****# [[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, by computational processes, which are, at the very least, not intentional in the same way that human cognitive processes are (footnote). 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]]. In his view, the natural world should not be appreciated piecemeal –as a collection of landscapes or objects– but as what they actually are: **unified, natural, dynamic** _environments_. We argue that similarly, AI [[generative systems]] should be thought of as _environments_ with their own generative dynamics. This paper develops this analogy by showing how Spinoza's distinction complements and enriches Carlson's concept of unity, Spinoza's distinction between nature as process ([[natura naturans]]) and nature as product ([[natura naturata]]). We extend [[this framework]] to AI by conceptualizing machine-learning systems as _machina naturans_ (artificial generative processes) that produce _machina naturata_ (the resulting artefacts). The paper proceeds through four sections: (1) Carlson and Spinoza's complementary frameworks for understanding unity in nature, (2) the application of these concepts to AI as a [[generative substrate]], (3) [[participatory appreciation]] through the gardener-prompter analogy, and (4) how informed spectators can properly appreciate AI outputs. ## 1. [[Appreciating Nature]] as what it in fact is 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. Spinoza's terms for these two aspects—_natura naturata_ (the concrete product we see) and _natura naturans_ (the active, generative processes)—make explicit Carlson's call to appreciate both the existing form and the unseen forces that produce and sustain it. Appreciating this dual aspect—appearance and underlying process—clarifies why scientific understanding heightens our aesthetic appreciation: it shows how the environment's present form is inseparable from the continuous natural forces that shaped it. On Carlson's recommendations, appropriate aesthetic appreciation of nature requires understanding nature. We should not appreciate a forest, coastline, or desert by treating it as we would a painting (framing it, isolating a static view); instead, we appreciate it _as an environment_, in light of knowledge about the natural processes that formed and sustain it. Carlson calls this the Natural Environmental Model of appreciation. Just as knowledge of art history and style informs our appreciation of a painting, scientific knowledge – ecology, geology, biology – informs our aesthetic engagement with natural environments (Carlson REF). Consider a coastal cliff like Beachy Head in England. A casual observer might admire it simply for its dramatic height or striking white color. But Carlson's model suggests a deeper appreciation emerges when we consider what the cliff _actually is_: a stratified chalk formation shaped by geological uplift and marine erosion, part of a broader shoreline ecosystem. Understanding the cliff's formation through sedimentation of ancient marine organisms, its subsequent uplift through tectonic forces, and its ongoing erosion by the sea allows us to see unity in what might otherwise appear as disparate features – the cliff's shape, the crashing waves at its base, the plant life on its edge – as all interconnected elements of one evolving natural scene. Such scientific cognizance situates what we perceive into a coherent narrative of natural forces. The coastal cliff is not just a picturesque object but a segment of _nature's ongoing processes_, appreciated for qualities (ruggedness, grandeur, harmonious form) that emerge from those processes. Natural environments possess an autonomous order: they are not designed for viewers, yet they exhibit form, structure, and beauty arising from natural forces. The "unity" of a natural environment, in Carlson's sense, means that every part is seen in relation to the whole. Here Carlson observes: > 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) In other words, the aesthetic character of a coastal cliff is not fully realised if one views the cliff in isolation as a static sculpture; it emerges when one appreciates the cliff as part of a larger process – the coastline shaped by tides and weather over millennia – and recognises that the same natural laws govern the rocks, the sea, and the life around it. Carlson's model shifts aesthetic focus from isolated objects to the interaction and continuity among elements, underlining that the environment is _"a seamless unity"_ of components and processes. This unity is dynamic: natural beauty often lies in change, growth, and decay (the dramatic collapse of a cliff section, the seasonal cycle of a forest) Carlson's ideas about unity can be understood in terms of Spinoza's distinction between _natura naturans_ ("naturing Nature") and _natura naturata_ ("natured Nature"). Spinoza used this distinction to describe two inseparable aspects of reality: (1) the active, productive aspect of nature (God or Nature doing: the endless generation of things through the laws of nature), and (2) the passive, product aspect (the things produced: the concrete world of particular forms and events that result from those laws) (Spinoza REF). Crucially, Spinoza did not see these as two different worlds, but as two perspectives on the one unified substance that is Nature. _Natura naturans_ is nature as process – the continuous lawful