You’ve been described as the “world’s most high-resolution architect.” How do you use technology in your practice to develop and deliver radical new approaches to architecture and urban design?

For over two decades, I’ve been working in architecture, focusing on computational and algorithmic multi-agent systems, as well as artificial intelligence. My primary interest lies in engaging with complex systems—what I refer to as ‘architecture for complexity’. The central question I’ve been exploring is how to address the challenges within built environments, which I see as dynamic, interconnected ecologies, through the lens of computation as a connective tool.

What excites me most is what that question makes possible. We now have access to big data, higher resolution, and computational power that allow us to reveal previously invisible patterns and relationships. And for the first time in history, we can engage with architecture, cities, urbanism, and infrastructure in ways that were once unimaginable. In science, increased resolution makes the invisible visible. Computation, at its best, does the same thing for design: it opens up the forces and logics that were always there, just out of reach.

You once called cities the most complex systems humans have ever created. How can we use algorithmic design tools to synthesize this complexity and inform the design of more intelligent urban plans?

Cities, and those who design them, require a fundamental shift in mindset. Today, we can simulate weather conditions at a one-kilometer resolution, track mobility patterns with precision, and model robotic construction in unprecedented detail. This capacity to test complex scenarios in controlled environments has changed what is possible, yet architecture and urban design still need to engage more fully with these possibilities. Buildings and cities operate as intricate systems that demand synthesis across multiple logics and data streams, far beyond isolated algorithms or aesthetic intuition.

The problem is that we still tend to treat algorithms as expressive instruments. They belong to broader systems. They are subject to environmental and contextual forces outside the model; the architect's intent is one input among many. That range of response demands a different kind of thinking: design as a search through an n-dimensional space, a mode of thinking closer to mathematics than to form-making. Machine learning opens this space and suggests options that once felt inaccessible within reach. The essential question remains: how do we, as humans, guide this search creatively, in collaboration with machines, to reveal the underlying patterns that shape our cities?

How did you start to apply these concepts to three-dimensional built structures?

After experimenting with waves, multi-agent systems, swarming behavior, and robotics—testing construction principles through prototypes like chairs and tables—I realized architecture needed a fundamental rethink in response to the planetary crisis. To stay relevant, we as architects must engage with systemic challenges and hack complexity to address urgent global issues. Moving beyond high-resolution objects like lattice structures, I explored how to create lightweight yet strong systems inspired by nature. This led to larger questions: How could landscape infrastructures and prefab architecture—long defined by repetition and monotony—be reimagined with greater richness?

The "AI Timber" project grew from this inquiry, premised on repetitive elements that form adaptable structures. Computational principles reveal that even simple building blocks (like binary code) can generate immense complexity. These discrete elements can produce formulas, images, music, or architecture, and they form systems that respond to external forces while they hold their internal logic. I focused on the joints: how machine learning and algorithms could extend human cognition to reveal new patterns and generate complexity from repetition. By adjusting a single angle, every component becomes unique while drawing on the same base elements. Higher resolution allowed parts to respond dynamically to light, temperature, and other factors, all within a stable underlying system. The result is a structure that appears to evolve organically while maintaining coherence, with considerable potential for future applications.

Many of your projects have a distinctive aesthetic expressed through fluid, nebulous forms based on complex adaptive systems found in nature. How can these ideas be scaled up for use in an urban or infrastructural context?

Nature's complexity has always been a deep source of inspiration. Desert dunes and rock formations, like those in the Grand Canyon, carry dense patterns that feel like vast computations of physics and matter. This notion of embedded complexity shapes my design approach, much like a musical composition where you assemble the instruments first, then explore their interplay. In computational design, I often introduce "noise" sparingly: subtle disruptions that generate patterns beyond simple scaling functions. The aim is to uncover hidden forces and dynamics within materials through computational tools, so the aesthetic takes shape from the underlying systems.

That is exactly what "Filling the Void" set out to test. The project applied these ideas to solar fields and reclaimed abandoned mining sites in the Australian desert as beautiful, site-specific infrastructure. Too often, large-scale projects like these are stripped of design and become ugly pieces of machinery that scar the landscape. That separation is the real failure. Designers belong inside the process from the start, as the intelligence that holds the aesthetic, technical, and contextual elements together.

