AI Writing in 2026: Your Complete Guide to Staying Creative, Authentic, & Informed

Discover how to navigate AI writing with confidence and integrity. Learn the ethics, best practices, tools, and strategies to establish your own AI-driven writing process while staying true to your creative vision.

By Julie Tyler Ruiz, PhD

Introduction. Navigating the AI writing landscape in 2026

By now, many of us are using generative AI for a variety of writing tasks or at least experimenting with AI to see what it can do, from automating content or generating pages of material in a matter of seconds. As this technology changes how writers tell stories, compose emails, conduct research, and even publish books, it's important to think critically about what AI means for human expression.

That's where I come in.

As a lifelong creative writer, author, blogger, mentor, and content creator with a vested interest in the future of storytelling, I've designed this article to guide you through the philosophical and practical aspects of AI writing, including:

• The ethics of using it

• Using AI to write: what you gain and lose

• How to prompt AI effectively to get the output you want

• Popular AI tools and events

• AI in publishing

I’ll also share my personal and professional views on AI in writing, where AI fits into my writing process and where it doesn’t, and my recommendations for anyone who wants to write creatively or professionally in 2026 and beyond. That way you can navigate the growing influence of AI in our craft and forge a path that aligns with your writing dream.

Chapter 1. What is AI writing?

At its simplest, AI writing refers to using generative AI technologies such as ChatGPT, Claude, or Gemini to generate or assist with written content. That might include sentences, paragraphs, articles, stories, scripts, or even entire books.

But by 2026, "AI writing" has become a much broader category than simply asking a machine to generate text.

Modern AI systems can analyze writing, search for current information, work with uploaded documents, compare sources, organize research, interpret images and other media, maintain context across a project, and in some cases use tools or carry out multi-step tasks. Depending on the system and how you use it, AI may participate in many stages surrounding the writing process without actually writing the finished work.

So throughout this guide, when I discuss AI writing, I'm referring broadly to the use of AI for generation, analysis, research, retrieval, editing, organization, and other tasks that support the creation of written work [1].

How generative AI works.

When you enter a prompt into a generative AI system, a large language model processes your instructions and generates a response based on patterns it learned during training. The exact sources and methods used to train models vary among AI companies and models, and training data shouldn't be understood simply as "everything available on the internet" [2].

There's another important distinction: what a model learned during training isn't necessarily the same thing as the information it can access while you're using it.

Some AI systems can now search the web, retrieve current information, analyze documents you provide, access connected sources, or use other tools. That means an AI response might draw from the model's existing capabilities, information retrieved specifically for your request, material you've supplied, or some combination of these [1].

For writers, you don't need to understand the underlying computer science to use these tools thoughtfully. But it helps to understand that asking AI to generate something from its general knowledge and asking it to research something from current sources are different processes—and should inspire different levels and types of verification.

AI generation can involve very different levels of human input.

The extent of human involvement in the writing process can vary significantly. Let's compare two examples:

Minimal human involvement:

You could tell an AI tool, "Write a love story in 1,000 words," and it could generate a complete piece of writing almost entirely on its own. This represents a high degree of AI involvement in the actual creation of the story.

Active human involvement:

To take a more active role, you could begin with your own idea and use AI to help you explore it. For example:

"I want to write a love story that's only 1,000 words. The lovers should be over the age of 70 and live in a remote area where it's hard to meet someone you're compatible with. The terrain is difficult to traverse, and the Wi-Fi or mobile signals are weak, at best, making it difficult for even passionate lovers to communicate. Can you suggest a few plot possibilities for me to consider?"

From there, you could evaluate the suggestions rather than simply accepting them. Perhaps one idea sparks something better. You might develop that possibility yourself, ask AI to identify complications you haven't considered, or reject everything it suggests and discover that your disagreement has clarified what you actually want to write.

Alternatively, you might ask AI to examine something you've already created:

"Here's the scene where the two lovers meet. Read it for character motivation and identify any moments that don't feel believable. Don't rewrite the scene."

In this case, AI hasn't generated the story at all. It's being used to analyze human-authored material.

These distinctions matter. Two writers can both say, "I used AI to write this," while describing fundamentally different creative processes.

AI research goes beyond asking a chatbot what it knows.

Another major development is the rise of AI-assisted research and source synthesis.

Earlier uses of generative AI often involved asking a chatbot a question and receiving an answer based largely on the model's existing knowledge. Increasingly, AI systems can undertake more involved research: searching for current sources, examining multiple documents or webpages, comparing findings, identifying disagreements, and synthesizing information into a report with citations [3].

For writers, this creates an important distinction between:

• Brainstorming or exploring a subject with AI based on its general capabilities
• Using AI to retrieve, analyze, and synthesize specific current sources

The second approach can make research considerably faster and help writers navigate large amounts of information. But it doesn't eliminate the writer's responsibility to evaluate the evidence. AI can misunderstand a source, overlook important context, prioritize weak information, or produce a citation that doesn't fully support its claim [4].

When I use AI for research, I still want to know: Where did this information come from? Is the source credible? Does the original source actually say what the AI claims it says? Is there another perspective or piece of evidence I should consider?

AI can help us move through information. It shouldn't relieve us of the responsibility to understand what we're ultimately claiming.

So, what counts as AI writing?

There's no longer one simple answer.

AI can write a complete story from a sentence-long prompt. It can also analyze a story you've spent three years writing without contributing a word to the manuscript. It can help locate research, interrogate an argument, organize notes, edit a paragraph, identify patterns across a large collection of material, or carry out parts of a larger workflow.

That's why I find it more useful to think in terms of degrees and types of AI involvement rather than dividing writing neatly into "AI" and "human."

The more important questions are: What is the AI actually doing? What is the writer doing? Where are the ideas, judgment, language, and creative decisions coming from?

Those are the distinctions we'll explore throughout this guide.

Chapter 2. Ethics of AI writing

Along with the speed, efficiency, and creative control AI offers, this technology also introduces complex ethical dilemmas. What does it mean for human creativity when machines assist, or even take over, the writing process? How do we ensure that AI-generated content upholds standards of originality, fairness, and integrity?

