At Paris Fashion Week, ANREALAGE’s Fall/Winter 2026 collection introduced garments embedded with LED displays designed to visually blend into their surroundings. Designer Kunihiko Morinaga presented coats, dresses, and structured outerwear that reproduced fragments of the projected runway environment, creating a camouflage-like effect as models moved through the space. The garments functioned as programmable visual surfaces, displaying imagery synchronized with the projections surrounding the catwalk. The concept draws on thermoptic camouflage, a fictional technology depicted in Ghost in the Shell, the cyberpunk manga created by Masamune Shirow.

Clothing as a Digital Display
The FW26 collection centered on garments that functioned more like modular display surfaces than traditional textiles. Coats, dresses, and structured outerwear incorporated LED panels capable of presenting animated imagery. During the show, large-scale projections formed the visual backdrop of the runway while the garments displayed fragments of the same visuals through embedded displays.

When alignment occurred between projection and garment, sections of the clothing visually blended with the background imagery, producing the illusion that parts of the wearer were dissolving into the environment. The effect relied on coordination between stage design and garment programming. LED displays embedded within the clothing rendered digital imagery synchronized with the projected visuals surrounding the runway.

According to the brand, Morinaga and his team spent several months developing the programming system used in the show. The process involved calibrating LED panels, synchronizing visuals with runway projections, and ensuring the garments remained structurally wearable. This differs from many projection-mapping runway presentations, where garments primarily serve as surfaces for projected imagery. In the ANREALAGE show, the garments themselves displayed digital visuals through embedded LED panels synchronized with the surrounding projections.

Cyberpunk References in Material Form
While the LED garments formed the technical core of the collection, the surrounding designs incorporated recognizable clothing elements. Prairie-style shirts, fringed denim, and softer fabrics appeared beneath or alongside the illuminated structures. This juxtaposition created a contrast between programmable surfaces and more conventional garment forms. Digital display components were layered over familiar silhouettes, combining electronic materials with traditional clothing construction.

Several looks incorporated structured, armor-like shapes alongside bright neon color palettes. Metallic accessories and hardware-like details appeared throughout the collection, reinforcing the technological aesthetic established by the LED garments. The collection also referenced thermoptic camouflage from Ghost in the Shell. The cyberpunk narrative imagines surfaces capable of reproducing their surroundings in order to conceal objects, a concept Morinaga translated into wearable display garments synchronized with the runway projections.

Fashion as a Testing Ground for Emerging Materials
Morinaga founded ANREALAGE in 2003 and has frequently incorporated experimental materials and technologies into his collections. Previous runway presentations have included ultraviolet-reactive fabrics, garments that change color under different lighting conditions, and pieces designed for projection mapping.

Many of these projects explore how clothing surfaces respond to light, projection, and environmental inputs. The FW26 runway extends this approach by integrating LED display systems directly into garments. While wearable LED technology has appeared in experimental fashion contexts before, the ANREALAGE presentation incorporated these displays into the structure of the runway environment itself. Background projections generated the visual environment while the garments displayed synchronized digital imagery.

Runway presentations provide a controlled setting where designers can experiment with new materials, visual systems, and interactions between clothing and digital media. In the FW26 show, thermoptic camouflage was explored through garments equipped with LED displays synchronized with the projections surrounding the runway.

Brain–computer interfaces (BCIs) have traditionally been developed within medical and neuroscience research. Systems capable of translating electrical activity in the brain into machine-readable signals have enabled patients to control prosthetic limbs, communication interfaces, and computer cursors through neural activity. Over the past decade, artists and designers have also begun experimenting with these technologies, exploring how neural signals can function as expressive inputs rather than purely clinical data.

Advances in affordable electroencephalography (EEG) headsets and open-source signal processing frameworks have made neural sensing increasingly accessible outside laboratories. Although EEG devices cannot decode thoughts, they can detect measurable patterns associated with attention, relaxation, and cognitive effort. Within creative practice, these signals are often treated not as precise commands but as generative inputs capable of influencing visual systems, sound synthesis, or responsive environments.

Across contemporary media art, neural interfaces are emerging as instruments that translate cognitive signals into visual, sonic, and spatial media. Early experiments in biofeedback art during the 1960s and 1970s similarly explored how physiological signals—heart rate, brainwaves, or skin conductance—could be translated into audiovisual systems. Today’s BCI-based artworks extend this lineage, combining neural sensing with machine learning, real-time computation, and networked media environments.

EEG Signals as Visual and Physical Media
One of the most direct artistic uses of EEG appears in the work of artist Lisa Park, whose installations translate brain activity into physical phenomena. Park frequently uses consumer EEG headsets to capture neural signals associated with shifts in attention and cognitive engagement.

In the performance installation Eunoia (2013), Park wears an EEG device while seated among a circular array of water-filled vessels. Each vessel is connected to a vibration transducer that responds to changes in the EEG signal. As the performer attempts to regulate her mental focus, the vibrations create visible ripples across the water’s surface. The system does not attempt to interpret specific thoughts. Instead, it converts fluctuations in neural activity into subtle physical transformations. The resulting patterns make cognitive dynamics visible, translating otherwise imperceptible neural activity into an observable environmental effect.

Park’s work demonstrates how neural interfaces can function less as instruments of control than as mechanisms for externalizing cognition. By turning EEG signals into physical motion, the installation foregrounds the fluctuating nature of neural activity while revealing how internal mental states can shape responsive systems.
Park extended this approach in the installation Blooming (2015), which translates EEG signals into kinetic movement within a spatial environment. In the work, an EEG headset captures fluctuations in neural activity associated with attention and mental engagement. These signals are transmitted to mechanical devices that animate artificial flowers placed throughout the installation.

As the participant’s brain activity shifts, the flowers open and close in response to variations in the EEG data. The installation creates a feedback loop between internal cognitive states and the surrounding environment. Small changes in attention or mental effort become visible through the movement of the flowers. Like Eunoia, Blooming does not attempt to decode thoughts. Instead, the system translates measurable neural patterns into a responsive physical system. The work highlights the instability and interpretive nature of neural signals while demonstrating how cognitive data can be transformed into expressive media.

Neural Signals in Computational Media
Artist and technologist Daito Manabe, founder of Rhizomatiks, investigates how neuroscience research and machine learning intersect with computational media. His work often examines how physiological and perceptual data can be translated into visual systems. Dissonant Imaginary (2019), developed by Rhizomatiks Research in collaboration with neuroscientist Yukiyasu Kamitani, draws on Kamitani’s research into neural decoding, where machine-learning models analyze patterns of brain activity associated with visual perception.

Rather than functioning as a conventional brain–computer interface, the work explores how neural data can be computationally interpreted and represented. Visual systems developed for the project reference research that reconstructs or predicts images from brain activity, highlighting how machine-learning models translate neural signals into visual information. By connecting neuroscience research with generative media systems, the project reflects the growing intersection between brain research and experimental media practice.

Human–Machine Perception and Cognitive Systems
Artist Justine Emard investigates how artificial intelligence and sensing technologies interpret human presence. Her installations frequently stage encounters between human bodies and intelligent systems capable of analyzing movement, gesture, and behavioral patterns.

In Co(AI)xistence (2017), developed with researchers associated with the Okinawa Institute of Science and Technology, Emard presents an interaction between a human dancer and a robotic arm equipped with AI-based vision systems. The system observes the dancer’s gestures through cameras and computational sensing technologies, generating responsive movements that unfold throughout the performance.

Rather than translating neural signals directly into media output, the project explores how machines construct representations of human behavior through sensor data and machine learning models. The installation highlights the interpretive frameworks through which computational systems perceive and model human activity. By situating the body within networks of sensors and intelligent systems, Emard’s work reflects the broader technological landscape in which neural interfaces are emerging. Brain signals represent only one layer within a growing ecosystem of biometric data streams captured and interpreted by computational systems.

Across these practices, neural interfaces appear less as tools for reading minds than as instruments that translate cognition into data streams capable of shaping media systems. The result is not mind-reading but a new class of responsive technologies in which attention, perception, and mental effort become parameters within computational environments.

Wendy Yu describes her practice as transforming movement into “living visuals.” Trained in dance at the Victorian College of the Arts, Yu has built a cross-disciplinary career spanning choreography, projection design, creative coding, and large-scale spatial installations. Her work operates across public art commissions, brand activations, performance design, and research-driven experimentation.

Rather than separating choreography from technical production, Yu integrates them. Movement informs system behavior; visual systems are structured through timing, sequencing, and embodied logic. Across her portfolio, choreography functions not only as performance content but as an organizing framework for responsive environments. Her projects frequently combine projection mapping, motion capture, generative visual systems, and site-specific architectural integration. The through-line is consistent: movement is translated into structured visual output across physical surfaces and digital platforms.

Public Projection as Data Visualization
Yu’s large-scale public works demonstrate how choreography intersects with architectural media and urban data. The Acts of Holding Dance, currently exhibited at the Art Gallery of New South Wales is a digital placemaking project that visualizes human movement data on the museum’s exterior surfaces. According to her project documentation, the work renders traces of community activity as persistent visual layers integrated with the building façade. The installation situates motion data within architectural context rather than isolating it within screen-based environments.

Similarly, the Boston Rose Kennedy Greenway Projection translates patterns of human movement into projected narratives across public space. The project uses projection mapping to anchor data-driven visuals to specific architectural sites, making patterns of circulation legible at urban scale.

At BLINK Cincinnati, an international light and art festival, Yu developed a projection mapping work structured around kishotenketsu, a four-part narrative framework that progresses without conflict. The piece animates a local legend through evolving architectural imagery, applying narrative sequencing principles to large-format projection design. These projects illustrate a consistent methodology: architectural surfaces become temporal canvases, and movement patterns become visual structure.

Performance Design and Brand Activation
Yu’s practice extends into theatrical production and commercial campaigns, where choreography informs both visual language and audience engagement. For the 2025 international theatrical production Legends of the Golden Arches, she designed projection content and real-time visual systems that support stage choreography and narrative development. The project integrates animated environments with live performance, aligning visual sequencing with the timing of movement on stage.

In 2022, Yu served as principal artist for the Adidas James Harden Vol. 6 campaign, producing projection-based activations around the Barclays Center in Brooklyn. The project translated athletic movement into large-scale projected visuals, integrating brand storytelling with choreographic motion principles.

Her collaboration with Arcitecta for supercomputing industry events integrates motion-captured dancers within LiDAR-scanned architectural environments. These works align performance visuals with representations of high-performance computing systems, connecting embodied motion with technological infrastructure in event-scale environments. Across these contexts—cultural, theatrical, and corporate—Yu applies similar structural thinking: motion informs projection behavior; projection systems respond to spatial constraints; timing and sequencing remain central.

Research and Generative Inquiry
Beyond commissioned work, Yu develops research projects that explore how choreography can be analyzed, visualized, and systematized. Scalable Choreography: SC2025 investigates generative performance frameworks that visualize distributed computing systems through mathematically derived movement structures. The project aligns choreographic patterning with computational concepts, structuring multi-performer movement using algorithmic logic.

Breath x Dance (2023) maps biometric breath data to visual outputs, making physiological processes perceptible through projected media. The project demonstrates how internal bodily rhythms can serve as input for real-time visual systems.

In Make Dance Comprehensible, Yu examines how choreographic structure can be translated into accessible visual languages. The research focuses on developing tools and representations that clarify temporal and spatial relationships within dance performance. These projects indicate a sustained interest in translating embodied processes into computational and visual form—without separating the body from the system that represents it.

Choreography as System Architecture
Across public installations, brand activations, theatrical productions, and research initiatives, Wendy Yu’s work centers on one consistent proposition: movement can structure technological systems. Her portfolio demonstrates that choreography can function as a design logic for projection mapping, data visualization, generative media, and immersive environments. Architectural surfaces become time-based media platforms. Motion becomes structured input. Narrative sequencing becomes programmable.

Yu’s designation as a choreographic technologist reflects this synthesis. The term does not position technology as a tool applied to dance; it frames choreography as an operational model for building responsive visual systems. By integrating movement, architectural media, and generative computation, Wendy Yu contributes to an evolving field in which embodied intelligence informs how interactive environments are designed, visualized, and experienced.

A comprehensive political and design theory of planetary-scale computation proposing that The Stack—an accidental megastructure—is both a technological apparatus and a model for a new geopolitical architecture.

What has planetary-scale computation done to our geopolitical realities? It takes different forms at different scales—from energy and mineral sourcing and subterranean cloud infrastructure to urban software and massive universal addressing systems; from interfaces drawn by the augmentation of the hand and eye to users identified by self—quantification and the arrival of legions of sensors, algorithms, and robots. Together, how do these distort and deform modern political geographies and produce new territories in their own image?

In The Stack, Benjamin Bratton proposes that these different genres of computation—smart grids, cloud platforms, mobile apps, smart cities, the Internet of Things, automation—can be seen not as so many species evolving on their own, but as forming a coherent whole: an accidental megastructure called The Stack that is both a computational apparatus and a new governing architecture. We are inside The Stack and it is inside of us.

In an account that is both theoretical and technical, drawing on political philosophy, architectural theory, and software studies, Bratton explores six layers of The Stack: Earth, Cloud, City, Address, Interface, User. Each is mapped on its own terms and understood as a component within the larger whole built from hard and soft systems intermingling—not only computational forms but also social, human, and physical forces. This model, informed by the logic of the multilayered structure of protocol “stacks,” in which network technologies operate within a modular and vertical order, offers a comprehensive image of our emerging infrastructure and a platform for its ongoing reinvention.

The Stack is an interdisciplinary design brief for a new geopolitics that works with and for planetary-scale computation. Interweaving the continental, urban, and perceptual scales, it shows how we can better build, dwell within, communicate with, and govern our worlds.

thestack.org

Nxt Museum’s Still Processing is a group exhibition that interrogates the mechanics of perception in a mediated world — a space where datasets and algorithms no longer sit behind screens but directly shape what we see, hear, and feel. Curated by Belgrade-born Bogomir Doringer and on view at Nxt Museum in Amsterdam from 7 February to 5 October 2025, the exhibition assembles seven contemporary artists whose works make tangible the often invisible processes of image generation, transformation, and interpretation.

At its core, Still Processing operates with a dual premise: first, that technological systems — from compression algorithms to AI synthesizers — materially shape the images and sounds circulating in our culture; second, that human sensory mechanisms themselves are active processors of information, constructing meaning through a complex interplay of cognition, expectation, and sensation.

The Exhibition’s Structural Logic: Algorithm and Embodiment
Rather than presenting a linear narrative, Still Processing is organized around staged encounters with work that foregrounds computational logic as a shaping force. Across seven large installations and transitional spaces, visitors move through perceptual registers where light, sound, motion, and digital representation are not just exhibited but activated.

A foundational strand in the show is Rosa Menkman’s suite of research-based works, which trace pivotal moments in the history of image technologies. Spanning pieces such as The Collapse of PAL and IM/POSSIBLE RAINBOWS, Menkman’s practice unpacks how standards, compression, and visual protocols influence what is preserved — and what is lost — when images are encoded, transmitted, and decoded digitally. These works perform a kind of archaeology of visual media, revealing the compromises and hidden biases that underlie seemingly neutral technical systems.

The exhibition also places computational aesthetics in dialogue with the body’s physical reception of stimuli. Gabey Tjon a Tham’s Red Horizon uses kinetic pendulums and light to generate patterns that produce afterimages on the viewer’s retina — a direct intersection of mechanical motion and human visual memory. Lumus Instruments’ Polynode XI similarly translates audiovisual data into an ambient field that continually evolves in response to environmental cues, collapsing the subject/object distinction between viewer and machine signal.

Between Synthetic Worlds and Sensory Conditions
In several works, the technologies of image and sound generation are more than instruments — they become subjects of inquiry. Geoffrey Lillemon’s Simulation in Blue situates AI-generated figures in an evolving audiovisual environment, where CGI and algorithmic choreography produce a hybrid form that defies conventional categorization.

Using tools associated with both entertainment and generative art, the piece raises questions about authorship and agency in synthetic media.
A contrasting spatial intervention comes from Children of the Light, whose ALL-TOGETHER-NOW creates a constellation of floating luminous rings inspired by the first iconic image of a black hole. Here, the interplay of light and spatial perception foregrounds the limits of visualization — especially when confronting phenomena that sit at the margins of human sensory capacities.

The exhibition’s commissioned project by Balfua, The Slollaleia, occupies a dedicated stage in the museum, deploying AI and digital world-building to evoke a speculative ecology of synthetic beings. Drawing on animation and algorithmic behaviour, this work positions machine logic not as an external force but as a generative partner in the creation of new forms.

Finally, Boris Acket’s Duration uses sound and light in a room-scaled structure that questions our experience of time itself. Instead of linear sequencing, the installation generates patterns that expand and collapse in real time, revealing how perceptual time — like image processing — is contingent, constructed, and continually re-negotiated.

Processing as Reflection, Not Metaphor
What distinguishes Still Processing from more typical tech-centric exhibitions is its insistence on processing as an active condition rather than a metaphor. The works do not simply illustrate technological influence; they materialize the act of translation between encoded signals and human interpretation. This emphasis on material processes — whether through algorithmic generation, kinetic motion, or sensory feedback — foregrounds the active, iterative nature of perception itself.
Across the exhibition’s trajectory, visitors can perceive how machine logic does not operate in isolation: it infiltrates standards, expectations, and even cognitive habits. Likewise, the human nervous system is shown not as a passive receptor but as part of a continuous loop of interpretation and re-interpretation. 

Still Processing thus situates the question of meaning not in static representation but in ongoing coupling between human systems and technological media. While the works diverge in media and mode — from archival probes into compression histories to speculative simulations of imaginary ecosystems — they share a focus on the seams where processing becomes visible, where data ceases to be inert and begins to shape experience. For a contemporary audience attuned to the ubiquity of digital mediation, the exhibition provides frameworks for understanding how meaning remains contingent, negotiated, and still very much under construction.

The stated premise was straightforward: create space to experiment. Each contributor worked within their respective discipline—Bosmans on design and illustration, Waltz on animation and project direction, and Poon on sound design—while responding to the evolving output of the others. The result is a tightly integrated audiovisual work shaped through reciprocal exchange rather than a fixed production hierarchy.

The collaboration was presented at the OOO Exhibition in Oslo in 2023 and is scheduled to be shown at the Northbridge Piazza Screen in 2024, positioning the work within both exhibition and public-screen contexts.

The project reflects a contemporary motion practice in which plugins, animation systems, and sound design tools operate as shared infrastructure across disciplines, supporting distributed authorship rather than isolated production roles.

Project Info
Year: 2023
Project Lead & Animation: Anders Waltz
Design & Illustration: Jerry-Lee Bosmans
Sound Design: John Poon
Tool: Flex plugin (aescripts)
Presented at: OOO Exhibition, Oslo (2023)
Screening: Northbridge Piazza Screen, Perth (2024) Image Credit: Jerry-Lee Bosmans, Anders Waltz, John Poon

Fluid Current consists of water, aluminium, an electric circuit, and an LED. As water moves through the aluminium structure, it completes an electrical circuit and triggers a pulse of light. The system is direct: conductivity produces illumination.

The work focuses on the relationship between material behavior and perceptual experience. Water—commonly understood as fluid and unstable—functions here as a conductive element within a simple electrical system. The circuit is not concealed; its operation unfolds physically through contact and flow.

Hagelaar has described his practice as engaging two related perceptual states: estrangement, when familiar materials reveal unexpected properties, and stilling, when subtle movement concentrates attention. In Fluid Current, both are present. The timing and intensity of the light depend on the dynamics of the water, introducing variation into an otherwise minimal setup.

By aligning a natural process with a technical response, Fluid Current frames perception as an encounter with cause and effect in real time. The piece does not simulate interaction; it stages it through material conditions.

Project Info
Artist: Sander Hagelaar
Materials: Water, Aluminium, Electric Circuit, LED

Year: 2020

Slow Technology Reader gathers contributions from diverse disciplinary fields and knowledge traditions to consider technology through a ‘Slow’ lens. By turns artistic, speculative, and academic the contents here probe alternative potentials for the digital entities proliferating in our midst, by invoking variable pacings and temporalities of engagement; reflecting through tools and techniques that have endured the test of time; and looking to non-Western and more-than-human sources to inspire technological development.

This new volume in the Slow Reader series gestures toward a fuller spectrum of what technology is and can be, moving beyond the limited perspectives and legacy structures that dominate technological development today. It includes the rich insights and intelligences of feminist, queer, Indigenous, activist, and ecological practices—offering them as vibrant data points for shaping more just and generative futures. At a time when the digital reaches into nearly every facet of planetary existence, this book aims to disrupt and recalibrate how we think about and relate/live with technology, illuminating more expansive pathways forward.

Contributions: Paula Albuquerque, Kader Attia, Aïsta Bah, Scott Benesiinaabandan, Pac.me B.ru, Cláudio Bueno, Derrais Carter, Raven Chacon, Joana Chicau, Guy Cools, Laura Coombs, Siobhán K. Cronin, Will Daddario, Edwidge Danticat, Thierno Dia, Mamadou Taslim Diallo, Henriette Essami-Khaullot, Silvia Federici, Mariana Fernández Mora, Ella Finer, Jem Finer, Mashinka Firunts Hakopian, Dakin Hart, Faïza Hirach, Candice Hopkins, Christine Hvidt, Carol R. Kallend, Theun Karelse, Danel Khojayeva, Suzanne Kite, Fran Kourouma, Jaron Lanier, Jason Edward Lewis, Pia Lindman, Gļeb(s) Maiboroda, Pierre Marchand, Michael Marder, Nanako Nakajima, Florence Okoye, Marina Orlova, Jogi Panghaal, Moisés Patrício, Rory Pilgrim, Elisabeth (elieli) Raymond, Milady Renoir, Oscar Santillán, Laurel Schwulst, Mindy Seu, Camila Sposati, Christel Stalpaert, Corey Stover, Melita Stover Janis, Carolyn F. Strauss, Foluke Taylor, Alberto Isifin Tchama, Ovidiu Ţichindeleanu, Rolando Vázquez Melken, Evelyn Wan, Halidou Wuandaougo, Arkadi Zaides, Joanne Zerdy, Martín Zícari

Support: Creative Industries Fund NL, Cultuurfonds

Design: Haller Brun

Real-time systems promise immediacy. In AI interfaces, gaming engines, financial markets, and live media, lower latency is equated with intelligence, efficiency, and control. Response time becomes a proxy for authority: the system that answers fastest appears most capable.

Latency, however, is not simply a technical metric. It is a distribution of responsiveness. It determines who speaks, who waits, and what counts as recognition. In contemporary performance and computational art, delay becomes material—staged, modulated, and made perceptible. Rather than treating latency as error, artists work with it as structure.

Across theater, immersive environments, and live electronic performance, Annie Dorsen, OpenEndedGroup, and Myriam Bleau and Myriam Bleau demonstrate how real-time systems redistribute agency through computational mediation.

Latency as Dramaturgy
Annie Dorsen’s theater projects foreground computation in real time. In Hello Hi There (2010), a live performance structured around algorithmically recombined philosophical debate, text is generated and delivered on stage through software systems drawing from pre-authored corpora. In The Slow Room (2018), Dorsen employed machine-learning systems trained on canonical dramatic texts to generate dialogue live, shaping each performance through probabilistic output rather than fixed script.

More recently, in Yesterday Tomorrow (2023), Dorsen incorporated large language models into a live performance structure, allowing text to be generated dynamically rather than performed from a predetermined script. The work extends her long-standing investigation of machine-authored speech as a theatrical condition.

Across these projects, language unfolds through computational processing rather than predetermined sequence. The audience witnesses dialogue emerging from a system in operation. Pauses between exchanges are structured not only by performers but by the processing dynamics of the software itself. Speech is selected, assembled, and delivered in real time.

Timing shifts authority on stage. The performer does not fully determine pacing; system response participates in shaping rhythm and tension. Silence becomes infrastructural rather than purely psychological. When language is generated live, the interval between prompt and output becomes part of the performance’s architecture. Consumer AI platforms are engineered to minimize visible delay in order to simulate fluency. Dorsen’s work instead keeps mediation legible. Response time is shown to be conditional and shared. Latency becomes dramaturgy.

Latency as Environmental Intelligence
If Dorsen stages latency as dialogue, OpenEndedGroup spatializes it. Founded by Marc Downie and Paul Kaiser, the collective works at the intersection of dance, computation, and immersive projection. Their installations and performances employ motion capture and real-time rendering systems that interpret human movement and generate visual response.

Gesture is captured, translated algorithmically, and returned as light, image, or spatial transformation. The response unfolds in near real time, though never without mediation. Action and output are coupled, but not fused. Tracking systems operate through predefined thresholds and parameters. Recognition depends on configuration. What appears seamless is structured by computational limits and decision rules.

This logic extends beyond the gallery. In biometric authentication, autonomous vehicles, and surveillance systems, response time governs classification and access. Speed often signals certainty; delay can register as ambiguity. OpenEndedGroup’s environments render that structure perceptible. The space responds—but on the basis of system interpretation. Latency here is not spectacle. It is the condition of interaction..

Latency as Kinesthetic Feedback
Myriam Bleau’s performance systems make computational response tangible. In Soft Revolvers (2014), she performs with a set of translucent, illuminated discs that function as both instrument and interface. Each object contains embedded sensors that track rotation and gesture, transmitting data to custom software that generates and modulates sound in real time.

Movement is translated into signal. Signal becomes audio output. The response is immediate but mediated through software interpretation. Unlike acoustic instruments, where vibration directly produces sound, Bleau’s system depends on computational mapping. Gesture must be read, processed, and rendered.

That interpretive layer structures timing. The performer adjusts not only to rhythm and composition, but to the responsiveness of the system itself. Expressivity emerges from the interplay between body and computational response.

In Second Self (2024), created with Nien Tzu Weng, Bleau extends this exploration of responsive systems through portable interactive LED screens. Inspired by Sherry Turkle’s concept of the “second self,” the work treats the interface as a bodily extension. Each gesture alters sound and visual output in real time, staging an audiovisual dialogue between human movement and technological mediation.

Here, latency is not framed as delay but as the condition of exchange. Timing shapes interaction, and interaction shapes authority.

Timing as Power
Across these practices, latency is not inefficiency. It is structure. In technical discourse, low latency is framed as progress. Edge computing reduces network delay by bringing computation closer to users. High-frequency trading firms compete on microseconds. AI inference optimization accelerates model response times. In many contexts, faster systems gain competitive advantage. Performance and computational installation complicate that narrative. Rather than eliminating delay, they foreground mediation. Response time becomes visible. Interaction is shown to be organized by timing.

Who receives immediate feedback? Who waits? Who determines the pace of exchange?

By foregrounding computational timing, Dorsen, OpenEndedGroup, and Bleau shift attention from output to responsiveness. The encounter becomes a study of how systems listen, process, and reply. Latency shapes rhythm, choreography, and perception. It also influences how authority is read. As real-time AI increasingly mediates speech, movement, and image, timing extends beyond performance. Every “real-time” interaction is structured by decisions about speed.

Each page is 3D printed with conductive TPU traces positioned beneath selected lines of text. These traces connect to capacitive touch sensors, allowing readers to activate audio output through direct contact with the printed surface. A microcontroller, speaker module, and electronic ink display are housed within a steel-infused PLA spine and cover, consolidating the computational components within the book’s physical architecture. The circuitry is not attached after printing; it is embedded into the page structure itself, making the book both printed artifact and functional device.

The project takes conceptual direction from Ray Bradbury’s short story “Ylla,” from The Martian Chronicles, which describes a metal book inscribed with raised hieroglyphs that emit sound when brushed by hand. METALLY interprets this description as a fabrication premise. The layered relief inherent to extrusion-based 3D printing—raised letterforms, surface ridges, volumetric marks—becomes both typographic strategy and tactile interface. In this configuration, touch is not symbolic; it is operational.

The development of METALLY builds on several years of experimental publishing at Hyper Press. Earlier research into flexible 3D printed substrates made it possible to produce page-like forms capable of bending while retaining embedded conductive pathways. Custom typefaces were designed specifically for extrusion-based production using a methodology termed path-trapping, which stabilizes letterforms during printing. Multi-material mechanical interlocking techniques, referred to as clotting, were developed to allow different printed materials to bind structurally without adhesives. These fabrication strategies converge in METALLY as a fully integrated workflow in which text, image, substrate, and electronics are co-produced.

By embedding circuitry directly into printed matter, the project situates 3D printed books within a longer lineage of press technologies and graphic design. It does not frame additive manufacturing as a departure from print history, but as an extension of it—revisiting typesetting, plate-making, and binding through digital fabrication and computational control. In doing so, the book expands beyond a purely visual medium. The page registers touch, produces sound, and incorporates dynamic display, introducing additional sensory and technical layers while remaining legible as a book-object.

METALLY (2025) was selected as Excellent Work in the Experimental Work category for the Tokyo Type Directors Club Annual Award 2026. The recognition positions the project within contemporary graphic design discourse, particularly in relation to expanded definitions of typography, material production, and the future of the printed page.

Project Info
Project: @darius_ou
Publisher & Research Platform: @hyper.press
Support: @feelers_feelers, @joandkapi
Guidance: @hiddnur, @sojamo, @msjospark, Dr. Aprille
Photography: @jonathantyl
Talent: @suvalidh

Generative AI is typically experienced through screens: chat windows, image feeds, dashboards. Increasingly, though, machine learning systems are being embedded into physical objects—arcade cabinets, kinetic installations, responsive environments—where computation unfolds through motors, water, light, and mechanical movement rather than pixels.

This shift does not reject software. It relocates it. The interface is no longer only a touchscreen but a surface, a sensor array, a hinge, a grid of valves. The output is not just a rendered image but a physical change in space. The move from screen to object becomes visible in the work of Ross Goodwin, Random International, and Universal Everything.
 
Ross Goodwin: Machine Learning as Physical Apparatus
Ross Goodwin is known for experiments in computational writing. In 2017, he created 1 the Road, a book generated during a cross-country car trip using sensors connected to a recurrent neural network. Cameras, GPS, microphones, and a clock supplied real-time data to the system, which produced text continuously and printed it on receipt paper as the journey unfolded.

The setup occupied a passenger seat. It required cameras, wiring, computing hardware, and a thermal printer. Text was generated and printed live during the trip, foregrounding the mechanics of machine inference rather than hiding them behind an interface. The published book was compiled from that output. Goodwin has also exhibited installations in which AI-generated text is printed in real time, making the act of computation materially visible. Paper feeds. Ink accumulates. Lines appear sequentially. Instead of interacting with a seamless interface, viewers encounter a machine performing inference in physical space.
Here, AI is not a background service. It is equipment.

Random International: Responsive Environments
The London- and Berlin-based studio Random International has long worked with robotics, sensing, and responsive systems. Their still-infamous project from 2012, Rain Room, uses motion tracking and a ceiling-mounted grid of electronically controlled valves to regulate falling water. Visitors walk through a downpour that parts around their bodies in real time. Rain Room, is AI, but its operation depends on real-time sensing and control systems. Cameras detect a visitor’s position; software calculates a response; hardware actuates a dense grid of individually controlled water streams. The result is an environment that reacts to human movement without relying on screens or projections.

Subsequent projects extend this approach. Random International has continued to develop responsive, system-driven installations. In Life in our Minds: Motherflock (2023), a coordinated group of robotic light elements moves collectively in response to real-time behavioral logic. Individual units operate as part of a distributed system, adjusting orientation and intensity according to programmed rules and environmental input. Computation is expressed through motors, control systems, and structural assemblies rather than screens.

Interaction remains spatial and bodily. Viewers enter the environment, and the system responds through coordinated physical motion. The technology is embedded in infrastructure rather than presented as a graphical interface.

Universal Everything: AI in Motion Systems
The UK-based studio Universal Everything is known for large-scale digital character works and immersive installations. In recent years, the studio has incorporated machine learning into projects where movement and form are generated or influenced algorithmically rather than pre-rendered as fixed sequences. In exhibition and public-facing installations, generative systems determine how digital figures evolve in response to real-time inputs. In some contexts, these outputs extend beyond the screen into architectural lighting systems or sculptural LED arrays, where software directly controls physical illumination patterns.

Instead of playing back completed animation files, systems calculate motion live. Sensors supply data; algorithms determine variation; lighting and display hardware execute change. The display remains, but it functions as part of a broader computational apparatus tied to physical infrastructure.

From Interface to Infrastructure
Across these practices, a pattern emerges. AI is not confined to a graphical user interface. It is integrated into cabinets, rooms, lighting systems, and mechanical assemblies. This approach is also visible in custom-built interactive devices presented at technology conferences and design fairs, where rule-based or generative systems are embedded directly into playable hardware. Buttons trigger algorithms. Joysticks modify system behavior. LED grids visualize live computation. The system is experienced through physical controls rather than abstract menus. Physical interfaces impose constraints. Motors have torque limits. Water systems require calibrated pressure. Printers jam. These conditions shape how generative systems are engineered and deployed. Latency becomes perceptible. Malfunctions appear as mechanical disruptions rather than rendering artifacts.

Re-embedding AI into hardware alters perception. When a language model produces text in a browser, the process remains opaque. When a system prints text onto paper in real time, the sequence becomes legible: sensor input, computation, mechanical output. Cause and effect are easier to trace because the system occupies space. Embedded computation is also present in certain architectural and fabrication contexts. Research studios have developed responsive partitions, adaptive lighting arrays, and sensor-driven prototypes that adjust position or illumination based on environmental input. In these cases, sensing and control systems are integrated directly into structural components rather than mediated solely through apps. What distinguishes these projects is not novelty but placement. The model is not accessed exclusively through a browser window. It is situated within the object.

Generative systems without screens do not eliminate digital media. They redistribute it. Computation moves closer to actuators and sensors. Interfaces become spatial rather than exclusively graphical. The visible output is movement, light, water, or ink. For a field saturated with software demos and chat interfaces, this marks a measurable shift. AI is not only something prompted through text. It is something that occupies space, consumes power, and moves matter.
Not on a screen. In the room.

Rather than generating text or images for a screen, NOEMA segments and reconstructs perception through sound. The system delivers spatialized audio experiences—ambient cues, narrative fragments, and musical elements—that respond to context and reshape how a listener interprets their surroundings. In this configuration, sound is not supplementary; it is the primary interface.

The project reframes AI as an atmospheric layer rather than a discrete output mechanism. Instead of presenting information visually, NOEMA positions machine intelligence as an interpretive presence—an inner voice that describes, questions, and reframes what is sensed. This shift foregrounds listening as an active mode of interaction and suggests alternative models for human–AI engagement rooted in embodiment and spatial awareness.

By relocating computation from screen-based display to acoustic space, NOEMA examines how perception changes when intelligence is encountered as environment rather than image.

Project Info
Developers: Melo Chen and Nomy Yu

Year: 2025
Image Credit: NOEMA by Melo Chen & Nomy Yu

Scale has defined machine learning for more than a decade. Foundation models are trained on scraped web corpora and optimized through centralized cloud infrastructure. Performance is measured in parameters and benchmarks. More data, more compute, more reach. Yet across artistic and research practices, a different logic is emerging—one that treats scale not as destiny but as design.

Dataset minimalism does not simply mean “small models.” It means interrogating how datasets are assembled, what they extract, and what their expansion requires. The politics of AI are embedded not only in model architectures but in the construction of their training ground. The contrast becomes clearer when looking at three distinct but interconnected approaches: Memo Akten’s constrained neural networks, Kate Crawford and Vladan Joler’s infrastructural cartographies, and Joana Moll’s quantification of digital extraction.

Building Within Limits: Memo Akten
In 2017, Memo Akten presented Learning to See, an interactive installation in which a neural network was trained on images captured live through a webcam. Rather than depending on a vast pre-scraped internet corpus, the system learned from a locally generated dataset shaped by participants in the space. Visitors could watch the training process unfold and observe how specific examples influenced the model’s output.

The project made a foundational aspect of machine learning visible: models reflect the structure of their training data. When the corpus is small and legible, causality becomes easier to trace. Misclassification is not an abstract glitch—it reflects what the system has or has not been shown. Akten has spoken about how bias and opacity are embedded in training data. By working with purpose-built datasets, the focus shifts upstream. Instead of treating bias as a downstream bug to patch, dataset construction becomes a primary design concern.

Mapping the Infrastructure of Scale: Crawford and Joler
In Anatomy of an AI System (2018), Kate Crawford and Vladan Joler mapped the supply chains behind a single consumer device: the Amazon Echo. The diagram traced lithium and rare earth mineral extraction, global logistics, data labor, and server infrastructure—demonstrating that AI systems depend on vast material and human networks. Their later project, Calculating Empires (2023), expanded this analysis historically and geopolitically, charting how computational systems have consolidated political and economic power across centuries.

Where Akten makes dataset construction legible at small scale, Crawford and Joler expose what large-scale systems obscure. The project suggests that scaling AI systems entails expanded material, labor, and energy infrastructures. Scale is infrastructural. Seen from this vantage point, dataset minimalism is not aesthetic restraint. It is a structural intervention. Limiting data aggregation limits the expansion of the infrastructures that sustain it. But infrastructure is not only visible in supply chains. It is measurable in energy consumption and tracking systems that operate quietly in the background of everyday digital life.

