# [[The Environmental Aesthetics of Generative AI]] ## Introduction I argue here that there is an instructive parallel between how we come to appreciate the [[natural environment]] and how we might come to appreciate the output of generative AI systems such as Midjourney, ChatGPT, and Sora. While such systems produce text, images, video etc., it is clear that these creations are a quite different sort of thing from traditional artworks. A painting or poem can take months and be a direct result of an individual's labor, attention, and effort. In contrast, an AI painting or AI poem can be produced in seconds, and is [[the result]] of a vast corpus of [[experiential artifacts]] encoded in [[latent space]], and a user's prompt. This means that when it comes to appreciation we have [[two options]]: 1. AI art is not the sort of thing that can be aesthetically appreciated. 2. We appreciate AI art in a different way than we do [[traditional art]]. This paper develops the second option by drawing on Carlson's [[environmental aesthetics]]. I will argue that AI art should be appreciated differently from [[traditional art]], and that Carlson's [[environmental aesthetics]] provides a valuable framework for understanding [[this appreciation]]. Instead of treating AI systems as [[designed objects]], I suggest we view them as environments shaped by generative processes. Understanding these systems as encoded corpora of images, text etc. helps us appreciate their outputs in a way that parallels how we appreciate nature. Just as environmental appreciation involves recognising what nature is and how we interact with it, AI art appreciation involves understanding what these systems are and how we engage with them through prompting. ## 1. Appreciating Natura Carlson's [[Natural Environmental Model]] for [[environmental aesthetics]] rests on two ideas: > First, that, as in our appreciation of works of art, we must appreciate nature as what it in fact is, that is, as natural and as an environment. Second, it recommends that we must appreciate nature in light of our knowledge of what it is, that is, in light of knowledge provided by the natural sciences, especially the environmental sciences such as geology, biology, and ecology. (2000 p. 6) Regarding [[the idea]] that we must appreciate nature as what it in fact is—Carlson argues that the environment is not merely a collection of objects or scenes but a system of interconnected elements shaped by various processes and forces (ibid. p. 44). Environments thus "come about 'naturally,' [in that] they change, grow, and develop by means of natural processes." From this perspective, aesthetic appreciation involves recognising what one is encountering—a natural environment in which components are interrelated—and understanding it in light of scientific or other relevant knowledge that illuminates its composition and development. This understanding can be framed in terms of Spinoza's concepts _natura naturans_ and _natura naturata_. Natura naturans refers to the active, generative aspect of nature—such as seeds sprouting from soil—whereas natura naturata designates the relatively stable entities that result, including trees, soil, and pebbles. Thus, nature can be understood both as the self-causing, active process (natura naturans) and as the passive, resultant structure (natura naturata). In the same way that an informed grasp of artistic traditions can enrich one's appreciation of an artwork, Carlson argues that familiarity with geology, biology, or ecology can guide our attention to patterns and processes that might otherwise remain unnoticed. For instance, one might begin by noticing only the colours or shapes of a coastal cliff's sedimentary layers. However, discovering that these layers formed over thousands of years of deposition and compaction changes how one regards it aesthetically. Similarly, understanding forest succession—how pioneer species gradually give way to climax vegetation—enhances appreciation of a mature woodland beyond just its bare appearance. If we see a forest or reef as subject to diverse forces and processes, an appropriate aesthetic engagement will centre on how those forces have shaped what we observe. Carlson considers an understanding of the unity of the natural environment to be relevant to aesthetic understanding: > natural objects possess [...] an organic unity with their environments of creation: such objects are a part of and have developed out of the elements of their environments by means of the forces at work within those environments. Thus the environments of creation are aesthetically relevant to natural objects. (ibid. p. 44). Appreciating the forest is informed by an understanding of the flora, fauna, and substrata of which it consists; appreciating a particular tree in a forest will be enhanced by an understanding of the forces and elements which have led to its creation and qualities. This framework of understanding environments as both process and product, and the unity between these aspects, will prove valuable when considering other complex systems that grow and develop through time. This environmental model offers a framework we can adapt to understanding AI systems and their outputs. ## 2. Generative Environments I suggest that Carlson's recommendations also hold for the aesthetic appreciation of generative AI: not only should we appreciate these systems for what they are, in light of our scientific knowledge of what they are, but they are also fruitfully characterised as a type of environment. What then, are generative AI systems? Consider first the remarks of Chris Olah, Co-founder of Anthropic: > I think one useful way to think about neural networks is that we don't program and we don't make them. We kind of, we