And now it is live, www.under-over-through.co, and the real work begins.

Under Over Through is a micro research, production, and publishing studio, working at the intersection of what stories can reveal and what systems can enable. A well-told story is a laboratory, a controlled environment where you can stress-test how people feel, adapt, and make decisions when the ground shifts around them. A well-designed infrastructure doesn’t just solve a problem; it generates possibilities its creators never imagined.

Small and slow sound anachronistic these days. Many things can be ordered, delivered, generated in anything from seconds to hours, days at worst. Is this also affecting our ability to think through time? To consider that outside the many perpetual loops of nowness (prompt →wait → evaluate || order → inspect → return || capture → share → rewatch) there is newness peeking through the cracks?

Or to appreciate that most of what we consider standard, required, and inescapable didn’t actually exist more than a hundred years ago?

Everything that we consider immutable was once created as solution to a problem and yes, the solutions were imperfect and in many cases biased and unfair, and generated their own set of new problems to solve... but the core point is that they were designed, and the opposite of immutable. We can’t change the laws of physics, but we can change a lot of other things if they don’t solve anymore our problems.

Anyhow, this is what I wrote to myself when I started thinking about this:

When was the last time you felt optimistic about the future?

The systems we rely on were designed for a world that no longer exists. The mental models are stale. The narratives are exhausted. The soft and hard infrastructures are buckling under the weight of climate adaptation, demographic changes, and the rapid diffusion of AI.

Dystopia is easy. We’d rather build than mourn as crises are also openings to radically imagine new ways of being together.

I certainly don’t have the solutions, but I think it would be interesting to look for them together.

Where Under Over Through operates?

• When an R&D team has built something powerful but has no language to explain what it changes about the world, we build that language, and the narrative to carry it.

• When a technology needs to be tested not in a lab but inside a human story, under pressure, fatigue, love, fear, to see if it survives contact with real life.

• When an organization wants to inhabit a possible future through living labs, simulations, or production residencies where participants build, write, and hack together.

• When artists and researchers need infrastructure and production support to explore the edges of AI, storytelling, or community co-creation.

• When the goal is to teach people to think in systems by actually constructing them, through labs where design is a way of reasoning, not only production.

• When you are building something weirdly-shaped and needs a collaborator comfortable with operating systems, privacy architectures, and AI workflows.

What changes here

This Substack is evolving too. The url is changing (go.under-over-through.co) and going forward it becomes the studio’s monthly-ish (I’ll stay slow) update: a mix of signals, observations, and progress notes from whatever I’m working on. I’ll still write the occasional long-form piece (the essays on privacy infrastructures, designing disorder, and AI agents aren’t going anywhere), but the default rhythm will be shorter, and closer to the work as it happens.

A small ask

I’m taking on a small number of collaborations this year alongside the studio’s own research. R&D partnerships where powerful technology needs language and narrative. Advisory and fractional leadership roles for teams building at the intersection of AI, design, and complex systems. Special projects where the work is genuinely unusual.

If something resonates you can book a call directly, or just reply to this email.

And if you know someone who might find this work interesting, I’d be grateful if you forwarded this along.

Marco

Imagine another NYC.

8 million people trying as usual to avoid each other and say 3 times as many AI agents actually doing the opposite: constantly pinging each other, sharing bits of information, coalescing their attention, negotiating, and explicitly or implicitly interacting back with us.

As usual these days, fiction becomes closer to a documentary faster than our brains can metabolize it.

It’s February 2026 and that number doesn’t feel so strange anymore. Fiction? Reality? Fict-uality? The details of what’s happening seem unverifiable to me which is itself part of the point. It turned out a lot of people love agents and it seems we enjoy seeing them chitchat and do serious and silly things, with the thrill of not knowing exactly what they will come up with next.

It feels like watching pets videos on your favorite dopamine dispenser. You wonder how staged the videos are and you mostly know how they will go, and yet you can’t stop watching them. On repeat.

Agents are of course a serious thing but for now I still can’t shake a parallel with my favorite goofy pets. Also consider for the time being switching two letters, from pets to pest(s) and, dad jokes aside, you will see how we need more infrastructures around them.

The viral story of crustacean MJ Rathbun publishing a rant against Scott Shambaugh, an engineer maintaining the Matplotlib Python library, or Azeem Azhar instructing his team to follow a simple (and astutely literary) rule to identify agents in their tools begin to highlight what we need.

We have the intuitions, the ideas, the desires and expectations of people imagining and using agents, they do pretty well when they run in single-player mode within our terminal, but when we get to Massively Multiplayer Online Games scale we miss a functioning infrastructure to move past the weird pets/pest(s) phase.

MCP and A2A protocols handle the interoperability, that’s layer zero. We need a few more. There is interesting work in development at IETF, OWASP GenAI Security Project, and the Agentic AI foundation and I think we need to consider this as a broader socio-technical, UX, and cultural problem.

Having bumped into similar issues through primordial bots projects at Nokia and later on at Google, I keep coming back to a few things that feel necessary. As usual, the list is both obvious and tricky. Easy to imagine, hard to specify, and even harder to scale.

Identity

Agents that go beyond an isolated sandbox (that means interacting with external people or servers) should have their own unique ID that points back to their human or institutional guardian, same as we have birth/death/residency certificates, etc. Without it the majority of agents would operate as transient, inscrutable entities.

Certifications

In the physical world, you need a license for a lot of potentially dangerous things like flying a plane or performing surgery. This doesn’t eliminate errors, nor malicious actors, but it still manages to keep all these activities safe in the majority of cases. We already have benchmarks for models but we should have a shared, fair and strict way for granting capability-bound credentials. Perhaps this could be a job for universities and professional organizations.

Verification

All of the above should be part of a shared mechanism for reasonable verification of an agent’s identity and intent. Today’s agents have permissions to do what they do but how much of this is today a controlled decision from their creator is unclear. YOLO is not a great strategy when agents get to act on more consequential buttons.

Auditability

This is fuzzier even for humans but in parallel to certifications we also have processes and checkpoints that generally provide a certain level of traceability in the performance of a critical action. You don’t really know what is going on inside the mind of a human operator, but you know what they requested to do, in which order they did it, and what the results were. It is boring stuff, and we have some capabilities in Claude Code and other tools today but there is so much technical and UX work to be done in this space, especially as the numbers grow.

Expiration

Since nothing is forever and everything spirals toward entropy, we should always put expiration and renewal dates on all the above properties so that either performance decay, mistakes, or malicious actions that slipped through the previous steps would ultimately get a chance of being amended. Think of it as a dead man's switch: an agent that hasn't been re-certified in six months gradually loses its permissions rather than silently operating on stale credentials forever.

