Bernabé Mallo
Doctor en Filosofía por la Universidad del País Vasco (UPV/EHU)
Investigador
en neurofilosofía, evolución humana y origen del arte. / PhD in
Philosophy – University of the Basque Country (UPV/EHU)
Researcher in neurophilosophy, human evolution, and the origins of art.
A review of the article by Hu Yang, Yunxian Zou, and Bo Gao (2026): From Digitization to the AI Era: Digital Technology Trajectories in Fine-Art Creation
Introduction: when the machine learns to look
What happens when a machine tries to "see" the world? Not with eyes, but with an algorithm that fragments millions of images into tiny visual fragments, associates them with words, and learns to recombine them according to statistical patterns? The answer, which may sound like science fiction, is the basis of artistic creation with artificial intelligence.
The article by Hu Yang, Yunxian Zou, and Bo Gao, published in 2026 in the Asia Pacific Economic and Management Review, analyses precisely this transformation. The authors propose a historical journey through the three phases of digital artistic creation —from early digitisation to the emergence of generative AI— and offer a conceptual framework for understanding how technology is reconfiguring authorship, originality, and the distribution of art .
The central concept of their analysis is the "data lens" of AI. Unlike a camera, which captures the light of a real scene, AI fragments billions of pre-existing images into "visual tokens" and associates them with linguistic tokens (words). In this way, the machine "learns the visual language of the world" and, once trained, can move fluidly and autonomously across artistic movements, styles, and eras.
This process, as we shall see, not only redefines what it means to "create" art, but also raises profound questions about artistic agency, intellectual property, and the role of the artist in the era of algorithmic automation.
Three phases of digital artistic creation
Yang, Zou, and Gao propose a practice-based periodisation that distinguishes three phases in the evolution of digital technology applied to fine art . Each phase not only introduces new tools, but transforms the artist's relationship with their work and with the public.
Phase I (1980–2000): medium translation
The first phase, which the authors call "medium translation" , is characterised by the digitisation of traditional media. Images become files, and logical processes —programming— enter the artistic craft . During these two decades, digital tools functioned primarily as mechanisms for extending analogue practices.
However, even in this early phase, later debates about agency and automation were prefigured. As the authors note, "even when digital tools largely functioned as translation and extension mechanisms, they already prefigured later debates about agency and automation by embedding creativity in operational sequences and delegating parts of form-making to computational processes" . The artist was beginning to be, not only a creator of objects, but a designer of procedures.
Phase II (2000–2010): platformised networking
The second phase, "platformised networking" , is marked by the emergence of Web 2.0 and social media . The publication and circulation of works become continuous, low-threshold, and socially integrated. Art no longer circulates as an isolated object, but as a node within communicative circuits.
This phase, however, has a dark side. The platformisation of artistic creation subjects artists to regimes of visibility governed by algorithms, metrics, and interfaces that determine what is seen and what is ignored. The authors describe this as a "distributed curation regime" in which circulation is determined by compatibility with interfaces and performance in terms of engagement, rather than by artistic merit . The political economy of creative labour becomes explicit: platforms extract value from the unpaid work of creators, who provide content, attention, and behavioural data without equivalent control or compensation .
Phase III (2010–present): algorithmic autonomy
The third and current phase is that of "algorithmic autonomy" . It is defined by the rise of machine learning systems that can synthesise images and styles through statistical inference, shifting artistic creation from direct pixel manipulation toward model-mediated generation.
The authors analyse two key technical milestones: Generative Adversarial Networks (GANs), which demonstrated that plausible synthetic images could be produced through adversarial learning, and diffusion models, which have since dominated the field for their ability to generate high-fidelity images and allow more flexible and controllable editing .
The crucial change, they note, is not only the improvement in image quality, but the relocation of creative labour. A visible portion of the artist's skill shifts toward specifying constraints, iterating instructions, testing variations, and curating results—activities that resemble direction, selection, and orchestration more than manual construction . The work of art emerges as a negotiated outcome of interaction with a probabilistic system.
The "data lens": how AI learns the visual language
The most suggestive concept in the article is the "data lens" of AI. Unlike the photographic camera, which captures the light of a real scene, AI does not see the world directly. Instead, it fragments billions of pre-existing images into "visual tokens" (minimal units of visual information) and associates them with "linguistic tokens" (words). In this way, the machine learns a "visual language of the world" that allows it, once trained, to move fluidly and autonomously across artistic movements, styles, and eras.