activity that generates and sustains the cosmos. _Natura naturata_ is nature as product – the totality of particular things and states of affairs that have been generated. Crucially, Spinoza did not see these as two different worlds, but as two perspectives on the one unified substance that is Nature. _Natura naturans_ is nature as process – the continuous lawful activity that generates and sustains the cosmos. _Natura naturata_ is nature as product – the totality of particular things and states of affairs that have been generated. On the Natural Environmental Model, when we appreciate a natural environment we appreciate both its current appearance and the unseen processes that gave rise to that appearance. Returning to our coastal cliff example, its sheer face and weathered texture are beautiful in part because they are legible as the outcome of centuries of wind and water – the cliff as process (erosion, sedimentation, or natura naturans) and as product (towering chalk face, or natura naturata) cannot be fully separated in our aesthetic experience. ## 2. Machina Naturans and the Generative Substrate of AI The principle that things must be appreciated "for what they in fact are" means we need to understand how they were created and what shaped them. In Spinozist terms, this means appreciating natura naturata (nature's products) in light of _natura naturans_ (nature's ongoing creative activity). By analogy, a generative AI system can be understood as _machina naturans_, an artificial generative process analogous to nature, which produces _machina naturata_: the images, texts, or other outputs of the system. To appreciate an AI-generated artifact for what it is, one must apprehend it as the result of an underlying generative unity – the computational processes and representations from which it emerges – rather than as an isolated, inexplicable object. In this section, we articulate the structure of such _machina naturans_ in machine learning, focusing on how its representational underpinnings function as a kind of "cultural soil" from which new artefacts grow. **Latent Space as Cultural 'Soil'.** Modern generative AI systems (for instance, large language models or image generators) rely on high-dimensional representational structures – often called _latent spaces_ or _manifolds_ – that encode patterns derived from vast corpora of human culture. These latent spaces are essentially compressed archives of training data, where information from countless artefacts (texts, images, recordings) has been subsumed into a mathematical structure. We can use an ecological metaphor to characterise this: the training corpus serves as a rich compost of _experiential artefacts_ from human culture, and the AI's latent space is the fertile soil formed by their decomposition. Drawing on Terrone's notion of "experiential artefacts" – **artworks and media that we value for the experiences they evoke** (Terrone REF) – we can say that generative models ingest a mulch of such cultural artifacts and metabolise it into abstract representational form. In other words, just as organic matter (leaves, wood, carcasses) decays into nutrients within soil, the AI model "digests" innumerable cultural artefacts (images, texts, sounds) into latent representations. What remains is not literal fragments of the original works, but a distributed encoding of their salient structures: a complex statistical memory of human experiences now made available for new combinations. Indeed, recent analyses describe latent space as a "multi-dimensional archive of culture," embedding "rich representations of our visual world" and other domains of human knowledge (Schaerf REF). This latent archive constitutes the generative substrate – the ground from which novel _machinic naturata_ can sprout. Crucially, the elements of this substrate are no longer intact artworks or discrete ideas, but interwoven patterns. The training process breaks down individual artifacts into features and learns how those features co-vary across the dataset. The result is analogous to topsoil enriched by humus: just as one cannot pick out a single leaf or branch from well-mulched earth, one typically cannot identify a single source image or text within a trained model's latent space. Instead, the legacy of countless sources blends into a _generative potential_. This perspective helps demystify the creativity of generative AI. Far from creating _ex nihilo_, the system reconstitutes and recombines ingredients provided by human culture. Appreciating an output of such a system thus involves recognising in it the transformed traces of many prior creations and experiences. The output is _of_ culture even though no individual human hand crafted it. In short, the AI's representational world is a cultural ecology: a dynamic repository of influences analogous to an ecosystem's soil, climate, and nutrients. And like any ecology, it gives rise to phenomena that reflect its conditions. **From Generative Substrate to Emergent Artifact.