At the beginning of the semester, we were asked to reflect on the role of artificial intelligence in the AEC industry. Looking back, my answer then came from a much more limited understanding. Revisiting the question now, after a series of lectures, I realize that what has changed is not so much how much I know, but how I frame and understand the topic itself.
Initially, my use of AI was mostly utilitarian. I relied on it for code debugging and solving isolated technical problems, and I had not really engaged with AI as a tool for image generation or early design exploration. AI felt peripheral — useful, but not central to architectural thinking.
The lectures helped shift this perception by presenting AI not as a single, generic tool, but as an ecosystem of highly specialized models. This became especially clear during Libny Pacheco’s lecture, which demonstrated how AI can operate directly within the design domain through data-driven urban analysis. By translating abstract environmental risk into spatially precise insights, his work showed that AI does not need to generate architecture to be impactful. Instead, it can guide where architecture must respond.
This reframing influenced how I began to use AI in my own work. I started using image generation to quickly explore atmospheres and spatial qualities, and I became more conscious of AI’s capacity for pattern detection, particularly when working with algorithms and rules and performance-related data. Even my use of AI for code debugging evolved, shifting from simply fixing errors to understanding underlying logic and improving workflows.
At the same time, the more I learned about AI, the more aware I became of the limits of my own knowledge. Rather than feeling a sense of mastery, I became increasingly conscious of the complexity of these systems, their limitations, and the risks of treating them as opaque or authoritative tools (black box).
Overall, the lectures helped me reposition AI not as a shortcut or a replacement for design thinking, but as a set of specialized tools that support exploration and decision-making. I now see working with AI as an ongoing process—one that requires critical awareness and ethical evaluation, as repeatedly emphasized throughout the course—particularly within the AEC context.
