Humor us for a moment and picture your favorite neighborhood restaurant. Ours is a corner spot in Fort Greene, Brooklyn. It has overflowing natural light, handmade textile seat cushions, a caramel wood grain throughout, and colorful ornaments dangling from the ceilings. Can you picture yours? Do you feel the warmth and spirit of the place?A Silicon Valley optimizer might say, “Well, they don’t brew their coffee at exactly 200 degrees. And the seats look a little ratty. And the ceiling ornaments don’t serve any function.”But we think that’s exactly the point. That these little, hand-crafted touches give our environment its humanity and spirit. In their absence, we’re left with something universal but utterly sterile — a space that may “perfectly” serve our functional needs, but leave our emotional needs in the lurch.

Operating systems were bubbly and evanescent, like nature. Apps were customizable, in every shape and size. And interfaces drew on real-life metaphors to help you understand them, integrating them effortlessly into your life.But as our everyday software tools and media became global for the first time, the hand of the artist gave way to the whims of the algorithm. And our software became one-size-fits-all in a world full of so many different people. All our opinions, beliefs, and ideas got averaged out — producing the least common denominator: endless sequels that everyone enjoys but no one truly loves.When our software optimizes for numbers alone — no matter the number — it appears doomed to lack a certain spirit, and a certain humanity.

In the end, we decided that we didn’t want to optimize for numbers at all. We wanted to optimize for feelings.While this may seem idealistic at best or naive at worst, the truth is that we already know how to do this. The most profound craftsmanship in our world across art, design, and media has long revolved around feelings.

When Olmstead crafted Central Park, what do you think he was optimizing for? Which metric led to Barry Jenkins’ Moonlight? What data brought the iPhone into this world? The answer is not numerical. It’s all about the feelings, opinions, experiences, and ideas of the maker themself. The great Georgia O’Keefe put it this way: "I have things in my head that are not like what anyone has taught me... so I decided to start anew."

James Turrell took inspiration from astronomy and perceptual psychology. Coco Chanel was most influenced by nuns and religious symbols. David Adjaye drew from Yoruban sculpture, and Steve Jobs from Zen Buddhism and calligraphy.

And yet, in so much modern software today, you’re placed in a drab gray cubicle — anonymized and aggregated until you’re just a daily active user. For minimalism. For simplicity. For scale! But if our hope is to create software with feeling, it means inviting people in to craft it for themselves — to mold it to the contours of their unique lives and taste.

You see — if software is to have soul, it must feel more like the world around it. Which is the biggest clue of all that feeling is what’s missing from today’s software. Because the value of the tools, objects, and artworks that we as humans have surrounded ourselves with for thousands of years goes so far beyond their functionality. In many ways, their primary value might often come from how they make us feel by triggering a memory, helping us carry on a tradition, stimulating our senses, or just creating a moment of peace.This is not to say that metrics should not play a role in what we do. The age of metrics has undeniably led us to some pretty remarkable things! And numbers are a useful measuring stick to keep ourselves honest.But if the religion of technology preaches anything, it celebrates progress and evolution. And so we ask, what comes next? What do we optimize for beyond numbers? How do we bring more of the world around us back into the software in front of us?

I kept looking for that one thing that defined me as a creative: you know, that thing that all designers seek. I felt the pressure of needing to have an aesthetic style or skill to stand out as a creative. I wish more people talked about how we don’t have to be pigeonholed, that we can just let the idea dictate the media.

I realized AR requires a combination of disciplines if you want to go beyond “awe.” Knowing how to make 3-D models is not enough. While I used publication design tools to organize information, I didn’t realize how much wayfinding would come into play. In the end, AR requires design for real physical spaces that go beyond a screen. Rules on viewing distance and material design, among other things, became important. Moreover, we experience AR through our mobile phones or glasses—both are experientially different. Having knowledge of interface design helped me navigate that.

In other words, when Kylie Jenner posts a petition demanding that Meta “Make Instagram Instagram again”, the honest answer is that changing Instagram is the most Instagram-like behavior possible.

The first trend is the shift towards ever more immersive mediums. Facebook, for example, started with text but exploded with the addition of photos. Instagram started with photos and expanded into video. Gaming was the first to make this progression, and is well into the 3D era. The next step is full immersion — virtual reality — and while the format has yet to penetrate the mainstream this progression in mediums is perhaps the most obvious reason to be bullish about the possibility.

The second trend is the increase in artificial intelligence. I’m using the term colloquially to refer to the overall trend of computers getting smarter and more useful, even if those smarts are a function of simple algorithms, machine learning, or, perhaps someday, something approaching general intelligence.

The third trend is the change in interaction models from user-directed to computer-controlled. The first version of Facebook relied on users clicking on links to visit different profiles; the News Feed changed the interaction model to scrolling. Stories reduced that to tapping, and Reels/TikTok is about swiping. YouTube has gone further than anyone here: Autoplay simply plays the next video without any interaction required at all.

