Words and Pictures

Essays on synthography, photography, and the machine

Apples in a glass bowl - synthographic artwork by davidname.

Synthography | A Definition of the Medium

Apples carry a lot of baggage. There is the apple in the Garden of Eden (temptation, knowledge, trouble); the poisoned apple in Snow White (beauty, vanity, collapse); Cézanne's apples (form, repetition, the slow dismantling of painting itself). There is the Big Apple, and there is Apple™ — a logo, a company, a device in your pocket. There are thousands of apple varieties worldwide, yet most of us encounter only a small handful in UK supermarkets. They are familiar, neutral, quietly perfect. We recognise them without thinking, which is precisely why apples are useful here. Because these are not apples. And this is not a photograph. What you are looking at is a synthograph — an image created with generative artificial intelligence. No camera. No fruit bowl. No light bouncing off real apples. Just language, probability, and intention, assembled into an image that behaves like a photograph.

Synthography | A Definition of the Medium

Apples carry a lot of baggage. There is the apple in the Garden of Eden (temptation, knowledge, trouble); the poisoned apple in Snow White (beauty, vanity, collapse); Cézanne's apples (form, repetition, the slow dismantling of painting itself). There is the Big Apple, and there is Apple™ — a logo, a company, a device in your pocket. There are thousands of apple varieties worldwide, yet most of us encounter only a small handful in UK supermarkets. They are familiar, neutral, quietly perfect. We recognise them without thinking, which is precisely why apples are useful here. Because these are not apples. And this is not a photograph. What you are looking at is a synthograph — an image made using generative artificial intelligence. No camera. No fruit bowl. No light bouncing off real apples. Just language, probability, and intention, assembled into an image that behaves like a photograph. If they look convincing, that is the point. If they look boring, even better. Sometimes the most radical thing an image can do is sit calmly and refuse to announce itself. Synthography is a form of image-making that uses generative AI to create synthetic images rather than capture the world. Like photography, it can produce images that appear convincingly real. Unlike photography, it does not record light from a scene, nor does it document an event or a moment in time. It produces synthetic media: images that look like evidence while remaining unbound from any single, actual occurrence. In simple terms, a synthographic image is not taken; it is authored into being. It begins not with a camera and a subject, but with intention, articulated through language, structure, and constraint.

The word synthography follows the same linguistic structure as photography. Where photography derives from the Greek phōs (light) and graphein (to write or inscribe), synthography replaces light with the synthetic: images written by computation rather than exposed by illumination. It is image-making through generation rather than capture, composition rather than exposure. The result may resemble a photograph — sometimes uncannily so — but its relationship to reality is fundamentally different. A photograph is indexical: it bears a physical trace of something that occurred. A synthograph does not. It is not evidence, nor a record of presence. It is a plausible artefact, designed to behave like an image we recognise while remaining untethered from the world it appears to depict. This distinction becomes clearer when compared with illustration and painting. Illustration is an act of direct translation: an image is intentionally drawn, painted, or rendered through deliberate mark-making. Even when mediated by software, the illustrator or painter constructs form through gesture, stroke, and explicit visual decisions, working materially against a surface. In synthography, the artist does not execute the image piece by piece. Instead, they define the conditions under which an image may emerge. Language initiates the process, systems generate possibilities, and the artist responds — selecting, refining, redirecting, or refusing outcomes. For this reason, synthography is sometimes described as painting with words: not because it resembles painting materially, but because it shares a similar posture of indirect control, where intention shapes form without dictating it outright. The image is not executed; it is negotiated.

What a synthographic image resolves from is not a scene, an object, or a memory, but a latent space: an abstract field shaped by patterns learned from training data drawn from vast numbers of images. This space does not contain pictures as such, but compressed visual possibilities — tendencies, correlations, and statistical relationships without fixed referents. When an image is generated, it emerges gradually, as structured noise resolves into form under conditions set by language, reference, and probability. The image feels photographic because it inherits the visual grammar of photography, yet it depicts no moment that was ever witnessed by a lens. What becomes visible is not a captured instant, but a synthetic probability made legible. In practice, synthography most often begins as text-to-image: written language used to initiate an image. From there, images are refined through selection and iteration, sometimes guided by existing images in image-to-image workflows or reference-based processes that steer composition, material, or tone. These approaches form a continuum of control rather than discrete stages, allowing the artist to move between broad instruction and precise adjustment. The tools vary — including systems such as DALL·E, Stable Diffusion, Midjourney, and Flux — but the underlying principle remains constant: language initiates a field of synthetic possibility; judgement shapes outcome. For this reason, “AI art” is an imprecise label. It names a technology rather than a practice. Synthography instead names a specific image-making medium, defined by process, decision-making, and intent.

