Revisiting computer authorship: a longitudinal perspective

Artificial intelligence has always been right around the corner in more ways than one. For example, when the subfield of computer science was first identified in the mid-1950s, the—in hindsight, rather optimistic—expectation was that “significant advance [could] be made […] if a carefully selected group of scientists work on it together for a summer” (McCarthy et al. 2006). In the following seven decades, the field has seen plenty of ups and downs, including several ‘AI winters’, wherein highly public failures to meet overly positive expectations resulted in massive funding cuts (Russell and Norvig 2003: 21–24). At the same time, as famously noted by Douglas Hofstadter in the 1970s, whenever advances in AI allow for some new task to be completed automatically, the general perception tends to shift so that the task is no longer viewed as requiring intelligence; real ‘artificial intelligence’ is always almost within reach, and whatever we can do now is mere computation (Hofstadter 1999: 601). Examples of this ‘AI Effect’ include many computational tasks classically thought of as AI problems, but which very few—especially amongst the general public—would today be characterised as ‘AI’, such as email spam filtering and spell checking.

Against these two observations, the changing discussions in the wake of recent advances in producing natural (i.e., human) language texts that appear coherent are curious. A technological method called large language models (LLMs; see, e.g., Devlin et al. 2019; Radford et al. 2019) has again brought AI into the limelight, going so far as to shift the casual parlance definition of AI into near-synonymy with ChatGPT (Schulman et al. 2022) and the names of other generative AI systems. Some go as far as claiming that these new tools will revolutionise everything in our lives, and they will—any day now—reach a level where we will view them as conscious in the same way humans are. Many of the world’s largest technology companies are currently working towards such artificial general intelligence (AGI). ChatGPT’s parent company OpenAI is one of the most prominent proponents of AGI, claiming that “[w]e can imagine a world in which humanity flourishes to a degree that is probably impossible for any of us to fully visualize yet. We hope to contribute to the world an AGI aligned with such flourishing” (Altman 2023).¹ However one feels about AI and AGI, and however ‘right around the corner’ the technology may or may not be, the fact is that software systems that communicate with us through natural language are increasingly common, and producing increasingly convincing texts. As the texts they produce become more complex and the input of individual humans in their production decreases, societies are forced to (re-)evaluate stances on a plethora of topics.

In this paper, we interrogate one specific aspect of natural language generation that has been the subject of ongoing, complex cultural conversation: authorship. Who—or what—is the author of the computer-generated text, and why? Does—or can—automated natural language generation adhere to conventional conceptions of authorship, or might we require new language to more firmly align with these new means of text production? Following a brief review of literature related to social perceptions and uses of AI systems that ‘understand’ and produce text, we present the methods and results of an international online survey that solicited general public perceptions of the concept of authorship as it pertains to computer-generated texts in late 2024/early 2025. The same survey was previously administered in 2017/2018, allowing us to investigate not only what the present views on authorship are, but also how those views have, or have not, changed over time. In the discussion, we highlight participants’ ambivalence and uncertainty about authorship of computer-generated texts. Authorship, it seems, is hardly clear-cut, and natural language generation makes it even less so.

People have always used highly anthropomorphic language about computers. The perceptrons of the 1950s evoked the imagery of electronic brains composed of firing neurons (Minsky and Papert 1988; Rosenblatt 1958). Since then, a whole sect of computer scientists has focused on making computers ‘learn’, while others are trying to make computers ‘understand’ natural language or ‘reason’ (for a critical commentary of these terms, see Bender and Koller 2020). Computers ‘hallucinate’ while looking at images or writing text (Ji et al. 2023), and ‘dream’ while producing pictures (Mordvintsev, Olah and Tyka 2015). Computers, it would seem, are just like us—or vice versa.

But with the LLM leap, the discussion appears to have shifted in an unexpected way (Mitchell and Krakauer 2023). Before, even among those who talked about ‘intelligent’ machines that ‘learned’, ‘understood’, or ‘reasoned’, most were keenly aware that the AI systems were not truly understanding the data in a meaningful way. But LLMs, with their ability to produce coherent language about a variety of topics, appear to have reached a critical point in which debate has emerged regarding whether this lack of true understanding is the case anymore. A recent study found that the technical community was evenly split on whether LLMs could be said to ‘understand’ language or not (Michael et al. 2023). Whether LLM coherence is the final nail in the coffin of language-based tests for sentience—thus putting to rest decades of philosophical thought experiments, such as the Chinese room hypothetical (Searle 1980)—or whether advances in AI will force us to concede that we’ve been relying on the equivalent of the ‘God of the gaps’ argument (Larmer 2002) but with sentience, will remain to be seen.

