The vacuum of “Why” is not an accident of the technology. It is what happens when a profession mistakes a capability for a direction.
Intent is a necessity, we as designers always need to ask "why?"
The design field is experiencing a shift as AI tools extend beyond physical capabilities to impact mental processes. While AI excels at symbolic tasks, it lacks understanding of spatial and conceptual elements crucial to design. Designers, particularly senior ones, must reclaim their role as the “More Knowledgeable Other” in the Human+AI collaboration, providing the necessary problem theory and grounding that AI cannot generate.
The author argues that designers should not be reduced to mere prompters for AI, as this undermines their unique cognitive abilities in spatial reasoning and problem-solving. Instead, designers should embrace Parametric Design, a practice that involves defining the governing parameters and constraints of a problem before AI generates any output. This approach empowers designers to shape the logic and boundaries within which AI operates, ensuring that the generated results align with the designer’s vision and the project’s requirements.
When “Can AI do it?” replaces “Why are we doing this?”
Chances are that there is a meeting happening right now somewhere in the world, where someone opens a brief, and before the problem has been properly read — before anyone has asked what success looks like, or who the product is actually for, or what it’s supposed to do — someone else opens an AI tool and starts generating options.
In an article titled When design stops asking why and starts asking, “Can AI do it?” Dolphia has articulated a name for what’s happening in that meeting: the Decision Flip. Teams have started asking “Can AI do it?” before they ask “Why are we doing it?” The sequence matters more than it might appear. When execution becomes the first question, intent becomes an afterthought. And when intent becomes an afterthought, you get what Michael Szeto has been documenting: AI-generated output that is formally competent and creatively lifeless. Technically resolved but conceptually empty. The visual equivalent of a sentence that is grammatically perfect and says nothing worth saying.
This is not a tool problem. Tools don’t evacuate meaning. People do — when they let the tool’s capability define the scope of the question.
The Inversion Error in AI models requires a structural fix at the foundational level, encoding physical and spatial constraints into the architecture itself. This necessitates designers working within AI labs, collaborating with mathematicians and researchers, to define the problem-solution space and provide the spatial ground truth and physical constraints that AI cannot generate. The era of the Architect of Constraints is upon us, demanding designers engage with AI systems as the More Knowledgeable Other, defining parameters and building the foundational “floor” for AI to operate upon.
