Creativity at a Crossroads

For centuries, creativity was viewed as a distinctly human gift, an ineffable spark linking imagination and expression. Yet the twenty-first century has disrupted this narrative. Artificial intelligence (AI) is no longer confined to industrial automation; it now crafts poetry, designs advertising campaigns, edits films, and composes music. As Marcus du Sautoy (2019, The Creativity Code) argues, algorithms are increasingly seen not only as tools but as participants in the “creative conversation” between humans and machines.

Today’s debates revolve around a central tension: is AI a collaborator or a competitor? If creativity is redefined as an interplay of human intentionality and machine-generated suggestion, authorship itself must be reconsidered.

Creativity has never been static, and its definitions shift alongside cultural and technological change. AI challenges us to confront the artificial boundaries we place around authorship, forcing a reconsideration of the human-machine relationship in imaginative work. One must ask: are we witnessing the erosion of originality, or the expansion of its possibilities?

It seems that the current unease about AI’s encroachment on creativity echoes a much older anxiety: the fear of losing ownership over the ineffable. Yet, what if the very condition of creativity is its permeability, its willingness to be unsettled by new interlocutors? Perhaps the true “crossroads” is not human versus machine, but whether we choose to approach this hybridisation with suspicion or curiosity.

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From Pen to Processor: A Brief History of Creative Tools

The evolution of creative tools has continually reshaped human imagination and expression. In ancient times, Plato’s Phaedrus expressed concern that writing would erode memory, as individuals might rely on external marks rather than internal recollection (Plato, trans. 1997). This scepticism toward new technologies as potential threats to cognition echoes throughout history, including contemporary anxieties about artificial intelligence (Rooted Edu, 2025).

The invention of the printing press in the fifteenth century marked a transformative moment in human creativity, enabling the mass production of books and the widespread dissemination of ideas. Literacy rates rose, Renaissance humanism spread, and access to knowledge became far less restricted (Eisenstein, 1979). By the nineteenth century, photography and film introduced new media for artistic expression, challenging conventional definitions of originality. Walter Benjamin’s seminal essay, The Work of Art in the Age of Mechanical Reproduction, critically examines how mechanical reproduction diminishes the “aura” of unique artworks, altering their cultural significance and reception (Benjamin, 1936).

In the digital age, tools such as word processors, graphic design software, and social media platforms have further transformed creative practices. Word processors streamlined writing and editing, graphic design software expanded the possibilities for visual storytelling, and social media democratized publishing, allowing creators to reach global audiences instantaneously (McLuhan, 1967; Negroponte, 1995). Shoshana Zuboff’s concept of “surveillance capitalism” highlights how digital platforms gather massive amounts of personal data to predict and influence user behaviour, raising ethical concerns about privacy and autonomy (Zuboff, 2019; Fast Company, 2023).

Artificial intelligence represents the latest frontier in this lineage of creative tools. AI systems now generate music, visual art, and written content that often rivals human output. This development challenges conventional notions of authorship and creativity, calling for a reevaluation of the boundaries between human and machine-made art (du Sautoy, 2019; Zephyr, 2023). As AI continues to advance, it raises questions about originality, intention, and the ethical responsibilities of creators working in hybrid human–machine environments.

Looking back, the arc of history shows a remarkable pattern: every so-called dilution of art, whether by writing, photography, or digital reproduction, has, paradoxically, expanded the human imagination. The discourse of loss rarely holds when tested against the record of practice. If AI unsettles us today, it is not because it diminishes creativity, but because it forces us to confront how fragile our definitions of originality truly are.

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AI as Co-Creator or Tool? Rethinking Authorship

The question of whether AI functions as co-author or tool remains central to contemporary debates on creativity and law. The crux lies in whether machines, devoid of consciousness and intention, can take on creative agency, or whether they remain advanced instruments mediated by human direction. The central question remains: is AI a co-author or simply a sophisticated instrument?

AI and Authorship: Theoretical Foundations

Michael A. Boden argues that true creativity requires intentionality, something AI, lacking subjective experience, cannot possess (Boden, 2004). Yet Janelle Shane suggests that AI acts as a “strange mirror,” generating unpredictable combinations that provoke human creativity in novel ways (Shane, 2019). Thus, while AI does not “intend,” its capacity to influence human authorship reframes creativity as co-constituted between human mind and algorithm.