Measuring Extraction: Joana Moll
Joana Moll’s practice quantifies the hidden material costs of digital systems. In CO2GLE, she calculates the carbon emissions generated by Google searches. In The Hidden Life of an Amazon User (2019), she traced the tracking mechanisms and data flows embedded in a single user session, revealing the complex web of third-party exchanges behind routine interactions.

Moll’s work does not train models; it measures the infrastructures that enable large-scale data capture. By translating digital processes into energy consumption, economic value, and environmental impact, she reframes scale as material burden. As datasets expand, storage and compute demands increase. As models scale, so do the data center infrastructures required to sustain them. The abstraction of “big data” carries measurable physical consequences.

Placed alongside Akten’s constrained training systems and Crawford and Joler’s cartographies, Moll’s quantifications complete the picture. Small datasets are not inherently virtuous, but they are more traceable. Their dependencies are easier to identify.

Designing Against Extractive Scale
Taken together, these practices reposition dataset minimalism as a design stance. Akten demonstrates how working with bounded corpora clarifies authorship and bias. Crawford and Joler reveal that large-scale AI systems depend on expansive material and geopolitical infrastructures. Moll measures the environmental and economic costs embedded in digital accumulation.

The through-line is visibility. At industrial scale, causality disperses across continents and supply chains. At smaller scales, relationships tighten. Decisions about inclusion, labeling, storage, and consent become more explicit. Dataset minimalism does not reject machine learning; it questions the assumption that performance must scale with extraction. It treats scale as optional rather than inevitable. As generative systems proliferate, pressure to expand training corpora will continue. These practices suggest another metric for rigor: not only how well a model performs, but how clearly its dataset can be traced—who contributed to it, what infrastructures sustain it, and what consequences it produces. In that sense, designing with less is not a retreat from AI. It is a demand for accountability in how intelligence is constructed.

High-end laptops equipped with dedicated GPUs and AI accelerators now handle real-time ray tracing, multi-stream 4K editing, parametric modeling, and generative image synthesis without tethering creators to fixed workstations. Platforms such as NVIDIA Studio—and comparable silicon from Apple, Intel, and AMD—are increasingly optimized for creative workloads. This may not simply a performance upgrade – perhaps its an infrastructure shift.

GPU acceleration and on-device AI tooling are compressing workflows that once required towers, external drives, or centralized studios into portable systems. Features such as AI upscaling, generative fill, denoising, background removal, and real-time simulation are increasingly accelerated at the hardware level. Creative software still runs across platforms—but it is increasingly optimized around specific silicon architectures.

As rendering, simulation, and AI inference move onto mobile hardware, more stages of production can occur outside fixed studios. Musicians can travel with synthesis, sampling, and mastering environments in a single device. Designers iterate on complex 3D scenes on site. For some teams, reliance on centralized render infrastructure decreases. The boundary between fieldwork and post-production becomes more fluid.

But mobility also consolidates ecosystems. Hardware acceleration is often aligned with specific software stacks. Drivers, neural engines, and GPU architectures are optimized for particular creative platforms, shaping workflows around tuned pipelines. Portability expands access—while subtly influencing how creative tools are configured and used.

The desktop is not disappearing. High-end studios, large simulations, and heavy training workloads still benefit from fixed infrastructure. But for designers reviewing models on site, musicians producing while touring, or small teams working across cities, this flexibility changes how and where work happens.

Rather than using a keyboard, sequencer, or touch screen, the instrument is played through two handheld electromagnetic pickups. These pickups sense variations in an electromagnetic field generated by the instrument’s surface. As the performer moves their hands through space, shifts in proximity and orientation modulate pitch, timbre, and amplitude.

The system is fully analog and polyphonic. Each hand controls an independent voice, enabling harmonic and intervallic relationships to be shaped through continuous gesture rather than discrete keys. The interface is touch-sensitive in the sense that it responds to embodied motion, but the sound is produced without direct physical contact. The performer navigates an invisible field.

Chromaplane repositions synthesis as a spatial practice. Instead of selecting presets or triggering predefined notes, the musician calibrates tone through subtle changes in distance and angle. This produces a mode of performance that foregrounds proprioception, fine motor control, and sustained attention.

In 2025, Chromaplane received First Prize at the Guthman Musical Instrument Competition, a competition hosted by Georgia Institute of Technology recognizing innovation in new musical instrument design.

Chromaplane is commercially produced and distributed by KOMA Elektronik.

Project Info
Artist: Passepartout Duo
Year: 2021 (development); 2024 (commercial launch)
Image Credit: Passepartout Duo / KOMA Elektronik

Friends. Lovers. Therapists. ‘Deathbots’. Artificial intelligence is now fulfilling new roles for millions of us every single day. How are these new ‘relationships’ changing how we view technology – and each other?

Beyond those who are using AI chatbots for administrative tasks, some people are now preparing to adopt children with their AI partners; others are reaching out to companies offering services to ‘resurrect’ deceased loved ones; others still look to bots to find treatment for their mental health issues.

In Love Machines, James Muldoon guides through these new forms of love, intimacy and connection, drawing on compelling interviews with users, developers and chatbots themselves. Along the way, he sheds light on the social conditions which have led to the exponential rise of the use of AI companions, and the unregulated corporations behind these technologies seeking to profit from users.

Inside Prague’s historic Old Town Market Hall, Signal Space has launched as a permanent venue dedicated to immersive and digital art. Its inaugural exhibition, ECHOES OF TOMORROW, assembles ten international artists and studios working across generative systems, laser environments, spatial audio, and data-driven visual media. On view through 31 March 2026, the exhibition unfolds as a sequence of large-scale installations embedded within the renovated hall.

This is not a collection of screens placed inside a historic shell. The works function as environments. Projection, light, sound, and computational processes operate at room scale. In several installations, generative logic and interactivity are not hidden layers—they determine how space behaves.

Systems, Not Screens
Computation sets the tone. Spanish studio Playmodes’ Signes runs on algorithmic processes that govern both image and sound. Sequences evolve according to coded rules rather than fixed timelines; each iteration is calculated, not replayed.

That systemic emphasis shifts form in Shohei Fujimoto’s intangible #form. Fine laser beams articulate geometric volumes in midair, appearing stable from one angle and dissolving from another. Light defines structure directly. Material and computation intersect in Crystal Pixels: Silent Reflections by the Preciosa Design Team. Digitally controlled lighting integrates with traditional Czech crystal, embedding programmable illumination within a craft lineage long associated with Bohemian glass.

Data becomes composition in Onformative’s Meandering River, which transforms datasets into evolving visual fields through algorithmic processing. Numerical input resolves as motion and pattern. Historical reference is reframed in Fighters / For Palestrina Sicut, a collaboration between Quayola and Max Cooper. Classical visual and musical structures are translated into digitally generated image and sound, processed through contemporary computational systems.
The pattern is consistent: projection is not display format; it is spatial logic. Sound is not accompaniment; it structures time.

Immersion as Infrastructure
The exhibition scales those systems to architecture. Czech digital artist Ondřej Zunka’s PHOTOSYSTEM II presents digitally constructed environments referencing natural systems through computational imagery. Synthetic landscapes unfold across the room rather than within a frame. In Weaving Nowhere, Belgian artists Porz A Park and Arnaud Gerniers combine spatial sound and visual structures into a multi-sensory field shaped by acoustic and optical composition. Interactivity sharpens that architectural logic in Body Sketches / Scale Studies by Zach Lieberman. Motion tracking converts visitor gestures into projected line drawings in real time. Movement alters output; the body enters the system.

Generative animation continues the emphasis on rule-based formation in Markos Kay’s aBiogenesis, where evolving patterns reference biological processes without direct simulation. At the largest scale, Nohlab’s EVERYTHING integrates projection and spatial sound into a continuous audiovisual environment occupying the room as a unified field. Projection systems, laser arrays, lighting rigs, and spatial audio operate as architectural components. The market hall’s masonry remains visible, framing computational media within a pre-digital civic structure. The contrast clarifies the premise: digital systems are not temporary overlays but spatial agents embedded in permanent infrastructure.

Exhibition as Platform
ECHOES OF TOMORROW foregrounds generative processes, laser technologies, spatial audio, and projection-based environments as primary artistic media. Algorithmic logic and interactivity are visible drivers of form rather than concealed mechanisms. As a permanent digital art venue, Signal Space provides the projection, lighting, and sound infrastructure required for immersive installations to function at architectural scale. The gallery is engineered for this mode of work.

The result is an exhibition organized around systems rather than objects. Projection defines volume. Laser defines boundary. Sound defines orientation. Computational processes shape how space is experienced.
Immersive media here operates as built environment. Not spectacle layered onto architecture, but infrastructure shaping it from within.

AI-Powered Conway’s Arcade is not a retro remake. Developed by SpecialGuestX and commissioned by Google, the project was presented at NeurIPS 2025 as an interactive installation that generates gameplay live rather than loading a fixed, preauthored title.

At first glance, the cabinet resembles a conventional upright arcade machine: joystick, mechanical buttons, compact footprint, pixel-style graphics. The difference lies in the software. Each session produces a distinct gameplay configuration in real time. Instead of replaying a static level, players encounter shifting arrangements of mechanics that echo classic arcade genres without replicating any single one. The project positions the arcade cabinet as a platform for exposing computational systems in action.

From Cellular Automata to Interactive Systems
The conceptual reference point is John Horton Conway’s Game of Life, the 1970 cellular automaton demonstrating how simple local rules can produce complex global patterns. In Conway’s Game of Life, cells switch states based on neighboring conditions. No central controller determines the outcome; structure emerges from rule interaction. AI-Powered Conway’s Arcade draws on this rule-based logic as inspiration. Rather than functioning as a conventional AI demo built around large-scale models or scripted scenarios, the cabinet generates gameplay configurations through structured rule interactions. Sessions unfold as dynamic arrangements of obstacles, agents, and motion patterns derived from this generative framework.

This approach distinguishes the project from standard procedural games. Traditional arcade software runs fixed binaries. Even procedurally generated titles typically operate within predefined templates and content libraries. Here, the emphasis is on recombining rule-driven behaviors at runtime, producing sessions that vary in structure rather than simply reshuffling assets. Players learn the system through interaction. Instead of following explicit instructions, they observe how elements behave within the current configuration. Patterns become legible through play.

Real-Time Variation, Legible Mechanics
Visually, the cabinet references early arcade graphics—flat color fields, geometric sprites, high contrast. The restrained aesthetic makes behavioral changes easier to register. When gameplay configurations differ between sessions, those differences are visible.

Individual runs may evoke familiar mechanics: paddle-and-ball exchanges, vertical obstacle navigation, projectile-based interactions. These are structural echoes rather than direct recreations. The system recombines recognizable arcade logics into new arrangements, producing experiences that feel referential without being fixed remakes. Because each session begins with a newly generated configuration, memorization alone offers limited advantage. Players instead attend to how movement, collision, and spatial constraints operate within the present setup. The cabinet foregrounds rule interaction as the primary site of engagement.

Hardware as Interface to Computation
The installation is housed in a custom-built aluminum cabinet designed for transport and exhibition contexts, including its presentation at NeurIPS 2025. While engineered for mobility, it retains the proportions and ergonomics of a traditional upright arcade machine.

The interface follows established conventions: joystick and mechanical buttons deliver immediate tactile response. The physical structure remains constant across sessions; what changes is the on-screen configuration. This separation between stable hardware and variable software underscores the project’s focus on generative systems. Installed within a research conference environment, the cabinet functioned as a playable demonstration rather than a commercial product. Instead of presenting computational models through diagrams or dashboards, the system was accessed through embodied interaction. Participants engaged with rule-based behavior directly, encountering generative logic as a playable system rather than an abstract concept.

AI-Powered Conway’s Arcade does not introduce a new arcade genre. It repositions the arcade machine as a container for real-time generative computation. By situating rule-based systems inside a familiar hardware typology, the project makes computational emergence observable through play—turning abstract logic into something tactile, visible, and testable.

name: Conway’s Arcade

agency: SpecialGuestx | @specialguestagram + @specialguestx
client: Google | @google
executive creative directors: Aaron A Duffy | @dufslam + Miguel Espada | @miguelvespada
creative directors: Marc Reisbig | @marc_reisbig + Carlos Font | @carlosfont
executive creative technologist: Federico Guardabrazo | @guardabrazo
user experience: Laura Fajardo | @laurafajardoc
lead producer: Francesca Palau del Mas | @francesca_pdm
lead creative coder: Bruno Barrán | @brunobarran
executive visual identity direction: 1stAve.Design | @1stave.design
visual identity: Polar, Ltda. | @polar.ltda
lead product designer: Seis Punyales | @seispunyales
product producer: Alicia Simon | @aliciasimon
product designer: Mike Fernández | @mikes.ink
product designer: Alicia Simon | @aliciasimon
product designer: Paco Fuster | @fuster.ferrer
lead electronics engineer: Alvaro Gordo | @creativengineer.jpg
technical consulting: Pedro Zambrana | @pedrozambrana
graphic design: Eder Larrondo | @edd.er
fabricated in barcelona by: Seis Punyales | @seispunyales
photographer: Paula Vázquez Guisande | @paulavguisande

At the February 2026 edition of New York Fashion Week, designer Maia Hirsch presented a garment that does more than animate—it responds. Her “Blooming Dress” (Peace Offer) mechanically opens when the wearer shakes someone’s hand. Embedded touch sensors detect physical contact, triggering internal actuators that expand petal-like structures outward from the body. In the same presentation, a second piece illuminated in response to motion, using integrated lighting activated by movement.

The engineering is compact and deliberate. Sensors, control circuitry, micro-actuation systems, and onboard power are integrated within the garment’s structure, enabling repeatable performance under runway conditions. Rather than treating electronics as decorative add-ons, the pieces operate as cohesive wearable systems: sensing input, processing signals, and executing mechanical output in real time.

Interactive fashion has precedents. Hussein Chalayan famously presented motorized dresses that transformed shape on the runway in the mid-2000s. Iris van Herpen is well know for having collaborated with engineers and architects to develop structurally dynamic couture that pushes material fabrication into computational territory. What differentiates Hirsch’s NYFW presentation is its use of social interaction as the trigger condition. A handshake becomes a switch. Proximity becomes input.

Does the high-profile runway, in this context, function as spectacle or more as a controlled deployment environment for experimental hardware? Garments are increasingly assembled through hybrid workflows that merge pattern-making software with PCB layout, conductive thread routing, and parametric modeling. The result is clothing that behaves less like static fabric and more like distributed interface. Wearable robotics has often been framed as futuristic theater. At NYFW 2026, it appeared as systems design operating at body scale.

The phrase “post-digital craft” is often misread as retreat—a soft-focus return to hand tools and pre-industrial methods. In practice, it signals something more structural: a recalibration of making after digital tools became default infrastructure. Post-digital craft does not reject computation. It absorbs it, retools it, and insists that technique, material intelligence, and embodied judgment still matter—even when the tools are robotic arms and parametric scripts.

As AI-generated imagery saturates feeds and manufacturing pipelines grow increasingly automated, a countercurrent is visible across design studios and fabrication labs. Authors are not abandoning machines. They are relocating authorship into systems—into parameters, toolpaths, calibration routines, and fabrication protocols. The result is hybrid practice: neither analog revival nor techno-optimism, but materially grounded and computationally fluent.

Hybrid Making After Automation
Digital fabrication expanded rapidly in the 2010s. Fab labs and makerspaces standardized access to CNC routers, laser cutters, and desktop 3D printers. That expansion democratized production—but it also normalized a recognizable visual language: parametric surfaces, algorithmic lattices, and toolpath aesthetics. Post-digital craft begins after that normalization. The machine is no longer the spectacle. The focus shifts to tuning—surface behavior, movement, structural performance—through iterative testing rather than frictionless output.

Amsterdam-based Studio Drift exemplifies this shift. Founded by Lonneke Gordijn and Ralph Nauta, the studio builds kinetic installations that combine custom engineering with material experimentation. Their work Shylight unfolds and retracts in sequences inspired by nyctinasty, the movement of flowers. The installation relies on precisely calibrated mechanical systems, yet the perceptual effect emphasizes timing, sensitivity, and variation. Technology functions as a tuned instrument rather than a productivity engine. The emphasis is not on speed or scale. It is on calibration.

Craft Knowledge in Code
If traditional craft has been described as tacit knowledge accumulated through repeated manual practice, contemporary digital fabrication distributes that expertise across software environments and robotic systems. In computational workflows, decisions about geometry, density, and structural reinforcement are encoded before material is shaped. Skill does not disappear; it migrates upstream.

Dutch designer Joris Laarman has explored this migration for more than a decade. His collaboration with MX3D on the MX3D Bridge in Amsterdam used robotic wire-and-arc additive manufacturing (WAAM), a process that deposits molten metal through an electric arc. WAAM requires precise control of heat input, deposition rate, and structural stability. The bridge’s form emerged through generative modeling and structural analysis, then underwent empirical testing to ensure performance. Here, the designer configures systems rather than directly manipulating material. Robotic arms execute programmed instructions, but human operators define parameters, oversee calibration, and evaluate outcomes. Iteration remains central: simulation informs fabrication; fabrication feeds back into code.

This redistribution of expertise alters how error functions. In software-driven workflows, modeling inaccuracies can propagate through fabrication if left unchecked. As a result, prototyping, mock-ups, and sensor data remain essential. Digital precision does not eliminate craft judgment. It makes it procedural. Institutions have acknowledged this shift. The Victoria and Albert Museum (V&A) has collected and exhibited works incorporating generative design and digitally fabricated components, including projects by Laarman. By situating these works within decorative arts and design collections, the museum frames robotic fabrication as part of a longer history of material practice rather than as a break from it.

Why Post-Digital ≠ Anti-Digital
The “post” in post-digital does not mean after technology. Media theorists have used the term to describe a condition in which digital systems are normalized—embedded so deeply in production and culture that they become infrastructural. The question is no longer whether to use computational tools, but how they are configured and governed. Post-digital craft rejects two simplifications: the assumption that automation inherently produces better outcomes, and the idea that authenticity lies only in manual labor. Instead, it treats computation as material—something to shape, constrain, and interrogate.

This has implications beyond aesthetics. Research in distributed manufacturing and circular design suggests that localized, small-batch production—when combined with repairability and modularity—can reduce transportation and tooling waste under certain conditions. Hybrid workshops that combine CNC machining with hand-finishing or robotic printing with manual assembly introduce adaptability into systems often optimized for scale. These outcomes are contingent, not guaranteed. But they demonstrate that digital fabrication is not structurally tied to mass standardization.

Time also re-enters the equation. Sociological analyses of technological acceleration describe how modern systems compress production cycles. Yet hybrid digital–manual workflows still require machine calibration, simulation testing, and physical prototyping. These phases slow processes down—not as inefficiency, but as method. Iteration becomes a site of evaluation rather than delay.The rise of generative AI intensifies this terrain. As automated systems produce images, text, and product concepts at scale, debates around authorship and agency expand. Post-digital craft responds by embedding authorship within process design—within how systems are structured, constrained, and executed. Originality shifts from surface novelty to procedural specificity.

Post-digital craft is not a revival of pre-digital romanticism. It is a condition in which computation is fully integrated into materially grounded practice. Silicon and steel coexist. Script and surface inform one another. Machines do not replace judgment; they expose where judgment resides. The nostalgia narrative misses the point. What is emerging is not a retreat from technology, but a demand that technology remain accountable to material intelligence, human calibration, and sustained attention.

Scientists in Bristol, England have demonstrated a prototype shape-shifting robot that deforms and moves under the influence of external electric fields, marking a noteworthy advance in soft robotics and electro-responsive materials research. The robot is constructed from a novel electro-morphing gel that reacts to changes in electric field configuration around it, rather than relying on conventional motors or rigid mechanical parts.

The prototype — visually likened to a “gymnast” during demonstrations — bends, stretches, twists and swings when electrodes around its body are energized in specific patterns. By altering the spatial distribution and intensity of these fields, researchers can prompt the material to undergo controlled deformations that translate into locomotion and shape change. This approach exploits the gel’s inherent material properties, which respond directly to electric forces rather than through intermediary mechanical linkages.

The development originates from work at the University of Bristol School of Engineering Mathematics and Technology, where engineers have been exploring how electro-responsive composites can be programmed to generate complex motion patterns. According to the report, the robot’s design leverages external electric field control to manipulate its morphology, rather than embedding actuators or circuitry within the body. This external actuation method simplifies the robot’s physical architecture but also highlights the challenge of integrating control systems for autonomous operation.

Shape-shifting robots like this prototype align with broader trends in soft robotics, where compliant structures and responsive materials replace traditional joints and rigid frames to enhance adaptability when interacting with unstructured environments. While practical applications remain exploratory, potential use cases include systems that must traverse confined or irregular spaces or interfaces that adapt dynamically to tasks without complex mechanical control layers.

The Bristol work underscores how material-centric design, paired with externally programmable fields, can expand how researchers think about robotic actuation and form modulation, particularly where conventional robotics faces limitations.

For years, computational design has been defined by modeling: parametric scripts generating geometry, simulations optimizing performance, machine learning systems producing form variations. Matter entered at the end of the pipeline—fabricated after computation had already concluded. Physical computation reverses that sequence. When behavior is encoded directly into material structure, topology, and compliance, computation no longer sits exclusively in silicon. It unfolds through deformation, phase change, distributed actuation, and multi-stable geometry. Under these conditions, design practice shifts. If materials compute, designers are no longer producing static forms—they are authoring behavioral systems.

Encoding Behavior in Matter
At the forefront of this shift is Lining Yao and her Morphing Matter Lab at Carnegie Mellon University. Yao’s research focuses on materials that change shape in response to environmental stimuli such as water and heat. Her team has developed 3D-printed hydrogels and bio-based composites whose microstructures determine how they swell, curl, or fold when exposed to moisture. In these systems, transformation is not animated externally. It is embedded in composition and geometry. Print patterns, fiber orientation, and material gradients encode how the structure will respond. Once fabricated, the object carries its own conditional logic.

This approach alters the workflow fundamentally. Instead of modeling a final geometry and simulating its performance, the designer defines a transformation rule: how much swelling occurs, along which axis, at what threshold. Form becomes a temporal expression of material instruction. Fabrication is no longer downstream of computation; it is computation. Such systems also operate with minimal external energy. Hygroscopic and hydrogel-based responses rely on environmental input rather than continuous electrical power. The substrate encodes its response behavior in material structure.

Projects such as MorphingSkin extend the lab’s morphing-material research into thin, programmable surface systems, where patterned structures encode how a material curls, folds, or textures in response to environmental input.

Topology as Instruction Set
While morphing materials encode behavior through chemistry and microstructure, programmable mechanical metamaterials encode behavior through geometry. At ETH Zurich, Kristina Shea leads research into multi-stable structures whose topology determines how they move and reconfigure under load. These latticed systems can snap between stable states without continuous energy input. In many cases, their behavior is governed by the arrangement of beams, hinges, and cellular patterns rather than continuous motorized control. A shift in topology alters how stress propagates through the structure. Multi-stability is achieved through structural design rather than embedded electronics.

In this model, geometry determines the system’s possible state transitions. The designer specifies relationships—angles, thicknesses, and connection points—that define how the structure responds to applied force. Once fabricated, the system behaves according to its geometry and material properties; its response is inseparable from its structural logic. For practice, this demands a shift from composing surfaces to engineering possibility space. The designer is not asking what shape to produce, but what behaviors to enable. In multi-stable metamaterials, analysis and fabrication converge: topology carries the behavioral rule.

The implications extend beyond laboratory prototypes. Such structures suggest architectural components, deployable systems, and adaptive assemblies that respond passively to load and environment. Maintenance, durability, and tolerance stacking become central design concerns. Wear and fatigue are operational variables embedded within the system’s long-term behavior.

Distributed Intelligence in Soft Robotics
If Yao’s work embeds behavioral response in material microstructure and Shea’s encodes it in topology, the soft robotics research of Daniela Rus shows how embodied mechanics scale into autonomous systems. At MIT’s Computer Science and Artificial Intelligence Laboratory (CSAIL), Rus and collaborators have developed both soft robotic systems—such as pneumatically actuated grippers—and self-reconfigurable modular robots like the M-Blocks platform. These projects explore how structure, material compliance, and distributed interaction contribute to adaptive behavior.

In soft robotics, body and control are tightly coupled: morphology and material dynamics can simplify control requirements by shaping how forces propagate and how the system responds to contact. Feedback emerges from interaction between material and environment, enabling behaviors that are difficult to achieve with rigid mechanisms alone. Designing these systems means configuring how sensing, actuation, and structure interrelate. Creative control shifts from sculpting a static object to specifying behavioral coupling—compliance gradients, actuator placement, and modular connectivity—then iterating through physical calibration as the robot’s behavior is validated in real operating conditions.

From Artifact to System
Across these examples, a consistent pattern emerges. The designer’s task is no longer limited to producing a finished artifact. Instead, it involves defining a rule-bound system that continues to operate after fabrication. This transformation has practical consequences. Iteration cycles expand beyond digital simulation into material calibration. Environmental variability—humidity, load, temperature—affects output. Tolerances, fatigue, and degradation become computational parameters. The system’s lifespan is part of its logic.

Physical computation also complicates narratives of decentralization. Many responsive systems still rely on embedded electronics and complex supply chains. Multi-stable structures may eliminate continuous energy input but require precision manufacturing. Soft robots reduce control overhead yet depend on sophisticated materials research. The shift from form-making to rule-making does not remove control; it redistributes it across structure, material, and feedback loops.

At the same time, as artificial intelligence increasingly automates symbolic production—generating images, text, and parametric scripts—physical computation anchors intelligence in tangible substrates. It positions material systems as active participants in sensing and response. In this framework, the designer becomes less a stylist of surfaces and more an author of conditions. Material choice is a computational decision. Topology is a behavioral script. Fabrication is an encoding process. When matter computes, design becomes the practice of shaping how systems act—not just how they look.

Researchers at The University of Texas at Austin, working with Sandia National Laboratories and other U.S. laboratories, have developed a 3D printing technique that enables fine-grained control over the internal structure of thermoplastics. The method, called Crystallinity Regulation in Additive Fabrication of Thermoplastics (CRAFT), allows printed objects to vary in stiffness, flexibility, and optical properties within a single material system.

CRAFT uses digital light processing (DLP) 3D printing and a widely available liquid resin, cyclooctene, that can be polymerized and crystallized in a controlled manner. By modulating grayscale light patterns during printing, the researchers adjust the degree of crystallinity at specific locations inside the object. Because crystallinity directly influences mechanical performance, this approach enables spatial tuning of properties without switching materials or print heads.

In demonstrations reported by the university, the team fabricated a model human hand with regionally differentiated mechanical behavior, approximating the relative rigidity of bone and the flexibility of surrounding tissues such as ligaments and tendons. A grayscale design controlled light intensity during printing, producing distinct crystalline and amorphous domains within a single feedstock material. Traditional multi-material 3D printing can achieve visual complexity by combining different inks, but these materials often adhere poorly at their interfaces, leading to structural weaknesses. The CRAFT method instead varies properties by regulating crystallinity during fabrication, eliminating the need to bond dissimilar materials and reducing interfacial failure.

The technique is compatible with commercially available digital light processing (DLP) or LCD printers capable of grayscale projection, including relatively low-cost systems. According to the university, potential applications include anatomical training models for medical education and energy-damping structures such as personal protective gear. The researchers also point to bioinspired materials with alternating hard and soft regions, similar to structures found in bone or tree bark, as another area of exploration. By enabling spatial control over mechanical and optical properties within a single printed object, CRAFT extends additive manufacturing beyond geometric complexity toward internal structural differentiation.

Cognitive Bloom is a speculative design concept developed through a collaboration between Chanwoo Lee, Map Project Office, and Lovelace Research. Rather than positioning artificial intelligence as a productivity accelerator, the project proposes an alternative: AI as a quiet companion for structured self-reflection.

The concept centers on an ambient display that visualizes personal wellbeing data as a living digital ecosystem. Areas of strain appear as yellowing leaves. Signs of progress surface as new buds. When aspects of wellbeing stabilize, those buds bloom. The interface avoids dashboards, scores, or clinical-style metrics. Instead, it translates abstract data into a metaphor most users intuitively understand: tending a garden.

Cognitive Bloom is presented as a domestic object designed to encourage what its creators describe as a ritual of reflection. Interaction is deliberately slowed. The system resists the logic of instant answers and frictionless optimization common to many contemporary AI tools. Rather than compressing cognition into efficiency metrics, it introduces pauses—moments intended for observation and consideration. The gardening metaphor extends throughout the project’s framing. Gardens demand attention over time. They respond to changing conditions. They cannot be rushed. In this way, Cognitive Bloom contrasts with engagement-driven digital systems that prioritize speed, scale, and behavioral capture.

As a speculative project, Cognitive Bloom does not claim full technical implementation. Instead, it operates as a design probe, asking how artificial intelligence might be shaped around wellbeing rather than extraction. By situating AI within a metaphor of care and cultivation, the project reframes the role of intelligent systems in domestic life—not as engines of acceleration, but as structures for presence.

Cultural circulation online is often framed as frictionless distribution: publish, post, upload, and consumed. In practice, visibility is governed by ranking systems, recommendation engines, and moderation pipelines that sit between creators and publics. Platforms such as TikTok, Instagram, and YouTube operate less like neutral conduits and more like infrastructural curators, determining what surfaces, what trends, and what disappears.

Algorithmic visibility is not simply a technical feature. It is a system of cultural governance embedded in code, policy, and economic incentives. Understanding who controls cultural circulation online requires examining how these systems rank, filter, monetize, and increasingly generate content.

From Chronological Feeds to Algorithmic Governance
Many major social platforms originally defaulted to reverse-chronological feeds, but over time shifted toward recommendation-driven ranking optimized for engagement signals. YouTube has described its recommendation system as using signals including clicks, watch time, and measures of viewer satisfaction derived from surveys. Reports have attributed a widely cited figure—around 70% of watch time—to recommendations, based on public remarks by YouTube’s leadership at industry events.  TikTok’s “For You” feed similarly ranks videos using factors such as user interactions, strong interest signals like watch completion, video information (captions/sounds/hashtags), and some device/account settings. Instagram, once anchored in a follower-based chronological feed, shifted to algorithmic ranking and has been described (including by platform explainers and reporting around the 2016 change) as prioritizing predicted interest, relationship, and timeliness when ordering content.  These systems do not merely sort content; they shape the conditions under which posts become discoverable.

Optimization as Aesthetic Constraint
Because recommendation systems prioritize metrics such as watch time, retention, and engagement, creators often adapt form and pacing to align with those signals. YouTube has publicly identified watch time and viewer satisfaction as central to its recommendation system. TikTok’s For You feed emphasizes watch completion and interaction signals, while Instagram has described how Reels and other recommendation surfaces rank content based on predicted interest and engagement.

Creator guidance materials frequently emphasize click-through rates, retention curves, and early engagement signals, encouraging thumbnails, titles, and opening seconds that maximize viewer response. When ranking systems reward frequent posting, high retention, and specific formats, creators adjust accordingly. Researchers and commentators have observed that this dynamic can contribute to format convergence over time, as visibility becomes tied to measurable engagement patterns. Monetization systems reinforce these incentives. YouTube’s Partner Program requires minimum subscriber and watch-hour thresholds before revenue sharing is enabled, creating economic gates that influence growth strategies. Visibility and monetization are structurally intertwined: ranking affects revenue potential, and revenue potential shapes production decisions.

The Politics of Amplification and Omission
Algorithms amplify some material while marginalizing other forms. This dynamic extends beyond virality to include classification, moderation, and training data. Artist and researcher Mimi Ọnụọha has examined how datasets reflect structural absence, arguing that what is missing can be as consequential as what is included. In the context of platform algorithms, omissions may stem from biased training data, automated moderation errors, or ranking systems whose optimization criteria deprioritize certain types of content. Platforms deploy automated moderation tools to detect hate speech, misinformation, and other policy violations, and publish transparency reports outlining enforcement metrics. However, the specific functioning of these models remains largely proprietary. Independent research has documented false positives and uneven enforcement, including cases in which language associated with marginalized communities is misclassified by automated systems.

Amplification also intersects with generative AI systems. Large language models and image generators are trained on vast corpora of publicly available data. Because widely circulated and highly indexed content is more likely to be scraped into training datasets, recommendation systems can indirectly influence the composition of future training data. This creates a recursive dynamic in which ranking systems shape what becomes broadly available cultural material, which in turn informs subsequent generative outputs. Such feedback loops can consolidate influence within infrastructures that are neither neutral nor evenly distributed.

Artists and Designers Working on the System
Some practitioners have treated algorithmic systems themselves as material. Hito Steyerl has examined how images circulate through compression, duplication, and digital distribution networks, tracing the relationship between visibility and power. Her essay “In Defense of the Poor Image” analyzed how distribution conditions shape meaning and authority. James Bridle has investigated opaque technological systems, including automated decision-making and machine vision, emphasizing the difficulty of understanding infrastructures that shape public life. Through research and artistic intervention, his work renders such systems visible and frames algorithms as active participants in cultural production rather than neutral utilities.

Artists including Tega Brain and Sam Lavigne have engaged directly with APIs, data scraping, and automated platforms, exposing how networked systems sort, classify, and monetize attention. Their projects treat code, metadata, and platform rules as sites of intervention, demonstrating that circulation itself is programmable. These practices do not dismantle platform power, but they surface its mechanics. By foregrounding the infrastructures behind recommendation engines, moderation systems, and data economies, they highlight the importance of understanding how algorithmic systems shape contemporary cultural circulation.

Who Controls Cultural Circulation?
Control over cultural circulation online is shaped by multiple actors, yet concentrated within a small number of dominant platforms. Platform companies design and continually adjust ranking systems. Advertiser requirements influence monetization policies and brand-safety standards. Machine learning engineers tune models using engagement and safety metrics. Users generate the behavioral data that enables personalization. Generative AI systems are trained on large corpora of publicly available content that reflect existing patterns of visibility and circulation.

The result is a multi-layered governance structure in which visibility emerges from interactions between code, policy, and market incentives. Cultural production is shaped at multiple points by recommendation systems and monetization thresholds. Algorithmic visibility has become a structural feature of contemporary networked platforms. As recommendation engines, automated moderation systems, and generative models become more deeply integrated, the central question is not simply whether algorithms influence circulation, but how that influence is designed, audited, and contested.

Georges’ broader practice spans literature, sound, photography, video, and 3D modeling. Since her debut novel La Mue de l’hermaphrodite (2001), she has examined how emerging technologies reshape concepts of subjectivity, memory, and embodiment. In recent years, her work has increasingly engaged artificial intelligence not as a novelty, but as a perceptual framework.

In Woven Presence, surface operates as both material reference and computational field. Textile logic—repeat, weave, ornament—intersects with generative image systems that mutate texture and form over time. Figures appear embedded within, or produced by, their own patterned environments. The distinction between fabric, skin, and simulation becomes visually unstable.

The project foregrounds mediation. Machine vision reorganizes the sensible world: tactility is rendered as pixel density; ornament becomes data structure; portraiture shifts toward probabilistic construction. The result is a work that situates intelligent systems within longer histories of image-making and material culture.

Woven Presence contributes to ongoing conversations in digital art and creative technology about authorship, hybridity, and the evolving relationship between algorithmic systems and embodied perception.

Project Info
Artist: Karoline Georges
Year: 2023
Image Credit: Courtesy of the artist

Design culture has spent decades optimizing for novelty: faster tools, smarter systems, more autonomous outcomes. But as infrastructures age and environmental volatility intensifies, the limits of mastery-first design are increasingly visible. Buildings, platforms, and material systems rarely fail because they were insufficiently clever; they fail because they were insufficiently maintainable. Designing for maintenance reframes durability not as permanence, but as the capacity to endure breakdown, repair, and care over time.

Across architecture, materials research, and community-led practice, a quieter design ethic is emerging—one that treats upkeep as a primary condition rather than a postscript. This approach resists speculative solutionism and instead foregrounds stewardship, repair cultures, and systems designed to fail gracefully.

Maintenance as a Primary Design Condition
Maintenance has long been treated as an operational concern, external to the design act itself. Yet research-driven practices are increasingly exposing how maintenance is inseparable from form, material choice, and system logic. Architect and researcher Lydia Kallipoliti has been central to this reframing. Through projects such as Closed World and her broader scholarship on material ecologies, Kallipoliti examines closed-loop systems not as seamless technological fixes, but as inherently leaky, contingent constructs. Her work demonstrates that circular material systems demand constant intervention: monitoring material fatigue, recalibrating flows, and managing inevitable loss.

This perspective matters because it destabilizes the myth of self-sustaining systems. Closed loops do not eliminate maintenance; they intensify it. Designing with recycled or hybrid materials introduces variability that must be anticipated, not engineered away. For designers, this shifts the goal from perfect control to informed custodianship—understanding how materials age, deform, and require care. Maintenance, in this sense, becomes a design parameter that shapes spatial logic and material expression. Systems are legible rather than hidden. Interfaces expose wear rather than conceal it. The result is architecture and infrastructure that communicates its own vulnerability—and, crucially, how it can be sustained.

