







































 

VOL. 13 
2024–2025 

 

Who Owns AI? 

Amy Whitaker 

New York University 

 

 

 

 

ABSTRACT: While artificial intelligence (AI) stands to transform artistic practice and creative in-
dustries, little has been theorized about who owns AI for creative work. Lawsuits brought 
against AI companies such as OpenAI and Meta under copyright law invite novel reconsidera-
tion of the value of creative work. This paper synthesizes across copyright, hybrid practice, and 
cooperative governance to work toward collective ownership and decision-making. This work 
adds to research in arts entrepreneurship because copyright and shared value is so vital to the 
livelihood of working artists, including writers, filmmakers, and others in the creative industries. 
Sarah Silverman’s lawsuit against OpenAI is used as the main case study. The conceptual frame-
work of material and machine, one and many, offers a lens onto value creation and shared 
ownership of AI. The framework includes a reinterpretation of the fourth factor of fair use under 
U.S. copyright law to refocus on the doctrinal language of value. AI uses the entirety of creative 
work in a way that is overlooked because of the small scale of one whole work relative to the 
overall size of the AI model. Yet a theory of value for creative work gives it dignity in its small-
ness, the way that one vote still has dignity in a national election of millions. As we navigate 
these frontiers of AI, experimental models pioneered by artists may be instructive far outside 
the arts. KEYWORDS: artificial intelligence, creative labor, Sarah Silverman, OpenAI, ChatGPT, 
copyright, fair use, governance, economic design. DOI: doi.org/10.34053/artivate.13.1.220 

In the summer and fall of 2023, a number of authors, including the comedian Sarah Silver-

man and the mystery writer John Grisham, sued OpenAI and other artificial intelligence 

(AI) companies for copyright infringement (David 2023; David 2023; Alter and Harris 

2023).1 Plaintiffs alleged that companies were using creators’ work as training data for 

 
1 A set of different lawsuits were filed. Grisham was part of a lawsuit that was filed by the Authors Guild 

and seventeen writers—with intent to bring a full class-action suit—in the Southern District of New York 

(David 2023). Silverman and the writers Christopher Golden and Richard Kadrey sued OpenAI and Meta 

in separate lawsuits filed in the Northern District of California (OpenAI: 



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their large language models. Writers, they argued, had not granted their permission, and 

the companies were benefiting from value they had not created.  

This group of lawsuits invites reconsideration of how we understand value around 

creative work, and also how a field such as the arts with such complexities in value can 

inform the economic models and governance structures of technology companies far be-

yond the arts. Key to this analysis is the uniquely dual nature of creative workers as both 

producers and investors. Arts entrepreneurship offers a framework for entrepreneurial 

thinking that can contend with the truly unknown frontiers of AI, in business model and 

also corporate form. The unusual economic circumstances of creative work and the exper-

imental political and economic structures being built in and adjacent to the arts—from 

worker-owned coops and decentralized autonomous organizations to speculative pro-

posals for collective ownership—offer a new lens on how AI offers a fulcrum moment in 

larger democratic societies to consider the role of large platform technologies companies 

and the possibilities of cooperative, networked, collectively owned technologies instead. 

We may take for granted the role of large companies like Google (Alphabet), Facebook 

(Meta), and Twitter (X). In the business models of those companies, users are the product. 

Data generated has value to those companies (which may have other business models as 

well, such as data storage). We may be habituated to these models, and thus there is an 

inertia to imagine that AI would—or even “should”—logically be invested in by large, 

powerhouse technology companies. Yet there are larger movements, some of which come 

out of blockchain discourse, to imagine systems of organization beyond large technology 

companies. In that discourse, large tech companies are referred to as “web 2” in which the 

large platform economies own their users’ data, in contrast to the quasi-utopian promise 

of the decentralized web was the idea of “web 3”—a world in which groups of users owned 

their own data. Even without any enthusiasm for blockchain, one can see the promise of 

shared ownership and cooperative models around data governance, for creative work and 

otherwise.  

This paper argues for reconsideration of the ownership and governance models 

around AI. To say creators should rightfully own shares to the proceeds of AI because it 

trains off of their work is to apply a logic that is analogous to but not dependent on the 

idea of web3. If we consider markets to be a design medium, then we are applying princi-

ples of economics to make prices represent values and principles of finance to make risk 

and return travel in lockstep. Currently, the price of AI does not include value because of 

externalities, including environmental costs, which are not priced in. And risk and return 

 
https://www.documentcloud.org/documents/23869693-silverman-openai-complaint?responsive=1&title=1, and 

Meta: https://www.documentcloud.org/documents/23869675-kadrey-meta-complaint?responsive=1&title=1) 

(Davis 2023). Paul Tremblay and Mona Awad joined the Silverman et al. suit though Awad exited it in Au-

gust 2023. They made six claims: “direct copyright infringement; vicarious infringement; violation of the 

Digital Millennium Copyright Act (DMCA) by removing copyright management information; unfair com-

petition; negligence; and unjust enrichment” (David 2024). The court dismissed five counts and held that 

Silverman et al. could still sue for direct copyright infringement (David 2024). 

https://www.documentcloud.org/documents/23869693-silverman-openai-complaint?responsive=1&title=1
https://www.documentcloud.org/documents/23869675-kadrey-meta-complaint?responsive=1&title=1


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do not move in lockstep because creators who have taken risk to generate work are con-

tributing value for which they are not rewarded. One does not need to assume that any of 

those creative actors are motivated toward profit, only that they are structurally entitled 

to it. While AI companies and their investors have taken some risk and are therefore them-

selves entitled to some reward, again under the basic algebra of markets, they are not en-

titled to all of the rewards, and a private nonprofit board is not entitled to the governance 

rights over proceeds that derive from a much greater good. 

