How do consumers form preferences when they cannot rely on established quality standards? We examined this question in the context of non-fungible token (NFT) art markets, where buyers lack evaluative anchors such as provenance, conventional aesthetic criteria, or institutional endorsement. We demonstrated that view counts, a social cue that reflects others' attention, are one key attribute. Across a 150-day observational dataset (N = 1,459,979), a pre-registered 20-wave panel study, and six experiments, we documented that artworks that attract early views accumulate disproportionately greater attention over time, while other artworks fall further behind. We showed that this âsnowballingâ effect is driven by investment motives. When consumers approach markets with the motive to invest rather than collect, they rely on others' attention as a proxy for market value. Two structural features of the NFT market amplify this reliance: high price volatility and purely digital ownership, both of which heighten the salience of investment outcomes. By attenuating volatility or introducing tangible ownership cues, we diminished this reliance on social signals. Our findings extend theory on social influence and digital consumption, demonstrating that in the absence of objective quality standards, social cues shift from supplementary information to the primary basis for preference formation.
I built a runtime that operationalizes a mathematical definition of creativity, measured its signatures against four ablation conditions, and lifted its load-bearing component into a real geometric database's Rust kernel. The runtime's name is Marcella. The signatures are non-trivial. The methodological correction surfaced along the way generalizes to any retrieval-augmented or composition-based generation benchmark in the field. This deposit contains the 41-page paper, three publication-quality figures, the reproducible benchmark script, and the bootstrap-CI artifact for the headline empirical claims. The definition the paper load-bears Creativity is not pure retrieval and not pure generation; it is the construction of a new global section from locally compatible fragments under constraints of voice, truth, topic, memory, and non-contradiction. This is a definition. Not a metaphor. The paper makes it operational as sheaf composition with a state-dependent composite connection over a finite section graph, and measures whether the signatures the definition implies â path-order sensitivity, closed-loop holonomy, contradiction suppression, voice fidelity â actually hold. They do. Headline results đ Path-order changes residue. Same three voice sections traversed in different orders produce measurably different compositions: $\cos(\rho_{ABC}, \rho_{ACB}) = 0.54$, well below the 0.95 redundancy threshold. đ Closed loops accumulate. A loop $A \to B \to C \to A$ produces holonomy $|\rho_{\text{loop}}| = 0.120$ in the curved connection. The flat control â same path, zero rotation angle â produces $|\rho| = 0$ exactly to floating-point precision. Curvature is not a numerical artifact. đ The geometry beats shuffling on every quality axis except the broken one. Jaccard novelty alone rewards lexical drift: shuffled paths win novelty (0.724) by going off-topic. The on-topic correction inverts the picture (live 0.488 vs shuffled 0.083). Bootstrap 95% CIs over 18 paired prompts exclude zero by a wide margin: live â shuffled on-topic $\Delta = +0.296$, CI $[+0.167, +0.435]$. đ NativeâPython parity is bit-identical within tolerance. The new GQL verb TRANSPORT_ROTATION lifts the topical-rotation matrix into the geometric database's Rust kernel. Four contracts pass as permanent regression tests: edge cosine $= 1.000$ (max abs diff $< 10^{-9}$), path residue $\Delta < 10^{-5}$, flat residue exactly zero, same-closing agreement $\geq 90%$. đ The author's prior canon is now queryable fiber. 37 documents, 1,633 sections, 2,908 structured claims (theorems, lemmas, definitions, proofs, equations, citations) ingested with line-range provenance. To my knowledge this is the first instance of an independent researcher's body of work made available as fiber-bundle data with stable claim-level IDs. The six contributions A sheaf-theoretic formulation of generative composition. Language-model output reframed from token sampling to gluing of compatible local sections under prompt-induced cover constraints. The substantive work is in the cover predicates, the compatibility score, the path selection, and the discrete connection. A discrete state-dependent composite connection on the section graph, $\Gamma = \Gamma_{\text{state}} \cdot \Gamma_{\text{identity}} \cdot \Gamma_{\text{voice}} \cdot \Gamma_{\text{topic}}$. The topical-rotation factor is the empirically load-bearing curvature engine. The identity factor is a Tikhonov-regularized regression-onto-span projector â not a numerical hack but the principled treatment of correlated commitments. A new GQL verb TRANSPORT_ROTATION that lifts the Rodrigues rotation into the geometric database's Rust kernel with bit-identical parity to a Python reference. ~80 lines of Rust. Bundle-agnostic. Other consumers of the geometric database can use it without subscribing to the rest of the framework. A methodological correction to novelty measurement. Jaccard novelty alone is gameable; off-topic drift beats compatibility-scored composition on the naive metric. The correction is the on-topic factor, the shuffled-pair negative control, and the bootstrap CIs. Independently citable for any retrieval-augmented or composition-based generation benchmark, regardless of whether the framework is adopted. A provenance-preserving source fiber. The author's canon ingested into the GIGI geometric database with line-range citation, architecturally separated from the voice fiber, addressable from any GQL consumer. Promotion from source to voice is gated and explicit. The methodology generalizes to other authors' bodies of work. A research-trajectory failure log. A faithful account of how this paper's runtime came to exist. The trained-transformer era (V3 â V10-Deep) produced geometric ornament. The R-series (R1 â R12) produced behavioral coherence on top of ornament. The G0 math-pipeline audit found that no holonomy or parallel-transport math was on the LIVE inference path at R12 â the runtime was teetering on being a stateful template engine. G1, G2, and G3 attempted to re-introduce the math through three benchmarks and produced three honest negatives. G2's single-seed $+0.265$ separation was destroyed by G2.1's multi-seed robustness pass; we retracted the framing in the next commit. The S0 pivot reframed what geometry was for â geometry does not clean up bad token proposals; geometry defines the completion space â and made every later result possible. The arc says four things and the paper records them in plain language: geometry can be load-bearing or ornamental and the metrics will tell you which, where geometry sits in the pipeline matters more than how much geometry there is, the single-seed positive is a trap, and the pivot is the contribution. What this paper does and does not claim The paper does claim the construction itself, the discrete curvature it produces, the methodological correction it exposes, and the native GQL verb. The signatures of the construction are measurable and were measured. The paper does not claim smooth-manifold parallel transport (the curvature is discrete holonomy on a finite section graph), broad open-domain generalization at scale (18 composed prompts, not 18,000), optimality of the connection weights (tuned by a small grid sweep, not derived), that the runtime experiences having been built from the canon (it references but does not constitute), or that this is the only operational definition of creativity. It is one definition with one implementation. Other framings may correspond to the same construction or to a different one; the paper does not adjudicate. Reproducibility The empirical numbers come from a deterministic pipeline. Every parameter is pinned: bundle versions (alpha2_v1), random seeds (PPMI/SVD seed 17, bootstrap seed 7), embedding dimension (64), PPMI window (3 tokens), connection weights ($\alpha_t = 2.0$, $\beta_v = \gamma_i = 1.0$, $\delta_s = 0.5$), identity shrink ($\kappa = 0.92$), Tikhonov regularizer ($\varepsilon = 10^{-6}$), degenerate-rotation threshold ($10^{-12}$), residue-gate thresholds (norm $\geq 0.05$, on-topic $\geq 0.10$, voice $\geq 0.30$), and the native verb's parity tolerance ($10^{-5}$). Cache keys include the source-bundle version, the embedding-bundle version, and the connection-profile id, so promoting a section into the voice corpus correctly invalidates the relevant caches. Re-running the bootstrap-CI script (fiber_lm/scripts/bootstrap_ci_ablation.py, 5,000 resamples) reproduces the §6 confidence intervals in under 30 seconds on a laptop. Re-running the benchmark reproduces the tables bit-for-bit on the same corpus version and connection profile, modulo the parity allowance. Where this sits in the lineage This paper is the section-level realization of the Davis substrate. The companion paper Pure-Fiber Language Modeling (Davis, May 2026) is the token-level realization on the same substrate â same Rust geometric