Long-Term Stewardship vs. Speculative Solutionism
Speculative design has played a valuable role in expanding the imagination of what systems could be. But when speculation hardens into solutionism, it often prioritizes dramatic intervention over lived continuity. Community-centered practices offer a counterpoint. The UK-based collective Assemble is frequently cited for its emphasis on process over product. Projects such as the Granby Four Streets regeneration in Liverpool were not conceived as finished solutions but as frameworks for ongoing repair. Assemble’s role was not to replace community knowledge with professional authority, but to scaffold conditions under which residents could continue to adapt, fix, and maintain their environment.

What distinguishes this approach is its temporal logic. Success is not measured at completion but years later, through occupancy, modification, and care. Materials are chosen for accessibility rather than novelty. Construction methods favor techniques that can be relearned and repeated by non-specialists. Maintenance is social as much as technical. This model exposes a blind spot in much speculative work: systems designed without clear stewards tend to externalize responsibility. When maintenance is someone else’s problem, failure becomes more likely. Assemble’s work demonstrates that durability emerges from shared ownership and embedded repair cultures, not from optimization alone.

Designing Systems That Endure Breakdown and Repair
If maintenance is inevitable, then breakdown must be treated as a design scenario rather than an exception. Few architectural programs embody this logic as consistently as Rural Studio, the long-running design-build initiative based in Alabama. Operating in contexts of limited resources, Rural Studio designs buildings that anticipate wear, modification, and local repair. Their projects are intentionally over-documented and materially straightforward, enabling residents to maintain structures long after the designers have left. The architecture does not assume stable funding streams or specialized maintenance crews; it assumes improvisation.

This orientation toward continuity through care produces a different aesthetic and technical outcome. Details are robust rather than delicate. Systems are redundant rather than optimized to the edge. Materials weather visibly, making deterioration legible rather than catastrophic. In doing so, Rural Studio challenges the notion that resilience requires advanced technology. Often, it requires humility about what can realistically be sustained. For those working in digital or hybrid systems, the lesson translates directly. Platforms and tools that assume uninterrupted uptime or ideal user behavior tend to fracture under real-world conditions. Designing for maintenance means accounting for partial failure, degraded performance, and user-driven repair—whether that repair is technical, social, or organizational.

From Mastery to Care
Designing for maintenance is not anti-innovation; it is anti-amnesia. It recognizes that every system persists beyond its launch moment and that endurance is shaped less by brilliance than by care. Closed-loop materials, community-led repair, and design-build stewardship all point toward a shared conclusion: continuity is produced through ongoing attention.

As climate instability, resource constraints, and infrastructural fragility become baseline conditions, the most relevant design intelligence may lie not in predictive mastery but in adaptive maintenance. Systems that acknowledge their own fragility—while making repair possible—are better equipped to survive the long arc of use. In this framing, maintenance is not a compromise. It is a form of authorship that unfolds over time, shared among designers, users, and caretakers alike.

Researchers at the University of Hong Kong have developed what they describe as the world’s first soft, three-dimensional, biocompatible semiconductor made from hydrogel, marking a significant step toward electronics that physically and functionally integrate with living tissue. The work was led by Shiming Zhang and his team in HKU’s Wearable, Intelligent, Soft Electronics (WISE) research group.

Unlike conventional semiconductors, which are rigid, flat, and mechanically mismatched with the body, the HKU device is water-rich, deformable, and volumetric. The hydrogel-based semiconductor can be fabricated into thick, three-dimensional structures while remaining soft—closer in texture and mechanical behavior to biological tissue than to silicon. According to the researchers, the material is also compatible with living cells, which can survive and grow within the electronic structure itself.

The team demonstrated three-dimensional hydrogel transistors capable of conducting electrical signals while hosting living cells, a combination that has not been achievable with previous electronic materials. This enables electronic architectures that operate not only on the surface of tissue but within tissue-like volumes, opening new possibilities for bioelectronic interfaces. The research, reported in Science, positions hydrogel semiconductors as a potential foundation for future neural interfaces, prosthetics, and regenerative medical technologies. Because the material reduces mechanical mismatch and supports biological integration, it addresses longstanding challenges in implantable electronics, including inflammation, signal degradation, and device longevity.

Rather than presenting electronics as external tools applied to the body, the HKU work reframes electronic systems as materials that can coexist with biological structures. As interest grows in brain–machine interfaces, soft prosthetics, and biointegrated healing technologies, this development points toward a class of electronics designed to blend into living systems rather than resist them.

At MIT, researcher and designer Cyrus Clarke has developed Anemoia, an experimental device that explores whether visual images can be translated into olfactory outputs through computationally mediated systems. Situated at the intersection of design research, sensory interfaces, and machine learning, Anemoia is not a commercial product or a claim about automating fragrance creation. It functions as a research prototype that examines how smell—one of the least standardized human senses—might be structured and mediated through technical systems.

Anemoia takes photographs as input and produces corresponding scent outputs via a controlled emission mechanism. Visual features are computationally mapped to olfactory representations informed by learned associations between images, descriptive language, and scent references. Rather than generating a single definitive smell, the system produces an approximation shaped by probabilistic translation rather than direct sensory measurement.

Smell as a Computational Problem
Olfaction has historically resisted digitization. Unlike vision or sound, it lacks a shared representational framework. There is no universal scale for smell, and language used to describe it varies widely across cultures and individuals. These inconsistencies make smell difficult to encode, compare, or reproduce computationally.

Anemoia does not attempt to resolve these ambiguities. Instead, Clarke’s work treats them as constraints. The system operates on the premise that smell can be approximated indirectly, through correlations rather than direct measurement. Visual cues—such as color, texture, and object category—are computationally associated with olfactory descriptors drawn from human-labeled references, which inform the system’s scent outputs. In this structure, smell is treated as probabilistic rather than absolute. A photograph of a forest path may align with descriptors such as woody or earthy, which map to certain aroma molecules. The system does not claim that this is how the scene “actually” smells. It reflects how such scenes are commonly described within the data it draws from.

How Anemoia Works
Technically, Anemoia combines image analysis with computational mapping to olfactory components. Images are processed to extract visual features, which are then associated with scent descriptors through trained models. These descriptors inform the system’s scent outputs, which are rendered through a physical emission mechanism. The scent output is delivered via a custom-built hardware interface designed to release controlled quantities of fragrance materials. This physical component is essential to the project. Anemoia is not purely a software model; it is a device that closes the loop between computation and embodied experience. The emitted scents allow users to encounter the system’s translations directly, rather than as abstract data.

Clarke has framed Anemoia as a design probe rather than a finished system. The device exposes the assumptions embedded in its mappings: which visual cues are prioritized, which scent descriptors are available, and how cultural conventions shape the dataset. These choices influence the resulting smells, making the system’s biases perceptible rather than hidden.

Designing Smell as an Interface
By translating images into olfactory outputs, Anemoia reframes smell as an interface layer. Scent becomes something that can be triggered, modulated, and synchronized with other media, rather than remaining ambient or incidental. This framing aligns olfaction with interaction design, where sensory outputs are treated as part of a broader system rather than isolated effects.

The project also foregrounds authorship at an infrastructural level. Decisions about datasets, descriptors, and mappings shape how the device interprets images and what it produces in response. These decisions are not neutral. They embed particular cultural and sensory norms into the system, determining which smells are legible and which are excluded. Anemoia does not suggest that machines can capture the fullness of human smell perception. Instead, it makes visible the gap between lived experience and computational representation. By doing so, it offers a way to examine how emerging technologies may increasingly mediate sensory experience—not by replicating it, but by approximating it through systems of translation.

As design and technology move further into multisensory territory, projects like Anemoia highlight the importance of treating perception itself as a designed system. Smell, once considered too subjective to model, becomes a site where technical, cultural, and aesthetic decisions converge.

Tezos and HEK Basel have announced a digital art partnership that brings blockchain into an institutional, research-oriented context within contemporary art. The collaboration connects Tezos, a proof-of-stake blockchain, with HEK Basel, a leading European institution dedicated to new media art, digital culture, and experimental practice.

HEK Basel has a long history of exhibiting and contextualizing work that engages software, networks, and emerging technologies within broader cultural and political frameworks. In this context, the partnership reflects a broader shift toward institutional and research-led engagement with blockchain, positioning it as part of digital art practice rather than primarily as a speculative market mechanism. Under the collaboration, Tezos will support HEK Basel’s institutional programming related to digital and blockchain-based art. Public materials emphasize engagement with decentralized technologies in artistic and curatorial contexts, alongside knowledge exchange and public-facing initiatives that address blockchain’s cultural implications.

For Tezos, the partnership aligns with a strategy of engaging established cultural institutions as part of its broader arts ecosystem. For HEK Basel, it provides resources to explore blockchain within existing curatorial and research frameworks while maintaining institutional independence. More broadly, the announcement points to a slower, institutionally grounded pathway for blockchain in the arts—one centered on sustainability, critical inquiry, and long-term cultural relevance. For artists and designers, it suggests a future in which decentralized technologies are treated as cultural infrastructure rather than short-term trends.

The installation originated within the eponymous music-theatre production directed by Ruben van Leer, with music by Tigran Hamasyan, which premiered at Holland Festival on June 8, 2024. Within that performance context, sculpture, text, sound, and choreography formed an integrated narrative environment.

The project references mythological traditions surrounding the firebird—figures associated across cultures with renewal and awakening. In Armenian tradition, the bird’s power resides in its voice. The installation extends this mythology into mechanical form: articulated wing structures suggest Leonardo da Vinci’s speculative flying machines, foregrounding the persistent human ambition to engineer flight.

Materially, the work stages a dialogue between natural and artificial systems. Fabric surfaces behave like membranes; motorized components impose programmed movement; tungsten light activates depth and shadow within ecclesiastical architecture, as seen in its 2026 presentation at Vilnius Light Festival in St. Catherine Church.

Rather than resolving illusion and engineering, the installation sustains their tension. The bird is neither purely symbolic nor purely technical—it is a constructed apparatus that makes visible the mechanics behind transcendence.

Project Info
Artist: Boris Acket
Year: 2024
Image Credit: Courtesy of the artist / production team (Truth.io)
Creative Direction: Boris Acket, Ruben van Leer
Inspired by the music of: Tigran Hamasyan
Creative Engineer: Merijn Versnel
Production: Truth.io
Supported by: Mondriaan Fund
Shown at: Vilnius Light Festival (2026), St. Catherine Church
Creative Production: Luiza Guidi
Software: Moos Rebolder

The Island is a site-specific project by Hito Steyerl, presented at Osservatorio, the exhibition space overlooking Milan’s Galleria Vittorio Emanuele II. Developed in close dialogue with the architecture and symbolic charge of its location, the work extends Steyerl’s sustained inquiry into how power operates through images, platforms, and infrastructures. Rather than isolating content from context, The Island treats the exhibition site as an active system—one shaped by visibility, circulation, and economic control.

The project can be read as mobilizing the island not as a place of retreat or autonomy, but as an operational condition. Across contemporary geopolitics, finance, and technology, islands function as zones of exception: special economic areas, offshore data centers, gated territories, and logistical enclaves. Steyerl’s installation foregrounds questions around how these spaces are produced, who benefits from their separation, and how their apparent isolation masks deep integration with global systems.

Osservatorio as an Active Infrastructure
Osservatorio’s position above the Galleria is central to the work. The space is elevated, transparent, and carefully controlled—neither fully public nor conventionally institutional. Below, the Galleria operates as a dense node of luxury retail, tourism, and urban spectacle. Rather than attempting to neutralize this setting, The Island can be read as leveraging it to foreground how architecture structures perception. The installation situates viewers in a position of observation that echoes contemporary systems of oversight: platforms that promise openness while managing access, attention, and value behind the scenes. Glass walls, sightlines, and spatial thresholds function as material conditions through which the work is encountered, situating visitors within a layered environment of visibility and exclusion.

This approach aligns with Steyerl’s broader practice, in which exhibition spaces are frequently treated as operative environments rather than neutral containers. At Osservatorio, the building’s hybridity—a cultural venue embedded within a commercial landmark—offers a case through which to consider how institutions participate in the systems they host.

The Island as a Logic of Separation
Rather than narrating a single story, The Island assembles fragments that can be read as pointing to separation as an infrastructural strategy. Visual and spatial elements emphasize enclosure, segmentation, and controlled access. The island emerges here as a logic through which contemporary power operates: carving out zones that appear autonomous while remaining tightly coupled to global flows of capital, data, and labor. Steyerl’s avoidance of linear explanation is consistent with her broader practice. The work does not present dashboards, speculative interfaces, or invitations to optimize or manage systems. Instead, it positions the viewer as an interpreter confronted with partial views and mediated perspectives—an experience that echoes how many real-world infrastructures function: opaque, distributed, and resistant to singular points of control. In this sense, The Island can be understood less as representation than as reenactment. Moving through the installation parallels forms of contemporary governance in which decisions are registered spatially and affectively, even as their mechanisms remain out of reach.

Against the Fantasy of Autonomy
The Island can be read as pushing against autonomy as a credible outcome. The island—often imagined as a refuge or self-sustaining unit—appears instead as a structure shaped by extraction and exclusion. This reading resonates within a technological landscape that frequently markets independence through platforms, automation, and smart infrastructures, even as risk and responsibility are redistributed downward.

At Osservatorio, the elevated viewpoint functions as a spatial condition through which this logic becomes legible. From above, the Galleria’s flows appear orderly and contained, while the distance of that perspective underscores how decisions made at one level shape experiences at another. The spatial arrangement invites consideration of who sees, who decides, and who absorbs the consequences of systemic design choices. The work’s restraint is evident in what it withholds. The Island does not propose alternatives or solutions, nor does it frame oversight as empowerment. Instead, it concentrates attention on the conditions through which separation is produced and normalized.

Creative Practice Under Conditions of Infrastructure
The Island can be understood as operating less as a statement than as a working example of how contemporary practice engages systems without visualizing or mastering them. The installation does not present speculative interfaces, data dashboards, or forms of performative transparency. Instead, spatial arrangement, restricted sightlines, and institutional framing shape how the work is encountered—reflecting the partial, indirect ways infrastructures are typically experienced.

This is especially legible in how agency is structured. Viewers are not invited to interact, optimize, or intervene; their role is largely limited to navigation and interpretation. Control remains present but displaced, embedded in architecture, access protocols, and perspective rather than explicit commands—a condition that echoes how many users encounter platforms, logistical networks, and governance systems. The Island offers a reference point rather than a solution. It shows how design can register systemic conditions without translating them into tools or outcomes. The exhibition functions as a kind of infrastructural cross-section, briefly exposing how visibility, elevation, and enclosure organize experience.

Situated within Osservatorio—a cultural venue embedded in a commercial landmark—the project brings cultural and economic infrastructures into the same frame. Rather than resolving this overlap, the work holds it in place, allowing institutional conditions to remain visible. In this sense, The Island aligns with a growing body of practice in which design is treated less as interface-making than as the arrangement of conditions under which interfaces, decisions, and futures take shape.

As artificial intelligence saturates design discourse, computation is still too often framed as something that happens inside software: code executing on silicon, data processed by remote servers, intelligence abstracted from matter. Yet a parallel lineage in design research challenges that assumption. Across architecture, materials science, and fabrication, designers are working with materials that sense, respond, adapt, and reorganize—performing forms of computation without electronics, sensors, or centralized control. This shift reframes intelligence not as an overlay added through software, but as something embedded in physical behavior itself. Under the right conditions, materials can compute.

Material behavior as distributed computation
At the heart of this approach is a consequential proposition: computation does not have to be confined to software or electronic control systems. In material-centered design research, behavior can be produced through distributed physical processes, where transformation is driven by how materials are structured rather than by step-by-step digital instruction. Instead of executing code, these systems rely on geometry, composition, and internal forces to produce predictable responses. Behavior emerges through interaction, not command.

The work of the MIT Self-Assembly Lab, founded and led by Skylar Tibbits, exemplifies this logic. The lab investigates self-assembly and programmable materials—systems in which components can autonomously fold, aggregate, or change shape when exposed to external stimuli. Across projects involving self-folding sheets and shape-changing composites, “programming” is achieved through material gradients, residual stresses, and environmental triggers such as heat or moisture, rather than through sensors or active control. Self-assembly is framed as a process driven by local interactions, where order emerges without centralized coordination.

What is at stake in this work is less technological novelty than a reallocation of control. Rather than relying on external computation to determine behavior in real time, decision-making is embedded into the material system itself. The material does not execute instructions in the conventional computational sense; it responds to conditions it has been designed to register. Folding, bending, swelling, or aggregation function as physically encoded responses, unfolding in parallel across a system and reducing reliance on continuous energy input or mechanical mediation. This approach aligns with research in morphogenesis and systems biology, where complex form arises from local interactions rather than top-down specification. Designers working with material computation similarly define constraints, thresholds, and material tendencies, allowing outcomes to emerge through interaction with environmental forces. Intelligence, in this framing, is not concentrated in an algorithm or controller, but distributed across a system’s material organization.

Environmental responsiveness vs. digital control
Material computation becomes especially legible when contrasted with digitally controlled responsiveness. Many smart buildings, adaptive façades, and responsive products rely on networks of sensors, actuators, and control software—technical layers that require continuous energy supply, calibration, and maintenance. Environmental data is captured, processed, and translated into action through centralized or semi-centralized control logic.

Materially responsive systems operate differently. Certain materials respond directly to environmental conditions through their physical properties. Hygroscopic wood expands or contracts with humidity. Bimetallic strips bend predictably with temperature variation. Fiber-based composites can stiffen, relax, or reorient under load depending on internal structure. These responses occur as continuous physical processes rather than discrete computational decisions, without relying on data capture, digital thresholds, or external controllers.

Research led by Achim Menges at the Institute for Computational Design (ICD), University of Stuttgart, has been particularly influential in advancing this approach within architecture. Through work on wood and fiber composite systems, Menges and his collaborators have shown how material behavior itself can act as a design driver, enabling structures to adapt to environmental conditions without motors, sensors, or mechanical actuation. Performance emerges from how materials are organized and fabricated, rather than from real-time digital control.

The implications extend beyond technical performance. Digitally controlled systems embed governance into software and infrastructure through decisions about which data matters, how thresholds are defined, and which responses are authorized. Materially responsive systems, by contrast, operate through embedded physical logic that is legible at the scale of the material itself and difficult to modify remotely. While they trade fine-grained precision for coarser forms of adaptation, they often favor robustness, continuity, and long-term stability—qualities increasingly relevant for designers concerned with autonomy, transparency, and durability.

Sustainability implications of non-electronic intelligence
The environmental stakes of material computation are difficult to ignore. As AI-driven systems expand, so does their dependence on energy-intensive infrastructure, including data centers, sensor networks, rare earth materials, and short-lived electronic components. In this model, intelligence is tightly coupled to extractive supply chains and ongoing energy consumption.

Materially based forms of intelligence offer an alternative. Systems that rely on physical behavior rather than electronics can operate without embedded sensors, firmware updates, or network connectivity, often drawing energy directly from environmental conditions such as humidity, temperature, gravity, or load. Because behavior is governed by material properties rather than software, degradation typically occurs through wear or environmental exposure rather than sudden technical failure, shifting maintenance toward material stewardship. At building and urban scales, this enables different design strategies: adaptive shading elements that respond to climate without motors, structural components that redistribute stress through geometry, and infrastructure designed to accommodate seasonal variation passively rather than through constant optimization.

Institutions such as the Institute for Advanced Architecture of Catalonia (IAAC) have been instrumental in translating these ideas into applied research and education. IAAC’s work on bio-based materials, climate-responsive systems, and low-energy fabrication positions material intelligence as a practical design strategy rather than a speculative abstraction. Here, computation is not removed from material reality but embedded within it, aligning performance with ecological constraint. This shift does not imply a rejection of digital tools. Simulation, modeling, and digital fabrication remain essential for designing material systems whose behavior is complex and nonlinear. The distinction lies in where intelligence ultimately operates: digital tools define conditions and parameters, while the resolution of behavior is delegated to material systems rather than continuously managed through software.

Designing with behavior, not control
As materially responsive systems gain traction, they prompt a reassessment of long-standing assumptions about authorship and control. When behavior emerges through interaction rather than direct instruction, the designer’s role shifts from specifying final form to shaping conditions and tendencies. Performance is evaluated less by precision than by adaptability.

As ecological pressures intensify and technological systems grow more opaque, interest in intelligence that is visible, local, and materially grounded continues to grow. Materially responsive systems do not promise total control. Instead, they demonstrate how design can operate within uncertainty. The most consequential forms of design intelligence may not run entirely on software, but through material processes that swell, bend, crack, and recover—quietly computing in plain sight.

At ArtScience Museum, The Drone Shepherd extends Liam Young’s long-running investigation into planetary systems, automation, and environmental governance. Presented as a VR graphic novel, the work reframes Young’s speculative project Planet City through a tighter narrative lens—one focused less on the scale of the city than on the mechanisms that manage it. Rather than presenting technological salvation or dystopian collapse, the work constructs an encounter with how control, care, and infrastructure converge at planetary scale.

Young’s practice has consistently treated architecture as a medium for storytelling about systems rather than buildings. The Drone Shepherd continues that trajectory, using immersive media to render abstract processes—ecological monitoring, autonomous decision-making, remote stewardship—into spatially legible scenes. The work is speculative, but its materials are familiar: drones, sensors, automated oversight, and data-driven management. What changes is how these elements are arranged and experienced.

From Planet City to the Operational Layer
Planet City proposed a radical spatial consolidation: all humans living in a single hyper-dense metropolis to allow the rest of the planet to recover. The Drone Shepherd does not revisit that premise directly. Instead, it occupies the operational layer beneath it—the everyday systems required to make such a world function. The VR narrative unfolds in fragments, presenting landscapes shaped by drone surveillance and automated environmental control.

This shift in focus is subtle but consequential. Rather than debating the feasibility of a planetary city, the work examines the logic that governs its aftermath. Drones patrol rewilded zones, manage ecological thresholds, and enforce boundaries. The environment is no longer untouched nature nor traditional urban space, but a managed terrain optimized through continuous monitoring. The viewer’s position inside the work reinforces this emphasis. You are not an external observer assessing a master plan. You move through scenes that suggest procedures rather than places, encountering infrastructure as lived condition. The speculative question becomes procedural rather than monumental: what does planetary care look like when it is executed by autonomous systems?

Graphic Novel Logic in Virtual Space
Formally, The Drone Shepherd resists the dominant aesthetic of VR as photoreal simulation. Young adopts the visual grammar of graphic novels—bold line work, flattened depth, and segmented compositions—translated into three-dimensional space. Scenes read as panels you inhabit rather than worlds you explore freely. By avoiding realism, the work foregrounds interpretation over immersion. The work encourages the reading of systems rather than the simulation of reality. Movement is deliberate, narrative pacing controlled, and visual density calibrated to guide attention rather than overwhelm it.

VR here functions as an interface for systems thinking. The medium is used to spatialize relationships—between drones and territory, governance and ecology—without collapsing into spectacle. The experience feels closer to navigating a spatial essay than inhabiting a virtual environment, aligning with Young’s broader interest in architecture as a narrative and critical tool.

Automation as Environmental Stewardship
The figure of the “shepherd” is deliberately ambiguous. In The Drone Shepherd, care is distributed across autonomous agents that monitor, decide, and intervene without human presence. Environmental stewardship is rendered as a continuous, automated process—less a moral choice than an operational mandate. This framing echoes contemporary developments in climate technology: AI-driven monitoring systems, automated conservation efforts, and remote sensing platforms designed to manage ecological risk. Young’s work does not dramatize these technologies as villains or saviors. Instead, it presents their logic as infrastructural fact.

Scenes emphasize thresholds, boundaries, and maintenance rather than growth or progress. The environment appears stabilized but tightly regulated. Life persists, but within parameters defined elsewhere. The viewer encounters a world where governance is embedded in code and logistics, operating persistently and quietly. The political tension emerges through accumulation rather than declaration. As scenes unfold, it becomes clear that planetary repair, when delegated to machines, also redefines agency. Decisions are made continuously, while deliberative processes remain largely invisible. The work invites viewers to notice this absence rather than resolving it.

Speculation as Design Method
The Drone Shepherd functions as a case study in how speculative practice can interrogate real systems without proposing solutions. The project does not offer a model to adopt or reject. It constructs a scenario precise enough to feel operational, yet open enough to remain contested.

Presented within ArtScience Museum’s programmatic focus on intersections between art, science, and technology, the work is notable for its restraint. There are no dashboards, no interactive controls, no invitation to optimize the system. The viewer’s role is interpretive, not managerial. This choice aligns with a growing recognition that design’s influence increasingly operates at the level of infrastructure. Decisions about automation, thresholds, and governance are rarely visible in finished artifacts, yet they shape behavior and possibility at scale. The Drone Shepherd makes these decisions spatially perceptible without foregrounding aesthetic spectacle. Young’s use of VR and graphic narrative demonstrates how emerging media can support critical design inquiry rather than dilute it. By translating planetary governance into a sequence of inhabitable scenes, the work reframes speculation as a method for examining power embedded in systems.

The Drone Shepherd does not predict a future. It constructs a lens through which existing trajectories—automation, environmental management, infrastructural governance—can be examined with clarity. Its value lies in this precision: a reminder that the most consequential design work often unfolds quietly, embedded in the systems that decide how worlds are maintained.

In a newly released report, the U.S. Copyright Office has clarified how U.S. copyright law applies to works created using generative artificial intelligence, reinforcing that copyright protection requires sufficient human creative control over the expressive elements of the work.

Published in January 2025 as Part 2 of the Office’s ongoing study on artificial intelligence, the report focuses specifically on the copyrightability of works that incorporate AI-generated material. Its central conclusion is consistent with prior Office decisions: content generated entirely by an AI system, without sufficient human creative input, is not eligible for copyright protection under existing law.

Crucially for designers, artists, and creative technologists, the report draws a distinction between using AI as a tool and delegating authorship to an automated system. The Office explains that simply entering prompts or selecting from AI-generated outputs does not, by itself, meet the threshold for human authorship. To qualify for protection, a human must exercise creative control over the expressive elements of the work—such as through selection, arrangement, modification, or integration of AI-generated material into a broader human-authored composition.

The guidance does not introduce new law, but it consolidates and formalizes principles that have emerged through recent registration decisions and public consultations. For creative professionals working with generative systems, the implications are practical rather than theoretical: copyright claims will depend on how AI tools are embedded within a broader creative process, and how clearly human contribution can be demonstrated.

The Office also emphasizes that its conclusions are grounded in current U.S. copyright statutes, leaving broader policy questions—such as whether the law should evolve to address AI authorship—to Congress. Additional parts of the Office’s AI study, including analysis of training data and fair use, are expected to follow. For a creative industry increasingly shaped by generative systems, the report underscores a clear message: AI may accelerate production, but authorship—and the legal rights attached to it—still hinges on human agency.

As climate volatility, resource constraints, and infrastructural fragility intensify, many artists and architects have already moved beyond treating computation as a neutral design aid. Across architecture, landscape, and spatial research, algorithms are routinely used not just for optimization or representation, but to model territorial processes, negotiate competing land uses, and structure infrastructures that respond to environmental change over time.

Often discussed under the rubric of algorithmic ecologies, this body of work positions computation as a medium of spatial governance as much as form-making. Research studios and experimental practices increasingly integrate planetary-scale datasets, environmental modeling, and systems thinking into design workflows. The result is an approach that treats cities, landscapes, and infrastructures as adaptive systems—assemblages shaped by feedback loops, thresholds, and uncertainty rather than fixed master plans or static end states.

From Computational Design to Territorial Intelligence
Early computational architecture largely emphasized geometry, performance optimization, and formal complexity. Contemporary algorithmic practices, by contrast, increasingly operate beyond the scale of the building envelope. They engage territorial conditions by integrating climate models, satellite imagery, logistical systems, and political boundaries into spatial analysis and design research. This approach is exemplified by Territorial Agency, founded by John Palmesino and Ann-Sofi Rönnskog. The practice is known for large-scale spatial investigations that combine cartography, environmental data, and political analysis. Rather than proposing discrete architectural objects, Territorial Agency develops visual and computational frameworks that examine how energy systems, climate dynamics, and geopolitical forces interact across regions.

Their work illustrates a defining characteristic of algorithmic ecologies: design as a mode of inquiry. Computational tools are used to surface relationships—between extraction and habitation, infrastructure and ecology—rather than to reduce complex territorial systems to singular solutions. For architects and designers, this reflects a shift away from deterministic planning toward scenario-based and probabilistic spatial thinking.

Speculation as Infrastructure: Design Earth
If Territorial Agency emphasizes analytical clarity, Design Earth foregrounds speculation as a critical design method. Led by Rania Ghosn and El Hadi Jazairy, the practice produces projects that combine environmental data, geopolitical research, and narrative visualization to examine how futures are framed and governed.

Design Earth’s work frequently employs computational mapping and modeling not to predict outcomes, but to construct alternative imaginaries. Their projects visualize offshore energy fields, planetary extraction zones, and post-carbon infrastructures as contested ecological systems. In this context, algorithms function less as tools of efficiency and more as instruments for critical storytelling, making visible the assumptions embedded in infrastructural decision-making. This speculative dimension is central to algorithmic ecologies. By rendering often invisible systems legible—whether atmospheric flows or logistical corridors—designers create space for political and ethical debate. Computation becomes a means of intervening upstream in how infrastructural futures are conceived.

Adaptive Infrastructures and Urban Feedback Loops
While speculative practices shape discourse, parallel research initiatives are translating algorithmic thinking into operational research frameworks. Future Cities Laboratory, a research initiative of the Singapore-ETH Centre, investigates how cities can adapt to environmental stress through integrated modeling of urban systems. The lab’s work spans climate-responsive urban design, mobility systems, and data-driven planning tools. Crucially, it treats cities as evolving systems shaped by continuous feedback—between climate, policy, behavior, and infrastructure. Computational models are used to simulate scenarios, test interventions, and evaluate trade-offs rather than to enforce static outcomes.

This orientation reflects a broader trend: adaptive infrastructure design. Instead of building for a single predicted future, algorithmic ecologies emphasize flexibility, redundancy, and responsiveness. Sensors, simulations, and data platforms are embedded into urban systems to enable adjustment over time—whether in flood management, energy distribution, or mobility networks.

Why Algorithmic Ecologies Matter
The significance of algorithmic ecologies lies less in technological novelty than in how creative practice engages power. Algorithms increasingly mediate access to land, resources, and mobility, and when designers participate in shaping these systems, they intervene—often indirectly—in forms of governance.

Artists and architects working in this space are also reconfiguring authorship. Projects can be less of a finished object than a set of conditions: rules, datasets, models, and scenarios that structure future decisions. This reflects a broader recognition that design operates as infrastructure, shaping behavior, priorities, and possibilities through everyday systems rather than isolated artifacts.

Algorithmic ecologies provide a framework for engaging complexity without retreating into abstraction. They demonstrate how computation can support ecological thinking at scale while remaining attentive to political and cultural contexts. As climate pressures intensify and urban systems face increasing uncertainty, these practices point toward a recalibration of design’s role—not as a standalone problem-solver, but as a critical participant in shaping adaptive and contested futures.

Working within the constraints of a highly regulated industrial environment defined by hygiene protocols, security systems, and advanced manufacturing standards, Blandet sourced discarded syringe components directly from the facility’s waste streams. These high-precision plastic elements—normally engineered for laboratory accuracy—were reassembled into two functional lighting objects.

Interaction is central to the work. The syringes act as dimming mechanisms: pressing them modulates light intensity. A calibrated medical gesture becomes a tactile lighting control. Through this translation, light is treated as a measurable and compressible medium, conceptually aligned with fluid handling technologies.

The project also foregrounds questions of material value and context. Components engineered for global pharmaceutical supply chains are redirected into design objects with distinct economic and cultural positioning. Their availability is strictly site-specific; the materials are accessible only within the industrial ecosystem that produces them.

Fluid Dynamics was presented by Faber (Timișoara) as part of the exhibition Design Signals, curated by Martina Muzi at Alcova, Milan, during Salone del Mobile 2025.

Project Info
Artist: Théophile Blandet
Year: 2025
Exhibition: Design Signals, Alcova, Milan (Salone del Mobile 2025)
Presented by: Faber, Timișoara
Image Credit: Eugenio Consentino; Alex Todirică
Video: Alex Todirică

Across architecture, materials research, and experimental manufacturing, living materials have transitioned from lab-scale experiments to early-stage deployment. Mycelium composites, bacterial cellulose, and bio-reactive facades now function as test cases for a broader shift in how materials are conceived and managed. Their relevance for designers lies less in biological novelty than in the way these systems redistribute agency: when materials grow or adapt, questions of sustainability, authorship, and responsibility become operational rather than abstract.

In this context, sustainability can no longer be treated as a checklist of lower emissions or recycled inputs. Biodesign instead frames materials as active participants within ecological systems, prompting more fundamental questions. Who is responsible for a material’s lifecycle when it is partially alive? How should designers intervene—or deliberately refrain—from controlling biological processes? And what does authorship mean when outcomes are co-produced with living systems?

Biofabrication as a Design Method, Not a Material Substitute
Biofabrication is often mischaracterized as a greener substitute for conventional manufacturing. In practice, it is better understood as a methodological shift. Techniques such as growing mycelium composites, cultivating bacterial cellulose, or producing microbial pigments replace extraction and machining with cultivation, environmental control, and care.

Companies like Ecovative have demonstrated that mycelium can be grown into structural packaging, insulation, and acoustic panels using agricultural waste as feedstock. While these materials are compostable and relatively low-energy to produce, their more consequential impact is conceptual: designers must account for growth rates, humidity, contamination risk, and material decay. Control becomes probabilistic rather than absolute.

Similarly, studios such as Modern Synthesis treat bacterial cellulose not as a fixed output but as a responsive system shaped by tension, scaffolding, and environmental conditions. Design decisions occur upstream—through constraints, protocols, and feedback loops—rather than through post-processing alone. This challenges industrial assumptions about repeatability and standardization, foregrounding variability and responsiveness as design parameters.

Living Materials and the Redistribution of Authorship
When materials grow, authorship disperses. Designers define parameters, but outcomes emerge through biological processes that introduce variability and constraint. This redistribution of control is not a flaw but a defining characteristic of living materials, requiring designers to relinquish assumptions of total mastery in favor of collaboration with non-human systems. A chair grown from mycelium or a textile cultivated from microbes is not inert matter shaped solely by human intention. It is the product of metabolic labor performed by organisms. While these systems are not sentient, they operate according to their own logics, which can be disrupted by over-optimization or extractive timelines.

This reframing aligns with broader shifts in systems thinking and post-human design theory. Authorship becomes infrastructural: the designer’s role is to set conditions, define thresholds, and anticipate downstream effects. Responsibility, in turn, extends beyond aesthetics to include care, maintenance, and end-of-life pathways.

From Biofabrication to Bio-Integrated Energy Systems
Living materials are not limited to objects and surfaces. They are increasingly embedded in architectural and energy infrastructures. One of the most cited examples is the algae-powered facade developed for the BIQ House in Hamburg, which integrates bioreactor panels into a building envelope to generate biomass and thermal energy. Developed by Arup in collaboration with SSC Strategic Science Consult, the SolarLeaf system demonstrates how biological processes can be integrated into urban energy systems. Algae growth within the facade responds to sunlight, producing heat and biomass while also acting as dynamic shading.

Crucially, this is not a decorative application of “green” aesthetics. It is a systems-level intervention that links material behavior, energy production, and environmental performance. Yet it also exposes ethical tensions: bio-integrated systems require ongoing monitoring, nutrient inputs, and technical oversight. Sustainability here is not passive; it is operational and labor-intensive.

Ethics Beyond Sustainability Metrics
The ethics of biodesign cannot be reduced to carbon accounting or biodegradability claims. While many living materials offer environmental advantages, their true ethical challenge lies in how they reconfigure responsibility. Designers must consider not only how materials are made, but how they are maintained, governed, and disposed of.

There is also a risk of bio-solutionism—using living materials as symbolic fixes for systemic environmental problems. A mycelium wall panel does little if embedded in a supply chain built on overproduction and short-term use. Ethical biodesign requires alignment between material innovation and broader economic and cultural shifts toward maintenance, repair, and sufficiency. Living materials demand longer timelines, interdisciplinary collaboration, and transparency about uncertainty. They also invite designers to think politically: about who bears the cost of care, who benefits from regenerative systems, and how biological labor is valued within capitalist frameworks.

Living and regenerative materials are reshaping design not by offering a single solution to sustainability, but by forcing a reconsideration of how design intervenes in the world. As biofabrication and bio-integrated systems move from research to deployment, the central question is no longer whether these materials work. It is whether designers are willing to accept the ethical responsibilities that come with designing alongside life.

Apple’s latest expansion of Creator Studio has prompted predictable comparisons to Adobe. But framing the tool as a potential “Adobe killer” misunderstands what is actually shifting inside creative industries. Apple is not competing at the level of production. It is consolidating control at the level of circulation.