Let's explore specific ethical concerns around AI:

1. AI slop and the competition for human attention

The ease of generating content with AI has led to an influx of low-quality, formulaic writing often called "AI slop." But the problem goes beyond quality. AI has dramatically lowered the time and effort required to produce content, making it possible to generate articles, books, social posts, images, and videos at enormous scale. Human attention, however, remains limited. Recent research on AI slop describes a digital culture in which content can become increasingly disconnected from meaningful communication and optimized instead to capture attention at scale [5].

For writers, this raises an important ethical question: Just because we can generate more content, should we? Our responsibility may extend beyond avoiding plagiarism and misinformation to creating work that is genuinely worth another person's time and attention.

• How can writers use AI to create more thoughtfully rather than simply create more?

• Does generating large quantities of disposable content make it harder for meaningful human work to find an audience?

• What responsibility do writers and publishers have to produce work worthy of the time and attention they ask readers to give it?

2. Plagiarism and attribution

AI tools generate content by analyzing vast amounts of existing material, sometimes producing outputs that closely resemble copyrighted works or other people's writing [4].

• Who is responsible when AI-generated content unintentionally mirrors someone else’s work?

• Should AI-assisted writing require disclosure or attribution?

3. The use of others' material in AI training

Many AI models are trained on publicly available content, including material protected by copyright. This raises concerns about whether AI-generated text is fair use or an unauthorized derivative of human-created works [2][4].

• Should AI companies be required to compensate or credit original creators?

• How can writers ensure their work isn’t unknowingly contributing to AI training without consent?

4. Diminished human creativity and critical thinking

When writers rely too heavily on AI, they may bypass critical thinking, deep reflection, and the personal creative journey that makes writing meaningful.

• Is AI a tool for enhancing creativity, or does it risk replacing human ingenuity?

• How can writers strike a balance between AI assistance and their own creative instincts?

5. Bias in AI-generated content

AI models inherit biases from the developers coding them and the data they’re trained on, which can lead to outputs that may not be completely objective or factual. In the worst cases, bias in AI-generated content may perpetuate harmful stereotypes, spread misinformation, or promote unbalanced perspectives [4].

• How can writers critically assess AI-generated content for bias and accuracy?

• What responsibility do writers have to fact-check AI-assisted work?

6. Displacement of jobs, including writing careers

AI-generated content is already being used in journalism, marketing, and creative writing, raising concerns about whether human writers will be replaced across industries.

• How can writers adapt to a world where AI is increasingly used for content creation?

• Is it possible for writers to future-proof their careers?

• What ethical considerations should businesses take into account when using AI instead of human writers?

7. The use of AI in education

AI can generate essays, research summaries, and even creative writing pieces, posing challenges for academic integrity and original thought.

• Should students be allowed to use AI in writing assignments, and if so, how?

• How can educators foster genuine critical thinking while acknowledging the role of AI?

8. Reader expectations and transparency

Readers may not always be aware when they’re consuming AI-generated content, which raises questions about authenticity and trust [4].

• Should authors even consider using AI to write a book?

• Should authors of AI-generated books, articles, or scripts disclose their use of this technology?

• Does AI-assisted writing change the way readers connect with a piece of work?

9. The environmental cost of AI at scale

Generative AI may feel intangible when we're typing prompts into a screen, but the technology depends on physical data centers that require substantial amounts of electricity and infrastructure to operate. According to the International Energy Agency, electricity consumption from AI-focused data centers grew 50 percent in 2025, even as improvements in hardware and software have made individual AI tasks considerably more energy efficient. More demanding uses, including AI reasoning, agents, and video generation, can also require far more energy than simple text generation [6].

This doesn't necessarily mean writers should feel guilty every time they ask AI a question. But it does add another dimension to responsible use, particularly when the same technology that allows us to generate unlimited content also makes it easy to consume computing resources without thinking about the physical infrastructure behind them.

• How can writers use AI intentionally rather than generating large amounts of unnecessary or disposable content?

• Should environmental impact factor into our decisions about when using AI provides meaningful value?

• What responsibility do AI companies have to improve efficiency and disclose the environmental costs of their systems?

Chapter 3. Using AI to write: what you gain and lose

AI writing tools offer undeniable benefits:

• Speed and scalability: AI can produce content in minutes that might take a human writer hours or even days. This makes it easier to meet tight deadlines or explore multiple versions of an idea quickly.

• Error detection and readability improvements: AI tools can flag grammar mistakes, awkward phrasing, and structural weaknesses, making it easier to produce polished, professional writing.

• Generating ideas and overcoming writer’s block: AI can suggest ideas, outlines, and even entire passages, helping writers break through creative blocks and find new angles on a topic.

• Consistency in tone and style: AI can help maintain a consistent voice, particularly useful in business or content marketing.

With these advantages come trade-offs. The writing process is more than just putting words on a page; it’s a journey of discovery, reflection, and refinement. When we offload too much of the heavy lifting onto AI, something essential is lost:

• The depth of the writing process: Traditional writing involves brainstorming, trial and error, rewriting, and deep reflection. These steps help writers develop their critical thinking, storytelling instincts, and self-awareness. AI speeds past these phases, potentially shortchanging growth and critical thinking.

• Intuitive discovery and creativity: Writing is often a process of finding meaning through exploration. Generating content with AI is efficient, but doing so may mean you sidestep the organic, unpredictable nature of human inspiration and miss out on the experience of intuitive leaps and creative connections.

• Personal investment and emotional depth: Writing is more than just conveying information. It's a way to explore the human condition, through your unique voice, perspective, and lived experience. Over-reliance on AI may mean losing touch with the wealth of insight you have inside.

• Discernment: Struggling with words, revising passages, and making tough creative choices are part of what shapes a skilled writer. Outsourcing to AI the struggles and decision-making inherent to the writing process potentially weakening a writer’s ability to evaluate and improve their own work.