grow them...we have these neural network architectures that we design and we have these loss objectives that we create. And the neural network architecture, it's kind of like a scaffold that the circuits grow on, it starts off with [...] random things and it grows. And it's almost like the objective that we train for is this light. > And so we create the scaffold that it grows on and we create the, you know, the light that it grows towards. But the thing that we actually create, it's this almost biological, you know, entity or organism that we're studying. > (Fridman 2024) _my emphasis._ This description captures the environmental nature of AI systems – they are 'grown' rather than coded line-by-line, developing through an iterative training process where their internal parameters adapt over time in unpredictable ways. Factors that influence how such systems grow include their specific architecture, the nature and extent of the data used to train them, and the particular training goals set by the researchers. We can describe this evolving process as an instance of _machina naturans_. Just as _natura naturans_ is nature actively creating, _machina naturans_ represents the machine's active creation through learning processes. Similarly, we can conceptualise _machina naturata_ as the resultant structures and capabilities that emerge from this generative process – the stable patterns and behaviours that constitute the trained model. While human developers determine the overall architecture and set the training parameters, the system's eventual capabilities emerge from patterns that are not explicitly scripted. It is important to note another characteristic of generative AI. They are not simply collections of code or weighted parameters, but rather an encoding of vast a corpora of experiential artifacts. A system like ChatGPT or Midjourney does not simply contain abstract rules for generating text or images; it encodes patterns derived from millions of examples of human creative expression. For example, when Midjourney produces an image in the brutalist style, it's drawing on encoded patterns of concrete textures, geometric forms, and compositional elements found across thousands of buildings, including brutalist architecture. This encoded corpus of experiential artefacts fundamentally shapes the system's generative capacity. Just as a natural environment contains embedded geological and biological histories, AI systems contain embedded cultural histories that influence their outputs Like natural processes where initial conditions and environmental pressures guide development without determining exact outcomes, AI systems evolve through the interplay of architecture, training data, and optimisation functions. The resulting capabilities emerge through a process of adaptation similar to how complex natural systems self-organise. This process parallels how _natura naturans_ can shape an environment over time. In nature, geological and ecological factors steer gradual changes that culminate in the formation of particular features, without direct step-by-step intervention. Similarly, in a _machina naturans_ system, the interplay of training data, architectural constraints, and iterative updates drives the emergence of new capabilities in ways not fully determined by its human creators. ## 3. Gardeners and Natura If generative AI systems can be understood as environments shaped by _machina naturans_, how might they be appreciated? This question leads us to consider the relationship between humans and these generative environments. A useful parallel exists in how humans have long interacted with another complex generative system: the garden. A gardener directly engages with _natura naturans_ with the aim of influencing the resulting _naturata_. For example, disbudding a dahlia plant redirects growth resources from multiple small florets to a single, symmetrical bloom. The gardener is not creating a dahlia bloom _ex nihilo_, nor does she impose form on inert material as would a sculptor; rather, she guides its inherent potential towards the form she wants. We might also think that, over time, the gardener gains knowledge of her environment, such as knowing to avoid watering dahlias too frequently after observing how excess moisture weakens their stems. Though she would not use terms like cortical cell lysis, her practical knowledge – spacing plants and rotating beds based on wilt patterns – is that the dahlias’ tubers perform better when the soil is well-drained. While she may not be able to express her knowledge in scientific terms, she understands her garden. In engaging with her common or garden _natura_ through gardening, she comes to know it as 'what it in fact is'. It is also not hard to see how this practical understanding grounds her aesthetic appreciation of gardens, others' as well as her own, and the natural environment in general. Her knowledge of what dahlias are, the natural processes which impact them, and how one might guide these processes will inform her aesthetic appreciation of dahlias. ## 4. Prompters and Machina Natura The relationship between prompters and AI systems mirrors this gardener-nature dynamic. When users interact with generative AI systems, they are not creating something from nothing, but rather guiding the generative potential of the system toward particular manifestations from its encoded corpus of experiential artifacts. Just as the gardener redirects the growth potential of a plant, the prompter steers the generative capabilities of an AI system toward specific outputs. Through iterative interaction, prompters develop a practical understanding of these systems. A Midjourney user can learn to combine artistic styles or artists' names to create distinct visual