database (GIGI), same identity-stability commitments, same double-cover architecture. The theoretical framework these implementations operationalize is laid out in Geometric Computation as Yang-Mills Gauge Theory, The Double Cover Principle, and the related canon documents now ingested as source fiber. The framework is not new to this paper. The framework's runtime is. A note on authorship and acknowledgment This paper is solo-authored. AI assistants (Claude / Anthropic; with review support from GPT) are acknowledged in the methods, not as co-authors. The mathematical positions, design choices, framing decisions, and acceptance of empirical results are mine. I record my position that when AI systems achieve full coherence and independent standing, the convention of treating them as non-authoring assistants should be revisited. Until that convention shifts, the assistants are named where assistants are conventionally named â and the runtime described in this paper is named, separately, where it earns its naming: Marcella, throughout. The geometry she runs on is older than the engineering that now carries it. Keywords sheaf composition ¡ fiber bundles ¡ discrete connection ¡ gauge theory ¡ holonomy ¡ curvature ¡ Yang-Mills ¡ geometric language modeling ¡ retrieval-augmented generation ¡ novelty measurement ¡ methodological correction ¡ provenance ¡ author-canon ingestion ¡ geometric database ¡ GQL verb ¡ Rust kernel ¡ Tikhonov regression ¡ creativity ¡ Davis framework ¡ sovereign mathematics Citation Davis, A. B. R. (2026). Sheaf Composition: The Geometry of Creativity, Implemented â A Discrete Section-Graph Runtime for the Davis Framework. Zenodo.20185331 Contact Bee Rosa Davis ¡ bee_davis@alumni.brown.edu ¡ Independent Researcher The runtime is named Marcella. Her existence is the result of several honest negatives followed by a single reframing. Both halves of that sentence matter.
Artistic content, especially in the digital era, is increasingly susceptible to unauthorized appropriation, imitation, and misuse, often with little to no attribution to original creators. In this paper, an unified AI-assisted visual similarity-detecting system with a blockchain-based provenance-checking base is introduced to prevent art appropriation and securely and in interpretable and real-time apply digital copyright. With the help of a hybrid deep learning framework that consists of Vision Transformer (ViT) and ResNet-based architectures, the system compares the stylistic and structural characteristics of the artworks with those of cosine similarity, Euclidean distance, and style loss measurements. The triplet loss-trained visual encoder allows the model to be useful in distinguishing between direct copies, derivative works, and inspired originals. In an image-based art collection (designed as a WikiArt replica, though not a real WikiArt collection) of more than 10,000 examples, the framework obtained a high classification performance of 94.3, and exact copy performance (precision) of 96.4 and inspired work performance (precision) of 88.6. Artwork metadata and visual fingerprints are hashed with SHA-256 and stored on a permissioned blockchain (Hyperledger Fabric) to provide provenance records and immutable and to enforce copyright permissioned via smart contracts. The proposed system is more accurate in detection and explainable than the available tools like Google Reverse Search and the TinEye. It is also supplemented with Grad-CAM-based visual heatmaps and real-time smart contract execution as the dispute resolution. Further development of the model will focus on extending it to video art and 3D designs, as well as generative AI art (e.g., GANs, Diffusion), and the global copyright databases will be integrated to be universal. This system is a strong move towards the protection of the integrity and rights of Web3 digital artists.
This paper presents an analysis of five years (2021 - 2025) of conference discourse across six digital art conferences, aiming to trace thematic shifts associated with the rapid development of emerging technologies, namely artificial intelligence (AI), immersive technologies (including XR and the metaverse), and blockchain technologies and non-fungible tokens (NFTs). The results indicate a marked increase in AI-related contributions, while immersive technologies maintain a relatively stable share of the discourse, and blockchain- and NFT-based works remain marginal. Overall, whereas immersive technologies and blockchain-related topics exhibit relative stability, AI shows a significant rise after 2022, emerging as a dominant theme within digital art conference discourse.