This process has profound implications. On the one hand, it allows unprecedented versatility: an AI model can generate an image in the style of Van Gogh, a hyperrealistic photograph, or a geometric abstraction with equal ease. On the other hand, it raises the question of what "originality" means when the system is trained on millions of existing works. As the authors note, the AI phase intensifies long-standing debates by redistributing artistic agency across instructions, model priorities, datasets, and post-selection .
Artistic agency thus becomes a distributed concept. It no longer resides solely in the artist, but is shared among:
Instructions (how the request is formulated).
Model priorities (what the algorithm is predisposed to produce).
Training data (which visual cultures are represented and how).
Post-selection (what is accepted, refined, and contextualised).
Technological humanism as a response
Faced with this scenario, Yang, Zou, and Gao propose a normative-analytical framework they call "technological humanism" . This approach sets out three conditions for responsible AI-mediated artistic creation:
Traceable delegation: the ability to follow and understand the decisions made by the system.
Situated cultural responsibility: the recognition that AI systems are not neutral, but reflect biases and cultural values.
Infrastructural transparency: the need for artists, educators, and museums to understand the technical and economic conditions under which generative systems operate.
Technological humanism is not a rejection of AI, but an invitation to use it with judgement, not to delegate to it decisions that require human discernment, and to maintain cultural integrity in the context of algorithmic production . The authors conclude their article with operational principles so that artists, educators, and museums can evaluate and sustain agency and cultural integrity under algorithmic production .
Connection with research on the origin of art (S/Y/C)
The analysis by Yang, Zou, and Gao resonates with the research we have been developing on the S/Y/C model of neuronal functioning and the Law of Biological Coherence (Mallo, 2023, 2025, 2026a, 2026b). The "data lens" of AI and its implications for artistic creation can be reinterpreted in light of our three dimensions.
The S (Survival) dimension manifests in the dependence of AI systems on large datasets that reflect existing visual cultures. This dependence is not neutral: data incorporate biases, exclusions, and power relations. AI, by learning from this data, can perpetuate and amplify those inequalities. Technological humanism, by demanding transparency and cultural responsibility, is a way of attending to the survival of diverse artistic traditions in the face of algorithmic homogenisation.
The Y (Symbolon) dimension is central to the tokenisation process described by the authors. AI fragments images into visual tokens and associates them with linguistic tokens, creating a bridge between the visual and the verbal. But this symbolon —this act of recognition through shared codes— is profoundly different from the one performed by a human artist. The artist does not merely combine tokens, but inhabits symbols, charging them with personal and cultural meaning. AI, by contrast, manipulates symbols from the outside, without a lived experience to sustain them.
The C (Wholeness) dimension manifests in the process of selection and curation that the authors identify as a central part of the artist's work in the AI era. The artist working with generative models does not merely generate images, but selects, refines, and contextualises, seeking a coherent totality. But this wholeness is different from the one sought by the traditional artist: the human artist seeks to close a form from inner experience; the artist with AI seeks to close a process from interaction with a probabilistic system.
Surgical Philosophy invites us to make a precise analytical cut in the analysis by Yang, Zou, and Gao. It is not about rejecting AI or accepting it uncritically, but about distinguishing levels: the level of technical production (where AI is a powerful tool), the level of aesthetic experience (where AI cannot replace embodied subjectivity), and the level of cultural responsibility (where technological humanism offers an ethical framework). AI can be an ally in artistic creation, but it cannot occupy the place of the artist as a source of intentionality and meaning.
Final considerations: art as an encounter between the human and the algorithmic
The article by Yang, Zou, and Gao has the merit of offering a lucid and well-informed vision of the digital transformation of art. Its periodisation into three phases helps us understand that the emergence of AI is not an absolute rupture, but the continuation of a process that began with digitisation and intensified with platformisation. And its proposal for a technological humanism offers an ethical framework for navigating this new territory.
AI, like the "data lens" the authors describe, does not capture the light of a real scene, but fragments and recombines humanity's visual legacy. This allows it impressive versatility, but also strips it of connection with lived experience. Art, in its origin and in its function, is not merely a combination of forms, but an encounter between embodied subjectivities. AI can facilitate this encounter, but it cannot occupy the place of either pole.