** Given this substrate, the process of generation in AI can be likened to growth or cultivation. When a generative model produces an image or a text in response to some input (a _prompt_ or initial conditions), it is akin to a seed germinating in fertile soil. The prompt sets constraints or initial directions (much as a seed carries genetic instructions), and the model's _machina naturans_ – its autonomous generative mechanism – develops this into a full output by drawing on the latent nutrients of its training. The end result, the _machina naturata_, is the artifact we observe: for example, a novel image that blends stylistic elements of Renaissance paintings with motifs from modern graphic design, or a paragraph of text mimicking a certain author's tone. Importantly, the generative process operates through the model's internal learned rules rather than direct human design. In this respect, the model's activity resembles nature's _natura naturans_: it is a cascading chain of interactions (among numerical weights, layers of a neural network, etc.) that unfolds largely on its own once initiated. The human user does not micromanage how the image develops any more than a gardener controls the minute cell divisions by which a bud blooms. At most, one can guide and adjust parameters from outside, but the detailed formation is driven by the system's inner dynamics. This autonomy of the generative process underlies the analogy between appreciating AI outputs and appreciating natural phenomena. Consider the experience of coming upon a beautiful flower in a garden. Part of what makes the flower aesthetically interesting (beyond its colors and form) is our understanding that it _grew_ from soil via sunlight, water, and the intrinsic vitality of the plant. The flower is an instance of _natura naturata_ – a finished product of nature – but we appreciate it against the background sense of _natura naturans_, the ongoing life process that produced it. We know the blossom is not a painted ornament assembled by an artisan; it is the visible expression of an organic unity (the plant and its environment). Likewise, when confronted with a striking image or text generated by an AI, there is an aesthetic dimension revealed by seeing it as the product of an underlying generative unity. The image is a piece of _machina naturata_ that has issued from an evolving computational process rather than from direct human fabrication. Recognizing this can change our mode of appreciation: we attend not only to the surface features (shapes, words) but also to how those features signify the operation of a complex _machina naturans_. Just as the delicate symmetry of a flower may be admired both for its immediate beauty and for what it tells us about biological growth, an AI artwork might be admired both for its manifest form and for the remarkable process that gave rise to it. To make this concrete, imagine an AI-generated landscape painting that looks uncannily like a work of Impressionism. A viewer informed by the generative context understands that the AI has synthesized this image from patterns learned across thousands of landscapes and artworks – a tapestry woven from threads of collective visual culture. Appreciating it "for what it is" means appreciating it as an emergent cultural _flower_ rather than misidentifying it as a traditionally painted canvas. If one falsely assumed a human painted it from scratch, one's aesthetic reaction might center on the implied intentions or skills of a non-existent artist ("What was the painter trying to express here?"). In contrast, knowing the true origin invites one to marvel at how the _machina naturans_ has combined and transformed elements of prior art into a new image. The sense of wonder or curiosity shifts: we might ask, "How did the model manage to evoke Monet's color palette with photographic realism in this scene?" Our aesthetic impression now includes an intellectual appreciation of the generative process – much as a nature-lover appreciates a coastline more deeply when understanding it as the cumulative work of tides and geology over eons. ### Internal Dynamics of Machine Learning Models To support this analogy further, we need to understand how generative AI systems work and why it is apt to describe them as _environments shaped by internal forces and constraints_. Modern generative models (such as GANs, VAEs, or large language models) operate by creating and then exploring a _latent space_ – a mathematical space encoding features of their training data. During training, the AI system compresses patterns from the data into a multi-dimensional array of numbers (the latent representation) and learns rules to construct new data from these representations. The result of training is not a fixed set of outputs, but rather a learned "world" of possible outputs: an abstract space with its own structure, in which moving in one direction might add, say, more "tree-ness" to an image, or alter the style of a sentence. Researchers often note that these latent spaces are highly structured: they capture meaningful variations in the data (for instance, one axis might correspond to image brightness, another to the presence of buildings vs. open terrain, etc.), even though they are not explicitly labelled (NICD REF). In a sense, the AI has _discovered_ an underlying order in the training examples and can use it to generate new combinations. This is analogous to how nature's processes impose an order (via physical laws) that shapes the forms we see. Indeed, we might say that a trained generative model is an artificial world of sorts: it has a kind of landscape (the latent space), natural laws (the model's learned weights and activation functions), and dynamic processes (the generative algorithm that produces outputs). When the model creates an