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“A conversation with OpenAI’s first artist in residence” refers to a March 29, 2024 interview by MIT Technology Review’s Will Douglas Heaven with artist and roboticist Alexander Reben. Its most important question is not whether an AI-generated image is automatically art. It is where authorship sits when an artwork moves through prompts, models, human judgment, robotic fabrication, and traditional craft.
Who is Alexander Reben?
Alexander Reben works across art, robotics, computation, and human-machine interaction. His background includes mathematics, robotics, and social robotics at the MIT Media Lab, and the interview identifies him as director of technology and research at Stochastic Labs. Christie’s later described him as an artist whose work has been shown at institutions and events including Ars Electronica, the Vitra Design Museum, and the Vienna Biennale.
Reben’s work predates his association with OpenAI. He has experimented with robotics, film, sculpture, generative systems, and the ways people respond to machines. That is why reducing him to an “OpenAI artist” misses the larger point. AI is one tool in a practice concerned with how technology changes human behavior and ideas about agency.
Reben also resists treating “AI artist” as a permanent artistic identity. Artists have historically adopted new tools—photography, video, digital editing, industrial machinery—and used them without allowing the tool to define everything they make. For Reben, the more useful questions are: What did the artist decide? What was delegated? What was rejected or revised? And what does the process reveal about technology?
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What was OpenAI’s artist residency?
The interview describes Reben as OpenAI’s first artist in residence. His formal residency began in January 2024 and was intended to last three months. Because he was the first participant, Reben said the arrangement was still being worked out and that the longer-term relationship with OpenAI was deliberately informal.
That distinction matters. The available account does not establish that Reben was a regular OpenAI employee, that the residency had a public application process, or that it continued in a particular form after the three-month term. It is better understood as an exploratory collaboration: Reben could work creatively with OpenAI systems while bringing an artist’s perspective into a company building generative technologies.
Reben had reportedly experimented with an early version of GPT-3 years before its public release. The residency therefore continued an existing interest rather than beginning his engagement with generative AI.
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Read the interview as reproduced by Maverick Studios. Christie’s also identifies Reben as OpenAI’s first artist in residence in its 2024 Art+Tech Summit biography.
Reben’s central idea: AI is a tool, not the artist
Reben’s position is more nuanced than either “AI makes art” or “AI can never make art.” He treats a generative model as one component in a human-directed system. The artistic work may lie in designing the conditions, choosing among possibilities, shaping the output, making the physical object, and deciding how an audience encounters it.
This shifts the debate away from the narrow question of whether a machine produced a particular image. A photograph may be created by pressing a shutter, but artistic photography still involves framing, timing, selection, sequencing, context, and intention. Reben sees a related possibility in generative AI: making an image may become easier, while deciding which image matters can remain difficult.
The comparison has limits. AI models are trained on large bodies of cultural material, generate text as well as images, and may disrupt a much wider range of creative and professional work than cameras did. Photography cannot settle today’s questions about training data, consent, copyright, employment, or synthetic media. It is a useful historical analogy, not proof that AI’s consequences will be harmless.
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Reben’s work is especially revealing because it does not stop at an image on a screen. His processes connect language models, image models, human artists, robots, and physical materials. “Ear we go again” is the clearest example.
- GPT-3 generated a description. The starting point was language: a model described an artwork that did not yet exist.
- DALL·E visualized the idea. The textual concept became an image.
- A human created a 3D model. The image had to be interpreted and translated into a form that could exist in three dimensions.
- A robot carved the sculpture. Robotic fabrication turned the digital design into marble.
- Human sculptors finished the details. Traditional skill and physical judgment remained part of the final object.
- GPT-4 generated the wall text. Even the museum-style explanation of the completed work became part of the experiment.
This sequence makes “who made it?” a complicated question. The language model supplied a description, the image model supplied a visual interpretation, a human 3D artist made technical and aesthetic decisions, a robot performed physical labor, and human sculptors refined the result. Reben’s role included establishing the structure of the process and determining what counted as a finished artwork.
The work does not prove that the machine and the humans contributed equally, nor does the presence of human finishing automatically resolve authorship. It demonstrates instead that artistic production can be distributed across a chain of people and systems.
“The Plungers” and the importance of selection
In “The Plungers,” Reben asked GPT-3 to complete museum-style descriptions of artworks that did not exist. From thousands of generated possibilities, he selected an unusual or humorous concept and turned it into a physical artwork.
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A free scan shows the junk files, broken settings and background clutter dragging Windows down - then fixes them in one click.Free scan · Windows 10 & 11The model generated language, but it did not independently decide which idea deserved attention. Reben’s selection was a form of curation. The physical realization required interpretation, production, and presentation. The work challenges the assumption that artistic authorship must be located in a single act of manual execution, while also showing why “the AI made it” is an incomplete description.
Selection is not merely a fallback justification for weak machine output. In a generative process, the artist may confront an enormous field of possibilities. Choosing a constraint, rejecting thousands of alternatives, recognizing a compelling accident, and giving one result a durable context can become the core practice.
Can an automatically generated image be art?