Copyright issues faced by writers, artists, and other creators of content animate larger 

questions of who owns AI, who decides, and who benefits. Who owns AI is one of the great 

political and economic questions of our time, and it points to larger underlying questions 

of how we organize ourselves as a body politic and as an economy. Even for those outside 

the arts who are not inherently interested in fine art, what is important about art is that it 

is a proxy for things that are valued but where people may not agree on what they are 

worth (Karpik 2010; Fourcade 2011) and that value may only come to be known over time 

(Caves 2000).  

This paper’s conceptual framework uses as an example the ownership of creative 

work in order to make the case for generative economic and governance models around 

AI. Particularly as companies like OpenAI begin to generate revenues, these questions are 

in urgent need of field-wide consideration and regulation, not to exert undue command-

and-control influence over areas of innovation but to set clear guardrails around who has 

contributed and therefore who benefits. AI is built on a paradox that it can only be in-

formed by the past yet relies on the work of creative courage of people who risked build-

ing frontiers of the future. The affordances of the U.S. Constitution to grant temporary mo-

nopoly rights to inventors and other creative workers exist to further creativity in the 

interest of the public good. In parsing AI, we need to design from the starting point of the 

public good and the risks of individual creators to build that shared resource.   

This paper uses the case of Sarah Silverman’s creative work and the company OpenAI 

(the parent company of ChatGPT and GPT-4). The paper presents a new framework that 

synthesizes a reimagination of the “market test” in copyright as a “market and value test” 

(Whitaker 2019), while applying theories from arts entrepreneurship on hybrid organiza-

tional practice (Whitaker 2023). By applying a “value” lens, we can make distinctions be-

tween the “material” and the “machine” of AI and consider the work of individuals in re-

lation to the work of the collective. This distinction can inform our understanding of 

copyright protections and clarify the role of creative producers as original investors in 

their own work (Gerber 2017). 

In addition, the company OpenAI offers a particularly salient case study for hybrid 

practice because of its unusual corporate form as multiple entities including a nonprofit 

governing body, a for-profit arm, and a manager entity. Here, hybrid practice is defined as 

the application of institutional entrepreneurship (Battilana 2004; Guo 2022) to arts entre-

preneurship (Callander and Taylor, 2023; Whitaker 2023) within the larger area of hybrid 



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organizational forms (Battilana and Dorado 2010; Battilana et al. 2012; Rushton 2014), 

including both amalgamations of legal form (e.g., a non-profit and related limited liability 

company (LLC) and newer legal forms such as public benefit corporations (PBCs) of which 

the crowdfunding site Kickstarter is emblematic. In PBCs, organizations can modify their 

profit-seeking fiduciary duty with a public benefit charter of other metrics to which they 

are beholden (Whitaker 2023).2 

Within this frame, OpenAI is a hybrid organization. It was founded in 2015 as a non-

profit “with the goal of building safe and beneficial artificial general intelligence for the 

benefit of humanity” (OpenAI 2024). In 2019, after the organization had experienced pro-

longed difficulty accessing capital to fund the large-scale infrastructure of their vision for 

AI, they founded a for-profit, though profit-capped, arm.3 That subsidiary has to date been 

granted $10 billion in funding from Microsoft (Metz and Weise 2023). The for-profit arm 

is governed by the nonprofit via a “manager entity” called OpenAI GP LLC. The for-profit 

subsidiary can distribute profits to shareholders but—not dissimilar to the functioning if 

not legal form of a public benefit corporation—must follow the mission of the non-profit. 

According to OpenAI, if the company makes profits above the cap set for the for-profit, 

those proceeds are “returned to the nonprofit for the benefit of humanity” (OpenAI 2024).  

This hybridity in their corporate governance structure adds to the complexity of who 

owns creative work. While it may seem laudable to return profits to a non-profit, it is also 

complicated to say that if there is a public benefit of collective creative work, the decisions 

about apportioning those funds will be made by a group of privately appointed directors 

of a nonprofit. This structure calls to mind critiques of philanthropy. In particular, as Gi-

ridharadas (2018) has argued, philanthropists have inherently vested interests, and some 

of the problems and opportunities that societies face need the mechanism of democracy 

instead. In this case, if creative workers—individually, as Sarah Silverman, and collec-

tively as the body politic of all creative workers—have generated value, why does a pri-

vate nonprofit determine the distribution of those funds for the benefit of humanity? In 

response, this paper considers both the role of democracy and the application of new mod-

els for cooperative and collaborative governance (Schneider 2018, 2024). Toward that 

end, the paper considers the case of Silverman and OpenAI as emblematic as a hypothet-

ical for theory-building around who owns AI for creative work.  

