## 3. Machina Naturans and the [[Generative Substrate]] of AI Carlson’s principle that nature must be appreciated “for what it in fact is” emphasizes informed awareness of generative context ( [[[Environmental Aesthetics]] ([[Stanford Encyclopedia]] of Philosophy)](https://plato.stanford.edu/entries/environmental-aesthetics/#:~:text=Carlson%20argued%20for%20it%20by,same%20reasoning%20to%20natural%20things) ). 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 artifacts 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 artifacts (texts, images, recordings) has been subsumed into a mathematical structure. We can use an ecological metaphor to characterize this: [[the training corpus]] serves as a rich compost of _experiential artifacts_ from [[human culture]], and the AI’s [[latent space]] is the fertile soil formed by their decomposition. Drawing on Terrone’s notion of “[[experiential artifacts]]” – artworks and media that we value for the experiences they evoke ([PEA - The Philosophy of Experiential Artifacts | PEA](http://pea.unige.it/node/2#:~:text=pursuing%20an%20alternative%20strategy,the%20philosophy%20of%20experiential%20artifacts)) – we can say that generative models ingest a mulch of such cultural artifacts and metabolize 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 artifacts (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 ([[2410.09094] Reflections on Disentanglement and the Latent Space](https://ar5iv.org/pdf/2410.09094#:~:text=scientists%2C%20digital%20artists%2C%20and%20media,dimensional%20space%20of%20potentiality)). 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 recognizing 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. 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 interactor 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. ## 4. 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 ( [Environmental Aesthetics (Stanford Encyclopedia of Philosophy)](https://plato.stanford.edu/entries/environmental-aesthetics/#:~:text=Carlson%20argued%20for%20it%20by,same%20reasoning%20to%20natural%20things) ). 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 ([](https://philarchive.org/go.pl?id=YOUGTI-2&proxyId=&u=https%3A%2F%2Fphilpapers.org%2Farchive%2FYOUGTI-2.pdf#:~:text=that%20a%20garden%20is%20a,bushes%20that%20grow%20over%20the)). 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” ([](https://philarchive.org/go.pl?id=YOUGTI-2&proxyId=&u=https%3A%2F%2Fphilpapers.org%2Farchive%2FYOUGTI-2.pdf#:~:text=and%20soil%2C%20but%20the%20distinct,A%20gardener%20can%20only%20coax)) – 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” ([Nick Young & Enrico Terrone, Growing the image: Generative AI and the medium of gardening - PhilArchive](https://philarchive.org/rec/YOUGTI-2#:~:text=outputs%2C%20which%20sets%20it%20apart,shaped%20by%20its%20inherent%20unpredictability)) ([](https://philarchive.org/go.pl?id=YOUGTI-2&proxyId=&u=https%3A%2F%2Fphilpapers.org%2Farchive%2FYOUGTI-2.pdf#:~:text=Drawing%20on%20this%20account%20of,incomplete%20control%20of%20the%20user)). They introduce the term “_machina naturans_” to characterize machines capable of autonomous generative behavior, with generative AI as the prime example ([](https://philarchive.org/go.pl?id=YOUGTI-2&proxyId=&u=https%3A%2F%2Fphilpapers.org%2Farchive%2FYOUGTI-2.pdf#:~:text=Drawing%20on%20this%20account%20of,incomplete%20control%20of%20the%20user)). In their analogy, the _prompter_ relates to the AI much as a gardener relates to a plot of fertile land ([](https://philarchive.org/go.pl?id=YOUGTI-2&proxyId=&u=https%3A%2F%2Fphilpapers.org%2Farchive%2FYOUGTI-2.pdf#:~:text=3,deployed%20the%20Latin%20expression%20natura)) ([](https://philarchive.org/go.pl?id=YOUGTI-2&proxyId=&u=https%3A%2F%2Fphilpapers.org%2Farchive%2FYOUGTI-2.pdf#:~:text=Drawing%20on%20this%20account%20of,incomplete%20control%20of%20the%20user)). 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 ([](https://philarchive.org/go.pl?id=YOUGTI-2&proxyId=&u=https%3A%2F%2Fphilpapers.org%2Farchive%2FYOUGTI-2.pdf#:~:text=text%20to%20image%20system%20is,one%20third%20of%20the%20canvas)) ([](https://philarchive.org/go.pl?id=YOUGTI-2&proxyId=&u=https%3A%2F%2Fphilpapers.org%2Farchive%2FYOUGTI-2.pdf#:~:text=Arguably%2C%20neither%20the%20gardener%20nor,be%20shaped%20and%20cut%20back)). 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” ([](https://philarchive.org/go.pl?id=YOUGTI-2&proxyId=&u=https%3A%2F%2Fphilpapers.org%2Farchive%2FYOUGTI-2.pdf#:~:text=As%20%E2%80%9Cthe%20gardener%20,be%20an%20artistic%20medium%20should)); by the same token, “the prompter is a creative agent who is nevertheless thoroughly dependent on the cooperation of artificial processes” ([](https://philarchive.org/go.pl?id=YOUGTI-2&proxyId=&u=https%3A%2F%2Fphilpapers.org%2Farchive%2FYOUGTI-2.pdf#:~:text=As%20%E2%80%9Cthe%20gardener%20,be%20an%20artistic%20medium%20should)). 