• The organic development of ideas: AI presents fully formed sentences instantly, but real thinking happens through iteration. When you wrestle with ideas, challenge them, and refine them over time, they mature and reflect your highest potential. Rushing toward AI-assisted conclusions and outcomes may rob you of the experience of witnessing how your skills and ideas evolve.

Chapter 4. AI prompt writing: best practices

As AI models have become more capable, effective prompting has become less about finding the perfect combination of words to unlock a good response. Clear instructions still matter, but increasingly, so does context: the information, examples, source material, previous conversations, project history, and constraints you give AI to work with [7].

This broader approach is sometimes called context engineering. For writers, you don't need to understand the technical side of the term. The practical takeaway is simple: instead of obsessing over writing the perfect prompt, think about what AI needs to know to help you well [7].

That might include passages from your manuscript, examples of your voice, research sources, information about your audience, decisions you've already made, previous drafts, or a description of where you are in the writing process.

The goal isn't to give AI everything. More information isn't automatically better. The goal is to provide the most relevant information for the particular job you're asking it to do [7].

Here are some updated best practices for prompting AI in 2026:

1. Start with a clear goal.

Before turning to AI, take time to determine what you actually want it to help you accomplish. What's the purpose of the task? Where are you in the writing process? What kind of assistance would be useful right now?

You don't necessarily need an elaborate prompt. As AI models become better at understanding natural language and complex instructions, a clear assignment can be more effective than a paragraph stuffed with unnecessary directions [8].

Examples:

• Vague: "Help me with this chapter."
• Clear: "I've drafted this chapter, but I'm concerned that the middle drags. Read it for pacing and identify the three places where reader interest is most likely to drop. Don't rewrite anything yet."

2. Build the right context.

Think beyond the individual prompt to the larger body of information AI has available while helping you. If you're working on a novel, for example, that context might include character profiles, earlier chapters, your synopsis, notes about the story, and decisions you've already made. For a nonfiction article, it might include your intended audience, previous articles you've written, research sources, interview transcripts, and your own expertise or experience [7].

You can also provide examples of your writing when voice matters rather than simply telling AI to "sound like me."

Examples:

• Limited context: "Help me make this introduction sound more like my writing."
• Better context: "Here are three passages from other articles that represent my natural voice. Compare them with this new introduction. Tell me where the introduction sounds noticeably different and why. Don't rewrite it yet."

The purpose of context isn't to hand over more of the writing process. It's to give AI enough information to respond to your particular project rather than generating the kind of generic answer it might give anyone.

3. Give AI a specific job.

Instead of assigning AI a broad identity, like "Be my book editor" or "Be an expert writer," define the particular job you want it to perform.

Ask it to examine character motivation. Look for holes in an argument. Find repetition. Generate alternative approaches. Challenge an assumption. Compare two versions. Identify questions a skeptical reader might ask.

The narrower the assignment, the easier it is for you to evaluate the response and remain in control of what happens next.

Examples:

• Broad: "Act as an expert editor and improve this scene."
• Specific: "Read this scene only for character motivation. Identify any moments when the protagonist's actions don't seem consistent with what we've learned about her so far. Explain why. Don't rewrite the scene."

This approach also allows you to use AI differently at different stages of the writing process rather than treating it as a machine that simply turns ideas into finished prose.

4. Set boundaries.

Tell AI not only what you want it to do, but what you don't want it to do. This is especially important for writers who want assistance without inadvertently outsourcing parts of the creative process they'd rather handle themselves.

Examples:

• Without boundaries: "Help me improve this chapter."
• With boundaries: "Identify the three biggest structural weaknesses in this chapter. Don't rewrite my prose or suggest replacement sentences. I want to solve the problems myself once I understand them."

Other useful boundaries might include:

• Don't change my voice.
• Don't introduce new facts.
• Don't write the scene for me.
• Flag uncertainty rather than guessing.
• Use only the sources I've provided.
• Give me options rather than choosing for me.

Boundaries turn AI from an open-ended generator into a more precisely controlled tool.

5. Separate evaluation from generation.

When something isn't working, resist the temptation to immediately ask AI to rewrite it. First ask AI to help you understand the problem.

For example, rather than asking, "Can you make this introduction better?" ask: "Read this introduction and identify anything confusing, repetitive, generic, unsupported, or tonally inconsistent. Explain the problems without rewriting the passage."

Then decide for yourself which observations you agree with and what you want to change.

This preserves an important part of the writing process: discernment. AI can offer an assessment, but you remain responsible for determining whether that assessment is useful and what creative decision should follow from it.

6. Ground research in sources.

AI can be incredibly useful for research, but it can also produce inaccurate information, misrepresent sources, or confidently fill gaps with plausible-sounding claims [4].

When accuracy matters, give AI clear instructions about what evidence it is allowed to use. Upload or link to primary sources when possible, ask for citations, and distinguish between what the sources establish and what the AI is inferring.

Example:

"Answer this question using only the sources I've provided. Cite the source supporting each major claim. If the sources don't contain enough information to answer something, tell me rather than filling in the gap from general knowledge."

Even then, don't treat citations as proof that the information is correct. Open the sources yourself, verify important claims, and make sure the cited material actually supports what AI says it does [4].

7. Refine through conversation.

You don't have to fit every instruction into one giant prompt. One of the advantages of working with AI conversationally is the ability to respond to what it gives you [8].

If the first response isn't useful, explain why. Ask it to go deeper on one observation. Reject an idea and explore another. Introduce new information. Correct its understanding of your project.

Example:

First prompt: "Identify three possible reasons this scene feels slow."

Follow-up: "I agree with your second observation, but not the first. Let's focus on the second. Show me where that pacing problem begins and ends, without rewriting the passage."

Over time, this can become less like issuing commands to a machine and more like conducting an extended inquiry into your own work.

8. Ask AI to challenge you, not just agree with you.

AI can be an encouraging collaborator, but encouragement isn't always what a writer needs. If you present an idea and ask whether it's good, you may receive a response that validates the premise rather than rigorously examining it.

Build productive disagreement into your prompts.