These questions of who owns work and who decides are structurally important no 

matter the scale of profits. At the same time, they become more urgent and important as 

AI companies start to make earnings. As of September 2024, OpenAI announced substan-

tial revenues and projected growth if still, by its own accounting, substantial operating 

losses (Isaac and Griffith 2024). Based on fundraising prospectus of the company, Isaac 

 
2 A variety of hybrid legal entities exist including flexible profit LLCs. For more information see Rushton 

2014 or Whitaker 2023.  
3 Per the OpenAI “Our Structure” section of their website: They had originally aimed to raise $1 billion but 

only received $130.5 million in donations (OpenAI 2024).  



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and Griffith (2024) reported that OpenAI made $300 million in revenue in August 2024, an 

increase of 1700% over the eighteen months to early 2023. OpenAI projected $3.7 billion 

in revenue for 2024, a number modeled to grow to $11.6 billion in 2025 and $100 billion 

by 2029.4 Adding to the urgency is not only the revenue but the costs, especially those such 

as the environmental impact of enormous energy usage that are borne by society at large. 

While those costs are difficult to quantify, we can see their scale indirectly in the rapidly 

growing real estate market for data centers (Batson 2024; Vincent 2024).5 

1. Method 
A key framework relied on in this paper is what Gu mu say and Reinecke (2024) have called 

“prospective theorizing.” They propose that prospective theorizing is the design of theory 

“to cultivate the creation of desirable futures by imagining, or helping others imagine 

them in the first place” (Gu mu say and Reinecke 2024: pp. 2-3). The authors posit this ap-

proach as a critique of and response to “the overriding temporal orientation within almost 

all theorizing towards the empirical past” (Gu mu say and Reinecke 2024: p. 2). By being 

circumscribed to what has happened before and what can be measured, empirically 

driven theory-building is, they argue, limited to projecting as opposed to imagining fu-

tures. Instead, theory-building can “inspire and guide meaningful change forward in the 

world” particularly given the likelihood that “with the looming planetary emergency . . . 

the future is unlikely to be anything like the past” (Gu mu say and Reinecke 2024: p. 2).  

AI itself has this bias toward the past, given that it trawls data facing backwards, cre-

ating what Gu mu say and Reinecke (2024) term followship and not leadership. Future-ori-

ented theorizing can still have “speculative rigor” (Gu mu say and Reinecke 2024: p. 16) 

based on generative potency (the ability of theory to support ideation about designing “de-

sirable futures,” process transparency (clarity about the means of theorizing and projected 

self-awareness or “reflexivity” about any normative assumptions or lack of representation 

in consulted co-designers), and speculative plausibility (internally coherent logic, and ad-

herence to basic “values guardrails such as human rights”). While there is moral complex-

ity to how the common good is defined, the overall approach aligns with artmaking as an 

emergent and entrepreneurial method (Callander 2019; Sarasvathy 2001) and an orienta-

tion to evaluate the future as a perpetually invented world more than as a knowable des-

tination or assured outcome (Whitaker 2016; Bureau et al. 2024). The approach also dove-

tails with the focus within arts entrepreneurship on pedagogy (Toscher 2019 White 2013, 

 
4 According to Isaac and Griffith (2024), OpenAI was aiming to raise $7 billion of new investment on a 

valuation of $150 billion, with 10 million current paid subscribers of ChatGPT at a monthly cost of $20. 
5 According to a JLL report (Batson 2024), the real estate market for co-located data centers has doubled 

in the past four years and new construction has gone up 700% over the past two years, but the vacancy 

rate is only 3%. Empty office buildings like One Wilshire in downtown Los Angeles have been retrofitted 

to house vast floors of servers and the infrastructure to cool them (Vincent 2024). 



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2015) and on critical and creative thinking skills (Kuhlke et al. 2016), particularly in the 

face of the growing presence of AI-assisted tools in education settings (Larson et al. 2024).  

In addition to employing this framework of “prospective theorizing” as a means of 

creating pathways toward unknown futures, this paper also employs a related approach 

of what Davies (2018) has called “economic science fictions.” Davies defines “economic 

science fictions” (in the introduction to an edited volume of that title) as a kind of story-

telling about the future that is “a political resource” because it “empowers the critic and 

the radical to see the present as amenable to conscious transformation” (Davies 2018: 16). 

The particularly economic piece of this kind of storytelling is in the idea of not only imag-

ining a narrative future but proposing new structures for how societies are organized 

around value and exchange. Economic science fiction can be a strategy across many aca-

demic disciplines, all synthesized into stories. As it is used here, it is also a strategy for 

bridge-building across political valences and environments of thought. At a time when 

news stories about politics and markets are often fueled by an economic model that needs 

advertisement clicks, the prospect of imagining and building the future can be reduced to 

rage-bait, zero-sum reductive thinking, as if the largest creative frontiers of society can 

only be addressed by a debate team. Instead, the stories we want to tell about the future 

can give us common ground in shared case studies (Yin 2013) that anchor theory-building 

in shared reference points—in this case of Silverman and OpenAI. That case study sits in 

the larger context of speculative work by artists, curators, and organizations to pilot forms 

of distributive justice, whether cooperative economies (Schneider and Glickstein, forth-

coming) or experiments in basic income (Acosta 2023).  