Now, what would happen if we transferred this same scheme —digitisation, assimilation, reformulation— to the biological system that gave rise to art? If AI begins with the digitisation of pre-existing images, the original nervous system began with the digitisation of knowledge of its lived niche: the information necessary for the most fundamental thing, survival. That niche was then assimilated according to its own biases, shaped by the relationship with the natural environment and by its own biological functions, whose first imperative was survival and, later, other equally fundamental needs.
Finally, there would remain what we might call the "prehistoric AI" : that primordial neuronal function capable of reformulating and conceptualising, from the possibilities of the biological system itself, the interest of survival and the search for wholeness, in order to apply them to practice. Was that the artificial intelligence of the past? Was that the first embodied algorithm that allowed our species to create symbols, tools, and art? The question remains open.
And it is precisely this openness that defines our research. Contemporary AI offers us an unexpected mirror: by observing how it fragments, assimilates, and recombines humanity's visual legacy, we can better understand how the nervous system itself, tens of thousands of years ago, fragmented, assimilated, and recombined the world to create something radically new. Art, from Makapansgat to generative algorithms, remains the testimony of a single function: that of a system that needs to survive, to symbolise, and to achieve wholeness.
References
Mallo, B. (2023). La construcción neuro-simbólica. Una aproximación al funcionamiento del cerebro desde una perspectiva multidisciplinar [Doctoral thesis, University of the Basque Country - Euskal Herriko Unibertsitatea]. ADDI Repository. http://hdl.handle.net/10810/62701
Mallo, B. (2025). Arte y biología: Una aproximación neurofilosófica al origen de la experiencia estética. https://www.amazon.com/dp/B0E8Y5WZMK
Mallo, B. (2025). Art and biology: A neurophilosophical approach to the origin of aesthetic experience. https://www.amazon.com/dp/B0E8Y6C2XN
Mallo, B. (2026a). De la filosofía quirúrgica a la ley de coherencia biológica S/Y/C: Hacia una investigación sobre el origen del arte en la especie Homo. https://isbn.bibna.gub.uy/catalogo.php?mode=detalle&nt=57196
Mallo, B. (2026a). De la filosofía quirúrgica a la ley de coherencia biológica S/Y/C: Hacia una investigación sobre el origen del arte en la especie Homo [Kindle edition]. https://www.amazon.com/dp/B0GYGTJD5C
Mallo, B. (2026b). From surgical philosophy to the law of biological coherence S/Y/C: Toward a study of the origin of art in the Homo lineage. https://isbn.bibna.gub.uy/catalogo.php?mode=detalle&nt=57197
Yang, H., Zou, Y., & Gao, B. (2026). From digitization to the AI era: Digital technology trajectories in fine-art creation. Asia Pacific Economic and Management Review, 3(1). https://doi.org/10.62177/apemr.v3i1.1086
Autor / Author
Bernabé Mallo
Doctor en Filosofía – Universidad del País Vasco / Euskal Herriko Unibertsitatea (UPV/EHU)
Investigador independiente en neurofilosofía, evolución humana y origen del arte.
Bernabé Mallo
PhD in Philosophy – University of the Basque Country / Euskal Herriko Unibertsitatea (UPV/EHU)
Independent researcher in neurophilosophy, human evolution, and the origin of art.
Enlaces / Links
Página de autor Amazon / Amazon Author Page: https://www.amazon.com/author/bernabemallo
ORCID: https://orcid.org/0000-0001-9002-9728
Plataforma EHUenRed / Link EHUenRed: https://www.ehu.eus/es/web/masterrak-eta-graduondokoak/red-latinoamericana-de-posgrados
Canal YouTube / Channel YouTube: https://www.youtube.com/@neuroideas815
Canal YouTube / Channel YouTube: https://www.youtube.com/channel/UCBsf6OZ482NjST6QA-hvYtQ
Publicaciones y proyectos en desarrollo / Publications and projects:
https://www.amazon.com/author/bernabemallo
https://ehuenred.theglocal.network/ideas/el-origen-del-arte-en-el-cerebro-de-makapansgat-al-moma-del-primate-al-sapiens