N/A

The infusion of artificial intelligence (AI) into the art domain has ignited profound discussions concerning the essence of artistry and its economic valuation. This study aims to provide a comprehensive examination of the impact of AI within the painting market, focusing on technological advancements with artistic and commercial perspectives. Utilizing qualitative methodologies, insights are gleaned from a diverse array of stakeholders, including art practitioners, and gallery proprietors. This research aims to explore different viewpoints to understand the essence of art, and determine the authenticity of AI-generated artworks.
By taking a close look at how paintings are valued and priced, identifying the subtle factors that determine the value of paintings made by humans compared to those made by AI. By comparing these factors, we aim to understand the different ways paintings are valued in today's art market. Additionally, the paper examines the creative abilities of both human artists and AI, predicting how they might change the way paintings are created in the future.
The research focuses on key questions aimed at understanding the important aspects of how human and AI artists work together, examining what gives paintings their value and how much AI can mimic or differ from human abilities in this regard. Additionally, taking a look at how AI affects the creative process and what this means for creativity, innovation, and artistic expression. Finally, explore how AI can improve the painting industry's value, paving the way for better collaboration and innovation.
This research targets to equip stakeholders with a comprehension of the intricate dynamics at the intersection of technology and artistry. By shedding light on both the collaborative potential and potential conflicts between human and AI artists, it brings a profound understanding of the intricate dynamics inherent in the coexistence of human and AI artists within the painting industry.

By illuminating the symbiotic relationship and potential tensions between these two entities, the empowerment for industry stakeholders to navigate this evolving landscape with discernment and foresight. Through strategic utilization and collaboration, stakeholders can harness the transformative potential of AI to not only invigorate but also elevate the painting industry's value proposition. This concerted effort not only fosters an environment conducive to innovation and growth but also propels the industry toward new horizons of creativity and value creation. Through informed decision-making and strategic planning, stakeholders can leverage the transformative capabilities of AI to not only invigorate but also enrich the painting industry. This concerted effort propels the industry toward uncharted realms of creativity and value creation, fostering an environment ripe for innovation and growth.

Bellaiche, L., Shahi, R., Turpin, M. H., Ragnhildstveit, A., Sprockett, S., Barr, N., & Seli, P. (2023). Humans versus AI: whether and why we prefer human-created compared to AI- created artwork. Cognitive Research: Principles and Implications, 8(1), 1-22.
Cui, J. (2017, July). Research on digital painting art and its diversified performance. In 2017 3rd International Conference on Economics, Social Science, Arts, Education and Management Engineering (ESSAEME 2017). Atlantis Press.
CELSYS. (2023, March 20). 40% of digital creators start Digital Art at the age of 15 or younger: survey of 6,000 European creators. Retrieved from https://www.celsys.com/en/topic/20230120
Davis, W. (1993). Beginning the History of Art. The Journal of Aesthetics and Art Criticism, 51(3), 327–350. https://doi.org/10.2307/431507
Elgammal, A., Liu, B., Elhoseiny, M., & Mazzone, M. (2017). Can: Creative adversarial networks, generating "art" by learning about styles and deviating from style norms. arXiv preprint arXiv:1706.07068.
Ashenfelter, O., & Graddy, K. (2003). Auctions and the price of art. Journal of Economic Literature, 41(3), 763-786.
Baumol, W. J. (1986). Unnatural value: or art investment as floating crap game. The American Economic Review, 76(2), 10-14.
Boden, M. A. (1998). Creativity and artificial intelligence. Artificial intelligence, 103(1-2), 347- 356.
Cetinic, E., & She, J. (2022). Understanding and creating art with AI: Review and outlook. ACM Transactions on Multimedia Computing, Communications, and Applications (TOMM), 18(2), 1-22.
Chamberlain, R., Mullin, C., Scheerlinck, B., & Wagemans, J. (2018). Putting the art in artificial: Aesthetic responses to computer-generated art. Psychology of Aesthetics, Creativity, and the Arts, 12, 177–192. https://doi.org/10.1037/aca0000136
Ekelund Jr, R. B., Ressler, R. W., & Watson, J. K. (2000). The "Death-Effect" in Art Prices: A Demand-Side Exploration. Journal of Cultural Economics, 24(4), 283-300.
Epstein, Z., Levine, S., Rand, D. G., & Rahwan, I. (2020). Who gets credit for AI-generated art?. iScience, 23(9), 101515.
78