Adobe’s Creative Suite remains the dominant environment where creative labor takes form. Photoshop, Illustrator, Premiere Pro, and After Effects define how images, video, and motion are made, revised, and finalized. These tools govern craft, technique, and authorship through file formats, interfaces, and increasingly, AI-assisted workflows. Apple’s Creator Studio operates elsewhere. Accessible primarily through Apple Music for Artists, it provides analytics, campaign attribution, and visibility into how work performs once it enters Apple’s ecosystem. Streams, listeners, geographic reach, playlist placement, and marketing pathways are rendered legible—but only in aggregate, and only within Apple’s terms.

This is not redundancy; it is layering.

Creators still rely on Adobe to produce work, but they increasingly adapt that work to the metrics, recommendation systems, and editorial pipelines that Apple controls. Neither platform needs to replace the other to exert power. Together, they structure the full lifecycle of creative labor—from production to visibility.

Creative power is no longer centralized in a single tool or platform. It is distributed across overlapping control layers: software that governs how work is made, and platforms that govern how work circulates, performs, and endures. For designers, artists, and creative technologists, authorship is shaped as much by analytics dashboards and distribution constraints as by brushes, timelines, or prompts. Understanding this layered governance is now a core professional skill.Apple’s Creator Studio does not threaten Adobe’s dominance in making. It reinforces a different truth: creative labor today is negotiated across multiple systems, each quietly defining what counts as success—without ever needing to eliminate the others.

As AI systems increasingly mediate access to information, services, and decision-making, trust has become a design problem—not a branding exercise. For interaction designers working with machine learning systems, transparency is no longer limited to explainability dashboards or model cards. It is expressed through interface choices, interaction flows, defaults, and the boundaries placed on what systems are allowed to do.

Across research, practice, and platform design, a growing body of work treats trust not as user compliance but as a negotiated relationship shaped by power, legibility, and accountability. Three strands of work—critical data design, participatory HCI, and applied industry research—offer concrete models for how ethical commitments can be embedded directly into AI-driven systems.

Designing Power, Not Just Interfaces
Much of the contemporary discussion around trustworthy AI focuses on technical transparency: opening black boxes, documenting datasets, or publishing system limitations. While necessary, these moves often overlook how power is exercised at the interface layer—where users encounter, interpret, and respond to AI systems in practice. Designer and researcher Catherine D’Ignazio has consistently argued that transparency without structural accountability can reinforce existing inequalities. Through projects such as Data Feminism (co-authored with Lauren F. Klein) and her work at MIT’s Data + Feminism Lab, D’Ignazio frames design as a site where values are operationalized, not merely communicated.

From this perspective, ethical AI design is less about revealing how a model works and more about deciding who benefits, who is harmed, and who has agency within a system. Interface decisions—what data is shown, what is hidden, what actions are possible, and which are blocked—become mechanisms of governance. Transparency, then, is inseparable from politics. This shifts the role of UX from usability optimization to power-aware mediation. Design choices actively shape how AI authority is perceived, challenged, or deferred to.

Participation as an Ethical Boundary
If power is encoded through design, participation becomes a primary method for rebalancing it. HCI researcher Christina Harrington has developed participatory design frameworks that center communities historically marginalized by technological systems, particularly Black and disabled communities.

Harrington’s work emphasizes that ethical boundaries cannot be retrofitted after deployment. They must be established upstream through co-design processes that surface lived experience, contextual knowledge, and alternative value systems. In AI-driven interfaces, this often means designing constraints rather than capabilities—limiting automation where it undermines human judgment, or foregrounding uncertainty instead of confidence.

Importantly, Harrington’s research demonstrates that transparency is not universal. What is considered “clear” or “intuitive” varies across cultural and social contexts. Trustworthy systems therefore require localized forms of legibility, shaped through sustained engagement rather than generalized personas. This reframes transparency as relational. Ethical UX is not achieved by exposing more information, but by aligning system behavior with the expectations and needs of the people most affected by it.

Translating Ethics into Product Systems
While critical and academic frameworks define the stakes, their influence on real-world platforms depends on how effectively they are translated into product development processes. This translation is the focus of Google’s People + AI Research initiative, Google PAIR, which has developed widely used tools such as the PAIR Guidebook, model transparency practices, and human-centered evaluation frameworks. Google PAIR’s contribution is not a singular ethical stance, but a set of operational methods for embedding human values into AI product teams. These include design checklists for responsible AI, participatory research protocols, and guidance on communicating system uncertainty through interface design.

Crucially, PAIR’s work treats UX designers as ethical agents within AI pipelines—not as downstream stylists. Designers are positioned to define system boundaries: deciding when automation should defer to human oversight, how confidence is communicated, and how users are invited to contest or correct system outputs. While corporate constraints inevitably shape what is possible, PAIR’s influence illustrates how ethics can move from abstract principles into repeatable design practices. Trust, in this model, is maintained not by claims of objectivity but by consistent, legible system behavior over time.

From Transparency to Accountability
Taken together, these approaches suggest that transparency alone is insufficient. Trustworthy AI systems require accountability structures that are experienced through interaction—not hidden in documentation.

The implication is clear: the politics of AI are increasingly negotiated at the UX layer. Interaction designers are not merely translating system logic for users; they are actively shaping how authority, agency, and responsibility are distributed within AI-mediated environments. Ethical boundaries—what a system can infer, recommend, automate, or refuse—are design decisions. Transparency becomes meaningful only when paired with participation, constraint, and the possibility of refusal. As AI systems continue to scale, trust will not be granted by default. It will be designed, tested, contested, and redesigned—one interface decision at a time.

While desktop 3D printer hardware has seen few meaningful launches in recent months, innovation in additive manufacturing is continuing elsewhere: in materials. 3D bamboo filament, including products such as Fibrolon® Bamboo developed by FILOALFA, illustrates how material development is increasingly driving differentiation in a maturing fabrication ecosystem.

Bamboo-filled filaments typically combine PLA with finely processed bamboo fibers, producing prints with a matte, fibrous surface and subtle tonal variation. These materials are not new, but their growing visibility reflects a broader shift away from machine-centric innovation toward material intelligence—how composition, texture, and behavior influence design outcomes. As printer performance stabilizes around speed, enclosure, and automation, designers are turning their attention to what comes out of the nozzle rather than how fast it moves. Bamboo filaments deliberately resist the smooth, uniform finish associated with industrial plastics. Layer lines remain visible, fibers interrupt surface continuity, and variation becomes part of the object rather than a defect to be eliminated.

For architects, industrial designers, and researchers, this matters. Bamboo filaments are being used for architectural models, exhibition components, and speculative prototypes where material legibility is as important as geometry. The filament’s visual and tactile qualities communicate process and constraint, aligning with post-digital approaches that foreground making over polish.

Critically, bamboo-filled PLA should not be overstated as a sustainability solution. While bamboo is a renewable resource, the base polymer remains a bioplastic, and lifecycle impacts depend on sourcing, processing, and disposal. The significance of these materials is cultural and methodological rather than purely environmental. In a period where 3D printing hardware innovation has slowed, bamboo filament functions as a material signal - additive manufacturing is entering a phase where expression, texture, and material choice are primary sites of experimentation. For many, this may be more consequential than another incremental machine upgrade.

Speculative design has matured beyond the gallery-bound provocation. Increasingly, it is being operationalized as a way to prototype governance itself—treating design not as an outcome, but as a protocol: a set of rules, assumptions, feedback loops, and decision logics that shape how futures are negotiated before they are fixed. In this framing, designers are not predicting what comes next. They are constructing systems that can hold uncertainty, surface trade-offs, and make values legible while futures are still unwritten.

Across experimental studios and civic labs, this shift reframes design as a method for institutional inquiry. Rather than producing artifacts, these practices prototype conditions—testing how policies might behave, how publics might respond, and how infrastructures could be reconfigured when social, environmental, and technological variables collide. Three practices in particular demonstrate how speculative protocols are moving from theory into applied civic experimentation.

Dark Matter Labs: Designing Institutions as Mutable Systems
Dark Matter Labs approaches design as an instrument for institutional transformation rather than aesthetic production. The studio works at the scale of governance, finance, land, and law—domains traditionally resistant to design intervention. Its projects treat institutions as dynamic systems that can be reprogrammed through alternative logics of value, ownership, and accountability.

Rather than proposing finished policy solutions, Dark Matter Labs constructs experimental frameworks that allow new forms of decision-making to be tested in real contexts. This includes rethinking land stewardship models, exploring post-growth economic mechanisms, and prototyping governance structures that respond to ecological limits. Design, here, functions as a protocol for inquiry: mapping incentives, identifying systemic lock-ins, and revealing how power circulates through institutional architectures.

What distinguishes this work is its refusal of speculative distance. The studio embeds within live policy conversations, working alongside governments, civic organizations, and communities. Speculation becomes actionable not because it predicts outcomes, but because it clarifies choices. By making assumptions explicit and systems visible, Dark Matter Labs uses design to create space for institutional imagination grounded in material constraints.

Extrapolation Factory: Futures as Participatory Governance Tools
Founded by Elliott P. Montgomery and collaborators, Extrapolation Factory is often associated with provocative future artifacts. Yet its deeper contribution lies in how those artifacts operate as civic interfaces. The studio’s work uses speculative scenarios to engage publics, policymakers, and institutions in conversations about long-term consequences that are otherwise difficult to address.

Extrapolation Factory’s projects are structured as experiential systems: exhibitions, workshops, and public engagements that simulate alternative regulatory, environmental, or technological conditions. These are not predictions, but carefully constructed extrapolations from existing trends. By extending present-day logics into plausible futures, the studio creates shared reference points for debate.

In governance contexts, this approach functions as a protocol for collective sense-making. Participants are invited to inhabit futures shaped by today’s policy decisions, making abstract issues tangible and emotionally legible. The value lies less in the speculative object than in the process it enables—one that distributes authorship of the future across diverse stakeholders. Design becomes a mediator between expertise and public imagination, allowing governance to be rehearsed before it is enacted.

Rachel Coldicutt: Embedding Design Protocols in Civic Technology
Rachel Coldicutt operates at the intersection of digital technology, public institutions, and social outcomes. Her work emphasizes how design protocols—standards, processes, and decision frameworks—shape the ethical and practical impact of civic technologies. Rather than treating technology as neutral infrastructure, she foregrounds how design choices encode values into public systems.

Coldicutt’s approach is notably pragmatic. Working with governments, funders, and cultural institutions, she focuses on improving how technologies are commissioned, evaluated, and governed. This includes advocating for inclusive design practices, transparent evaluation metrics, and accountability mechanisms that extend beyond deployment. Speculation, in this context, is not futuristic storytelling but anticipatory governance: asking how systems might fail, exclude, or produce unintended consequences over time.

By embedding design thinking into procurement and policy processes, Coldicutt demonstrates how speculative protocols can operate quietly but effectively within institutions. The future is shaped not by grand visions, but by everyday decisions about data, accessibility, and responsibility. Design becomes a means of institutional self-reflection—an ongoing practice rather than a one-off intervention.

From Speculation to Stewardship
Taken together, these practices signal a broader shift in how speculative design is understood and applied. The focus is no longer on imagining distant futures, but on designing systems capable of adapting to uncertainty. Protocols replace predictions. Governance becomes a design space. This reframing expands the field’s responsibility. Working at the level of systems requires fluency in policy, economics, and institutional behavior, as well as a willingness to engage with slow, complex change. It also demands rigor: speculative protocols must be testable, legible, and accountable if they are to influence real-world decision-making.

As climate instability, technological acceleration, and social fragmentation continue to challenge existing institutions, the ability to prototype governance itself may become one of design’s most consequential roles. Not as authors of the future, but as architects of the conditions under which futures can be collectively negotiated.

This week, NVIDIA formally introduced Earth-2, an AI-accelerated climate and weather modeling platform designed to run high-resolution environmental simulations at unprecedented speed and scale. Announced at the American Meteorological Society meeting, the system combines machine-learning models with physics-based simulation to generate localized forecasts, including short-term “nowcasts,” using significantly less compute than traditional numerical methods.

While Earth-2 is positioned as infrastructure for climate science, disaster preparedness, and energy planning, its implications extend well beyond meteorology. For designers, architects, and creative technologists, Earth-2 reframes simulation as an active design medium rather than a downstream analytical tool. Unlike conventional climate datasets—often static, coarse, and retrospective—Earth-2 operates as a continuously updating model of environmental behavior. This enables iterative testing of scenarios at the scale of neighborhoods, coastlines, or regions, making climate conditions a live constraint within design workflows. For practices working in parametric architecture, responsive urban systems, or environmental visualization, the platform signals a shift toward climate-informed form-finding driven by near-real-time data rather than historical averages.

Earth-2 also marks a broader evolution in NVIDIA’s strategy. Building on the logic of Omniverse and digital twins, the company is moving from simulating discrete objects and spaces toward modeling interdependent planetary systems—weather, infrastructure, energy, and risk—within shared computational environments. The result is a form of simulation that increasingly overlaps with governance, planning, and policy.

The critical question is not whether these simulations are more accurate, but how they shape decision-making. As platforms like Earth-2 become embedded in planning and design processes, they quietly establish defaults: what variables matter, what futures are modeled, and which risks are rendered visible—or ignored. Earth-2 is not just a forecasting tool. It is a signal that planetary simulation is becoming a foundational layer of contemporary design practice.

Apple’s Vision Pro is increasingly being framed less as a consumer entertainment device and more as a professional spatial computing platform. While early attention focused on immersive media and personal productivity, recent software updates, developer messaging, and partnerships signal a deliberate shift toward enterprise, engineering, and design-oriented use cases.

Since launch, Apple has emphasized Vision Pro’s ability to run familiar desktop-class applications in spatial environments. Native support for macOS screen mirroring allows professionals to scale multiple high-resolution virtual displays, positioning the headset as an alternative to multi-monitor setups rather than a replacement for traditional computing. This approach aligns Vision Pro with production workflows already common in design studios, architecture firms, and engineering teams.

Enterprise-focused collaborations reinforce this direction. Companies including Adobe and Autodesk have introduced Vision Pro-compatible tools that extend creative and technical workflows into three-dimensional space. Rather than promoting novelty, these integrations emphasize continuity: familiar software, spatially expanded. Apple has also highlighted use cases in product design review, medical visualization, and industrial training—areas where immersive context adds measurable value.

Notably absent is aggressive consumer marketing around gaming or social experiences. Instead, Apple’s messaging increasingly mirrors that of a professional workstation vendor. Vision Pro’s precise eye tracking, low-latency hand input, and high pixel density are framed as instruments for accuracy and focus, not spectacle. This positioning aligns with Apple’s broader strategy of targeting high-margin professional markets before mass adoption.

The implication is clear. Vision Pro is less about lifestyle augmentation and more about spatial computing as infrastructure—a tool for thinking, prototyping, and collaboration. As software ecosystems mature and costs eventually come down, Apple appears to be laying groundwork for spatial computing to become a normalized layer of professional production rather than a niche entertainment format. From this perspective, Vision Pro is not an experiment. It is a quiet bet that future creative work will happen in space, not just on screens.

A new research training group funded by the German Research Foundation brings together the University of Stuttgartand the University of Freiburg to investigate smart materials and responsive building envelopes. The initiative focuses on envelope systems that adapt to environmental conditions through material behavior, with particular emphasis on bio-inspired materials and compliant mechanisms as alternatives to electronically controlled adaptive systems.

The research is situated at the intersection of architecture, engineering, and materials science, addressing long-standing challenges in how buildings manage environmental performance while limiting mechanical complexity.

Rethinking the Building Envelope as an Active System
Building envelopes have historically been designed as largely static assemblies, optimized for insulation, weather protection, and visual performance. Over the past two decades, performance-driven design approaches have introduced dynamic shading systems, kinetic facades, and automated ventilation strategies. These systems have improved environmental responsiveness but often depend on electromechanical components, centralized control systems, and ongoing maintenance.

The research training group focuses on alternative approaches to adaptive envelopes, investigating systems that respond to environmental stimuli such as humidity, temperature, and solar radiation through material behavior and structural configuration. Rather than relying on motors, sensors, or software-driven control, the research explores how responsiveness can be achieved through the intrinsic properties of materials and the way they are assembled.

Drawing on bio-inspired principles, the work examines how movement and adaptation can emerge from material composition, geometry, and compliant mechanisms. In natural systems, environmental responsiveness is often achieved without centralized control, offering models for architectural components that operate passively and predictably over time.

Smart Materials Beyond Embedded Electronics
The research training group emphasizes approaches to smart materials that foreground material behavior and structural logic rather than electronically controlled systems alone. Research areas include bio-inspired composites, anisotropic materials, and hybrid structures whose performance can be influenced during design and fabrication. Computational design plays a central role in this process, serving as a tool for modeling, simulating, and predicting how material systems respond under environmental loads. These digital methods are integrated with physical workflows, including robotic fabrication and material testing, allowing researchers to evaluate behavior across multiple scales and conditions.

By exploring material-driven responsiveness, the research investigates alternatives to mechanically complex adaptive systems. These approaches are examined for their potential relevance to durability, energy use, and architectural expression, particularly in contexts where simplicity and passive performance are critical.

Training Researchers at the Intersection of Design and Science
The research training group will support approximately 20 doctoral researchers over a five-year period beginning in 2026. The program brings together doctoral projects across architecture, engineering, materials science, and biology within a structured interdisciplinary framework, reflecting the complexity of adaptive building systems.

Doctoral researchers engage with a range of scales and methods relevant to responsive envelopes, including material development, component prototyping, computational modeling, and experimental testing. As with other DFG Research Training Groups, the program includes coordinated supervision, seminars, and collaborative research formats designed to support interdisciplinary exchange and methodological rigor.

Implications for Architecture, Industry, and Design Culture
While the research training group is focused on building envelopes, the material systems under investigation relate to broader developments in adaptive structures and bio-inspired materials research. Similar approaches are being explored across architecture, construction, and adjacent design fields where lightweight, passive, and materially efficient performance strategies are increasingly relevant.
Within architectural discourse, the research aligns with ongoing interest in material-driven responsiveness and alternatives to digitally controlled adaptive systems. By combining computational design with material experimentation, the program contributes to wider discussions about how environmental performance can be addressed through structural and material behavior rather than through additional technological layers.

The European Union is moving the AI Act from legislative framework to active enforcement faster than many platforms anticipated. While the law formally entered into force in 2024, recent actions by the European Commission signal a sharper focus on early compliance, particularly around high-risk and general-purpose AI systems. For design-led AI tools, the shift marks a transition from policy interpretation to operational consequence.

Under the AI Act, prohibitions on so-called “unacceptable risk” systems—including certain forms of biometric categorization and manipulative AI—are among the earliest provisions to apply. At the same time, obligations for general-purpose AI models, including transparency requirements and technical documentation, are being clarified ahead of the Act’s full phased rollout. National authorities are now standing up supervisory bodies, while the Commission is publishing guidance intended to standardize enforcement across member states rather than leaving interpretation entirely to local regulators.

For designers and creative technologists, the immediate impact is less about consumer-facing bans and more about upstream accountability. Tools that generate images, video, text, or audio at scale will increasingly be expected to disclose synthetic content, document training data practices at a high level, and assess whether outputs could fall into regulated risk categories. Platforms operating across the EU will need to demonstrate that compliance is embedded in product design, not bolted on at launch.

The enforcement acceleration also reshapes timelines for startups and studios relying on third-party AI models. Liability and compliance obligations can extend through the supply chain, meaning creative teams may need clearer contractual assurances from model providers. As enforcement mechanisms solidify, the AI Act is becoming a design constraint as real as accessibility standards or data protection—one that will shape how creative tools are built, deployed, and governed in Europe over the next year.

Materials have historically been treated as stable substrates—chosen, specified, and deployed to perform predictably under known conditions. That assumption is increasingly inadequate. Across architecture, textiles, and industrial design, materials are now developed to behave: to respond to environmental stimuli, to change state over time, and to be tuned through computational feedback rather than fixed recipes. Beyond aesthetics, this behavior is structural. It reshapes how performance is specified, how failure is assessed, and how responsibility is distributed across design, fabrication, and maintenance.

Rather than designing a finished form, practitioners are increasingly defining behavioral envelopes—ranges of action materials can perform under varying conditions. Intelligence, in this context, does not necessarily mean embedded computation. It often resides in geometry, composition, fabrication logic, or in the workflows used to discover and optimize material properties.

Programmable textiles beyond embedded electronics
Smart textiles were historically dominated by add-on approaches—sensors stitched into fabric, electronics laminated onto garments, and conductive threads treated primarily as carriers for hardware. Current research increasingly targets the material substrate itself. At Aalto University, projects such as SuperTextil investigate conductive, bio-based textiles using scalable graphene coatings, targeting functions such as electrical heating and sensing while reducing reliance on metals and discrete electronic components. The project foregrounds research priorities including durability, washability, and compatibility with existing textile manufacturing processes.

In parallel, Aalto’s AI-yarn project applies machine learning techniques to textile material development itself. Rather than relying solely on manual trial-and-error, researchers use computational optimization to explore how variables such as coating thickness, material composition, and processing parameters influence performance outcomes. Here, “intelligence” resides not in the fabric alone, but in the development pipeline that shapes it.

Taken together, these efforts point to a broader methodological shift. Materials are no longer specified solely through fixed recipes; they are increasingly developed through data-driven iteration, with designers working alongside scientists and engineers to define target behaviors rather than static characteristics.

Self-transforming systems and programmable matter
Among the research groups shaping contemporary discourse on programmable materials is the MIT Self-Assembly Lab, led by Skylar Tibbits. The lab’s work focuses on engineered systems designed to change shape, properties, or function in response to external stimuli such as heat, moisture, or mechanical force.

Importantly, these materials are not framed as autonomous or “alive.” They are deliberately constrained systems whose behaviors are specified in advance. The central design challenge lies in determining how and when transformation occurs, and in ensuring that such changes remain legible and predictable. This requires a form of design literacy that integrates material science, computation, and fabrication logic. For architecture and product design, the implications are significant. Walls, panels, and textiles can no longer be evaluated solely through static metrics. Performance unfolds over time, making testing, simulation, and verification integral to the design process.

Fabrication workflows catch up to material complexity
As materials become more behaviorally complex, traditional construction documentation often struggles to keep pace. Soomeen Hahm Design addresses this gap by integrating computational design, robotic fabrication, and augmented reality into construction workflows.

The studio’s Steampunk Pavilion, presented at the Tallinn Architecture Biennale, employed steam-bent timber elements assembled on site with guidance from augmented reality headsets, reducing reliance on conventional two-dimensional drawings. The project was not concerned with “smart” timber, but with adaptive fabrication—using digital tools to accommodate material variation during construction.

This approach is increasingly relevant. When materials vary, bend, or behave unpredictably, rigid documentation can become a liability. Designers must instead develop systems that absorb variation while maintaining structural integrity and spatial intent.

Designing for legibility, not novelty
The growing availability of programmable materials introduces a critical concern frequently noted in design and systems discourse: opacity. As materials respond, transform, or adapt, failures can become more difficult to diagnose, repair pathways less clear, and responsibility more diffuse. For designers working with behavioral materials, legibility becomes a central requirement. Clear stimulus–response relationships, modular systems, and transparent documentation are necessary if such materials are to move beyond experimental installations into durable, maintainable environments. From this perspective, the future of programmable matter is likely to hinge less on how much intelligence is embedded in materials than on how carefully behavior is governed—whether systems remain understandable, repairable, and ethically deployed over time.

Generative AI is now embedded across creative workflows, yet the systems that enable it—cloud platforms, data pipelines, and large-scale training cycles—carry environmental and political costs that remain largely out of view. As models expand and inference becomes ubiquitous, the material footprint of computation is becoming a critical design concern. Circular data, a framework centered on minimizing, reusing, or localizing datasets and compute cycles, offers a way to rethink how creative AI systems are built and sustained. Across the field, practitioners are exposing the hidden resource flows behind digital systems and proposing alternative infrastructures that prioritize ecological and ethical responsibility.

Making AI’s Materiality Visible
Joana Moll has long challenged the assumption that digital interactions are immaterial. Her investigations into the operational complexity and energy implications of online services—such as The Hidden Life of an Amazon User, which exposes the dozens of scripts and tracking processes triggered by a simple purchase—reveal how digital activity depends on continuous resource consumption. Her broader work examines the carbon footprint of tracking and advertising infrastructures, making visible the environmental impact of data-intensive services that typically operate out of sight.

While Moll does not focus specifically on generative AI, her research on the material costs of digital systems provides a critical foundation for understanding the environmental pressures that AI development amplifies. Large-scale models rely on electricity, specialized hardware, and distributed cloud infrastructure at every phase of their lifecycle, from dataset preparation to training and inference. Recognizing these resource flows is essential for any effort to build AI systems with a reduced ecological footprint. A circular data approach builds on this awareness by shifting attention from unbounded dataset growth toward more intentional, efficient, and context-driven data practices.

Mapping the Politics of Infrastructure
If Moll foregrounds the material costs of computation, Ingrid Burrington reveals the infrastructures that make those costs possible. Her documentation of fiber-optic cables, data centers, and the territorial footprints of networked systems offers a lens through which to understand the physical architecture that AI now depends on. Burrington’s research surfaces the civic and geopolitical dimensions of the cloud: the energy grids that power data centers, the land they occupy, and the regulatory structures that govern them.

Working with generative models, this perspective reframes cloud usage as an infrastructural choice rather than a neutral technical detail. Decisions about where data is stored, which providers host training runs, and how models interface with networks all have environmental and political consequences. Circular data extends naturally from this view, emphasizing locality, transparency, and an awareness of how computational workloads circulate across global infrastructures.

Designing Alternative Pipelines
Creative technologists are also demonstrating how pipeline design itself can counter the default logic of large-scale, cloud-dependent AI. Björn Karmann’s work offers concrete examples of this shift. Project Alias places a programmable, user-controlled device over the microphones of commercial smart speakers. It continuously emits noise to block passive listening and, when activated, plays a custom wake word that allows the speaker to receive commands. The device inserts a local layer of control between users and cloud-based assistants, reducing unnecessary data transmission and challenging the assumption that continuous upstream audio is required for usability.

Karmann’s Paragraphica likewise explores alternative relationships between data and output. Instead of using photographic light capture, the system gathers geolocation and environmental data—such as place, time, or nearby points of interest—and uses those inputs to generate an image via existing pre-trained models. While these models are still built on large-scale training, the project shifts emphasis away from dataset accumulation toward contextual sensing and recombination. For creative technologists, this approach demonstrates how constrained, situational data can open new modes of experimentation without defaulting to building or retraining large models.

These practices reflect the core principles of circular data: reduce transmission, use what is already available, localize where possible, and design workflows that avoid unnecessary compute cycles.

Data Circularity as an Ethical and Political Framework
Circular data is not only a technical proposition. Joana Varon and the Brazilian organization Coding Rights expand the conversation to include the rights-based and geopolitical implications of data practices. Their work examines how data collection and AI development reflect broader patterns of extraction, often disproportionately affecting marginalized communities in the Global South. Coding Rights’ analyses of data colonialism, governance, and AI accountability highlight how training datasets—whether newly created or repeatedly reused—shape the power dynamics embedded in computational systems.

Adopting circular approaches, this raises critical questions: What forms of reuse respect community consent? How can localized datasets support autonomy rather than reinforce inequities? As models become more powerful and accessible, sustainable AI must also engage with social and cultural dimensions of data stewardship. Circular data, approached through this lens, integrates ecological responsibility with ethical governance.

Building Toward Sustainable AI Systems
As AI becomes a routine part of creative production, the environmental and infrastructural realities of computation need to become part of the design vocabulary. Circular data offers a way to reframe the lifecycle of training and inference—reducing redundancy, rethinking storage and transfer, and encouraging the use of contextual or localized data. It signals a shift from scale-driven development toward sufficiency, adaptability, and transparency.

Moll’s environmental investigations, Burrington’s infrastructural mapping, Karmann’s speculative pipeline design, and Varon’s rights-based critique together illustrate how a more sustainable AI ecosystem might emerge. Each contributes a different but complementary insight: computation has material limits, infrastructure is political, pipelines can be reimagined, and data governance matters.

The challenge, of course, is to integrate these insights into practice—designing systems that acknowledge their dependencies, minimize their footprint, and align with broader commitments to ecological and social responsibility. Circular data does not solve the environmental pressures of AI outright, but it provides a framework for building systems that consume less, reveal more, and operate with greater accountability as generative computation continues to evolve.

The aesthetics of machine vision don’t begin with neural networks; they begin with the dataset. As image models, recommendation systems, and civic algorithms weave into daily life, artists and curators increasingly treat datasets not as neutral inputs but as archives, lenses, and infrastructures of power. Gene Kogan, Hannah Redler Hawes and the Open Data Institute’s Data as Culture programme, and James Bridle approach this from different directions, yet all foreground the dataset as a site of authorship, control, and critical inquiry.

The Dataset as Medium, Not Just Fuel
For decades, “data” functioned largely as back-end substrate—an engineering concern that remained separate from the aesthetic decisions of designers and artists. That boundary has eroded. As machine learning becomes embedded in creative practice, the dataset itself has moved into view as an expressive and conceptual medium.

Artist and programmer Gene Kogan has been central to this shift. His long-running project ml4a (Machine Learning for Art) is a public collection of open-source code, tutorials, workshops, and an in-progress textbook designed to help artists work directly with machine learning systems. The project is explicitly described as “a collection of tools and educational resources which apply techniques from machine learning to arts and creativity,” and was initiated and maintained by Kogan as an open resource between 2015 and 2021.

Kogan has reinforced this pedagogical approach through institutional teaching, most notably his Machine Learning for Artists course at New York University’s Interactive Telecommunications Program (ITP). In that course—outlined in his Medium posts and course materials—artists and designers are introduced not only to neural network architectures and generative models but also to dataset curation, data preprocessing, and labeling practices. The emphasis is clear: critical engagement with AI requires understanding how training data conditions what a model can perceive, classify, or generate.

Kogan’s later research extends this argument into collaborative, decentralized systems. His project Abraham, introduced in a paper for the NeurIPS 2019 Creativity Workshop and expanded in subsequent writing, proposes an “autonomous artificial artist” built and governed by a distributed network of contributors. Abraham is conceived as a long-term, open project in which participants help design, train, and refine a generative art agent. In this model, the dataset becomes a collectively authored cultural object—its contents determining the aesthetic scope, tendencies, and biases of the artificial “artist.”

This logic reframes a foundational idea for creative AI practice: dataset authorship is aesthetic authorship. Decisions about what the machine sees—what images, languages, histories, or bodies populate its training corpus—are not neutral technical steps. They are formative aesthetic acts, shaping the system’s expressive vocabulary as directly as any gesture, material choice, or compositional decision made by a human artist.

Data as Culture: Curating Data’s Social and Civic Aesthetics
If Kogan opens up the technical stack, Hannah Redler Hawes builds institutional infrastructures around data as cultural material. As Director of the Data as Culture art programme at the Open Data Institute (ODI), she works at the intersection of contemporary art, data, and the social and ethical questions raised by digital technologies.

Data as Culture has, since 2012, commissioned and exhibited artworks that use data as an art material while interrogating its social and ethical implications. The ODI frames the programme as a way to engage diverse audiences with artists and works “that use data as an art material,” situating artistic practice alongside its broader work on open and trustworthy data ecosystems. Early commissions—documented in ODI catalogues and talks for the 2014 Data as Culture exhibition—focused explicitly on surveillance, privacy, and personal data, using pneumatic machines, satellite imagery, data-collection performances, and “knitted data discrepancies” to explore how open data and monitoring intersect with everyday life.

A key anchor for the programme’s long-term trajectory is Julie Freeman’s online work We Need Us (2014–ongoing), co-commissioned by the ODI and The Space and later exhibited with NEoN Digital Arts. The piece pulls live metadata—data about data—from the citizen science platform Zooniverse, transforming the collective activity of over a million volunteers into a dynamic environment of animated forms and sound. Rather than visualising scientific results, We Need Us focuses on the rhythms, density, and volatility of participation itself, exploring what Freeman has described as “the life of data.”

More recently, Freeman’s Allusive Protocols (2023), commissioned by Data as Culture at the ODI with support from Invisible Dust, uses kinetic, data-driven sculptures to reflect on networked infrastructure and the fragility of connectivity. The work responds directly to ODI research on power and diplomacy in data ecosystems, considering how control over protocols and networks underpins contemporary infrastructure and the distribution of power.

Across more than a decade, Data as Culture has presented over 100 works—including 27 commissions across 11 exhibitions and partnerships—and has developed research-led themes such as Copy That?, which asks how “true” the “data you” really is and how many versions of that self exist online. This is curation as infrastructural critique: datasets are treated as civic artefacts, and their aestheticisation is tied to questions of accountability, access, and governance.

James Bridle: When Datasets Become Governance
Artist and writer James Bridle pushes the aesthetics of the dataset into the domain of political technology. Known for coining the “New Aesthetic” and for books such as New Dark Age: Technology and the End of the Future (Verso, 2018) and Ways of Being (2022), Bridle traces how computation, automation, and data infrastructures reorganise perception and power.

In the solo exhibition Failing to Distinguish Between a Tractor Trailer and the Bright White Sky at NOME Gallery in Berlin (2017), Bridle took the self-driving car as a central motif. The title comes from an accident report into a fatal Tesla crash in which the vehicle’s Autopilot system failed to distinguish the white side of a tractor-trailer against a brightly lit sky—an incident widely reported as exposing limits in the car’s sensors and system design.

The exhibition gathered works that probe machine learning and machine vision: how autonomous driving systems are trained, how they “see” the world, and how those ways of seeing can fail. Bridle worked with “software and geography” to create components for his own self-driving car—an autonomous vehicle “which learns to get lost”—using freely available tools and research papers. Through installations, image series, and the video essay Gradient Ascent, which documents test drives on Mount Parnassus, the exhibition linked technical processes such as optimisation and training to questions of labour, responsibility, and the political opacity of complex systems.

Autonomous Trap 001 (2017) condenses these concerns into a single scene: a car encircled by a salt line and road markings laid out as inward-facing “no entry” signs. As Bridle’s own description notes, the work uses ground glyphs “to trap autonomous vehicles using ‘no entry’ and other glyphs”; the car, programmed to obey those markings, cannot leave the circle without breaking its encoded rules. The trap here is not magical but infrastructural—an environmental configuration that reveals how rule-bound systems can be manipulated, and how much agency is ceded to code, training regimes, and the datasets on which they depend.

In New Dark Age and Ways of Being, Bridle extends this critique beyond the gallery. Both books argue that contemporary knowledge is increasingly routed through computational systems—search engines, models, and platforms—that are trained on vast, uneven datasets and embedded in economic and political structures. Rather than treating data as neutral input, Bridle shows how data-driven infrastructures decide what is visible, what is legible as “information,” and whose experience is excluded. In that sense, datasets become epistemological tools: they shape what counts as knowledge for human institutions and for machines alike, and their blind spots often mirror the social and environmental hierarchies in which they are produced.

Curating the Machine’s Eye
Taken together, these practices point to a shared cultural shift. Kogan equips artists and designers to work directly with training data and models, treating machine learning literacy as a creative skill set. Redler Hawes and the Data as Culture programme develop public-facing contexts in which data-driven artworks are exhibited, questioned, and contextualised—from live metadata works such as We Need Us to commissions that address energy systems, connectivity, and the power dynamics of networked infrastructures. Bridle, meanwhile, shows how training data, sensor inputs, and encoded rules can function like policy in practice, influencing what systems are able to perceive, how they behave, and where their failures become visible.

For a design-savvy, technologically fluent audience, this is the real frontier of AI aesthetics. Style transfer and text-to-image prompts represent only the surface layer. The deeper work lies in curating the machine’s eye: determining which archives matter, which signals count as noise, whose worlds become legible to computation, and how those decisions are opened up to scrutiny, collaboration, and repair. The aesthetics of the dataset are, ultimately, the aesthetics of the systems we build—and the futures we enable through them.

Urban design is shifting away from a human-exclusive worldview. Climate instability, biodiversity collapse, and the spread of networked sensors are exposing the limits of cities built solely for human optimization. A new model is emerging—one where organisms, machine systems, and environmental processes become co-authors of urban space. This is not an aesthetic turn toward biomorphism; it is a strategic realignment of how infrastructure works, how it is maintained, and who it serves.

Across landscape architecture, urban technology, civic art, and architectural research, a set of practitioners is building systems that treat oysters, air particulates, seeds, sensors, and autonomous digital agents as operational components of the city. Their work signals a future in which urban resilience depends on symbiosis, feedback, and distributed intelligence rather than control and centralization.

Ecological and Atmospheric Systems as Infrastructure
Landscape architect Kate Orff and her studio SCAPE exemplify how ecological processes can function as core infrastructure. Projects like Living Breakwaters demonstrate how engineered structures can be designed for biological colonization, allowing oysters to mitigate wave energy, strengthen shorelines, and improve water quality. This model challenges the convention of static hard infrastructure by substituting it with evolving ecological systems that require long-term stewardship rather than one-time construction.