Examples:

• "What's the strongest argument against the position I'm taking here?"
• "What might an intelligent reader find unconvincing about this?"
• "Give me three reasons this plot solution might create new problems later in the novel."
• "What assumptions am I making that I haven't supported?"

This turns AI into a tool for testing your thinking rather than merely reinforcing it.

9. Remember your own role.

Whether you're writing a single prompt or building an ongoing AI-assisted workflow, you remain responsible for the creative vision and final product.

Think of yourself as:

• The visionary, deciding what the work is ultimately trying to become.
• The writer, bringing your experience, imagination, expertise, observations, and voice to the page.
• The editorial manager, deciding which AI suggestions are useful and which should be ignored.
• The fact-checker, verifying research and taking responsibility for what you ultimately publish.
• The decision-maker, establishing which parts of your writing process you're willing to share with AI and which you deliberately preserve for yourself.

The skill writers need in 2026 isn't simply knowing how to write clever prompts. It's knowing how to build a productive relationship with AI while retaining the judgment, curiosity, and creative authority that make the work their own.

Chapter 5. AI writing tools

The AI writing landscape has changed considerably since tools first emerged that specialized in generating blog posts, marketing copy, or other forms of written content. Today, some of the most useful options for writers are general-purpose AI systems that can move between brainstorming, research, analysis, writing, editing, and other tasks within the same environment.

Rather than trying to keep track of every new tool that appears, it can be more useful to understand the major categories and decide where AI might (or might not) fit into your particular writing process.

General-purpose AI for writing and reasoning

General-purpose AI tools can assist with many stages of a writing project, from exploring an initial idea to analyzing a completed draft. Depending on their features, they may also search the web, work with uploaded documents, maintain context about a project, analyze sources, or perform multi-step research [1][3].

Popular tools:

• ChatGPT: A versatile AI system from OpenAI that can help writers brainstorm, research, analyze writing, work with files, refine content, and develop ideas through ongoing conversations.
• Claude: Anthropic's general-purpose AI assistant, particularly useful for working with long documents, analyzing text, brainstorming, and providing feedback on writing.
• Gemini: Google's AI assistant, which combines writing and reasoning capabilities with access to Google's broader ecosystem of tools and services.

These systems increasingly overlap in what they can do, and their capabilities change frequently. Rather than choosing one based solely on a list of features, writers may want to experiment with several and consider which best complements the way they naturally think and work.

AI tools for fiction writers

Some AI tools are designed specifically around the needs of novelists and other creative writers. These platforms may combine generative AI with features for organizing characters, settings, plotlines, scenes, and other elements of a long-form manuscript.

Popular tools:

• Sudowrite: An AI writing platform built specifically for fiction, with tools for brainstorming, developing characters and plots, describing scenes, revising passages, and generating prose.
• Novelcrafter: A fiction-focused writing platform that combines manuscript organization, planning, worldbuilding, and AI assistance. Writers can build a structured collection of information about their story and work with different AI models while drafting and developing a manuscript.

These tools can be useful for writers who want AI integrated directly into a fiction-writing environment. But the same questions about authorship and creative ownership still apply: a tool capable of generating scenes or prose doesn't necessarily need to be used that way. Writers can decide whether they want AI to generate material, analyze their own material, brainstorm possibilities, or simply help them organize a complex project.

AI for editing and enhancement

AI-powered editing tools can help improve grammar, style, clarity, and readability. Some now include generative features as well, blurring the distinction between traditional editing software and AI writing assistants.

Popular tools:

• Grammarly: A writing assistant for grammar, spelling, clarity, tone, and revision, with additional generative AI features.
• Hemingway Editor: A readability-focused editor that identifies dense sentences, passive voice, and other issues that can make prose difficult to read.
• ProWritingAid: A writing and editing platform with detailed reports on grammar, style, readability, repetition, pacing, and other aspects of prose.
• Wordtune: An AI-powered writing assistant focused on rewriting, paraphrasing, summarizing, and adjusting tone.

For creative writers in particular, editing suggestions should still be evaluated against artistic intent. A sentence that an algorithm considers too long or unusual may be doing exactly what you intended it to do.

AI for research and source-based writing

AI can also help writers gather, organize, compare, and understand information. One of the most important developments in this category is the ability to ground AI responses in specific sources rather than relying entirely on what a model already knows [3].

Popular tools:

• Perplexity: An AI-powered search and research tool that retrieves information from the web and provides links to its sources.
• Gemini Notebook: Google's source-based research and writing tool. Users create notebooks from their own collection of sources—such as documents, websites, PDFs, and other materials—and then ask questions, generate summaries, and explore connections based on that material.
• Elicit: An AI research tool designed primarily for academic literature, helping users locate papers, extract information, and synthesize research.
• ChatGPT, Claude, and Gemini: General-purpose AI systems also offer research capabilities, including various combinations of web search, source analysis, uploaded files, and more extensive research workflows.

Whatever tool you use, AI-assisted research still requires human verification. Follow citations to the original source, make sure the source actually supports the claim being made, and prioritize authoritative and primary sources whenever possible [4].

AI for personalization and ongoing projects

Another shift in AI writing is from isolated prompts toward ongoing working environments. Instead of explaining the same project every time you open a chatbot, some tools allow you to create persistent projects, provide reference materials, establish instructions, or customize how AI works with you.

Depending on the platform, writers can provide background information about a book or content project, upload reference material, establish preferences, and create specialized AI assistants for particular tasks.

Examples include custom GPTs and Projects in ChatGPT, Projects in Claude, and Gems in Gemini.

This can make AI considerably more useful for long-term writing projects, but it also makes the principles discussed in Chapter 4 even more important. The quality of an ongoing AI collaboration depends not simply on the prompt you type today, but on the context, source material, instructions, and boundaries surrounding the project.

AI agents: from answering questions to taking action

One of the biggest changes in AI since the early days of generative writing tools is the emergence of AI agents. Agents go beyond generating a response to a prompt. They can independently carry out parts of a workflow toward a larger goal, potentially using tools, searching for information, working with files, making decisions about what step comes next, and taking actions along the way [9].

