Artificial Intelligence for Creative Professionals

Chapter 10 of Mostly Harmless AI

The following is a first draft of my upcoming book Mostly Harmless AI. This one is about AI as a tool for augmenting creativity. I hope you find it interesting, and please, do leave me your feedback in the end.

Photo by Mike Petrucci on Unsplash

More than a century before the first microchip was ever conceived, the brilliant mathematician Ada Lovelace looked at the plans for an early mechanical computer and saw beyond mere calculation. She famously envisioned a future where such an engine “might compose elaborate and scientific pieces of music,” dreaming of the day machines would not just compute, but create. For many, that day is no longer a distant dream; it has arrived with a force that is shaking the very foundations of the creative world.

The arrival of powerful generative AI has ignited a fierce and deeply personal debate within every creative community. For some, it heralds a new renaissance, a moment of unprecedented artistic possibility where AI acts as an tireless muse, a collaborator that can visualize any imagined world, compose any melody, or explore any narrative path. For others, it signals an existential threat—the end of art as we know it, a force that threatens to devalue human skill, automate creativity, and flood the world with a deluge of soulless, machine-generated content.

It is crucial to acknowledge a third, equally valid perspective. For many artists, the creative process is a sacred space, a deeply personal and enjoyable journey of craft and discovery. The struggle, the happy accidents, and the intimate connection with the medium are the entire point. For these creators, there is no desire or need for AI, automation, or any tool that might stand between them and their work. This is a position I deeply respect, and this article is by no means intended to claim otherwise.

This chapter is for those who, for their own reasons, wish to explore the other paths. It makes no normative claim on whether AI is “good” or “bad” for art. Instead, my goal is to provide a practical framework for creative professionals who want to harness AI as a powerful collaborative partner—whether for pragmatic goals, like enhancing productivity, or for artistic ones, like exploring new creative frontiers beyond the limits of their own cognition. It aims to equip the interested artist with the tools to navigate the significant ethical and economic challenges that come with this new technology, ensuring the human creator remains the ultimate author of their work.

Can Artificial Intelligence be Creative?

Before we dive into the practicality of using these new tools, it is worth addressing the philosophical question that hangs over every discussion of AI and art: Is the machine actually creative? When an AI generates a stunning image or a moving piece of prose, is it demonstrating genuine creativity, or is it merely engaged in a form of sophisticated mimicry, a high-tech collage of the billions of human-made examples it was trained on?

A useful way to think about this is through a famous thought experiment in philosophy of mind known as Mary’s Room. Imagine Mary, a brilliant neuroscientist who has spent her entire life in a black-and-white room. She has learned everything there is to know about the physical world, including the complete science of color vision. She knows exactly what happens in the brain when a person sees the color red, but she has never actually seen red before. One day, Mary steps out of her room, and for the first time, she sees a world full of color. The question is, does she learn something fundamentally new?

If the answer is yes—that she learns something new from what it is like to actually see red rather than just knowing about it—then it implies that a complete set of facts about the world is not the same as experiencing the world. This is the crux of the issue with generative AI. Like Mary, these models have read everything. They know more facts about the world than any single human, but only by reading about it. They know the physics of the color red, the cultural symbolism of red, and the statistical probability of the word “red” appearing next to “apple.” But they have never experienced what seeing red means.

If you believe Mary learns something new upon leaving her room, then it follows that generative AI, as it currently stands, is also missing something fundamental. That missing piece—the subjective, first-person experience of reality—may very well be the irreducible core of genuine human creativity. And I’m with you on this. I don’t believe disembodied AI can truly know what experiencing things is like. Embodied AI, now that’s a different question.

However, as fascinating as this debate is, it can also be a distraction. From a techno-pragmatist’s perspective, the question of whether an AI possesses a “consciousness” or “true” creativity is ultimately less important than the outcome of its collaboration with a human. Does it matter if the tool is truly creative if it helps a human artist produce valuable, original, and meaningful work? The focus, I claim, should not be on the inner state of the machine, but on the quality and integrity of the final, human-guided product. At least for the time being.

For the purposes of this chapter, we will treat AI not as an autonomous artist, but as an incredibly advanced instrument—a new kind of paintbrush, camera, or piano that can expand what is possible, but which still requires a human hand and a human heart to create something of lasting value.

AI as a Cognitive Partner for Creatives

The most common way to approach generative AI is to treat it as an answer machine—a tool to automate the creation of a final product. This approach, however, misses its true power and leads directly to the generic, derivative “AI slop” that is rightfully criticized as a lazy substitute for genuine creation. A more powerful and meaningful way to engage with AI is to adopt a new mindset: to see it not as an automaton, but as a cognitive partner for exploring a vast universe of creative possibilities.

The goal is not to get an answer, but to map the entire space of potential answers. In this human-centric process, you are the director of the exploration. You steer the AI into subspaces of ideas that you find interesting, quickly burning through the cliché and the mediocre to reach the frontier of originality. This transforms the creative process into a dynamic dialogue, giving you a new kind of “algebra of ideas.” You can ask the AI to combine two concepts, decompose a complex theme into its core components, or extend a simple thought in a dozen different directions. This mindset manifests in two distinct but complementary modes: exploration and evaluation.