On a parallel track, Studio Roosegaarde explores how atmospheric sensing can become a public interface. The Smog Free Tower, for example, uses ionization to remove particulate matter while transforming air-quality metrics into a visible, spatial experience. Here, environmental data is not a background diagnostic but an active element that shapes public awareness and behavior. Together, these approaches illustrate how biological and technological systems can operate as co-infrastructures—each responding to conditions humans alone cannot manage.

Multispecies Participation and Networked Governance
The more-than-human city is not solely a technological or ecological construct; it is also a civic one. The collective Futurefarmers builds participatory frameworks that link everyday urban life to ecological and agricultural systems. Seed libraries, mobile research units, and collaborative workshops demonstrate how public engagement becomes a critical layer of urban metabolism. Their work shows that multispecies urbanism depends not just on environmental engineering but on shared governance, ecological literacy, and cultural practices capable of sustaining long-term ecological care.

Meanwhile, architectural research studio Space Caviar addresses the other major non-human actor embedded in cities: digital infrastructure. Their investigations into sensor networks, supply-chain logistics, algorithmic governance, and platform-based housing reveal how non-human digital systems influence spatial access and resource distribution. These systems operate alongside biological actors—shaping flows, mediating decisions, and generating new forms of urban governance.

Toward Symbiotic Urbanism
What emerges from these practices is not a single vision but a shared operational shift. Cities are becoming ecological-technological hybrids where oysters, particulate sensors, microbial systems, autonomous agents, and human communities co-produce resilience. Designing for this reality requires architects, technologists, and planners to orchestrate relationships rather than impose fixed solutions. It also demands regulatory frameworks capable of supporting living, adapting, and sensing infrastructures that exceed traditional engineering categories.

The more-than-human city takes shape through these experiments: ecological infrastructures that grow stronger through biological activity, public spaces that react to atmospheric signals, civic practices that cultivate multispecies responsibility, and architectural research that makes algorithmic systems visible. As these models scale, the city becomes not a human-only environment but a shared platform for biological, technological, and cultural interaction—one that is better equipped for the uncertainties ahead.

Apple has rolled out an updated hardware revision of its Vision Pro headset, now powered by the company’s M5 chip. The upgrade introduces a next-generation 10-core CPU, more powerful GPU, and enhanced Neural Engine under a 3 nm process — improvements that collectively yield sharper visuals, smoother performance, and accelerated AI tasks.

Among the headline specifications: Micro-OLED displays rendering 10 percent more pixels than the prior model, and an increased refresh rate of up to 120 Hz to reduce motion blur during passthrough and virtual-display use. Apple also ships the headset with a new “Dual Knit Band,” designed for improved comfort and stability. 

Yet, despite the technical leap, the reception remains cautiously measured. According to Ars Technica’s recent review, while the M5-equipped Vision Pro delivers smoother performance — especially for graphics-intensive and AI-powered spatial apps — its value proposition remains constrained by persistent issues: limited content catalog, high price point, and ergonomic trade-offs relative to lighter AR glasses. 

For designers, developers, and creative professionals, the M5 refresh improves the headset’s viability for pro workflows: real-time 3D visualization, spatial UI, and AI-assisted design tools may now run more reliably. But as the market pivots toward accessibility and portability, Vision Pro still straddles an uneasy middle ground: powerful, yet niche.

Micro-robots are moving a step closer to real-world use as several new peer-reviewed studies published in 2024 and 2025 report advances in propulsion, power delivery, and in-vivo navigation—longstanding bottlenecks that have limited the field to controlled laboratory tests.

One of the most cited developments comes from Cornell University, where optical microrobots powered by on-chip photovoltaics demonstrated improved untethered motion when steered by focused laser light. The system, developed by the Paul McEuen and Itai Cohen groups and reported in Science Robotics and Nature, shows how cell-scale robots can operate without wired power or bulky magnetic equipment. At the University of Pennsylvania, researchers in the GRASP Lab have advanced magnetically actuated micro-swimmers capable of navigating viscous fluid channels with greater stability than earlier designs. Their 2024 Science Robotics paper details improved steering precision using rotating magnetic fields—an increment that brings microrobots closer to functioning in complex biological environments.

Biomedical applications remain a driving force. Multiple groups, including teams at ETH Zurich and the Max Planck Institute for Intelligent Systems, have recently demonstrated micro-scale devices performing targeted transport tasks in animal models. None of these platforms are approved for human clinical use, but early in-vivo studies show controlled motion with minimal tissue disruption.

Environmental testing is also expanding, primarily through lab-based micro-swarm prototypes for pollutant sensing and microplastic capture. Despite momentum, major hurdles persist: power constraints, long-term biocompatibility, and the difficulty of manufacturing large, coordinated swarms. Still, the growing volume of reproducible results suggests micro-robotics is shifting from speculative prototypes toward application-driven systems with implications for medicine, environmental monitoring, and materials inspection.

Design has long borrowed from biology, but the current shift goes further than biomimicry or nature-inspired form. A growing movement toward regenerative intelligence positions living systems as collaborators in design—systems that respond, adapt, and evolve rather than remain fixed. The focus is no longer on extracting efficiency from ecological forms; it is on working with ecological processes to build resilience, feedback-driven infrastructures, and multispecies forms of cohabitation.

Across architecture, materials research, and landscape design, a set of practitioners is reframing intelligence itself as ecological: distributed, cyclical, and interdependent. Their work suggests a structural transition in design culture, one that moves away from optimization as a dominant paradigm and toward strategies that regenerate the conditions that make environments livable.

From Optimization to Ecological Feedback
Most design systems still operate around the logic of optimization—streamlining resource flows, reducing waste, or maximizing performance. These goals matter, but they often freeze environments into static configurations, leaving little capacity for adaptation. Regenerative intelligence shifts the emphasis to systems that evolve through feedback, where materials and environments adjust to external pressures rather than resist them.

This perspective aligns design with ecological realities. Forests redistribute nutrients during stress; wetlands modulate water flows; coral reefs build architectures through interaction, not control. Regenerative intelligence treats these processes as operational models rather than metaphors.

In practice, this means designing infrastructures that learn from disturbances, not simply withstand them. It also means integrating biological actors—microbes, plants, algae, or soil systems—into built environments in ways that allow for ongoing negotiation. Instead of optimizing for a narrow set of parameters, regenerative design embraces variability, succession, and transformation.
Studio Ossidiana’s practice embodies this shift. Their work often centers on material ecologies shaped by geological, biological, and cultural dynamics—projects where matter is not inert but part of a responsive system. Their installations and landscapes explore how materials behave over time, interacting with water, light, and other species rather than holding a fixed form. By foregrounding change instead of stability, their work argues for a design approach that accepts—and leverages—environmental flux.

Designing with Living Systems, Not Around Them
Regenerative intelligence also reframes the role of living systems in design. Rather than treating biological matter as a resource or aesthetic reference, it positions organisms as active participants whose agency shapes the outcome.

Anna Heringer’s architectural practice is a clear example. Her work with earthen construction demonstrates how local ecosystems, communities, and materials can collaborate to form resilient structures. Earthen buildings respond to humidity, temperature, and repair differently than industrial materials. They invite maintenance as a shared cultural practice and emphasize the social and ecological dimensions of building. Her projects show how regenerative principles—circularity, low-energy processes, and local ecological alignment—can produce architecture that strengthens rather than depletes environmental systems.

This orientation toward ecologically responsive processes is also reflected in the emerging field of biofabrication, where materials such as mycelium, algae, or bacterial cellulose become structural elements. These materials grow, metabolize, and decay according to environmental conditions. Designing with them requires accommodating biological agency, not imposing rigid parameters. The result is a move away from linear manufacturing toward metabolic cycles. Regenerative intelligence treats these cycles not as constraints but as sources of intelligence—systems that sense, respond, and heal. As climate pressures increase, this form of intelligence becomes a critical design asset.

Mapping Multispecies Realities
If regenerative intelligence asks designers to collaborate with living systems, it also requires a deeper understanding of the infrastructures that shape ecological relations. Feral Atlas, led by a collective of researchers and artists and published by Stanford University Press, offers one of the most comprehensive recent attempts to map these relationships. The project documents “feral” entities—organisms, infrastructures, and hybrid ecologies that have emerged from human systems but exceed human control.

Rather than presenting a stable view of environmental impact, Feral Atlas highlights the micro-politics and material entanglements that shape contemporary ecologies. It underscores how industrial infrastructures, colonial legacies, and global supply chains have produced hybrid ecosystems that cannot be reduced to either nature or technology. This form of mapping expands the scope of regenerative intelligence by revealing where design interventions need to occur and where unintended ecological actors already operate. By articulating how systems behave at multiple scales—from microbial interactions to geopolitical infrastructures—Feral Atlas offers a framework for designers working toward regenerative outcomes. It shows that resilience is not simply a material property; it is produced through entangled relationships.

Building for Resilience, Not Permanence
A critical component of regenerative intelligence is the recognition that resilience emerges from diversity, redundancy, and continuous feedback. It stands in contrast to the architectural pursuit of permanence or the technological pursuit of control. Instead of designing structures that resist change, regenerative systems absorb, redirect, and adapt to it.

Studio Ossidiana’s landscapes shift with tidal rhythms and seasonal variations. Heringer’s buildings invite maintenance as a living process. Feral Atlas reveals the way unintended ecologies form in the gaps of infrastructure. Each of these practices challenges the assumption that stability is the ultimate design goal.
Regenerative intelligence suggests that the environments most capable of withstanding climate uncertainty are the ones that can reorganize themselves. These systems are not simply efficient; they are alive in the sense that they maintain relationships, exchange matter and energy, and renew themselves over time.

For designers, this translates into a new set of priorities: designing for feedback, fostering ecological co-production, and planning for ongoing transformation. The shift is subtle but significant. Instead of asking how design can control environments, the question becomes how design can participate in ecological regeneration. As more practices engage with living systems—through biofabrication, community-embedded construction, feedback-driven landscapes, and multispecies mapping—regenerative intelligence is emerging as a foundational lens for designing the future.

Interfaces now operate as policy. Every toggle, dropdown, and confirmation flow encodes a worldview—sometimes intentionally, often by default. As digital systems scale across finance, civic infrastructure, and everyday communication, interaction design has become an engine of soft governance. Not the legislative kind, but the procedural rules that determine how people coordinate, what actions are possible, and who gets to participate.

This shift isn’t ideological. It’s structural. Distributed networks depend on UI scaffolding to make complexity manageable. Corporate platforms use interface architecture to frame what counts as consent. Civic tools convert municipal processes into clickable workflows that either expand or shrink public agency. Governance is migrating from law to layout. The question is no longer whether design shapes power. It’s how deliberately we’re willing to treat it as a governance layer.

Interfaces as Protocols: The Narrow Bandwidth of Decentralization
Blockchain systems make this visible. Ethereum’s mechanics are abstract, but wallets, dashboards, and explorers function as translation layers that define how the network is experienced. These aren’t neutral windows—they’re governance surfaces.

Designer and researcher Kei Kreutler describes how DAO interfaces shape the mental models of collective ownership. The metaphors—treasuries rendered as financial dashboards, voting as a slider bar, membership as a colored graph—preset expectations about what participation means. When the UI compresses governance into a single action, it narrows the imagination of what governance could be.

Artist and developer Sarah Friend extends this further by designing systems where participation requires sustained care or social negotiation. Her work makes visible how UI mechanics can enforce norms: a button that expires, a screen that requires collaboration, an interaction that demands accountability. The interface becomes a rulebook, not an illustration.

Decentralization often promises openness, but UI constraints determine what “open” actually looks like. A multisig workflow that’s confusing or rigid reshapes who can coordinate. A wallet recovery path that requires technical fluency alters who feels safe participating. Governance, in practice, becomes whatever the interface allows.

Corporate UX and the Quiet Politics of Permission
At the opposite end of the spectrum sit tightly controlled ecosystems. Here, governance emerges not through collective coordination but through UI-defined boundaries. Apple’s privacy settings are a prime example. When the company pushed tracking permissions into explicit pop-ups, mobile advertising economics shifted almost instantly. A modal window became a policy instrument powerful enough to reroute revenue across entire industries. That’s interface as regulation, executed through design rather than legislation. But Apple’s patterns also show the limits of corporate-defined agency. Whether a user selects “allow once,” “allow while using the app,” or “don’t allow,” the interface frames privacy as a series of choices that fit Apple’s architecture—and Apple’s business priorities. What isn’t presented isn’t considered.

Designer and researcher Caroline Sinders has examined how reporting tools and moderation flows on major platforms codify permissible forms of expression. The labels in a dropdown menu define the boundaries of harm. The number of steps in a submission process influences whether people speak up. These structures aren’t decorative; they’re forms of governance embedded in UX. Corporate UX, then, operates as a policy membrane. It decides how much friction users encounter, which decisions require confirmation, and which are silently automated. Each choice carries political weight.

Civic Interfaces and the Architecture of Participation
Cities are increasingly governed through portals, dashboards, and consultation tools. Civic tech aims to make processes legible, but the interface often becomes the primary gatekeeper.

Architect and urbanist Pablo Sendra, alongside sociologist Richard Sennett, has explored how participatory systems fail when they simulate openness without enabling actual influence. The translation of civic processes into interface elements—the comment box, the vote button, the feedback map—can either democratize decision-making or reduce it to symbolic input.

Researcher Chris Speed’s work in digital place-making shows how design choices affect local agency. Interfaces can foreground community narratives, or they can abstract them into datasets. They can amplify local knowledge, or flatten it in the name of efficiency. The UI becomes a mediator between public values and institutional workflows. Net art pioneer Olia Lialina offers another important legacy. Her advocacy for the expressive, empowered “user” stands as a counter-model to contemporary platforms that treat individuals as endpoints. Civic interfaces that draw from this lineage expand agency; those that ignore it risk eroding it. In each case, design decides which publics count.

Designing Power: Toward More Negotiable Systems
When governance moves into the interface layer, design gains new responsibilities. Interaction patterns become policy instruments. Screen flows become behavioral constraints. Defaults become political positions.

The challenge is not to eliminate this power—governance is unavoidable—but to design for systems that remain legible, adjustable, and open to critique. For decentralized networks, that means interfaces that expose rather than conceal complexity, allowing communities to renegotiate structures. For corporate platforms, it means acknowledging that privacy, security, and agency are shaped through design decisions, not legal disclaimers. For civic systems, it means building UI architectures that expand—not compress—public influence. Interfaces are no longer front ends. They are governance engines. And as more decisions migrate into digital systems, designers increasingly shape the rules of engagement. The future of governance will be built in screens, flows, and interaction models. The question is whether we build them with accountability—or simply inherit them as default infrastructure.

Google has introduced Antigravity, a new development environment built around the capabilities of Gemini 3. Rather than functioning as a traditional code assistant, Antigravity positions autonomous agents as active participants in the development process—capable of writing code, running terminals, navigating a browser, and generating verification artifacts within a single interface.

The platform offers two working modes. Editor View mirrors a conventional IDE but adds a persistent agent sidebar for planning and task execution. Manager View expands this into a coordination layer where developers can supervise multiple agents working across different workspaces. The system is designed for structured delegation: users outline a task, agents carry it out in stages, and Antigravity records the process through task lists, screenshots, and browser captures.

While Gemini 3 is the default engine, Antigravity also supports selected third-party and open-source models, signaling Google’s intent to position the environment as a flexible platform rather than a model-locked tool. Antigravity launches in public preview with generous rate limits, giving developers early access while allowing Google to evaluate how autonomous agents perform in real-world conditions. Early feedback will likely influence how agent autonomy evolves.

For now, Antigravity’s impact is practical. It accelerates setup tasks, testing routines, documentation lookup, and iterative coding. Its agent-first structure introduces a more traceable and coordinated approach to AI-assisted development than previous generations of coding assistants.

When Affinity relaunched as a unified, no-charge design application this week, it sent a ripple through the creative-software market. The shift from a paid-license model to a “free-forever” core app—while gating select AI features behind a separate subscription—places the product squarely in competition with Adobe’s subscription ecosystem.

The new Affinity app merges photo editing, illustration, and layout tools into a single workspace, integrating much of the functionality of Affinity Photo, Designer, and Publisher. Designers are already testing whether it can serve as a daily-driver alternative to Photoshop, Illustrator, or InDesign. While many welcome its zero-cost entry point, others remain cautious about how the freemium model—and the reliance on premium AI tools—will sustain long-term development and professional reliability.

By offering a professional design suite at no cost, Affinity is challenging long-held assumptions about creative-tool economics. The move pressures legacy platforms to justify subscription pricing and raises broader questions for designers navigating increasingly AI-augmented workflows: Who controls the toolchain? Who owns the data? And what does creative autonomy mean when the platform itself is free but monetized elsewhere?

Whether Affinity’s strategy sparks a broader shift or simply establishes a loyal open-access community, it underscores a pivotal shift in how creative-technology companies compete—not just on features, but on access, interoperability, and trust.

A team led by researchers at Massachusetts Institute of Technology (MIT) has developed a new algorithmic layer that harnesses generative-AI techniques to design quantum-capable materials — shifting generative art beyond pixels and into atomic architectures.

Traditional generative-AI models in materials science excel at proposing vast libraries of compounds but struggle to home in on the atomic lattice geometries that yield quantum phenomena like spin liquids or flat-band magnetism. The MIT group’s solution, dubbed “SCIGEN,” embeds physics-based constraints into diffusion-model workflows, steering the generation process toward crystal motifs — for example, Kagome or Lieb lattices — that foster exotic magnetic behaviour. 

In practice, the pipeline generated over 10 million candidate structures, sorted them via stability filters and high-fidelity simulations, and ultimately yielded at least two synthesized compounds whose measured magnetic characteristics matched predictions.The result offers a blueprint for bridging generative design, materials science and quantum technology.

MIT’s SCIGEN research marks a genuine inflection point. The algorithm doesn’t just generate images or interface layouts — it generates atomic structures engineered for specific physical behaviours. By embedding structural and physics-based constraints into diffusion-model workflows, the system effectively lets AI propose stable crystal frameworks with target magnetic or quantum properties. MIT researchers suggest that such workflows could soon integrate with autonomous lab systems, enabling an end-to-end pipeline from generative model to synthesized material — a tangible step toward AI-assisted quantum materials engineering.

Industrial designer Ross Lovegrove has teamed with Google DeepMind to chart a new territory where generative-AI meets industrial prototyping. According to a blog post from the research lab, the collaboration involved fine-tuning a text-to-image model (based on Imagen) with a curated dataset of Lovegrove’s sketches and design lexicon — enabling the system to internalise his signature biomorphic forms.

The project selected a chair — a conventional object with defined function but open formal possibilities — as the testbed. Lovegrove’s studio, in concert with DeepMind engineers, developed a design vocabulary and deliberately avoided the word “chair” in prompts to coax novel outputs. From concept output to physical manifestation, the workflow passed through AI generation, material and viewpoint exploration using Google’s Gemini tools, and then to metal 3D printing.

The outcome: a tangible prototype that embodies Lovegrove’s aesthetic, yet extends it through human-machine co-creation. The case indicates a shift in industrial-design workflows — not AI replacing the designer, but augmenting the designer’s generative capacity. As Lovegrove puts it, “For me, the final result transcends the whole debate on design. It shows us that AI can bring something unique and extraordinary to the process.”

For design professionals, this signals that mastering prompt-crafting, semantic modelling of a design language, and physical-manufacturing pipelines are becoming core skills. In operational terms, the experiment maps a workflow: dataset → fine-tuned model → prompt-driven generation → CAD refinement → additive fabrication — a template for future studio-AI collaborations.

Over the past two decades, generative design and digital manufacturing have reshaped how furniture and material systems come into being. What began as a series of experimental prototypes has evolved into a mature design ecosystem—one driven by algorithmic logic, simulation, and robotic fabrication. Form now emerges not from sketch or style but from computation: structures refined through topology optimization, additive manufacturing, and data-driven performance criteria. What was once an industrial process of assembly has become one of emergence.

Generative Intelligence and Material Behavior
At its core, generative design marks a shift in authorship. Instead of drawing fixed forms, designers define parameters—constraints, materials, forces, and goals—and allow algorithms to iterate through thousands of potential solutions. Topology optimization, originally developed for aerospace and automotive engineering, has moved into product and furniture design. It minimizes material while maintaining strength, yielding forms that resemble natural growth patterns: lattices, branches, and bone-like geometries that are mathematically efficient and visually organic.

A landmark example is the AI Chair by Philippe Starck, developed with Kartell and Autodesk. Using Autodesk’s generative design software, Starck set design goals for comfort, stability, and minimal material use. The software produced multiple configurations optimized for those parameters, from which Starck selected and refined the final version. The resulting skeletal form was manufactured through additive processes before being adapted for production. While marketed as co-designed with artificial intelligence, the project more accurately illustrates algorithmic generativity—a designer guiding computation toward structural intelligence.

Joris Laarman’s Bone Chair (2006), created with engineers from BMW’s Rapid Prototyping Division, also embodies this synthesis of algorithmic form and fabrication. Based on Claus Mattheck’s bone-growth simulation, the algorithm removed material where it wasn’t structurally necessary, mimicking biological efficiency. The result became a milestone in computational furniture design and a statement on design as a negotiation between biology, engineering, and aesthetics.

Today, these early experiments have evolved into full-fledged design workflows. Tools such as nTopology and Autodesk Fusion 360 integrate simulation, performance analysis, and generative modeling within a single environment. Designers can test loads, material behavior, and fabrication parameters in near real time, allowing form to evolve with mechanical data. The studio increasingly resembles a hybrid lab—where intuition, code, and material computation co-produce new forms of making.

Printing Structures, Not Surfaces
Additive manufacturing has matured from prototyping tool to production method. For furniture designers, it enables geometries that traditional fabrication cannot achieve, especially when paired with generative modeling. The result is not just new aesthetics but new material logics.

 Hot Wire Extensions, originally developed by Fabio Hendry and Christoph Hauf at the former Studio ilio, exemplifies this shift. The London-based designers introduced a process of heating recycled nylon powder with a custom hot-wire rig to create furniture with a porous, coral-like texture. Rather than concealing the irregularities of additive manufacturing, the designers amplified them—turning digital imperfection into material expression.

By contrast, The New Raw, a Rotterdam-based studio founded by Foteini Setaki and Panos Sakkas, explores large-scale robotic printing with recycled plastics. It's Print Your City! initiative invites citizens to transform plastic waste into urban furniture—benches, planters, and playground structures—via parametric design software. Users customize form and color before fabrication, revealing a direct feedback loop between data, participation, and physical outcome.

Hybrid fabrication pushes this logic further. Zaha Hadid Architects’ Striatus Bridge (2021), designed with ETH Zurich’s Block Research Group and Incremental3D, demonstrates how robotic extrusion and structural modeling can produce complex geometries assembled without adhesives. Though architectural in scale, it illustrates a principle that furniture designers are adopting for modular joints and adaptive assemblies—where variation within constraint replaces repetition as the guiding logic.

Two decades after these early explorations, generative furniture design has entered a new phase. Studios such as Nagami and Gantri are extending these principles into scalable production systems, combining computational modeling with circular materials and distributed manufacturing. Nagami’s Circular Lab (2023–24) advances closed-loop polymer research and AI-driven form optimization, while Gantri continues to scale 3D-printed lighting production through parametric customization and sustainable, plant-based materials. Together, these developments signal a shift from isolated experimentation to system-level transformation—where generative principles are embedded within industrial production and everyday design.

Data as Craft
Digital manufacturing often seems to replace the hand with the machine. Yet across contemporary practice, it has evolved into a new kind of craftsmanship—a dialogue between data and material. The designer’s role shifts from manual execution to curatorial intervention: defining parameters, refining algorithmic outcomes, and interpreting feedback between digital process and physical result.

Patrick Jouin’s Solid C2 Chair (2004) exemplifies the early days of this negotiation. Produced through selective laser sintering (SLS) in collaboration with Materialise, it was among the first pieces of 3D-printed furniture to move beyond the prototype stage. Jouin digitally modeled the chair as part of his Solid Collection, exploring how additive manufacturing could produce seamless, complex geometries impossible to achieve through traditional molding or assembly. The resulting piece—a single, lightweight form with intricate internal structures—demonstrated how digital fabrication could extend, rather than replace, the designer’s craft.

The Growth Chair by Mathias Bengtsson series extends this computational materialism further. Developed through custom software simulating cellular growth, the works are fabricated in composite materials or cast from 3D-printed molds. Bengtsson treats code as a living agent, allowing form to evolve through virtual processes analogous to natural morphogenesis. The resulting structures blur the line between digital automation and artisanal authorship, showing computation as an extension—not an erasure—of craft.

In parallel, Layer Design (2023) is developing AI-assisted seating prototypes generated through cloud-based simulation. These projects suggest how human–machine co-creation is entering mainstream industrial design, translating the craft of computational iteration into adaptable, mass-customizable systems.

Toward Adaptive Fabrication
What connects these projects is not the tools themselves but the paradigm behind them: a move from fixed design to adaptive systems. Generative algorithms, simulation engines, and robotic fabrication all operate through feedback loops, iteratively responding to data to refine structure, performance, and material behavior. This convergence enables a new kind of design ecology in which material distribution, assembly, and even end-of-life scenarios are driven by performance metrics rather than static convention.

At ETH Zurich’s Digital Building Technologies (DBT) group, researchers are advancing these principles at architectural scales, using 3D-printed molds and computational workflows to optimize concrete components for strength, efficiency, and form. Projects such as Smart Slab (2018) and Concrete Choreography (2019) demonstrate how algorithmic design and robotic fabrication can minimize material while maintaining structural integrity. Though developed for buildings and pavilions, these methods point toward near-future applications in furniture and product design—where objects could be locally printed on demand and tuned to specific users, materials, and contexts.

Generative design is not merely producing new aesthetics; it is redefining how designers conceive of making itself. The emphasis shifts from static form-giving to dynamic system-tuning—from the object as an endpoint to design as an evolving process. Data becomes a design material, shaping how density, mass, and performance are distributed across a structure. The furniture that emerges from these workflows is not simply digitally fabricated—it is computationally conceived, the product of continuous negotiation between algorithm, material, and maker.

The global market for generative-design tools could reach US $8.45 billion by 2029, according to a new forecast from The Business Research Company (TBRC). The report projects a compound annual growth rate of about 17 percent, driven by advances in artificial intelligence, cloud computing, and simulation technologies.

Generative design—once a niche within engineering and architecture—is now influencing manufacturing, product development, and creative workflows. The technology uses algorithms to explore thousands of design iterations, optimizing for parameters such as performance, material efficiency, or cost. TBRC notes that adoption is accelerating across industries from automotive and aerospace to construction and consumer products, particularly with the rise of AI-assisted modeling and additive manufacturing.

Other firms offer different market projections, reflecting varying definitions. Straits Research estimates the generative-design market could reach US $11.87 billion by 2033 (CAGR ~16 % from 2025). Meanwhile, Fortune Business Insights projects a smaller figure of US $926 million by 2030 (CAGR ~20.1 % from 2023), reflecting its narrower scope.

For designers, the upward trend points to a deeper shift in authorship and workflow. As algorithms evolve from assistants to collaborators, generative design is becoming less a technical method than a creative infrastructure—one reshaping how form, function, and computation converge across disciplines.

Adobe Inc. has unveiled AI Foundry, a new platform enabling enterprises to build custom generative-AI models tuned to their own brand assets, tone, and creative identity. Announced in October 2025, the initiative extends the company’s Firefly model family into an enterprise-grade framework designed to align with creative workflows across Adobe Creative Cloud and Experience Cloud.

Rather than a self-service platform, AI Foundry operates as a managed collaboration between Adobe and enterprise clients. The company works directly with organizations to develop deeply tuned models using proprietary brand data—ensuring results that reflect specific visual languages and corporate identities. These customized Firefly derivatives can generate brand-consistent images, videos, and 3D assets while maintaining legal and ethical safeguards around data provenance and intellectual property.

For creative departments, the implications are substantial. Design teams now have access to bespoke generative systems that are tuned to their brand’s visual vocabulary, narrative tone, and campaign parameters. Rather than generic tools, this offering positions AI Foundry as an extension of the creative stack—one that collaborates with human teams and Adobe experts alike, and is built to scale content production while preserving brand integrity and creative oversight.

The launch signals a broader shift in creative workflows: design teams are increasingly acting as orchestrators, leveraging brand-specific AI models rather than generic tools. With AI Foundry, Adobe is betting that enterprise creativity will evolve around customized AI systems built to each organization’s unique identity.

The boundaries between art, design, media, and technology are dissolving. Once distinct disciplines now operate within an expanded field where digital systems and material environments merge into new forms of hybrid creativity. The result is not simply a digital transformation but a shift in how creative practice itself is conceived—distributed across software and sensors, data and gesture, virtual and physical space.

From interactive installations and robotic sculpture to generative architecture and responsive environments, today’s design-tech landscape is defined by convergence. Artists and designers are no longer working with technology—they’re working through it, using computation and connectivity as the raw material of expression.

Hybrid Interfaces
The most compelling examples of this continuum treat technology not as a tool but as a collaborator. Studio Drift’s Fragile Future series, for instance, merges dandelion seeds and LED circuits into delicate light sculptures that fuse natural fragility with technological precision. The work makes tangible the interdependence of organic and artificial systems—an aesthetic logic echoed in their recent drone-based performances like Franchise Freedom, where hundreds of autonomous drones mimic the collective behavior of starlings in flight.

Similarly, Random International’s Rain Room (2012–ongoing) transforms environmental control into an embodied interface. Using motion sensors and real-time data processing, the installation allows visitors to walk through falling rain without getting wet, collapsing the distinction between environmental experience and algorithmic control. The body becomes a responsive node within a cyber-physical system.

Such works signal a broader trend: interaction as material. Rather than static displays, creative systems now respond dynamically to movement, sound, or data. This is evident in the installations of Refik Anadol, whose Machine Hallucinations series translates massive datasets—ranging from architectural archives to satellite imagery—into immersive, AI-generated visual environments. These works are not representations but processes, blending computational abstraction with sensory immersion.

The Living Circuit
In parallel, designers and technologists are exploring how biological and synthetic systems might inform one another. MIT’s Mediated Matter Group, led by Neri Oxman, pioneered this approach through projects like Aguahoja (2018), a series of biodegradable structures fabricated from chitin, cellulose, and pectin using robotic extrusion. The result is a material ecology that repositions design as a living interface between nature and computation.

This bio-digital approach extends to projects like Hylozoic Ground by Philip Beesley, where sensor-embedded structures appear to “breathe” in response to human presence. Here, architecture behaves as an organism—a responsive ecosystem driven by feedback loops between machine intelligence and environmental stimuli.

Across these works, technology becomes less about efficiency and more about emergence. Designers operate as system choreographers, setting up conditions for interaction and adaptation rather than dictating fixed outcomes. The resulting artefacts—whether soft robotics, kinetic architecture, or responsive textiles—embody a new kind of intelligence, one that is distributed, embodied, and situational.

Immersive Systems
Media artists have long experimented with immersive environments, but recent advances in sensing, real-time rendering, and spatial computing are expanding the field’s vocabulary. Tokyo-based collective teamLab has become a global reference point for this mode of practice. Their installations—such as Borderless in Tokyo and Planets in Osaka—dissolve the physical boundaries between viewer and artwork. Motion tracking, projection mapping, and multi-sensory cues generate a seamless field of interaction, where digital flora respond to touch and presence.

Beyond spectacle, immersive systems are being used as instruments for inquiry. Marshmallow Laser Feast’s We Live in an Ocean of Air (2018) visualizes the exchange of oxygen and carbon dioxide between humans and trees using real-time data and VR. By translating invisible ecological processes into perceptual experiences, the work reframes immersion as a medium for environmental awareness rather than escape.

Meanwhile, designers like Es Devlin are using AI-driven projection and choreography to stage performances that integrate audience movement and collective data. Her Memory Palace (2019) installation at Pitzhanger Gallery transformed historical narratives into spatial storytelling, blending physical architecture and digital mapping. These projects foreground narrative as a spatial, networked experience—an architecture of information as much as of form.

Designing the Continuum
What ties these practices together is a shift from static object-making toward systems thinking. The creative act is no longer confined to producing artifacts but to designing the conditions through which matter, data, and behavior interact. The continuum between digital and physical is now an operational space—where feedback, automation, and embodied experience converge.

This perspective is increasingly visible in architecture and product design. Firms like MAD Architects, Zaha Hadid Architects’ CODE group, and MX3D are developing adaptive systems that combine parametric modeling, robotic fabrication, and environmental sensing. MX3D’s 3D-printed steel bridge in Amsterdam, equipped with sensors and data visualization tools, stands as a literal manifestation of cyber-physical infrastructure—a structure that monitors its own behavior while serving as public space. In industrial design, practitioners such as Ross Lovegrove and Formafantasma explore material intelligence through digital modeling and sustainable processes. Their works translate the logic of computation into tangible form, blurring the line between algorithmic precision and artisanal craft.

As boundaries dissolve, the creative process becomes less about authorship and more about orchestration. The designer’s role shifts toward that of mediator—navigating between hardware and software, code and culture, human and non-human systems. The challenge is not simply to integrate technology but to question how it redefines creativity, ethics, and agency within interconnected environments.

Toward Cyber-Physical Practice
The convergence of art, design, and technology is not a passing phase but a structural evolution of creative culture. As AI, robotics, and sensing technologies permeate material and spatial practice, the question is no longer whether technology belongs to design, but how design can responsibly shape hybrid worlds.
Cyber-physical creativity points toward futures where the boundaries between virtual and tangible dissolve, where data behaves like matter, and where imagination operates as both biological and computational intelligence. From drones that mimic nature to structures that sense and respond, the design-tech continuum signals a cultural shift: creativity as a dynamic interface—alive, adaptive, and shared between human and machine.

Synthetic landscapes rethink the relationship between design and ecology. At the Southern California Institute of Architecture (SCI-Arc), the term defines a field where the artificial and the natural can no longer be separated. The program describes the built environment as “always continuous with what we have found,” positioning design as part of a living, evolving system rather than something imposed upon it.

This approach departs from traditional landscape design focused on preservation or stewardship. Instead, it examines how architecture, computation, and environmental systems intersect to generate new forms of coexistence and beauty. Design operates within networks of human and non-human agents, linking the biological and the technological to imagine how future environments might take shape. From speculative ecosystems that respond to pollution to living architectures that grow and adapt, these projects suggest that ecology itself has become a designed medium—revealing how entangled our built and natural worlds have always been.

Speculative Ecosystems
Artist-designer Pınar Yoldas frames the polluted oceans as a “plastic soup” and asks: if life were to begin now, in that synthetic milieu, what organisms might evolve? Her installation, An Ecosystem of Excess, imagines life forms capable of metabolizing plastic waste, offering a speculative biology of the Anthropocene rather than a “restored nature.”

By treating waste and human-technological detritus as ecological media, Yoldas reorients the landscape—not as something to preserve, but as something already irreversibly altered and now to be designed. Her work exposes how synthetic landscapes can emerge through cultural-ecological reflexivity: design that interrogates our ecological turning point rather than simply mitigates it.

Bio-Hybrid Built Form
On the more technical end of the spectrum, the EU-funded Flora Robotica project explores symbiotic relationships between plants and robots to prototype forms of living architecture. Supported under the Horizon 2020 program (Grant No. 640959), the research team developed systems where robotic structures braid soft scaffolds, guide plant growth, and sense environmental change. As the plants mature, they overtake and integrate these braided supports, forming hybrid constructions that are both biological and artificial.

Flora Robotica’s publications describe this as an “architectural system combining living natural plants and distributed robots,” in which “plants and robots work together to create a living organism that is inhabited by human beings.” The project investigates how robotic control, plant tropisms, and environmental sensing can generate architectural forms through continuous growth rather than assembly.

The result is a model of design based on process and co-evolution. Architecture becomes something that grows, adapts, and repairs itself—guided not only by human intention but by the behavior of living systems. Flora Robotica situates architecture within a dynamic ecology of sensors, materials, and biological intelligence, suggesting how the built environment might one day sustain itself through collaboration between machines and living matter.

Generative Ecosystems
Beyond fixed installations and architecture, another trajectory of synthetic landscapes emerges through generative systems that integrate living, algorithmic, and material flows. The Synthetic Ecologies Compendium—organized by the Serpentine—centres on “fermentation, invisible scales of life and the intersections of biology, culture and computation.”

More recently, the research project Simulacra Naturae combines neural activity, agent-based simulation, and living plants to create a multisensory ecosystem where biological processes and generative code evolve together. These systems shift the design question from formal composition toward emergent dynamics—designing for process, thresholds, and interaction rather than static objects. The synthetic landscape here is less a site than a looping system of feedback among living, artificial, and computational agents.

Design as Ecology
Across these projects, ecology becomes a design interface—adaptive, coded, and alive. In Yoldas’s An Ecosystem of Excess, plastic-polluted oceans become a habitat for speculative organisms that metabolize synthetic waste. In Flora Robotica, plants and robots braid scaffold structures and guide each other’s growth toward architectural form. And in Simulacra Naturae, neural activity, agent-based simulation, living plants, and generative systems intertwine to create evolving environments of material, biological, and computational feedback. Each reframes “landscape” not as a static backdrop but as an ever-shifting system of interaction.

