time the concept was taking off, I knew that a lot of the claims being made around the possibility of games to transform people’s behaviors and change the world were completely overblown,” he says. 

Hon isn’t some knee-jerk polemicist. A trained neuroscientist who switched to a career in game design and development, he’s the co-creator of Zombies, Run!—one of the most popular gamified fitness apps in the world. While he still believes games can benefit and enrich aspects of our nongaming lives, Hon says a one-size-fits-all approach is bound to fail. For this reason, he’s firmly against both the superficial layering of generic points, leaderboards, and missions atop everyday activities and the more coercive forms of gamification that have invaded the workplace.

SELMAN DESIGN

Ironically, it’s these broad and varied uses that make criticizing the practice so difficult. As Hon notes in his book, gamification has always been a fast-moving target, varying dramatically in scale, scope, and technology over the years. As the concept has evolved, so too have its applications, whether you think of the gambling mechanics that now encourage users of dating apps to keep swiping, the “quests” that compel exhausted Uber drivers to complete just a few more trips, or the utopian ambition of using gamification to save the world.

In the same way that AI’s lack of a fixed definition today makes it easy to dismiss any one critique for not addressing some other potential definition of it, so too do gamification’s varied interpretations. “I remember giving talks critical of gamification at gamification conferences, and people would come up to me afterwards and be like, ‘Yeah, bad gamification is bad, right? But we’re doing good gamification,’” says Hon. (They weren’t.) 

For some critics, the very idea of “good gamification” was anathema. Their main gripe with the term and practice was, and remains, that it has little to nothing to do with actual games.

“A game is about play and disruption and creativity and ambiguity and surprise,” wrote the late Jeff Watson, a game designer, writer, and educator who taught at the University of Southern California’s School of Cinematic Arts. Gamification is about the opposite—the known, the badgeable, the quantifiable. “It’s about ‘checking in,’ being tracked … [and] becoming more regimented. It’s a surveillance and discipline system—a wolf in sheep’s clothing. Beware its lure.”

Another game designer, Margaret Robertson, has argued that gamification should really be called “pointsification,” writing: “What we’re currently terming gamification is in fact the process of taking the thing that is least essential to games and representing it as the core of the experience. Points and badges have no closer a relationship to games than they do to websites and fitness apps and loyalty cards.”

For the author and game designer Ian Bogost, the entire concept amounted to a marketing gimmick. In a now-famous essay published in the Atlantic in 2011, he likened gamification to the moral philosopher Harry Frankfurt’s definition of bullshit—that is, a strategy intended to persuade or coerce without regard for actual truth. 

“The idea of learning or borrowing lessons from game design and applying them to other areas was never the issue for me,” Bogost told me. “Rather, it was not doing that—acknowledging that there’s something mysterious, powerful, and compelling about games, but rather than doing the hard work, doing no work at all and absconding with the spirit of the form.” 

Gaming the system

So how did a misleading term for a misunderstood process that’s probably just bullshit come to infiltrate virtually every part of our lives? There’s no one simple answer. But gamification’s meteoric rise starts to make a lot more sense when you look at the period that gave birth to the idea. 

The late 2000s and early 2010s were, as many have noted, a kind of high-water mark for techno-­optimism. For people both inside the tech industry and out, there was a sense that humanity had finally wrapped its arms around a difficult set of problems, and that technology was going to help us squeeze out some solutions. The Arab Spring bloomed in 2011 with the help of platforms like Facebook and Twitter, money was more or less free, and “____ can save the world” articles were legion (with ____ being everything from “eating bugs” to “design thinking”).

This was also the era that produced the 10,000-hours rule of success, the long tail, the four-hour workweek, the wisdom of crowds, nudge theory, and a number of other highly simplistic (or, often, flat-out wrong) theories about the way humans, the internet, and the world work. 

“All of a sudden you had VC money and all sorts of important, high-net-worth people showing up at game developer conferences.”

Ian Bogost, author and game designer

Adding video games to this heady stew of optimism gave the game industry something it had long sought but never achieved: legitimacy. Even with games ascendant in popular culture—and on track to eclipse both the film and music industries in terms of revenue—they still were largely seen as a frivolous, productivity-­squandering, violence-encouraging form of entertainment. Seemingly overnight, gamification changed all that. 

