The symbiotic relationship between writing vocations, creative expression, and the development of artificial intelligence (AI) is becoming increasingly evident. A revolution is underway, driven by the remarkable advancements in AI. AI has transformed various industries, including creative domains like writing, art, and music composition. Through sophisticated algorithms and deep learning models, AI systems can generate content that mimics human creativity and expression. The emergence of generative AI marks a paradigm shift in how we conceive, produce, and consume written content. From predictive text applications to predictive analytics, the writer of the future is poised at the intersection of human creativity and machine intelligence. As AI continues to advance more and more, the question arises: Could the need for human writing diminish over time?

Will AI replace authors? Authors, human writing and AI in question

Predictive AI and execution issues

The overall allure of AI lies in its ability to predict, generate, and refine text with unprecedented speed, efficiency, and accuracy. AI tools have become valuable aids for writers, providing inspiration, generating ideas, and even assisting in drafting content. These tools can analyze vast amounts of data, identify patterns, and offer suggestions, augmenting the creative process for human writers. No longer confined to mere spell-checking or grammar correction, AI applications now extend to predictive writing, offering tantalizing glimpses into the future of authorship. But amidst the promise of seamless execution, one truth remains immutable: execution still matters.

"Proofreading AI": the uncertain future of proofreaders

While the specter of writer's block, specific language barriers, and other linguistic errors may vanish in the face of AI assistance, problems still persist as challenges to be overcome. The advent of fact-based developmental editing threatens the traditional role of proofreaders. It's putting the destiny of issues of continuity and consistency all alone in the hands of editors. While proofreaders focus primarily on correcting errors in written material, editors are involved in a broader range of tasks. These include improving the overall quality, clarity, and effectiveness of the text. Up to this day, both roles have been essential in the editing and publishing process and have often complemented each other to produce polished, professional writing. But times are changing.

Book genres and AI-generated books

Writers have always been adept at adaptation. The rise of self-publishing stands as a testament to our resilience in the face of technological upheaval. While AI may excel in certain domains, the realm of fiction remains a bastion of human creativity. Complex genres such as autobiography and historical fiction offer sanctuary from the encroaching tide of automation. Nuance, subtlety, and the human touch defy replication by even the most sophisticated algorithms. The future may herald a new era of human-AI collaboration, but the sanctity of artistic expression surely endures. Anyhow, will readers accept AI-assisted (or AI-generated non-fiction) books? The answer remains uncertain. The hybridization of human and AI creativity offers alluring possibilities, and the future belongs to those who embrace change.

The death of the author: a haunting possibility

You may think that the subtitle is a deliberate reference to the infamous "death of the author" theory of Roland Barthes to introduce some criticism of AI intervention on the basis of postmodern thoughts, but the truth couldn't be further. Ghostwriters, once stalwarts of the publishing industry, now face obsolescence in the wake of AI's ascendancy. They find themselves marginalized as AI technologies continue to advance. Traditional ghostwriting involved a symbiotic relationship between the author and the ghostwriter. The latter lent their expertise to manifest the former's vision. Nowadays, AI tools can, without a doubt, assist authors with these tasks and processes. Consequently, the role of the ghostwriter faces existential threats as AI algorithms increasingly fulfill the demands of content creation, challenging established notions of authorship and creative ownership.

Text detectors

Text detectors are essential tools at the intersection of human writing and AI, designed to identify and analyze textual content for various purposes. These detectors leverage machine learning algorithms to classify, extract, and interpret information from text, enabling tasks such as sentiment analysis, spam detection, and content categorization. By recognizing patterns and semantic nuances within text data, text detectors help writers streamline their research, enhance language comprehension, and ensure content meets specific criteria or standards. With ongoing advancements in natural language processing, text detectors play a pivotal role in refining writing processes, improving communication effectiveness, and fostering greater efficiency in content creation across diverse digital platforms.

AI mistakes vs. human successes

Yet, even as AI promises to revolutionize marketing and productivity, we must remain vigilant to its inherent fallibility. Despite its remarkable capabilities, AI is not infallible. AI fails. Although the very notion of what it means to be a writer is in flux as algorithms encroach upon creative territory once deemed exclusively human, editing, refinement, and the cultivation of nuance remain quintessentially human endeavors. Human writing is not just about generating content; it's about craftsmanship and storytelling prowess. Human writers refine their skills through practice, reflection, and the pursuit of mastery. This is a journey that cannot be replicated by AI alone.