You mentioned the need for architecture to address urgent global issues. How do you see the discipline evolving in response to these concerns, and what technologies are emerging that will enable architects to propose innovative solutions?

Most architects are driven by a genuine concern for the planet. Embracing complexity and ecology as a mission is how that concern translates into agency. The difficulty is structural, though. The field has long been dominated by the celebrity paradigm, where a single visionary drives the design, but today's challenges are too complex for that model. We need collective intelligence within teams and across disciplines, working with scientists, engineers, and specialists to achieve polymathic synthesis.

That kind of collaboration, though, requires tools that can actually support it.

Architects, engineers, and builders still operate in fragmented digital ecosystems and rely on incompatible software. That needs to change. With agentic AI, we can synthesize ideas more fluidly. Platforms like Omniverse already enable real-time simulation of complex scenarios and accelerate the design process through generative AI. Multimodal AI that integrates language, visuals, video, and 3D further expands these capabilities. The question at the center of it all is: how do we merge different modes of intelligence and think creatively across disciplines?

Architecture has always been about integration: client needs, site conditions, budgets, materials, technology, and cultural context. Computational power and AI push that capacity into new territory, and with it, what architects can actually attempt. In the future, design goes beyond individual buildings. It brings circularity, life cycles, and ecology into focus, and it asks architects to work with complexity to shape environments that can adjust over time.

Some architects and designers today are using tools like artificial intelligence to generate fantastical images of structures that defy physics and can, therefore, never be built. How important is it to focus instead on using these technologies in ways that will genuinely benefit society?

The true potential of AI is to redirect that creativity toward real complexity: matter, physics, the systems that govern how we build. Image-making is only the surface. It is seductive, but it is also a detour, and the ambition must be larger: to reimagine how we design and think about cities as interconnected, evolving ecologies.

That ambition has historical precedent. The most impactful architecture, whether Gothic or Modernist, was deeply connected to its context and moment. Today we face a similar crossroads, and the pressure to think boldly has never been greater. Many architects still focus on objects, form, and beauty, yet the stakes now demand a tighter link between those concerns and the world we actually inhabit. Form and beauty matter most when they are intrinsic to the challenges we must confront, not separate from them.

That orientation also changes the scale of the work. The field needs to move from singular buildings toward systemic frameworks, rules, and protocols that allow architecture to evolve dynamically and arrive as a living outcome. In this context, the future architect needs to be more of a systems designer than a form-maker.

That shift toward systems thinking also changes what collaboration with AI actually means. As the technology takes on a more prominent role in the design of buildings and cities, how do you define the architect's role within that framework, and where do you see risks?

Generative algorithms transform design from a linear, predetermined process into a process of discovery. The fixed path disappears; in its place, a search space opens, with algorithms that evolve alongside the designer's intent. Both the designer and the algorithm grow together, in something closer to gardening than engineering. The process, at its core, becomes an algorithmic synthesis: a dynamic interplay between creativity, exploration, and the iterative refinement of ideas.

What makes this really exciting is that AI cognition works differently from our own, and that difference is the point. It opens up possibilities that would have seemed unthinkable before. In that sense, architects are no longer sole authors. They become collaborators, co-creating with AI. The technology cannot yet make decisions or follow design intent on its own, but it shapes ideas in often unexpected and insightful ways. That distinction is worth keeping in mind.

The risk, though, is real. AI can inspire creativity, but it can just as easily become formulaic and drift from the real world. Thinkers like Nick Bostrom have raised this concern about optimization: an AI trained purely to optimize will pursue efficiency at the expense of human sensibility and creative intent. For designers, the opportunity is to hold that creative power alongside AI, so design keeps its human touch and the emotional intelligence that gives it true meaning.

Looking ahead, how might these programs evolve, and how will their enhanced capacities influence the future of architectural practice?

Generative computational tools are evolving quickly, however, there are certain significant thresholds that they cannot yet pass. For instance, transitioning from two-dimensional images to three-dimensional spaces is exponentially more complicated and, therefore, incredibly expensive computationally. But these problems can and will be solved, so the question remains: how can we use these tools to redefine architectural practice, and how can we be more ambitious about the scale of the challenges we choose to confront? It’s vital that we work collectively and use these tools to confront the planetary pressures facing us.