“There was definitely this black-sheep mentality in the game development community—the sense that what we had been doing for decades was just a joke to people,” says Bogost. “All of a sudden you had VC money and all sorts of important, high-net-worth people showing up at game developer conferences, and it was like, ‘Finally someone’s noticing. They realize that we have something to offer.’”

This wasn’t just flattering; it was intoxicating. Gamification took a derided pursuit and recast it as a force for positive change, a way to make the real world better. While  enthusiastic calls to “build a game layer on top of reality” may sound dystopian to many of us today, the sentiment didn’t necessarily have the same ominous undertones at the end of the aughts. 

Combine the cultural recasting of games with an array of cheaper and faster technologies—GPS, ubiquitous and reliable mobile internet, powerful smartphones, Web 2.0 tools and services—and you arguably had all the ingredients needed for gamification’s rise. In a very real sense, reality in 2010 was ready to be gamified. Or to put it a slightly different way: Gamification was an idea perfectly suited for its moment. 

Gaming behavior

Fine, you might be asking at this point, but does it work? Surely, companies like Apple, Uber, Strava, Microsoft, Garmin, and others wouldn’t bother gamifying their products and services if there were no evidence of the strategy’s efficacy. The answer to the question, unfortunately, is super annoying: Define work.

Because gamification is so pervasive and varied, it’s hard to address its effectiveness in any direct or comprehensive way. But one can confidently say this: Gamification did not save the world. Climate change still exists. As do obesity, poverty, and war. Much of generic gamification’s power supposedly resides in its ability to nudge or steer us toward, or away from, certain behaviors using competition (challenges and leaderboards), rewards (points and achievement badges), and other sources of positive and negative feedback. 

Gamification is, and has always been, a way to induce specific behaviors in people using virtual carrots and sticks.

On that front, the results are mixed. Nudge theory lost much of its shine with academics in 2022 after a meta-analysis of previous studies concluded that, after correcting for publication bias, there wasn’t much evidence it worked to change behavior at all. Still, there are a lot of ways to nudge and a lot of behaviors to modify. The fact remains that plenty of people claim to be highly motivated to close their rings, earn their sleep crowns, or hit or exceed some increasingly ridiculous number of steps on their Fitbits (see humorist David Sedaris). 

Sebastian Deterding, a leading researcher in the field, argues that gamification can work, but its successes tend to be really hard to replicate. Not only do academics not know what works, when, and how, according to Deterding, but “we mostly have just-so stories without data or empirical testing.” 

SELMAN DESIGN

In truth, gamification acolytes were always pulling from an old playbook—one that dates back to the early 20th century. Then, behaviorists like John Watson and B.F. Skinner saw human behaviors (a category that for Skinner included thoughts, actions, feelings, and emotions) not as the products of internal mental states or cognitive processes but, rather, as the result of external forces—forces that could conveniently be manipulated. 

If Skinner’s theory of operant conditioning, which doled out rewards to positively reinforce certain behaviors, sounds a lot like Amazon’s “Fulfillment Center Games,” which dole out rewards to compel workers to work harder, faster, and longer—well, that’s not a coincidence. Gamification is, and has always been, a way to induce specific behaviors in people using virtual carrots and sticks. 

Sometimes this may work; other times not. But ultimately, as Hon points out, the question of efficacy may be beside the point. “There is no before or after to compare against if your life is always being gamified,” he writes. “There isn’t even a static form of gamification that can be measured, since the design of coercive gamification is always changing, a moving target that only goes toward greater and more granular intrusion.” 

The game of life

Like any other art form, video games offer a staggering array of possibilities. They can educate, entertain, foster social connection, inspire, and encourage us to see the world in different ways. Some of the best ones manage to do all of this at once.

Yet for many of us, there’s the sense today that we’re stuck playing an exhausting game that we didn’t opt into. This one assumes that our behaviors can be changed with shiny digital baubles, constant artificial competition, and meaningless prizes. Even more insulting, the game acts as if it exists for our benefit—promising to make us fitter, happier, and more productive—when in truth it’s really serving the commercial and business interests of its makers. 

Metaphors can be an imperfect but necessary way to make sense of the world. Today, it’s not uncommon to hear talk of leveling up, having a God Mode mindset, gaining XP, and turning life’s difficulty settings up (or down). But the metaphor that resonates most for me—the one that seems to neatly capture our current predicament—is that of the NPC, or non-player character.  