Arts entrepreneurship (Chang and Wyszomirski 2015; Essig 2015a, 2015b; Taylor 

2015; White 2015) informs the larger disciplinary methods by which we understand AI, 

including the modeling of entrepreneurship itself as an art form (Callander and Cummins 

2021), and the separation of entrepreneurship from profit-seeking (Benz 2009), with 

more multiplicity of options relative to more canonical definitions of entrepreneurship 

(Shane, S., & Venkataraman 2000). Arts entrepreneurship offers particular resources as 

an effectual discipline (Callander 2019; Sarasvathy 2001), that is, one in which the refer-

ence points such as known markets are themselves changing.  

Even with these rapidly evolving technological frontiers, the principles of economics 

and finance can still apply within this design framing. Sarasvathy (2001) describes effec-

tuation as akin to cooking from a pantry instead of from a recipe. By that same token, we 

have a set of resources and ingredients with which to say that, if creative producers invest 

time and resources to make new work that then has value, it makes sense structurally that 

they would own upside in the value that they helped to create (Whitaker 2016). Yet, the 

formats by which they would do that are difficult to understand and require new attention 

in relation to technological processes and the possibility that existing models of entrepre-

neurial investment lack dimensionality for the task at hand. If creative producers own this 

upside, the question also arises as to whether they are taking risk together collectively, in 



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which case one would need to design both a format for collective ownership and for the 

design of decision-making rights—governance—over those proceeds. Digital-first initia-

tives such as decentralized autonomous organizations (DAOs) (Catlow and Rafferty 2022) 

offer models that complement a longer history of cooperative forms in the United States 

(Schneider 2018). Particularly given the economic structure of AI development—fixed 

cost intensive with potential tendencies toward natural monopoly, the design of regula-

tion and policy generally (Wilkerson 2012) may be important modes of entrepreneurial 

thinking. 

2. Field Scan 
AI is a broader term for technology-driven intelligence, up to and including the idea that 

a computer can think in a quasi-sentient way, whereas machine learning (ML) is generally 

a more tactical idea of teaching a computer to perform analytic tasks. Computer vision is 

used to isolate the aspects of ML that focus on the analysis of images and capacity of com-

puters to “read” visually (Stork 2024a, 2024b; Columbia Engineering n.d.). While AI may 

formally denote the capacity of computers to develop intelligence, that is, the ability not 

only to perform computational tasks but to learn, in this paper, I use AI as a holdall to 

encompass ML with a focus on the development of large language models (LLMs) that take 

vast fields of input data—text, number, image, structure. Within Jebara’s (2004) distinc-

tion of generative and discriminative AI, I focus on the former, as it particularly dovetails 

with creative processes of content generation.  

I focus more here on the question of computational models that can draw from a 

range of training data—including that created by artists and writers—in order to remix 

and produce new outputs. It is this aspect of AI which is the focus here for theory-building 

on how we model forms of value creation—of the original underlying content producers 

and the AI tools—with regard to evolving tests of existing U.S. copyright law and the pos-

sibilities of new models and frameworks for individual and collective ownership of these 

outputs. This focus informs our understanding of how we credit ownership, distribute de-

cision-making rights, and design systems of governance and cooperative economics over 

creative work, especially given that creative work is both a form of production and of in-

vestment (Gerber 2017). 

From a start-up investment standpoint, AI is a rapidly growing field. According to the 

Organisation for Economic Co-operation and Development (OECD), estimated venture-

capital (VC) investment in AI rose from 5% of all VC investment in 2012 to 20% of all VC 

investment in 2020 (Tricot 2021; Berg et al. 2023). However, VC investment has not yet 

synced up to revenue. Goldman Sachs (2024) estimated that a trillion dollars of direct cap-

ital expenditure would be required across companies in order to develop AI. The invest-

ment bank’s own economist, Joseph Briggs projected that generative AI “will ultimately 

automate 25% of all work tasks and raise U.S. productivity by 9% and GDP growth by 6.1% 



ARTIVATE 13 

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cumulatively over the next decade” (Goldman Sachs 2024, p. 3). However, leading aca-

demic researchers cited in the same report estimated only a 0.5% increase in worker 

productivity and corresponding 0.9% increase in GDP growth (Acemoglu 2024; Goldman 

Sachs 2024).  

One significant unknown is the required concurrent investment in public utilities 

given the substantial taxing of the power grid to service data centers for these technolo-

gies, costs which are not currently priced into these private investment calculations via 

economic tools such as emissions permits (Coase 1960) or via regulation and taxation. 

Researchers have also expressed skepticism about whether AI’s infrastructure invest-

ment requirements are matched by foreseeable revenue. In a report for the VC firm Se-

quoia Capital, Cahn (2024) has used data about Nvidia, the company which makes graphics 

processing units (GPUs) used by many other AI companies, in order to back out assump-

tions for how much revenue the industry would need to make to justify the capital ex-

penditures.6 Cahn has found that, even with OpenAI’s reportedly surpassing $1 billion an-

nualized revenue in 2023 (Efrati and Holmes 2023) and $3.7 billion annualized revenue 

by the autumn of 2024 (Palazzolo and Woo 2024; Isaac and Griffith 2024), overall the in-

dustry would have needed to find $200 billion in additional revenue in 2023 (Cahn 2023) 

and $600 billion in the 2024 analysis (Cahn 2024)—a substantial gap between infrastruc-

ture buildout and demonstration of product use. These projects are arguably useful infor-

mation in and of themselves and also a reminder of the methods of this paper, namely, the 

idea that the future is still in formation and that the model itself is still in creative flux.  