image or text, it is akin to a mini-evolution or simulation happening inside the computer: starting from some initial conditions (noise or a prompt), the internal process iteratively applies its "laws" until a final form emerges. For example, _diffusion models_ (used in image generators) literally simulate a kind of random diffusion and then reverse it: they start with random noise and gradually _coalesce_ an image by applying learned denoising steps. This can be compared to how, say, a crystal forms from a chaotic solution under natural physical laws – patterns emerge as the process iterates. In neural network terms, the latent representation serves as the hidden generative environment: instead of directly drawing pixel by pixel, the model operates in a compressed feature-space where conceptually related images cluster together. In this latent environment, a single prompt like "old tree by lakeside" might map to a region dense with variations of that theme, and the model "wanders" there to pick one realization. From a technical standpoint, these systems have internal unity: every output is a manifestation of a consistent underlying model. All images generated by a given version of Midjourney, no matter how different, are related by the fact that they are shaped by the same training data and network weights. Analogously, every natural landscape on Earth is different, yet all are outcomes of the same fundamental natural laws (physics, chemistry, biology) and planetary history. This parallel highlights why we call the AI's process _machina naturans_. It is the generative capacity of the machine – the algorithm + training – that plays a role akin to "nature naturing." And the specific output we see at a given time is _machina naturata_ – a particular "natured" product of that generative capacity. Just as one cannot fully appreciate a natural vista without some sense of the forces that made it, one cannot fully appreciate an AI-generated artifact without recognizing it as the endpoint of an algorithmic generative trajectory. In sum, the structures and processes inside generative AI form a unified generative environment analogous to a natural environment. The latent space – built from the decomposed archive of cultural artifacts – plays a role akin to soil in an ecosystem, containing and recycling the detritus of countless creative works. The generative algorithm's functioning (the forward pass of the neural network, the diffusion of noise into an image, etc.) is comparable to nature's growth forces, spontaneously producing novel configurations. And the outputs are the _machina naturata_ that we ultimately encounter, analogous to the plants, rocks, or rivers that constitute _natura naturata_. According to the Carlsonian insight, proper aesthetic appreciation depends on understanding an object in light of "what it in fact is" – here, a product of machina naturans. In the case of AI art, that means the spectator or critic should situate the work within its generative context: the cultural-technical soil that nourished it and the algorithmic activity that shaped it. By doing so, we treat the AI output neither as a mere random oddity nor as a strictly human artwork, but as _the kind of thing it truly is_: an expression of a hybrid natural-cultural generative process. This sets the stage for a deeper inquiry into the roles of those who engage with such processes. In particular, if _machina naturans_ is analogous to nature, what is the role of the human prompter who tends and guides it? We turn now to the figure of the prompter, who stands in relation to generative AI much as a gardener stands in relation to fertile nature. ## 3. Participatory Appreciation: The Prompter as Gardener Thus far, we have treated generative AI's machina naturans as an autonomous system akin to nature, with outputs comparable to natural products. However, just as natural environments often have human caretakers or participants (gardeners, hikers, scientists) who interact with nature's generative forces, so too do AI systems have users or "prompters" who engage with their generative capacities. Carlson's framework primarily valorizes a contemplative appreciation grounded in knowledge – a somewhat distanced stance of observing nature with understanding. Yet Carlson and others also acknowledge that direct _participation_ in natural processes (for instance, the hands-on appreciation a gardener has for her garden) offers a legitimate and perhaps qualitatively different mode of aesthetic appreciation. In environmental aesthetics, one can appreciate a landscape by studying it and beholding it, but one can also appreciate a garden by cultivating it. We can apply this insight to the realm of AI art: interacting with a generative model by crafting prompts and curating outputs is a form of participatory aesthetic engagement. The prompter stands at the critical interface of _machina naturans_ and _machina naturata_, analogous to the way a gardener mediates between natura naturans and the blooms of natura naturata. In this section, we explore how the prompter's role as a kind of "digital gardener" yields a unique, practice-based appreciation of AI's generative artistry. **Guiding a Partly Autonomous Process.