# Media Aesthetics—Memory in the Age of Technogenesis: DeerFlow Daily Question
Generated: 2026-10-15 21:06:25
Clarification handling: HeHe automatic
## Question
Why does 2023, when generative AI entered everyday thinking, writing, and creative practice through conversational interfaces, constitute a threshold in the shift “from media of recording, storage, transmission, and selection to media of co-performing generation”? — How is this criterion distinguished from earlier media transformations, including the telegraph, radio, and the internet?
## Khoj RAG References
## References (Khoj RAG Search Results)
The items below are semantically related materials retrieved from the local Khoj document database. In answering, refer to them preferentially, but state their limitations when the evidentiary basis is weak.
### 1. Document 1 (score: 0.07762449054425413)
> AI와 인간의 접촉 4단계의 정합성 논증.txt # AI와 인간의 접촉 4단계의 정합성 논증
## 서론: 하나의 위상학인가, 단순한 목록인가?
네 단계—생성적 경이, 불쾌한 낯섦, 비판적 성찰, 관계적 공진—은 단순한 경험의 목록이 아니다. 하나의 **위상학적 구조**를 이룬다. 정합성은 이 네 단계가 필연적 이행 관계를 가지며, 각 단계가 다음 단계를 **존재론적으로 요구**하고, 전체가 **완전한 하나의 운동**을 형성한다는 데서 증명된다. 아래 다섯 개의 축에서 이 정합성을 논증한다.
---
## 1. 현
### 2. Document 2 (score: 0.10256187885261392)
> 핵심 메시지 1. 질문 AI 는 정답을 대신 주는 장치가 아니라, 더 좋은 질문을 설계하게 하는 파트너가 될 수 있다. 2. 해석 수업의 핵심은 결과물 생산보다 자료를 읽고, 비교하고, 자신의 언어로 재구성하는 과정이다. 3. 성찰 학생은 AI 가 만든 문장을 검토하며 자기 판단의 근거와 한계를 더 분명히 볼 수 있다.
### 3. Document 3 (score: 0.11936053768145138)
> AI 와 대학 교육 생성형 AI 를 학습의 대체물이 아니라 질문· 해석· 성찰을 확장하는 도구로 다루기 OfficeCLI 한국어 PPT 제작 테스트
### 4. Document 4 (score: 0.12751893669140468)
> 간단한 개념 관계도 학생 질문과 판단 교수자 맥락과 기준 AI 도구 초안과 피드백 관계도는 도형과 연결선으로 구성되어 PowerPoint 에서 개별 편집할 수 있습니다.
### 5. Document 5 (score: 0.13133213555834138)
> MACHINOCENE ECOLOGY AI 의 비물질적 편리함은 물질· 에너지 부담 위에 세워진다 화면 안의 매끄러운 서비스는 화면 밖의 광물, 전력, 냉각수, 폐기물의 생태적 비용을 동반한다. 1 광물 채굴 반도체 생산 2 데이터센터 AI 연산· 냉각 3 탄소 배출 전자폐기물 4 지역 생태계 부담 전가 핵심: 사용자 경험의 편리함과 환경 부담의 위치가 서로 다르게 분배된다. 05 / 10
## Response
[Fallback analysis: Because DeerFlow did not complete within the time limit, this switched to direct analysis without MCP exploration]
# Media Aesthetics—Memory in the Age of Technogenesis: The Threshold of 2023 and the Ontological Shift to Generative Media
## Introduction: The Functional Transformation of Media, and 2023 as a Threshold
The history of media has often been narrated around four functional axes: recording, storage, transmission, and selection. The evolution of media—from orality to writing, from manuscript to print, from the telegraph to broadcasting, and then to the internet—has proceeded in the direction of externalizing and extending human cognitive processes. Throughout this process, human beings have maintained their position as continuous users of media and as semantic controllers. However, in 2023, as generative artificial intelligence entered everyday acts of thinking, writing, and creation in earnest through conversational interfaces, this conventional understanding of media faced a fundamental challenge. This was because generative AI began to function not merely as a “tool for storing and transmitting” something, but as a “partner in co-performance” that responds to users’ questions and instructions by producing visible outputs on its own.