A key related area of analysis has been projecting how much AI will affect worker 

productivity and the economy overall. Eloundou et al. (2023) estimate that LLMs could 

affect 10% of tasks for 80% of U.S. workers, or up to 50% of tasks for a more concentrated 

subset of 19% of workers, though the researchers explicitly do not commit to a timeframe 

on the adoption and therefore the impact of such models. Researchers have tried to iden-

tify which industries will be most heavily affected by AI (Felten et al. 2023, 2021). Peng et 

al. (2023) found that computer programmers completed an assigned task 55.8% faster 

when paired with an AI tool.7  

While automation has traditionally focused on factory labor, researchers have stud-

ied the impact of AI on non-manufacturing labor. Noy and Zhang (2023) had college-edu-

cated workers complete writing and interpretive tasks (“incentivized writing tasks”, e.g., 

writing press releases, short reports, or sensitive emails) with an experimental group 

 
6 Nvidia has a variety of products including the development of GPUs and related data centers which al-

low the computational power of AI. Nvidia was a partner in Refik Anadol’s Unsupervised 2022-23 installa-

tion at the Museum of Modern Art, New York (MoMA, n.d.; Kuo et al. 2022).  
7 All programmers in the study had the same instructional material. The control group completed the task 

by themselves. The experimental group used Copilot, a GitHub tool that employs Codex, a generative tool 

made by OpenAI (Chen et al. 2021; Peng et al. 2023: p. 3). That task was to “implement an HTTP server in 

JavaScript as quickly as possible” (Peng et al. 2023: p. 3).  



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assigned to use ChatGPT. Relative to the control group, the group using ChatGPT on aver-

age completed the task 40% more quickly (saving 11 minutes on average time to comple-

tion of 27 minutes) and their output was rated as 18% better (as scored by external eval-

uators who rated originality as well as quality of both writing and content). Those 

participants who had performed best prior to the introduction of ChatGPT maintained the 

quality of their output while saving time, whereas those who had performed more weakly 

saw both an increase in quality and in time savings. This study shows the potential im-

portance and benefit of ChatGPT, though it leaves open the questions of this paper, namely 

the crediting of those content creators whose work supports these gains in quality and 

productivity.  

When we consider the extrapolation of these studies to the arts, we start to see a 

number of factors. First and foremost, the scale of the arts is very different from that of AI. 

The global art market—sales of art—is estimated at approximately $65 billion (McAn-

drew 2024). Apart from the scale of the art market per se, one can consider the input of 

creative work generally. Legal scholars have begun to weigh in on the role of AI. Carys 

(2024) has argued that copyright is an inadequate and limiting tool, though that view em-

phasizes the possibilities of AI companies over the economic inputs of artists. In an anal-

ysis of unfair competition of AI data scraping, Rouser (2024) argues that the data scraping 

AI companies conduct merits consideration under the Federal Trade Commission’s unfair 

competition standards. Rouser also provides a broad literature review on the state of AI 

development as well as problematic racial bias in AI (Rouser 2024; Obermeyer et al. 2019).  

AI has been used to create innumerable works of art. One notable example is the sale 

of the first AI-generated artwork at auction. In October 2018, Christie’s auction house sold 

an artwork Portrait of Edmond Belamy by a French art collective called “Obvious” (Flynn 

2018). The Obvious collective was made up of three students. To make the artwork, they 

used an algorithm that was created by Robbie Barrat, who had made the code in his spare 

time at the age of seventeen and who was nineteen by the time of the auction. Barrat cre-

ated the algorithm by training it on thousands of artwork images that he trawled from 

Internet pages. Barrat posted the code to the repository Github, where the members of 

Obvious found it. An artwork that was estimated to sell for $10,000 sold for $432,500. Who 

should own the proceeds of that sale? In an interview, one of the members of Obvious, 

Hugo Caselles-Dupre , said that Obvious had not substantially modified Barrat’s code but 

that they had put more substantial time and effort into working on the computer, with the 

code, to make the final artwork (Vincent 2018).  

The field of AI has received substantial investment from VC firms and large companies 

such as Microsoft. The technologies are capital intensive to produce but have not yet seen 

demonstrated markets. However, the ability of users to see the outputs have led to con-

sideration of who has consented to be in the training data. In addition, preliminary schol-

arly studies of productivity point to large-scale gains, even within computing. In the hu-

manities and education, the use of AI had substantial impact on what work is made, how 



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people think, and how culture represents societies. AI has been demonstrated to show 

biases and to present substantial challenges to basic academic research protocols (e.g., 

informed consent). At the same time its economic model is unresolved. Particularly where 

creative work and research are concerned—forms of knowledge building—gains made by 

individuals and by society at large are privatized to AI companies without structures for 

shared ownership and governance. 