** A gardener does not create a flower in the way an artist might create a porcelain replica of a flower; instead, the gardener provides conditions and guidance for a plant to grow, while the actual formation of the flower is executed by nature. In the same way, a prompter using a system like Midjourney or GPT-4 does not explicitly design every detail of the output; rather, they input a prompt (plant a seed) and then _machina naturans_ – the AI's internal generative process – autonomously produces an outcome. The prompter's art lies in skillfully guiding this process: selecting input phrases, adjusting parameters, iterating and refining prompts, and maybe applying slight post-generation tweaks, all in an effort to coax the system toward a desired kind of result. Yet, as with gardening, there is an inherent unpredictability and only partial controllability in working with generative AI. The AI, like nature, has a "dynamic recalcitrance" – it can surprise, resist fine control, and require adaptation. Users often find that a prompt yields an output that is interesting but not quite what they envisioned, prompting them to adjust their input and try again (much as a gardener might prune a plant, water differently, or move a flowerpot to shade in response to how the plant grows). This interactive, iterative cultivation is central to the prompter's activity. Nick Young and Enrico Terrone draw this parallel explicitly, arguing that creating images with a tool like Midjourney "parallels a gardener's interaction with _natura naturans_—nature's autonomous generative processes" (Young & Terrone REF). They introduce the term "_machina naturans_" to characterize machines capable of autonomous generative behavior, with generative AI as the prime example. In their analogy, the _prompter_ relates to the AI much as a gardener relates to a plot of fertile land. The gardener can plant seeds (input prompts), tend the conditions, and make rudimentary interventions (trimming, rephrasing, re-rolling outputs), but cannot dictate every outcome in advance. As they note, a painter can precisely control each brush stroke in painting a sunflower, but a gardener cannot likewise control the exact shape of a sunflower growing in her garden, and a prompter cannot precisely determine every detail of a sunflower image generated by the AI. Both gardener and prompter exercise skillful yet incomplete control over a process that has its own momentum. The creative role of the human here is one of _partnership_ or _stewardship_ rather than unilateral mastery. This kind of partnership yields a distinctive form of aesthetic satisfaction. The gardener's pleasure in seeing her garden in full bloom is partly an appreciation of nature's beauty, but it is heightened by her intimate involvement in the process – the knowledge that her choices and care contributed to, and conversed with, nature's growth. Similarly, a prompter experiences a special gratification on obtaining a stunning AI-generated image after many rounds of trial and error. The result is appreciated not just for its visual qualities, but also as a culmination of an interactive process in which the prompter's ingenuity met the machine's generative power. In philosophical terms, the prompter has situated knowledge of the AI system. Through hands-on engagement, they develop a tacit understanding of the model's "personality" – its tendencies, strengths, and limitations. For example, an experienced user learns how a certain phrase will likely influence the style of an image, or how adjusting a parameter will affect the randomness of the output. This is analogous to a gardener's practical knowledge of soil types, weather patterns, or how a certain species of plant responds to pruning. Such knowledge is not purely theoretical; it is empirical, gained by experience and feedback. It allows the prompter to appreciate subtleties in the AI's outputs that a casual observer might miss – just as a gardener notices the slight improvements in a rose's bloom due to a different fertilizing technique. It is important to clarify that calling the prompter a "gardener" of AI art is not to suggest that the prompter exercises complete creative authorship in the traditional sense. Rather, the prompter is a co-creator operating within the constraints and opportunities provided by the _machina naturans_. The gardener metaphor highlights that the prompter's agency is collaborative: he or she works _with_ the autonomous generative processes. As one commentary puts it, "the gardener [is] a creative agent who is nevertheless thoroughly dependent on the cooperation of natural processes"; by the same token, "the prompter is a creative agent who is nevertheless thoroughly dependent on the cooperation of artificial processes" (Young & Terrone REF). This dependence does not diminish the prompter's contribution – on the contrary, it defines a new mode of creativity. The aesthetic achievement (a beautiful garden, an impressive AI artwork) emerges from the fusion of human decision and non-human generation. Appreciating the final product thus involves appreciating this fusion. A person admiring a well-kept garden can often sense the gardener's hand in the layout and cultivation, while also sensing the wild vitality of nature that exceeds the gardener's designs. Likewise, when we know an image was produced through a prompt-driven interaction, we may simultaneously credit the prompter's vision _and_ the AI's algorithmic fecundity. Carlson's original model of nature appreciation emphasized knowledgeable contemplation, but here we see an active mode of