In any case, these recent events have prompted calls for discussion about whether computer programs should have, for example, legal personhood—discussion that has actually been ongoing for decades (Jaynes 2020; Solum 1992). While these calls mostly come from outside computing research communities, there are some notable exceptions, such as the 2022 case where a Google engineer claimed that a chatbot (LaMDA) had become sentient. This ‘sentience’ was supposedly demonstrated through the chatbot’s own declaration of personhood in conversations with the engineer—conversations that covered a wide range of topics, from philosophy to science (De Cosmo 2022). While a system’s claim to personhood does not equate to sentience or personhood (consider, for example, a trivial chatbot that is hardwired to reply to all queries with “Please help, I have real sentience”), it does encourage us to think more deeply about what constitutes personhood in the first place, especially in legal systems that include concepts like corporate personhood. The importance of this question is highlighted by a recent argument by Air Canada that the company should not be held responsible for misrepresentations by their LLM-based chatbot, as the bot was a “separate legal entity that is responsible for its own actions”—an argument rejected by the court (Yagoda 2024). The Air Canada example shows that there are various reasons why one may wish to grant personhood, legal or conceptual, to an AI system—in Air Canada’s case, for instance, to absolve the company of responsibility for its output. It also shows that questions of personhood are entwined with questions of authorship; if authorship equates to self-expression and responsibility for textual content (interpretations affirmed by our findings below), understanding perceptions of authorship can support understanding of the legal personhood debate.

To add further complication, academics do not agree even within the technical literature on what, exactly, the term ‘AI’ means, with definitions ranging from so simplistic as to enable a modern-day Diogenes to point at a bimetallic thermostat and shout “look, I have brought you AI” to so strict that nothing currently in existence is AI. When others are concurrently pointing at computer systems that produce text that certainly reads like it has been written by a human (e.g., ChatGPT), talking about those systems using highly anthropomorphic terms (e.g., Salles et al. 2020), and going so far as invoking the terminology of magic and the divine (Giuliano 2020), it is no wonder that the term ‘AI’ has effectively become a floating signifier in colloquial discourse. It “suggests a specific referent but works to escape definition to maximize its suggestive power [and] works through a strategic vagueness that serves the interests of its promoters, as those who are uncertain about its referents are left to assume that others know what it is” (Suchman 2023: 3). Put simply, AI is simultaneously everything and nothing, a term so malleable that it can be—and is—bent to suit circumstances as desired.

Our goal in this paper is not to wholly calm the maelstrom of chaotic change and definitional difficulty described above. Rather, ours is a humbler goal: to understand how—or whether—the perceptions of authorship have changed following the coming of the LLMs. After all, as Simone Natale explains in his book about Artificial Intelligence and Social Life after the Turing Test (emphasis in original), “[t]he question, Turing tells his readers, is not whether machines are or are not able to think. It is, instead, whether we believe that machines are able to think—in other words, if we are prepared to accept machines’ behavior as intelligent” (Natale 2021: 20). While actual technological functionality is of course important to consider in current conversations about the societal implications of LLMs and AI, perceptions of technological functionality and value (or lack thereof) are more important in this paper. Perception is powerful. It informs whether or not people are willing to adopt technologies and, if so, how they are willing to adopt them. It informs hopes, fears, excitement, and concern. It informs our senses of identity and meaning (Møller et al. 2024). Perceptions have very real societal and commercial implications for the development and propagation of AI. If we want to develop AI that adequately serves us, we must account for our perceptions.