A simple way to understand the progression is:

Chatbot → assistant → agent

• A chatbot answers. You ask a question or give it an instruction, and it generates a response.
• An assistant works with you. It may retain relevant context, work with your documents and other information, and help you across multiple stages of a project.
• An agent acts toward a goal. You give it an objective, and it may determine and execute several of the intermediate steps required to accomplish it, sometimes using external tools or information along the way [9].

For writers, that could eventually mean asking an AI system not merely to suggest sources for an article but to search for them, evaluate their relevance, compare their findings, organize the research, and return a sourced report for you to review. An agent could potentially perform other multi-step publishing or business tasks as well.

This increased autonomy also introduces new risks. One is prompt injection, in which instructions embedded in external material, such as a webpage or document an agent encounters, attempt to manipulate the AI into following those instructions instead of the user's. The risk becomes more consequential when an AI system can do more than generate words and has permission to access information or take actions [10].

Writers don't need to become cybersecurity experts to use these tools, but we should understand that giving AI greater autonomy changes our responsibility as users. The more an AI system can do on our behalf, the more important it becomes to understand what information and tools it can access, limit unnecessary permissions, review important actions, and maintain human oversight.

AI agents don't eliminate the need for writers to think, research, edit, or make decisions. Ideally, they allow us to automate carefully chosen parts of a workflow while keeping human judgment in the places where it matters most.

Chapter 6. AI writing events

AI is transforming writing and publishing, and the best way to stay informed and have a say in how it unfolds is to join the conversation. AI-focused writing events bring together authors, editors, publishers, and industry experts to explore both the opportunities and challenges of AI in content creation, marketing, and publishing.

Attending these events is more than just an opportunity to learn; it’s a chance to make your voice heard. You don’t need to be an AI expert to contribute ideas. Writers of all levels should be part of shaping how these tools are used. Whether you’re excited about AI or deeply concerned about its implications, your perspective matters. By participating, you help influence the direction of AI in writing, ensuring that creativity, originality, and ethical storytelling remain at the forefront.

Many AI writing events have already taken place in the past few years and more are on the horizon. To stay involved, follow these organizations and sign up for upcoming discussions.

AI-specific writing conferences

• AI Writing Summit: A multi-day online event, hosted by Infostack, featuring expert panels, discussions, and workshops on AI’s role in writing. (I served as a panelist for two years in a row.)

• AI for Writers Summit: An online event, hosted by Marketing AI Institute, focusing on how AI can enhance writing productivity, creativity, and storytelling for marketers.

• The AI Copywriting Intensive: A multi-week online event, hosted by the American Writers & Artists Institute (AWAI), exploring AI’s future in copywriting.

Traditional writing conferences featuring AI discussions

Even at traditional writing conferences and book fairs, AI is becoming a major topic of discussion. If you attend a writing or publishing event, check the schedule for AI-related panels, workshops, or keynotes. These sessions often explore AI’s role in storytelling, marketing, publishing, and ethics, giving you the chance to learn from industry experts and contribute to the conversation.

Chapter 7. AI in book publishing: industry shifts

Artificial intelligence is no longer a hypothetical issue for book publishing. Publishers, authors, agents, and other industry professionals are already using AI for a variety of tasks, while courts and industry organizations grapple with questions about copyright, compensation, consent, and ownership.

A 2026 report from the Book Industry Study Group (BISG) and BookNet Canada offers a useful snapshot of where things stand. Among 559 North American book industry professionals surveyed, 46 percent reported personally using AI and 48 percent said their organizations used it. The most common applications included administrative and operational work, marketing, and data analysis, not simply generating books. At the same time, 86 percent of respondents cited inadequate controls around copyrighted material as a concern [11].

In other words, the publishing industry isn't simply embracing or rejecting AI. It's figuring out where this technology belongs and what protections need to surround it.

AI adoption in everyday publishing

Much of AI's current role in publishing is less dramatic than predictions about machines replacing authors might suggest. Publishers and other book professionals are experimenting with AI for marketing, data analysis, administrative tasks, metadata, discoverability, and other parts of the publishing workflow.

This doesn't mean concerns about AI-generated books have disappeared. AI has made it possible to produce enormous amounts of content quickly, raising questions about quality and the saturation of online marketplaces. AI is also being used or explored for translation, audiobook narration, editing, and other tasks traditionally performed by human publishing professionals.

But the industry's growing adoption of AI suggests that the central question is no longer simply, Will publishing use AI? Increasingly, it's How will AI be used, who gets to decide, and what protections will authors and other creative professionals have?

AI copyright cases are becoming more consequential

One of the most significant developments came in July 2026, when a federal judge gave final approval to a $1.5 billion settlement in the copyright class action against Anthropic. The case involved copyrighted books Anthropic had downloaded from pirate libraries while developing its large language models. Eligible authors and publishers are expected to receive approximately $3,000 per infringed work [12].

The ruling contains an important distinction. An earlier decision in the case found that using lawfully acquired copyrighted books to train large language models could constitute fair use, while Anthropic's downloading and permanent storage of pirated copies violated copyright law. The settlement therefore represents a major victory for authors and publishers over the acquisition of pirated books without necessarily settling the broader question of whether training AI on copyrighted material is itself unlawful [12].

Meanwhile, publishers are becoming direct participants in the legal fight. In July 2026, Hachette Book Group, Cengage Learning, Elsevier, and author Scott Turow filed a proposed class action against Google, alleging widespread copyright infringement involving texts used to develop its Gemini AI models [13].

These cases could have enormous implications for how books are acquired, licensed, and compensated when they're used in the development of future AI systems.

AI rights are becoming part of publishing contracts

For individual authors, one of the most practical developments of 2026 may be the growing importance of addressing AI directly in publishing contracts.

In April, the Authors Guild released an expanded collection of model contract clauses covering AI training, subsidiary rights, audiobook narration, translation, artwork, authors' own use of generative AI, and publishers' use of AI. The Guild recommends that authors either expressly reserve AI rights or negotiate and receive compensation for specific uses they choose to license [14].