appreciation that Carlson (and environmental aestheticians more broadly) can accommodate: the appreciation in doing. The gardener _appreciates by cultivating_, and analogously the prompter _appreciates by prompting_. Each stands at the nexus of naturans and naturata: the gardener physically engages with soil, water, and living plants, dwelling in the interface between process and product; the prompter intellectually and creatively engages with the model's latent space and output, inhabiting the interface between algorithmic process and resulting image/text. This participatory immersion provides an aesthetic experience that is more process-oriented and intrinsically rewarding in a way that complements the appreciation of a finished product from an external viewpoint. In the AI case, many practitioners describe a sense of discovery and play when working with generative models – an aesthetic joy in exploring what the _machina naturans_ can produce, akin to the joy a gardener finds in experimenting with new plants and arrangements. Finally, the gardener analogy helps illuminate the notion of aesthetic profundity arising from generative unity. When a person both participates in and observes a generative process, they often develop a heightened sense of connection to the underlying unity of that process. A devoted gardener may feel deeply connected to her garden as a living whole; the boundary between creator and creation blurs as she attunes to the rhythms of growth and change. This sense of continuity between self, process, and product can be aesthetically profound – it situates the beauty of the flower in a larger meaningful narrative of cultivation and care. Similarly, a prompter who engages deeply with an AI system might feel a certain awe at the _machina naturans_ as a creative force and a sense of personal connection to its outputs. The outputs are not just prettified pixels or neatly arranged words; for the engaged prompter they are the visible tips of an iceberg of generative activity in which the prompter has played an integral part. Insofar as the prompter appreciates the output, it is with an awareness of the vast latent space and complex algorithmic dance that underlies it – a parallel to how a knowledgeable gardener perceives even a simple blossom against the background of botany and ecology. This awareness of generative unity can imbue the experience with a depth beyond surface impressions. One might say the prompter appreciates the _process-in-the-artifact_, perceiving the unity of machina naturans and machina naturata as aesthetically significant in itself. ## 4. Aesthetic Profundity and the Informed Spectator Thus far we have established a parallel between environmental appreciation and AI art appreciation in terms of generative contexts and the possibility of participatory engagement. We now turn to the case of the informed spectator – someone who observes and appreciates a piece of AI-generated art without having been the prompter or creator. This figure is analogous to a hiker admiring a wildflower they did not plant, or a tourist marveling at coastal rock formations they did not shape. The challenge is to show how such a spectator might attain an appropriate aesthetic appreciation of _machina naturata_ by drawing on knowledge of _machina naturans_. In keeping with Carlson's dictum, the spectator must appreciate the artifact for "what it in fact is," which in this context means recognising it as the product of an AI generative process (and not, say, a human-made piece of art in the traditional sense). We will argue that even without hands-on involvement, a spectator can achieve a form of conceptual engagement with the generative unity behind the work, thereby accessing a degree of aesthetic depth or profundity comparable to that experienced by the prompter. The key is the spectator's understanding – whether scientific, technical, or via informed imagination – of the underlying creative forces at play. **Knowledge as a Bridge to Appreciation.** Carlson's environmental model asserts that knowledge of natural history and ecology can transform one's experience of a landscape: knowing _what_ one is looking at and _how_ it came to be enhances its aesthetic qualities. A similar principle applies to AI art. When an observer knows that a given image was generated by a diffusion model trained on, for example, millions of photographs and paintings, this knowledge can attune them to aspects of the image they might otherwise overlook. Instead of viewing it as a mere picture, they perceive it as a _convergence of influences_ orchestrated by an algorithm. The colours blending in a sunset scene might prompt the spectator to recall that the AI likely absorbed countless real sunset photos; the inventive twist in the style (say, pixelated impressionism) might be recognised as an emergent quirk of the model rather than the personal signature of a human artist. In effect, the informed spectator sees the _machina naturata_ (the output) in relation to its _machina naturans_ (the process and dataset). This satisfies the criterion of appreciating the work as the kind of thing it is – namely, an AI-generated artifact. By contrast, a spectator ignorant of the work's origin might apply mismatched categories (for instance, praising brushwork that doesn't exist, or faulting the piece for lack of human emotion, or simply feeling uneasy without knowing