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** (the third insight we aim to integrate). 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. In conclusion, the role of the prompter exemplifies a participatory route to aesthetic appreciation in AI art, analogous to the role of the gardener in environmental aesthetics. The prompter cultivates and learns the _machinic_ environment, developing a practical understanding that informs their aesthetic judgments. This engagement produces a distinctive appreciation – one grounded in interaction, skill, and a felt connection to the generative process. However, not every admirer of AI-generated art will be a prompter; many will encounter the machinic naturata after its creation, as spectators rather than gardeners. The question then arises: how can an informed spectator (who did not personally generate the work) still aesthetically appreciate an AI artifact in a way that captures its connection to machina naturans? We address this in the next section by examining the role of knowledge and imagination in the appreciation of AI art by those who stand outside the direct creation process. ## 5. 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 recognizing 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 ( [Environmental Aesthetics (Stanford Encyclopedia of Philosophy)](https://plato.stanford.edu/entries/environmental-aesthetics/#:~:text=Carlson%20argued%20for%20it%20by,same%20reasoning%20to%20natural%20things) ). 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 colors 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 recognized 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. To summarize, even a spectator who is not a prompter can develop an enriched aesthetic appreciation of AI-generated art by understanding, at least in broad outline, the _machina naturans_ behind the scenes. Whether through direct explanation, education, or imaginative insight, such a spectator comes to appreciate the piece **as AI art**, grasping the unity of process and product that defines this new medium. In doing so, the spectator joins the prompter and the creator in treating the AI artifact “for what it in fact is.” The aesthetic merit of the piece – its beauty, intrigue, or sublimity – is thus partly perceived in light of the generative context, which can render the experience more profound. Just as our sense of the sublime in nature often stems from recognizing the vast natural forces implicit in a scene, our sense of profundity in AI art can stem from recognizing the expansive generative network implicit in the artifact. This parallel with environmental aesthetics not only helps validate AI art as a subject of serious aesthetic appreciation, but also enriches our understanding of what it means for something to be appreciated on its own terms. By acknowledging the role of both practical engagement (as in gardening or prompting) and informed contemplation (as in educated spectatorship), we arrive at a coherent and comprehensive account of aesthetic appreciation in the age of generative AI – one that maintains an elegant symmetry with the appreciation of natural environments. **References:** (Selections cited in text) Carlson, Allen. _Aesthetic Appreciation of the Natural Environment_ (various works, 1979–2007). Cooper, David E. _A Philosophy of Gardens_. Oxford, 2006. Spinoza, Benedict. _Ethics_ (esp. Part I, on _natura naturans_ and _naturata_). Terrone, Enrico & Young, Nick. “Growing the Image: Generative AI and the Medium of Gardening.” _Philosophical Quarterly_ 75(1), 2025 ([](https://philarchive.org/go.pl?id=YOUGTI-2&proxyId=&u=https%3A%2F%2Fphilpapers.org%2Farchive%2FYOUGTI-2.pdf#:~:text=Drawing%20on%20this%20account%20of,incomplete%20control%20of%20the%20user)) ([](https://philarchive.org/go.pl?id=YOUGTI-2&proxyId=&u=https%3A%2F%2Fphilpapers.org%2Farchive%2FYOUGTI-2.pdf#:~:text=As%20%E2%80%9Cthe%20gardener%20,be%20an%20artistic%20medium%20should)). Terrone, Enrico. **PEA – Philosophy of Experiential Artifacts** (ERC Project Description) ([PEA - The Philosophy of Experiential Artifacts | PEA](http://pea.unige.it/node/2#:~:text=pursuing%20an%20alternative%20strategy,the%20philosophy%20of%20experiential%20artifacts)). Schaerf, Ludovica. “Reflections on Disentanglement and the Latent Space.” xCoAx Conference 2024 ([[2410.09094] Reflections on Disentanglement and the Latent Space](https://ar5iv.org/pdf/2410.09094#:~:text=scientists%2C%20digital%20artists%2C%20and%20media,dimensional%20space%20of%20potentiality)).