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Why Contemporary Dance Still Cannot Be Replaced by Machines

  • Writer: Brainz Magazine
    Brainz Magazine
  • 7 minutes ago
  • 7 min read

Dr. Dragana Favre is a psychiatrist, psychotherapist, and seeker of the human psyche's mysteries. With a medical degree and extensive neuroscience education from prestigious institutions like the Max Planck Institute and Instituto de Neurociencias, she's a seasoned expert.

Executive Contributor Dragana Favre

In the contemporary world, “creativity” is increasingly measured by outputs: speed, novelty, variability, and scale. Artificial intelligence thrives under that regime. It can generate fluent text, plausible images, and even choreographic sequences that look like dance when rendered as pose data, animation, or notation. Yet contemporary dance, especially in its most searching, process-based forms, keeps exposing a limit that is not merely technical but ontological, the difference between movement as produced pattern and movement as lived event.


Dancers in colorful outfits reach toward towering robots with glowing eyes, set against a vibrant, fiery backdrop. Energetic and dramatic.

This is not a romantic claim that dancers possess mystical powers and machines do not. It is a more precise claim: contemporary dance is embodied meaning-making that depends on lived kinesthesia, situated relationality, and unconscious integration. Current AI systems can approximate these dimensions as representations, but they cannot inhabit them as a first-person experience. That gap matters socially and philosophically because it marks what remains irreducible in a culture that increasingly treats creativity as a commodity, dance’s capacity to negotiate, live, in public, between the personal and the collective, the conscious and the unconscious, the individual body and the social field.


AI’s role in dance is real and expanding. It supports ideation, prototyping, editing, documentation, and iteration in choreographic workflows, for example, systems designed to help choreographers explore movement variants while keeping human control central. Generative models trained on motion capture or pose sequences learn correlations among visible postures, transitions, timing, and sometimes musical structure. From that standpoint, AI can be impressive. It can propose countless “possible dances,” recombine stylistic signatures, and surface patterns that a choreographer may not notice in their own habits.


But these capabilities clarify the level at which AI “understands” dance. The model optimizes plausibility relative to examples. That is not trivial, but it is also not the core of contemporary dance as a lived art. Contemporary dance is not primarily a catalogue of positions. It is an inquiry into how a body becomes meaningful in time, under gravity, under fatigue, in relation to other bodies, under affect, and under risk.


When creativity is defined as selecting among already available options, pattern engines excel. When creativity is defined as form emerging from the not yet known, especially from the unconscious, pattern engines face a different kind of problem. Not “generate another plausible sequence,” but risk transformation.


A post-Jungian argument for dance’s irreducibility begins with a phenomenological one. The body is not merely a physical container for the mind. It is the primary site of perception, orientation, and sense-making. In Merleau-Ponty’s terms, the body is our being in the world made concrete, an intelligence that precedes explicit thought. Contemporary dance intensifies this. Knowing is often pre-verbal. You know where you are because you feel weight through the foot. You know timing because you sense suspension. You know relational distance because your skin, peripheral vision, and proprioception register a moving field.


Philosophers of movement, such as Sheets-Johnstone, push this even further. Movement is not a secondary expression of cognition. Movement is a ground of thinking and feeling. That matters for AI because AI’s “body” is not a lived centre of orientation. It has no kinesthetic felt sense. It does not inhabit gravity. It does not experience pain, shame, exhilaration, or fear as internal states that reorganise meaning. It can model or describe these things. But in dance, embodiment is not a metaphor. It is the medium.


Contemporary dance is also social and political because it is live. Even when mediated, its claim to meaning depends on a co-presence that cannot be fully reproduced by repetition. Performance theory has long argued that liveness is not just technical, it is cultural. Live performance disappears as it happens, and that disappearance is part of its force.


AI intensifies an economy of repetition. It can generate endless variations, endlessly. Contemporary dance, by contrast, often values the singular encounter, this room, these bodies, this risk, this breath. The dancer is accountable to the moment in a way a model is not. A dancer can fail publicly, be changed mid-performance, receive the audience, and respond without fully knowing how. That responsiveness is not decoration. It is a core artistic material, a live negotiation with the social field.


A post-Jungian frame adds what phenomenology alone does not. The unconscious is not merely hidden information. It is a generative psychic reality that speaks through image, affect, and symbol. Jung’s transcendent function names a process by which conscious and unconscious positions are brought into relation, producing not a compromise but a Third standpoint, a transformation.


Many contemporary dance processes, especially improvisational, somatic, or depth-oriented practices, work exactly here. The dancer does not simply express a known feeling. They encounter something unknown through movement, an impulse, a blockage, an inner image arriving as sensation. This is why dancers describe moments of sudden coherence, an “aha,” where a phrase clicks not because it is technically correct, but because it is psychically true.


Practices influenced by Jungian active imagination make this explicit. Unconscious material is given form, not to control it, but to relate to it. Authentic Movement, historically linked to Jungian ideas through Mary Starks Whitehouse and later practitioners, frames movement as receptivity to inner impulses and images, with meaning arising through witnessing and integration.


