Design in the Age of Predictive Architecture: From Digital Models to Parametric Code to Latent Space

Abstract

Over the last three decades, architecture has undergone a sustained digital transformation that has progressively displaced the ontology of the geometric generator, understood here as the primary artefact through which form is produced, controlled, and legitimized. This paper argues that, within one extended digital epoch, three successive regimes have reconfigured architectural agency. First, a digital model regime, in which computer-generated 3D models become the main generators of geometry. Second, a parametric code regime, in which scripted relations and numerical parameters supersede the individual model as the core design object, defining a space of possibilities rather than a single instance. Third, an emerging latent regime, in which diffusion and transformer systems produce high plausibility synthetic images as image-first generators and subsequently impose a post hoc image-to-geometry translation requirement. To make this shifting paradigm comparable across time, the paper uses the blob as a stable morphological reference and develops a comparative reading of four blobs, Kiesler’s Endless House, Greg Lynn’s Embryological House, Marc Fornes’ Vaulted Willow, and an author-generated GenAI blob curated from a traceable AI image archive, to show how the geometric generator migrates from object, to model, to code, to latent image-space. As a pre-digital hinge case, Kiesler is selected not only for anticipating blob-like continuity, but for clarifying a recurrent disciplinary tension, “ form first generators” that precede tectonic and programmatic rationalization. The central hypothesis is that GenAI introduces an ontological shift not primarily at the level of style, but at the level of architectural judgement and evidentiary legitimacy. The project can begin with a predictive image that is visually convincing yet tectonically underdetermined. To name this condition, the paper proposes the plausibility gap, the mismatch between visual plausibility and tectonic intelligibility, as an operational criterion for evaluating image-first workflows, and for specifying the verification tasks required to stabilize them as architecture. Selection establishes evidentiary legitimacy, while a friction map and Gap Index externalize the translation pressure required to turn predictive imagery into accountable geometry, making the plausibility gap operational rather than merely asserted. The paper concludes by outlining implications for authorship, pedagogy, and disciplinary judgement in emerging multi-agent design ecologies.