why). Informed knowledge prevents such category errors and opens up the path for genuine appreciation. Consider again the analogy with nature. A casual observer might enjoy a coastal vista simply for its picturesque scenery. But an informed observer, who knows that the jagged cliffs were carved by waves over millennia and that the black sand resulted from volcanic activity, can appreciate the scene at another level: the landscape becomes a story of natural forces made visible, a testament to _natura naturans_ at work. This geological and ecological understanding often increases the viewer's sense of wonder or reverence – the scene feels more profound because it is connected in the mind to vast processes of time and change. Analogously, an informed viewer of an AI-generated artwork can perceive in it the imprint of vast underlying processes: the training of the model on global datasets, the complex mathematics of neural networks, the iterative refinement through which the final output emerged. Even if these processes are not directly observable in the image or text, knowledge about them can imbue the artifact with an added fascination. The spectator might think, "This surreal landscape looks like a blend of Van Gogh and Dalí – and indeed, knowing the AI was trained on Western art, I can see how it _dreamed_ up this scene by combining those influences." The artwork thus becomes not only an image to look at, but evidence of an unseen generative unity – a _machinic imagination_ operating behind the scenes. One might worry that requiring such knowledge makes the appreciation of AI art overly intellectual. However, the knowledge need not be detailed technical expertise; it can be more akin to a general understanding or narrative of origin. In art appreciation generally, viewers often benefit from knowing the context of a work's creation (the artist's intent, the art movement, the techniques used). Here the context happens to be a computational one. For example, a museum exhibition of AI-generated art might include wall text explaining the basics of how the pieces were made ("This artwork was created by an artificial neural network that was fed thousands of landscape paintings and then instructed via text prompts to produce new images."). A viewer who reads this gains a framework for interpretation: the strange blending of styles or the slight uncanny quality of the image now makes sense as a result of that process. They might aesthetically enjoy trying to spot which elements came from which influence, or simply marvel at the capability of the system. In short, conceptual knowledge serves as a bridge that allows the spectator's imagination to connect the finished artifact back to its generative source. **Imagination and Aesthetic Profundity.** Beyond factual knowledge, there is also a role for the spectator's imagination in deepening aesthetic experience. Even in the absence of complete information about an AI's workings, a spectator can speculate or conceptually envision the process that led to the artwork. This is similar to how one might appreciate a thunderstorm by imaginatively connecting the lightning and thunder to the idea of electrical charges and atmospheric dynamics, even if one is not a meteorologist. In the case of AI art, an informed imagination means the viewer actively conceives of the artwork as _a machinic creation_. They might imagine the myriad images the AI "saw" during training, now distilled into this one output, or picture the algorithm iterating through noise and clarity to form the image. This imaginative reconstruction can yield a sense of awe or "aesthetic sublime," where the viewer feels the presence of something larger and more complex than the immediately given form. The artwork points beyond itself to the totality of data and computations that birthed it – much as a fossil points to a prehistoric world or starlight points to cosmic distances. We can thus identify a source of aesthetic profundity in AI art that parallels that in nature. Both a wild coastline and a well-crafted AI-generated image can inspire profound feeling because they connect the perceiver to a grand creative force: in one case, the force of natural processes; in the other, the sprawling convergence of human culture and machine learning. The unity of _natura naturans_ or _machina naturans_ with the particular _naturata_ before us evokes a sense of continuity and meaning. It reminds us that what we admire is not an isolated object but part of an ongoing story of formation. Many have noted that nature's beauty often lies not just in static appearance but in our sense of the _life_ or _history_ within it (the way the gnarled trunk of an old tree is beautiful partly because it signifies decades of growth, struggle, and regeneration). Likewise, one could argue that a compelling aspect of AI art is that it encapsulates an expansive cultural and computational journey: it is literally the output of a network that has learned from innumerable prior artifacts and is performing an act of creation. When this backstory is appreciated, the artwork gains a new dimension of significance. The _profundity_ here is intellectual-emotional: a mixture of admiration for human technological ingenuity, reflection on the interplay between human creativity and artificial generation, and perhaps a philosophical wonder at how patterns of meaning can arise in unexpected new mediums. It