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Demystifying Generative Aesthetics

‍This is the second part in a series on building a framework for appreciating generative art. In the first part, “Demystifying Generative Art,” Peter Bauman (Monk Antony) builds the case for such a framework. In this essay, he takes a closer look at one of the five framework components, Results—the outputs of a generative system. Peter spoke to Tyler Hobbs, Kim Asendorf, Andreas Gysin, Leander Herzog, Erick Calderon, William Mapan, Lauren Lee McCarthy, Sougwen Chung, Christiane Paul, Patricio González Vivo, Linda Dounia and Golan Levin for the piece.
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Tyler Hobbs, Fidenza #575, 2021. Courtesy of the artist and owned by Le Random


Demystifying Generative Aesthetics

This is the second part in a series on building a framework for appreciating generative art. In the first part, “Demystifying Generative Art,” Peter Bauman (Monk Antony) builds the case for such a framework. In this essay, he takes a closer look at one of the five framework components, Results—the outputs of a generative system. Peter spoke to Tyler Hobbs, Kim Asendorf, Andreas Gysin, Leander Herzog, Erick Calderon, William Mapan, Lauren Lee McCarthy, Sougwen Chung, Christiane Paul, Linda Dounia, Patricio González Vivo and Golan Levin for the piece.

“One has to examine the conventional design wisdom and see whether traditional design knowledge actually maps on to the kinds of things that we need to know and need to be able to do in the next five, ten, fifteen years when the technology enables us to do much, much more than we can today.”

-Muriel Cooper, “Information Landscapes” (1994)


What makes generative art special? What makes it interesting art? How can we think about and communicate these thoughts to others more clearly? In “Demystifying Generative Art,” I proposed a Framework (image below) for appreciating generative art, which examines artistic intent and suggests approaching work from multiple perspectives: Process, Results, Concept, Autonomy and Context. But taken individually, these perspectives also function as lenses, giving you the ability to examine a work from a single, more attuned point of view. This article investigates one such lens—Results and their aesthetics—to better understand how appreciating the visual outputs of a generative system can provide a deeper connection to the work as a whole.

Generative Art Analysis Framework from "Demystifying Generative Art"



Whether you are a Systemist or a Resultist—that is, whether you prioritize a generative project's process or outputs—it is critical to understand aesthetics, the way art looks, sounds or feels and the emotional response it elicits. Aesthetics, from the Greek meaning “perception,” is our sensory perception of a work, where nearly all experience with an art object begins. In generative art that art "object" is typically treated as the results. Erick Calderon (Snowfro) spoke to me about their significance: "Ultimately, what the community has responded to and been in the deepest dialogue with—other than the deepest thinkers or the artists themselves—is the result.” 1

The results are our most direct contact with the work. When we see a Sonia Delaunay painting, hear a John Cage composition or watch a Lillian Schwartz computer animation, an emotional response gets triggered. This instantaneous process can be difficult to verbalize or even understand internally. How can we better appreciate, interpret and speak about our sensory perception of art? We have to consider aesthetics—the aim of this essay.

Traditional, Digital & Algorithmic Aesthetics

Novel ideas on aesthetics have long been proffered and refined. In 1924, Andre Breton's Surrealist Manifesto advocated for an art, “exempt from any aesthetic or moral concern.” Hal Foster wrote of postmodernism's denial of "the idea of a privileged aesthetic realm" in his 1983 book of essays, The Anti-Aesthetic. Muriel Cooper in 1994 (above) called for “new design principles” for a “new information environment.” Yet not all movements were so radical. Tom DeWitt, in his 1989 essay “Dataism” advocated for an embrace of traditional aesthetics enabled by computer programming. Others, like Sean Cubitt in “Digital Aesthetics,” seem to suggest that pinning down a “digital aesthetic” is meaningless when the digital can express so much visual variety. To appreciate generative art today, do we need new design principles or can we merely embrace traditions of the past? We need both but that is still incomplete.