United States: Legal Certainty Through Human-Authorship Mandate

Under U.S. law, authorship remains strictly human. In Thaler v. Perlmutter, Stephen Thaler’s attempt to name his AI (“Creativity Machine”) as the author of a piece titled A Recent Entrance to Paradise was rejected by the U.S. Copyright Office (USCO) and later reaffirmed by both a federal district court and the U.S. Court of Appeals for the D.C. Circuit in March 2025—on the grounds that copyright requires a human author (D.C. Cir. 2025) (Justia News, CNBC). Similarly notable was the case involving Kristina Kashtanova’s Zarya of the Dawn: while the text and creative arrangement were copyrightable, the AI-generated images themselves were not (Wikipedia). The USCO’s 2025 guidance emphasises that AI-generated material “prompted” by humans requires additional human modification or arrangement to qualify for protection, and all assessments remain case by case (Synthtopia, Designboom, AP News).

Europe and the UK: Legal Gaps and Patent Parallels

In the European Union, the recently passed AI Act has been criticized for creating a “devastating” copyright loophole, text and data mining exemptions, originally intended for non-commercial research, are now being leveraged by large tech firms in ways that may undermine creator rights (The Guardian). Meanwhile, UK patent law mirrors similar principles: Thaler’s attempt to list DABUS as a patent inventor failed in the UK Supreme Court. As under U.S. law, only natural persons can be recognised as inventors, reinforcing the human-centric framing of intellectual ownership (iclg.com).

Poland: Copyright Through Human Creativity, Legal Ambiguities Remain

Under Polish copyright law, aligned with EU norms, only works that originate from a natural person’s creative effort enjoy protection. AI-generated content lacking sufficient human involvement is unlikely to be protected; modest human action, such as prompt selection or output editing, may tip the balance, though the line between tool usage and authorship remains legally unsettled (Dudkowiak & Putyra). Contracts and organisational IP policies are critical to navigating this uncertainty in commercial and creative contexts.

Comparative Insights

• Intentionality and Authorship: Across jurisdictions, the presence of human intent remains the decisive factor in determining authorship.
  
• Legal Precedence vs. Legislative Lag: The U.S. provides a clearer judicial pathway, with established case law. Europe and Poland face evolving regulation and gaps in clarity.
  
• AI as Amplifier, Not Author: All systems affirm that AI can assist and augment, but cannot originate authorship independently

It can be revealed that we continue to measure authorship through the lens of intention, as though meaning were a possession rather than a negotiation. AI unsettles this model not by “thinking” like us, but by showing us how much of our creativity already depends on recombination, influence, and systems larger than the self. The law may insist on locating the human author, but intellectually, the ground beneath that figure is already trembling.

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AI Tools for Content Creators – Who Uses Them and Why?

Artificial intelligence is no longer niche software; it is embedded across industries. From classrooms to marketing firms, creators use AI to accelerate workflows, polish style, and expand imagination.

Here is a comparative overview:

Tool	Popular Users	Uses	Sources
OpenAI ChatGPT	Marketers, bloggers, educators	Drafting articles, brainstorming, lesson planning	Tech & Learning; Reddit
Jasper.ai	Content marketers, agencies	SEO-driven copy, branded campaigns	Medium; Switcher Studio
Copy.ai / Writesonic	Small businesses, freelancers	Ads, emails, product descriptions	Analytics Vidhya; exemplary.ai
Grammarly	Writers, academics	Grammar, tone, plagiarism checks	Medium; identicalcloud.com
Wordtune	Writers seeking variety	Paraphrasing, style adjustment	Wikipedia; Gennova
Surfer SEO / Frase	SEO experts	Keyword research, optimisation	Analytics Vidhya; How To Make Money Online
Canva (Magic Write)	Educators, entrepreneurs	Visual + text content creation	Medium; exemplary.ai
Midjourney / DALL·E	Designers, influencers	AI-generated images, branding	Reddit; Wikipedia
Lumen5 / InVideo / Runway	Social media creators	Video editing, animations	Analytics Vidhya; singularitybyte.com
Synthesia / Colossyan	Corporates, trainers	AI avatars for presentations	Elastic Email; Wikipedia
ElevenLabs / Murf.ai / Play.ht	Podcasters, video producers	Voiceovers, audiobooks	singularitybyte.com
Adobe Firefly	Designers, photographers	AI-assisted, IP-aware image generation	Vox
Wix AI Website Builder	Small businesses	AI-powered web design	TechRadar
Notion AI	Teams, researchers	Summarisation, note-taking	The Verge

What emerges from this overview is not simply a catalogue of tools, but a shifting ecology of creative practice. Each platform subtly redefines what it means to “create”: speed replaces deliberation, optimisation replaces intuition, and collaboration with code becomes a tacit norm. As I observe these patterns, I am struck less by the novelty of AI than by its quiet domestication, how swiftly it has moved from experiment to infrastructure, and how seamlessly we have come to rely upon it without fully reckoning with the implications.