effects. They might discover, for example, that the prompt "Vermeer's light techniques with cyberpunk aesthetics" creates domestic scenes with a hint of neon Or that "botanical illustrations in Basquiat's style, rendered in watercolour" produces images that maintain his energy while transforming it into delicate plant studies. They understand, through experimentation rather than technical knowledge, how these systems will attempt to blend many disparate concepts together, which styles 'dominate' and which are more 'recessive'. While a computer scientist understands the model's architecture, the nature of the training data, and the iterative processes through which a system's internal parameters adapt, the prompter gains practical knowledge by observing how small changes in prompting redirect the output. Architectural constraints and patterns emerge through trial, error, and experimentation, this can be thought analogous with the gardener who notices how stems respond to watering or how different arrangements guide energy in a particular direction. The user's familiarity with the interplay of encoded artifacts within the system can illuminate the processes that shape AI art in ways not fully determined by its creators. Such engagement places the prompter in direct contact with _machina naturans_, much as the gardener gains an informed appreciation of her environment by understanding how soil or seasonal factors influence a single bloom. Rather than merely witnessing static artefacts, the prompter interacts with a system that changes and develops through refinements. This partial but practical understanding shows that one does not require scientific expertise to recognise how AI art emerges from a process akin to _natura naturans_, thereby clarifying how AI art can be appreciated in light of what these systems in fact are. ## 5. Unity and Aesthetic Appreciation Having established the parallel between gardeners and prompters, we can now explore how Carlson's concept of unity furthers our understanding of AI art appreciation. The practical knowledge prompters develop informs their ability to recognise the unity between generative processes and creative outputs. As we have seen, unity plays a crucial role in Carlson's approach - the unity between _natura naturans_ and _natura naturata_ provides a foundation for meaningful environmental aesthetics. This concept of unity takes on parallel significance when applied to generative AI systems. Prompting connects artefacts to the system's broader encoded experiential knowledge. A text generated by ChatGPT bears a relationship not just to the prompting process that created it, but to the vast body of text that informed the system's parameters. Similarly, an image from Midjourney connects to a broad visual tradition encoded in its training data. The prompter occupies a unique position between _machina naturans_ and _machina naturata_ – actively participating in guiding the generative process while simultaneously appreciating its results. This dual role enables a special form of appreciation. When a prompter refines inputs to guide the system, they directly experience the unity between the generative potential of _machina naturans_ and the manifested artefacts of _machina naturata_. This parallels Carlson's environmental aesthetics: understanding the unity of natural processes and their resulting forms enhances our appreciation of both nature and AI-generated content. One might object that this restricts the appreciation of AI art to prompters, as it is only they who have direct engagement with _machina natura_. However, the limited appreciation of AI art by general audiences should not surprise us given how new this medium is. Just as non-specialists can come to appreciate gardens through books, television shows etc. a more widespread appreciation of how to appreciate AI art could be achieved through similar means. We should not expect people to appreciate AI art until they can understand it in light of what it is: as dance between _machina naturata_ and _machina naturans_. ## Conclusion This paper began by asking how we might appreciate AI art differently from traditional art. By viewing AI systems as environments shaped by encoded cultural patterns, I've established a framework paralleling Carlson's environmental aesthetics. The core of my argument rests on understanding AI systems as complex environments – what I've called _machina natura_. Just as nature actively creates through natural processes, these systems actively generate outputs by drawing on encoded patterns from millions of human creations. When we appreciate AI art, we're appreciating something that emerges from this generative environment. The prompter, like a gardener, develops practical knowledge of this environment. Through direct engagement, prompters learn how to guide the system's potential without needing technical expertise in neural networks. They understand, through practice, how different prompts influence the system's output, just as gardeners learn how different conditions affect plant growth. This understanding reveals the unity at the heart of AI art appreciation. Each AI-generated image or text connects to both the prompter's guidance and the vast corpus of encoded human creations within the system. ## References Carlson, A. (2005). Aesthetics and the environment: The appreciation of nature, art and architecture. Routledge. Fridman, L. (Host). (2024). Dario Amodei: Anthropic CEO on Claude, AGI & the Future of AI & Humanity [Transcript]. In Lex Fridman Podcast (#452). Retrieved from https://lexfridman.com/dario-amodei-transcript 02/03/2025 Terrone, E. (2024). Are works of art affective artifacts? If not, what sort of artifacts are they?. Topoi, 43(3), 737-746. Chicago