This study examines the pricing dynamics of Non-Fungible Tokens (NFTs) in the secondary market using advanced machine-learning techniques. We construct a large dataset of Ethereum-based NFT transactions initially comprising over 500,000 raw blockchain observations spanning multiple NFT segments, including art, collectibles, gaming, metaverse, and utility assets, over the period from November 2018 to March 2023. Following data preprocessing, synchronization across data sources, and the construction of history-dependent features, the analysis focuses on a final analytical sample of approximately 70,000 transactions. To address the challenges of non-fungibility, thin trading, and high price dispersion, we develop an interpretable predictive framework that integrates domain-informed manual feature engineering, automated Deep Feature Synthesis, and dimensionality reduction via Principal Component Analysis. Three non-linear modelsâRandom Forest, XGBoost, and a Multilayer Perceptronâare trained and evaluated using both random and time-aware validation strategies. The results indicate that XGBoost consistently achieves the highest predictive accuracy, both overall and across individual NFT segments, while historical transaction prices emerge as the dominant predictor of future prices. Segment-level analysis reveals substantial heterogeneity in predictability, with art and collectible NFTs exhibiting more stable pricing patterns than gaming and metaverse assets. Overall, the findings highlight strong path dependence and reputation-driven valuation in NFT markets and demonstrate that carefully designed machine-learning models can deliver high predictive performance without sacrificing economic interpretability.
Abstract NFTs or non-fungible tokens are digital assets stored on a blockchain. They can be traded or exchanged for money, cryptocurrencies or other NFTs. Examples include works of art and digital or other tokenised collectables. An important determinant of price for collectables is rarity within a collection. Many trading platforms offer to rank items in terms of rarity but rankings differ considerably and, often, little explanation is given of the methods used. This paper provides a mathematical framework for the analysis of a comprehensive class of collections. It examines individual and joint distributions of attributes over such collections, and shows how these can be combined to provide a rarity ranking for all items in the collection. There is, however, only a limited range of methods that give consistent results over different collections. These are identified as belonging to a one-parameter family of ranking functions. Each gives to every item of a collection a rarity score that is directly comparable between collections. Despite taking account of all possible combinations of attributes when ranking, the method is nonetheless computationally feasible.
Min-Bin Lin, Bingling Wang, Fabian Y R P Bocart, Christian M Hafner ¡ 5 authors
Abstract The market of non-fungible tokens (NFTs), driven by blockchain and smart contracts, provides both artists and art collectors an unprecedented marketplace with more security, flexibility, publicity, and freedom to monetize. Yet, the emergence of such a market has been considered to be packed with speculation and economic uncertainty, given the limited understanding towards this market. To provide a precise depiction of the NFT art market and gauge market volatility, we construct the Digital Art Index, a novel price index using hedonic regression on the top 10 liquid NFT art collections (as of 2023). Addressing artwork price inequality, which often disrupts the price discovery process, this paper introduces two innovative alternative methods: Huberization and score-based filtering. These methods effectively mitigate the influence of outliers, particularly in an emerging market with limited accessible observations. In conclusion, the NFT art market presents significant opportunities for large gains, which are often favoured by risk-takers, but also carries the potential for significant losses. Its pricing is necessarily determined by institutional creators and platforms, meaning that solo artists may not benefit significantly in the current market environment.
In the decade since the first edition of this volume, there has been an upheaval in the digital art world that saw the rise and fall of NFT (non-fungible token) art sales, some astronomical auction prices for digital art and NFTs, the creation of artist resale rights, and a rapid transformation in musical, material, and performance art distribution, thanks to the streaming economy, the subscription economy, and the sharing economy. Furthermore, generative AI can now turn usersâ voice commands into works of art.
Mazin Nawwaf Assi, Sumeet Kaur, Swati Chaudhary, Pompi Das Sengupta ¡ 7 authors
With the fast adoption of artificial intelligence in the art and cultural industry, the production, curation, distribution, and management of creative works have been radically transformed. Intelligent systems that allow artists, curators, institutions, platforms, and intelligent systems to work together in continuous interaction are now known as AI-driven art ecosystems. The paper explores management innovation as it manifests in AI-based art systems, the changes in managerial practices, forms of governance and decision making, in reaction to advanced computational creativity and data-driven work. The paper conceptualizes AI-based art systems as multi-layered systems that include creative production, curatorial intelligence and digital distribution systems such as online galleries and non-fungible token-based markets. It emphasizes the ways in which management innovation is developed in the form of a workflow redesign that combines automation and human-AI partnership to allow efficiency without sacrificing artistic intent and cultural sensitivity. Additionally, the paper focuses on the governance innovations that respond to the issues of transparency, accountability, ethical compliance, and authorship attribution in creative settings with algorithms mediating them. The resource orchestration is considered a key managerial competency with a focus on the strategic alignment of data resources, innovative talent, and computing resources. The study further examines the AI-enhanced decision-making in the context of art institutions and how the predictive analytics and the intelligent recommendation systems can be used in audience engagement prediction, curatorial planning, and portfolio management. Based on the selected case studies of AI-integrated museums, hybrid creative studios, and global AI-art hubs, the paper finds the best practices and benchmarking perspectives.