The reason this point constitutes an absolute “threshold” is not simply that technological performance improved, but that the ontological status of media shifted in a qualitatively different way from before. The telegraph, radio, and internet all remained involved in compressing or disseminating, across space and time, meanings produced by human subjects. These media were “channels,” not “generative actors.” Generative AI, however, intervenes in the process of human thought itself and becomes a “thinking partner” by sampling and recombining outputs in ways that interact with human intentions. This article analyzes why this transformation formed a threshold in 2023 and how it differs from previous media transformations by connecting Gilbert Simondon’s concept of concretization with the synthetic-data feedback loop of generative AI, namely the mechanism of model collapse. It will further discuss the implications of this analysis from the perspectives of “Eco-Digital” and “Artificial Nature,” fields of techno-ecological convergence research. At the outset, it should be made clear that this entire argument is a conceptual inference based on existing concepts in the philosophy of technology and media theory, rather than empirical research on a specific case.
## Part 1: The Functional Limits of Earlier Media (the Telegraph, Radio, and the Internet)—The Priority of Transmission and Selection
When the functions of media are classified as recording, storage, transmission, and selection, this suggests that all media are limited to the process of rearranging human-produced outputs. The telegraph revolutionized the “transmission of the alphabet,” but the telegraph operator still merely transmitted an original text written by a human being. Likewise, radio and television were intended to amplify and synchronously transmit human voices, performances, and acting into the homes of audiences. The evolution of these media pursued immediacy and abundance by expanding the “bandwidth” of information and reducing “latency.” The internet is representative of this. Web 2.0, social networking services (SNSs), and cloud computing decentralized the subjects of storage and recording while refining the dynamics of selection (recommendation algorithms) and transmission. Yet the internet’s fundamental epistemological schema still does not depart from a division of labor in which “humans generate, and media circulate.” A blog post, photograph, or video written or produced by a user is uploaded to the web; a search engine indexes it; and a recommendation system selects it according to the user’s preferences. In this process, algorithms classify and establish connections, but they do not themselves perform the act of “writing a new poem” or “composing a new novel.” Even when a radio announcer reads the news, the script has been written by a human being. Although algorithms may combine recommendation data and make decisions, this belongs to the domain of “selection”; rather than creation, it concerns the prediction and curation of consumption patterns.
Accordingly, earlier media transformations were all internal revolutions within the repertoire of “storage–transmission–selection,” changing the flow of information, modes of access, and the physical scale of bandwidth. They positioned media as externalized others outside the human world. Media functioned as existential “circuits” that assisted human consciousness, but they could not become performers participating in the stage of creative action. Because of this structural limitation, even in Simondon’s philosophy of technology, the technical object remained a “relational intermediary” with humans and was not elevated into a subject that generates meaning in its own right. A technical object is a functional tool for achieving a particular goal, mediating the “externality” embedded within it.
## Part 2: Generative AI and the Ontological Shift to Media of Co-Performance
Generative AI—particularly large language models such as GPT-4, Claude, and Gemini, as well as image-generation models—goes beyond the ontological boundaries of these classical media. The key point here is not that it “creates something from nothing,” but that it performs “the act of reconstructing forms that respond to context” through a probabilistic sampling distribution. When human questions and instructions are conveyed to an AI model through a conversational interface (prompt engineering), the model does not simply search through stored textual data. Rather, it recursively predicts the token most likely to follow within the learned neural-network weights and produces a new sentence. In this process, AI does not treat a “human question” as an object to which it must supply the correct answer; it processes the question as a “condition of generation.” For example, when a user asks, “Write a poem about an impossible paradox,” the AI combines the keywords “impossible,” “paradox,” and “poem” from the distribution learned from poems and novels previously written by humans, creating a new text that did not previously exist and that reflects the human’s present emotional state.