Mode 1: AI for Exploration

Every creative project begins with a spark. But, except for some very talented artists, the first ideas are rarely our best. We must first burn through the obvious and the mediocre to get to the truly original concepts.

A common ideation workshop game illustrates this perfectly. Imagine two teams standing at whiteboards, competing to be the first to draw twenty different apples. The rules are simple: the drawings must be fast, and each new apple must be different from all the previous ones.

What happens next is always the same. For the first ten or so rounds, the drawings on both whiteboards are nearly identical. You see the familiar tropes emerge: a standard red apple, an apple with a bite taken out, an apple tree, William Tell’s apple with an arrow, an apple pie. But then, something magical happens. Around the tenth apple, the easy answers are exhausted. The teams are forced to stretch. Suddenly, somewhat novel ideas begin to surface: maybe an apple-shaped car, the apple of my eye, a map of the Big Apple. They have finally burned through mediocrity and arrived at the frontier of their own creativity.

AI can be used to open this idea faucet at full blast. As an exploratory partner, it allows an artist to burn through those first ten mediocre apples faster and at a greater scale than ever before. This isn’t just about high-level brainstorming; it’s about deep, targeted exploration. A visual artist can ask for twenty variations of a single texture. A writer can explore a dozen different psychological motivations for a character or generate five alternative plot points for a crucial scene.

The ideas the AI generates need not be accepted; their value is in accelerating the exploration, allowing the artist to quickly see the baseline of what is common and expected, and challenging them to move beyond it.

Mode 2: AI for Evaluation

Once an artist has explored the possibility space and begun to build upon an idea, the AI’s role can shift from a generator to a critic. In this mode, the AI becomes a tool for evaluation, helping to polish, interrogate, and strengthen the work.

Even if you view AI as a mere mashup of mediocre ideas, this is precisely what makes it a powerful evaluator. Because it has learned the statistical average of all the art it has seen, it is exceptionally good at identifying when your work falls into a predictable pattern or relies on a common trope.

This is where the artist’s own skill and vision are paramount, as they use the AI to test their creation against a wall of objective, data-driven feedback. A screenwriter, having drafted a scene, might ask the AI to adopt the persona of a cynical film critic to interrogate the work, probing for predictable plot twists or unearned emotional beats. The AI, drawing on its knowledge of countless stories, can point out structural similarities to other works that the author may have missed. Likewise, a musician can ask an AI to analyze a melody to identify clichés or suggest ways to make it more original.

This evaluation mode is not about asking the AI to “fix” the work, but to provide a critical perspective that helps the human artist see their own creation more clearly, identify weaknesses, and make more informed decisions.

The Creative Loop

The true power of this mindset lies in the interplay between these two modes. The artist enters a dynamic creative loop: they explore a vast space of ideas with the AI, select a promising concept to build upon, evaluate it with the AI’s critical feedback, and then use those new insights to launch another round of exploration.

This process transforms the AI into an infinite canvas. Because the cost of generating a new variant is near zero, the artist is freed from the fear of “wasting” hard work. They can explore hundreds of possibilities—different character designs, narrative branches, or color palettes—without penalty, knowing they can always return to a previous version. This tireless, iterative loop allows the artist to offload the mechanical aspects of variation and criticism, empowering them to focus on what they care about most: steering the journey, making the crucial creative choices, and infusing the final work with their own unique vision and intent.

The Challenges and Opportunities of the New Creative Landscape

Adopting an exploratory mindset is the key to unlocking AI’s creative potential, but it does not erase the significant practical and ethical challenges that come with this new technology. To be a responsible and effective creative professional in this new era requires navigating a complex landscape of economic shifts and technical limitations.

The Economics of Creative AI

The fear of job loss is real and cannot be dismissed. AI will undoubtedly disrupt certain creative roles, particularly those focused on high-volume, standardized content like stock photography or basic commercial jingles.

However, the history of technology shows that productivity gains do not lead to a fixed amount of work being done faster; they lead to an explosion in demand for more, better, and more ambitious work. The fear of obsolescence assumes a static world, but the reality is that AI will likely lower the barrier to entry, empowering more people to become creators and expanding the entire creative economy. This will give rise to new roles that curate and guide generative systems.

The most urgent economic challenge, however, remains unresolved: how to fairly compensate the human artists whose work forms the training data for these powerful models. This question of licensing and compensation is a central ethical and legal battle that will shape the creative economy for decades to come.

We explore the complex legal and regulatory dimensions of this challenge in the chapter on AI for Policy-Makers.

Navigating the Limitations

Working with AI requires a deep understanding of its inherent flaws. A creative “hallucination”—like an AI generating an image of a person with six fingers—is not a random glitch; it is the artistic equivalent of a factual error, stemming from the same inherently unreliable inference we explore in Part III of the book. Artists must learn to spot and correct these errors.

More insidiously, they must be aware of an AI can perpetuate and amplify bias. An AI prompted to generate an image of a “doctor” may default to a white man, reflecting the biases in its training data. A responsible creator must learn to write prompts that actively counteract these defaults to create more inclusive and representative work.