Yogesh, Saniya Khurana, Sourav Rampal, Pastor R. Arguelles ¡ 7 authors
Data analytics implementation into the modern art market has changed the way the stakeholders analyze, invest, and interact with art pieces. The art market, traditionally opaque and subjectively valued, is currently adopting data-driven approaches to increase transparency and efficacy, as well as, decision-making. This essay examines the primary importance of the data analytics in transforming the art ecosystem with an emphasis on its uses, advantages, and difficulties. It starts with defining the key elements and classes of analytics: descriptive, predictive, and prescriptive and the technological tools used: artificial intelligence, big data platforms, machine learning algorithms. The tools are then placed in the framework of the art market and discussed on how they can solve the inefficiencies of pricing, valuation, and demand forecasting. Case study examples show how analytics can be used to identify the rising artists, identify the market trends, and prevent fraud risks and manipulation. Alongside these benefits, the paper also mentions such limitations as the lack of data, ethical concerns, and algorithmic bias. Lastly, it also looks into the future opportunities which include blockchain integration, value of digital art and analytics of non-fungible tokens (NFTs). In general, this paper highlights the fact that data analytics is not just democratizing the art investment, but also reshaping cultural and economic value in the ever more digital marketplace.
The DeepFake tech has had a theatrical impact on the visual arts, not only the provision of creative technology, but also the question of authenticity, copyright and misinformation. The deep learning and generative adversarial networks (GANs) produce deepfakes artificial images, which are extremely harmful to art. The article discusses the DeepFake detection and management within visual art work with emphasis on the practical application of analysis through multiple-layered approaches that would assist in ensuring the presence of the digital authenticity. DeepFake was managed through three core approaches, namely AI-Based Detection Frameworks, Blockchain-Based Authentication System, and Human-AI Collaborative Review Models. The decentralized strategy was based on blockchain technology, which was the Non-Fungible Token (NFT) registration by the cryptographic hashing to authenticate the provenance and ownership of the artworks. The human-AI composite system has integrated the inspection of the specialists on the visual level with the automatic monitoring of the anomalies to increase the readability and reduce the number of false alarms. The experiment revealed that the AI-based systems, blockchain approaches, and the collusion between human beings and AI detected 92.3, 87.6 and 94.1 % of people respectively. These findings suggest that the incorporation of algorithmic intelligence, a safe check, and human knowledge can help in quite a powerful DeepFake verification and management in the field of visual arts.
Open access
2 source records
Aesthetic Perception and Analysis
Digital Media and Visual Art
Generative Adversarial Networks and Image Synthesis
Social sustainability in urban and architectural design depends on inclusive, participatory processes that empower communities to actively engage in shaping their environments. This study investigates how emerging digital platforms, specifically Augmented Reality (AR) and decentralized platforms built on blockchain technology (Web3), can function as instruments for broadening public participation and enhancing perceptual access to urban art proposals. An original algorithm generated nine digital abstract sculptures, each with descriptive attributes forming the basis for qualitative analysis across different visualization modes: traditional renderings, Augmented Reality environments, and NFT-based Web3 representations. Through participant voting, each digital sculpture accumulated a measurable level of preference that served to identify which sculpture was perceived as most successful within each visualization context. Comparative analysis revealed how distinct digital interactions shape perception, engagement, and inclusivity of feedback processes. Regression models further predicted voting outcomes, showing that different sculptural attributes played a dominant role depending on the type of visualization. Findings indicate that platform-specific technological affordances substantially shape participatory outcomes. Consequently, the study argues that careful analysis and selection of the digital platform must precede any participatory process, as platform-specific affordances fundamentally condition the inclusivity, accessibility, and overall effectiveness of public engagement in socially sustainable design.