What can make generative art particularly challenging to assess is coping with its staggering visual complexity. Outputs can vary from the static-painterly to the real-time-digital. Understanding generative art’s aesthetics means first considering:

  • Traditional aesthetics: Principles that have been established and practiced over time in the realm of art and design, rooted in historical and cultural contexts and encompassing concepts such as form, balance and proportion
  • Digital aesthetics: Characteristics and features of the digital—infinite, real-time, interactive and much more
  • Algorithmic aesthetics: Areas native to coded art, a vital sub-section of digital aesthetics

Finally, this essay considers what these three mean for generative aesthetics today by recalling Max Bense’s 1965 aim.

I. Traditional Aesthetics

Foundational to understanding formal expression regardless of medium are the elements and principles of design that have guided artists and art historians for centuries. Appreciating traditional aesthetics requires a considered look at a work’s surface in an effort to discern the artist’s visual—or auditory or tactile—message.

These expressions that we typically consider when formally analyzing visual artwork, according to MoMA and UC Berkeley, include the following, which can all relate to generative work:

Design Elements  

  1. Line: a point’s path. Lines are of particular importance to digital and computational art due to their reliance on pen plotters for decades. The work of the Algorist Jean-Pierre Hébert as well as Colette Bangert and her husband Jeff display masterful devotion to line, with Colette writing in Ruth Leavitt's Artist and Computer, "I always seem to be in the process of learning about line and land forms."
  2. Shape/form: distinct forms. Particularly important to pioneers such as Manfred Mohr and Vera Molnár.
  3. Space: the distances between components. Golan Levin spoke to me about the importance of space: “Sometimes there are voids and the voids establish contrast with the places that are filled. And that's interesting. I need voids and to do that, I have to omit. I have to make space to make space.” What reminded Levin of this foundational principle? A list! He explains: “That's an example where Christopher Alexander's 15 Fundamental [visual] Properties [1981] made me realize I should think about voids.” 2
  4. Texture: an object’s surface quality. Sougwen Chung has described the importance of texture in her practice, remarking to Digitalplug that her work invites viewers into “intricately textured worlds that echo beyond the surface of the image.”
  5. Color/value: William Mapan demonstrates a particular sensitivity to color and value—the relative lightness or darkness of colors. Mapan spoke to me about the importance of color in his work: "I'm interested in how color can affect someone, how it can serve as a vessel of emotion." His fxhash project Dragons with its simulated use of chiaroscuro and carefully selected palettes demonstrates Mapan's exquisite control over algorithmic color and value.
William Mapan, Dragons #404, 2021. Courtesy of the artist and fxhash


Principles of design

  1. Balance: how forms are arranged, including symmetry. The most aesthetically pleasing pieces in a collection often display—or subvert—formal balance, which also highlights what makes generative art special. It is a characteristic that must be considered in advance and programmed in; only the best algorithms can achieve this, such as Meridian by Matt DesLauriers. Meridian #7 (left) and #165 (middle) both showcase the algorithm’s exquisite use of balance, while #178 (right) shows the sophistication of the algorithm to elegantly subvert it.

Matt DesLauriers, Meridian #7 (left), Meridian #165 (middle), Meridian #178 (right), 2021. Courtesy of the artist and Art Blocks


  1. Variety: the use of several design elements. In generative work, this can mean variety within an algorithm as well as the visual variety of a single output. Observe the variety of the three Meridian outputs above and how they differ from one another in various ways based on line, form and color.
  2. Rhythm (movement): This can refer to either the simulation of movement in a static piece or the actual movement in an animated piece. In Meridian #178 (right, above), notice how the juxtaposition of straight and curved lines simulates movement in the static image—the same lines that disrupt the piece's balance. Observe the various ways patakk's char conveys rhythm below, evoking the long history of capturing motion with static imagery, beginning with Muybridge and Duchamp.