Finally, there is the risk of homogenization. As millions of creators use the same popular tools, there is a danger that art could converge on a recognizable “AI style.” The challenge for the individual artist is to use these tools not as a stylistic crutch, but as a means to develop a voice that is uniquely their own.

Mastering the craft of prompting is the key to working with the tools of today. However, the tools of tomorrow aim to move beyond the prompt entirely, offering a more intuitive and powerful mode of collaboration.

The Next Frontier in Creative Tools

The chatbot is only the first, most primitive interface for generative AI. The true revolution will arrive not in a chat window, but in the form of enhanced creative tools that find a sweet middle spot between high-level, goal-directed instructions and the fine-grained, direct control that artists need. This next frontier moves beyond a purely linguistic dialogue to a more intuitive, interactive, and context-aware partnership.

Creative tools have always existed on a spectrum. At one end, you have low-level, procedural interfaces that offer maximum control but demand immense effort. Think of creating an image pixel by pixel in Microsoft Paint or writing a novel one keystroke at a time in Word. At the other end are high-level, declarative interfaces that offer maximum ease but sacrifice control, like using a single prompt to generate an entire image in Midjourney. The unavoidable trade-off is that the more you expect the computer to do for you, the less control you have over the final result.

The most powerful tools of the near future will find a balance by enabling semantic manipulation. Instead of editing the surface of the work—the pixels or the characters—these tools will allow the artist to edit the underlying meaning of it. Imagine an AI-generated image of a landscape at sunset. Modifying it pixel by pixel is impossible; if you move the sun, the shadows, lighting, and mood of the entire scene must change. Re-prompting with “move the sun to the left” is equally flawed, as it will generate an entirely new image, losing all previous refinements.

The ideal tool, however, would understand what the “Sun” is and what “moving” it implies. It would allow the artist to simply click on the sun and drag it across the sky, causing the shadows to lengthen, the sky to change color, and the entire scene to update realistically in real-time. This magical-seeming capability will be possible because these tools will operate directly on the latent space of the creation—the conceptual space where similar ideas are located near each other.

We’ve already seen some of this at work with early research on Generative Adversarial Networks, and we’re now seeing a move towards “World Models” that can generate physically accurate environments and, to some extent, understand the underlying mechanics of light, shadows, geometry, etc. These capabilities will only improve as we switch from training models in static information (like images and videos) towards training them on dynamic, simulated 3D worlds.

Conclusion

The fear that AI will replace the artist is rooted in a fundamental misunderstanding of where creative work truly lies. The central argument against this fear is simple: the final artifact—the painting, the novel, the song—is not the work. It is merely the residue of the work.

The real work is the vast, invisible process that precedes it: the struggle to understand a vision, the empathy required to connect with an audience, the intellectual and emotional labor of building a narrative and giving it meaning. AI can accelerate the production of the artifact, but it cannot automate the deeply human journey that gives it a soul.

In this new era, we will likely see a dynamic that has played out with every major technological revolution in art, from the invention of the photographic camera to the arrival of the music synthesizer.

Two distinct paths for creative professionals will emerge. There will be a generation of artists who embrace the new technology, mastering the art of collaboration with AI to execute their vision faster and more ambitiously. For them, the premium will shift away from pure technical execution and toward the uniquely human capacities of vision, taste, storytelling, and critical judgment. They will create new forms of art.

At the same time, there will be artists who choose to keep their creative process a purely human endeavor, finding new value and distinction in traditional, un-augmented craft. They will keep the existing forms of art alive.

Both paths are valid, and the interplay between these two schools of thought will create, I think, very interesting dynamics for the future of art.

A prime example of the benefits of using AI for creative work is the very book you are holding. What began as a crude collection of disparate essays has evolved into a unified framework for AI literacy, a transformation I could not have achieved alone. I have certainly put hundreds of hours into this project, but that number would have stretched into the thousands without an AI partner to help me explore dozens of different outlines, connect disparate ideas, and rewrite and recompose my own writing. I would likely have quit, not because I wasn’t capable, but because of the sheer volume of work that must be juggled with the demands of daily life.

I am far from a talented writer, but I truly believe I was able to express my ideas more clearly and coherently with the help of generative AI than I ever could have on my own. And it’s not just me. Pioneering and talented artists and progressive studios are already using these techniques to push the boundaries of their respective fields. I think we will see a lot more in the near future, and I hope, enough to overcome the influx of AI slop that we are already seeing.

This brings us back to the book’s human-centric thesis. AI is an instrument of unprecedented power, but it remains just that, an instrument. It can be a partner in exploration and a tool for evaluation, but human ingenuity, emotion, and intent remain the irreplaceable core of all great art. The future of creativity is not one of automation, but of augmentation.

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Thanks for reading!

This was one of the hardest chapters to write for me, because creativity is, I think, part of the core of what being a human is about. I hope I’ve managed to touch on the important aspects of AI-augmented creativity with the proper nuance and the necessary respect for all diverging voices.

Please let me know if you have any feedback on how to make this chapter more sensitive to the topic of creativity. I’d love to hear your thoughts!