Digital Disruption in Art: A Comprehensive Analysis of AI and NFT Market Dynamics

Michał Włodarczyk

Abstract


Theoretical background: The dynamic development of generative artificial intelligence such as ChatGPT has transformed the perception of creative work. In the graphic realm, AI systems like Midjourney, DALL-E, or Adobe Firefly allow the creation of high-quality graphics without the need for artistic skills or hiring a talented designer. Concurrently, the emergence of cryptocurrencies and the associated non-fungible tokens (NFTs) has resulted in radical changes in the creative sector, especially in the art market.

Purpose of the article: The aim of this article is to examine the development pace and impact of AI-generated art and NFTs on the global art market, focusing on market trends, dependency on energy prices, segmentation, and how these changes influence artwork pricing and artists’ livelihoods.

Research methods: The author has assessed the popularity and development of the NFT market, as well as the main reasons for its collapse in 2022. The author identified possible scenarios for the development of the art market, pointed out the primary potential threats, and highlighted the key determinants of future digital asset valuation.

Main findings: Despite initial euphoria, buyers depreciate digital goods, especially those generated by artificial intelligence. They are considered inherently inferior and less valuable. Overproduction of works combined with the availability of AI solutions means that the traditional supply and demand mechanism lowers the price of assets and thereby forces more and more graphic designers, photographers, and painters to abandon their professions. Competition from artificial intelligence is subject to the same supply and demand mechanisms, which reduces the cost of access to quality graphics for entrepreneurs and individuals. Simultaneously, as the market becomes saturated with synthetic goods, there will be a delineation at the segment level, similar to what has happened with artisanal beers, hand-assembled cars, and furniture, or handmade ceramics. An unwritten, "made by humans" certificate will result in a 300–500% higher price for similar goods made by humans compared to works of artificial intelligence. This situation will resemble the division of the clothing or furniture market into mass-produced goods and designer items. Amid all this, the increasingly prominent role of NFTs will be evident, which will also appreciate in value, but due to the ecological taxation resulting from energy consumption that is 100,000 times higher than that of a regular bank payment.


Keywords


artificial intelligence; NFT; AI-generated art; digital art; cryptocurrency

Full Text:

PDF

References


Adobe Firefly. (2023). https://www.adobe.com/pl/sensei/generative-ai/firefly.html

Alexander, P., Arneth, A., Henry, R. et al. (2023). High energy and fertilizer prices are more damaging than food export curtailment from Ukraine and Russia for food prices, health and the environment. Nature Food, 4, 84–95. https://doi.org/10.1038/s43016-022-00659-9

Canva. (2023). https://www.canva.com/pl_pl/

Cascone, S. (2021). Sotheby’s is selling the first NFT ever minted – and bidding starts at $100. Artnet News.

Cetinic, E, & She, J. (2021). Understanding and creating art with AI: Review and outlook. arXiv:2102.09109v1

Cheng, M. (2022). The creativity of Artificial Intelligence in art. Proceedings, 81(1), 110. https://doi.org/10.3390/proceedings2022081110

Cire.pl. (2021). GUS: Zużycie energii elektrycznej w gosp. domowych wzrosło o 3% r/r w 2020 r. https://www.cire.pl/artykuly/serwis-informacyjny-cire-24/gus-zuzycie-energii-elektrycznej-w-gosp-domowych-wzroslo-o-3-rr-w-2020-r

Cochintu, C. (2023). Natural Gas Forecast & Price Predictions for Today, 2023 and Beyond: Rebound Could Extend. https://capex.com/en/overview/natural-gas-price-prediction

Coeckelbergh, M. (2017). Can machines create art? Philosophy & Technology, 30, 285–303. https://doi.org/10.1007/s13347-016-0231-5

Crypto stamp. (2023). https://crypto.post.at/

Dilmegani, C. (2023). Synthetic Data vs Real Data: Benefits, Challenges in 2023. https://research.aimultiple.com/synthetic-data-vs-real-data/

Dorrier, J. (2022). OpenAI says DALL-E is generating over 2 million images a day – and that’s just table stakes. https://singularityhub.com/2022/10/03/openai-says-dall-e-is-generating-over-2-million-images-a-day-and-I-just-table-stakes/