patakk, char #109 (left), char #7 (middle), char #214 (right), 2022. Courtesy of the artist and fxhash


  1. Stress: the emphasis of a work. For generative artists, this must be predetermined and programmed into the piece so that, on occasion, you get pieces with striking features. Staying with our Meridian triptych, we can again look for opposition as a clue. See how the contrasting red with the blue in Meridian #165 makes the red seductively pop. In Meridian #178, the contrast emerges from parallel lines on the left initially suggesting a seamless continuation—like the ones above them—before jutting upward like a wave.
  2. Proportion: the distance between objects. We can observe this in Meridian through the relationships between its colorful repetitious forms.
  3. Contrast: compositional differences within a piece or collection. How many different ways can you find that Meridian expresses contrast? You can write an entire essay on it. We see contrast employed in char largely through the use of computer characters alone.
  4. Cohesion: the organization of both individual pieces and the entire output space of an algorithm to achieve a desired effect, e.g., oneness. By looking at the three pieces from char, the viewer gets the sense that—despite quite obvious compositional contrast—there are structures that tie the three together. These include visual elements like background color, prominent use of characters and the border, as well as choices like constraint. What else ties char together?

Next time you view a generative work, consider it within the context of these formal elements of analysis. When you collect, base a choice on one or several of these elements that speak or appeal to you rather than choosing a floor piece or a rare trait. Okay, we covered the very basics of aesthetics as understood for hundreds of years—at least. Surely, that’s all we need.

II. Digital Aesthetics

Welllll, I'll let Leander Herzog explain. “I struggle to understand why so many people and such a big part of the space are really focused on static imagery that references the aesthetics of the past century,” Herzog told me. “That feels extremely weird to me. I think more and more people should and will focus on real-time, code-based art.”

How can we do that? What even are the aesthetics we should consider with “real-time, code-based art”? Herzog continues:

“It’s about pixels, performance, motion and interaction—all the ways the architecture of contemporary technology manifests in process and result. It is the opposite of faking paint or making work look like it’s created on paper. It means not trying to create just a static image with a fixed size but a moving, dynamic, ‘new’ kind of work."

Kim Asendorf, monogrid 4d, 2021/10. Courtesy of the artist and owned by Le Random



Even how we consume the digital, a key factor in digital aesthetics, should not rely on ideas from the past, argues Andreas Gysin. He believes we should be utilizing the digital canvases that surround us to consume art, telling me, “I really like the idea of an artwork that can be consumed on the computer or on a phone. And it doesn't necessarily have to live on something that we drag along from the past.”

For Patricio González Vivo, the power of the digital lies in its ability to take advantage of the medium’s inherent characteristics: “I like to constrain the temptation of doing things that are not real-time. Being generative or procedural in real-time for me is important.” 3

Kim Asendorf spoke to me about what computers offer him aesthetically: “I really like the color—they’re illuminative colors. It's not just a print where the color basically is just a reflection of the sunlight or whatever. It's really coming out of a box and that already adds a certain aesthetic. I really want to embrace that and put that up front.” 4

By considering the digital aesthetic, we can better appreciate what the artist has done to highlight, comment on or question an element of that aesthetic that may reveal insight into their way of seeing the world—one of our ultimate goals as the viewer. Identifying and appreciating the elements and concerns of the digital aesthetic is essential to understanding generative art. I compiled the below list—sure to be incomplete—by synthesizing the ideas of Christiane Paul, John Maeda and Levin.