Erdem, T., Swait, J., & Louviere, J. (2002). The impact of brand credibility on consumer price sensitivity. International Journal of Research in Marketing, 19(1), 1-19.
Frey, B. S., & Pommerehne, W. W. (1989). Art investment: an empirical inquiry. Southern Economic Journal, 396-409.
Ford, M. (2015). Rise of the Robots: Technology and the Threat of a Jobless Future. Basic Books.
Fortuna, P., & Modliński, A. (2021). A(I)rtist or counterfeiter? Artificial intelligence as (D)evaluating factor on the art market. The Journal of Arts Management, Law, and Society, 51(3), 188-201.
Galbraith, J. W., & Hodgson, D. J. (2018). Econometric fine art valuation by combining hedonic and repeat-sales information. Econometrics, 6(3), 32.
GilPress. (2024). AI Art Generator Statistics. What’s the Big Data. Retrieved from
https://whatsthebigdata.com/ai-art-generator-statistics/
Goodfellow, I., Pouget-Abadie, J., Mirza, M., Xu, B., Warde-Farley, D., Ozair, S., & Bengio, Y. (2020). Generative adversarial networks. Communications of the ACM, 63(11), 139- 144.
Gu, L., & Li, Y. (2022). Who made the paintings: Artists or artificial intelligence? The effects of identity on liking and purchase intention. Frontiers in Psychology, 4605.
Hernando, E., & Campo, S. (2017). Does the artist's name influence the perceived value of an art work?. International Journal of Arts Management, 46-58.
Hertzmann, A. (2018, May). Can computers create art?. In Arts (Vol. 7, No. 2, p. 18). MDPI.
Li, Y., Ma, M. X., & Renneboog, L. (2022). Pricing art and the art of pricing: On returns and risk
in art auction markets. European Financial Management, 28(5), 1139-1198.
Lobo. (2020, July 13). The history of painting. The evolution of art. Lobo Pop Art. Retrieved
from https://lobopopart.com.br/en/the-history-of-painting/
Liu, X. (2020, October). Artistic reflection on artificial intelligence digital painting. In Journal of
Physics: Conference Series (Vol. 1648, No. 3, p. 032125). IOP Publishing.
Lu, L. (2017). Analysis and research on the creative characteristics of digital painting and
traditional painting. Beauty Times (middle), 7, 23-24.
Mandel, B. R. (2009). Art as an investment and conspicuous consumption good. American Economic Review, 99(4), 1653-1663.
79

Martindale, C. (1986). Aesthetic evolution. Poetics, 15(4-6), 439-473.
Marinkovic, B. (2021). Tacit knowledge in painting: From studio to classroom. International
Journal of Art & Design Education, 40(2), 389-403.
Mazzone, M., & Elgammal, A. (2019, February). Art, creativity, and the potential of artificial
intelligence. In Arts (Vol. 8, No. 1, p. 26). MDPI.
McCormack, J., Gifford, T., & Hutchings, P. (2019, April). Autonomy, authenticity, authorship and intention in computer-generated art. In International Conference on Computational Intelligence in Music, Sound, Art and Design (part of EvoStar) (pp. 35-50). Cham: Springer International Publishing.
O'Reilly, D. (2005). Cultural brands/branding cultures. Journal of Marketing Management, 21(5- 6), 573-588.
O’Neil, K. M. (2008). Bringing art to market: The diversity of pricing styles in a local art market. Poetics, 36(1), 94-113.
Polanyi, M. (1958). Personal Knowledge: Towards a Post-Critical Philosophy. London: Routledge, 2003. Print.
Sherman, A., & Morrissey, C. (2017). What is art good for? The socio-epistemic value of art. Frontiers in Human Neuroscience, 11, 411.
Stetco, I. (n.d.). Value of art: Analysis of the factors influencing the prices of paintings. Sun, Y., Yang, C. H., Lyu, Y., & Lin, R. (2022). From Pigments to Pixels: A Comparison of
Human and AI Painting. Applied Sciences, 12(8), 3724.
Ursprung, H. W., & Wiermann, C. (2011). Reputation, price, and death: An empirical analysis of
art price formation. Economic Inquiry, 49(3), 697-715.
Varela, F. J., Thompson, E., & Rosch, E. (2017). The Embodied Mind, Revised Edition:
Cognitive Science and Human Experience. MIT Press.
West, D. M. (2018). The Future of Work: Robots, AI, and Automation. Brookings Institution
Press.
Yin, S. (2022). Analysis on the Inner Relationship between Computer Digital Painting and
Traditional Painting. Mobile Information Systems, 2022.
Zhu, Y., Gao, T., Fan, L., Huang, S., Edmonds, M., Liu, H., & Zhu, S. C. (2020). Dark, beyond deep: A paradigm shift to cognitive AI with humanlike common sense. Engineering, 6(3),