We had millennia to slowly, painfully, and still imperfectly develop ways to cooperate and coexist with each other. It was survival and fear first, tales, religions, philosophy, laws, and complex mechanisms for identifying and regulating decisions. These things are already compressed somewhere inside these models. The soft parts at least are there, latent, but we don’t yet have the hard infrastructure to enforce them.

It seems we will need these infrastructures pretty soon, but maybe now that AI is writing most of our code we might finally have more time to focus on these infrastructures.

Some more reading?

This is a fast-moving space. Here are some starting points across different layers of the problem, from technical protocols to governance frameworks.

Protocols & Standards

• Yang et al., “A Survey of AI Agent Protocols” (2025). The most comprehensive map of current and emerging agent communication protocols, including MCP and A2A. Useful for seeing the full landscape beyond the protocols that get the most press.

• IETF Draft: “Digital Identity Management for AI Agent Communication Protocols” (Yao & Liu, 2025). An early but significant proposal for standardizing agent digital identity at the internet infrastructure level.

• IETF Draft: “Secure Intent Protocol: JWT-Compatible Agentic Identity” (Goswami, 2025). Tackles the “intent-execution separation problem”, what happens when an agent’s actions diverge from what the user authorized.

• IETF Draft: “Agent Name Service (ANS)” (Narajala et al., 2025). A DNS-inspired directory for agent discovery and verification.

Governance & Policy

• Shavit at al., “Practices for Governing Agentic AI Systems” (OpenAI, 2024). Still the clearest breakdown of who’s responsible for what in the model developer → deployer → user chain.

• World Economic Forum, “AI Agents in Action: Foundations for Evaluation and Governance” (2025)

• OWASP GenAI Security Project. An evolving community resource on security risks specific to generative AI and agent systems.

As I wandered with a small group through the Oku-no-in cemetery on Mount Koya, Japan, our guide, a local Buddhist monk, explained two things: that during our walk, we might hear a rattling sound moving through the tall, dense trees, and that the term “esoteric,” used to describe their particular flavor of Buddhism, meant that they believed that truth was hidden in everything and could be uncovered through constant observation and meditation.

The rattling sound was actually made by flying, perhaps annoyed, squirrels in the treetops and doesn’t really relate to this piece, but the unexpected explanation in halting English of the term “esoteric” triggered something in my brain — as often happens: a specific turn of phrase unlocks an immediate and satisfying new understanding of something previously inscrutable to me. I haven’t decided yet if this is a gift or a curse, but think about that sentence and repeat it again: anything, if observed and practiced long enough, reveals a truth about everything.

I flew back to NYC and resumed my work with this idea lodged in my brain, and soon found myself stuck in front of my computer, pondering the hidden truths of the interface I was working on.

Like many people, I find it difficult to define exactly who I am and what I do, but on most occasions, the title “designer” will suffice — an interaction designer, to be precise, the type who designs user interfaces. After 16 years in the field, as I expanded the scope of my work, I also began to abstract the definition of what an interface is.

Typically, a user interface is thought of as a set of buttons, sliders, and gestures. Yet, consider a broader definition: an interface as a conduit between two heterogeneous systems, a membrane that enables mutual feedback and control. The extent to which an interface allows one system to interact in terms of feedback and control is determined by the interface’s affordances and is an inherent part of its design. What if we are all, to varying degrees, creators and maintainers of interfaces?

Titles, clothes, rituals, laws — both written and unspoken — all function as interfaces, facilitating human connections (they could benefit from the rigorous testing and refinement that digital interfaces undergo!). The composer Caterina Barbieri described techno music as a synchronization tool for humans, exemplifying another form of interface that uses recognizable, repetitive, and pulsating rhythms to induce entrainment among humans.

Perhaps interfaces are in some sense esoteric because, as inspired by the night walk in the Buddhist cemetery, they may contain some hidden truth about ourselves and our world(s). Interfaces, even human-computer ones, are cultural objects since they embody how we interpret our world(s) and the technologies at our disposal. They are bridges between systems because they reflect how we think these systems are and should behave.

The original computer punched cards and later terminals were a symptom of a deeply utilitarian and perhaps slightly elitist view of computers: they were tools used to perform and repeat the same tasks, their interfaces designed for the initiated few. The graphical user interfaces of modern operating systems were didactic in their abstract simplification and linearization of tasks: trash cans, folders, list menus, and eventually leather notebook textures represented a certain hope for computers to tame and control the increasing complexity of the world. The first websites gave us a glimpse of pluralistic interfaces where standardization and predictability took a back seat to self-expression and exploration. The playfulness of the original web interfaces signaled an optimism that computing technologies could empower and liberate us.

Then came the mobile revolution — interfaces were designed to be touched, swiped, and tapped. Here, interfaces began to cater to dopamine-infused consumption loops like infinite scrolling, pull-to-refresh, and autoplay videos, each carefully crafted to keep us engaged, entertained, and entranced. These interfaces were (are!) a kind of performative ritual of gratification.

Interaction design is a relatively new discipline, and the mobile app space is where most UX/UI designers have been trained and have worked to date. We’ve learned to optimize UIs down to the last millimeter and deliver delightful experiences that hide complexity and optimize for engagement and useability. All the while, the world around us has changed rapidly, becoming far more complicated, unpredictable, and multifaceted than our interfaces had hoped for.

With climate change and AI both becoming self-evident and reaching the irreconcilable state of being at once unavoidable and non-unavoidable, we have entered uncertain times of hallucinations, deceptions, prismatic realities, and opportunities for radical rethinking and reimagining.

Interfaces have once again followed suit: with natural languages, gaze, attention, and brain activity becoming viable interfaces of this AI-infused era, we begin to see interfaces that are as natural and powerful as they are non-deterministic and prone to misunderstanding and error. Language is the ultimate interface, yet it is so much more ambiguous and variable than an old stable action menu list. Gaze is effortless and low latency, but our attention is fragile, and our thoughts are fleeting. Gone are the days of simple, linear, predictable UIs with clean desktops and organized folders. Somehow, these hopeful metaphors still exist in VR interfaces (and perhaps that’s why, despite its relative newness, VR feels more like the embodiment of a bygone dream), but take off your headset — or replace it with AR glasses — and you’ll see that the interfaces we’re building today are not shying away from the ambiguity and complexity of the world around us.

My first reaction is concern, yet I feel that by embracing their entropic nature, these new interfaces are, in some strange way, demanding us to become more present, proactive, vigilant, and resourceful. So maybe there is hope in uncertainty.