  1. Infinite: Maeda describes the digital as an "alien substance" of “invisible machines” capable of performing “forever.” Levin, in his 1994 MIT thesis, relates the digital to “a magical substance” that’s inexhaustible and infinitely variable—an extraordinary power for a visual artist. Code can run art forever. It seems ridiculous for artists not to take advantage of that.
  2. Real-time: Digital art can be time-based and dynamic, allowing for greater creative flexibility and control. Asendorf explained to me the power of real-time animation: “I more or less feel the urge that my work has to be coded. It’s because I can write an artwork in 10 kilobytes of code but it can expand into something that is an endless stream of data. If I tried to capture that in video, it would end up in countless gigabytes and the quality also could not be replicated.” 
  3. Personal: Perhaps odd at first glance, but what knows you better, a piece of paper or the Instagram algorithm? The digital can learn about you, understanding you better than you even know yourself. Lauren Lee McCarthy explores how our interactions with algorithms impact our personal well-being and social interactions. By combining technology with performance, she told me how she invites participants into a shared vulnerability: “There's a bond or a shared experience that emerges and I'm interested in the intimacy of what can happen in that space.” The digital is not all cold code.
  4. Living: In his book How to Speak Machine, Meada explains how the digital allows artists to simulate living systems. Examples abound throughout history, including early algorithms such as boids and Langton’s ant, as well as the work of Karl Sims, William Latham and Christa Sommerer.
Ciphrd and Alexander Mordvintsev, Genomes #1358, 2024. Courtesy of the artists and fxhash


  1. Interactive: Undoubtedly, one of digital art’s defining characteristics is ease of interactivity. Herzog told me, “It’s very new, very exciting and something that is completely ignored often. Interaction is definitely very underrated still and is a huge part of this digital aesthetic, if there is such a thing.”
  2. (Im)material: The digital is often thought of as immaterial but can express itself in countless material forms. Paul elaborated to me, “By materiality, I could also mean software art. I know that's a very meta level. But what that requires, when you dig down a little bit, is an understanding of the medium itself—whether that's the medium of painting or whether that's the medium of digital art.” Astonishingly, the digital can range from the painted work of Chung to the real-time animation of Asendorf to clothing to glass.
  3. Modular: Both Paul and Lev Manovich identify modularity, the ability to assemble larger units from smaller ones, as a key characteristic of the digital. Modularity eliminates the need for artists to start from scratch, thereby speeding up their learning and productivity.
  4. Procedural: Procedural generation in digital art involves creating algorithms or systems that generate content automatically based on predefined rules or parameters. Most long-form collections on Art Blocks or fxhash would fit into this category. We discuss "Algorithmic aesthetics" is greater detail next.
  5. Multi-sensory: The digital can combine visual, auditory or tactile elements, allows artists to create immersive experiences that stimulate beyond two-dimensional visuals. This can give the viewer a more holistic and immersive engagement with an artistic vision.
  6. Networked: Meada also speaks to the importance of art, artists and viewers connecting and interacting with each other through the Internet, or now blockchains.
  7. AI: There is increasingly an argument being made for an AI aesthetic, which could be a topic for an entire other essay. Ranging from hyper-photorealistic to distorted and sinister—mostly depending on technology—AI tends to convey dream-like, fantastic qualities. Appreciating AI’s inherent surface-level features can enrich the viewer’s understanding of a work, as with Linda Dounia’s subversion of the photorealistic in Flore Perdue. "I'm hiding some of the details, forcing the viewer to miss them," she told me.
Mario Klingemann, Neural Glitch (left, detail), Error Correction (right, detail), 2018. Courtesy of the artist and owned by Le Random