This is significant because "AI rights" may encompass far more than allowing a company to train a large language model on a book. Emerging uses include AI-generated summaries, retrieval systems that answer questions using books, "chat with a book" applications, AI translation, digital narration, and other products that may not yet be standard parts of a traditional publishing agreement [14].

For authors negotiating publishing contracts, that means AI rights are becoming another area where it's important to understand exactly what you're granting rather than assuming existing contract language will protect uses that barely existed when many publishing conventions were established.

Who is allowed to put your manuscript into AI?

Another emerging issue turns the familiar question about writers and AI around. Instead of asking only Should an author use AI to write a manuscript? writers may also need to ask: Who else is allowed to put my manuscript into an AI system?

In April 2026, the Authors Guild raised concerns about reports of publishing professionals uploading authors' manuscripts and personal information into consumer AI systems for tasks such as generating summaries, assessments, and marketing copy without author permission. The organization recommended contract language requiring written permission before publishers make such uses and prohibiting publishers from using AI to substantively edit manuscripts, aside from basic spelling and grammar tools [15].

This introduces a new dimension to creative ownership. Writers may have their own carefully considered boundaries around AI, but those boundaries mean little if an editor, agent, publisher, or other professional can independently upload their unpublished work into an AI system.

As AI becomes embedded throughout publishing workflows, transparency may therefore need to work in both directions: publishers increasingly want to know how authors use AI, while authors have legitimate reasons to know how publishers are using AI with their work.

The fundamental copyright question remains unsettled

Despite settlements, lawsuits, new contract language, and years of debate, one of the biggest questions surrounding generative AI remains unresolved: Can AI companies train their models on copyrighted works without permission or compensation under the doctrine of fair use?

The stakes became even clearer in September 2026, when the U.S. Department of Justice entered the copyright battle between The New York Times and OpenAI, arguing in a court filing that the use of copyrighted material to train AI models can qualify as fair use. The Times disputes that position and argues that AI companies should compensate creators for the work they use [16].

At a G20 technology meeting that same week, U.S. Commerce Secretary Howard Lutnick similarly urged countries to develop frameworks that allow AI training on copyrighted creative works under fair-use principles while also protecting creators [17].

Authors and publishers, meanwhile, continue bringing lawsuits challenging the unauthorized use of their work.

For writers, perhaps the most important takeaway is that the rules governing AI and books are still being written. Court decisions, contracts, licensing agreements, industry standards, and government policy are all shaping what comes next.

That's why staying informed matters. AI isn't simply changing how books can be written or produced. It's forcing the publishing industry to reconsider some of its most fundamental questions: 

• Who owns creative work? 
• Who gets to use it? 
• Who gets paid when it creates value? 
• And how much control should authors retain over what happens to their words after they leave their hands?

Chapter 8. How I use AI to write: my dos and don'ts

In this chapter, I want to be transparent about my own use of AI. Throughout this article, you've likely picked up on my approach, but I'll be more direct here in case it helps you establish your own standards and rules of engagement with generative AI technology.

The reality is, I use AI in some capacity around almost every writing project, including this very article on AI writing (yes, I recognize the irony). But my use of AI has also evolved beyond asking a chatbot to brainstorm ideas, conduct research, or help me revise a passage. Increasingly, I use it as an ongoing thinking environment surrounding my writing and creative life.

That distinction matters to me. I don't want AI to replace the experiences, observations, expertise, imagination, and sometimes difficult thinking from which my writing emerges. I want it to help me examine those things more closely.

My role as visionary, writer, and storyteller

Before turning to AI, I generally already have something: an idea, an experience, a question, a problem, a passage I've written, or a project I'm trying to develop.

For fiction, I still do the imaginative work of developing characters, dialogue, scenes, and stories. For nonfiction, I draw from my own expertise, research, observations, and lived experience. AI can help me examine or develop that material, but I don't want it generating the substance of my creative life for me.

That foundation also makes me better able to judge what AI gives back. I reject suggestions. I redirect conversations. I notice when something doesn't sound like me. Sometimes an AI response helps me recognize an idea I hadn't fully articulated yet; other times, seeing the wrong answer clarifies what I actually think.

The goal isn't to make AI responsible for producing something good. It's to use my own knowledge of writing and my vision for the project to determine what good means in the first place.

AI as a thinking partner

I often have lengthy "conversations" with ChatGPT rather than using a single prompt to produce a finished result. I might bring in a passage I've written and ask where the logic breaks down, explore the advantages and disadvantages of two directions for a project, ask for questions that expose a blind spot, or test whether an idea holds up under scrutiny.

Sometimes I don't even want a solution. I want resistance.

I might ask: What's the strongest argument against this? What am I overlooking? Why might this story decision create another problem later? What questions should I ask myself before deciding?

Used this way, AI becomes less of a content generator and more of a place to externalize thought. The conversation gives me something to react to, challenge, develop, or reject. And those reactions often help me understand my own ideas better.

Creating a searchable record of my creative life

One of the most valuable developments in my AI practice has little to do with generating prose.

Over time, I've built up conversations about multiple creative projects, including fiction, nonfiction, websites, publishing, business ideas, and the experiences in my personal life that inevitably influence what I write. I can record an observation when it happens, return to an idea months later, continue developing a project across many conversations, or step back and look for larger themes and patterns.

In that sense, AI has begun to function somewhat like a journal or creative archive—but with capabilities a physical notebook or collection of documents doesn't have.

A traditional journal lets me look back and see what I wrote. An AI-supported archive can also help me interrogate the record. I can ask what themes have appeared repeatedly, how my thinking about something has changed, what ideas I've abandoned and returned to, or how developments in one part of my life might relate to a creative project somewhere else.

This is particularly interesting for personal writing. The subjects we write about don't exist separately from the lives we're living. Motherhood, marriage, ambition, money, faith, work, creativity, identity, and the passage of time can all shape what a writer notices and wants to say. Looking across a larger record of my own thoughts can sometimes reveal connections that would be difficult to see entry by entry.