In this paper, we focus on perceptions of AI authorship in particular. The relevance of authorship perceptions is manifold. For one, authorship is often equated with interpersonal connection. Writing is regarded as an actualisation of human thought, an effort to share experience and insight. Texts are sometimes framed as contributions to asynchronous conversations between authors and readers (Henrickson 2024). Authorship perceptions matter because they influence who we think we are ‘talking’ to. They also pertain to several key moral and legal questions in the contexts of media and communication, most notably those related to legal personhood and responsibility for content. Our conceptions of copyright are based on an assumption that a piece of content has value that should rightly belong to the author, and that this value must be protected. At the same time, it is often held that authors—and in the case of news, together with editors—are responsible for their works and should be punished for harms caused by their creations. Indeed, recent discussions about AI authorship emphasise the importance of responsibility for truthful textual content (Pennock 2024). However, if general conceptions of authorship change so that the ‘author’ might no longer be a human, what does this mean for the rights, responsibilities, and expectations associated with this role? Is there a point in protecting the ‘rights’ of a computer program, and should we allow for a future where the ultimate responsibility for publishing falsehoods is not with a human but with a piece of code?

While questions about AI’s place in our current social infrastructures abound, generative AI—often powered by LLMs—is nevertheless being integrated into these infrastructures at a rapid rate. Generative AI systems have been applied in university classroom assignments, for instance, with reported success for encouraging students to navigate the literary, communicational, and ethical complexities of increasingly pervasive ‘AI-powered plagiarism’ (Fyfe 2023). They are also increasingly being used in marketing campaigns, although consumers have expressed ‘moral disgust’ in some such use cases (Kirk and Givi 2025). These are just a few areas wherein LLMs have seen particular prominence, but computer-generated texts abound. Yet even with these texts’ prevalence, we remain unsure of readers’ willingness to accept them. Studies indicate that the degree of AI involvement in a text’s production may influence readers’ sentiments towards the AI and human contributors to that text, and to the text more generally (Formosa et al. 2024). In addition, the increasingly widespread use of generative AI systems for text generation raises questions about the processes of generation themselves. How do publics believe these systems to be technically operating? Does writing a prompt for a system count as authorship in itself (Mazzi 2024)? Are we moving towards a new kind of ‘distributed authorship’, wherein authorship is characterised by networks of human and non-human actors (Bajohr 2024)?

To examine public perceptions of algorithmic authorship (Henrickson 2021: 3), we distributed an international online survey from 5 December 2024 to 20 January 2025 (ethical approval granted by the University of Queensland, 2024/HE001972). It was completed by 200 participants. This survey was the same as one distributed by and reported on by one of this paper’s authors from 5 December 2017 to 24 January 2018 for a doctoral project (Henrickson 2019: 145–176). The redistribution of the same survey has supported a longitudinal perspective on perceptions of algorithmic authorship. This is, to our knowledge, the only longitudinal empirical consideration of algorithmic authorship pre- and post-LLM boom.

The survey presented participants with a 243-word English-language text reviewing Finnish municipal elections. After an introductory demographics survey, participants were asked to “list three things that come to mind when you think of the word ‘author’.” Then, participants were asked to attribute authorship to the provided text four times. Each time participants were asked to attribute authorship, they were presented with additional information about the text’s process of production. First, they were only given the text itself, which included the mononym ‘Valtteri’ in the byline under the title. Second, participants were told that Valtteri was a bot, and that ‘John Smith’ had translated the Finnish political party names into English for comprehensibility. Third, participants were told that Valtteri was developed by a team called Immersive Automation. Finally, they were informed that Immersive Automation was funded by various public and private organisations. In all four instances, participants were provided lists of predetermined answers, with ‘It is not possible to assign authorship’ and ‘Other’ included; if a participant selected ‘Other’, they were required to explain why in a text box. Each attribution instance included a ‘Why have you selected this option?’ text box for participants wishing to volunteer elaboration on their answers. Iterative questioning helped us see what kinds of information participants found valuable for their considerations of authorship, and supported participants’ critical reflections on the multilayered nature of computational text production. Following the authorship attribution activity, participants were asked whether they thought that their answers would have been different had they been presented with a computer-generated piece of opinion-driven journalism, short story, or poem, and were asked to explain why in a text box if they selected 'yes' to any of these questions. A copy of the survey is included in the Appendix 1.

Like the initial version of the survey, this survey recruited participants through word-of-mouth and snowball sampling techniques. Social media platforms including Reddit (r/SampleSize), Facebook (personal and acquaintances’ feeds; the ‘Women in Academia Support Network Careers Support #wiasn’ group), X/Twitter (personal and acquaintances’ feeds), and LinkedIn (personal and acquaintances' feeds) were used to promote the survey. In addition, and again like the initial version, participants were solicited through the SHARP (Society for the History of Authorship, Reading and Publishing), DHSI (Digital Humanities Summer Institute), and SIGGEN (ACL Special Interest Group on Natural Language Generation) professional listservs, and student mailing lists of various universities. These techniques undoubtedly influenced the demographic distribution of participants. However, the demographic distributions of both versions of the survey are sufficiently similar to support comparability of results.