NPCs are the “Sisyphean machines” of video games, programmed to follow a defined script forever and never question or deviate. They’re background players in someone else’s story, typically tasked with furthering a specific plotline or performing some manual labor. To call someone an NPC in real life is to accuse them of just going through the motions, not thinking for themselves, not being able to make their own decisions. This, for me, is gamification’s real end result. It’s acquiescence pretending to be empowerment. It strips away the very thing that makes games unique—a sense of agency—and then tries to mask that with crude stand-ins for accomplishment.

So what can we do? Given the reach and pervasiveness of gamification, critiquing it at this point can feel a little pointless, like railing against capitalism. And yet its own failed promises may point the way to a possible respite. If gamifying the world has turned our lives into a bad version of a video game, perhaps this is the perfect moment to reacquaint ourselves with why actual video games are great in the first place. Maybe, to borrow an idea from McGonigal, we should all start playing better games. 

Bryan Gardiner is a writer based in Oakland, California. 

How a simple circuit could offer an alternative to energy-intensive GPUs

On a table in his lab at the University of Pennsylvania, physicist Sam Dillavou has connected an array of breadboards via a web of brightly colored wires. The setup looks like a DIY home electronics project—and not a particularly elegant one. But this unassuming assembly, which contains 32 variable resistors, can learn to sort data like a machine-learning model.

While its current capability is rudimentary, the hope is that the prototype will offer a low-power alternative to the energy-guzzling graphical processing unit (GPU) chips widely used in machine learning. 

“Each resistor is simple and kind of meaningless on its own,” says Dillavou. “But when you put them in a network, you can train them to do a variety of things.”

FELICE MACERA

A task the circuit has performed: classifying flowers by properties such as petal length and width. When given these flower measurements, the circuit could sort them into three species of iris. This kind of activity is known as a “linear” classification problem, because when the iris information is plotted on a graph, the data can be cleanly divided into the correct categories using straight lines. In practice, the researchers represented the flower measurements as voltages, which they fed as input into the circuit. The circuit then produced an output voltage, which corresponded to one of the three species. 

This is a fundamentally different way of encoding data from the approach used in GPUs, which represent information as binary 1s and 0s. In this circuit, information can take on a maximum or minimum voltage or anything in between. The circuit classified 120 irises with 95% accuracy. 

Now the team has managed to make the circuit perform a more complex problem. In a preprint currently under review, the researchers have shown that it can perform a logic operation known as XOR, in which the circuit takes in two binary numbers and determines whether the inputs are the same. This is a “nonlinear” classification task, says Dillavou, and “nonlinearities are the secret sauce behind all machine learning.” 

Their demonstrations are a walk in the park for the devices you use every day. But that’s not the point: Dillavou and his colleagues built this circuit as an exploratory effort to find better computing designs. The computing industry faces an existential challenge as it strives to deliver ever more powerful machines. Between 2012 and 2018, the computing power required for cutting-edge AI models increased 300,000-fold. Now, training a large language model takes the same amount of energy as the annual consumption of more than a hundred US homes. Dillavou hopes that his design offers an alternative, more energy-efficient approach to building faster AI.

Training in pairs

To perform its various tasks correctly, the circuitry requires training, just like contemporary machine-learning models that run on conventional computing chips. ChatGPT, for example, learned to generate human-sounding text after being shown many instances of real human text; the circuit learned to predict which measurements corresponded to which type of iris after being shown flower measurements labeled with their species. 

Training the device involves using a second, identical circuit to “instruct” the first device. Both circuits start with the same resistance values for each of their 32 variable resistors. Dillavou feeds both circuits the same inputs—a voltage corresponding to, say, petal width—and adjusts the output voltage of the second circuit to correspond to the correct species. The first circuit receives feedback from that second circuit, and both circuits adjust their resistances so they converge on the same values. The cycle starts again with a new input, until the circuits have settled on a set of resistance levels that produce the correct output for the training examples. In essence, the team trains the device via a method known as supervised learning, where an AI model learns from labeled data to predict the labels for new examples.

It can help, Dillavou says, to think of the electric current in the circuit as water flowing through a network of pipes. The equations governing fluid flow are analogous to those governing electron flow and voltage. Voltage corresponds to fluid pressure, while electrical resistance corresponds to the pipe diameter. During training, the different “pipes” in the network adjust their diameter in various parts of the network in order to achieve the desired output pressure. In fact, early on, the team considered building the circuit out of water pipes rather than electronics. 