Synthography is collaborative by nature, but it is not automated. The machine generates images without understanding meaning, coherence, context, or restraint. Those responsibilities remain human. The artist works through writing prompts, setting parameters, refining direction, and evaluating results. Authorship resides in selection, refusal, iteration, and curation — and, increasingly, in the craft of post-production, where synthetic images are edited, stabilised, and made consistent with a larger body of work. This distinguishes synthography from novelty-driven generation or one-click production. Like photography, illustration, or printmaking, it rewards patience and discipline. Images are tested, rejected, adjusted, and contextualised; series are constructed over time rather than accumulated. Generative image-making did not appear overnight. It developed gradually through decades of research in computer vision, procedural art, neural networks, and machine learning, long before it entered public awareness. What has changed in recent years is not the existence of generative systems, but their accessibility and visual fluency. The current moment marks a point of maturity, where synthetic image generation has become stable enough to support sustained artistic practice rather than isolated experimentation. As a result, synthography is already in active use across the creative industries — in fashion, advertising, editorial, and brand-led image-making — where precision, consistency, and cultural literacy matter as much as visual impact. In these contexts, generative AI functions not as an autonomous artist, but as a studio instrument: a camera for ideas, not for light. To use an analogy, synthography can be understood as a form of digital alchemy: where alchemists once sought to turn base matter into gold, synthographers transform language into images.

Thumbnail of mid-century armchair with swirling background - synthographic artwork by davidname.

Armchair reflections | Generative AI and Me

When I look online at what passes for AI-generated art, I see an aesthetic that repeats itself endlessly. It is a default look that dominates social feeds and community platforms: endless glow effects and hyper-detailed surfaces, particle storms and glittering textures, stitched together with familiar tropes — glowing orbs, vaporwave neon, fantasy portraits, over-saturated dreamscapes. Again and again, the same genres reappear: cyberpunk skylines, armoured warriors, dragons circling floating castles. These images are designed to grab attention but not to hold it. They impress at a glance, but only for a moment. They are surface without substance — glossy, algorithmic spectacles made for the scroll, not for the wall. Of course, there are talented synthographers producing strikingly original work, but I am speaking of the broader flood of images that now dominate search results. When so many use the same tools, models, and prompt clichés, the results converge into a uniform aesthetic. You can often spot the AI look in a second, whatever the subject.

My own work is a deliberate departure from that default. I aim for restraint rather than excess, cohesion rather than chaos. I build projects that are structured, not scattered; each series has its own palette, its own logic, its own internal consistency. Rather than relying on fantasy clichés or algorithmic spectacle, I choose motifs that are grounded — objects, flowers, landscapes, textures — and treat them with care. I want the images to breathe, to hold their place, to live with you over time. This difference matters. It is the distinction between making "AI art" and making art with AI. The tools are the same, but the intention is not. Where most see an engine for excess, I see a way to work with precision — to explore subtle variations, to create quiet illusions, to test how far restraint can go. My projects are not one-off images but carefully developed bodies of work, each carrying its own logic from first prompt to final curation. I'm not against bold colour, but I'm against when it's paired with formulaic spectacle. In a culture of visual abundance, choosing calm, order, and refinement is not timidity; it is a position. It says: an image can be generous without shouting; it can be decorative without being empty; it can be new precisely because it refuses the easy, overused tricks. That refusal is not a limitation — it is the work.

Thumbnail of swirling psychedelic pattern - synthographic artwork by davidname.

The Third Image | Defining Synthography

AI art is a misleading label. We don't call painting "pigment manipulation" or photography "mechanical picture-making." Every medium has needed a name, not just a description of its tools. Synthography is that name: a recognition that these images belong to a practice distinct from painting, photography, or digital collage. To call them simply "AI art" is to blur them into a mass of outputs with no authorship, no discipline, no method. Names matter, because they give shape to a medium. They tell us how to look, and how to judge. Synthography is not everything that passes through a machine; it is the work of making something with intention.

Synthography begins with collaboration. The artist works not with brushes or cameras, but with prompts — language turned into form. Whether in DALL·E, Stable Diffusion, Midjourney, Flux, or any model yet to come, the principle remains the same: the artist writes, selects, iterates, and curates. The machine generates possibilities, but the work emerges through human direction — the patience to refine, the judgement to choose, the vision to create series with internal logic. This is not coding or automation, nor the casual pressing of a button. It is a practice built on discipline, taste, and clarity of intention. The machine is vast, but directionless. The artist provides the path.