À propos mistakes: AI hallucination

The risks of AI hallucination, in which synthetic intelligence generates attainable but fake information, may be closely related to AI writing. AI writing relies on algorithms that process substantial amounts of textual content to generate human-like content material, but it additionally runs the risk of amplifying misinformation or producing misleading narratives if not nicely monitored. The difficulty of the task lies in ensuring that AI writing structures are educated on dependable information and geared up with strong truth-checking mechanisms to mitigate the propagation of falsehoods and hold consideration within the information they generate. Thus, addressing the perils of AI hallucination is pivotal for reinforcing the integrity and reliability of AI writing applications in various domains.

Who owns AI-generated content?

In the complex landscape of copyright and intellectual property, the emergence of AI-generated work poses novel challenges and raises fundamental legal questions about ownership, authorship, and accountability. Unlike traditional creative processes where human authors or creators assert clear ownership over their works, the landscape becomes murkier when artificial intelligence algorithms autonomously generate content. One of the central inquiries revolves around determining who should be credited as the creator or owner of AI-generated works. In traditional copyright law frameworks, the creator or author is typically granted exclusive rights to reproduce, distribute, and display their work. However, when AI systems autonomously produce content, there's often no human author in the conventional sense. Instead, there are programmers who design and train the algorithms, but their role in the creative process may be tangential compared to the machine's contribution.

The question of ownership becomes even more convoluted when considering the various forms of AI involvement. Some AI systems merely assist human creators in generating content. Others operate autonomously, making decisions and creating content without direct human intervention. In such cases, distinguishing between the contributions of the AI system and the human designer becomes increasingly challenging. Furthermore, proving AI infringement or defending against accusations of automated plagiarism presents significant legal hurdles. Traditional methods of establishing infringement, such as comparing similarities between works and assessing the intent of the alleged infringer, may not easily apply to AI-generated content. Additionally, the sheer volume of content produced by AI systems exacerbates the difficulty of monitoring and enforcing copyright protections.

So why is authenticity important?

We can read and hear a lot about how the pressure to conform to online personas and social media standards complicates our understanding of authenticity. People often present curated versions of themselves online, filtering out imperfections and projecting idealized images to garner validation and acceptance. This curated authenticity, though, often falls short of genuine expression. This leads to a paradox where the more we strive to appear authentic, the more artificial our interactions become. The question of human authenticity gets murkier when AI comes into the picture. In this digital landscape, the erosion of authenticity seems inevitable as AI's capabilities burgeon. The very essence of human expression, once cherished for its unique insights and imperfections, now confronts a formidable competitor in artificial intelligence. The allure of efficiency and productivity offered by AI systems can overshadow the nuances of human creativity. This is a doorway to a homogenization of voices and ideas.

Human writing, AI and emotions: empathy, the Savior

While AI can replicate certain aspects of human creativity, it lacks the depth of human experience and emotion. Human writers possess a unique ability to infuse their work with authenticity, empathy, and personal perspective, resonating with audiences on a profound level. So, despite the advancements in AI, human writing and thinking remain indispensable. The human capacity for compassion, imagination, and critical thinking fuels the evolution of literature, philosophy, and culture. As stewards of the written word, human writers play a vital role in shaping the narrative of humanity's collective journey. As writers, we must learn to navigate this brave new world, where the tools and processes of our craft undergo profound transformation.

Fighting the dangerous big data: towards data democratization

"Machine drift", aka surrender to algorithms

Before we delve into the topic of data concentration, algorithmic opacity, and data democratization, firstly, I would like to give a concrete example of how algorithms and machines born from dangerous big data can take control of our lives. The question increasingly arises: Has AI already taken over? The nihilistic concept of "machine drift," coined by American technology columnist Kevin Roose, underscores the perils of relinquishing control to opaque algorithms and data-driven decision-making.