AI doesn't determine what those experiences mean. But it can help me look across a larger body of my own material while I decide what meaning to make from it.

Managing multiple creative projects

I also use AI around the larger ecosystem in which my writing exists.

At any given time, I may be working on a novel while developing nonfiction, maintaining StoryBold, and building my sister sites: Eating With Babies and Babies After 40. Those projects have different audiences and purposes, but they're all part of my broader creative and professional life.

AI helps me move between those layers. I can explore publishing possibilities, think through website structure, develop an article, evaluate how projects relate to one another, revisit decisions I've made previously, or consider how my available time and larger life goals affect what I should work on next.

This doesn't mean I want AI running my creative life. Quite the opposite. Having a place where I can examine so many moving pieces together can help me make more intentional decisions about where my attention belongs.

Speeding up tedious tasks

There are also plenty of less philosophical reasons I use AI.

It can help me consolidate paragraphs, identify repetition, tighten sentences, organize notes, generate SEO possibilities, compare versions, summarize research, and perform other tasks that don't necessarily represent the part of writing I most want to spend my limited time doing.

When researching, I can use AI to locate and organize information or help me understand a complicated subject before returning to the original sources myself. These efficiencies can free up more time for the parts of writing I deliberately don't want to automate: imagining, reflecting, discovering, drafting, reading deeply, and making difficult creative decisions.

Efficiency is useful to me precisely because I don't believe every part of writing should be made efficient.

Boundaries I've set

For all of this extensive AI use, I still maintain boundaries:

• I don't want AI inventing my stories for me. 
• I don't want to become dependent on it to generate prose from nothing. 
• I don't want its fluency to convince me that something is insightful when it isn't.
• I don't want the availability of an instant answer to eliminate the periods of uncertainty, experimentation, and discovery that have always been part of my development as a writer.

I also distinguish between AI helping me think about my writing and AI doing the writing. Asking AI to analyze a scene I wrote, identify a gap in an argument, organize my own notes, or help me examine recurring themes across my work is different from asking it to generate a chapter and then editing the result until I'm willing to put my name on it.

Where that boundary belongs will differ from writer to writer. This is where I've chosen to put mine.

What I'm comfortable sharing with AI

Using AI as extensively as I do also means thinking about what information I'm willing to put into these systems.

I share passages of my own writing, ideas, creative plans, and personal reflections because the ability to work across that material provides real value to me. But I don't think writers should make that decision casually. AI companies have their own policies and settings governing how user data may be stored, processed, or used to improve their systems, and those policies can change [18].

Before uploading an unpublished manuscript, confidential material, someone else's private information, or anything else you would be uncomfortable sharing beyond its intended context, understand the privacy and data controls of the particular service you're using [4][18].

My own willingness to share my material with AI is ultimately a trade-off I've considered rather than a declaration that everyone else should make the same choice.

The larger principle behind my AI practice is simple: I want technology to expand my capacity to think about my work without outsourcing the part of me that creates it.

That's an evolving boundary. As the technology changes and as I change as a writer, I'll keep examining where I want that line to be.

Chapter 9. How to establish your own AI writing practice

As I've reflected on my own use of AI, and on the conversations writers are increasingly having about it, I've become less interested in drawing a universal line between "good" and "bad" AI use.

The more useful question is: What kind of relationship with AI supports the kind of writer you want to become?

We're living through an extraordinary technological shift. Writers now have access to tools that can help us research, analyze, organize, brainstorm, edit, question, and generate at speeds that would have been unimaginable only a few years ago. I think we should remain open to those possibilities.

But possibility doesn't have to mean acceleration.

One of the greatest temptations of generative AI is to equate more with better: more ideas, more articles, more books, more posts, more output. I would rather use these tools to create space for deeper exploration, stronger thinking, and better work.

Here are the principles I recommend considering as you establish your own AI writing practice:

1. Stay open to what AI makes possible.

Writers don't have to choose between embracing every new technology and rejecting AI altogether.

Experiment.

Ask what these tools make possible that wasn't practical before. Perhaps AI helps you examine hundreds of pages of your own notes, uncover patterns across a long project, understand an unfamiliar field before beginning deeper research, challenge an argument, organize an unwieldy manuscript, or remove tedious administrative work from your creative day.

Some experiments will prove useful. Others won't.

Approaching AI with curiosity gives you the freedom to discover where it genuinely expands your capabilities without assuming that every available feature deserves a permanent place in your process.

2. Choose depth over speed.

AI makes speed incredibly easy.

You can generate ten headlines instead of thinking through one. Produce an outline in seconds. Summarize a book instead of reading it. Draft an article before you've fully decided what you think.

Sometimes that efficiency is exactly what you need. But writing isn't merely the production of words, and efficiency isn't always the same thing as progress.

Some of the most important parts of writing are inefficient: sitting with an unanswered question, following an unexpected line of research, struggling with a scene, changing your mind, noticing a contradiction, writing a bad paragraph and slowly discovering the better one underneath it.

Before using AI to remove friction, ask whether the friction is actually part of the work.

Use technology to eliminate the tasks that keep you from thinking deeply, not the thinking itself.

3. Master your craft.

The more capable AI becomes, the more valuable your own judgment becomes.

If you understand storytelling, you can recognize when an AI suggestion would flatten a character or resolve tension too neatly. If you understand nonfiction, you can distinguish a compelling argument from something merely polished and plausible. If you've developed your own voice, you'll notice when AI begins sanding away its edges.

Continue learning from writers, editors, teachers, books, and your own repeated practice. That might include:

• Taking a creative writing course
• Hiring a mentor, book coach, or developmental editor
• Seeking feedback from fellow writers
• Reading deeply in the genre you want to write
• Studying work you admire to understand why it succeeds

Craft knowledge isn't becoming obsolete because AI can produce fluent prose. It may be becoming even more important because writers now need the discernment to tell the difference between prose that merely sounds competent and writing that actually works.