Every new image technology has entered history under suspicion. Photography was dismissed as a mechanical trick, cinema as vulgar entertainment, digital tools as shortcuts. What unites these histories is not just resistance to change, but the failure to recognise a new way of seeing. Synthography is no different. It is not an extension of photography or a branch of painting, but something separate: a third image. Where painting organises pigment and photography fixes light, synthography shapes possibility itself. It uses language as raw material, building images from fragments of thought that unfold into form.

To define synthography is not to lock it down, but to give it space. A name makes a medium visible, distinct from the vague cloud of "AI art." For me, synthographic artwork is structured, deliberate, and project-based. It is not spectacle for the feed, but a body of work that can be lived with, returned to, and thought through. The definition matters because it resists the idea that the machine replaces the artist. Instead, it shows how collaboration can create something neither could produce alone. The third image is not the end of the history of pictures, but its continuation — another way of seeing, another way of making, another way of asking what an image can be.

Thumbnail of grapes in a glass bowl on a wooden table - synthographic artwork by davidname.

Digital Alchemy | The Synthetic Image

All images are illusions. From prehistoric cave paintings to oil portraits, from photographs to film, every image has always been a construction: pigment on plaster, silver on paper, pixels on a screen. The arrival of generative AI has not changed this; it has only made the illusion more explicit. What appears to be glass, wood, or metal in my work is none of those things — it is language turned inside out, hallucinated into form by a machine. The result is not a copy of reality, but a convincing fiction: images that ask to be believed, while admitting they were never real to begin with. New technologies of image-making have always been met with suspicion. Generative AI inherits the same doubt — treated as novelty or threat rather than possibility. Yet, like every tool in the creative industries, its value depends not on the engine itself but on how it is used. In careless hands, it produces clichés; in deliberate ones, it opens new frontiers. My work begins here, testing how far illusion can stretch before it breaks.

I call this process digital alchemy. Where alchemists once tried to turn base matter into gold, I transform words into images. Glass that cannot shatter, flowers that cannot wilt, metal that will never tarnish, light that never fades. Each project is an experiment in persuasion, seeing how far illusion can go before it collapses. And in a culture saturated with altered images — from filtered portraits to deepfakes, from staged social feeds to fabricated news — the distinction between truth and fiction is already unstable. We live inside simulations, whether we acknowledge them or not. My work does not attempt to disguise this condition; it makes it visible. These are not objects, but synthographic artworks: simulations that may take the form of vessels, figures, surfaces, or gestures, precise enough to feel physical yet forever intangible. To call them synthetic is not to diminish them — it is to recognise their nature. All images are synthetic. The difference is that here, illusion is not a failure of vision but the very material of the work. In my practice, this means creating series that test illusion across different motifs: glass that seems to refract, ceramics that seem to crack, bodies that appear sculpted. Each is impossible, but convincing — not because the machine is flawless, but because the artist knows where to push, and where to stop.

Thumbnail of a handsome man with buzzcut hair - synthographic artwork by davidname.

The Perfect Stranger | When the Image Looks Back

Generative AI can imitate almost anything: driftwood, glass, metal, fabric, the weathered grain of a tabletop. It can make imaginary objects feel as solid as those pulled from a kiln or carved from stone. But the human face has never been just another surface. A portrait is an encounter — a negotiation between two people. Painters interpret it. Photographers witness it. AI, until recently, only hallucinated it. What looked convincing at first glance dissolved under scrutiny; the realism was there, but the reality wasn't. I felt that gap every time I worked with Flux.1. The men it generated resembled perfect strangers: symmetrical, compliant, frictionless. They asked nothing of me and revealed nothing of themselves. And that emptiness eventually became impossible to ignore.

Projects like Gymnos and Kalos were fuelled by desire and fantasy — classical bodies, queer yearning, the erotic charge of looking. But the more I worked with AI-generated faces, the more uneasy I felt. These invented men carried no history, no agency, no consent. Their beauty was shaped by an internet-trained appetite: youthful, porn-adjacent, optimised for admiration. I began to worry that I was feeding a machine that flattened the complexity of the male body into a glossy commodity. Even the flaws — too many fingers and toes, plastic skin, the waxy stare — were reminders that the model didn't understand what it was making. It didn't know what a face is. It didn't know what a body means.

