To incorporate bioremedial functions into the performance of buildings and to balance generative architecture's dominant focus on computational programming and digital fabrication, this thesis first hybridizes theories of autopoiesis into extended cognition in order to research biological domains that include synthetic biology and biocomputation. Under the rubric of living technology I survey multidisciplinary fields to gather perspective for student design of bioremedial and/or metabolic components in generative architecture where generative not only denotes the use of computation but also includes biochemical, biomechanical, and metabolic functions. I trace computation and digital simulations back to Alan Turing's early 1950s Morphogenetic drawings, reaction-diffusion algorithms, and pioneering artificial intelligence (AI) in order to establish generative architecture's point of origin. I ask provocatively: Can buildings think? as a question echoing Turing's own "Can machines think?" Thereafter, I anticipate not only future bioperformative materials but also theories capable of underpinning strains of metabolic intelligences made possible via AI, synthetic biology, and living technology. I do not imply that metabolic architectural intelligence will be like human cognition. I suggest, rather, that new research and pedagogies involving the intelligence of bacteria, plants, synthetic biology, and algorithms define approaches that generative architecture should take in order to source new forms of autonomous life that will be deployable as corrective environmental interfaces. I call the research protocol autopoietic-extended design, theorizing it as an operating system (OS), a research methodology, and an app schematic for design studios and distance learning that makes use of in-field, e-, and m-learning technologies. A quest of this complexity requires scaffolding for coordinating theory-driven teaching with practice-oriented learning. Accordingly, I fuse Maturana and Varela's biological autopoiesis and its definitions of minimal biological life with Andy Clark's hypothesis of extended cognition and its cognition-to-environment linkages. I articulate a generative design strategy and student research method explained via architectural history interpreted from Louis Sullivan's 1924 pedagogical drawing system, Le Corbusier's Modernist pronouncements, and Greg Lynn's Animate Form. Thus, autopoietic-extended design organizes thinking about the generation of ideas for design prior to computational production and fabrication, necessitating a fresh relationship between nature/science/technology and design cognition. To systematize such a program requires the avoidance of simple binaries (mind/body, mind/nature) as well as the stationing of tool making, technology, and architecture within the ream of nature. Hence, I argue, in relation to extended phenotypes, plant-neurobiology, and recent genetic research: < Architecture = Nature > Consequently, autopoietic-extended design advances design protocols grounded in morphology, anatomy, cognition, biology, and technology in order to appropriate metabolic and intelligent properties for sensory/response duty in buildings. At m-learning levels smartphones, social media, and design apps source data from nature for students to mediate on-site research by extending 3D pedagogical reach into new university design programs. I intend the creation of a dialectical investigation of animal/human architecture and computational history augmented by theory relevant to current algorithmic design and fablab production. The autopoietic-extended design dialectic sets out ways to articulate opposition/differences outside the Cartesian either/or philosophy in order to prototype metabolic architecture, while dialectically maintaining: Buildings can think.

Being an architect means dealing with data. All architectural thinking—whether it is done with pen and paper or the most advanced modeling softwares—starts with data, conveying information about the world, and ultimately outputs data, in the form of drawings or models. Reality is neither the input nor the output. All architectural work is abstractions of reality, mediated by data. What if data, the abstractions of reality that are crucial for our work as architects, was to be used more literally? Could data actually be turned into architecture? Could data be turned into, for example, a volume, a texture, or an aperture? What qualities would such an architecture have?  These questions form the basis of this thesis project. The topic was investigated first by developing a simple design method for generating architectural forms from data, through an iterative series of tests. Then, the design method was applied to create a speculative design proposal for a combined data center and museum, located in Södermalm, Stockholm.