Epstein, Z., Levine, S., Rand, D.G., & Rahvan, I. (2020). Who gets credit for AI-generated art? iScience, 23(9). https://doi.org/10.1016/j.isci.2020.101515

Eurostat. (2023a). Culture statistics – cultural employment. https://ec.europa.eu/eurostat/statistics-explained/index.php?title=Culture_statistics_-_cultural_employment

Eurostat. (2023b). Culture statistics – international trade in cultural goods. https://ec.europa.eu/eurostat/statistics-explained/index.php?title=Culture_statistics_-_international_trade_in_cultural_goods

Gangadharbatla, H. (2021). The role of AI attribution knowledge in the evaluation of artwork. Empirical Studies of the Arts. https://doi.org/10.1177/0276237421994697

Garay, J., Kiayias, A., & Leonardos, N. (2017). The bitcoin backbone protocol with chains of viarable difficulty. In J. Katz & H. Shacham (Eds.), Advances in Cryptology (pp. 219–323). Springer.

Gartner. (2021). Maverick* research: forget about your real data – synthetic data is the future of AI. https://www.gartner.com/en/documents/4002912

Gratas, B. (2023). 50 ChatGPT Statistics and Facts You Need to Know. https://blog.invgate.com/chatgpt-statistics

Guan, Y., Yan, J., Shan, Y. et al. (2023). Burden of the global energy price crisis on households. Nature Energy, 8, 304–316. https://doi.org/10.1038/s41560-023-01209-8

Harrison, P.J. (2021). Non-fungible token market grew by 299% in 2020. The Fintech Times, https://thefintechtimes.com/non-fungible-token-market-grew-by-299-in-2020/

Hayward, A. (2023). NFT sales in 2022 nearly matched the 2021 boom, despite market crash. Decrypt. https://decrypt.co/118438/2022-versus-2021-nft-sales

Hedera. (2023). How much energy do NFTs use? https://hedera.com/learning/nfts/nfts-energy-use

Icecap. (2023). https://icecap.diamonds/

International Energy Agency (IEA). (2023). Natural gas. https://www.iea.org/energy-system/fossil-fuels/natural-gas

International Monetary Fund (IMF). (2023). Global price of natural gas, EU. https://fred.stlouisfed.org/series/PNGASEUUSDM

Internet world stats. (n.d.). Internet world users by language. https://www.internetworldstats.com/stats7.htm

Kardaś, S. (2022). Rosja: kolejne ograniczenie dostaw gazu do Europy. OSW. https://www.osw.waw.pl/pl/publikacje/analizy/2022-06-17/rosja-kolejne-ograniczenie-dostaw-gazu-do-europy

Karras, T., Laine, S., Aila, T. (2019). A style-based generator architecture for generative adversarial networks. arXiv:1812.04948.

Knez, S., Šimić, G., Milovanović, A., Starikova, S., & Županič, F. (2022). Prices of conventional and renewable energy as determinants of sustainable and secure energy development: Regression model analysis. Energy, Sustainability and Society, 12(6). https://doi.org/10.1186/s13705-022-00333-9

Known origin. (2023). www.knownorigin.io

Köbis, N., & Mossink, L.D. (2021). Artificial intelligence versus: Experimental evidence that people cannot differentiate AI-generated from human-written poetry. Computers in Human Behavior, 114, 106553. https://doi.org/10.1016/j.chb.2020.106553

Lean, H.H., & Lee, C. (2022). Energy economics and energy finance in developing and emerging countries. Frontiers in Energy Research, 10. https://doi.org/10.3389/fenrg.2022.814273

Lima, G., Kim, C., Ryu, S., Jeon, C., & Cha, M. (2020). Collecting the public perception of AI and robot rights. Proc. ACM Human-Computer Interaction, 4, 1–24. https://doi.org/10.1145/3415206

Lima, G., Zhunis, A., Manovich, L., & Cha., M. (2021). On the social-relational moral standing of AI: An empirical study using AI-generated art. Frontiers in Robotics and AI, 8(2021). https://doi.org/10.3389/frobt.2021.719944

Matney, L. (2021). The Cult of CryptoPunks. TechCrunch.