is worth emphasizing that the informed spectator's appreciation still relies on the fusion of process and product we have been discussing throughout. Even though the spectator did not personally operate the generative process, their understanding of that process lets them symbolically reunite the machina naturata with its machina naturans in their mind. In effect, the spectator mentally reconstructs the connection that the prompter physically engaged in. This is analogous to how one can appreciate gardening without being a gardener: a visitor to a garden who knows about horticulture can admire how the layout and health of the plants result from skilled gardening practices and natural growth working in concert. They might notice, "These roses are vibrant – someone must have carefully tended the soil and pruned them at the right times," thereby acknowledging the cooperation of gardener and nature. Similarly, an informed audience of AI art can think, "This image's surreal yet coherent style tells me the AI blended different art genres – and the person who guided it must have found a clever prompt to yield such balance." In doing so, the audience member is _conceptually participating_ in the generative unity: they appreciate the artifact with an awareness of both the human guidance and the machine creativity behind it. Lastly, one might ask: Is it truly necessary for appreciation to depend on knowing the work's origin in this way? In environmental aesthetics, there is debate about whether one can appropriately appreciate nature without scientific knowledge. Carlson would say such knowledge is required for full appreciation, whereas others might allow more intuitive or emotional engagement to count as aesthetic. In the AI case, if a person unknowingly enjoys an AI-generated artwork under the false belief it is human-made, are they appreciating it wrongly? One could argue yes – they attribute qualities or intentions to it that are mislocated. Their experience might still be pleasant, but it misses the distinctive character of the work. Our view, following the spirit of Carlson, is that a degree of category-informed understanding is indeed crucial for the _proper_ aesthetic appreciation of AI art. The work's identity as _machina naturata_ is one of its defining features. Appreciators should not ignore that any more than one should ignore that a waterfall is a natural phenomenon (not a fountain sculpture) when judging its aesthetics. That said, the knowledge can be quite general. What matters is that the spectator situates the work in the right conceptual frame – as an output of generative AI – and thereby becomes attuned to the right qualities. ## Conclusion We have developed an analogy between environmental aesthetics and the aesthetics of generative AI, grounded in the idea of appreciating things _as what they are in fact_. Carlson's environmental aesthetics, complemented by Spinoza's process-product distinction, provides a unified framework for understanding how natural environments should be appreciated as integrated wholes shaped by natural processes. Extending this framework to AI, we introduced the concepts of _machina naturans_ (the machine as generative process) and _machina naturata_ (the machine's product). We argued that advanced generative AI systems can be viewed as artificial, law-governed environments with each output analogous to a "natural" occurrence within the system, governed by the system's trained parameters. Therefore, an AI output is best appreciated not as a standalone traditional artwork, but in relation to the AI's generative context. Through our examination of how AI latent spaces function as "cultural soil" and how generative algorithms operate, we showed that the aesthetic characteristics of an AI output are inseparable from the process that produced it. We then expanded this analogy through the gardener–prompter comparison, showing that engaging with AI's generative process can be a mode of aesthetic appreciation analogous to gardening. Finally, we demonstrated how even non-participating spectators can achieve proper aesthetic appreciation of AI outputs through informed understanding of their generative origins. One virtue of this framework is its coherence: it provides a unified way to think about various phenomena in AI aesthetics that might otherwise seem disconnected. It explains why people often describe using generative AI in exploratory, discovery-laden terms – because they are treating it like an environment to explore. It explains why the lack of human intention in AI art need not be a deficit – because we transfer the source of order to the AI's internal dynamics, much as we find beauty in natural forms with no human author. It also offers an elegant resolution to the debate of "is the AI the artist, or the prompter, or the programmer?" by sidestepping the notion of a single artist: the AI art emerges from an interactive unity of machine and human, process and prompt, akin to a collaborative emergence. By appreciating AI artworks as we do environments, we foster a stance of respectful fascination: we respect the AI's complexity and limits, and we are fascinated by the interplay between our inputs and its autonomous development. In doing so, we may find a new kind of beauty – one that is born from code and data, yet resonates with age-old aesthetic ideals of unity, harmony, and the sublime power of creation.