In design studios and research labs, the shift is tangible. Climate and soil data flow into texture maps. Neural networks train on sap flow and plant electrophysiology. Bioreactors and hydroponic systems feed fabrication workflows where code directs photosynthesis with the same precision once applied to CNC milling. Architecture and computation move beyond representation—they become ecological participants.

As synthetic landscapes proliferate, authorship disperses. The designer’s hand gives way to calibration: tuning parameters, guiding microbial metabolisms, steering robotic motion, adjusting generative algorithms. What emerges is not an imitation of nature, nor its conquest, but a mutual composition—an ecology written with code, mycelium, and design intelligence.

OpenAI’s newly launched ChatGPT Atlas merges web browsing with conversational AI. Its most distinctive—and controversial—feature is “Memories,” a function that allows the browser to retain contextual information about a user’s activity across sites. The goal is to create continuity: Atlas can recall prior searches, revisit ideas, or suggest relevant actions based on past behavior. But this same continuity is sparking concern among technologists and creatives who see in it a potential for overreach.

Data Retention Meets Personalization
OpenAI states that browsing content isn’t used to train models by default and that users can view, edit, or delete their stored data. Yet, “memory” transforms a transient browsing act into a persistent behavioral record. For designers, researchers, and creative professionals whose workflow depends on iterative exploration, this could blur lines between assistance and surveillance. The convenience of context-aware AI comes with the trade-off of deeper data visibility—how much the browser knows about your habits, projects, and even creative process.

Trust as Interface
The question isn’t just technical—it’s relational. Atlas redefines the browser from a neutral interface to an active participant in knowledge work. The tension lies in trust: can users rely on a system that both assists and archives? As OpenAI pushes toward agentic AI—browsers that act rather than observe—the debate over memory, privacy, and agency will shape the future of creative technology. For now, Atlas offers a preview of a more intimate web, one that remembers as much as it serves.

At the Design Museum in London, Superflux’s Nobody Told Me Rivers Dream positions artificial intelligence not as a tool of extraction, but as an act of listening—reorienting technology toward sensitivity, attention, and reciprocity. Conceived for the River Thames, the project transforms data into a collaborative sensory dialogue with the living world.

Superflux—founded by designers Anab Jain and Jon Ardern—has long used speculative design to probe the social, political, and ecological implications of emerging technologies. Projects such as Mitigation of Shock (2019) and Refuge for Resurgence (2021) imagined how communities might adapt to environmental change. Nobody Told Me Rivers Dream extends that inquiry, shifting from preparation for crisis toward cultivating awareness within the present.

Listening, Not Measuring
The installation features three handcrafted sensor-objects conceived for placement along the Thames, each capturing a facet of the river’s ecosystem—birdsong, tidal flow, and atmospheric conditions—through embedded sensors and microphones. Environmental data from these instruments feeds into a generative AI process that produces poetic, reflective outputs rather than analytical ones.

In form, the sculptures are neither machines nor monuments. Made from organic materials, copper leaf, and naturally patinated metal, they appear as if grown from the riverbank itself. Their biomorphic language draws from shells and sediment, merging technological sensing with ecological materiality.

By emphasizing noticing over measurement, Superflux reframes AI as an instrument for perception. The river becomes an active participant—its tides, birds, and weather shaping the system’s behavior. The exchange invites humans to listen rather than command, reconfiguring the usual hierarchy between intelligence and environment.

AI Beyond the Lab
AI, as typically conceived, remains an invisible infrastructure—coded in data centers, optimized for efficiency, and scaled across global networks. Nobody Told Me Rivers Dream counters that abstraction by localizing intelligence: embedding it in specific materials and sensory experiences. A compact computational device synthesizes the captured data into generative visual and textual responses. What matters is not processing power but orientation. Here, AI becomes an act of attunement, surfacing resonances between the river’s rhythms and human language.

The choice of the Thames is deliberate. Both infrastructural and symbolic, the river embodies centuries of trade, industry, and ecological transformation. By situating the project in this context, Superflux prompts reflection on what forms of knowledge—ecological, cultural, or sensory—have been excluded by dominant technological systems. The work draws on folklore and ecological wisdom that regard rivers as living entities, sources of insight rather than resources to manage.

Within ongoing debates around ecological AI, the project questions scale, energy use, and epistemology in data systems. Instead of building ever-larger models, Superflux proposes a modest, situated intelligence—one that listens, adapts, and remains responsive to its environment.

Design as Relationship
For Superflux, design is a mode of relationship-building between humans, technologies, and the more-than-human world. Nobody Told Me Rivers Dream extends this philosophy through multi-sensory layering: sculpture, sound, data, and language form a continuous exchange between systems.

Each sensor-object acts as both scientific instrument and cultural artifact—listening to the environment while performing an act of care. The generative outputs, whether textual or visual, are less interpretations than invitations: traces of how AI might help humans perceive the subtle intelligence of ecosystems.

The project’s aesthetic restraint is central to its impact. It avoids interactive screens and spectacle-driven visualizations, cultivating instead a quiet presence. Viewers encounter the sculptures not as interfaces but as living objects that emit light and motion in quiet sympathy with the river. This minimalism distinguishes the installation from the theatricality often associated with media art, proposing that technological imagination can operate through humility rather than control.

Toward Ecological Intelligence
At its core, Nobody Told Me Rivers Dream asks who or what defines intelligence. By treating the river as both subject and collaborator, Superflux blurs the boundary between computation and ecology. Intelligence becomes relational—distributed across sensors, tides, and the humans who perceive them. The title itself functions as a provocation. If rivers dream, what do they dream of? The phrase gestures toward forms of consciousness and communication that lie beyond human perception yet persist in the natural world. It invites designers and technologists to reconsider the narratives driving AI development, shifting focus from extraction and automation toward reciprocity and care.

The project is a unique demonstration of how emerging tools can cultivate environmental awareness, how digital systems and material craft can coexist, and how speculative design can foster futures grounded in empathy rather than dystopia. As AI infrastructures expand, Nobody Told Me Rivers Dream reminds us that intelligence need not be built from code alone—it may already flow through the living systems that sustain us.

Virtual reality was once synonymous with immersion—a headset, a digital world, and the promise of total escape. But as artists and designers increasingly blur the lines between the physical and the virtual, immersion is no longer about isolation from reality; it’s about deepening our connection to it. Across galleries, forests, and data centers, a new generation of immersive projects is rethinking experience as an embodied, relational, and multisensory event.

From Marshmallow Laser Feast’s ecological simulations to Hyphen-Labs’ speculative activism, these works reveal that immersion isn’t just about entering an artificial world—it’s about expanding perception within the one we already inhabit.

From Simulation to Sensation
London-based collective Marshmallow Laser Feast (MLF) has become emblematic of this shift. Their projects, such as We Live in an Ocean of Air (2018) and Breathing with the Forest (2023), merge scientific data with aesthetic sensibility to explore the invisible systems that sustain life. In We Live in an Ocean of Air, participants wear VR headsets and biometric sensors that visualize breath as streams of light intertwining with the respiratory patterns of trees. The experience doesn’t separate human and environment—it visualizes their entanglement. MLF’s approach rejects VR as an escapist medium and repositions it as a tool for ecological empathy. The forest, reconstructed from LiDAR scans and volumetric data, becomes a living interface. Instead of simulating an external environment, MLF renders the unseen dimensions of the real world—airflow, carbon exchange, and respiration—reminding participants that immersion can also mean awareness.

As MLF creative director Barney Steel has noted in past interviews, the aim is not to recreate nature but to shift perception of the natural world through technology. This recalibration—where immersion mediates sensory understanding rather than sensory overload—reflects a growing cultural turn toward ecological experience design.

teamLab and the Continuum of Space
Few collectives have shaped the language of immersive art as profoundly as teamLab, the Tokyo-based interdisciplinary studio merging digital technology, architecture, and performance. Their sprawling exhibitions—such as Borderless (2018) and Planets (2018)—turn spatial design into an algorithmic choreography of light, motion, and sound. At teamLab Borderless in Tokyo, visitors navigate through a labyrinth of responsive projections that flow seamlessly between rooms. The artworks are not static; they migrate, transform, and overlap. The viewer’s movement becomes part of a computational ecosystem where human presence triggers generative behavior.

This is immersion as continuity rather than containment. Unlike traditional VR, which isolates the body in a headset, teamLab’s installations extend perception across shared physical space. The environment reacts to touch, proximity, and motion, collapsing distinctions between artwork and audience. In this sense, teamLab’s work embodies a broader shift in immersive design—from digital simulation to environmental computation.

The installations are not digital replicas of nature but algorithmic counterparts that model interdependence and flux. The experience is less about entering a virtual world than about understanding how systems—biological, social, and digital—coexist in feedback loops.

Hyphen-Labs and the Politics of Immersion
If MLF and teamLab focus on sensory and ecological entanglement, Hyphen-Labs approaches immersion as an instrument of cultural and political reprogramming. Their landmark project NeuroSpeculative AfroFeminism (2017) premiered at Sundance New Frontier and reimagined virtual reality through a lens of Black feminist futurity. Set in a speculative beauty salon run by women of color, the installation merges VR, neuroscience, and product design. Visitors don a headset and enter a world where cultural rituals and advanced technologies intersect—a space of empowerment and narrative reclamation.

Unlike much of the VR industry, which often prioritizes technological spectacle, Hyphen-Labs uses immersion to critique how design encodes power. The beauty salon becomes both a literal and symbolic site of transformation. Objects such as “Octavia Electrodes” and “Mnemo Electric” reimagine neurotech through aesthetic and cultural agency, subverting the white, male-dominated gaze of mainstream tech culture. By positioning VR as a speculative framework rather than a neutral tool, NeuroSpeculative AfroFeminism reframes immersion as political embodiment. It invites participants to inhabit identities and perspectives excluded from the dominant narratives of innovation.

This reorientation—from technological novelty to cultural agency—signals a crucial evolution in immersive practice. Immersion is not only sensory but ideological: it determines who gets to imagine the future and who is rendered visible within it.

Ryoji Ikeda and the Sublime of Data
If MLF explores ecology, teamLab space, and Hyphen-Labs identity, Ryoji Ikeda turns immersion inward—toward the architecture of data itself. The Japanese composer and artist is known for transforming raw information into audiovisual environments that probe the limits of human perception.

In works like data-verse (2019) and test pattern (2008–ongoing), Ikeda translates scientific and computational datasets into rhythmic flashes of light and sound. The resulting spaces are overwhelming yet precise—minimalist in form, maximalist in sensation. Visitors stand inside rooms pulsing with binary patterns and frequencies, their senses compressed by the density of information. But beneath the spectacle lies a critical tension: the beauty of data versus its abstraction. Ikeda’s installations don’t merely aestheticize information; they expose the dissonance between human scale and machine logic. By transforming code into choreography, Ikeda redefines immersion as a confrontation with the invisible infrastructures that shape digital life. His work speaks to a contemporary condition in which experience is increasingly mediated through data—where perception itself becomes computationally entangled.

The Next Layer of Immersion
Across these projects, immersion evolves from spectacle to inquiry. MLF visualizes interspecies connection; teamLab designs spatial intelligence; Hyphen-Labs stages cultural intervention; Ikeda renders data perceptible. Each reframes immersion as an active process—sensorial, ethical, and epistemic.

This redefinition coincides with a broader cultural realignment in experience design. As extended reality technologies merge with spatial computing, the boundaries between physical and digital experience are dissolving. The goal is no longer total escape but total engagement—a recalibration of attention across environments, bodies, and systems. Immersion beyond VR is not about stepping outside the world. It’s about stepping more deeply into it.

Across the global creative sector, contraction is replacing growth. The Otis College Report on the Creative Economy (2025) recorded a 0.9 percent drop in California’s creative jobs—around 6,700 positions lost—while U.S. national figures show a decline of 35,000 jobs. The downturn is not limited to any single discipline. From media production to design and visual arts, freelancers and small studios face reduced workloads, delayed payments, and intensified competition from AI-assisted workflows.

A Creative UK survey found that 66 percent of creative freelancers have seen a drop in work since 2020, and more than one-fifth say generative AI has already reduced demand for their services. The World Economic Forum now lists graphic design among the professions most at risk of automation. This convergence of labor precarity, platform volatility, and technological acceleration exposes a deeper vulnerability: creative economies are still built on informal networks and unstable infrastructures.

Sustainability adds another layer of strain. Reports from the Creative PEC and the Centre for Sustainable Design note that most creative sectors still operate on linear “take–make–waste” models, with little systemic support for reuse, circular production, or low-carbon digital practices. Virtual production, data-intensive workflows, and material waste in set and exhibition design all contribute to an expanding ecological footprint that creative industries struggle to reconcile with their cultural ambitions.

Yet amid contraction, a recalibration is underway. “Creative value” is being redefined to include resilience, social impact, and ecological accountability alongside revenue and reach. The future of the creative economy may depend less on growth than on redesign—of systems, incentives, and infrastructures capable of sustaining creative work as a form of collective intelligence rather than extractive output.

Melbourne’s National Communication Museum (NCM) is reframing what it means to befriend a machine. Opening November 1, 2025, FRIEND gathers pioneering robotics, interactive installations, and contemporary artworks that probe the emotional, ethical, and aesthetic dimensions of human–machine companionship.

Rethinking Companionship in the Age of Robotics
The exhibition marks the first Australian appearance of WABOT-2, the legendary humanoid robot developed at Waseda University in the 1980s, known for reading musical scores and performing on an electronic organ. Its presence situates FRIEND within a lineage of human–robot interaction that predates today’s AI revolution. “We are delighted to exhibit WABOT-2 in Australia and explore future collaborations with NCM,” said Professor Tetsuya Ogata, Director at Waseda University.

Beside this historic artefact stands a new generation of robots that challenge conventional ideas of perfection and functionality. From the Interaction and Communication Design (ICD) Lab in Japan come Weak Robots—deliberately fragile, emotionally open machines that short-circuit the industrial myth of robotic precision. As ICD Lab’s Dr. Michio Okada notes, these creations “invite audiences to rethink what robots can be, creating relationships that are more human, not less.”

When Technology Becomes Tender
The emotional tone of FRIEND is neither utopian nor dystopian—it’s intimate. “Changes in AI, robotics, and genetics are shaping how people live with and relate to machines in unprecedented ways,” explains Jemimah Widdicombe, NCM’s Senior Curator. “FRIEND brings together artists, industry, and researchers to explore companionship, creativity, and collaboration.”

Opening weekend programming expands the exhibition into a live experiment. International guests will join NCM for talks, demonstrations, and interactive sessions—among them a “FRIEND dating” event pairing visitors with machines, and a rare live demonstration of JIZAI ARMS, the wearable robotics system from the University of Tokyo’s JIZAI Body Project. Developed by Professors Masahiko Inami and Shunji Yamanaka, the project explores the augmentation of human capability and identity through networked prosthetics. One visitor will even have the chance to test the robotic arms in real time. The event underscores how human–machine relationships are moving beyond productivity or novelty, toward affective and performative exchange. As Dr. Belinda Dunstan, one of the opening weekend speakers, notes, “The museum provides a playful, thought-provoking space for exploring human–machine connections. I can’t wait to see how visitors engage with the work.”

The Museum as Experimental Interface
NCM’s in-house Studio division contributes several original works to FRIEND, positioning the museum itself as a living research lab. The Furby Choir—an installation of modified Furbies that respond to visitors who ask, “What is a friend?”—blends nostalgia with natural language processing to explore social AI in its most approachable form. Another piece revives ELIZA, the 1960s text-based chatbot that pioneered computational empathy, now reimagined on vintage hardware and an emulated mainframe. By reintroducing early AI systems alongside contemporary robotics, FRIEND reveals continuity between decades of experimentation in human–machine dialogue. These installations emphasize communication as both technological and emotional infrastructure—an evolving conversation between code and care.

Beyond the NCM Studio works, the exhibition introduces a range of companion robots and interactive installations that blur boundaries between animate and artificial. Yukai Engineering’s Qoobo and Petit Qoobo—catlike therapy robots with responsive tails—and Amagami Ham Ham, designed by Shunsuke Aoki (one of teamLab’s original founders), evoke comfort, humor, and a strange form of affection. Elsewhere, Pepper and Cruzr, familiar figures in social robotics, meet visitors face-to-face, while Brandon Tay’s DOOMSCROLL DREAMMACHINE and EJ Son’s 재주없는곰: Trickless Bear bring the digital unconscious into tactile, sculptural form. The exhibition also features Protective Seal by Elena Knox, shown in Australia for the first time—an artwork examining how robotic interfaces mediate intimacy, gender, and trust. Each piece contributes to a broader dialogue about how technology learns not just to see and speak, but to care.

Designing Futures of Empathy
For Dr. Emily Siddons, NCM’s Artistic Director and Co-CEO, FRIEND embodies the museum’s broader mission. “At NCM, we explore the evolving relationship between humanity and technology,” she says. “FRIEND surveys how rapidly human–robotic interaction is transforming, inviting visitors to consider their own relationships with machines in new and meaningful ways.”

This framing positions NCM among a new generation of cultural institutions—like Tokyo’s Miraikan or London’s Science Gallery—operating as hybrid spaces for research, art, and public inquiry. The exhibition avoids spectacle, favoring curiosity: robots as collaborators, not commodities.

Running through April 26, 2026, FRIEND represents a pivotal moment in Australia’s engagement with robotics culture. It arrives as social AI and assistive technologies become embedded in healthcare, education, and creative industries, raising questions about agency, empathy, and design ethics.

For audiences, the exhibition offers more than a glimpse into the future of robotics—it asks what kind of relationships we want to build with the systems that increasingly accompany our lives.

At The Regent’s Park, Frieze London 2025 positions itself less as a marketplace and more as a system for cultural circulation. Now in its 22nd edition, the fair runs from October 15–19 alongside Frieze Masters, hosting over 280 galleries from 45 countries and reaffirming its status as a global hub for contemporary art.

This year’s edition introduces Echoes in the Present, a new curated section exploring how artists rework historical narratives to address contemporary urgencies. The fair’s redesigned layout—developed by architecture studio A Studio Between—reflects that ambition. It opens the tent to more fluid spatial navigation, blending commercial and curatorial zones in ways that hint at the fair’s hybrid identity: part exhibition, part economy.

Beyond the booths, Frieze London doubles down on collaboration. The Artist-to-Artist mentorship program, supported by Tiffany & Co., highlights intergenerational exchange, while satellite events expand the fair’s footprint across the city. These activations suggest a shifting model—away from transactional spectacle and toward distributed networks of influence.

Still, the art market context looms large. With global sales softening, fairs like Frieze are recalibrating around experience and engagement rather than acquisition. The tension between visibility and value—between art’s market and its public—defines Frieze’s current experiment.

In 2025, the question isn’t whether art fairs still matter. It’s how they evolve into infrastructures that sustain not just sales, but the shared cultural systems art depends on.

At Singapore’s ArtScience Museum, Another World Is Possible reframes futurity as a design challenge rather than a distant dream. Running through February 2026 and co-curated by speculative architect and filmmaker Liam Young, the exhibition brings together architects, artists, and storytellers who treat the future as an open-ended prototype—one that must be iterated, not merely imagined.

The curatorial premise is clear—futures are not abstract or inevitable; they are designed systems shaped by cultural, ecological, and technological choices. The exhibition situates this argument within Singapore’s unique context: a city-state long invested in long-term planning, sustainability, and infrastructural experimentation. The result is an exhibition that oscillates between global speculation and local grounding.

From Speculative Fiction to Spatial Prototypes
Liam Young’s curatorial approach builds on his ongoing work in cinematic architecture and world-building. Another World Is Possible extends this methodology into the museum space, treating exhibitions as immersive story engines. Visitors encounter works by international and regional practitioners—Björk, Ming Wong, Jakob Kudsk Steensen, Osborne Macharia, Ong Kian Peng, and Torlarp Larpjaroensook among them—whose practices merge narrative media, sound, simulation, and spatial design. These installations operate less as static artworks and more as dynamic ecosystems where fiction becomes a material of design.

Architectural studios such as WOHA and Pomeroy Studio contribute speculative models that explore urban resilience and environmental adaptation: vertical biodiverse cities, amphibious housing typologies, and floating agricultural infrastructures. Rather than futuristic fantasies, these are tangible thought experiments—grounded, testable, and positioned within Southeast Asia’s environmental realities. The exhibition’s scenography reinforces this hybridity. Digital projections, miniature architectures, and sonic environments are staged in fluid relation, dissolving disciplinary boundaries. The result is a spatial essay on how design disciplines can collaborate to construct shared imaginaries.

Beyond the Dystopian Frame
Another World Is Possible consciously resists the dystopian fatigue that defines much of contemporary futurism. Its optimism is not naive; it is infrastructural. The works on display ask how design might foster new forms of collective agency, even amid planetary complexity. In this context, narrative becomes infrastructure—a recurring concept throughout the exhibition. Storytelling is treated not as escapism but as a mode of governance, ethics, and coordination. By constructing alternative narratives, designers can also reshape the systems—economic, ecological, and technological—that sustain them.

This framing aligns with broader shifts in speculative design and critical world-building. Rather than forecasting or predicting, these practices propose prototyping as critique: using fiction to test the social and material consequences of emerging technologies. The exhibition extends this logic into public culture, inviting visitors to consider themselves participants in design futures rather than passive spectators. The museum amplifies this engagement through “We Dream in Futures,” a public symposium held alongside the exhibition’s opening. Bringing together practitioners from architecture, game design, and media art, the program situates the show within a broader discourse on the politics of imagination—how cultural institutions can mediate between speculation and implementation.

The Museum as Platform for Collective Imagination
As an institution, ArtScience Museum has positioned itself at the intersection of design, science, and cultural speculation. Another World Is Possible represents its most integrated articulation of that agenda to date. The museum functions here as a platform for systems thinking—an experimental site where design research, environmental ethics, and cinematic storytelling converge.

This orientation signals a wider institutional shift. Museums are no longer content to archive the past; they are increasingly engaged in prototyping the future. By partnering with creative technologists and speculative practitioners, ArtScience Museum asserts that curatorial work can shape civic and environmental imagination as effectively as policy or engineering.

Singapore’s own history reinforces this narrative. Its urban development has long been guided by anticipatory design—city planning as a speculative act. Another World Is Possible reframes that logic through cultural inquiry: how might imaginative foresight extend beyond urban systems to encompass social, ecological, and planetary ones?

A Blueprint for Possible Futures
Across its installations and programs, Another World Is Possible challenges visitors to think of futures not as timelines but as negotiations—between growth and sustainability, autonomy and cooperation, technology and ecology. It reframes creativity itself as a civic act: a collective capacity to envision alternatives and build toward them.

What emerges is less a vision of the future than a blueprint for future-making. Through architecture, film, and narrative design, the exhibition models how imagination can become a method of governance and how cultural institutions can mobilize that imagination toward public agency.

For a museum often associated with digital spectacle, this exhibition marks a shift toward critical synthesis. It invites audiences to move beyond aesthetic immersion and toward strategic reflection—to ask not only what futures look like, but who designs them and under what conditions.

At London’s Serpentine South, Peter Doig: House of Music (October 10, 2025–February 8, 2026) expands the painter’s practice into sound and space. The exhibition brings together Doig’s paintings, prints, films, and a selection of restored analogue speakers from his personal collection, including rare Klangfilm Euronor and Western Electric systems originally used in cinemas. The result is a multisensory environment where sound operates not as accompaniment, but as material.

According to the Serpentine, House of Music explores the intersection of visual art and listening culture. The gallery will host “Sound Service,” a live program of Sunday listening sessions curated by guest artists and musicians. These events are designed to activate the space through collective experience rather than spectacle.

Doig’s approach reflects a wider institutional interest in sound as a spatial and social medium. Recent Serpentine projects—from Ryoji Ikeda’s Test Pattern to Grace Wales Bonner’s Dream in the Rhythm—have similarly treated sound as an architectural force. Rather than isolating artworks, such exhibitions emphasize how sensory experience can reframe the gallery’s function.

By foregrounding listening as a form of engagement, House of Music positions the gallery as an active site of resonance. The exhibition’s fusion of painting, film, and analogue sound asks what happens when the walls that contain art also begin to listen back.

The Internet of Things (IoT) didn’t begin with smart thermostats or factory sensors—it emerged from decades of experimentation linking computation, communication, and control. Long before the term was coined, researchers, artists, and hobbyists were already building prototypes of a world where objects could sense, respond, and interact. The story of IoT is as much about creative inquiry as it is about industrial evolution—a shift from speculative cybernetic systems to the connected infrastructures that quietly govern everyday life.

Feedback Loops and Early Cybernetics
The conceptual roots of IoT lie in mid-20th century cybernetics, a field exploring feedback and control in biological and mechanical systems. Norbert Wiener’s 1948 book Cybernetics: Or Control and Communication in the Animal and the Machine proposed that systems—living or artificial—could regulate themselves through feedback loops. This idea seeded a generation of research into automated sensing and machine-environment interaction.

By the 1970s, these principles extended into design and architecture. At MIT’s Architecture Machine Group, Nicholas Negroponte and his collaborators developed systems that responded to human behavior, anticipating what would later be called “smart environments.” These projects explored how computation could embed itself into physical space, long before the networked sensors of modern IoT. In the late 1980s, Xerox PARC researcher Mark Weiser advanced this vision with the concept of “ubiquitous computing.” He imagined a future where technology would fade into the background—calmly embedded in objects and environments rather than isolated in screens. Weiser’s work reframed computing from an interface problem to an ecological one, setting the stage for IoT’s evolution as a distributed system of awareness rather than a collection of devices.

From RFID to the Idea of the Internet of Things
The technical foundation of IoT began forming in the 1990s as advances in wireless communication and miniaturized computing converged. The widespread adoption of RFID (Radio Frequency Identification) systems allowed objects to be tagged and tracked, creating a digital identity for physical things.

In 1999, British technologist Kevin Ashton used the term “Internet of Things” while working at Procter & Gamble, describing a vision where objects could automatically communicate data through the internet without human input. Around the same time, MIT’s Auto-ID Center began developing global standards for RFID networks—essentially designing the first architecture for connecting physical objects to digital databases. These developments were driven largely by logistics and manufacturing, but the conceptual implications were much broader. For the first time, the infrastructure existed to turn the physical world into a data environment—where materials, products, and even cities could be monitored, optimized, and analyzed in real time. Yet the early 2000s also marked a cultural shift. Outside corporate R&D labs, artists and designers began experimenting with sensors, networks, and interactivity not to optimize systems, but to question them.

The Birth of DIY IoT: Makers, Artists, and Networked Experiments
If industrial IoT was born in warehouses and factories, its creative counterpart emerged in studios and hacker spaces. The early 2000s saw a convergence between open-source hardware, creative coding, and a growing maker culture that democratized the means of experimentation. Arduino, launched in 2005 by Massimo Banzi and David Cuartielles, was pivotal. The low-cost microcontroller gave hobbyists and students an accessible way to prototype networked devices. Combined with open-source software frameworks like Processing and later Raspberry Pi, Arduino transformed IoT from a specialized engineering challenge into a participatory culture of invention.

Artists were among the first to push these tools beyond functional use. Natalie Jeremijenko’s Feral Robotic Dogs (2002) repurposed robotic toys to detect pollution, turning consumer electronics into environmental activists. Usman Haque’s Pachube (2007)—a platform for sharing real-time sensor data—pioneered open environmental sensing, laying conceptual groundwork for today’s data-driven sustainability projects. Collectives such as Proboscis and designers like Timo Arnall and Jack Schulze explored “internet-connected things” as social and cultural interfaces rather than mere technical systems.

This DIY IoT movement reframed connectivity as a material for design thinking. It encouraged a generation of creators to view sensors and networks as expressive media, capable of revealing relationships between people, objects, and environments. Long before the smart home became a commercial category, these prototypes explored how data and material form could intertwine.

From Consumer Gadgets to Global Infrastructure
By the early 2010s, IoT had shifted from experimentation to mass-market adoption. Cloud computing, mobile connectivity, and cheap sensors created the conditions for scalable applications. Nest Labs launched its learning thermostat in 2011; Philips introduced Hue smart lighting in 2012; and Amazon’s Echo (2014) brought voice control into the home. The “smart home” became IoT’s public face, marketed as convenience through automation. However, this commercial wave also exposed critical tensions. Fragmented platforms, security vulnerabilities, and data privacy issues revealed the risks of connecting everyday life to corporate clouds. The 2016 Mirai botnet attack, which hijacked insecure IoT devices to cripple parts of the internet, highlighted how quickly convenience could turn into systemic vulnerability.

As hype subsided, IoT’s center of gravity moved toward the enterprise. Industrial IoT (IIoT) applies the same principles of connectivity and data-driven feedback to manufacturing, logistics, energy, and urban infrastructure. Sensors embedded across machines and environments enabled predictive maintenance, resource optimization, and real-time analytics—forming the backbone of what became known as Industry 4.0. Meanwhile, the artistic and critical dimensions of IoT persisted in parallel. Projects by Tega Brain and Sam Lavigne, Addie Wagenknecht, and James Bridle continued to interrogate the social, ethical, and ecological implications of networked objects—insisting that to connect things is also to connect responsibilities.

From Things to Ecologies
The trajectory of IoT—from cybernetic experiments and hobbyist prototypes to industrial ecosystems—marks a profound cultural transition. What began as a question of “smart objects” has become a question of living systems: how intelligence, sensing, and agency distribute across networks that now mediate the material world. Today’s IoT extends beyond homes and factories to planetary scale—monitoring climate systems, managing supply chains, and orchestrating energy grids. Edge computing and AI integration allow these systems to act autonomously, sometimes beyond direct human oversight.

But the creative origins of IoT still matter. The experimental ethos of early artists and makers—who viewed connected objects not as products but as inquiries—remains vital for navigating what comes next. As IoT evolves into the invisible infrastructure of everyday life, its design and governance will shape how agency, accountability, and meaning persist within the vast web of connected things.

Each unit is driven by custom PCBs and Teensy microcontrollers. The subwoofers are fitted with 3D-printed cones, enabling the controlled generation of air vortexes and low-frequency turbulence. Rather than producing a stable musical composition, the system operates as a fluctuating pressure field.

As visitors move through the structure (approx. 3000 × 4000 × 2200 mm, variable), they encounter shifting zones of vibration and airflow. Sound resists clear localization. Air currents are felt across the body, redistributing perceptual emphasis from vision to touch and hearing.

Installed, among other venues, at Schemerlicht Festival in Nijmegen (NL), the project constructs what the artist describes as a “sensory whitespace”: a temporary withdrawal from visually dominant environments in favor of embodied listening. The installation functions simultaneously as architecture, instrument, and atmospheric system.

BORA: BORA received an Honorary Mention in the Digital Musics & Sound Art category at the 2025 edition of the Prix Ars Electronica.

Project Info
Artist: Zhao Zhou
Year: 2024–ongoing
Concept, Design & Production: Zhao Zhou
Sound Design: Mint Park
Embedded Programming: Nathan Marcus
Mentorship: Zalán Szakács
Documentation: Riccardo De Vecchi, Ira Grünberger
Medium: Custom PCB, Teensy microcontrollers, modified subwoofers with 3D-printed cones, plexiglass, aluminum, stainless steel
Dimensions: Variable, approx. 3000 × 4000 × 2200 mm
Image Credit: Riccardo De Vecchi and Ira Grünberger (Schemerlicht Festival, Nijmegen, NL)

When museums digitize their archives, it’s usually to expand access. UNESCO’s new Virtual Museum of Stolen Cultural Objects does something radically different: it uses technology to make visible what has been taken—and to eventually erase itself. Launched in 2025, the online platform brings together 3D models of stolen, looted, or illicitly trafficked cultural artifacts from across the world. But unlike most museums, it measures success not by how much it collects, but by how much it loses. Each time an object is repatriated to its rightful home, it disappears from the virtual gallery. This inversion of the museum’s traditional logic—accumulation—signals a new phase in the ethics of digital heritage. The project reimagines not only how we exhibit lost culture, but how institutions can use virtual tools to advocate for cultural justice

A Museum Designed to Shrink
Developed by UNESCO in collaboration with INTERPOL and national heritage authorities, the Virtual Museum of Stolen Cultural Objects currently features around 200 digital artifacts—ranging from bronze sculptures and manuscripts to ceremonial objects and archaeological fragments—each meticulously rendered in high-resolution 3D. Visitors can navigate its immersive galleries as they would a physical museum, exploring the contours and textures of objects that have been missing, in some cases, for decades.

Each entry is accompanied by a layered narrative: where the object originated, how it was stolen, and what efforts are underway to recover it. What initially reads as an impressive digital archive quickly reveals itself as a global index of cultural loss. The curatorial logic is deliberately paradoxical. The museum’s collection is meant to shrink over time, with each disappearance marking a successful restitution. As UNESCO’s Assistant Director-General for Culture, Ernesto Ottone Ramírez, explained at MONDIACULT 2025 in Barcelona, the museum’s goal is for its collection “to shrink, not grow.” Its gradual disappearance would signify a world in which stolen heritage has been returned.

Cultural Memory as Digital Infrastructure
The museum’s design takes inspiration from the symbolic form of the baobab tree—a central metaphor chosen by UNESCO to represent endurance, community, and continuity. Often called the “tree of life” across many African traditions, the baobab embodies resilience and rooted connection. Its branching form becomes a metaphor for the museum’s structure: decentralized, interlinked, and alive.

Each digital gallery functions as a node in a wider network of cultural memory. The 3D reconstructions—produced using a combination of photogrammetry and laser scanning—are developed in partnership with museums, universities, and law enforcement agencies. These digital replicas serve multiple purposes: as visual evidence in efforts to track illicit trade, as educational resources for cultural institutions, and as public-facing artifacts that restore a measure of access to displaced heritage.

At its core, the project is also a statement about digital sovereignty. By storing and presenting these objects in a globally accessible virtual space, UNESCO bypasses many of the physical and jurisdictional limits that have long constrained restitution. The museum becomes a shared space for accountability and remembrance, where access—not possession—defines value.

The Politics of Visibility
The virtual museum launches at a time when global debates over restitution are accelerating. European institutions, including the British Museum and the Musée du quai Branly, have faced sustained pressure to return colonial-era artifacts. Yet restitution is rarely straightforward—it involves complex legal frameworks, diplomatic negotiation, and painstaking questions of provenance that can take years to resolve.

The UNESCO platform doesn’t settle these disputes, but it reframes them. By visualizing the scale of cultural theft, it shifts restitution from abstract policy to tangible visibility. Seeing around two hundred stolen artifacts assembled in one digital space makes it difficult to ignore the systemic nature of cultural displacement. At the same time, the museum raises questions of representation and consent. Who determines which objects appear? How are communities of origin involved in shaping their narratives? UNESCO has emphasized that the platform was developed in consultation with national cultural authorities and heritage agencies, though the ongoing challenge remains ensuring that restitution stories are told with, not merely about, the affected communities.

The digital medium adds further complexity. The 3D scans—precise, detailed, and endlessly reproducible—blur the boundary between visibility and replication. When a virtual copy circulates freely, what does that mean for ownership? Can a digital rendering of a looted object ever be neutral, or does it risk becoming another layer of appropriation?

The Museum as Future Prototype
As a prototype for how institutions might use digital media for ethical advocacy, the UNESCO Virtual Museum is conceptually groundbreaking. It transforms the museum from a repository into a temporal mechanism—an evolving ledger of accountability.

The “shrinking museum” model challenges how cultural institutions measure value. Instead of equating prestige with size, the project inverts the hierarchy: absence becomes proof of success. Conceptually, it offers a framework that could influence how virtual archives address other global forms of loss—from climate-displaced architecture to endangered biodiversity—using digital preservation not as a substitute for loss, but as a prompt for repair. Still, the project faces tangible constraints. High-fidelity 3D modeling is costly and technically demanding. Maintaining the infrastructure over decades requires funding, cybersecurity, and long-term stewardship. And technology alone cannot restore trust or undo colonial histories of extraction. The museum’s power lies less in resolution than in visibility—in its capacity to convene attention and sustain accountability.

UNESCO’s Virtual Museum of Stolen Cultural Objects is not a static monument to loss; it is an evolving system for memory, advocacy, and repair. Its paradox is deliberate: a museum that succeeds by disappearing. As it does, it invites cultural institutions everywhere to reconsider what it means to hold, display, and ultimately let go.

Qualcomm’s acquisition of Arduino marks a turning point for one of the most influential open-source platforms in tech history. The deal, announced this week, brings the 20-year-old Italian company—long synonymous with DIY electronics, education, and prototyping—under the wing of a global semiconductor giant.

Financial terms were not disclosed, but the implications reach far beyond the balance sheet. For millions of developers, artists, and educators who learned hardware design through Arduino boards, the move raises a crucial question: what happens to openness when a grassroots ecosystem is absorbed by a corporate one?

Arduino insists it will remain open-source and vendor-neutral. Qualcomm, for its part, has pledged continued support for the brand and community. Yet its influence is already visible. Alongside the acquisition, Arduino unveiled the UNO Q, a hybrid board combining a traditional microcontroller with a Qualcomm processor capable of running Linux and AI inference—effectively merging hobbyist tinkering with edge computing.