3. Conceptual Framework 
The case of Obvious collective’s Portrait of Edmond Belamy encapsulates the complexity 

of economic models for AI. Artists—as a proxy for all creative workers—have always 

drawn from their surroundings and often from the work of specific people. For example, 

when the artist Shepard Fairey was sued by the Associated Press (AP) for using an under-

lying AP-owned photograph to make the iconic Hope Poster, for Barack Obama’s 2008 

presidential campaign (a project that Fairey, like Barrat, made on a volunteer basis in his 

free time), a group of leading intellectual property attorneys who represented Fairey pro 

bono cited the painter Paul Cezanne’s use of a photograph underlying his painting The 

Bather (1895), which is owned by the Museum of Modern Art and considered a master-

piece within Cezanne’s body of work (Fisher et al. 2012: 272, 332). How do we conceptu-

alize the role of the photographer underlying the Cezanne painting? What is the frame-

work for balancing the input of others as an organic part of creative process and finding 

structures and methods of sharing in upside for those whose work meaningfully helped 

to create value? While these questions have been active in creative disciplines for dec-

ades—with artists such as Andy Warhol or Richard Prince who engage with popular cul-

ture and methods of appropriation—the technology of AI creates a new scale to the prob-

lem.  

Answering this question requires engagement with both the U.S. frame of economic 

incentives and the European framework of moral rights over one’s own creation. This pa-

per focuses primarily on the economic rights and the U.S. context of the case studies while 

acknowledging that AI produces a much larger and thornier said of questions about moral 

rights beyond art including rights to one’s own voice or image.8 The answer to this ques-

tion needs in some way to undertake the especially difficult task of answering whether 

Barrat’s algorithm is different from that of OpenAI. Does it matter that one is programmed 

by an individual—and a seventeen-year-old—and the other is a corporate effort that, as 

Silverman can attest, did not ask permission? Obvious quickly credited Barrat; as 

 
8 Scarlett Johansson threatened to sue OpenAI for a chat voice uncannily similar to her own (Allyn 2024). 

Rouser (2024) cites the case of two high school students who sued for infringement when their likeness was 

used as a recognizable input to AI-generated pornographic images (Harris, S. & Moshtaghian 2023). There are 

substantial issues with AI outside the scope of this paper including in policing and emergency response 

(NAACP n.d.; Feldkamp and Neusteter 2023) and in military contexts (Greenland 2023).  

 



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Silverman’s lawsuit alleges, OpenAI did not retain the copyright information within the 

texts it scraped from the internet.  

Silverman is a stand-up comic, performer, and author by background. She was a cast 

member of Saturday Night Live (1993-94) and the principal of The Sarah Silverman Show 

(2007-2010) in addition to acting in a variety of other works. She is the author of Bedwet-

ter, an autobiography published in 2011. Her lawsuit against OpenAI was allowed to pro-

ceed in February 2024 on the grounds of direct copyright infringement.  

Within this larger context, we can focus on the case study of Sarah Silverman’s work 

and her case against OpenAI. If OpenAI’s ChatGPT-3.5 model scraped over 300 billion 

words from publications found in the internet (Rouser 2024, Hughes 2023), we can con-

textualize Silverman’s work within that field. As Chiang (2024) has written, creative work 

is defined by a series of choices—each word put down on the page, each brushstroke cho-

sen. That choice has associated with it labor, skill, and intelligence. The labor is in the form 

of time spent, including by those who have used AI as inputs. The skill pertains to the AI 

model as well and its capacity to act based on past experience. Intelligence, as Chiang 

(2024) defines it, is the capacity to learn. Drawing on the methodological framework of 

this paper, intelligence is the capacity to synthesize and to move forward into untemplated 

circumstances—to risk creating value, that is, original work that may later go from opus 

to input.  

In the U.S., copyright is governed by the Copyright Act of 1976 (U.S.C 1992), which 

codifies a ruling from the court case Folsom v. Marsh (1841). The U.S. Copyright Act eval-

uates “fair use”—meaning the use of another’s creative work without infringement on 

their creative works—by a test of four factors: 

(1) the purpose and character of the use, including whether such use is of a commer-

cial nature or is for nonprofit educational purposes;  

(2) the nature of the copyrighted work;  

(3) the amount and substantiality of the portion used in relation to the copyrighted 

work as a whole; and  

(4) the effect of the use upon the potential market for or value of the copyrighted 

work. 

These factors are particularly understood through the prism of a highly influential 

essay by Judge Pierre Leval (1990) in the Harvard Law Review. Leval defined the idea of 

“transformative use” as a synthesis of the fair use factors. This transformativeness is gen-

erally judged by judges, who are at times put in the position of being art connoisseurs or 

parsing the intentionality of testimony by artists (Adler 2016). As argued previously by 

Whitaker (2019), the fourth fair use factor is commonly described as a “market” test but 

is in fact written as a “potential market . . . or value” test. This shift from market to potential 

market and value allows the frame of imagining future constructs for AI. In addition, the 



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third fair-use factor—the portion of the work used—provides grounding for the initial 

challenge of answering who owns AI, which is the scale of use: one versus many.  