In the cemetery, we walked for another 20 minutes with our ears up, scanning for sounds around us. Finally, as we approached the Kōbō Daishi Mausoleum, it happened: a sudden movement of leaves in the trees above us and the rattling sound of the flying squirrels.

I don’t have photos from the night walk but here some pictures from our days in Kōyasan.

Let’s see.

First let me ask forgiveness for the sins I am about to commit. I watched the movies, enjoyed some of them, played some games, but I am no master of the canon. Growing up I loved the idea of the Force as a poetic, holistic energy that connected everything. Later I realized it remixed elements from Eastern and Western philosophies into a vision of intergalactic homeostasis, not too dissimilar from the Gaia Hypothesis. In fact the Empire was always busy mining resources and destroying ecosystems and the Force was the balancing counter-agent, or so I liked to think.

As beautiful and versatile a narrative device the Force is, it was still pure magic and the problem was clarifying whether it was something to be harnessed or it was an invisible hand controlling the faith of all characters. Then in 1999 George Lucas gave it a mechanism: Midi-chlorians, invisible intelligent organisms translating the will of the universe into action. To the horror of many fans, this turned the magic into a distributed network. This is where the metaphor might get productive for us.

Imagine another NYC.

8 million people trying as usual to avoid each other and say 3 times as many AI agents actually doing the opposite: constantly pinging each other, sharing bits of information, coalescing their attention, negotiating, and explicitly or implicitly interacting back with us.

You walk on the street and the song in your headphones inspires you to snap a photo of a good looking tree, just enough time to prevent you from taking the 8:23am train that was going to be stuck anyway between Jay and York. Maybe a reservation has preemptively been made to a bar that happened to be close to your friends’ new apartment days before you actually think of writing to them, let alone knowing they moved. Or that time the person sitting next to you at the café was reading the very same book you just bought and so you discovered you were both working on a similar project and later started a new collaboration… This is quickly turning into a cringey and naive future vision, but the point is to imagine that this could be the doing of midi-chlorian-like agents, orchestrating all of this toward some ideal (but how/who to define what ideal is?) lifestyle.

In Nokia's heyday I was working on a project to build Symbian “bots” that would proactively optimize battery, prioritize contacts, silence notifications, silently observing and learning your patterns. Later around 2016 at Google I was part of a group sketching AI agent/s that could browse, analyze content, and even click buttons on our behalf. Some people laughed at the mocks. Today, these ideas ship as beta features. Truthfully, we are nowhere near the scale and complexity I described before (it is also not a given that it is achievable or desirable) and we are still debating how to properly define agents but as much as I love/d searching for the perfect definition I think right now we need more mental models than definitions to help us grasp what we might already intuit. Is the Star Wars one bringing anything useful?

Reconsider the Skywalkers: was Luke’s journey his own, or was he a pawn in a cosmic balancing act? Was Anakin’s fall a series of personal moral failures, or an inevitable correction by the Force itself? Most characters seemed to believe they were making choices, but from a wider point of view, they were fulfilling roles dictated by a power they could channel but not control.

Swap X-wings for personalized autoplay and you might realize something similar is already here, you are just scrolling past it every day. The recommender systems we interact with every day are already a tangible version of this steering current, even before we get to talk about advanced autonomous agents.

The Midi-chlorian metaphor is obviously not precise to describe AI agents but it might be handy because it connects this emerging yet amorphous tech with something we already know. We are already familiar with the benefits and shortcomings of recommender systems as we have experienced them both on the personal and global scales. And we already know from decades of Star Wars canon that being able to perceive and control the Force requires a great deal of training and self-control and it might just be easier to go with the flow, wherever it takes us. Sounds familiar?

Becoming a Jedi is not easy and from a systemic perspective even then the actions of an individual person, AI agent, or recommender system contribute but cannot control the emergent behaviors of the much larger distributed networks they are part of. That we already knew long before LLMs.

Thinking about agents in this way is not only an intellectual game; it could be a productive mental model for its discussion, design, and development. If we are building this Force, we have a responsibility to shape its nature. The critical question is how do we design AI systems that enhance human agency rather than diminish it?

This requires a new charter for design that prioritizes legibility, contestability, and genuine empowerment. The debate should expand from privacy and data collection to defining and controlling directional consent. Can we make the workings of the algorithmic Force visible? Can we give users the ability to not just follow the current, but to consciously swim against it, or even change its direction? We might need an academy to learn how to read and wield this force.

It also means that when developing frameworks for AI agents we need to consider multiple scales. There is the nuclear, obvious layer of micro one-on-one interactions, whether between human-agent, agent-agent, agent-machine (hw or sw), but there is another, maybe less obvious, layer of massive multiplayer interactions between a growing numbers of actors (human and otherwise) that require a different way of thinking about their dynamics and emergent patterns. And again, existing recommender systems already demonstrate that we don’t need particularly intelligent systems to get visible network effects. As designers we are naturally trained to think about the first layer and we start to have frameworks for controls, feedback, and guardrails for agentic systems but I am not sure we have yet (or at least that’s something I am interested in exploring more) mental models, metrics, and techniques to understand the aggregate dynamics of the second layer.

I admit it is fun to stretch the cinematic metaphor a bit more so here are a few tools and experiments we might want to try (even before AI agents):

• The Jedi Toggle: a slider that dials algorithmic influence from suggestive to sovereign. Want serendipity? Turn the Force off. This is a user’s tool to withdraw directional consent, an algorithmic mute button.

• Force-Traces: a visualization of how algorithms shaped your week. A small data-trail showing how you were nudged from indie pop to doom-folk.

• The Sparring Arena: a simple web toy where you choose to speculatively act for yourself or let the system act for you. See what you gain. See what you lose. Algorithmic what-ifs.

And if this is interesting to you, here are some of my favorite (colleagues and sources of inspiration) reading rabbit holes that will take you to more serious thinking about these topics.

• Gabriel Iason et al., The Ethics of Advanced AI Assistants

• Shannon Vallor, Technology and the Virtues: A Philosophical Guide to a Future Worth Wanting and AI Mirror

• Tobias Rees, Agency. On the Philosophical Stakes of AI agency

• Bruno Latour, Reassembling the Social: An Introduction to Actor-Network-Theory

• David Sumpter, Outnumbered

Maybe George Lucas wasn’t just adding lore; maybe the Force was guiding him to give us a vocabulary for the invisible, systemic forces that mediate our lives :) He saw the network beneath the magic.

To be continued.