The interview leaves this question deliberately unresolved. Reben raises the possibility of increasingly autonomous systems in which one generated image becomes the input for another, creating a long-running chain with little direct human intervention.
That possibility exposes several different meanings of “making art”:
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- Material production: creating pixels, text, sound, or a physical object.
- System design: selecting a model, dataset, prompt structure, goal, and constraints.
- Curation: choosing which outputs to preserve and which to discard.
- Interpretation: giving the work a title, setting, narrative, or exhibition context.
- Accountability: accepting responsibility for the work’s effects and the conditions under which it was made.
Reben argues that even an automated process can be traced back to human choices: someone selected the dataset, chose the model, defined the objective, decided when to stop, and presented the result. That is his philosophical claim, not a settled legal rule or a universal definition of artistic authorship.
A short prompt followed by an unedited image is not equivalent to Reben’s multi-stage workflows. Nor does extensive human intervention automatically make a work ethically unproblematic. The relevant questions are the nature of the intervention, the labor involved, the source material, and the meaning created by the final presentation.
The strongest objections are not only aesthetic
Criticism of AI art often gets reduced to a dispute over skill: does prompting count as drawing, painting, or sculpture? The interview points toward larger issues:
- Training data: What works were used to develop a model, and were artists informed or compensated?
- Employment: Which illustrators, designers, photographers, writers, fabricators, and other workers may lose opportunities?
- Copyright and ownership: Who can claim rights in material generated from a model trained on existing cultural work?
- Disinformation: How does abundant synthetic media affect trust in images and video?
- Oversupply: If images become nearly free to produce, does attention—and therefore artistic value—become scarcer?
Reben acknowledges these concerns. He suggests that public debate often concentrates on harmful uses and overlooks artists who use generative tools constructively. That is not a dismissal of the objections. It is an argument for examining both the risks and the creative possibilities.
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The distinction is important for painters and other traditional artists. A tool can expand what an artist can attempt without making every use of it responsible, original, or valuable. Conversely, difficulty of execution is not the only basis on which art is judged.
Artistic value is not the same as labor or price
Reben separates economic value from critical or artistic value. A market price reflects what someone is willing to pay; it does not provide a complete measurement of conceptual importance, emotional power, historical significance, or cultural influence.
Contemporary art already includes works whose physical execution may be simple but whose meaning depends on an idea, a decision, or a context. Reben uses this fact to question the assumption that more manual labor always means more valuable art.
That argument should not be confused with saying that labor is irrelevant. Craft, time, skill, scarcity, and the conditions of production can all shape a work’s value. The point is that they are not the only factors. In an AI-assisted artwork, value may also depend on the originality of the setup, the quality of the artist’s judgment, the human labor retained in the process, the work’s social meaning, and the clarity with which its making is presented.
Why humor and absurdity matter
Reben’s playful concepts are not simply technological demonstrations. Humor makes abstract questions tangible. A strange generated description or an improbable machine-made sculpture can reveal assumptions about what counts as intelligent, creative, useful, or human.
Absurdity also exposes the limits of automation. A system may be able to generate an answer, but that does not mean the answer has a purpose. By placing machine outputs into physical objects and museum-like contexts, Reben asks viewers to notice the human decisions that normally disappear behind finished artworks.
What the interview suggests about the future
Reben expects AI to alter artistic fields as earlier technologies did. Artists may use generative tools to produce work, to explore ideas, or to distinguish themselves from generic machine output. Some future artworks may involve very little direct human execution. Others may deliberately return to painting, carving, drawing, or other traditional practices precisely because those forms signal a different relationship to labor and attention.
These are forecasts, not established outcomes. The interview does not provide a threshold for how much human involvement is required, settle copyright disputes, or demonstrate that automation will benefit artists overall. It also does not document the residency’s contract, compensation, internal access, deliverables, or later institutional status.
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1Fix the driver behind crashes, sound loss and screen glitches2Clear out junk files and repair common Windows errors3Scan for outdated or missing drivers - takes under a minuteWhat it does provide is a practical way to think about authorship. Instead of asking only whether AI generated the visible object, examine the entire chain:
- Who chose the system and its source material?
- Who defined the goal or constraint?
- Who reviewed and rejected possible outputs?
- Which people supplied technical or physical labor?
- Who made the final decisions about form and finish?
- How was the work framed for an audience?
- Who is accountable for its cultural and social consequences?
In later coverage, Reben described related text-to-object experiments involving Sora-derived rotating sculpture clips, 3D information, and robotic marble carving. That 2024 account is later context, not part of the original March interview, but it shows the continuing direction of his interest: moving from generated images toward objects, machines, and material experience. See the later Autre interview.
The human question remains
Reben’s work does not answer whether an AI system can be an artist in its own right. Instead, it makes the question harder to avoid. A model can generate descriptions and images. A robot can carve stone. But a finished artwork still enters a human world of selection, interpretation, labor, institutions, audiences, and consequences.
For painters, the lesson is not that traditional making has become obsolete. It is that the definition of artistic decision-making is widening. Some artists will use AI for sketches, references, or conceptual prompts. Others will reject it. Still others will build elaborate systems in which human judgment, machine generation, fabrication, and craft are deliberately interdependent.
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