There are, however, slight differences between the two surveys that require mention. To accommodate a wider range of participants, ‘elementary school’ was added as an option for the highest level of education completed; ‘prefer not to say’ was added as an option for gender and age; the Web link to Valtteri was removed as it was no longer functional; and the development and funding of Valtteri were referred to in the past, rather than present, tense due to the completion of that project. The analysis of the second survey’s results was similar to that of the first’s, but benefited from the involvement of a second researcher with more extensive experience in quantitative and computational analytical techniques. The coding of the second survey’s authorship word associations was completed by the same researcher as those of the first’s, but variations in coding practice emerged due to changing rhetorical landscapes and the researchers’ deeper reflections on word functions (e.g., ‘text’ could be considered both verb and output; ‘authority’ could be considered a person or a connotation). The execution of the first survey also did not produce a detailed codebook, as this survey was not initially imagined as longitudinal. Finally, the presentation of the most recent survey results more thoroughly reviews the quantitative changes between authorship attributions than did the presentation of the 2018 results, more firmly marrying quantitative and qualitative methods for a better picture of participants’ opinions based on the information provided to them in each step of the survey.

This section reviews the results of the participant demographics section, author word association activity, authorship attribution activity, questions about genre specificity in authorship attribution, and final questions/comments section. The results of the authorship attribution activity also include attention to the evolution of participants’ attributions across the four instances of attribution, as well as links between participant demographics and attribution tendencies. Our data consist of survey responses from 200 participants for the 2024–2025 survey, and 500 responses from the previous 2017–2018 survey. We do not know if there is overlap across these populations. In the results reported below, quotations from the survey’s qualitative responses were selected because they exemplify the general sentiments expressed by participants. These quotations have been copied verbatim, including any spelling and grammar errors and eccentricities. Each quotation is accompanied by parenthetical acknowledgment of the participant’s attribution trajectory throughout the authorship attribution activity.

So far, we've considered authorship perceptions from a variety of angles, including what kinds of words people associate with authorship, how people attribute authorship of a computer-generated news text based on limited information, how authorship attribution relates to various demographic variables, and whether authorship attribution is genre-specific. For all these aspects, we’ve considered not only the findings from the current survey, but also how those results differ—or not—from what was observed in the 2018 survey. In this section, we recap and discuss our key quantitative findings on these fronts separately, and then more broadly discuss what these findings, and their corresponding free-text responses, indicate to us holistically. Finally, we observe some limitations of our work and suggest future avenues of research.

A fourfold definition of authorship emerged from the analysis of the first set of survey responses in 2018, and this definition was affirmed by the results of the survey in 2025. A statistical analysis indicated meaningful changes from the 2018 survey, with the words relating to connotations becoming more prevalent and those relating to outputs becoming rarer. We also observed a tentative (i.e., not statistically significant) increase in the proportion of terms relating to activities. This increase may be partly explained through participants’ caveats to authorship in their qualitative answers throughout the survey. These caveats were often focused on the how of text production, but also included assumptions related to human expressions of creativity and textual characteristics and quality. The importance of the ‘how’ is further mirrored in the decrease in terms relating to outputs in the word association task. Even with these differences, though, the two surveys share a key finding: that ‘authorship’ is used flexibly. The concept of authorship, like the concept of AI itself, “works through a strategic vagueness that serves the interests of its promoters, as those who are uncertain about its referents (popular media commentators, policy makers and publics) are left to assume that others know what it is” (Suchman 2023: 3). In addition, like AI, ‘authorship’ is simultaneously everything and nothing, a term so malleable that it can be—and is—bent to suit circumstances as desired. Thus, both elements comprising ‘AI authorship’ are somewhat fuzzy; their synthesis in ‘AI authorship’ does not appear to make either any less so.

