Vision Setting and Problem Solving: AI in Architecture is Changing Design

Zach Mortice
April 19, 2024|


Artificial Intelligence in architecture is becoming a pervasive, powerful tool – and it’s also a technology that finds itself at an awkward, intermediate stage of development. AI can solve simple, practical problems, like how to arrange a floor plan, with unmatched speed and variation. And it can paint broad, creative visions culled from the entire Internet’s worth of imagery with just a brief text prompt. But connecting these two parts of the design process, the fundamental science and art of architecture, has proven elusive.

Feeding a string of architectural description (“Eco-topia Flintstones California Bungalow at the La Brea Tar Pits”) into AI image generators like MidJourney doesn’t result in anything buildable. And endless algorithmically generated floor plans can’t be scaled up to express anything more than an efficient use of space – yet. But joining these two capabilities together will be, perhaps, the most profound design-technology advance of AI’s age.

Benefits of using AI in architectural design

AI in architectural design is most useful for rapidly completing mundane, repetitive tasks and for optimizing designs by small increments, often referred to as artificial narrow intelligence. AI is most effective where these types of tasks overlap, as they often do. AI can instantly fill a residential tower with apartments shaped to fit the developers’ specifications and can tune them to varying degrees of material and cost efficiency. Additionally, image generators can work as an unapparelled “mood board” for design inspiration, offering a quick visual synthesis drawn from vast image libraries. These detailed images can give architects an aesthetic target to aim for as they define structural and engineering systems.

In both of these scenarios, architects take on a more broad-based curatorial role instead of maintaining granular control of every design decision; they are defining parameters, selecting and discarding options, and offering advice and guidance to algorithms. That’s a radical change in how architecture has been practiced. The dividing line is still being defined: Is this new tool a labor-saving device, just as CAD or BIM have become, or does it represent a fundamental shift in the creative process?

Will AI replace architects?

While computer capabilities bring more opportunity to balance humans and machine intelligence, humans are still better at open-ended creative solutions – for now.

Given how new AI is in the architecture field, it’s difficult to say how it will affect architecture jobs – though it’s hard to imagine that the tasks AI excels at, like assembling technical details and plans, won’t reduce the need for the entry-level designers who typically focus on these things. And while AI’s potential to free architects from detail drudgery is real, the temptation for employers to use this labor-saving tool to increase the pace of production is also well-established.

Today, there many areas of architectural design that AI hasn’t penetrated. AI can’t yet define the constraints that come with a building project, such as the program, size, audience, material or geographic context. These parameters come from interactions with clients, which also can’t be outsourced to AI. Th technology also has little understanding of how people move through space and interact with objects, and it can’t yet generate 3D imagery via text prompt with the richness and detail with which it creates 2D imagery.

Additionally, the fantastical visions dreamed up by MidJourney and DALL-E don’t come with supporting construction documents. Across the architecture, engineering and construction industry, AI has been least used in robotics applications that interact with building sites or buildings directly – though this is changing, with reality capture robots that have some level of independence but still require a human for guidance.

AI in architecture is also limited by fundamental economic and selection-bias dynamics that affect the quality of data these applications draw on. AI algorithms are limited by how much data they have to learn from – in architecture, this data can be proprietary, which creates a disincentive to share it with potential rivals working on their own AI applications. Also, image-creation AI can only resynthesize what it has already seen, so if the Internet’s bank of imagery is culturally or regionally biased (with, say, an overrepresentation of architectural imagery from rich, Western nations), the results will be similarly biased.

AI is an evolution of automation, and automated processes are already integral to design; they’ve just been labeled differently.

Improved computing capabilities are providing more opportunity to balance human and machine intelligence, letting each do what it’s best at.

Zach Mortice is an architectural journalist based in Chicago.

About the Author: Zach Mortice

Zach Mortice

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