McMorrow, R. (2022). China’s YMTC asks core US staff to leave due to chip export controls. Financial Times. https://www.ft.com/content/97147102-a02c-48df-b3a0-28c77c4c298f

Microsoft. (2023). https://designer.microsoft.com

Midjourney. (2023). https://docs.midjourney.com/docs/plans

Molenaar, K. (2023). NFTs statistics – sales, trends and more [2023]. https://influencermarketinghub.com/nfts-statistics/#toc-0

Nakamoto, S. (2019). Bitcoin: A peer-to-peer electronic cash system.

NBA top shot. (2023). https://nbatopshot.com/

Open AI. (2023). https://openai.com/pricing

Prnewswire. (2023). Artprice by Artmarket.com publishes its 26th annual report. https://www.prnewswire.com/in/news-releases/artprice-by-artmarketcom-publishes-its-26th-annual-reportart.e-art-market-in-2022-reveals-a-16-increase-in-art.tern-art-auction-turnover-as-the-united-states-regained-its-first-place-and-the-world-posted-a-record-number-of-art--301771682.html

Qian, L.Y. (2023). Why do people buy NFTs: 2023 study. Coin Gecko. https://www.coingecko.com/research/publications/why-people-buy-nfts

Roose, K. (2022). An A.I.-generated picture won an art prize. Artists aren’t happy. The New York Times. https://www.nytimes.com/2022/09/02/technology/ai-artificial-intelligence-artists.html

Saber, D. (2014). Almost 70 percent of people have never bought art; have you? Take our survey. https://www.oregonlive.com/entertainment/2014/04/almost_70_percent_of_people_ha.html

Savage, N. (2023). Synthetic data could be better than real data. https://www.nature.com/articles/d41586-023-01445-8

Składanek, M. (2017). Sztuka generatywna. Wyd. UŁ.

Statista. (2023a). Bitcoin average energy consumption per transaction compared to that of VISA as of May 1, 2023. https://www.statista.com/statistics/881541/bitcoin-energy-consumption-transaction-comparison-visa/

Statista. (2023b). Sales value of the art market worldwide from 2007 to 2022. https://www.statista.com/statistics/883755/global-art-market-value/

Statista. (2023c). Total sales value of art and collectibles non-fungible tokens (NFTs) worldwide from 2019 to 2022. https://www.statista.com/statistics/1299636/sales-value-art-and-collectibles-nfts-worldwide/

Superrare. (2023). https://superrare.co/

Turner, J. (2018). Robot Rules: Regulating Artificial Intelligence. Springer.

Wang, Q., Li, R., Wang, Q., & Chen, S. (2021). Non-fungible token (NFT): Overview, evaluation, opportunities and challenges. arXiv:2105/07447.

Wehrmann, B. (2023). What German households pay for electricity. https://www.cleanenergywire.org/factsheets/what-german-households-pay-electricity

Wiv. (2021). https://www.wiv.io/

Wong, J.I. (2017). The ethereum network is getting jammed up because people are rushing to buy cartoon cats on its blockchain. https://qz.com/1145833/cryptokitties-is-causing-ethereum-network-congestion

Wood, G. et al. (2014). Ethereum: A secure decentralized generalised transaction ledger. Ethereum Project Yellow Paper, 151(2014), 1–32.

Yan, S., & Fang, Y. (2021). The influence of artificial intelligence on art design in the digital age. Scientific Programming, 2021, Article ID 4838957. https://doi.org/10.1155/2021/4838957

www1: https://www.christies.com/img/LotImages/2018/NYR/2018_NYR_16388_0363_000(edmond_de_belamy_from_la_famille_de_belamy).jpg

www2: https://www.youtube.com/@StacMnie/community




DOI: http://dx.doi.org/10.17951/h.2024.58.2.171-193
Date of publication: 2024-07-05 15:45:02
Date of submission: 2023-09-06 17:51:01


Statistics


Total abstract view - 943
Downloads (from 2020-06-17) - PDF - 0

Indicators



Refbacks

  • There are currently no refbacks.


Copyright (c) 2024 Michał Włodarczyk

Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 International License.