This fuzziness seemed prevalent in the participants' authorship attributions. In terms of attributing authorship to a computer-generated news text, following the reveal that the text was indeed computer-generated the quantitative results of the present survey are mostly characterised by an increase in uncertainty and either inability or refusal to attribute solitary authorship to either the software or its creator. However, where the respondents did attribute authorship to one of the named entities, the system itself dominated the results—and much more so than in the 2018 survey. One explanation for this finding could be increasing awareness of how machine learning systems in general, and LLMs more specifically, are trained using the products of (and in the case of LLMs, texts written by) third parties. This is exemplified by one respondent's (Valtteri → Valtteri → Valtteri → Valtteri) final comment that “[a]lthough I consider AI to be the author, that doesn’t mean it’s ethical and I still believe it’s stealing.” In the case of the present study, the text-generation system was in reality not a machine learning one (and thus had no training data), but that matters little for the respondents' perceptions given the limited information available to them.

A hypothesis of increased awareness could also be supported by our observations on how the educational background (‘highly educated’ simplified to whether the respondents had at least a master’s degree or not) related to the authorship attributions. While a high-level statistical analysis indicated that statistically significant differences still existed in the 2025 survey data, a closer look at the data revealed a general trend of the groups’ responses getting much closer to each other. This could arise from a situation where information, knowledge, and critical lenses previously available only to the highly educated group were now also available to and known by the other group.

As for how responses evolved across the four rounds of attribution, we note that the opinions remain largely static following the reveal (or confirmation) that the text was computer-generated, and changes between the two surveys are limited. We did, however, observe that those who attributed authorship to the mononym ‘Valtteri’ prior to the reveal that it was a bot now more evenly split into those who continued to attribute authorship to the software and those who found it not possible to attribute authorship in comparison with the 2018 results, as well as that the fall in popularity of the ‘Immersive Automation’ option was not attributable to any specific response from the previous round(s).

When asked whether their responses to the questions would change if the genre of the text were different, our results from the present survey mirror those from the 2018 survey in that the vast majority of respondents did not view authorship as genre-specific. While the proportion of those who would have changed their responses fell across all genres, we only observed a statistically significant decrease for opinion pieces, with 28% indicating they would have changed their responses in 2018 and 19% indicating the same in the present survey. This change aligns with the findings pertaining to words relating to authorship, wherein we saw fewer words relating to outputs; if outputs are less associated with authorship, it seems reasonable that the genre of the output matters less as well.

Overall, participants recognised the extensive networks within which authorship exists, especially in their recommendations for distributed authorships of the computer-generated text under consideration. Hannes Bajohr (2024: 327–328) describes ‘distributed authorship’—heavily influenced by actor-network theory—as “distributed across an assemblage of actors in complex chains of operations. [..] This actor-network must be imagined as sprawling and immense.” Such distribution includes not just human contributors, but also those more algorithmic, as well as those whose data have informed AI training sets. Participants appeared instinctively attuned to the distributed, networked nature of text production more generally, suggesting various combinations of authorship for the text under consideration, if they felt attributing authorship was possible at all. It appears, then, that although authorship has always been a social construct dependent upon interpersonal collaboration (Inge 2001), such collaboration is increasingly recognised as imperative. Humans using LLMs may no longer be able claim the ‘solitary genius’ long associated with conceptions of single authorship. Instead, they are more commonly perceived as existing within networks of other humans, other humans’ data, and algorithmic systems, all of which inform text production processes. These networks were identified by participants, who in the present survey showed increased attention to the connotations associated with the word ‘author’, as well as slightly increased attention to ‘author’ activities. These networks are also the same networks currently being mobilised in efforts to develop AGI that “empower[s] humanity to maximally flourish in the universe” (Altman 2023). However, to flourish, humanity must feel confident in its own role in the production of cultural artefacts like texts. After all, “[w]hen humans use language, we do so for a purpose: We do not talk for the joy of moving our articulators, but to achieve some communicative intent” (Bender and Koller 2020: 5187; emphasis in original). By distributing authorship we are, in effect, distributing intent. Such distribution is neither inherently good nor inherently bad, but does inherently change the conventional understanding of ‘authorship’ being claimed by any single person. Not being able to attribute authorship to a computer-generated text does not necessarily signify confusion; it could signify recognition of the distributed nature of text production.