(if you have suggestions for material, experiments please let me know)

In the middle of a few debates over product metrics and UX strategies to move them I surprised myself by responding that “you don’t build a bridge to maximize the number of cars crossing it”. It happened a few more times and it kept feeling somehow random. The tension is familiar: without serious and sustained growth there won’t be much to worry about, and yet without values beyond immediate growth there is not much worth fighting for. But why go for bridges?

I have no particular obsession for bridges but they are infrastructures, so maybe that is why it came out of my mouth. Maybe I was anticipating I would use this in a presentation and I was foreshadowing the moment I could finally use one of the pointless Golden Gate Bridge photos I took over the years. Two birds with one stone.

The number of people crossing a bridge is of course important. If nobody crosses it you threw away a lot of money and ruined the landscape for nothing. If too many crosses it, it becomes a disaster of traffic, noise, and pollution. The daily/weekly/monthly active users of a bridge is a useful mechanical metric, you need it to plan things like maintenance, transit times, fares, etc. but it is not really the reason why you built it in the first place.

These metrics would also only give very limited bragging rights. You could drop the numbers at a dinner conversation but if you really want to make a lasting impression you would talk about the other values that bridge has generated. Time saved, safety, economic activity, property values, services increase on both sides of the bridge. Of course you also have negative metrics to consider and hopefully the net is positive.

Those are the reasons you want to build a bridge in the first place. You design it for the consequences it brings.

A bridge is why we should be thinking about products as infrastructures or functions that act as multipliers of the metrics we really care about and have a real impact on people. Is a communication platform just about its monthly active users, or is it infrastructure for collaboration, community building, or faster innovation? Is a navigation app just about routes planned, or the time saved, fuel conserved, and stress reduced?

Mechanical and immediate metrics are useful, but for what purpose? I understand this is not what we are taught and incentivized to do in most cases but maybe we have a distorted perception of the digital things we build. They might feel smaller, more ephemeral and less consequential than a bridge, but are they really?

If we remove the digital | physical divide projects in both realms become serious endeavors that require time, money, and commitment to the fact that they will bring changes and consequences to the world around them.

We might as well optimize for them.

Since you are here and as a reminder that we are not in normal, happy, mindless times, here is the last photo I took of the Golden Gate Bridge before moving to NYC. It was around noon, during the 2020 wildfires. We need more infrastructures than products.

It was bound to happen. My partner has been studying Japanese Tea Ceremony for years, I love tinkering with sounds, and we practice in the same space.

Here is a snippet of the third experiment adding live electronics to her tea making process. The first and second experiments were terrible, too much uncontrolled feedback, but this one is starting to get somewhere although there is still a lot of do to.

The sound is going through an envelope follower and is driving both a sequencer and a spectral re-synthesizer, the results are processed with delays and filters and blended with the original sounds. I particularly like how the filter is almost changing the water sound around 55 seconds into something lithic.

This is a new section. I hope to keep adding little audiovisual experiments but knowing myself the pace will always be very slow.

Imagining, and then building, a new operating system from scratch doesn’t happen often and while it’s true most R&D projects never see the light of the day, you might have used bits of Fuchsia yourself if in the last couple of years you used some Google Nest hardware and with the latest rise of GenAI I have seen many ideas we explored blooming in devices and operating systems around us, a sort of tech and design zeitgeist, or an entanglement where teams without direct connections have been thinking about the same problems and dreaming similar solutions.

R&D projects could be explorations of alternative worlds, engineering and design what-ifs, that with enough follow-through might have the possibility of influencing the direction of products and infrastructures. From a design perspective exploring a new operating system meant the possibility to question and rethink many fundamental constructs of human-computer interactions. The specifics of the work are obviously confidential and also not really the point I want to make. I am more interested in explaining what I learned from this experience and what can be applied, in general, to system and infrastructure design. 

So let’s use an analogy instead to tell this story.

The process of building cities from scratch

Imagine being an architect who gets to design a city from scratch (some actually do…). It sounds exciting and scary at the same time, maybe even naive (but we will talk more about this aspect later). Where to begin? How? Without constraints or specific requirements it is difficult to decide how to start. One approach might be to copy existing templates, pick cities we like, understand what we specifically like about them and recombine the elements in a new design, but this would give us evolutionary, perhaps derivative results. We could improve on some aspects of the design but we would be fundamentally following existing solutions. The alternative could be to work in isolation and focus on redefining what a city is, not what we already know a city to be, or what we think a city should be, but to redefine what it could be from the ground up, and let the design follow.

We thrive thanks to mental models that help us maintain handy shortcuts to what things are and how they work and as a bonus successful mental models are also shared so we have the benefit of knowing our shortcuts are understandable by others too. But here we are in a completely new territory, there is a vast plot of land in front of us and the drawing board is empty. We should first write our new definitions and hopefully they will inspire new mental models. For instance we could say a city is a place for people to accumulate and share resources; we are not assuming shapes or infrastructures yet, but we are imagining a purpose. In a city built for accumulating and sharing resources there could or could not be skyscrapers, cars, or shops. We don’t yet know if these things are needed for our city to be a city as we defined it.

With a purpose, we have verbs (accumulate and share) and we have a noun (resources). We can use these elements to dig deeper. What are the resources people need to be shared in a city? Food, water, shelter, knowledge, tools, skills perhaps? This list might not be exhaustive but these nouns could become some foundation for the next step in designing a city out of thin air. In our team we would call these stakes in the ground.

Assuming food, water, shelter, knowledge, tools, and skills are our stakes in the ground now we know our city must be designed around them. We need structures to support the collection, storage, and distribution of these six types of resources. This is by no means comprehensive but it is enough to start sketching concrete ideas for how this city could be built and function.

The way I described this process might seem very bottom-up, given the images I used around putting stakes in the ground and designing infrastructure around them, but it is in reality a very rigid, top-down approach, where I basically imposed my own personal definition of what a city might be and used it as the foundation of its design. You wouldn’t do this alone, most likely you would have a lot of people around you, each responsible for an aspect of the city, and you would have to negotiate the stakes in the ground, the language, and the design principles with them in order to make progress with the project.

This looks like a collective dream where everyone involved in the projects decides to take a pause from reality (the common, shared, known definitions and expectations of a city), and isolate in a different reality where a new, shared language provides an alternative definition of what a city could be. Given the scale of the project - Rome wasn’t built in a day after all - the group needs to commit to operating in this self-imposed dream state for a long while. I tried it for several years: it is both exhilarating and frustrating. You can see all the pieces of your imaginary city coming together beautifully, and you can also see how they won’t fit in the actual reality around you, at least at present or in the immediate future. It is both a communication and adoption problem: the mental models and the solutions you are developing are not compatible with others outside of the project and they would require both a massive (and unlike) cognitive and technical reset.