Visual Concerns

  1. Computational bias: as Hobbs described to me. “The amount of information needed to describe a rectangle is significantly lower than a lumpy, hand-drawn oval. Computers are geared toward a perfect representation of things. That's what comes out by default and is the foundation that everything else is built on. There are going to be massive effects from that.” If painting and the real world are messy by default, as Hobbs explains, computers are the opposite. This is merely a fact that artists must consider when working with digital materials such as code. It’s fascinating to observe how different artists embrace, combat or work with the bias of the formally perfect.
  2. Resolution: how clear and detailed the image appears. Herzog informed me about the importance of its consideration, an extreme challenge when this varies from user to user: “Screens are different, resolutions are different. And that's just a reality that we embrace because we think ignoring it is stupid.” He continues: “Adapting to this is basically the bare minimum that you have to do to actually display an artwork properly the way it is intended to.”
  3. Aspect ratio: the size of the digital frame. A frame analogy is appropriate, as cropping an image to stuff it on a screen with the wrong aspect ratio is like cutting off a painting to fit it into a frame—unthinkable. Hobbs told me more about what separates the most highly attuned: “Some artists think it's good enough to put their work on a 16:9 screen—even if that doesn't match the aspect ratio of the artwork—and to just put that screen on a wall and call it a day. There's a certain kind of laziness that the traditional art world doesn't accept in terms of presentation.” Presentation should never be the reason an artwork is not properly considered.
  4. Display/Presentation: Resolution and aspect ratio are ultimately considerations of the digital’s unique presentation challenges. Hobbs detailed further, “I think visual artists historically pay much more attention and detail—at least for good work—to exactly how the work is presented. With video art and new media artwork, often they are very specific; the presentation layer itself is part of the artwork.” He continues, “I think that focusing on improving that presentation layer can go a long way towards improving the quality of the [NFT] work and how it's received.”
  5. Longevity/Preservation: Ensuring the work can never be lost due to technological shifts, such as the discontinuation of support for Flash, must remain in consideration for artists, even regarding aesthetics. If you can no longer perceive it, is it still art?

III. Algorithmic Aesthetics

Beyond considerations of traditional and digital aesthetics, algorithmic art work, including most digital generative art, requires special attention as a viewer. Hobbs related to me: “Algorithmic art has its own distinct aesthetics and capabilities that you, as a viewer, absolutely should take into consideration.” 

He continues: “Some of the new doors that are opened are around precision, scale and complexity, and algorithmic art has a completely different handle on these things than, say, painting or photography.”

The randomness that artists can simulate through algorithms is perhaps the medium’s defining characteristic. Sougwen Chung disclosed to me: “Iteration is at the heart of my generative exploration, a rejection of needing to control the outcome or have a road map.” For Chung, exploring with algorithms presents a original way of making.

Visual exploration, innovation and even human intuition can be enhanced with the incorporation of algorithms into an artistic practice. Below, we look at how identifying these concerns augments our ability to understand algorithmic aesthetics.

Algorithmic characteristics and concerns

  1. Randomness: the ultimate algorithmic feature and concern, utilized by nearly all algorithmic processes. Randomness is often what leads to the unexpected, which drives generative art’s visual exploration. Randomness increases the visual output space available to artists and viewers, enhancing our human qualities even. Vera Molnár explains with the quotation from which Le Random takes its name: “There is one thing that can replace intuition; it’s randomness." [Il y a une chose qui peut remplacer l’intuition; c’est le random]. Randomness gives artists an augmented human quality.
  2. Recursion: the description of something with itself; also called “Droste” effect or feedback loops. These have long played an important role in algorithmic aesthetics since Max Bense’s cybernetics. Maeda speaks of their importance in How to Speak Machine for an entire chapter (of six).
  3. Iteration: making changes and refinements to a piece over time. Iteration breeds experimentation; it broadens the visual landscape for algorithmic artists. Hobbs writes how code is easily changeable, meaning there is a low cost of failure to experiment: “I believe this freedom to experiment cheaply allows the artist to develop new styles more quickly.” Iteration has long been a valuable expressive quality, as Mark Wilson told Travess Smalley for Le Random: “The wonderful and exciting thing about using a computer is that sometimes, just by changing some numeric value slightly, you get something totally different.”
  4. Scale and curation (edition size): Algorithmic scale refers to the edition size that the artist chooses to produce from any particular algorithm, based on curation. For Hobbs, scale is his first consideration—before even the project’s visuals—when considering a generative work. “Is this an open-ended, infinite output? Were these curated? Were these direct from the algorithm? And if they're direct from the algorithm, did the artist take ten, a hundred or a thousand of them? It’s knowing the size and dimensions of the output space that we're looking at and then it's an instinctual response in terms of aesthetics.”
  5. Precision: the ability for artists to manipulate form on an infinitesimally small scale. Asendorf spoke to me about its importance to an algorithmic practice: “Precision is important. The crispness of the rendering means that a pixel can really be just one pixel. That is just beautiful; I cannot put it in any other medium.” 
  6. Complexity: With artists able to incorporate randomness, loops and forms algorithmically at incredibly small scales, complexity quickly emerges. Often, the challenge for artists is controlling the chaos of complexity while harnessing it for some of generative art’s most compelling visual work.
Jared Tarbell, Substrate, 2019. Courtesy of the artist and Kate Vass Galerie