AI can generate novelty, but novelty is not individuation. The Jungian question is not “Is it new?” but “Does it transform the relationship between conscious life and the unconscious?” That transformation requires a subject who can be changed, who can resist, surrender, integrate, and live differently afterward. AI can simulate the story of transformation. It cannot undergo it.


Even if we set Jung aside, dance’s soul-body-mind oneness has empirical correlates. Studies in neuroscience show that watching dance recruits motor-related brain systems differently depending on the observer’s motor expertise. Trained dancers often show stronger activation when viewing movements they can perform. This supports a simple but powerful point. Dance meaning is enacted partly through motor resonance, a bodily understanding not reducible to verbal semantics. We read dance with our bodies as well as our eyes.


AI can classify or generate movement, but it does not participate in this resonance as lived reciprocity. It does not feel the flinch of empathy, the held breath, or the visceral recognition of a gesture as grief or defiance. A dancer, by contrast, is transmitter and receiver in a loop of embodied communication. That loop is social, affective, and physiological, precisely where “soul” becomes a grounded word again, not supernatural substance, but the felt unity of aliveness.


None of this implies AI is irrelevant to dance. The most interesting AI and dance work often appears when artists refuse replacement narratives and use AI as constraint, collaborator, or mirror. Choreography support tools can expand a choreographer’s search space, reveal habits, document iterations, and provoke novel structures while keeping human agency and embodied evaluation central. Generative duet models likewise become most responsible when developed in close conversation with dancers and choreographers, because relational and ethical dimensions cannot be abstracted away.


So the claim is not “AI can’t make dances.” It is that the core creative act in contemporary dance is not finding a new sequence. It is integration, converting lived experience, affect, memory, shadow, and desire into form that changes the mover and can change the witness.


In an era that wants creativity without vulnerability, contemporary dance insists on a different definition of intelligence. The courage to be moved by what you do not yet understand.


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Read more from Dragana Favre

Dragana Favre, Psychiatrist and Jungian Psychotherapist

Dr. Dragana Favre is a psychiatrist, psychotherapist, and seeker of the human psyche's mysteries. With a medical degree and extensive neuroscience education from prestigious institutions like the Max Planck Institute and Instituto de Neurociencias, she's a seasoned expert. Her unique approach combines Jungian psychotherapy, EMDR, and dream interpretation, guiding patients towards self-discovery and healing. Beyond her profession, Dr. Favre is passionate about science fiction, nature, and cosmology. Her ex-Yugoslav roots in the small town of Kikinda offer a rich backdrop to her life's journey. She is dedicated to helping people find their true selves, much like an alchemist turning lead into gold.

References:

  • Agre, P. E. (1997). Toward a critical technical practice: Lessons learned in trying to reform AI. In G. C. Bowker, L. Gasser, S. L. Star, & W. Turner (Eds.), Bridging the great divide: Social science, technical systems, and cooperative work. Lawrence Erlbaum.

  • Auslander, P. (1999). Liveness: Performance in a mediatized culture. Routledge.

  • Calvo-Merino, B., Glaser, D. E., Grèzes, J., Passingham, R. E., & Haggard, P. (2005). Action observation and acquired motor skills: An fMRI study with expert dancers. Cerebral Cortex, 15(8), 1243–1249.

  • Cross, E. S., Hamilton, A. F. d. C., & Grafton, S. T. (2006). Building a motor simulation de novo: Observation of dance by dancers. NeuroImage, 31(3), 1257–1267. 

  • Dreyfus, H. L. (1972). What computers can’t do: A critique of artificial reason. Harper & Row. 

  • Jung, C. G. (1969). The transcendent function (R. F. C. Hull, Trans.). In H. Read, M. Fordham, & G. Adler (Eds.), The collected works of C. G. Jung (Vol. 8). Princeton University Press. (Original work published 1916/1958)

  • Liu, Y., et al. (2024). DanceGen: Supporting choreography ideation and prototyping with generative AI. In Proceedings of the ACM on Human-Computer Interaction (ACM).

  • Merleau-Ponty, M. (1945). Phenomenology of perception. Gallimard. (English trans. Routledge, 1962)

  • Sheets-Johnstone, M. (2011). The primacy of movement (Expanded 2nd ed.). John Benjamins. 

  • Stromsted, T. (2009). Authentic Movement: A dance with the divine. Body, Movement and Dance in Psychotherapy, 4(3), 201–213.

  • Suchman, L. A. (1987). Plans and situated actions: The problem of human-machine communication. Cambridge University Press. 

  • Wang, Z., et al. (2025). Dyads: Artist-centric, AI-generated dance duets (arXiv:2503.03954).

  • Zhong, Y., Fu, X., Liang, Z., Chen, Q., Yao, R., & Ning, H. (2025). The application of artificial intelligence technology in the field of dance. Applied System Innovation, 8(5), 127.

This article is published in collaboration with Brainz Magazine’s network of global experts, carefully selected to share real, valuable insights.

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