For the maker community, this could be both an upgrade and a warning. Qualcomm’s resources could modernize Arduino’s toolchain and push embedded AI into classrooms and small studios. But consolidation risks narrowing the platform’s independence, a tension familiar across open hardware and software movements.

As the lines blur between DIY innovation and corporate infrastructure, the future of the maker class may depend on whether Arduino’s open ethos can survive at Qualcomm scale.

Voice technology has become part of everyday infrastructure—embedded in phones, cars, and smart homes. Yet few artists examine how these systems decide what counts as a voice. In Up and Away, commissioned by Hyundai Artlab, Christine Sun Kim turns the mechanics of a voice-controlled web game into a study of power, access, and listening itself. What looks like simple play becomes a quiet critique of how technology encodes difference—who it listens to, and who it leaves out.

Reprogramming the Mechanics of Speech
At its surface, Up and Away resembles a standard browser-based voice game: players use sound to move through a sparse digital landscape of icons and floating forms. But Kim’s version resists the logic of entertainment. Instead, it redirects the familiar format of vocal command toward a deeper cultural question—how technologies define “normal” speech and decide whose voices register.

Hyundai Artlab frames the project as a work that challenges conventions of how speech should sound and whose words are given authority. Each icon within the interface reveals a fragment from Kim’s world, referencing personal and cultural narratives that shape her relationship to sound. Through these small interactions, the microphone becomes more than a control device—it’s a site of critical inquiry.

Kim, who has been deaf since birth, has long examined how sound structures social power. Her drawings, performances, and caption-based installations explore the ways hearing and speech confer authority, belonging, or exclusion. In Up and Away, that investigation moves into digital space, questioning the systems that translate—or fail to translate—human expression into data. Rather than relying on spectacle or technical complexity, the work operates with deliberate simplicity. Its icons and visual cues function like musical notation, inviting players to think of sound as relational—mediated by culture, technology, and access. The piece aligns with Kim’s broader effort to reimagine listening itself, positioning the act of speaking—or choosing silence—as a form of authorship. Ultimately, Up and Away reframes voice technology as more than an interface. It becomes a mirror for how societies value certain kinds of sound while rendering others invisible.

The Politics of Listening
The deeper you move into the work, the clearer it becomes that Up and Away is less about gameplay than awareness. It uses the logic of a voice-controlled system to expose how listening is structured—by code, by culture, and by expectation. Kim has described her practice as mapping the “social currency of sound,” and here that investigation becomes interactive. Each icon and waveform within Up and Away reflects fragments from Kim’s world—references to the social and technological systems that define what counts as a voice. The game’s minimal interface becomes both score and map, translating lived experience into a symbolic language of sound and silence.

Voice recognition technology has become embedded in daily life, yet it still struggles to accommodate the full range of human expression. Accents, tonalities, and speech patterns are often misread or erased. For Deaf and hard-of-hearing users, these tools frequently exclude by design. Up and Away doesn’t attempt to correct those flaws—it surfaces them, asking what it means to be audible in a system that decides who can be heard. Visually, the work mirrors Kim’s broader aesthetic: minimalist, graphic, and rhythmically precise. The interface disarms through simplicity, yet every line and icon functions like a caption awaiting meaning. As in her drawings and performances, Kim treats the act of reading sound as a political gesture.

A New Grammar of Voice
Up and Away extends Kim’s long-standing critique of how societies define intelligibility—who is heard, who is translated, and who remains unreadable within dominant systems of communication. Her earlier works, including Degrees of Deaf Rage (2022) and The Star-Spangled Banner (Third Verse) (2020), used notation, performance, and humor to show how institutions choreograph the act of listening. The recent Hyundai Artlab commission brings that inquiry into the space of digital interaction, where voice recognition has become both interface and identity.

The work gestures toward an alternative design logic—one that treats difference not as a flaw to be corrected but as a condition to be understood. By situating the microphone as both medium and metaphor, Kim invites audiences to imagine technologies that might one day listen otherwise: systems that register silence, accent, or ambiguity not as errors but as meaningful input.

In doing so, Up and Away continues Kim’s project of making the architectures of sound and power visible. It doesn’t claim to fix the inequities of voice technology, but it renders them perceptible—reminding us that every act of listening carries a politics of recognition.

OpenAI has launched Sora, an AI-powered video app that allows users to generate short clips featuring lifelike versions of themselves or their friends. Built on the company’s Sora 2 model, the app lets users record brief facial and vocal samples to create realistic digital avatars that can perform scripted scenes or respond to prompts.

While OpenAI frames Sora as a creative tool for personal expression and entertainment, the platform immediately reignited debates over consent and copyright. The app’s early access terms indicate that content from public datasets—potentially including copyrighted material—was used to train the model. That disclosure prompted criticism from artists, filmmakers, and legal analysts who argue the company’s opt-out approach shifts responsibility onto creators rather than the AI developer.

OpenAI says Sora includes safeguards against non-consensual or harmful use, such as restricting explicit or violent content and notifying users when their likeness is used in another video. Still, questions remain about ownership: who controls a likeness generated by AI, and what happens when digital identities become remixable assets?

Sora’s release marks a major step in consumer-facing generative video, but also underscores a larger reckoning—where creativity, consent, and copyright are no longer clearly distinct.

As the rings turn, their alignment produces shifting geometric configurations. Recognizable figures briefly cohere before dissolving into abstraction. The sculpture does not rely on a single stable form; instead, it operates through continuous recomposition. What appears solid in one moment fragments in the next.

The work situates movement as a primary material. Rather than using motion as embellishment, CYCLES depends on rotation to generate its visual logic. Perception becomes time-based: depth, symmetry, and figure-ground relationships fluctuate according to the rings’ relative positions.

By structuring the piece around synchronized rotation, SPY Studio constructs a system in which equilibrium is dynamic rather than fixed. The installation invites sustained looking, as patterns only fully register through duration.

Project Info
Artist: SPY Studio
Year: 2025
Location: Madrid, Spain
Image Credit: Rubén P. B

A recent Financial Times analysis suggests that social media usage may have plateaued, with engagement declining across major platforms and younger users signaling fatigue. The findings raise questions about whether the growth engine that has defined digital life for nearly two decades is slowing—and what this means for design and culture.

Facebook continues to lose relevance among younger demographics, while TikTok, once positioned as unstoppable, is showing slower growth in key markets. Even Instagram reports lower session times, a shift that analysts link to both platform saturation and changing user expectations. For Gen Z, social media is less an entertainment hub than an obligation: a place of performative visibility, often weighed down by anxiety rather than excitement.

The implications extend beyond market valuations. If “peak social media” has arrived, designers and technologists must shift focus from scaling user numbers to cultivating meaningful engagement and cultural legitimacy. Questions of trust, authenticity, and sustainable interaction are now at the center of platform strategy.

Social media may not be disappearing, but its dominance as the defining digital medium is no longer guaranteed. For those shaping its future, retention and resonance could matter more than reach.

Designing a platform isn’t just a technical exercise. It’s a cultural intervention. Every toggle, ranking system, or community rule shapes how people express themselves, what voices rise to the surface, and which practices are normalized. Platforms don’t just organize data; they organize daily life.

Platforms Build Cultures, Not Just Code
A platform's design choices ripple outward as social norms. TikTok’s recommendation engine didn’t just optimize video discovery—it reshaped how music is produced, promoted, and consumed, creating a culture where 15-second hooks can launch global careers. Similarly, Reddit’s recent IPO was less about market valuation than about the culture clash between community governance and corporate growth.

Design decisions—ranking algorithms, voting mechanics, or monetization models—extend far beyond usability. They influence which communities thrive, how information spreads, and what forms of labor or creativity are rewarded. The technical becomes cultural almost immediately.

Design Decisions Encode Values
Interface choices are never just aesthetic. Default privacy settings, notification hierarchies, or the prominence of “share” buttons are value statements. They encourage some behaviors while discouraging others.

A system that prioritizes frictionless posting privileges virality, while one that enforces identity verification privileges accountability. Both are forms of cultural engineering. Even seemingly minor design features—double taps, swipes, ephemeral stories—become rituals. They alter how people signal intimacy, negotiate status, or perform identity online. When scaled to millions, these micro-affordances create shared behaviors that feel cultural rather than technical. A single interface tweak can shift how communities communicate, collaborate, or even protest.

Platforms Shape Public Life
As platforms have become central to public discourse, the stakes of design decisions have escalated. Content moderation debates are framed as policy issues, but they’re fundamentally cultural: who defines acceptable speech, and on what terms?

The rise of decentralized platforms illustrates the same point from another angle. Mastodon and blockchain-based social systems aren’t only experiments in architecture—they embody cultural propositions about autonomy, governance, and trust. They ask whether communities should be managed top-down or self-organized across distributed networks. AI-infused platforms add yet another layer. Recommendation systems trained on biased datasets decide which music, art, or commentary circulates and which gets buried. They don’t just surface content; they shape cultural memory and attention. Designers and engineers may frame these as technical optimizations, but they are effectively drafting the rules of public life.

Platforms as Cultural Propositions
If platform design is cultural design, then designers need more than technical fluency. They need cultural literacy—the ability to anticipate social consequences, recognize embedded biases, and see users as co-authors rather than passive consumers. This requires designing with awareness of how features reshape labor, identity, and expression, and supporting systems flexible enough to accommodate diverse values rather than enforcing uniformity. Platforms increasingly dictate how culture is produced, distributed, and remembered. They don’t simply run on culture—they manufacture it. Every feature encodes a proposition about how communities should operate and how individuals should engage with one another. To design a platform is to take a position on culture itself.

The pressing question is not whether platform design is cultural design, but whether those building the platforms are prepared to accept—and be accountable for—the cultural power they hold.

As cities expand, the survival of pollinators—bees, butterflies, moths, and other insects vital to global food systems—becomes increasingly precarious. Habitat loss, monocultural landscapes, and urban fragmentation have driven global declines in pollinator populations. Architecture has typically addressed this crisis through surface-level gestures, such as green façades or rooftop gardens. Wild Futures, a project by Infra-Architecture Lab, takes a different position. It treats pollinator survival as a central architectural challenge, asking how buildings themselves might function as ecological infrastructure.

Founded by Rafael Luna, the lab operates at the intersection of speculative design and research-driven prototyping. Developed in the context of the University of Technology Sydney, Wild Futures extends beyond a single prototype toward a broader question: what would it mean if the form, structure, and ornamentation of buildings were shaped by the needs of nonhuman species?

Pollinator Tectonics as Design Logic
Infra-Architecture Lab frames its approach as pollinator tectonics: embedding cavities, niches, and surfaces into architecture that double as nesting zones, feeding sites, and movement corridors. Instead of treating plants or habitats as decorative add-ons, the project integrates them into the core of the building’s performance.

The Sydney prototype illustrates this principle. The structure is porous and layered, with gradients between interior and exterior spaces. Cavities are sized and angled to host native bees, while modular planters provide nectar and pollen sources. By weaving ecological function directly into the building’s skeleton, Wild Futures shifts architecture from sealed mass to living infrastructure.

This approach reinterprets ornament, long debated in architectural history. Rather than aesthetic embellishment, ornament here is treated as productive—serving a role in biodiversity. It positions architecture not as a barrier to ecological systems but as a participant in them.

From Prototype to Urban Strategy
While the Sydney prototype demonstrates feasibility, Wild Futures is conceived as a system that could scale across urban contexts. Widespread adoption could generate pollinator corridors—networks of habitat distributed through dense neighborhoods that reconnect fragmented ecosystems.

Scaling this idea requires more than design innovation; it demands infrastructural change. For pollinator-conscious architecture to become standard, it would need to integrate with biodiversity policies, zoning codes, and building regulations. The shift parallels earlier moments when sanitation, fireproofing, or ventilation became codified as essential to public health. In this frame, habitat provision for nonhuman species could become equally fundamental. The challenges are significant. Different pollinators require different conditions: soil depth, cavity size, plant species, or microclimates. Wild Futures addresses this by emphasizing modularity—adaptable components that can be tailored to different ecological contexts. Yet ecological performance is difficult to standardize, and its effectiveness requires collaboration with ecologists, horticulturalists, and policymakers.

Architecture as Ecological Justice
What sets Wild Futures apart from many green architecture projects is its emphasis on ecological justice. Urban development has historically privileged human needs while displacing other species. By centering pollinators in the design process, Infra-Architecture Lab reframes architecture as a tool for rebalancing those inequities.

This orientation connects the project to a wider movement in design research that expands the definition of infrastructure beyond human use. Cooking Sections’ Climavore rethinks how food and architecture adapt to shifting ecological cycles, while Design Earth has mapped planetary-scale infrastructures as speculative design. Wild Futures shares these ambitions but grounds them in a modular, buildable system with immediate ecological relevance. The project also demonstrates the need for disciplinary hybridity. To design architecture that supports pollinators, architects must engage with biology, horticulture, and climate science. This aligns with the growing discourse on posthuman design, where the value of creative practice is measured not only by cultural impact but also by ecological function.

Toward a Wild Urbanism
The future of Wild Futures depends on factors beyond architecture. Municipal policies, cultural perceptions, and investment in biodiversity infrastructure will determine whether pollinator-conscious design remains experimental or becomes integrated into the urban fabric. Yet the project is valuable precisely because it pushes the boundaries of architectural discourse.

For architects, Wild Futures is a provocation: buildings are not inert shells but active participants in ecological systems. For urban planners, it presents a low-tech, distributed approach to biodiversity infrastructure that could scale incrementally across neighborhoods. For cultural observers, it frames architecture as part of a multispecies commons.

As biodiversity loss accelerates, architecture cannot remain isolated from ecological responsibility. Wild Futures shows how rethinking structure and form around nonhuman needs can generate new directions in design. Its prototypes are modest, but its implications are expansive: cities that serve as habitats not just for humans, but for the species that sustain life itself.

The infrastructures we depend on—data cables, surveillance networks, logistics chains, energy grids—tend to recede into the background, becoming visible only when they fail. Viewed through the lens of artistic and design practice, these hidden systems become sites of inquiry into how power, accountability, and fragility circulate within the networks that structure contemporary life. From forensic analysis of state violence to speculative visions of planetary governance, this work reframes infrastructure not as neutral support but as contested terrain.

Making Systems Legible
Forensic Architecture has become one of the most recognized collectives in this field, pioneering the use of architectural modeling, open-source intelligence, and digital forensics to investigate human rights abuses. Their meticulous reconstructions of bombings, shootings, and environmental crimes are not only legal evidence but also a form of public pedagogy, teaching audiences to read the built environment as testimony. By transforming satellite imagery, smartphone videos, and architectural plans into evidence chains, the group underscores how infrastructures of violence and surveillance can be scrutinized with the same technical tools that enable them.

A recent investigation, A Cartography of Genocide (2023–24), exemplifies this approach. Produced with partners in Palestine, the project assembled an 827-page report and interactive platform that mapped the systematic destruction of homes, schools, hospitals, water networks, and agricultural sites in Gaza. By analyzing patterns of bombardment and infrastructural collapse, Forensic Architecture demonstrated how assaults on life-sustaining systems function not as collateral damage but as an organized strategy of dispossession. The project reframes infrastructure itself—electricity grids, water systems, food supplies—as central evidence in the politics of survival.

Mapping Invisible Networks
Shu Lea Cheang has long examined how digital networks shape identity, sexuality, and control. Her practice brings into view the ways infrastructures of surveillance extend older systems of confinement and regulation into the present. In 3x3x6 (2019), Taiwan’s pavilion at the Venice Biennale, Cheang used surveillance cameras, facial recognition, and 3D scanning to interrogate how monitoring technologies echo historic regimes of incarceration. The title refers to the dimensions of a standard prison cell—three meters by three meters—watched by six cameras, collapsing architectural space and digital oversight into a single system of control.

Through immersive projections and interactive interfaces, 3x3x6 reframed surveillance infrastructure as both a technological apparatus and a cultural script, binding together sexuality, criminality, and state power. Cheang’s work highlights how contemporary infrastructures are not just logistical or material but also deeply entangled with the politics of visibility—determining who is monitored, how they are represented, and under what conditions they are allowed to exist.

Designing at a Planetary Scale
While some artists make visible what already exists, others speculate on futures where infrastructures themselves are redesigned. Liam Young’s planetary-scale fictions, including Planet City, reimagine governance and logistics beyond national borders, aligning them instead with ecological systems and global resource flows. Using cinematic worldbuilding, Young constructs visual narratives that imagine how architecture, supply chains, and collective governance might operate if infrastructures were designed with planetary crises in mind.

These speculative futures are not prescriptions but provocations. They encourage audiences to confront the mismatch between existing systems of governance and the challenges of climate change, resource depletion, and mass migration. By rendering these alternate realities in vivid detail, Young collapses the distance between speculative fiction and infrastructural planning, showing how imagination itself becomes a tool of governance.

Infrastructure as Cultural Inquiry
Taken together, these practices reveal how infrastructure has become a cultural, political, and artistic question as much as a technical one. Forensic Architecture transforms data into evidence of state violence and infrastructural collapse; Shu Lea Cheang exposes surveillance as both a technological apparatus and a cultural script; Liam Young visualizes speculative futures of planetary governance and logistics. Each project highlights that infrastructures are not static or inevitable—they are designed, contested, and open to reimagination. These works serve as both warnings and prompts. They insist that understanding systems is a prerequisite for reshaping them, and that critical attention to infrastructures may be one of the most consequential arenas for creative practice today.

ArtPrize is an open, citywide art competition in Grand Rapids, Michigan, where the entire downtown transforms into a sprawling gallery. For several weeks each fall, museums, public spaces, and even perfumeries become exhibition spaces, drawing artists from around the world and inviting the public to explore, debate, and vote on the works that reshape the city. At Aroma Labs, one piece in particular is pulling people in through quiet intensity: Aqua Limina. Visitors interact with a virtual river world alive with spirits rendered through motion capture, AI, and CGI. Their flickering anatomies, drawn from myth and performance research, shift as players move. The work feels like an experiment in progress, where a water cycle doubles as a database of ancestral memory.

Mesmerizing as it is, Aqua Limina is also unapologetically technical. “Worldbuilding in game engines is built on the relativist knowledge that we live in a world of many realities,” says lead creator Yiou Wang.

Through independent intellectual curiosity, Wang expanded upon her Masters of Architecture from Harvard Graduate School of Design to “materialize wild imagination.” Departing from notions of formalism, her work in interactive hologram and XR has been featured by the MIT Museum, SXSW, and many more. Since her 2024 collaboration with performance artist Alina Tofan, Wang has been developing immersive environments where water links performance, 3D art, and interactivity into a single current.

The project, built with motion capture by Tofan, sound by Yuj Archetype, and Wang’s technical fluency, reflects a broader shift in contemporary art. Technical literacy has become a medium in its own right. Arts organizations increasingly favor works that merge creativity with research and hard skills. 

Across festivals and awards cycles, many of the most talked-about works are research experiments. Think of X.S. Hou’s slime-mold installations at OFFLINE Gallery or Christie’s auction of Refik Anadol’s Memory Temple, a data-sculpture that replayed Leo Messi’s most cherished goal. 

This emphasis on research extends beyond new media. A recent exhibition at the Nairobi National Museum, Debe: A Container for Material Culture, exemplified how research and industrial design skills can amplify the impact of an installation. The solo artist featured, Adam Yawe, is trained as a biomedical engineer. 

 “Very often, I have an idea for a work I want to make or a process I want to engage with, but I stop myself…I spend time reading, talking to people or just walking around trying to find a connection or spark that would trigger the work,” Yawe reflects. “I think of this as a more abstract form of literature review, engaging with the existing ‘literature’ of the world, and making a connection to the work in order to give it some weight.”

Yawe’s work reconfigures mass-produced functional tools into aesthetic cultural objects by expanding upon the fabrication skills imparted during his technical training. Contemporary matatu (bus) speakers are converted into traditional instruments. Industrial clamps become handbags. Concrete drainage systems transform into modernist furniture. His work is creative, beautiful, and functional while remaining ideologically cohesive, exercising technical skill to serve concept and narrative.

For some creatives, the title of “researcher” now holds just as much weight as “artist.” While Yawe reconfigures material culture, Italian researcher-designer Tiziana Alocci probes memory and perception. Trained in industrial design, her practice begins with painstaking observation: mapping responses, studying acoustics, modeling interactions. Only after months of listening and measuring does she begin to build visuals.

“Everything I create stems from extensive research,” Alocci says. “This research could span months, capturing visual, behavioural, biometric, sonic, and spatial data. More simply (or perhaps more romantically), it's an act of intentional observation and listening. These practices become powerful tools for focus and care.”

Her latest installation, Frequencies of Belonging, lit up Torre Piacentini in Genoa with generative audiovisuals shaped by emotional data and voice archives. For Alocci, research isn’t background—it’s the foundation: “The marriage of research and art gives my practice a meaning that goes beyond borders and aesthetics… Today, I cannot begin any work—not even at the conceptual stage—without inspiration from research, observation, or listening.”

Today’s leading artists are as much investigators of the world as they are makers of its images, objects, and systems. In Alocci’s work, as in Aqua Limina and Debe, the art is both object and experiment: a laboratory for experience, inquiry, and discovery. These projects exemplify a new paradigm in contemporary art, one in which technical skill, research fluency, and conceptual ambition converge. The resulting works are immersive, intellectually charged, and, above all, alive.

For designers, the stakes of artificial intelligence are not just about models but about the infrastructure shaping what kinds of experiences can be built. The current race—across chips, data pipelines, developer tooling, and platform incentives—will define the creative constraints and opportunities for future interfaces.

Nvidia has become the de facto gatekeeper with its GPUs, driving strategic alliances with companies like OpenAI and Microsoft. Meanwhile, Meta’s decision to open-source Llama internationally signals a push for broader adoption and developer lock-in. Even cloud providers such as AWS and Google are retooling services to win not just workloads, but the workflows around them.

This competition is not only about computational horsepower. Infrastructure choices set the boundaries for what designers and users can experience. When inference costs plummet, new AI-native interfaces become viable. When access to training data narrows, innovation can stall. Developer tooling, from fine-tuning platforms to multimodal APIs, increasingly determines whether an AI product feels seamless or clunky.

For design and UX teams, these battles matter. A startup constrained by limited GPU access might optimize for text-first interactions, while a platform backed by abundant compute can experiment with fluid, multimodal experiences. Incentives built into infrastructure—pricing tiers, distribution channels, or integration frameworks—quietly shape how creative applications surface.

The AI infrastructure arms race is less about who builds the flashiest demo and more about who controls the conditions of possibility. As hardware bets converge with platform consolidation, the interfaces of tomorrow will be defined as much by supply chain strategy as by design vision.

Designers and artists have long sought ways to give shape to the invisible—through maps, models, and diagrams that render abstraction legible. What distinguishes current practice is the deployment of digital fabrication, AI, and responsive systems to materialize datasets in physical form. From algorithmically generated textiles to kinetic sculptures and architectural installations, data is no longer confined to the screen but staged as something to walk around, touch, or inhabit. The move from intangible to tangible has always been fraught. Once translated into objects—whether 3D-printed forms, sculptural memorials, or built environments—datasets do more than visualize. They surface the infrastructures, assumptions, and politics that shape how information is collected and used.

From Code to Object
Early work established the lineage for today’s practice. Artists like Addie Wagenknecht and Golan Levin were less focused on literal “data sculpture” than on making invisible digital systems perceptible. Wagenknecht’s practice, spanning robotics, open-source fabrication, and handmade processes, exposes how power and surveillance shape technological infrastructures. Levin, known for pioneering computational art and interaction design, created works that reveal the inner logics of code and networks through visual and performative form.

While not always producing 3D-printed artifacts, both helped set the stage for thinking about how abstract systems could be embodied and scrutinized through design. Jer Thorp extended this trajectory with projects like Just Landed (2008), which transformed Twitter data into patterns of international movement. Though originally screen-based, Thorp also fabricated data into laser-cut and 3D-printed artifacts, demonstrating how datasets could be confronted as objects rather than fleeting graphics.

Formafantasma’s Ore Streams (2017–19) reframed the conversation. Instead of visualizing numerical datasets, they worked with e-waste materials to expose the infrastructures of digital culture. Here, the material itself is the data: the flows of minerals, plastics, and discarded electronics that underpin technological systems. Nervous System, the Massachusetts-based studio of Jessica Rosenkrantz and Jesse Louis-Rosenberg, carried this ethos into generative design. Their Kinematics series (2014 onward) simulated folding and movement in 3D-printed textiles, embedding computational logic directly into wearables.

Matthew Plummer-Fernandez’s Disarming Corruptor (2013) took a more critical approach, distorting and encrypting printable files to expose the cultural politics of digital fabrication. And Shinseungback Kimyonghun’s Cloud Face (2012) turned facial recognition errors into tangible artifacts, materializing the flaws of AI vision systems.
Together, these works set the foundation: treating data not only as information to be displayed but as a material to be shaped, embodied, and critiqued.

Expanding the Field, 2023–25
Recent years have seen a resurgence of interest in making data tangible, driven by advances in fabrication, sustainability concerns, and new cultural questions about AI and infrastructure.

At Oregon State University’s PRAx building, Data Crystal: OSU (2024) by Refik Anadol transforms 10,000 hours of forest audio collected by an array of 1,200 microphones into a crystalline, AI-generated sculpture. Rather than printing datasets, the project uses algorithmic translation to turn ecological monitoring into a physical presence in the lobby, collapsing sound into sculptural form.

At Art Dubai 2025, BREAKFAST unveiled Carbon Wake, a monumental kinetic installation driven by real-time energy consumption data from more than 100 cities worldwide. Thousands of mirrored tiles shift in rippling waves, responding to fluctuations in fossil fuel use and renewable output. The sculpture turns the abstraction of carbon dependency into a dynamic, visceral experience of global infrastructure.

The architectural scale is evolving too. The null² pavilion, designed by Yoichi Ochiai with NOIZ for Expo 2025 in Osaka, integrates mirrored membranes, robotic actuation, voxel-like structures, and acoustic vibrations. While not a dataset in the conventional sense, the pavilion itself is a real-time materialization of “digital nature,” where robotic systems and visitor interactions produce a constantly shifting environment.

Sustainability has also become central. Rain Gauge (2024, University of Minnesota) 3D-prints 80 years of precipitation data in ceramic form, each month projecting outward on a cylindrical surface. By replacing plastics with clay, the project foregrounds the ecological footprint of physicalization itself. Similarly, IAAC’s 3D-Printed Earth Forest Campus (2024, with Crane WASP) uses local soil and fibers in large-scale robotic printing. Here, environmental performance data is not visualized but embedded in the fabrication process itself, aligning construction with material circularity and site conditions.

Finally, institutional installations are embedding data physicalization in educational spaces. At Stanford’s CoDa building, Camille Utterback’s Fathom (2025) creates an interactive installation tracing the history of encoded data. Panels and light-responsive surfaces let visitors explore how information has been recorded across time, turning abstract information theory into a tactile museum experience.

From Abstraction to Weight
What unites these projects is not a single medium but a shared strategy: making abstract systems tangible. Whether the subject is rainfall, energy infrastructure, forest soundscapes, or computational folding, physicalization transforms the immaterial into something you can hold, enter, or stand before.

This shift is more than aesthetic. By materializing data, designers expose its infrastructures, politics, and costs. Formafantasma’s Ore Streams reveals the material afterlife of electronics, exposing the infrastructures and supply chains embedded in every device. BREAKFAST’s Carbon Wake translates the world’s carbon dependency into a moving surface of kinetic tiles. Rain Gauge questions the ecological footprint of fabrication itself. And Data Crystal reframes environmental monitoring as cultural object.

As data grows ever more pervasive, the ability to translate it into matter is becoming a critical form of design literacy. These materializations do not only communicate information—they reshape our relationship to the systems we build and inhabit.

Buildings account for nearly 40 percent of global carbon emissions—more than aviation and shipping combined. For years, architectural “innovation” has leaned on high-performance glass façades, algorithmic skylines, and smart systems promising optimization. Yet the projects that now look most advanced don’t resemble gleaming towers or sensor-packed campuses. They look almost primitive: walls of mud, structures cooled by natural airflow, houses pieced together from salvaged timber and brick.

The paradox is that methods once dismissed as backward are proving to be among the most credible tools for designing in an age of climate constraint. Across the globe, architects are using digital fabrication not to escape vernacular traditions but to extend them—scaling local knowledge, circular materials, and low-energy systems into the mainstream of design. Low-tech, it turns out, may be the sharpest form of innovation.

Climate Logic, Scaled
Francis Kéré’s buildings in Burkina Faso show how climate performance can be achieved without mechanical cooling or imported infrastructure. Clay walls and carefully positioned apertures regulate air and temperature, while construction is carried out by local communities. These are civic structures built from the ground up—literally.

London-based Material Cultures pushes that same logic into industrialized contexts. Led by Paloma Gormley, the studio works with hemp, straw, and timber to create modular systems that can scale into housing and infrastructure. Their proposition is blunt: if construction keeps relying on steel and concrete, it locks in planetary failure.

By reframing agricultural byproducts as credible replacements for carbon-heavy materials, Material Cultures positions “low-tech” as a viable industrial strategy, not just an artisanal experiment. Together, Kéré and Material Cultures demonstrate that climate-responsive architecture doesn’t require exotic technologies. It requires aligning design intelligence with what is already abundant—soil, straw, and labor—and treating them as assets rather than limitations.

Reuse as Cultural Technology
If materials are one axis of the low-tech turn, adaptive reuse is another. Assemble Studio’s Granby Four Streets project in Liverpool revived a neighborhood of derelict Victorian terraces through collective repair. Working with the Granby Four Streets Community Land Trust, the group salvaged building elements and developed Granby Workshop, which produces hand-made tiles, fireplaces, ceramics, and furnishings from reclaimed and locally sourced materials. These were not just technical fixes but became central to the project’s aesthetic—patchwork surfaces and visible joins that celebrate repair.

Reuse here isn’t simply about carbon savings. It operates as a cultural technology—redistributing authorship and making construction a shared civic act. Assemble’s approach turns neighborhoods into producers of architecture, not passive consumers of glossy development. The design language is imperfect, sometimes messy, but deliberately so: resilience made visible.

Speculative Vernaculars
Other practices are pushing low-tech into speculative territory. Nairobi-based Cave_bureau—founded by architects Kabage Karanja and Stella Mutegi—works directly with geological and indigenous knowledge systems. Their ongoing Anthropocene Museum project, staged in natural caves such as the Nairobi Lava Tube, positions the cave as both the oldest form of architecture and a lens for imagining post-carbon futures. By combining oral histories, spatial installations, and field research, Cave_bureau reframes “low-tech” as a design intelligence rooted in deep time.

Meanwhile, TRAME, a studio based in Paris, TRAME, a Paris-based studio founded by Ismail Tazi, uses computational tools to extend craft traditions, particularly those rooted in Moroccan heritage. Digital weaving and parametric modeling are paired with artisanal techniques and local materials, producing hybrid works that resist the binary of tradition versus technology. Their practice suggests that digital fabrication doesn’t have to erase vernacular knowledge; it can scale and adapt it to contemporary needs.

Together, Cave_bureau and TRAME demonstrate that low-tech futures are not monolithic. They can be speculative, computational, or indigenous—but always grounded in situated knowledge rather than imported templates.

Redefining Innovation
The throughline across these projects is a redefinition of innovation itself. Glossy façades and sensor-packed systems may still dominate architectural marketing, but the more consequential work is happening elsewhere: in buildings that breathe naturally, in materials that regenerate rather than deplete, in neighborhoods that organize around repair, in traditions recalibrated through digital tools.
Low-tech is not nostalgia. It’s a design frontier that privileges continuity over novelty, resilience over spectacle. And for architects still chasing skyscrapers and “smart cities,” the provocation is stark: if the future of architecture is low-tech, what happens to those who refuse to step off the high-tech escalator?

Artificial intelligence is only as good as the data it consumes. Yet the datasets driving machine learning are far from neutral—they reflect histories of exclusion, erasure, and cultural dominance. From natural language processing to facial recognition, the question of whose knowledge gets encoded is increasingly unavoidable. To decolonize the dataset is not just a technical problem; it is a cultural and political challenge that demands a rethink of authorship, representation, and participation in AI design.

Missing Data, Missing Voices
Artist and researcher Mimi Ọnụọha shows that what’s absent from datasets can be as revealing as what’s included. Her project The Library of Missing Datasets (2016–ongoing) collects examples of information that institutions routinely fail to gather—such as statistics on undocumented communities, instances of police violence, or other marginalized experiences. By naming these absences, Ọnụọha makes visible that missing data is rarely accidental; it reflects structural conditions and decisions about whose lives are counted and whose are left out.

She argues that datasets are cultural artifacts shaped by power, not neutral repositories of facts. When these omissions carry into AI training sets, they scale into systems that overlook or misrepresent entire populations, reinforcing inequities in who technologies can “see” or “hear.”

Training Machines Differently
Some practitioners are reimagining what dataset authorship could look like. Stephanie Dinkins, an artist working at the intersection of AI and community organizing, is known for her long-term project Conversations with Bina48, in which she engages a humanoid robot built on the likeness and memories of a Black woman. For Dinkins, these dialogues are not only about human–machine interaction but about how cultural specificity and family narratives can shape AI. She extends this inquiry through projects such as Not The Only One—a community-based AI trained on oral histories from people of color—where local participants help generate the training data.

Rashaad Newsome takes a parallel approach with his AI entity Being the Digital GRIOT trained on archives of Black cultural production and critical theory. Rather than presenting AI as a universal or objective intelligence, Being performs, lectures, and interacts with audiences from within a distinct cultural lineage.
Together, these projects move beyond critique into practice, offering models for how AI might be trained through community-grounded processes. They open space for cultural knowledge that has historically been excluded from technological infrastructures.

Collective Infrastructures
Decolonizing datasets also requires rethinking how infrastructure itself is built. The Masakhane Project, a grassroots research collective across Africa, develops open-source natural language processing datasets, models, and tools for African languages. Most mainstream NLP systems privilege English and a handful of global languages, sidelining hundreds of millions of speakers. Masakhane addresses this imbalance by mobilizing a distributed network of contributors who build resources in their own languages, reshaping who participates in AI development.

The Indigenous Protocol and AI Working Group challenges the Western epistemologies that dominate AI systems. Their work asks what it would mean to embed Indigenous worldviews into machine learning—where training is grounded in relationship rather than extraction, and knowledge is guided by protocols of respect, consent, and reciprocity. Instead of treating datasets as resources to be mined, this approach repositions them as living cultural entities. Critical scholarship reinforces these shifts. Cognitive scientist Abeba Birhane has described large-scale datasets as a form of “automated colonization,” reproducing extractive logics of empire in digital form. She advocates for relational ethics in AI, where the value of data is situated in context, community, and ongoing dialogue rather than in scale or efficiency.

In Latin America, the feminist tech collective Coding Rights addresses similar issues through activism and creative interventions. Known for projects like Chupadados, they expose how algorithmic systems perpetuate discrimination—particularly against women and marginalized groups—and link dataset politics to broader struggles around rights, surveillance, and digital governance. Together, these initiatives show that alternatives are not only possible but already in motion. They demonstrate that decolonizing AI is less about “fixing” biased systems from the outside and more about designing infrastructures that begin from different worldviews.

Beyond Inclusion
Decolonizing AI cannot be reduced to adding more data or expanding categories of representation. As many researchers note, inclusion alone risks integrating marginalized knowledge into systems that remain extractive at their core. The projects above point instead to a deeper shift: reframing what counts as data, how it is gathered, and who has agency in the process. From Ọnụọha’s mapping of absence to Dinkins’ and Newsome’s culturally specific training practices, from Masakhane’s distributed infrastructures to the Indigenous Protocol group’s epistemic reframing, the momentum is toward plurality. The future of AI is unlikely to be shaped by a single universal dataset but by many situated ones.

As Birhane and Coding Rights emphasize, the politics of datasets extend beyond technical domains. They touch on cultural survival, digital rights, and the governance of knowledge itself. The challenge is not only to demand representation but to build AI infrastructures that reflect and sustain the diversity of human experience.

Developed in reference to the centenary of Frei Otto, the project does not attempt to digitally replicate Otto’s tensile structures. Instead, it explores similar questions of form-finding through physical experimentation. Industrial robotic arms, fitted with custom tools, are choreographed using animation workflows to interact with soap films—one of the most minimal surface-forming systems available.

Soap films are structurally precise yet materially unstable. Their geometry is governed by surface tension and environmental conditions rather than rigid constraints. By introducing robotic precision into this system, Gentle Systems stages a controlled encounter with unpredictability.

The robots execute programmed movements, but the resulting forms are contingent: stretching, collapsing, and reforming in response to micro-variations in air, humidity, and motion. The work shifts robotic fabrication away from repeatable output toward ephemeral formation.

The project positions it as a participant in material negotiation. Computation sets parameters; matter completes the form.

The result is a series of experiments in robotic choreography—where motion, timing, and material resistance produce geometries that cannot be fully predetermined.