IV. Generative Aesthetics

“The aim of generative aesthetics is the artificial production of probabilities of innovation or deviation from the norm.”

-Max Bense, "Projects of generative aesthetics" (1965)


We end at the beginning. Max Bense was thinking about aesthetics, computers and feedback loops in the early 1950s, recognizing computation’s ability to alter humanity’s relationship with visual forms. He understood that a key feature of generative work is its reliance on emergent forms—the unexpected. In other words, Bense understood how “artificial production” would allow artists to create with superhuman powers. More importantly, he recognized that at the heart of that innovation was generativity.

So what are generative aesthetics today? It’s about assessing the, as Hobbs refers to it, “output space of the generative procedure combined with the curation model.” To do that effectively, the viewer is tasked with synthesizing the tools and approaches from traditional, digital and algorithmic aesthetics. Hobbs explains how he assesses generative work differently from traditional media. “For traditional art, I have a higher bar for how polished I expect some of those elements to be versus generative work because of the difference in difficulty in achieving that.” A similar degree of difficulty is placed on the generative art viewer.

That’s where this essay can help. As viewers, we can think of any digital-generative artwork in terms of these aesthetic layers. The exact process will vary from person to person but could be approximated as follows:

  1. Assess traditional aesthetics’ concerns. Consider the principles and elements of design, like we did above, from a generative perspective.
  2. Evaluate elements of the digital. What element stands out about the work? What does it uniquely express? What makes it special? What character of the digital does the piece draw attention to? How does the piece make you think about one of these elements differently?
  3. Address algorithmic concerns. These include questions about the edition size, cohesion and consistency of the algorithm. Again, what does the piece excel at or fall short of in those terms? How can our understanding of the algorithm inform our appreciation of the work?
  4. Understand the work in terms of its generative aesthetics. If we put the first three pieces together, we can perhaps begin to get a more holistic view of the piece from an aesthetic or Results level. Each new aesthetic lens reveals more about the work, uncovering an angle or layer we would have otherwise missed.

Understanding these 4 realms of aesthetics serves as another layer in a dragonfly-eye approach to comprehensively considering a work of generative art. Zooming in reveals greater detail and endless opportunities for further exploration. Zooming out reveals the original Framework and further areas on which to focus (process, concept, context, etc.) This method recalls the potency of recursion—or as Maeda calls it—a “magical power source” for exploration.

Coming in Part 3: “Demystifying Generative Systems”



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1 Interview conducted between Erick Calderon and Peter Bauman on November 28, 2023.

2 Interview conducted between Golan Levin and Peter Bauman on January 8, 2024.

3 Interview conducted between Patricio González Vivo and Peter Bauman on February 9, 2024.

4 Interview conducted between Kim Asendorf, Andreas Gysin, Leander Herzog and Peter Bauman on November 24, 2023.



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Peter Bauman (Monk Antony) is Le Random's Editor-in-Chief.

Special thanks to Tyler Hobbs, Erick Calderon, Kim Asendorf, Andreas Gysin, Leander Herzog, Lauren Lee McCarthy, Sougwen Chung, William Mapan, Christiane Paul, Golan Levin and Patricio González Vivo.