AI as a Design Medium - Harvard Design Magazine

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AI as a Design Medium

ESSAY12 mins read time

ERIC RODENBECK

Rodenbeck

is a lecturer in architecture at the Harvard GSD and founder of Stamen Design.

HDM DIGITAL

PUBLISHED

May 4, 2026

While the first article in this series on artificial intelligence (AI) explored how designers may be uniquely positioned to confront the technology’s implications, this second essay examines AI as a design medium—one that can reshape how designers think and produce knowledge.

Eric Rodenbeck, a lecturer in architecture at the Harvard Graduate School of Design and founder of Stamen Design, argues that AI is most generative when treated not as a system for producing answers, but as a condition for inquiry. Challenging prevailing narratives of efficiency and automation, he proposes a different trajectory: AI as a material to be tested, misused, and interrogated. Here, prompts become sketches, outputs become sites of critique, and design becomes a practice of engaging with complexity rather than resolving it.

For the past two years I have been teaching a course at the Harvard Graduate School of Design (GSD) called “Re-imagining the Archive.” The premise is simple: take collections that are supposed to stabilize knowledge and treat them instead as something you can work on, work through, and sometimes work against.

We have been inside the archives of the Museum of Modern Art (MoMA), the Harvard Art Museums, the Institute of Black Imagination, and the American Museum of Natural History. Our goal is not to visualize these archives cleanly, nor to make them legible faster, but rather to see what happens when you stay with them long enough that their seams start to show, when the gaps emerge as new sources of meaning. We do not treat data visualization as a neutral exercise in creating and communicating understanding. My students and I are pursuing, evaluating, and critiquing rhetorical and aesthetic gestures in the pursuit of knowledge creation through these archives.

At the same time, the ground has been shifting. Large language models (LLMs) and related systems have moved from curiosity to constant presence. Every day there is another invitation in my inbox: come talk about prompts, come talk about the future, come talk about what this replaces. The academy seems to be obsessed with these new tools, which are moving so fast we can barely keep up (if we do at all).

Recently, at the GSD, Edward Eigen gave a talk where he compared LLMs to the Talmud—not because they are sacred, but because they are dense, generative, and endlessly interpretable. You do not read them once; you return to them, argue with them, annotate them, build traditions around them. That framing stuck with me because most of what I hear about artificial intelligence (AI) still sounds like management consulting. Efficiency. Acceleration. Staff reduction. Faster pipelines. Better throughput. That is not how AI behaves in the studio. There, AI is much closer to ink. We treat it, and the data it shuffles, as material.

Prompts as sketches, models as material

If you treat AI as a tool, you ask: how do I get the right answer? If you treat it as a medium, you ask: what happens if I push this?

The difference is immediate. A prompt stops being an instruction that returns a correct (or not) answer and starts being a sketch—something provisional, something you can revise, distort, overwork. The point is not to nail it on the first try. You are trying to see what these models do under pressure.

In my class, the first principle is simple: Do not take what comes back from prompts at face value. Interrogate it. Iterate on it. Stay with it longer than feels efficient.

One of the clearest examples of this came from Roy Zhang, a student in the Master of Design Studies (MDes) program who graduated in 2025, who asked what sounds like a trivial question: what happens if you ask the same thing over and over again?

He took a single image from the collection at Harvard’s Houghton Library and asked ChatGPT to generate twenty keywords describing it. Then he did it again. And again. Twenty times using the same prompt. He compared the returned lists of keywords with each successive list and found something fascinating.

Roy Zhang, detail of project for "Re-imagining the Archive," spring 2025. Zhang repeatedly prompted ChatGPT to generate keywords for a single archival image, then analyzed the results.

Zhang, detail of project for "Re-imagining the Archive."

Zhang, repeated keyword lists generated by ChatGPT for "Re-imagining the Archive" project, revealing increasing standardization of terms. By using repetition as a method, he showed that human interactions with AI unfold along a linguistic and temporal continuum that can be mapped, analyzed, and critiqued.

What he found is something you can feel once you see it: the system settles. Early responses are wide, exploratory, a little...