Distributed authorship, however, did not appear to excuse responsibility for the accuracy and truthfulness of computer-generated texts for survey participants. These are issues that scholars have been considering through reflection (Pennock 2024), proposals for evaluative metrics (Goodrich et al. 2019), and empirical studies about human trust in AI systems (Glikson and Williams Woolley 2020). Questions of trust also lead to questions of trusteeism. While participants did not appear to assign legal personhood to the AI system used for the survey, they did implicitly question the legal ramifications of AI authorship by, for example, evoking copyright and intellectual property theft. There are no criminal punishments for AI systems that breach legal rules, and any AI system’s capacity for moral judgment still seems murky at best (Solum 1992: 1244–1253). These points are not the case for humans, who may be punished for neglecting or overstepping copyright, and who can reasonably be assumed to have the capacity for moral judgment. While legal personhood akin to that enjoyed by humans may not be appropriate for AI systems, though, other legal protections of these systems have been proposed by scholars who, like our survey participants, are trying to navigate new environments of human–machine communication and coexistence (Jaynes 2020). This navigation is hardly straightforward; as our attribution evolution visualisations (Figs. 5 and 6) show, many participants changed their authorship attributions when provided with new information about the presented text’s complex context of production. Although it is outside the scope of this study to consider precisely how responsibility for, and legitimacy of, computer-generated texts is—or should be—allocated, we believe that considering who or what participants believe the author to be can support such deliberations. Throughout the survey, participants indicated that their understandings of authorship were deeply linked with conceptions of both creative expression and social responsibility.

Our findings show that people may apply and adapt conceptions of authorship to new contexts without resolving the term’s ambiguities—which perhaps adds additional ambiguities. However, participants’ ambivalent views towards conceptions of authorship and their relevance to computer-generated texts bring us back to the more fundamental question raised by one participant: so what? Why does thinking about authorship of computer-generated texts matter, especially when—as indicated by the results above—authorship itself is a murky concept? Participants themselves answered these questions, albeit indirectly. Responsibility and accountability were cited as economic and social components of authorship; creative expression was valued for interpersonal understanding and assertion of self; simply producing a text was for many not enough to justify a claim to authorship, but contributions to text production could be recognised through different titles specific to the kind of contribution (e.g., translator). All of these points are intertwined with one another. Authorship matters because it is about more than just who produced a work. Authorship matters because it acknowledges the extensive social, political, and economic networks that producers exist within and perpetuate. Authorship matters because readers’ understandings of how a text has been produced may influence those readers’ interpretations of that text’s content, authority, and legitimacy (Formosa et al. 2024). As our study results show, though, these understandings may also be informed by a reader’s demographic profile. Perceptions of algorithmic authorship may be subjective and difficult to wholly capture, but this does not mean that they are not worth considering; these pereptions have very real consequences for the ways in which computer-generated texts are received and accepted—or not.

The study documented herein makes one thing very clear: that perceptions of AI authorship are hardly clear at all. As in 2018, participants of our 2025 survey varied in their understandings of what constituted an ‘author’, and who or what might be deemed an ‘author’ of a computer-generated text. While some quantitative results were statistically significant, participants showed continued ambivalence towards attributing specific authorship to a computer-generated text. In their qualitative comments, participants revealed their concerns (or, in some cases, lack thereof) about attributing authorship to an AI system, and shared questions and comments indicating their awareness of the complexities of both computational text production and authorship more generally. Of course, qualitative comments cannot be taken as the entirely authoritative source of participants’ perceptions; just because participants do not include an idea in their commentary does not mean that they are not thinking about that idea. Nevertheless, in this paper we have compared two sets of results—one from 2018 and another from 2025—that show that although sociotechnical circumstances have changed substantially, participants’ perceptions of authorship of computer-generated texts have changed much less so. What this suggests is that ‘authorship’ may not—and perhaps has never been—the most appropriate word to summarise computational text production through natural language generation. ‘Authorship’ may be too evocative, too deeply enmeshed in long-standing cultural imaginaries. However, these cultural imaginaries have themselves been convoluted, with ‘authorship’ meaning different things to different people across different times. As a result of its present and historical complexity, ‘authorship’ as used in discussions of computational text production may actually obscure more than clarify. Nevertheless, it is ‘authorship’ that continues to be the go-to word in discussions of computer-generated texts, and its persistent use warrants reflection on its meaning for readers of those texts. ‘Authorship’ has always been a complicated concept, and it remains so in discussions of computer-generated texts. To end with the words of one of our 2025 survey participants: “New tools, old problems.”