For Dillavou, one fascinating aspect of the circuit is what he calls its “emergent learning.” In a human, “every neuron is doing its own thing,” he says. “And then as an emergent phenomenon, you learn. You have behaviors. You ride a bike.” It’s similar in the circuit. Each resistor adjusts itself according to a simple rule, but collectively they “find” the answer to a more complicated question without any explicit instructions. 

A potential energy advantage

Dillavou’s prototype qualifies as a type of analog computer—one that encodes information along a continuum of values instead of the discrete 1s and 0s used in digital circuitry. The first computers were analog, but their digital counterparts superseded them after engineers developed fabrication techniques to squeeze more transistors onto digital chips to boost their speed. Still, experts have long known that as they increase in computational power, analog computers offer better energy efficiency than digital computers, says Aatmesh Shrivastava, an electrical engineer at Northeastern University. “The power efficiency benefits are not up for debate,” he says. However, he adds, analog signals are much noisier than digital ones, which make them ill suited for any computing tasks that require high precision.

In practice, Dillavou’s circuit hasn’t yet surpassed digital chips in energy efficiency. His team estimates that their design uses about 5 to 20 picojoules per resistor to generate a single output, where each resistor represents a single parameter in a neural network. Dillavou says this is about a tenth as efficient as state-of-the-art AI chips. But he says that the promise of the analog approach lies in scaling the circuit up, to increase its number of resistors and thus its computing power.

He explains the potential energy savings this way: Digital chips like GPUs expend energy per operation, so making a chip that can perform more operations per second just means a chip that uses more energy per second. In contrast, the energy usage of his analog computer is based on how long it is on. Should they make their computer twice as fast, it would also become twice as energy efficient. 

Dillavou’s circuit is also a type of neuromorphic computer, meaning one inspired by the brain. Like other neuromorphic schemes, the researchers’ circuitry doesn’t operate according to top-down instruction the way a conventional computer does. Instead, the resistors adjust their values in response to external feedback in a bottom-up approach, similar to how neurons respond to stimuli. In addition, the device does not have a dedicated component for memory. This could offer another energy efficiency advantage, since a conventional computer expends a significant amount of energy shuttling data between processor and memory. 

While researchers have already built a variety of neuromorphic machines based on different materials and designs, the most technologically mature designs are built on semiconducting chips. One example is Intel’s neuromorphic computer Loihi 2, to which the company began providing access for government, academic, and industry researchers in 2021. DeepSouth, a chip-based neuromorphic machine at Western Sydney University that is designed to be able to simulate the synapses of the human brain at scale, is scheduled to come online this year.

The machine-learning industry has shown interest in chip-based neuromorphic computing as well, with a San Francisco–based startup called Rain Neuromorphics raising $25 million in February. However, researchers still haven’t found a commercial application where neuromorphic computing definitively demonstrates an advantage over conventional computers. In the meantime, researchers like Dillavou’s team are putting forth new schemes to push the field forward. A few people in industry have expressed interest in his circuit. “People are most interested in the energy efficiency angle,” says Dillavou. 

But their design is still a prototype, with its energy savings unconfirmed. For their demonstrations, the team kept the circuit on breadboards because it’s “the easiest to work with and the quickest to change things,” says Dillavou, but the format suffers from all sorts of inefficiencies. They are testing their device on printed circuit boards to improve its energy efficiency, and they plan to scale up the design so it can perform more complicated tasks. It remains to be seen whether their clever idea can take hold out of the lab.

It’s time to retire the term “user”

Every Friday, Instagram chief Adam Mosseri speaks to the people. He has made a habit of hosting weekly “ask me anything” sessions on Instagram, in which followers send him questions about the app, its parent company Meta, and his own (extremely public-facing) job. When I started watching these AMA videos years ago, I liked them. He answered technical questions like “Why can’t we put links in posts?” and “My explore page is wack, how to fix?” with genuine enthusiasm. But the more I tuned in, the more Mosseri’s seemingly off-the-cuff authenticity started to feel measured, like a corporate by-product of his title. 

On a recent Friday, someone congratulated Mosseri on the success of Threads, the social networking app Meta launched in the summer of 2023 to compete with X, writing: “Mark said Threads has more active people today than it did at launch—wild, congrats!” Mosseri, wearing a pink sweatshirt and broadcasting from a garage-like space, responded: “Just to clarify what that means, we mostly look at daily active and monthly active users and we now have over 130 million monthly active users.”