Project Info
Developers: Gentle Systems
Year: 2025
Image Credit: Haw-lin Services
Project Team:
Engineering Direction · @dev.90
Design Direction · @lucasdiem
Robot Programming · @sambrego.tt
Tool Design + Concept Development · @no_mrzy
Mechanical Engineering · Jan Thröner
Tool Design Support · @t_silluzio
Tool Design + Concept Development · @koritzmoch
Photography · Haw-lin Services (@hawlinservices)
Post-production · Studio Wolfram (@studiowolfram)

In April 2022, a consortium led by Eindhoven University of Technology installed the Smart Circular Bridge in Almere—a 15-meter pedestrian span built from flax-fiber biocomposites and embedded with sensors. More than a demo, it’s a live trial for bio-based load-bearing structures. Add to that the field deployment of self-healing concrete on Dutch bridges and the U.S. Air Force Research Laboratory’s biocement tests for rapid runway build-outs, and the signal is clear: biomaterials are moving from prototypes to infrastructure—roads, bridges, and utilities designed to adapt rather than just endure.

From Niche Materials to Structural Contenders
For years, biomaterials circulated in design as clever alternatives to plastics and concrete: mushroom-based packaging, bacterial dyes, or bio-bricks pressed from crop waste. But the ambition has shifted. Research centers and start-ups are pushing these materials into structural applications, aiming for durability at the scale of infrastructure.

Take bio-cement, where microbes mineralize sand into solid forms. U.S.-based start-up BioMASON is commercially producing masonry units grown by bacteria, avoiding the high emissions of traditional kilns. At the University of Colorado Boulder, researchers are developing “living building materials” using photosynthetic cyanobacteria to grow prototype structural blocks that actively sequester carbon as they cure. In Europe, the Living Architecture project has created microbial bricks that can clean water and generate electricity while contributing to structural assemblies.

Mycelium, meanwhile, is moving from speculative installations toward applied research. Dutch design labs such as Studio Klarenbeek & Dros are piloting mycelium-based façade panels, exploring how fungal composites could provide insulation and carbon capture. These aren’t yet bridges, but they point to modular bio-components that might slot into tomorrow’s building systems.

Infrastructure That Learns and Repairs
Traditional infrastructure is built to resist change. Roads, tunnels, and pipes are cast, buried, and maintained until they crack or corrode. Biomaterials flip that logic. Because they are living or semi-living systems, they introduce adaptability into what has historically been rigid.

Consider self-healing concrete infused with dormant bacteria that activate when cracks form, sealing fractures before they spread. Dutch company Basilisk has moved this technology out of the lab and into pilot projects across Europe, applying bacterial concrete to tunnels, bridges, and sections of roadway. By reducing maintenance costs and extending lifespan, it reframes infrastructure from a liability into a regenerative system.

Other experiments are testing more responsive or metabolic structures. Research into fungal composites suggests building skins could expand or contract with humidity, adapting passively to environmental shifts. In Hamburg, the BIQ building has been running since 2013 with algae-filled façade panels that generate shade, produce biofuel, and contribute to local carbon cycling—a hybrid model of infrastructure that functions less like a static wall and more like a metabolic system. This reframes infrastructure not as a finished object but as a responsive process. The new design challenge is less about how to reinforce structures against inevitable decay and more about how to choreograph cycles of growth, adaptation, and repair.

Cities as Bio-Networks, Not Machines
Infrastructure defines how cities breathe, move, and metabolize resources. Treating it as a living network rather than a mechanical system opens a radically different urban design agenda. Imagine wastewater treatment plants seeded with microbial communities that both filter waste and generate energy. Roads under study that deploy bio-based chemical reactions to repair micro-fractures before potholes appear. Building skins that photosynthesize, offsetting emissions and producing usable biomass. These are not speculative fantasies but directions already tested through funded research and pilot projects.

The ecological stakes are high. Global infrastructure demand is projected to triple by 2060, locking in enormous material footprints. Cement and steel alone account for around 15 percent of global CO₂ emissions. Biomaterials that sequester carbon while providing structural function could help redirect that trajectory, reframing infrastructure as a partner in ecological cycles rather than an extraction-heavy burden.

Reliability, however, is the choke point. Infrastructure failures are catastrophic, and the unpredictability of living systems makes regulators cautious. For now, hybrid models—where bio-based composites are integrated with conventional materials—are the likeliest route to adoption. Success will depend not just on novelty but on demonstrating consistency across decades of use.

The Next Material Transition
Treating biomaterials as infrastructure raises deeper questions than whether they “work.” Who governs materials once they are alive within public works? How do building codes adapt to substances that grow, regenerate, or decay? And how should value be calculated when performance includes carbon sequestration, biodiversity, or air purification alongside traditional metrics of strength and longevity?

The answers will determine whether biomaterials remain experimental or become foundational to urban life. The promise of roads that repair themselves, bridges informed by microbial processes, and façades that metabolize carbon is less about aesthetics than about systemic change. Infrastructure has always been invisible until it fails; biomaterials demand that we see it differently—not as a fixed asset but as a living partner in the design of cities.

For designers, engineers, and policymakers, this is not about swapping concrete for fungi. It is about rewriting the cultural and technical frameworks that define infrastructure. Biomaterials suggest that the cities of the future may not only be built but cultivated—systems that grow with us rather than against us.

Google’s unveiling of Android XR at I/O 2025 reframes the XR landscape from fragmented experiments into a more unified platform strategy. Unlike earlier attempts at standalone AR wearables, Android XR ties directly into Google’s broader ecosystem—leveraging Gemini AI, Play Store compatibility, and support for WebXR and OpenXR standards.

The hardware side will be the real test. Google has partnered with Samsung, Gentle Monster, and Warby Parker to design lightweight XR eyewear, while Xreal’s Project Aura was introduced as one of the first optical see-through smart glasses announced for the system. Powered by Qualcomm’s XR-optimized silicon, Aura blends everyday eyewear with real-time overlays, positioning itself closer to consumer-ready design than the bulkier headsets that have dominated the space.

For developers, Android XR consolidates tools once scattered across Snapdragon Spaces and individual OEM platforms. Early indications suggest smoother pathways for porting immersive content across devices—an essential step if XR is to move beyond the silo problem that slowed earlier efforts.

Enterprise applications are evolving in parallel. Hololight’s Stream SDK 2025.0.0 now enables sub-50ms latency XR streaming across Apple Vision Pro, Meta Quest, and desktop browsers, making collaborative reviews and simulations more fluid. Meta, PIXO, and Vuzix are also advancing XR tools for workforce training and industrial use.

The convergence of AI, cross-platform standards, and wearable design suggests a turning point. For creative practitioners, the question is less whether XR will reach maturity, and more which ecosystems will shape how we design, prototype, and tell stories in spatial media.

Soft robotics has long promised machines that are safer, lighter, and more adaptive than their rigid industrial counterparts. Yet the field has been slowed by practical constraints: heavy pneumatic pumps, external tubing, and limited modes of movement. Researchers at the Singapore University of Technology and Design (SUTD) believe they have found a way forward with a deceptively simple innovation. Their “FiBa” modules—short for film-balloon actuators—combine curved polymer films with 3D-printed balloons to create lightweight, flexible units that can power a new generation of untethered robots.

The FiBa breakthrough, published in Science Robotics in July 2024, is not just a story about clever engineering. It is also a story about design—specifically, the translation of biological strategies for movement into mechanical form. By looking to turtles, inchworms, bats, and ladybugs, the Soft Fluidics Lab at SUTD has turned animal motion into a blueprint for robotic locomotion.

Material Simplicity, Structural Intelligence
At the heart of the FiBa module is a combination that is as accessible as it is effective. A thin polymer film is curved transversely, echoing the spring-like structure of a carpenter’s tape measure, and paired with a compact pneumatic balloon. When pressurized, the balloon pushes against the film to produce movement. This design allows the actuator to generate strength and flexibility while remaining exceptionally light. Because the modules weigh so little, they can carry onboard pumps, valves, batteries, and controllers. In practice, this removes the need for external tubing and tethered power supplies, a long-standing bottleneck for soft robotics.

What emerges is a system that is not only functional but autonomous. While many robotics breakthroughs are driven by advances in computation, FiBa demonstrates how structural and material design can also provide solutions.

Biomimicry in Motion
Rather than showcasing the modules in abstract movements, the research team turned to nature. A crawling robot drew inspiration from the low, stable gait of a turtle, designed to illustrate steady progress across flat surfaces. Another prototype climbed in the looping rhythm of an inchworm, revealing how contraction and extension could carry a robot vertically. A third design perched on ledges with the stability of a bat, showing how soft structures can maintain position in static environments. The most ambitious of the group unfolded like a ladybug, deploying hidden wings mid-air to achieve controlled flight. Each of these movements underscored a different dimension of the FiBa’s potential, illustrating how biological strategies for efficiency, adaptability, and protection can be translated into robotic performance.

The ladybug design in particular shows how compactness and adaptability can be combined. On the ground, the robot remains small and protected, but once airborne it expands into a flyer. Nature’s own solution—hardened wing covers concealing delicate flight wings—proved a useful model for balancing resilience with functional mobility. The takeaway for soft robotics is that reconfiguration, rather than fixed form, may be one path to versatility.

From Laboratory Prototype to Potential Applications
The FiBa modules are still in the early stages of research, but their implications are broad. A robot that can crawl, climb, perch, and fly without external hardware points toward future applications in environments where traditional machines often fall short. In disaster response, lightweight climbers or crawlers could one day move through rubble to locate survivors without the encumbrance of cables. In healthcare, perching or soft-bodied devices might operate safely around humans, reducing the risks posed by rigid machinery. In aerospace, compact robots that deploy larger structures when needed align with the priorities of space engineering, where weight constraints are critical.

These scenarios remain speculative, but they highlight the kinds of domains where FiBa’s lightweight autonomy could matter. Just as importantly, the project suggests a way of reframing robotics development as a material problem rather than a purely computational one. Instead of relying on algorithms to compensate for mechanical limitations, the FiBa modules embody solutions directly in their physical design. This perspective positions robotics closer to design disciplines, where form and material choice are recognized as central to function.

A Design Language for Soft Robotics
The FiBa initiative is the product of collaboration across institutions, led by Dr. Terry Ching during a joint PhD between SUTD and the National University of Singapore, with Professor Michinao Hashimoto of SUTD’s Soft Fluidics Lab as a corresponding author. The work also involved the Aerial Innovation Research Lab at SUTD and the MicroTE Laboratory at Queensland University of Technology. This interdisciplinary collaboration—spanning fluidics, aerial mechanics, and fabrication—underscores that no single discipline holds all the tools needed to advance soft robotics.

Since its publication in 2024, no major public updates have emerged from the project, indicating that FiBa remains a foundational proof of concept. Yet even at this stage, it points to how robotics might evolve. The research demonstrates that autonomy does not always require heavy infrastructure, and adaptability can be achieved through material ingenuity. By borrowing strategies from nature, the Soft Fluidics Lab has outlined what could become a new design language for robotics—one where intelligence is as much about structure as it is about code.

BEIJING — UCCA Center for Contemporary Art is presenting Pipilotti Rist: Your Palm is My Universe from July 19 to October 19, 2025. The exhibition marks a major commission by the pioneering Swiss artist, whose video-based installations have defined new modes of spatial and sensorial engagement since the 1980s.

Curated by Yan Fang, the exhibition anchors itself around the newly commissioned Your Palm is My Universe (2025), Rist’s most ambitious installation to date at UCCA. Spanning 1,800 square meters of the museum’s Great Hall, it is the largest single work the institution has commissioned since its founding. The installation is accompanied by a layered sound composition from experimental multi-instrumentalist Surma (Débora Umbelino). Together, image and sound form a multidimensional environment that situates visitors inside Rist’s evolving inquiry into perception, embodiment, and ecology.

The Great Hall as Living Body
At the center of the exhibition, Rist transforms UCCA’s Great Hall into a symbolic body. She deploys draped, translucent fabrics as projection surfaces that function like skin—porous, tactile, and open to touch. Videos move across the surfaces in saturated loops: close-ups of hands, feet, and faces shift into landscapes and back again, collapsing the distance between human anatomy and natural terrain.
The work deliberately unsettles orientation. Images play across uneven folds, recalling the way human eyes invert visual information before the brain corrects it.

Visitors are encouraged to lift and move the fabric, shaping their own experience while altering the perspectives of others nearby. This emphasis on collective perception dismantles the passivity often associated with screen-based culture, reframing the gallery as a shared sensory field.

Ecological Systems and Everyday Materials
While the installation immerses audiences in bodily sensation, it also reaches outward toward ecological reflection. Palm lines resemble topographic maps; close-up shots of leaves dissolve into vast fields. Rist positions the human body not as separate from the environment but as one element within a reciprocal system of organisms and habitats.

Other works extend this ecological and material focus. The Innocent Collection (1985–c.2054), installed along UCCA’s street-facing glass façade, assembles discarded translucent packaging into a sculptural constellation of “instant diamonds.” The piece underscores how overlooked materials can be reactivated through attention, turning waste into a site of perception. In the Open Gallery, Spring Chaoyang Chandelier (2025) strings pink swimsuits into a fountain-like chandelier, while Heaven on Earth (2025) saturates walls with radiant color fields layered with a poem by Rist and a curatorial preface. These works extend her ongoing play with intimacy, public space, and material transformation—where humor and sensuality destabilize familiar codes.

From Early Experiments to Expansive Installations
The exhibition also situates Rist’s new commissions alongside her early single-channel videos, screened weekly in the UCCA Auditorium. Works such as I’m Not the Girl Who Misses Much (1986) and (Entlastungen) Pipilottis Fehler (1988) remix pop culture and feminist critique through glitch, distortion, and repetition. These early experiments reveal the foundation of her later practice: treating the moving image not as passive representation but as a tool for unsettling identity and perception.

By juxtaposing these early tapes with monumental immersive environments, the exhibition demonstrates the continuity of Rist’s project. Her installations have grown in scale, yet their conceptual drive remains consistent: collapsing the screen-viewer divide, engaging the body, and reconfiguring the boundaries between technology, nature, and self.

About the Artist
Pipilotti Rist (b. 1962, Grabs, Switzerland) is one of the most influential video and installation artists working today. Since the mid-1980s, her work has been exhibited globally, from the New Museum in New York to the Louisiana Museum of Modern Art in Denmark. Recent solo shows include Electric Idyll (Doha, 2024), Behind Your Eyelid (Hong Kong, 2022), and Big Heartedness, Be My Neighbor (Los Angeles, 2021–2022). She lives and works in Zurich.

Exhibition Details
Pipilotti Rist: Your Palm is My Universe
UCCA Center for Contemporary Art, Beijing
July 19 – October 19, 2025

Berlin, September 5, 2025 — Artificial intelligence is at the center of this year’s IFA 2025, with exhibitors across computing, gaming, audio, and home technology presenting products that integrate AI as standard functionality. The trade show runs through September 9 at Messe Berlin.

Intel introduced its Panther Lake processors, emphasizing AI acceleration for performance gains. Lenovo’s Legion Go 2 and MSI’s Claw 8 AI+ handhelds highlighted how gaming devices are shifting toward predictive optimization, with systems learning user patterns to improve battery life and frame stability.

Samsung unveiled its “AI Home” initiative, presenting a connected ecosystem where appliances, entertainment systems, and climate controls share data for automated adjustment. The focus is on interoperability across categories rather than isolated smart devices.

In consumer audio, manufacturers showcased AI-driven earbuds equipped with Bluetooth 6.0. Features such as adaptive sound tuning and background noise profiling, previously limited to premium models, are now being positioned for mass-market availability.

Across categories, exhibitors are treating AI not as an optional upgrade but as baseline design. The question for developers and consumers is how these systems will balance automation with transparency and user control.

When Arm introduced PlasticARM, a prototype processor fabricated from plastic instead of silicon, it marked a quiet but significant shift in how computing might embed itself into everyday materials. The device runs a streamlined version of Arm’s Cortex-M0 microcontroller and contains over 18,000 logic gates. By performance standards it is modest, but its real significance lies in where it can go that silicon cannot.

Traditional processors are rigid, fragile, and expensive to manufacture. PlasticARM, built on a flexible substrate, is thin, lightweight, and inexpensive—qualities that open new avenues for design. A processor that bends could be integrated into smart packaging, wearable textiles, or single-use medical sensors without the constraints of silicon’s brittleness. This shift extends computation into new surfaces and contexts. Imagine packaging that tracks freshness, or adhesive bandages that monitor wound healing. The goal is not more powerful computing, but pervasive, low-cost intelligence embedded in the overlooked objects of daily life.

Still, the possibilities come with trade-offs. PlasticARM is limited in speed, memory, and energy efficiency, making it unsuitable for complex tasks. More critically, the idea of disposable processors raises environmental questions. If computation becomes as throwaway as cardboard, the challenge of electronic waste intensifies—a concern designers and engineers must confront if the technology scales.

PlasticARM remains a research project, but it signals a future where processors are less like devices and more like materials. For the design community, it suggests a new palette: computation not just on screens or boards, but woven into the very surfaces that shape how we live.

The rise of generative AI has produced an explosion of synthetic imagery, from near-photorealistic portraits to speculative design renderings. This is not breaking news. Nonetheless, as these systems flood culture with machine-made visuals, the question of intellectual property has moved from theoretical to urgent: who owns the image? The current debate continues to unfold across four interlinked arenas—copyrightability, training data rights, legal enforcement, and policy response—each exposing the fault lines between creative labor, corporate power, and algorithmic production.

Copyrightability: The Human Authorship Barrier
Copyright law has historically rested on the principle of human creativity. In the United States, the Copyright Office has made it clear that purely AI-generated works are not eligible for copyright protection. If an image is produced entirely by a system like Midjourney or DALL·E, based solely on a user’s text prompt, it cannot be copyrighted—meaning others may freely use it. Neither the model developer nor the individual user is considered the author. Yet the boundary is less clear when humans exert meaningful creative control. When a designer iteratively refines prompts, edits outputs, or integrates AI-generated fragments into a larger composition, some copyright protection may apply. Recent guidance from the U.S. Copyright Office suggests that such hybrid works can be registered, but only the human contributions—not the underlying AI elements—are covered.

Other jurisdictions are experimenting with different approaches. The United Kingdom’s Copyright, Designs and Patents Act recognizes “computer-generated works,” assigning authorship to the person who makes the arrangements for their creation. Meanwhile, a Beijing court recently upheld copyright protection for an AI-generated image, highlighting how global interpretations diverge. The underlying issue is conceptual: should originality require a human mind, or can human-directed machine outputs cross the threshold of creative expression? Until a consensus emerges, copyrightability will remain a contested and highly contextual determination.

Training Data Rights: Fair Use or Mass Infringement?
The debate extends beyond outputs to the inputs that make generative systems possible. Training data for image models often includes billions of images scraped from the internet, many of them copyrighted. Developers argue that using these datasets should qualify as “fair use”—a transformative practice akin to how humans learn by exposure to existing works. Rights holders counter that mass scraping without consent amounts to systematic infringement. This tension has already sparked high-profile litigation. Getty Images sued Stability AI in the U.K. for allegedly copying millions of photographs to train Stable Diffusion without authorization, with the case set for trial in 2025. In the United States, Disney and Universal filed lawsuits against Midjourney in 2025, alleging that its training and outputs drew on copyrighted characters such as Darth Vader and Elsa. A coalition of artists has also brought suits against Stability AI and Midjourney, claiming their works were misappropriated to generate derivative images; while some claims were dismissed, others—such as direct infringement—are still moving forward.

Beyond the courtroom, the dispute raises ethical questions. Many artists see generative AI as a form of uncompensated labor extraction—models are effectively trained on years of creative effort without acknowledgment or licensing. On the other side, developers warn that restricting training data too tightly could stifle innovation and limit the capabilities of AI systems.

Legal Enforcement: A Growing Patchwork
As these disputes unfold, courts worldwide are establishing precedents that could shape the future of creative AI. The Getty Images case, scheduled for trial in the U.K. in summer 2025, may clarify how courts view the unlicensed use of copyrighted works in training datasets. In the United States, Disney and Universal’s lawsuit against Midjourney could set important standards for how character likenesses and fictional universes are protected against algorithmic replication.

Smaller but significant cases are also chipping away at the issue. In 2023, a U.S. federal judge dismissed parts of the artists’ lawsuit against Stability AI but allowed claims of direct infringement to proceed. Authors and publishers, including the New York Times, have filed suits over AI training on text, showing that the conflict is not limited to visual media. The result is a fragmented legal environment where outcomes vary across jurisdictions and contexts. For creators and companies working globally, this patchwork complicates risk management and makes liability difficult to predict.

Policy Response: Transparency, Disclosure, and Labeling
Policymakers are beginning to step in, but responses remain uneven. In the United States, the Generative AI Copyright Disclosure Act, introduced in 2024, would require developers to disclose whether copyrighted materials were used in training datasets at least 30 days before releasing a model. The act stops short of banning such use but seeks to bring transparency to a process that has largely been opaque. The U.S. Copyright Office has also released a series of reports in 2025—one addressing the copyrightability of AI-generated outputs, and another examining the legality of training practices.

The European Union has taken a more regulatory stance through the AI Act, which obliges providers to indicate whether copyrighted works were used in training and to label AI-generated outputs. These provisions build on the EU’s framework for text-and-data mining (TDM) exceptions, which allow the use of copyrighted material for machine learning unless rights holders explicitly opt out. Elsewhere, approaches remain less developed. The U.K. is pursuing a code of practice on AI and copyright through its Intellectual Property Office, while China has signaled more openness by recognizing copyright in some AI-generated works through recent court decisions. Each approach reflects distinct cultural and legal priorities, suggesting that global harmonization is unlikely in the near term.

The Unfinished Framework
The question of who owns the image in generative AI is not just a technical legal matter—it’s a cultural and economic struggle over how creativity is valued in an age of algorithmic production. Current law leans heavily toward protecting human authorship, while AI-only outputs remain ineligible for copyright protection. At the same time, the battle over training data pits notions of fair use against the rights of creators whose work underpins these systems. For now, the landscape is defined by uncertainty. Courts are testing arguments, lawmakers are drafting disclosure requirements, and artists are demanding stronger protections. The eventual framework will likely blend legal standards, industry practices, and technical safeguards such as provenance metadata or opt-out systems for training data.

What remains clear is that the stakes are high: the outcome will shape not only the economics of creative labor but also the cultural legitimacy of machine-made art. The fight over generative AI and intellectual property is less about ownership of any single image and more about defining the contours of authorship in a computational age.

When designers talk about tools, they usually mean instruments that extend precision, efficiency, and control. Software updates promise smoother interfaces, better prediction, and fewer glitches. But not all value lies in seamlessness. Let’s consider a few generative design tools the embrace unpredictability, treating algorithmic error and emergent behavior as part of the process. These systems complicate the relationship between human and machine, asking: do we want tools that standardize outcomes, or ones that resist our expectations?

From Optimization to Emergence
Mainstream creative software is built on optimization. Auto-complete, template-driven interfaces, and predictive rendering reduce error and accelerate output. While this culture of optimization speeds production, it also risks flattening creative expression. By contrast, emergent tools prioritize instability over prediction. Instead of clean, expected results, they produce strange, contradictory outputs that force designers into dialogue with the system.

Projects like Runway Gen-2 expose the limits of seamlessness. Marketed as a professional-grade generative video tool, it frequently produces warped hands, collapsing physics, or flickering textures. For commercial workflows, these are bugs, but for artists, they reveal how probabilistic systems think. On the other end of the spectrum, Stable Diffusion demonstrates emergence through open-source experimentation. Its community of users trains custom models on partial, idiosyncratic datasets, producing outputs that are inconsistent and often eccentric. These results remind us that generative tools are not just technical systems but social ones, shaped by the biases and quirks of their communities. Together, Runway and Stable Diffusion demonstrate how moving beyond prediction repositions design, shifting from issuing commands to negotiating with emergent behavior.

Failure as Cultural Material
Some projects treat failure not as a by-product but as the foundation for new forms of authorship and curation. Botto, an AI-driven “artist” governed by a DAO, generates thousands of images every week—most of them awkward or incoherent. Instead of discarding them, the community votes, curates, and elevates select works into its evolving corpus. Failure becomes raw material for collective agency, transforming the way cultural value is assigned.

Similarly, Wekinator, Rebecca Fiebrink’s machine learning toolkit for musicians, foregrounds instability as an integral part of live performance. A system trained on a handful of gestures rarely behaves predictably on stage; performers must improvise alongside its divergences. The feminist design and research group Feminist AI Projects: Bits & Bytes experimented with Wekinator as a way to explore ideas around machine learning. They later integrated it into Unity, where it was used to create small-scale interactive protests that encouraged public participation. Failure becomes part of the fabric of collaboration, weaving human and machine into shared improvisation. Here, emergent systems shift attention away from polished results toward the infrastructures of evaluation and performance. Authors, curators, and performers become facilitators of instability.

The Edges of Machine Creativity
At the boundaries of generative culture, projects like Google’s Magenta reveal how machine creativity falters—and why those falterings matter. Magenta’s compositions often loop awkwardly or fall out of harmonic resolution. Against human standards, they appear incomplete. Yet these “failures” highlight the assumptions embedded in training data and raise questions about what cultural forms machines are capable of inventing.

Across Runway’s distortions, Stable Diffusion’s eccentricities, Botto’s awkward images, Wekinator’s unstable gestures, and Magenta’s off-kilter melodies, failure is not a breakdown of functionality but a site of possibility. This has broader implications. If algorithmic failure is embraced, creative labor could evolve in ways that treat unpredictability as a valuable resource rather than a liability.

Art schools, publishers, and cultural institutions may have to adapt to practices that resist seamlessness instead of privileging it. And the question remains whether these approaches can scale into mainstream workflows or whether they will persist as counterpoints to the dominant drive for optimization. Designing with failure reframes the role of the designer. Instead of engineers debugging systems, practitioners become curators, performers, and collaborators. Failure becomes a method, not a malfunction—an essential resource for cultural inquiry.

The Las Vegas Sphere has unveiled its most ambitious production to date: an AI-enhanced staging of The Wizard of Oz. Debuting August 28, 2025, the event combines advanced image restoration with immersive spectacle, transforming the 1939 classic into a 21st-century experience.

Working with Warner Bros. and Google DeepMind, the Sphere’s creative team used machine learning to sharpen and restore the film’s visuals without altering performances. The result is projected across the Sphere’s 22-story interior screen, surrounded by synchronized drones, wind effects, and spatial audio. The production brings the film’s iconic sequences—yellow brick road, Emerald City, tornado—to life with unprecedented scale.

More than 2,000 artists, engineers, and technicians contributed to the project. Rather than a remake, the production preserves the original cast and narrative, embedding them in a multi-sensory environment. Judy Garland’s performance remains intact, while synchronized effects—drones, wind, haptics, and spatial sound—extend the film into an experience that bridges cinema, live performance, and themed spectacle.

For the entertainment industry, the Sphere’s Wizard of Oz highlights how AI and immersive architecture can transform legacy films into new cultural formats. Instead of simply re-screening classics, studios and venues are beginning to pair machine learning with large-scale environmental effects to create experiences that merge cinema and live event. In this way, the Sphere operates not just as a venue but as a prototype for the future of cinematic exhibition.

For decades, the screen has been the primary surface of interaction: from CRTs to smartphones, pixels have defined how we access information, navigate tools, and communicate. But design futures are increasingly shifting away from glass rectangles. Gesture, voice, and multimodal systems are positioning themselves as the next frontier—interfaces less about tapping icons and more about embodied interaction.

From Screens to Embodied Interfaces
Touchscreens revolutionized personal computing by making interaction tactile, but they are now reaching their limits. Fatigue, distraction, and ergonomic constraints show that screens are not always the best interface for work, play, or learning. Smart speakers, mixed reality headsets, and sensor-rich environments suggest alternatives where voice, gesture, and environmental cues become primary channels.

Gesture recognition, once confined to gaming experiments like Nintendo’s Wii or Microsoft’s Kinect, is maturing into infrastructure. Apple’s Vision Pro, released in 2024, sets a new benchmark by relying on micro hand gestures and eye tracking as its primary navigation method. Instead of controllers or keyboards, interaction is choreographed directly through embodied cues. Advances in platforms like Ultraleap further extend hand-tracking precision for VR/AR and training simulations, showing how gesture is moving from novelty to infrastructure.

Voice is undergoing a similar transformation. Early smart assistants revealed the limits of rigid command systems, but large language models now enable more conversational and adaptive voice interaction. Google’s Project Euphonia pushes this further, training systems to better understand atypical speech patterns—making voice a more inclusive modality. These advances not only transform accessibility but also enable hands-free workflows across fields from surgery to manufacturing.

Designing Multimodal Futures
The trajectory is not gesture or voice—it is multimodal interaction. The most powerful systems combine input types: sketching with gestures, refining with voice, finalizing on screens. A surgeon might navigate 3D models with hand motions while dictating notes, while a designer could manipulate virtual forms in space while using spoken commands to control tools.

Research labs are actively prototyping these futures. The MIT Media Lab’s Fluid Interfaces Group has developed projects such as AlterEgo, a wearable device that interprets silent speech through neuromuscular signals, and the Reality Editor, which uses augmented reality overlays to let users control IoT devices through spatial gestures. At Disney Research, projects like EM-Sense explored how a smartwatch could recognize objects by detecting their electromagnetic signatures. Taken together, these initiatives illustrate how multimodal systems—combining gesture, wearables, and environmental sensing—are moving toward context-aware, embodied computing.

On the developer side, XR design toolkits from Unity and Unreal are providing frameworks for combining gaze, voice, and haptic input, giving designers the building blocks for multimodal ecosystems. For practitioners, this shift demands a new design vocabulary. Wireframes and static flows give way to movement scripts, sonic cues, and conversational prototypes. Instead of designing only for visual layout, designers must choreograph how different inputs complement each other in sequence, ensuring they work as a coherent interaction rather than competing signals

Ethics, Bias, and Access
The promise of post-screen interaction carries significant risks. Gesture and voice interfaces often depend on always-on cameras and microphones, raising privacy and surveillance concerns. Voice recognition systems still struggle with accents and dialects, and gesture tracking can misinterpret across body types or cultural norms.

Inclusive design requires broad datasets, intentional testing, and safeguards against bias. Initiatives like Mozilla Common Voice, which builds open datasets for training speech models across languages and dialects, are critical steps toward equity. Designers must also resist novelty for novelty’s sake, ensuring that new modalities genuinely reduce friction and expand access rather than complicate interaction.

Toward Ambient and Adaptive Environments
The long view points to computing dissolving into environments. Screens will remain useful but will be only one part of a distributed interface ecology that includes sensors, AI agents, and adaptive spaces. The emphasis shifts from devices to presence: how interaction aligns with natural human movement and communication. For creative practice, this unlocks new possibilities. Installations like Random International’s Rain Room (2012), where human movement alters environmental behavior, hint at how embodied interaction can transform cultural experience. Projects such as TeamLab’s Borderless in Tokyo, where digital projections respond to visitor movement, or Meow Wolf’s Convergence Station in Denver, where immersive installations adapt to presence and touch, further illustrate how design is becoming environmental, adaptive, and multisensory.

Design tools that allow manipulation of 3D models in midair or voice-responsive museum guides point toward environments that adapt dynamically to visitors. Done well, these systems reduce friction and make technology feel less like a barrier and more like an extension of human expression.

From Pixels to Presence
Gesture and voice are not simply add-ons to existing systems; they are reconfiguring the grammar of interaction. As design moves beyond the screen, it becomes more embodied, contextual, and multisensory. The challenge is to choreograph these new interactions with cultural sensitivity, accessibility in mind, and awareness of surveillance risks. The future of design will not be measured in pixels alone but in how effectively we shape the invisible interfaces that surround us.

Pirouette: Turning Points in Design at New York’s Museum of Modern Art looks at design as an agent of change rather than static form. Organized by Paola Antonelli with curatorial assistant Maya Ellerkmann, the show is on MoMA’s Floor 3 and runs through November 15, 2025; it opened January 26, 2025.

Drawn largely from MoMA’s collection, the exhibition assembles more than 100 works spanning from the late 19th century to today. Familiar touchpoints—Milton Glaser’s original I ♥ NY sketches, the Sony Walkman, and Apple’s Macintosh 128K—appear alongside the updated Accessible Icon, underscoring how symbols and consumer devices have reshaped communication and everyday behavior. 

The checklist adds depth with Susan Kare’s early Macintosh icon drawings; Ed Hawkins’s climate visualizations Warming Stripes and Global Temperature Spiral; and Golan Levin and Shawn Sims’s open‑source Free Universal Construction Kit, which bridges incompatible toy ecosystems. These sit with work like Gabriel Fontana’s Multiform series, extending the show’s attention to social and cultural contexts. 

MoMA complements the galleries with a 15‑track audio playlist featuring reflections from designers and writers. A one‑day Pirouette Abecedarium program (February 21, 2025) framed 26 “turning points” as an A‑to‑Z of design ideas; a recording is available. Together, these elements position the exhibition as an inquiry into leverage points where design redirects expectations around technology, consumption, and expression. 

Rather than a retrospective, Pirouette reads as a map of influence: how everyday objects and visual systems—labels, logos, interfaces, and tools—quietly reorganize habits, access, and meaning over time. The result is a clear view of design’s cultural work across more than a century. 

Generative storytelling isn’t about automation. It’s about reconfiguration. When models write, recombine, or adapt text, stories stop being fixed scripts and start behaving like adaptive systems—shaped by both human input and machine inference.

Authorship in Flux
The first assumption generative storytelling unsettles is authorship. Who owns a story co-produced by human prompts, training data, and machine inference? Shekhar Kapur’s Warlord, a sci-fi series in development with Studio Blo, takes a collaborative approach. Built with generative AI, the project is framed as a shared-IP universe where creators—and eventually audiences—can expand the narrative. Kapur calls this democratization. But dispersing authorship raises questions: who maintains coherence, and who bears responsibility for ethics or biases encoded in the system?

Natasha Lyonne’s Uncanny Valley, developed with AI studio Asteria, pushes hybridity further. Asteria’s custom model, “Marey,” trained on licensed datasets, shaped both visuals and narrative logic. Here, AI functions not just as a production tool but as a narrative presence, blurring the line between authorship and story. The experiment highlights both promise and pitfalls: originality, credit, and accountability become murkier, while AI’s statistical tendencies risk flattening distinctive voices.

Narrative Systems
If authorship disperses, narrative itself becomes unstable. Stories operate less like texts and more like adaptive systems. Guy Maddin’s Seances, co-created with the National Film Board of Canada, makes this instability explicit. Each screening is a one-off, algorithmically assembled short film from an archive of pre-shot footage—then erased after viewing. With billions of permutations, the work shifts attention from definitive story to generative process.

The Diary of Sisyphus, written entirely by GPT-NEO, extends the experiment into feature-length cinema. Critics called it “mechanical poetry”—slow, surreal, uneven, but philosophically resonant. The film proves AI can sustain narrative at scale, but coherence and emotional depth remain fragile.

Hidden Door, an AI storytelling game in early access, takes the opposite approach. Players move through structured “story cards” and guided arcs designed to constrain generation. The system preserves coherence but sacrifices surprise. The trade-off points to a broader design tension: how much freedom can be given to generative systems before narrative breaks down?

Personalization and Cultural Meaning
Generative storytelling also alters how audiences experience text. Instead of a shared version, each person may encounter a personalized flow shaped by preferences or interactions. Google DeepMind’s ANCESTRA, which premiered at Tribeca, shows this hybrid model. Developed with Primordial Soup, it combines live-action with AI-generated sequences from DeepMind’s Veo and Imagen models. The film demonstrates how human and machine imagination can merge seamlessly. Yet it surfaces a cultural dilemma: if every narrative can splinter into individualized versions, what happens to stories as collective reference points?

The Wizard of Oz reimagining at Las Vegas’s Sphere takes another approach. Using generative AI to restore and upscale the 1939 classic into a 16K immersive projection, the project reintroduces a cultural icon through spectacle. Supporters see revitalization; critics call it revisionism. The question lingers: how much alteration before a classic ceases to be itself?

The Dream Within Huang Long Cave pushes personalization into intimacy. Visitors interact with YELL, a virtual avatar modeled on the artist’s father, whose dialogue adapts to participants in real time. Here storytelling becomes psychologized, raising ethical questions about emotional influence when narrative is mediated by AI.

Designing for Difference
The path forward lies in hybrid practices that shape AI critically rather than consume it passively. Seances and Hidden Door show how design can balance contingency and coherence. Warlord and Uncanny Valley illustrate both the allure and the complications of collaborative authorship. Huang Long Cave points to data-driven narrative experiments where ethical stakes are inseparable from aesthetics.

The debate is no longer whether AI belongs in storytelling—it already does—but how it should be used. Will generative systems churn out formulaic variations, or can they become frameworks for inquiry, collaboration, and genuinely new narrative forms? Generative storytelling is not just a tool for faster scripts or infinite content. It is reshaping the logic of narrative itself: distributing authorship, destabilizing structure, and personalizing meaning. The challenge is not whether AI can tell stories, but what kinds of stories we want it to tell—and what it means to tell them together.