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Examples in Practice: Humans and Machines at Work

Concrete examples reveal the growing hybridisation of authorship across diverse creative domains and jurisdictions:

Literature
In 2016, an AI-supported short story titled The Day a Computer Writes a Novel (“Konpyūta ga shōsetsu o kaku hi”), co-created by researchers at Future University Hakodate and an AI, passed the first round of Japan’s Hoshi Shinichi Literary Award competition. Human researchers provided plot parameters and vocabulary, while the AI aggregated these inputs into coherent prose, an impressive demonstration of collaborative narrative generation (Engadget 2016; ScienceAlert 2016) (engadget.com, ScienceAlert). More recently, acclaimed Japanese novelist Rie Qudan acknowledged using ChatGPT to compose approximately 5% of her prize-winning novel Tokyo Sympathy Tower, specifically crafting the AI’s dialogue—an intentional and thematic choice rather than mere expedience (Wikipedia).

Journalism
In the United States, the Associated Press (AP) has relied on Automated Insights’ Wordsmith, a natural language generation platform, to transform raw earnings data into narrative summaries. This automation has enabled AP to produce around 4,400 quarterly corporate earnings articles, over a tenfold increase, while freeing journalists to pursue investigative reporting (The Guardian 2014; Poynter 2015; BestPractice.AI 2020) (The Guardian, poynter.org, Best Practice AI, Wikipedia). AP’s cautious optimism is reflected in its ongoing guidelines, which restrict AI-generated material from being published unsourced by humans and instead use AI for ideation, not authorship (AP News 2023) (AP News). In the UK, the Press Association has been experimenting with “robo-journalism” to generate succinct sports and election reports, citing increased accuracy and speed without job losses (WIRED UK 2016) (WIRED). Broader automated journalism initiatives include Reuters’ financial news automation (via Tracer and Quakebot) and data-driven athletic reporting, highlighting the global trend toward mechanised news generation (Wikipedia—Automated Journalism) (Wikipedia).

Film and Visual Media
In the United States, generative video tools such as Runway’s Gen-1 and Gen-2 models are reshaping filmmaking. Used even in major productions like Everything Everywhere All at Once, these technologies allow creators to generate complex visual sequences from text or imagery, a filmmaker’s studio, condensed into the browser (Axios 2023) (Axios, Wikipedia). Academic innovations like Script2Screen unify scriptwriting with live audiovisual generation, enabling writers to iteratively sculpt scenes with control over gestures, emotion, and camera angles, further evidence that AI complements rather than replaces manual direction (ArXiv 2025) (arXiv).

Education
Educators worldwide have integrated ChatGPT and similar tools into lesson design, adapting materials or brainstorming prompts. Though widely used, such AI interventions spark intense debate over academic integrity, the authenticity of student work, and equitable access, a nuanced balancing act between utility and ethical responsibility.

Music
Compositional tools like Amper and AIVA illustrate how AI composes ambient or soundtrack music, often indistinguishable from human-generated scores. These platforms are widely used in media and film yet raise questions about emotional authenticity, creative agency, and the value of human nuance in music.

Regional Snapshot: Poland and Europe
While large-scale AI adoption in journalism and film remains more pronounced in the US and UK, early Polish initiatives mirror these trends. Polish news outlets and universities have begun experimenting with AI-assisted text generation for routine reporting and academic summarisation, though legal frameworks rooted in EU and Polish copyright law demand substantial human input for authorship, making full AI replacement unlikely in the near term. AI in Polish creative sectors remains primarily assistive, requiring careful human curation and oversight.

These vignettes reveal a shared dynamic: AI seldom displaces human intelligence but enhances and redirects it. Whether in Tokyo or New York, the point is not that AI can “write,” but how it prompts us to reimagine what it means to the author. Creation today is less an act of solitude than a choreography, a human-machine duet shaped by context, intention, and judgement.