Consider the “amount and substantiality” of the use of Silverman’s Bedwetter by 

OpenAI’s ChatGPT. One the one hand, OpenAI is using 100% of Silverman’s book. On the 

other, that book is a small drop in the bucket of an estimated 300 billion words used to 

train GPT-3.5 (Hughes 2023). The kneejerk reaction is to think Silverman’s work is such a 

small input that it does not matter. But this is not the case. Copyright exists from the cre-

ator’s point of view, not only the user’s. That is because the original logic of copyright is to 

grant individual economic incentive to create works that contribute to the public good—

that “promote the Progress of Science and useful Arts” (U.S. Constitution). While there is 

no hard and fast rule as to what percentage of a work constitutes fair use, university li-

braries often guide the usage of less than 10 to 15% of a written work. Even though a 10% 

rule does not exist, suppose for the sake of argument that OpenAI is allowed to use 10% 

of Silverman’s work and of any work by any other writer or other creative producer in the 

class of the prospective class-action lawsuit. We know that OpenAI used all of the work, 

whether the output shows all of the work or not. But what if they used all of it in the way 

someone—say, a professor—might read an entire book but then assign less than 10% of 

it to a course of college students? Imagine a scenario in which a user of ChatGPT makes 

the following requests of the AI tool: 

(1) Summarize Silverman’s work 

(2) Recreate a complete version of Bedwetter 

(3) Design a new work in the style of Bedwetter 

(4) Combine Bedwetter with another work to create a new style 

Under fair use, (1) AI is allowed to summarize the work, so long as it does not plagia-

rize and it cites sources. Citing sources would require OpenAI to include copyright infor-

mation in its input files and in the way in which its algorithm is trained. Such a require-

ment, if required by law, would be a meaningful intervention. It is relatively clear that (2) 

recreating the complete version of Bedwetter is a violation of Silverman’s copyright.9 In 

the case of (3) a writer could read Bedwetter and write a memoir informed by Silverman’s 

style without violating her copyright. At issue is the vast labor differential in a single indi-

vidual reading a single book in a single lifetime and an AI algorithm ingesting hundreds of 

thousands of books. The case of (4) mimics questions in copyright scholarship about “re-

mix” culture. It is hard to say how sampling and recombination are parsed by existing cop-

yright law. The general principle proposed below does not circumvent the complexity of 

bringing individual cases of to court in particular circumstances. 

 
9 Readers may know the obscure and conceptual case of Borges’ (1962) short story “Pierre Menard of the 

Quixote” in which an author happens to have recreated a section of Don Quixote of Cervantes verbatim, 

but this is a symbolic rather than structural comparison. 



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There is no claim to fair use under the third factor because the entire work is being 

used. Though the use of the entire work is mitigated in many cases by its small scale rela-

tive to the overall, the creative work still has dignity in its smallness the way that one vote 

still has dignity in a national election of millions. The fourth factor creates more analytic 

power and possibility for the analysis of AI. The “value” part of the fourth factor is not 

generally considered in legal cases of fair use. Judges tend to evaluate the existing market 

and whether the source artist’s market is infringed. A whole-cloth change in lens, but 

maintaining of doctrine, would be to look instead at value and potential market. Value 

encapsulates the “added value” of a contribution. Thus the question becomes: How would 

the OpenAI algorithm change without the inclusion of Silverman’s work? Perhaps this 

question is academic and in many cases Silverman’s work would not affect the impact. 

However, if Silverman is emblematic of the class of all creators, if all creators left OpenAI, 

then the algorithm would not work at all. And, in some salient cases, a user might try to 

recreate Silverman’s work. Given that she has not granted consent, this is a problem for 

which OpenAI is, in fact, arguably responsible.  

Even traditional copyright law make space for the singularity of an individual’s con-

tribution. In the case Salinger v. Random House, Inc. (1987), the author J.D. Salinger suited 

over a biography that relied heavily on the novelist’s unpublished letters. The lawsuit al-

leged overreliance on Salinger’s “dazzling prose.” It was as if the biographer were the ma-

chine relying on the content made by the individual creator. The author had not given 

consent; the letters were found files, not unlike manuscripts scraped from the Internet. 

The court upheld the author’s right to some control over the use of his work out of its 

original—private—context.  

To synthesize these problems of creator and tool—material and machine—with the 

place of the individual creator in the landscape, imagine as a framework is a 2 x 2 matrix 

in which we weight the nature of content creation—“material” against the role of an algo-

rithm—“machine” on one axis. In the other axis, we can consider the logic of many and 

one—that is, whether the derivation of the material or the machine is the work of many 

or of one.  

 Material Machine 

One  Bedwetter, work of an indi-

vidual artist 

Robbie Barrat’s algorithm 

Many All work pulled into a 

training algorithm 

OpenAI 

 
This analysis of the part versus the whole is instructive for the economic logic of cop-

yright. As argued previously by Whitaker (2019), fair use has been construed as having a 

zero-sum nature: Either a use is fair or it is not. The doctrine does not allow for economic 

complements to copyright and for ideas of shared ownership. Fair use exists in the one-



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material box of work made by an individual creator. Of course that creator may have relied 

on many sources and influences. The creation of a machine or tool by a solo creator—for 

example, Barrat—merits special consideration because of the individuality of the creator. 

That said, Barrat placed his work in a public code repository, and the Obvious collective 

used it and made meaningful developments within it. We could consider something like 

Adobe Photoshop to be analogous to Barrat’s algorithm. It is a machine that allows the 

realization of an artistic vision, a set of a tools used by a creator. OpenAI can be a set of 

tools used by a creator, but crucially it is trained on prior content whose origins must be 

considered in the “value” analysis of the work. 