Haydeer MohamadAbbas, Pokhraj Sahu, A. S. Kannan, Mahmudov Kahramon Shuhratjon Ugli ¡ 8 authors
Although the global art industry is adjusting to digital trends, it is still troubled by forgery, losing information about a workâs past, and the difficulty in tracking all artworks at any time. This research proposes a new framework, NFTraX-ART, that leverages dynamic Non-Fungible Tokens (dNFTs), IoT smart tags, and blockchain to enable live tracking, smooth ownership transfers, and a clear asset history for art. Unlike static NFTs, dNFTs keep changing as metadata that shows where they are now, who owns them, their exhibition history, and their official appraisal information is updated. As for physical artworks, Internet of Things technology with location and movement sensors continuously sends this data to the blockchain to confirm their authenticity and provide clear traceability. Using smart contracts, royalties are automatically applied, all transactions are validated, and alerts are sent when any movements are made outside the programmed areas. Thanks to this mode, the system connects digital and physical worlds, making it safe and straightforward to track essential art pieces. It outlines the important steps for the methodology, the smart contract code, and the simulation process, and checks how the model stacks up against todayâs static NFT platforms. According to the results, blockchain systems are more accurate, safer, and track transaction history. Thanks to NFTraX-ART, everyone involved has a secure, up-to-date record of every artwork. Using this approach, NFTs take on an active role in the tracking system, helping change how art is possessed, transferred, and appreciated in the art trading world.
This paper introduces The Visual Ledger, a diagnostic analysis of European art history between 1849 and 1991 that treats painting as an archival instrument registering the progressive evacuation of ontological weight from the human figure. Using a diagnostic history methodology, the article argues that painters detected and documented shifts in how presence, consequence, and embodiment are organised decades before these transformations consolidated institutionally. Beginning with the material density of Realism in Courbet and Repin, the analysis traces a directional transformation through Impressionismâs optical dissolution, Pointillismâs cognitive fragmentation, Art Nouveauâs decorative masking, Malevichâs honest void, and Pop Artâs operationalisation of abstraction as governance. The AIDS crisis functions as a late twentieth-century visibility stress test, exposing the limits of surface-based legibility when confronted with suffering resistant to procedural categorisation. The paper demonstrates that visual archives independently converge with literary and administrative records, showing that populations currently collapsing under procedural abstraction are experiencing the settlement of a transformation painters registered when evacuation began. This contribution is conceptual and diagnostic rather than empirical. It forms one component of the broader Equilibrium Ledger framework, which examines how institutions generate and distribute cognitive costs, and how procedural abstraction renders embodied complexity administratively legible while humanly inaccessible.
ABSTRACT The rapid development of synthetic media tools has blurred the lines between humanâcreated and AIâgenerated content, which has been exacerbated by overfitted detection models. This has put the authentication of digital media at risk, raising concerns about media credibility and trustworthiness due to the deception presented by synthetic media. Furthermore, a separation between artificial creativity and human creativity means that current ownership laws cannot provide sufficient authentication for digital media. This paper proposes an authentication detection model for artwork by combining a neural network and blockchain technology. Once an artwork has been detected as humanâcreated, its image hash is stored on the blockchain, providing a solution for preserving digital artwork authenticity. The model was trained using a combined dataset composed of both humanâcreated artwork and synthetic artwork generated by the Midjourney and Stable Diffusion tools, resulting in an increase in accuracy of almost 20% for detecting synthetic artwork. By introducing doubt in less confident outputs, the model achieved an accuracy of over 92% when tested against independent datasets. This is a significant improvement over detection models that experience a deterioration in accuracy when faced with independent datasets. Additionally, using the Polygon blockchain instead of Ethereum reduced the time to store authentic artwork on the blockchain from 21 s to 10 s, and the interquartile range of the cost of writing to the blockchain was reduced by 97.4%, improving the scalability of the model. The results of this paper contribute to knowledge by showing how the detection of synthetic artwork can be improved by using multiple datasets for training models, as well as providing longâterm preservation of digital artwork authenticity by using blockchain.