Material-based software can also have gentler learning curves, because the user only needs to learn a small set of rules about how different metaphors in the software interact with each other rather than learning deep hierarchies of menus and terminologies. In the best case, users can continue to discover new capabilities and workflows long after the initial release of the software.

Take nonlinear video editing software, like Final Cut Pro and Premiere Pro, for example. Though they have their fair share of menu complexity, it’s pretty intuitive for anyone to understand the basic building blocks of video and audio clips sitting on a layered timeline. Here, the video and audio clips are composed of a software material that have their own laws of physics: they can grow and shrink to take up more or less time on the timeline. They can be moved around and layered in front of or behind other clips, but they can’t occupy the same space on the same “track” of the timeline. The timeline that runs left-to-right is a kind of “world” in which the materials of audio and video exist.

In complexity science, ‘emergence’ describes the way that interactions between individual components in a complex system can give rise to new behavior, patterns, or qualities. For example, the quality of ‘wetness’ cannot be found in a single water molecule, but instead arises from the interaction of many water molecules together. In living systems, emergence is at the core of adaptive evolution.

Design for emergence prioritizes open-ended combinatorial possibilities such that the design object can be composed and adapted to a wide variety of contextual and idiosyncratic niches by its end-user. LEGO offers an example — a simple set of blocks with a shared protocol for connecting to one another from which a nearly infinite array of forms can emerge. Yet as we will see, design for emergence can generate value well beyond children’s toys.

In contrast to high modern design, user-centered design takes a more modest position; the designer does not inherently know everything, and therefore she must meticulously study the needs and behaviors of users in order to produce a good design. User-centered design remains the dominant design paradigm today, employed by environmental designers, tech companies, and design agencies around the world.

In this paradigm, design is about gaining knowledge from the user, identifying desirable outcomes, and controlling as much of the process as possible to achieve those outcomes. ‘Design’ remains synonymous with maximizing control.

But consider even the ‘desire path’ example pictured above. The modal user may be well supported by paving the desire path indicated by their behavior, but what good is a paved path leading to stairs for a wheelchair user? In practice, user-centered design tends to privilege the modal user at the expense of the long-tail user whose needs may be just as great.

User-centered design has a better track record than high modern design, but it still exerts a homogenizing effect. The needs of the modal user are accommodated and scaled through software or industrial manufacturing, while power users and those with edge cases can do nothing but actively petition the designer for attention. In most cases, diverse users with a wide variety of niche use cases are forced to conform to the behavior of the modal user.

In design for emergence, the designer assumes that the end-user holds relevant knowledge and gives them extensive control over the design. Rather than designing the end result, we design the user’s experience of designing their own end result. In this way we can think of design for emergence as a form of ‘meta-design.’

In other words, to address the long-tail problem, the tool must be flexible enough that it can be adapted to unexpected and idiosyncratic problem spaces—especially those unanticipated by the tool’s designer.

In contrast to user-centered design, design for emergence invites the user into the design process not only as a subject of study, but as a collaborator with agency and control.

What all these tools have in common is support for open-ended adaptation to highly contextual problems without the need for technical knowledge. Rather than building a static, purpose-built solution to a single common problem with lots of users (and lots of competitors), they’ve won robust user bases by supporting a broad swath of long-tail user needs.

Design for emergence is composable. It provides a limited ‘alphabet’ and a generative grammar that’s easy to learn and employ, yet can be extended to create powerful, complex applications. As Seymour Papert once remarked, “English is a language for children,” but this fact, “does not preclude its being also a language for poets, scientists, and philosophers.”

It creates more generic-looking interfaces that may perform well in numbers but fall short of appealing to our senses.

It’s easy to make data-driven design decisions, but relying on data alone ignores that some goals are difficult to measure. Data is very useful for incremental, tactical changes, but only if it’s checked and balanced by our instincts and common sense.

It became clear to the team in that moment that we cared about more than just clicks. We had other goals for this design: It needed to set expectations about what happens next, it needed to communicate quality, and we wanted it to build familiarity and trust in our brand.We could have easily measured how many customers clicked one button versus another, and used that data to pick an optimal button. But that approach would have ignored the big picture and other important goals.

Not everything that can be counted counts. Not everything that counts can be counted.Data is good at measuring things that are easy to measure. Some goals are less tangible, but that doesn’t make them less important.While you’re chasing a 2% increase in conversion rate you may be suffering a 10% decrease in brand trustworthiness. You’ve optimized for something that’s objectively measured, at the cost of goals that aren’t so easily codified.

Design instinct is a lot more than innate creative ability and cultural guesswork. It’s your wealth of experience. It’s familiarity with industry standards and best practices.

Overreliance on data to drive design decisions can be just as harmful as ignoring it. Data only tells one kind of story. But your project goals are often more complex than that. Goals can’t always be objectively measured.