4. Decide what you want to keep human.

Instead of asking only, What can AI do for me? ask another question:

What parts of writing do I want to preserve precisely because doing them myself matters to me?

Maybe that's drafting fiction from a blank page. Maybe it's discovering an argument while writing an essay. Maybe it's journaling by hand, conducting your own interviews, reading complete books rather than summaries, or spending an afternoon wrestling with a paragraph until you finally understand what you're trying to say.

Your answer doesn't need to match mine.

But establish some boundaries deliberately rather than allowing convenience to establish them for you.

You might decide AI can help you organize research but not interpret a novel. Or critique a scene but not write it. Or brainstorm possibilities but not decide which possibility becomes the story.

Those choices help protect not only the originality of the finished work but the experiences that help you develop as a writer.

5. Maintain ownership, transparency, and informed boundaries.

Not every use of AI is equivalent. Using AI to organize your own notes or challenge an argument is different from incorporating AI-generated passages into a manuscript and presenting them as your own writing. As AI policies develop, those distinctions increasingly carry professional and legal consequences as well as ethical ones [4].

The Authors Guild's 2026 best practices, for example, recommend disclosing substantial AI-generated text and caution writers that incorporating AI-generated material can affect copyright registration and publishing contract warranties about original authorship [4].

Know what your publisher, publication, client, school, or other institution requires. If AI-generated material becomes part of the work itself, understand when disclosure is appropriate or required.

You should also think about what happens to your work after you create it. Review publishing contracts for AI-related rights, ask how editors or other professionals may use AI with your manuscript, and decide what permissions you're willing to grant [14][15].

Ownership in the AI era isn't only about what you put into your writing. It's also about what other people are permitted to do with it.

6. Take an active role in shaping what comes next.

AI is changing writing and publishing too rapidly for writers to treat these questions as something only technology companies, lawyers, and publishers should decide.

Writers have a stake in how our work is created, licensed, edited, translated, narrated, marketed, distributed, and used to develop future technologies.

You don't need technical credentials to participate in those conversations. Follow developments in publishing. Read guidance from writers' organizations. Understand the contracts you sign. Ask questions of editors and publishers. Talk with other writers about what's working, what concerns you, and what kind of literary culture you want to preserve.

Most importantly, don't allow the capabilities of the technology to dictate your values.

AI can help us work faster.

It can also help us look across more information, ask better questions, explore more possibilities, and spend less time on work that doesn't require our deepest attention.

I'd rather judge its value by those possibilities.

The goal isn't to produce the maximum number of words in the minimum amount of time. It's to use the remarkable tools available to us without surrendering the curiosity, discernment, imagination, and lived experience that make writing worth doing in the first place.

Sources

1. IEA. “Executive Summary: Key Questions on Energy and AI.” https://www.iea.org/reports/key-questions-on-energy-and-ai/executive-summary.
2. Cultural Studies. “Degenerative AI: AI Slop, Brainrot, and the Gimmick of Generative Culture.” https://journals.sagepub.com/doi/full/10.1177/13675494261468638.
3. OpenAI. “Prompt Engineering Best Practices for ChatGPT.” https://help.openai.com/en/articles/10032626-prompt-engineering-best-practices.
4. OpenAI. “Best Practices for Prompt Engineering with the OpenAI API.” https://help.openai.com/en/articles/6654000-how-to-use-our-models-to-their-fullest-potential-with-prompting.
5. Anthropic. “Effective Context Engineering for AI Agents.” https://www.anthropic.com/engineering/effective-context-engineering-for-ai-agents.
6. Reedsy. “The Best AI Writing Tools for Authors (2026).” https://reedsy.com/studio/resources/ai-writing-tools/.
7. OpenAI. “A Practical Guide to Building Agents.” https://openai.com/business/guides-and-resources/a-practical-guide-to-building-ai-agents/.
8. OpenAI. “Designing AI Agents to Resist Prompt Injection.” https://openai.com/index/designing-agents-to-resist-prompt-injection/.
9. BookNet Canada. “Results from the AI Use Across the North American Book Industry Survey.” https://booknetcanada.ca/blog/2026/04/27/results-from-the-ai-use-across-the-north-american-book-industry-survey/.
10. Publishers Weekly. “Judge Gives Final Approval in $1.5 Billion Settlement in Anthropic Copyright Case.” https://www.publishersweekly.com/pw/by-topic/digital/copyright/article/100888-judge-gives-final-approval-in-1-5-billion-settlement-in-anthropic-copyright-case.html.
11. Publishers Weekly. “New Lawsuit Aims to Stop Google from Copyright Infringement in Creating AI Models.” https://www.publishersweekly.com/pw/by-topic/digital/copyright/article/100820-new-lawsuit-aims-to-stop-google-from-copyright-infringement-in-creating-ai-models.html.
12. The Authors Guild. “Authors Guild Releases New AI Model Clauses and Issues Updates.” https://authorsguild.org/news/authors-guild-releases-new-ai-model-clauses-and-issues-updates/.
13. The Authors Guild. “AI Model Clauses.” https://authorsguild.org/advocacy/artificial-intelligence/ai-model-clauses/.
14. The Authors Guild. “Use of Consumer AI Systems in Publishing: Statement and New Model Contract Clauses.” https://authorsguild.org/news/use-of-ai-in-publishing-and-new-model-contract-clause/.
15. The Authors Guild. “AI Best Practices for Authors.” https://authorsguild.org/resource/ai-best-practices-for-authors/.
16. Reuters. “US Government Backs OpenAI in New York Times Copyright Case.” https://www.reuters.com/legal/litigation/us-government-backs-openai-new-york-times-copyright-case-2026-09-02/.
17. Reuters. “US Urges G20 Countries to Allow AI Training on Creators' Work.” https://www.reuters.com/business/media-telecom/nvidia-ceo-urges-g20-avoid-ai-rules-theoretical-harms-2026-09-02/.
18. OpenAI. “Data Controls FAQ.” https://help.openai.com/en/articles/7730893-data-control.
19. OpenAI. “ChatGPT Privacy Settings.” https://openai.com/consumer-privacy/.