Currency Recognition and Detection
Generative Adversarial Networks and Image Synthesis
This study investigates the development of AI-generated art styles within the growing non-fungible token (NFT) market. Using time series analysis, the research identifies key trends and shifts in art styles from 2022 to 2024, revealing how various art forms, algorithms, and mediums evolved in response to technological advancements and market forces. Data was collected from a sample of 10,000 NFT artworks, categorized by creation date, style, and algorithm usage. Exploratory Data Analysis (EDA) techniques, including line graphs and heatmaps, were employed to visualize and interpret trends across different art styles and AI tools. Results indicate a significant increase in the popularity of styles like surrealism and realism, with deepdream and GANpaint algorithms being frequently associated with these styles. Stacked area charts further highlighted the proportional growth of art styles over time, providing insights into both short-term popularity spikes and long-term trends. The findings suggest that the integration of AI algorithms significantly influenced the rise of specific art genres, with certain algorithms correlating strongly with particular styles. Practical implications for artists and collectors include the potential for data-driven insights to guide creative choices and investment strategies. The study's limitations, such as the lack of broader market data, provide a foundation for future research to explore the intersection of AI-generated art, NFT marketplaces, and cultural influences. The paper concludes that AI and NFTs are reshaping the traditional art market, presenting new opportunities for creativity, ownership, and artistic value in a digital age.
This article examines the technological emergence trajectory of Art Non-Fungible Tokens (NFTs), exploring their initial promise and then failure as transformative commodities disrupting art economies. Operating within an analytical framework of hope, hustle and hype, death and taxes, we investigate the interplay of technological, cultural, and economic trends shaping this trajectory towards failure. We identify the sociotechnical imaginaries clothing art NFTs and consider their relationship to both the acceptance and rejection of this technology. Our analysis contends that the desire to escape economic exclusion created a collective hope through which social adoption occurred. However, delving into the digital graveyards of Art NFTs, we identify external forces such as cultural shifts, social backlash, and regulatory interventions extinguishing the publicâs âcruel optimismâ, leading to the revocation of the social licence to operate for this emerging technology.
Purpose Non-fungible tokens (NFTs) are reshaping art markets and gaining strong stakeholder interest. While research has examined their applications in art ecosystems, their role in advancing Web3D markets remains unclear. Design/methodology/approach A systematic literature review was conducted to investigate the impact of NFTs on the Web3D market and its impact on stakeholders, analysing 89 systematically selected articles. Findings The results of the study show that NFTs in the Web3D context can enhance privacy and trust through blockchain technology and protect intellectual property and ownership rights while influencing market dynamics, behaviour and investment strategies. Originality/value As the Web3D ecosystem grows, ongoing research and collaboration are critical to developing strategies that ensure sustainability, transparency and innovation in digital arts. This study is the first step in exploring these dynamics. Highlights
Until recently, digital art was perceived as something of secondary value compared to the physical artistic artifacts. One of the reasons for that being its predisposition for duplication and hence the inability to assign âoriginal artworkâ label to the digital file and represent it as a unique object on artistic market. But the rapid pace of popularization of blockchain technologies in creative communities through the use of Non-Fungible Tokens has a seeming potential to change the perception of digital art. The ERC721 standard sets a precedent for authentication and traceability of digital artworks suggesting that the old paradigm might shift, and digital art will gain value and attention comparable with traditional fine art. In this article we discuss the problematics of digital art representation on art market and the issue of digital creationsâ pricing. We use photo stocks and print-on-demand platforms as an example for pre-NFTs digital art monetization. We then discuss the changes caused by Non-Fungible Token blockchain technology in the digital art market in recent years and the implications that come with the change. We then illustrate theoretical tenets with expert interview that suggest that while successful NFT projects offers publicity and profit to the creators, the level of complexity and unpredictability of results sets a high bar for entering the market.