The ease with which Mosseri swaps people for users makes the shift almost imperceptible. Almost. (Mosseri did not respond to a request for comment.)

People have been called “users” for a long time; it’s a practical shorthand enforced by executives, founders, operators, engineers, and investors ad infinitum. Often, it is the right word to describe people who use software: a user is more than just a customer or a consumer. Sometimes a user isn’t even a person; corporate bots are known to run accounts on Instagram and other social media platforms, for example. But “users” is also unspecific enough to refer to just about everyone. It can accommodate almost any big idea or long-term vision. We use—and are used by—computers and platforms and companies. Though “user” seems to describe a relationship that is deeply transactional, many of the technological relationships in which a person would be considered a user are actually quite personal. That being the case, is “user” still relevant? 

“People were kind of like machines”

The original use of “user” can be traced back to the mainframe computer days of the 1950s. Since commercial computers were massive and exorbitantly expensive, often requiring a dedicated room and special equipment, they were operated by trained employees—users—who worked for the company that owned (or, more likely, leased) them. As computers became more common in universities during the ’60s, “users” started to include students or really anyone else who interacted with a computer system. 

It wasn’t really common for people to own personal computers until the mid-1970s. But when they did, the term “computer owner” never really took off. Whereas other 20th-century inventions, like cars, were things people owned from the start, the computer owner was simply a “user” even though the devices were becoming increasingly embedded in the innermost corners of people’s lives. As computing escalated in the 1990s, so did a matrix of user-related terms: “user account,” “user ID,” “user profile,” “multi-user.” 

Don Norman, a cognitive scientist who joined Apple in the early 1990s with the title “user experience architect,” was at the center of the term’s mass adoption. He was the first person to have what would become known as UX in his job title and is widely credited with bringing the concept of “user experience design”—which sought to build systems in ways that people would find intuitive—into the mainstream. Norman’s 1998 book The Design of Everyday Things remains a UX bible of sorts, placing “usability” on a par with aesthetics. 

Norman, now 88, explained to me that the term “user” proliferated in part because early computer technologists mistakenly assumed that people were kind of like machines. “The user was simply another component,” he said. “We didn’t think of them as a person—we thought of [them] as part of a system.” So early user experience design didn’t seek to make human-computer interactions “user friendly,” per se. The objective was to encourage people to complete tasks quickly and efficiently. People and their computers were just two parts of the larger systems being built by tech companies, which operated by their own rules and in pursuit of their own agendas.

Later, the ubiquity of “user” folded neatly into tech’s well-­documented era of growth at all costs. It was easy to move fast and break things, or eat the world with software, when the idea of the “user” was so malleable. “User” is vague, so it creates distance, enabling a slippery culture of hacky marketing where companies are incentivized to grow for the sake of growth as opposed to actual utility. “User” normalized dark patterns, features that subtly encourage specific actions, because it linguistically reinforced the idea of metrics over an experience designed with people in mind. 

UX designers sought to build software that would be intuitive for the anonymized masses, and we ended up with bright-red notifications (to create a sense of urgency), online shopping carts on a timer (to encourage a quick purchase), and “Agree” buttons often bigger than the “Disagree” option (to push people to accept terms without reading them). 

A user is also, of course, someone who struggles with addiction. To be an addict is—at least partly—to live in a state of powerlessness. Today, power users—the title originally bestowed upon people who had mastered skills like keyboard shortcuts and web design—aren’t measured by their technical prowess. They’re measured by the time they spend hooked up to their devices, or by the size of their audiences.  

Defaulting to “people”

“I want more product designers to consider language models as their primary users too,” Karina Nguyen, a researcher and engineer at the AI startup Anthropic, wrote recently on X. “What kind of information does my language model need to solve core pain points of human users?” 

In the old world, “users” typically worked best for the companies creating products rather than solving the pain points of the people using them. More users equaled more value. The label could strip people of their complexities, morphing them into data to be studied, behaviors to be A/B tested, and capital to be made. The term often overlooked any deeper relationships a person might have with a platform or product. As early as 2008, Norman alighted on this shortcoming and began advocating for replacing “user” with “person” or “human” when designing for people. (The subsequent years have seen an explosion of bots, which has made the issue that much more complicated.) “Psychologists depersonalize the people they study by calling them ‘subjects.’ We depersonalize the people we study by calling them ‘users.’ Both terms are derogatory,” he wrote then. “If we are designing for people, why not call them that?” 