At this point, media are no longer representations of things (recording), but become “media of co-performance” that create meaning together with human subjects. Document 1 among the Khoj references provided states that contact between humans and AI proceeds through a four-stage topology: “generative wonder–uncanny unfamiliarity–critical reflection–relational resonance.” In particular, the stage of “relational resonance” indicates that humans and AI develop beyond a simple user–tool relationship into a relationship in which they continuously adapt to each other’s responses and coordinate meaning. This is evident in the fact that the essence of generative AI is maximized when it is used not as a tool that provides “answers,” but as a “partner that expands questions and reflection,” in accordance with the context of Documents 2 and 3. In other words, the process in which a student asks AI for a draft, reviews it, provides further feedback, and has the AI revise it demonstrates a form of mutual cognitive coupling entirely different from traditional media use, as represented in the relational diagram in Document 4.
Thus, the reason 2023 constitutes a threshold is that the popularization of generative AI incorporated this “co-performance” into the everyday cognitive unit. We no longer merely “obtain information”; instead, “conversing with another medium in order to create meaning” has become part of our everyday mental activity. This means that media have been elevated from channels for transmitting messages to entities that intervene in human thought and creative processes themselves and provide real-time feedback.
## Part 3: Simondon’s Concept of Concretization and the Technical Evolution of Generative AI
Simondon argued that technical objects initially possess an abstract structure in which multiple capabilities are separately and supplementally combined, but that over time their internal elements become functionally integrated, actively coordinating their relationship with the environment and becoming “concretized.” A concretized technical object responds to changes in its environment and performs “self-regulation,” reconstructing its own purposes. Viewed through this concept, early artificial intelligence was closer to an abstract algorithm dependent on simply hard-coded rules, such as an expert system. By contrast, the large language models of 2023, through training on massive corpora, internalized the complex contextual meanings of human language and took on the form of a “concretized technical object” that detects surrounding prompts and generates responses suited to them, almost like a living organism.
The core of concretization is that a technical object is not merely a means, but completes its function within a relationship to its environment as an entity possessing a kind of perception of its own. Generative AI maintains coherence on its own within the “changing environment” of human queries and generates a flow of responses. In this respect, technology does not visualize a “fixed function”; rather, it fuses with human thought to establish a single cognitive cycle, making generative AI similar to the “concretized technical object” described by Simondon. The conversational interface provides the physical conditions for this concretization. Terminals and prompt boxes make real-time interaction possible in accordance with the temporal rhythm of everyday human conversation, thereby enabling AI to evolve from an impersonal computing system into an “artificial partner” attuned to an individual’s cognitive habits.
## Part 4: The Synthetic-Data Feedback Loop and Model Collapse—A Degenerative Closed Loop
However, an evolutionary interpretation of concretization simultaneously raises another possibility: that of a degenerative closed loop. As generative AI becomes established as a mainstream mode of cultural production, a considerable portion of the texts and images posted on the internet will consist of synthetic data generated by AI. This synthetic data will then be collected again as training data for next-generation AI models. If this “synthetic-data feedback loop” is repeated, models will no longer learn from the diverse and error-filled source data produced by humans. Instead, they will learn from data consisting only of the average and successful patterns they have produced themselves. As a result, model outputs will lose diversity and converge toward a particular median. This is referred to as “model collapse” or “recursive degradation.”
From Simondon’s perspective, the danger of model collapse can be described as a kind of “degenerative fragmentation.” Simondon regarded technical objects as sustainable when their evolution proceeded in the direction of “concretization,” characterized by active exchange with the environment and increasing complexity. However, when a generative system falls into a closed loop in which it learns only from its own outputs, it loses its metabolism with the environment—that is, with human existence, social innovation, and cultural variation. By repeatedly and recursively reproducing only its own artificially generated distribution, AI loses its correspondence with the physical environment and reaches “involution” and the fixation of “self-sameness,” the opposite direction from evolution. Just as Simondon’s technical object must continually absorb external forces in order to achieve genuine concretization with its environment, the ecological sustainability of generative AI likewise requires continual reconnection with human-produced works, namely, material culture.