[E]ventually, I began feeling that surrendering my daily decisions to machines wasn’t making me happier or more productive. Instead, it was turning me into a different person—a shallower one, with more fixed routines and patterns of thought, and an almost robotic predictability in my daily life. I started calling this feeling “machine drift,” and I first noticed it happening to me a few years ago. (Roose 2022 [2021], 81)

Roose's introspection, based on his own experience, illuminates the insidious nature of machine drift. Initially seduced by the allure of automation and efficiency, he found himself ensnared in a web of predictability and routine. Instead of enhancing his productivity or fostering a deeper sense of fulfillment, surrendering to machines left him feeling hollow and diminished. In our quest for optimization and convenience, we risk sacrificing the very essence of what makes us human: our capacity for spontaneity, creativity, and genuine connection. Machine drift erodes our agency, reducing us to mere spectators in our own lives. We become slaves to algorithms, tethered to predetermined patterns of behavior and thought.

Data concentration concerns

Data concentration and algorithmic opacity are pivotal issues in the landscape of AI-driven systems. Especially when considering their implications for human writing and communication. The term "data concentration" refers to the centralization of vast amounts of data in the hands of a few powerful entities. In the realm of human writing and AI, data concentration means that a handful of tech giants and platforms possess an immense amount of textual data generated by users worldwide. This concentration of data can lead to monopolistic control over information, limit the diversity of perspectives, and potentially stifle creativity. Moreover, it raises privacy concerns as these corporations amass extensive knowledge about individuals' writing styles, preferences, and linguistic patterns.

Algorithmic opacity instead of algorithmic transparency

Algorithmic opacity exacerbates these concerns by shrouding the decision-making processes of AI algorithms in secrecy. When it comes to human writing, algorithmic opacity means that the mechanisms determining which pieces of content are prioritized, amplified, or suppressed remain obscure. So, this is the antonym of the desired algorithmic transparency. Writers and users may find it challenging to discern why certain texts receive more visibility or engagement than others. This leads to a lack of transparency and accountability in the dissemination of information. This opacity can perpetuate biases and misinformation, undermining the integrity of written communication in the digital sphere.

What is data democratization?

Addressing these challenges requires concerted efforts to promote data decentralization, enhance algorithmic transparency, and establish mechanisms. We must verify the authenticity of written content in an AI-driven world! Initiatives such as open data sharing protocols, algorithmic accountability frameworks, and authenticity verification tools can help mitigate the risks associated with the aforementioned concerns. These initiatives foster a more inclusive, transparent, and trustworthy environment for human writing and communication in the digital age. Supporting data democratization through accessible data repositories and collaborative platforms can empower individuals and communities to leverage information for innovation, research, and societal progress. By democratizing access to data and ensuring equitable participation in its creation and utilization, we can harness the collective intelligence of diverse perspectives. Driving meaningful advancements and addressing systemic inequalities in data access and utilization will be easier this way.


The journey ahead of unifying human writing and AI may be fraught with challenges, but it is also replete with boundless opportunities for those bold enough to seize them. In the age of AI, the writer of the future is not an obsolete relic of a bygone era. The writer is a pioneer charting a brave new course toward literary innovation. Rather than fearing obsolescence, human writers can embrace collaboration with AI as a means of amplifying their creativity and expanding their repertoire. It's also good to remind ourselves that, as with any technological innovation, the hype surrounding AI may eventually cool, just as the buzz around wearable technology has waned. As AI capabilities expand, writers have the opportunity to explore new narrative forms, collaborate with AI systems, and push the boundaries of storytelling while still devoting time to their so-called "human hours." If we get entangled in the World Wide Web and technology's virtual reality in general, leveraging Roose's concept of the "human hour" can make us feel grounded again:

Every weekday, around the same time—for me, it’s usually five to six p.m.—I try to spend at least an hour away from screens, doing something I genuinely enjoy doing, like playing tennis, cooking, or taking my dog for a run. Choosing purely optional activities is key—I don’t cross items off my todo list during human hour or take care of household chores. The point is to spend an hour a day getting back in touch with my own needs and priorities by doing things that make me feel more human, while escaping the web of incentives and invisible forces that tug on me throughout the rest of the day. (Roose 2022 [2021], 94)


Kevin Roose. 2022 [2021]. Futureproof: 9 Rules for Humans in the Age of Automation. New York: Random House.