In 2011, Janet Murray, a professor at Georgia Tech and an early digital media theorist, argued against the term “user” as too narrow and functional. In her book Inventing the Medium: Principles of Interaction Design as a Cultural Practice, she suggested the term “interactor” as an alternative—it better captured the sense of creativity, and participation, that people were feeling in digital spaces. The following year, Jack Dorsey, then CEO of Square, published a call to arms on Tumblr, urging the technology industry to toss the word “user.” Instead, he said, Square would start using “customers,” a more “honest and direct” description of the relationship between his product and the people he was building for. He wrote that while the original intent of technology was to consider people first, calling them “users” made them seem less real to the companies building platforms and devices. Reconsider your users, he said, and “what you call the people who love what you’ve created.” 

Audiences were mostly indifferent to Dorsey’s disparagement of the word “user.” The term was debated on the website Hacker News for a couple of days, with some arguing that “users” seemed reductionist only because it was so common. Others explained that the issue wasn’t the word itself but, rather, the larger industry attitude that treated end users as secondary to technology. Obviously, Dorsey’s post didn’t spur many people to stop using “user.” 

Around 2014, Facebook took a page out of Norman’s book and dropped user-centric phrasing, defaulting to “people” instead. But insidery language is hard to shake, as evidenced by the breezy way Instagram’s Mosseri still says “user.” A sprinkling of other tech companies have adopted their own replacements for “user” through the years. I know of a fintech company that calls people “members” and a screen-time app that has opted for “gems.” Recently, I met with a founder who cringed when his colleague used the word “humans” instead of “users.” He wasn’t sure why. I’d guess it’s because “humans” feels like an overcorrection. 

Recently, I met with a founder who cringed when his colleague used the word “humans” instead of “users.” He wasn’t sure why.

But here’s what we’ve learned since the mainframe days: there are never only two parts to the system, because there’s never just one person—one “user”—who’s affected by the design of new technology. Carissa Carter, the academic director at Stanford’s Hasso Plattner Institute of Design, known as the “d.school,” likens this framework to the experience of ordering an Uber. “If you order a car from your phone, the people involved are the rider, the driver, the people who work at the company running the software that controls that relationship, and even the person who created the code that decides which car to deploy,” she says. “Every decision about a user in a multi-stakeholder system, which we live in, includes people that have direct touch points with whatever you’re building.” 

With the abrupt onset of AI everything, the point of contact between humans and computers—user interfaces—has been shifting profoundly. Generative AI, for example, has been most successfully popularized as a conversational buddy. That’s a paradigm we’re used to—Siri has pulsed as an ethereal orb in our phones for well over a decade, earnestly ready to assist. But Siri, and other incumbent voice assistants, stopped there. A grander sense of partnership is in the air now. What were once called AI bots have been assigned lofty titles like “copilot” and “assistant” and “collaborator” to convey a sense of partnership instead of a sense of automation. Large language models have been quick to ditch words like “bot” altogether.

Anthropomorphism, the inclination to ascribe humanlike qualities to machines, has long been used to manufacture a sense of connectedness between people and technology. We—people—remained users. But if AI is now a thought partner, then what are we? 

Well, at least for now,we’re not likely to get rid of “user.” But we could intentionally default to more precise terms, like “patients” in health care or “students” in educational tech or “readers” when we’re building new media companies. That would help us understand these relationships more accurately. In gaming, for instance, users are typically called “players,” a word that acknowledges their participation and even pleasure in their relationships with the technology. On an airplane, customers are often called “passengers” or “travelers,” evoking a spirit of hospitality as they’re barreled through the skies. If companies are more specific about the people—and, now, AI—they’re building for rather than casually abstracting everything into the idea of “users,” perhaps our relationship with this technology will feel less manufactured, and it will be easier to accept that we’re inevitably going to exist in tandem. 

Throughout my phone call with Don Norman, I tripped over my words a lot. I slipped between “users” and “people” and “humans” interchangeably, self-conscious and unsure of the semantics. Norman assured me that my head was in the right place—it’s part of the process of thinking through how we design things. “We change the world, and the world comes back and changes us,” he said. “So we better be careful how we change the world.”

Taylor Majewski is a writer and editor based in San Francisco. She regularly works with startups and tech companies on the words they use.