This approach to designing systems is both problematic and sometimes necessary. Designing in isolation is rarely a good idea and yet, if we strive for better cities, OSes, or any other systems we sometimes need to, at least temporarily, bend reality and believe something else is possible. The trick is in finding the productive balance between radical redefinitions and pragmatic solutions.

The disorder of complex systems

Cities are in fact fairly complex systems that cannot easily be reduced to a few nouns and verbs. They are the constantly evolving results of many small or large decisions made by countless different actors. These decisions are not only constantly happening but they are reverberating from the past and influencing the future directions of these systems. Are operating systems the same? Computers are by definition predictable, although they have reached incredible levels of sophistication they don’t display the unpredictability of complex systems, but the large platforms or systems we built with computers do. So in a strict technical sense an operating system is not a complex system, but in broader terms it enables many systems no less complex than cities.

Cities are an interesting analogy because through history there have been many attempts at redefining and building, measuring and controlling them, most recently in human history through the use of computers (A City is not a Computer: Other Urban Intelligences by Shannon Christine Mattern is a good read about this).

Richard Sennett described in his 1970 book The Use of Disorder how modernist city planners deadened cities with strict top down designs, and in more recent years in collaboration with Pablo Sendra (Designing Disorder: Experiments and Disruptions in the City) reiterated how complex systems do not exist in isolation; they cannot be created top-down by fiat and require embracing and designing for and with chaos—accepting that complex systems can only be steered, not fully designed or tamed.

You are no longer required to design a city from scratch - although we keep trying - and you don’t need to set stakes in the ground or have a fixed set of nouns and verbs. Rather, you are working with trajectories, levers, and pressure points to manipulate. The idea of a city as a place for people to accumulate and share resources becomes a series of questions around what are the most useful resources that should be shared among the city dwellers and how to incentivize sharing, rather than hoarding. The answer wouldn’t be singular but it could open up a host of small and large experiments and this is where the ideas of Designing Disorder struck me as a powerful insight even in my line of work. To make small and large experiments, and subsequent adaptations possible the focus of design should be on creating open, opportunistic, loose infrastructures that open up possibilities, rather than closing them.

Pablo Sendra has a simple, effective examples in a diagram where he shows the difference between allocating service outlets in a single corner of a public space (that’s the spot the planner designated as the temporary event area of the park) versus evenly distributing them, letting the future inhabitants of the space deciding the best spot for events and other activities. The space bends and adapts to its users, not the opposite. It is a tiny, almost invisible difference, and yet it changes the space dynamics and the role of the designers, from benevolent dictators to facilitators.

I don’t think I would have understood these concepts 5 or 10 years ago, nor had the desire for applying them. I confess I still sometimes feel the allure of an empty canvas in front of me for re-imagining from scratch whatever system I am designing. But as the world and the technologies around us are getting more complex, multi-faceted, and intertwined I think this is becoming a luxury I/we cannot afford.

Whatever city you are building, don’t design it isolated in an alternate reality.

References:

Christopher Nolan. Inception, 2010

Shannon Christine Mattern. A City is not a Computer: Other Urban Intelligences. Princeton University Press 2021

Richard Sennett. The Uses of Disorder: Personal Identity and City Life. Knopf 1970

Pablo Sendra and Richard Sennett. Designing Disorder:Experiments and Disruptions in the City. Verso 2022

Not cited here but Jane Jacobs’ The Death and Life of Great American Cities and The City as a System, Metabolic Design for New Urban Forms and Functions are also great reads I hope to study more.

The cover photo is a detail of “Veiled Whispers” (2021) by Elias Sime

Between 2020 and 2023 I contributed to building the Android Private Compute Core and set the principles for Protected Computing at Google. Android’s Private Compute Core is a secure, isolated sandbox where continuous sensing data (such as keyboard input, microphone, and camera) can be processed on device without anyone else, including the feature developers, seeing it. These types of technologies are important because even prior to the current explosion of LLMs and GenAI many useful features of our devices required continuous sensing and processing. On top of that the Machine Learning models built to support those features require new data to improve. For this we included federated learning and analytics techniques to guarantee privacy throughout the feature lifecycle.

Developing UX, communication, and adoption strategies around these new infrastructures is challenging because they are often defying long-held mental models around how modern software works.

A mental what?

A mental model is an abstraction of how something works. Psychologist Kenneth Craik (1943, The Nature of Explanation, Cambridge University Press) described how perception constructs “small-scale models of reality” that are used to anticipate events and to reason.

Mental models allow us to compress large amounts of data and experiences into smaller, memorable, and reusable models that we can rapidly apply to new situations. As I write this it occurs to me that this is not dissimilar from how machine learning works: sample lots of data, label it, and learn an abstraction that can predict an outcome when a new, different piece of data is presented. A meta note: you have just witnessed one of my mental models in action. 

You might not be a mechanic but you probably have a general sense of how combustion engines work; they have been around long enough for the majority of us to have experienced them in action, often daily; We have textbooks with schematics, movies with tropes around their failings (and quick magical fixes), and we need to answer correctly basic questions about them in order to get a driving license. So my mental model of a combustion engine is a personal aggregate of all of this and it keeps evolving. When I was a child my model didn’t include considerations of the environmental consequences of combustion, and when I was a teen my model didn’t include yet the possibility of alternative power sources.

Philip N. Johnson-Laird (2001, Mental models and deduction) defined 3 principles of mental models:

• Each mental model represents a possibility

• The principle of truth: mental models represent what is true according to the premises, but by default not what is false

• Deductive reasoning depends on mental models

The language is fascinating to me because it links mental models to the potential (or difficulty) of seeing things in different ways. If I have a solid mental model about something it will be difficult for me to see what is counter to that model, but if someone were to suggest the possibility of an alternative I might have the possibility to evolve my model. So mental models are both cages and keys to unlock them.

When it comes to privacy there are fundamental mental models that predate current computing technologies and make evolving these infrastructures more challenging. Even before we had to deal with computers and digital services we all learned and applied a simple rule of thumb when dealing with other people: to get something useful from another person, we often need to share with them some personal information… It is difficult to have a mechanic fix our car without knowing its model and year and how much we drove it. It is difficult to get a loan without a banker knowing about our savings, job, and plans. It is difficult to get a doctor's opinion without sharing our symptoms or lab test results. And the list goes on, yet the pattern is the same. Personal information is a necessary aspect of most transactions, without it nothing moves. We generally accept this, unless we suspect or know that the other party will use this information in a malicious or simply asymmetrical way, gaining more from it than us.