What emerges from this overview is not simply a catalogue of tools, but a shifting ecology of creative practice. Each platform subtly redefines what it means to “create”: speed replaces deliberation, optimisation replaces intuition, and collaboration with code becomes a tacit norm. As I observe these patterns, I am struck less by the novelty of AI than by its quiet domestication, how swiftly it has moved from experiment to infrastructure, and how seamlessly we have come to rely upon it without fully reckoning with the implications.

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The Risks and Ethical Crossroads

Plagiarism and Originality

MIT researcher Kate Darling aptly notes, “machines remix, but humans read meaning into the remix,” reminding us that AI’s outputs are inherently derivative (Darling, 2021). This resurfaces deeper questions about authorship and ownership: to what extent is remixing tool usage versus theft? In the US, legal standards still demand substantial human creative input for copyright (USCO, 2025), while the UK and EU maintain similar thresholds, though they are inconsistently enforced. In Poland, copyright law clearly restricts protection to works with substantial human involvement, reinforcing the importance of originality in layered AI-human processes.

Bias and Cultural Homogenisation

AI systems reflect and amplify biases embedded in their training data. Caliskan et al. (2016) found that standard word embedding models encode human-like gender and racial stereotypes, underscoring how AI can perpetuate historical inequalities (arXiv). More recently, a study of GPT-4-o-mini revealed that AI-generated narratives across 236 countries followed a near-uniform plot structure, minor conflict resolved via tradition, which illustrates how AI enforces narrative conformity rather than cultural specificity (arXiv). Large Language Models show skewed value alignment towards Anglo-Protestant cultural norms, though ‘cultural prompting’ can improve alignment for many nations (arXiv). In Europe, these biases risk flattening diverse cultural expressions unless models are trained with broader representation. Poland, with its distinct cultural identity, would benefit from AI models and prompts that intentionally preserve local nuance.

Labour Displacement and the Freelance Market

AI’s disruption of creative labour is increasingly evident. A Brookings study (2025) found that freelancers in AI-exposed fields lost an average of 2% of contracts and 5% of earnings post-AI introduction (Brookings). A comprehensive study by Imperial College, Harvard, and DIW confirmed a 21% drop in freelance jobs in writing and coding, and a 17% decrease in design-related roles following the arrival of ChatGPT and AI image tools (c3.unu.edu, Reddit). In Poland, job postings for copywriters and content creators fell sharply in early 2024—copywriter positions down by 64% and content roles by 88% (LTV Reports). Still, data suggests a shift: demand is moving from easily automated tasks to more advanced, human-centric roles involving critical thinking, emotional intelligence, and creative oversight (Complexity Science Hub).

Cultural Homogenisation Revisited

Building on Benjamin’s concerns, the mechanisation of creativity now occurs at AI scale, risking the erosion of cultural specificity through algorithmic standardisation. When generative systems condense narratives into safe, banal scripts, they undercut the vibrancy of diverse storytelling traditions. This matters greatly across pluralistic regions, far more than the uniform output of a large language model would suggest.

These risks are intertwined. Plagiarism, bias, displacement, and homogenisation are not discrete problems, but facets of a broader ethical architecture that must shape how we design, deploy, and regulate AI in creative work. Across continents, from Silicon Valley to London to Warsaw, the real test lies in whether we can harness AI without flattening the human texture of culture, creativity, and livelihood.

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Beyond the Author

To write with AI is not to surrender authorship but to renegotiate it. Creativity has always been hybrid – between memory and imagination, solitude and collaboration, tradition and innovation. AI merely makes this hybridity explicit.

The “author” of the twenty-first century may no longer be a solitary genius with a quill, but a curator of systems, orchestrating algorithms, prompts, and cultural contexts into new forms of expression.

Just as code-switching reveals identity in motion, AI-assisted authorship reveals creativity in flux. To be a hybrid author is to dance with unpredictability, to allow machines to provoke but not dictate. If code-switching taught us to hear the rhythm between languages, then AI teaches us to hear the rhythm between minds, organic and synthetic.

The future of creativity will not be either/or but both/and. The question is not whether AI will write, but whether we will learn to read the hybrid text it produces. To do so requires a new mode of perception — a capacity to see authorship differently, to grasp its hybrid nature without anxiety. 

In short: to be eng-sighted.

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