Sitting area in the Parents' Realm of the Sala House - Christopher Alexander, Architect (photo by Ekyono; file is licensed under the Creative Commons Attribution-Share Alike 4.0 International license).
For years I’ve wondered what role AI might play in architectural design—not in the routine domains of optimization, code compliance, or energy modeling, but in the harder question of how buildings acquire coherence, depth, and human resonance. Much of the current discourse avoids that question. It gravitates toward what is measurable and leaves the deeper structure of design unexamined.- Levels of scale
- Strong centers
- Thick boundaries
- Alternating repetition
- Positive space
- Good shape
- Local symmetries
- Deep interlock and ambiguity
- Contrast
- Gradients
- Roughness
- Echoes
- The void
- Simplicity and inner calm
- Not‑separateness
Portion of an analysis of the Sala House image, by Danny Raede's 15 Fundamental Properties of Wholeness Analyzer.
A deeper constraint applies to any image‑only system: a single photograph is never a building. It freezes one viewpoint, one moment, one lighting condition. Many of Alexander’s properties are inherently experiential. Thick boundaries reveal themselves in section. Gradients unfold in movement. Not‑separateness depends on context beyond the frame. Positive space requires reading the shape of the outdoor room. Judging wholeness from one static image is necessarily partial.
After using Raede’s tool, I wanted a more relational reading. I turned to a large language model (Microsoft Copilot) and prompted it to analyze the same images through the lens of the fifteen properties: “Consider relationships across scales, massing, thresholds, centers, voids, interlock, context, and overall coherence.”
The difference was immediate. The model produced something closer to the kind of relational analysis an architect might offer. It could do this because it was not limited to pixel‑level cues. It could integrate the image, the conceptual structure of the fifteen properties, and the contextual hints in the prompt. This does not overcome the limits of a single photograph, but it allows the model to make sense of the available information in a more integrated way than a vision‑only classifier can.
Design for a renovation and expansion of the McMinnville Public Library (1982). The original Carnegie library is on the left; the proposed addition is on the right, which opens toward McMinnville City Park.
A vision‑only classifier
cannot produce this kind of relational reading.
These experiments raise a larger question: could any future tool reliably detect living structure? Even with far more capable models, the answer is probably no. Wholeness is emergent. It arises from iterative negotiation among site, program, structure, light, materials, craft, and human inhabitation. These relationships unfold across scales and through time. Many require embodied, situated judgment. AI may one day illuminate hidden patterns or diagnose coherence more reliably, but the deeper life of a building is unlikely to spring from software alone. Design remains a recursive field grounded in construction’s realities.
Even so, the potential is real. If future systems combine multiscale visual reasoning, spatial‑relationship understanding, datasets annotated for the fifteen properties, and insights from environmental psychology and cognitive science, AI could become a useful diagnostic partner. It might help architects see strengths, weaknesses, and missed opportunities. Whether Alexander’s properties remain the definitive rubric is debatable. They endure because they describe persistent, cross‑cultural patterns rooted in human perception. But they demand a relational intelligence that challenges even skilled humans to articulate fully. Any meaningful AI engagement with them will require moving beyond feature detection toward something closer to architectural judgment.
My own exploration echoes themes in Salingaros’s work on living geometry’s measurable effects on cognition and wellbeing. The takeaway is modest but clear: relational coherence in buildings is not an aesthetic preference; it has consequences for how people perceive and inhabit space. For now, AI’s most immediate value lies in conversation, as a tool that helps us see our work more sharply, test assumptions, and notice relationships we might otherwise overlook. In my experiments, it has already done that. If future systems can extend this capacity without losing sight of the relational nature of design, they may become useful partners in the ongoing effort to create environments that are coherent, grounded, and humane.