We can see a set of possibilities that center two things: the presence of individual ar-

tistic labor at all and the collective circumstance of all artistic labor. Each of those inputs 

matter. Again, like a vote in an election, it can be individually subtracted out without 

changing the overall, but if all of the votes were subtracted out, the election would not 

work. We have a sense of individual value—of the specificity of Silverman’s and others’ 

individual contributions—and we have the sense of the collective—the body politic of hu-

man creativity, which is the yardstick and the common resource that copyright exists to 

protect and incentivize in the first place.   

4. Discussion and Implications 
The technological level at which OpenAI operates makes clear that technology has moved 

far enough forward to allow the amalgamation of vast quantities of individual data. This 

technological promise is currently centralized under a single organization and under liti-

gation from individual creators whose work is an input to that centrally owned machine. 

Models in the arts, from blockchain and from artists’ cooperatives, show that it is possible 

for groups of creative workers to band together and own their own work. Although still 

speculative, we see these experiments in some blockchain-based experiments including 

the “invisible economy” of mutual support imagined by the founders of the art-making 

collective “DADA” (Franceschet and Read 2022) and the artists’ collective Transfer Data 

Trust (Schneider and Glickstein, forthcoming) in which artists take care of and store back-

up copies of each other’s art and operate in some regards as an economic cooperative. 

Experimental art collectives are a much larger topic and area of extensive recent experi-

mentation through decentralized autonomous organizations (DAOs) (Rafferty and Catlow 

2022).  

OpenAI itself embodies an ethos of art: Investment, without demonstration of suc-

cess, or even certainty that a success is possible, following from an animating question, 

sense of purpose, and audacious goal. OpenAI’s own corporate story acknowledges the 

tension between the need for concentrated capital investment to get the airplane of the 

idea off the ground and the reality that a whole host of actors share responsibility and 

credit for the success. The system by which OpenAI took investment is set up to credit a 



ARTIVATE 13 

15 

lone corporate actor when a collective has created an important part of the underlying 

value. The idea of web3, which is associated with blockchain, could also characterize co-

operative models that reflect the realities of shared risk-taking and collective efforts be-

hind AI. 

It would be theoretically possible for OpenAI to operate as both a private company 

and a cooperative of all of the inputs it trains on. It would need to cite its sources, that is, 

have it be possible to know whose input mattered. We have probabilistic systems of re-

verting rights—and revenues—to creators. Models include DACS, the resale royalty and 

copyright management system in the United Kingdom, and ASCAP and BMI which pay roy-

alties to musicians and split the proceeds between artists and music publishers. What we 

do not have is the political will to shift from VC models of concentrated, privatized risk to 

public models in which the collective owns the proceeds of the collective’s work. Individ-

uals with special influence could receive an outsized portion, but even to credit the collec-

tive is to say that companies are privatizing public benefits, like the output of artists, writ-

ers, and other content creators.  

We could make inroads to credit those systems with simple proposals that require 

companies like OpenAI to credit their inputs. They effectively need to pay for raw materi-

als and to do so on an equity basis—that is, to pay a royalty for use rather than a fee for 

the input. Such a system recognizes the risk and flexibility needed to chart an unknown 

future. Artists do not have access to the same kind of capital investment as AI. We are only 

just beginning to center a meaningful conversation about the precarity of artistic labor 

(Roberts 2024) and the embeddedness of artists’ lives in the need to make money in order 

to keep making creative work. Creativity that benefits all of society requires individual 

risk, and we need ways of mapping this and thinking about it structurally as both a prob-

lem of governance and of economic structuring.  

Content creators have stared down blank pages and empty canvases, rearranged their 

working lives to have the flexibility to do so, taken on enormous and ongoing risk—even 

for those at the top of their creative fields—and contributed to a greater good in a way 

that, if they are performing at the very top of their field, they have probably made look 

easier than it was. If we wish collectively to incentivize that work, we need to enact—even 

in small and experimental ways—systems to distribute not only economic proceeds but 

also decision rights. OpenAI’s nonprofit performs social benefit while reserving decision 

rights for a small and privately appointed group of directors. Instead, the proceeds asso-

ciated with the creative work that trains the AI models needs to be governed more demo-

cratically—whether by government or by cooperatives of creative workers.  

5. Conclusions 
The lawsuits brought by creators against AI companies call for new ways of thinking sim-

ultaneously about economics as a creative endeavor itself—a design medium—and about 



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16 

creative workers as members of a larger whole. That larger whole asks more than sym-

bolic representation by the charitable aims of a private nonprofit. It asks—in a structural 

and not merely normative way—for us to consider the creation of value and to design 

economic models that are faithful representations of the entrepreneurial risk asked of all 

artists and creative works in their artistic practices.  

To develop such systems is not to overlook or defensively react against the seismic 

changes brought on by AI itself. Those changes may deepen the work existing creators can 

do and expand and democratize access to creative labor more broadly. But this use of AI 

does not obviate the responsibility to engage in a meta-level creativity and entrepreneur-

ship. Just because each of us is a small input to AI, doesn’t mean we don’t count. The invi-

tation of AI is to design structures that can allow creators to benefit in upside they help to 

create, share risk they collectively face, and grant decision-making or governance rights 

in the ultimate generativity of generative AI—in allowing investment in the ongoing work 

of creative research and development that benefits us all.  

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