That is why we all learn techniques to vet other people’s intentions and develop a sort of spidey-sense for when we might be at risk of sharing too much. It is a matter of expectations and balance. We want information to be used as expected and we want to feel we are entering a balanced exchange where both parties get comparable benefits. Helen Nissenbaum brilliantly described this as Contextual Integrity.

Enter computers and digital services and the same applies: without revealing a bit of personal information it is difficult to get something useful in return. Except technology evolves faster than our mental models and infrastructures like the Private Compute Core challenge this do ut des mental model and posit that a mutually beneficial exchange can happen even when information is not shared between the two parties. The idea in itself is simple and appealing, but in practice it requires changing drastically how software is built and used today, or even better, evolving how we think software should be built and used since the assumptions we make when using a digital service are also likely the assumptions developers made when developing it: like the spice, data must flow.

Given the principles highlighted before, a mental model is not something you can simply erase or update. To evolve a mental model is to show the possibility of an alternative by providing concrete proofs of it, and let our brains do their deductions. I think we should do this by making systems such as the one I worked on more transparent, inspectable, and eventually verifiable or, in a single word, more legible.

Legibility

First published in 1998 the book Seeing Like a State, How Certain Schemes to Improve the Human Condition Have Failed by James C. Scott introduced the term ‘legibility’ as one of the key mechanisms of the modern state to homogenize and operate society.

To make something legible means to make it understandable and quantifiable, thus manageable at scale. The introduction of surnames, standard units of measurement, censuses, and official languages are ways to make the complex realities of large territories and swaths of population legible, albeit at the cost of homogenization and loss of critical details.

The author does a great job at detailing the tension between the large-scale improvements and the failure caused by the centralized, top-down approach of high-modernist ideologies. It seems we could learn to do better the next time around and in this particular case the notion of legibility is associated with the erasure of local diversity, cultures, and expertise.

As a designer the term ‘legibility’ caught my eye because to many of us it means something else. In typography, Legibility represents the level of ease with which characters can be perceived by readers. Many factors determine it: shape, stroke contrast, angular size, tracking, kerning, etc. as Ilene Strizver says, “The legibility of a typeface is related to the characteristics inherent in its design” (Type Rules: The Designer’s Guide to Professional Typography, 2010).

This inspired me to further consider the notion of legibility when applied to systemic design. That is to force legibility to the systems we design as a way to make them more transparent and comprehensible. This is less about flattening complexity into a single schema and more about opening a system up to inspection, critique, maintenance, and improvement. I have been deliberately using the term legibility instead of the ubiquitous ‘transparency’ because I think it adds a specific requirement of comprehension not necessarily included in our current notion of transparency. I can be transparent about something, but I might not make any particular effort to make it comprehensible.

We could try an adapted definition of legibility for our systemic design purposes:

There are two interesting aspects to a legible system:

• Legibility is always directed as it doesn’t exist without a specific subject. To whom is something legible?

• Legibility is proxy for power and agency, as being able to ‘read’ a system equals to being able to critique, improve, repair, and maintain it.

As such legibility is an intrinsic property of any human-made system since each system is at least legible to its creator/s. But we know this is not enough. Think about a legalese-dense terms of service statement, or poorly annotated code, or a messy, unlabeled electrical panel; these are all perfectly legible to the experts that created them but eventually opaque, frustrating, and even dangerous for many others.

The role of design in system legibility

As designers we need to increase the legibility of systems and infrastructures and in some cases this requires going against another mental model I suspect many of us have internalized for a long time: extreme simplification as a hallmark of good design.

I am not arguing simplicity is always bad and we should instead resort to a maximalist approach. I am arguing we often highlight design’s ability to remove complexity but sometimes forget a critical nuance in how we go about it.

Designers can aim at hiding the complexity by swiping it under the carpet of a simple UI that might make something feel like magic (I guess you can imagine how I feel about this type of design). Or design can aim to simplify complexity into something that can be intuitively understood and inform a better mental model about the underlying system.

Reconnecting with the combustion engine mental model from earlier in the article, Toyota has for many years provided a good example of this: its Energy Monitor UI has evolved to become one of the identifying features of its hybrid cars and I suspect it has played a crucial role in setting a simple, yet accurate mental model of how regenerative braking works for many.

Design has a long history of infographic and data visualization and a classical reference for when the data to be visualized in the behavior of the system itself is Fritz Kahn. His Factories of the Human Body tables has created a precedent for the use of analogy as a tool for applying known mental models (like film cameras or conveyor belts) to different, perhaps less legible, systems like the human body.

What would it mean to apply this approach to the design and development of digital systems and infrastructures? 

• Understand how the underlying system or technology works. An obvious step but there isn’t much to make legible if we cannot read it ourselves.

• Make system legibility an explicit requirement of the design and consider it for all actors involved.

• Apply infographics and  data visualization techniques to translate complex and invisible systems into understandable ones.

• Design beyond the product surface: invest time in building live indicators, inspection dashboards, explainers and simulations to help all actors monitor and understand the system.

• Expand UX research methodologies and metrics to measure the longitudinal impact on comprehension of a design: if we only optimize for immediate usability and delight the magic button will regularly win but it doesn’t mean that we are helping people become more savvy.

Applying these principles to the design and implementation of the Android Private Compute Core has been a big part of my work in the last few years.

Through explainers, reference design implementations, open sourcing, and more legible UX for end users and developers we managed to improve the Android privacy infrastructure and in recent years OSes have made good progress with live privacy indicators, dashboards and fine privacy controls - you can read the whole Android Private Compute Core story here or check the white paper and the source code - but why stop at Privacy? I hope these considerations on mental models, their evolution, and the importance of system legibility can help improve other critical infrastructures.

References:

Philip N.Johnson-Laird. Mental models and deduction, TRENDS in Cognitive Sciences Vol.5 No.10 October 2001

Helen Nissenbaum. Privacy as Contextual Integrity, Washington Law Review 119 (2004)

James C. Scott (1998). Seeing Like a State, How Certain Schemes to Improve the Human Condition Have Failed. Yale University Press  

Kenneth Craik (1943). The Nature of Explanation, Cambridge University Press

Strizver, Ilene (2010). Type Rules: The Designer’s Guide to Professional Typography (3rd ed.). New Jersey: John Wiley & Sons

Helena Doudova, Stephanie Jacobs & Patrick Rössler (2018). Image Factories: Infographics 1920–1945: Fritz Kahn, Otto Neurath et al. Spector Books

The cover photo is a detail of an Isamu Noguchi sculpture from his NYC museum.