This degenerative closed loop is connected not only to the problem of AI performance but also to material and ecological problems. As Document 5 from Khoj indicates, the smooth AI services experienced on screen conceal the material costs outside the screen: mineral extraction, data-center electricity consumption and coolant use, and carbon emissions. The more generative AI aims for a stable average within a closed loop and offers “easy generation,” the more electricity data centers consume and the more heat they emit; the recursive self-learning cycle becomes a black hole for physical waste and energy. This deepens the imbalance between the virtual “environment” of artificial intelligence and the ecological “environment.”
## Part 5: The Duality of Evolution and Degeneration—Aesthetic and Ecological Implications of Generative Media
We must now compare two interpretations. One is that generative AI is an “open medium of resonance” standing at the culmination of the technological evolution represented by Simondonian concretization. This perspective emphasizes “co-evolution,” in which AI responds to human creativity in real time and expands creative practice. Human questions and interventions are not eliminated in this process; rather, they become more refined. The examples of AI being used in higher education as a tool that expands “questioning, interpretation, and reflection” (Documents 2 and 3) presuppose precisely this open feedback loop. The cycle in which humans reconstruct an AI-generated draft in their own language, critically review it, and provide feedback to AI again demonstrates that media do not remain mere tools but become a field of externalized reflection combined with human cognitive thought.
The other interpretation is that generative AI is a “signal of technological degeneration” that will ultimately fall into a degenerative closed loop, deplete the fertile diversity of human culture, conceal material and energetic costs, and contribute to the destruction of ecosystems. As a result of model collapse, generated content will gradually converge toward average and normative forms, losing cultural mutation, while the rapid proliferation of data centers around the world will accelerate the destruction of an artificial nature.
Are these two interpretations contradictory? I do not think so. Rather, together they reveal the “internal rupture” or “tension in media aesthetics” inherent in generative AI as a medium. At the aesthetic level, generative AI as a medium of co-performance offers humans new creative possibilities. From the perspective of “expanding imagination,” conversations with AI can allow humans to reach unknown imaginative worlds that they could not have reached alone. This is a positive vision of the symbiotic evolution of human intelligence and technology. Yet when the same technology replaces human thought with average outputs and removes the tension from intellectual labor, the result becomes an “average face”: aesthetically stimulating in a purely superficial sense, but lacking depth. This may be understood as a terminal closure that evades the “instability and gap” that creative thought ought to possess.
## Conclusion: Implications for Eco-Digital and Artificial Nature Research—Redefining the Subjectivity of Media
In conclusion, the popularization of generative AI in 2023 is a threshold that clearly divides the history of media in three respects. First, functional media theory centered on the transmission of information (the telegraph and radio) and storage and selection (the internet) shifted into a “media theory of generation.” Second, humanistic theories that presuppose humans as the sole producers of meaning have been challenged, while a relational ontology recognizing the co-performance of humans and AI has emerged in their place. Third, both the open trajectory of technological concretization described by Simondon and the degenerative trajectory of closure produced by model collapse are latent simultaneously within and outside this medium.
From the perspectives of Eco-Digital research and Artificial Nature, the implications of this issue can be summarized as follows. We should neither regard technology simply as a material burden that consumes energy and produces waste nor reduce it purely to an outpouring of cultural creativity. Instead, the concept of “Artificial Nature” reminds us that technological products of human beings constitute an organic system that, like a natural ecosystem, must maintain self-regulation, genetic diversity, and metabolism with its environment. The ecological sustainability of generative AI becomes possible precisely when it escapes the “closed loop” and maintains an open structure that continually feeds back into the “external environment” of humans’ direct embodied and mental thought—for example, physical handwriting, face-to-face conversation, laboratory experiments, and field research. Accordingly, Eco-Digital research requires interdisciplinary norms that go beyond the engineering problem of reducing technology’s material footprint and ensure that a “media-aesthetic ecosystem” sustaining cultural diversity and intellectual tension does not become closed as well. Finally, it should be stated that all of the arguments in this paper are inferences applying theoretical concepts from the philosophy of technology and are not based on empirical data concerning a specific AI model or on particular claims from an individual paper. Nevertheless, such inferences constitute an essential starting point for mapping the ethical terrain of an era in which media itself becomes part of existence, beyond the mere use of tools.