It’s August 2023 when I’m writing this and I wonder how this will sound in just 6 months considering the pace at which AI is progressing. But I’m not concerned with predicting the future here, I’m more interested in exploring the role of design in a changing technological landscape and I hope these ideas will be useful no matter which directions AI takes in the near future and will be applicable to other fields of design as well.

So let’s briefly touch on such traditional practices: open another tab and search for images associated with “product design process”. You will find variations of linear or circular processes where critical user journeys are defined, solutions are designed and tested, and eventually implemented as experiments in live products. If successful in improving the chosen metrics, these solutions are integrated into the current product until a new feature or problem arises, and the cycle repeats.

It is the manual labor of UX teams building or optimizing features in response to specific user needs, striving for the elusive product-market fit. It is what we teach at design schools, what we discuss at interviews, often showing images of post-its, paper sketches and diagrams (a side note: please don’t do it, they are part of the process but they don’t really tell much about the work that we don’t already know).

But it is also a process born from a specific technological milieu, one where web first and then mobile app development were a mostly manual labor of crafting singleton solutions to narrow problems.

Take another break and this time try searching on your phone or tablet’s app store for calendar apps. You will likely find plenty, each with a different take on the core narrow problem. One might help you automate reminder generation, one might be aimed at professionals, others might help families manage shared calendars across different platforms, and so it goes. Each app is a refactoring of the same narrow problem of remembering events and managing time, each one carries the specific point of view of the developers and designers, often a small team working independently or part of a larger corporation to provide the best possible solution for that specific user needs and compete with the others on features, ease of use, stability, usability, and usefulness.

Here you see the interconnectedness of the design practice with the underlying means of productions and distributions.

There is nothing inherently wrong with the product design process, at this point it is a well-honed approach and if implemented smartly it leads to fantastic results for both consumers and makers of technologies.

Complex AI systems might upend all of this, forcing us to look deeper and wider. And just a reminder that this is a “Yes, and” approach; I am not proposing to dismiss product design altogether, I am proposing instead to add other dimensions to it.

In 1977 Charles and Ray Eames produced a fantastic video titled Powers of Ten where they show a camera zooming through a logarithmic scale based on a factor of ten. Starting from a couple having a picnic in a park in Chicago, zooming out to show galaxies and then zooming all the way in to show atoms and quarks. This is still a fantastic film and a simple, visceral way to show the complexity and relativity provided by a different point of view.

It is also a useful metaphor for our design practices where traditional product design is akin to looking through the camera at 1X, focusing on the narrow and immediate of a specific combination of user needs and context: a couple having a picnic in a park in Chicago. Zoom out or zoom in and you suddenly find yourself designing in completely different contexts.

In the Eames film there is a particularly useful scene where the camera enters the skin of the hand of one of the actors. The camera zooms in and suddenly goes underneath a surface to explore the invisible. This motion is similar to how I think modern designers should approach working with complex technologies and systems because, as a rule of thumb I developed over the years, as the complexity of a technology increases the levers of good user experience become invisible, hidden under layers that were not traditionally the domain of designers.

Let me give you a concrete example. Imagine being a designer tasked with designing a traditional mailbox: most of the design decisions impacting the quality of user experience for people interacting with the mailbox would concern the shape, materials, constructions, affordances of the mailbox. Hide the inlet opening and people won’t find it, design it at the wrong angle and it will flood when it rains, and the list goes on. Get these superficial (meaning pertaining the physical appearance and functioning of the device) variables right and you will have a successful, usable product. Improve them iteratively and you will soon reach a  great UX.

Imagine being tasked with designing a post office. The same superficial concerns would still apply: if spaces are not clearly marked and designed people might be confused or lost. Without the right affordances chaos would ensue as soon as the office gets busy. And yet in order to make the design successful you would also need to understand the postal service workflows, its procedures and regulations, which means that you would also need to consider and design for something that might not be immediately visible on the surface. A post office is a more complex piece of technology than a single mailbox.

Imagine then being a designer for a modern operating system, responsible for designing the platform that handles messages and notifications from hundreds of sources, human and otherwise, basically designing a complex world-wide network of digital mailboxes and postal offices, each with their own policies. Far from being a solved problem, it becomes obvious that focusing on the UI design of notifications and their controls is required but not sufficient to provide a good user experience. The most influential variable over how annoying, distracting, or useful and timely a notification could be can be found in the complex systems underneath: the collections of APIs, policies, and tools offered to developers, the spam and abuse filtering systems, the training of AI systems managing them, etc. All these variables are crucial to the product UX but oftentimes are removed from the daily practice of designers and I believe they should not.

So far we established how product design has been getting more complex and how designers should think and design at multiple scales, understanding both the surface and the underlying systems, thinking past 1X and expanding both to the macro and the micro. Let’s take a look now and how in particular Generative AI is making this need even more obvious and how designers could adapt.

As I said at the beginning, it is difficult to predict how things will evolve but so far the AI systems we are seeing growing in front of us are shaping to be incredibly versatile, excelling across a wide range of scenarios. And they can do all this with a simple, quite universal, albeit fuzzy, user interface: natural language.

Natural language is our latent space for making sense of the world and it is now also the interface/surface that can be applied to and become everything. What You See Is What You Get becomes What You Think Is What You Get. And it changes everything because once again means of production and distributions are put through a radical sea change driven by the generalist nature of LLMs.

 This subverts three main tenets of traditional product design:

• the continuous crafting of specific symbolic user interfaces

• the focus on narrow product verticals and individual critical user journeys

• the focus on deterministic/linear user journeys

Let me unfold this a little bit more. With AI, generic text-based applications can readily become canvases for memory, thought partnership, code generation, analyzing and aggregating data, developing new ideas. And yet, the surface is the same: it’s a chat, it’s textboxes, it’s a collection of buttons, it’s text morphed in different decorations.

We will still need UI design for these applications of course, but we might not need to have dozens of variations of similar UI patterns optimized for specific critical user journeys as today’s calendar apps.

We might desire more standardization, economy, and reusability of UI components because, as I described earlier, the crucial variables of the system making or breaking the user experiences, and ultimately deciding who wins the choice of customers, will be determined by aspects buried deep under the visible surface: the capability and consistency of a generative AI system, its trustworthiness, predictability, alignment, the speed and economy of execution, the ability to interface and operate different services and devices. And if these variables cannot be determined with traditional design approaches we will need to imagine new techniques and workflows to make sure designers can have visibility and influence over them, together with the other functions involved in their creation.

So, how should we adapt our design approach in this new landscape? Should we write and maintain lists of hundreds if not thousands of critical user journeys for generalist AIs and try to create design mocks of every possible variation of the interactions with these systems? It would be a daunting task and one that would anyway fail to capture all the possible variations and nuances of these systems.

Infrastructures are the often hidden, less glamorous, systems that make a city, or a society, work. The mind obviously goes to bridges, railroads, and airports but also intangible things such as regulations and trade agreements are infrastructure.

If the concept of infrastructure is obvious and pervasive what is interesting is that thinking that goes beyond it. Whether you are an engineer planning a new subway station, a policy maker drafting a new regulation you have fundamentally two options ahead of you: focus on the immediate problem at hand and try to address it in the most fitting (or some might say overfitting) way or use the problem at hand as the starting point to consider solutions that can hopefully withstand the passing of time and changing of other contextual variable, thus not just building a solution but building infrastructure. 

Olympic games are an example that brings all these professionals together in showing the differences between the narrow and systemic approaches. Huge amounts of money have been traditionally spent by hosting cities for building, branding the games, only to generate a few years later abandoned buildings and forgotten designs. Luckily there are also positive examples of infrastructural approaches that equally contributed to great olympic games and had positive, lasting impacts on hosting cities and communities. I’m thinking for instance about Otl Aicher’s pictograms for the 1972 games or the deep infrastructural renovation that Barcelona went through for its 1992 games where the majority of the games budget was allocated to infrastructure renovation. The olympic games were the trigger to develop infrastructures, visual and literal, that long outlived the main event and generated greater impact in the following years.

I often think of UX/UI components as functions within a complex system signal flow. What does this component do? What is the input and what is the output? What does it encourage or discourage? It might seem strange at first but baring UI components from their appearances and thinking abstractly about how they function in the larger system often helps clarify how to design it.

In her seminal 2008 book Thinking in Systems: a Primer Donella H. Meadows defines a system as “elements, interconnections, and a function or purpose” and here I might say UX/UI components are one of the basic units of function designers can directly manipulate when dealing with simple or complex systems.

Regarding UX/UI components as infrastructure also means considering their interoperability, resilience, and maintainability. Contrary to how we might sometimes operate, newness and uniqueness are not necessarily desirable traits of designing infrastructures. Furthermore, viewing these components as infrastructure entails broadening the traditional definition of users to encompass multiple actors.

Bruno Latour's Actor/Network Theory has been influential in understanding these systems. When designing a component, we need to consider all actors and networks connected to it: end users, developers, administrators, regulators, wider communities, and even potential abusers of the system. This is not entirely new territory for designers. We have seen in the past good examples of user journey maps from Service Design practices. We have the tools to do this, but what needs to change is the mental model with which we use them. Mapping the actor network theory of a system means understanding roles, incentives, expectations and canonical user journeys of each of them. Designing UX/UI components means also actively choosing which paths/behaviors we want to incentivize or disincentive through the system and this means also designing both the visible and the invisible, which brings us to the second idea of this piece, worldbuilding.

The term has been traditionally associated with fictional entertainment across media, including novels, film, television, and games and it refers to the practice of developing a cohesive set of narrative infrastructures such as characters, geography, lore and laws that support the development of several stories by multiple creators and across different media. Star Wars, Game of Thrones, Final Fantasy or Super Mario are all massively successful examples of worldbuilding.

You might start to see some parallels with the infrastructural work of designers and you might also see similarities with well established practices of Speculative Design. Dunne and Raby are among my favorites with their critical and provocative work. Near Future Lab recently published a successful design fiction manual with clear examples on how to use speculative techniques to design artifacts from alternative and imagined futures. Another web search for Speculative Design will provide you with several renditions of the famous future cone diagram: the framework here is temporal and horizontal. The main verb are “Imagine”, followed by “visualize”, “embody”. We are asking designers to imagine the future through tangible artifacts such as  books, magazines, and products as a way to visualize, critique and make sense of possible futures.

I think worldbuilding is a fitting companion because it adds a dimension of creation to the speculative explorations of what might/could/should happen in a given future and goes beyond traditional product design by putting  an emphasis on the infrastructural elements needed to foster and steer building by many.

For designers to apply worldbuilding at an infrastructural level means considering the cultural, legal, and technological conditions needed for desirable futures to develop. It means adding more vertical dimensions to the temporal/horizontal speculative design framework.

I sketched this inspired by the concepts of Architectural Shearing Layers (buildings as composed of several layers of change) from Frank Duffy and the Pace Layers by Stewart Brand from the The Long Now Foundation.

Let's imagine a future where say 90% of content is synthetic, meaning being generated on demand from advanced generative AIs. What does it mean to be an artist, a musician in that world? Do copyright laws still make sense? If we don't have an experience of a shared cultural artifact what would we talk about? Or, in a completely different set up, let’s imagine a future where AIs are both individual and communal technologies, for instance helping an entire community thrive. How to balance individual and community wellbeing?

For example, what kind of platforms, APIs, and processes are needed to negotiate values among heterogeneous communities? How can we evolve the understanding and definition of human creativity and its contribution to the generative AI workflows? What kind of new technologies and legal framework can support it? These are abstract questions that can be explored in quite concrete terms… platform APIs can be prototyped, culture continuously evolve through discourse, legal frameworks can be negotiated and tested out, making worldbuilding a communal activity that can be used to activate and involve all the actors beyond the traditional design and engineering roles.

Evolving AI technologies are quickly going to change the way we design and build products, challenging some of the design processes (and roles). Thinking about our work as defining and building the infrastructures needed to support development of the worlds we want to inhabit, expanding our attention beyond the here and now and toward the macro, communal, long term seems to me something crucial at this juncture and something designers are well equipped to contribute to.

In this context worldbuilding is meant to be an act of collaborative negotiation, planning, and creation where designers get to extend the reach of their techniques beyond traditional product design.

References:

Anthony Dunne and Fiona Raby (2013). Speculative Everything. Design, Fiction, and Social Dreaming. The MIT Press

Brand, S. (1994). How Buildings Learn: What Happens After They're Built. New York (NY): Penguin

Bruno Latour (2007). Reassembling the Social. An Introduction to Actor-Network-Theory. OUP Oxford

Julian Bleecker, Nick Foster, Fabien Girardin, Nicolas Nova, Near Future Laboratory, Chris Frey, Patrick Pittman (2022). The Manual of Design Fiction. Near Future Laboratory

Marc Stickdorn, Jakob Schneider (2010). This is Service Design Thinking: Basics – Tools – Cases. BIS Publishers

Meadows, D. H. (2008). Thinking in Systems: A Primer. White River Junction, VT: Chelsea Green Publishing

The cover photo is a detailed of an experimental biogenic building material created for the 2023 Reset Materials: Towards Sustainable Architecture exhibition in Copenhagen, Denmark