So I stepped away. Not out of fear, but clarity. I chose objects, materials, abstractions — places where illusion could roam freely without trespassing on the territory of the real. Portraiture felt dangerous because it worked too well and too cleanly. It produced beauty without depth, likeness without life, desire without presence. I didn't want to be complicit in that. Then Flux.2 arrived. And suddenly the ground shifted. What struck me first wasn't the resolution or the detail. It was the weight. Bodies made with Flux.2 Pro behave like bodies. Joints articulate cleanly, muscles sit properly on the frame, hands — the old enemies — arrive with surprising reliability. Skin carries pores, texture, warmth, asymmetry. Light settles on the body the way light does on a real one, not like a gloss smeared across a synthetic surface. For the first time, the men I generated didn't feel assembled. They felt observed — even though they are still, of course, nothing but invention. This changed everything.

The ethical concerns do not disappear; if anything, they sharpen. The closer AI comes to photographic believability, the more conscious I must be of what I'm making and why. But the aesthetic leap matters. When anatomy was unreliable, the unease was technical as much as philosophical. Now that the body feels convincing, the question becomes subtler: can I use this new believability knowingly? Can I treat the AI-generated figure not as a counterfeit of a real man, but as a deliberate fiction — a construct, an avatar, a mirror held up to the culture that shaped him? Flux.2 opens that door.

Working with it feels less like wrestling with artefacts and more like collaborating with a medium that finally understands its own illusions. It doesn't make portraiture "safe," but it makes it meaningful again. The images no longer collapse under their own awkwardness; they hold a kind of gravity, a tension, a presence that echoes painting more than photography. They allow for expression instead of perfection. They let atmosphere return.

So if I return to AI portraiture — and Selfie suggests I already have — it will be with a different awareness than before. The men I create are still constructs, still strangers, still born of probabilities. But now they feel like propositions rather than mistakes. They ask something of me. They look back. This essay remains a record of where I stood before Flux.2 arrived — before the medium learned a new way of seeing. What comes next will answer it in its own language, its own light, its own flesh made of pixels. The perfect stranger is still a stranger. But he is no longer hollow.

Thumbnail of man taking a selfie with his gym buddy - synthographic artwork by davidname.

Body Double | Technology and the Desire to See Oneself

To understand the modern male selfie, we must look further back than the invention of the smartphone. Long before anyone had heard of an iPhone, the Austrian painter Egon Schiele was effectively creating the naked selfie. Standing before a mirror, Schiele observed his own body and drew it with feverish urgency. These self-portraits were not the idealised, heroic figures of Greek sculpture; they were raw, cropped, and charged with sexual tension.

Functionally, Schiele's work is the closest pre-digital equivalent to the mirror pics seen on social media today. He used the same visual language: the cropped torso, the implied genitals, the tilted pelvis, the lifted shirt. He was performing for his own reflection, capturing a moment of self-inspection and arousal using chalk, ink, and oil. The desire directed at the viewer is identical to today's digital self-portraits; the only difference lies in the technology used to capture it.

When photography arrived, the first true selfies emerged through a combination of mirrors and film cameras. Men have always had the desire to look at themselves, but they needed technology to make those images reproducible. Bathroom mirror selfies certainly existed during the film era, but they were rare: awkward, grainy, badly lit, and undeniably human.

The behaviour didn't flourish then because film placed too much friction between desire and execution. Taking a risqué mirror picture involved risk and effort: worrying about lab technicians seeing your nudity, wasting expensive exposures, waiting days for results. The outcome was a physical object that was difficult to hide. Film made people cautious, forcing the libido to wait.

The arrival of the Polaroid changed this dynamic instantly. With instant gratification and private development, Polaroids became the first true private erotic technology. For the first time, straight men, gay men, couples, and narcissists alike could capture intimate moments without outside interference. While the process still required preparation and lighting, the immediacy was thrilling. This era marks the true beginning of modern self-documentation.

As technology evolved into webcams and early digital cameras, the erotic mirror became more democratic. Through MSN Messenger, MySpace, and early dating platforms, the culture of the gym selfie began to take shape. Men experimented more freely with bathroom mirrors, locker rooms, and bedroom shots. Yet even with digital cameras, friction remained: images had to be uploaded, cables connected, files transferred. Desire moved quickly, but the technology struggled to keep pace.

The release of the iPhone marked the true revolution. The smartphone is the libido's perfect tool because it eliminates every barrier: it is instant, flattering, private, and available twenty-four hours a day. With vast storage, seamless sharing, and the safety of disappearing messages, the friction of the film era vanished completely. Now we have Reddit progress pics, Instagram transformation culture, Snapchat thirst traps, Grindr torsos, and OnlyFans monetisation.

With the smartphone, men no longer simply take pictures of themselves; they perform themselves. Every gym mirror becomes a stage, every bathroom a studio, every body a form of content. This shift has produced a global phenomenon in which straight men routinely create imagery that borrows from homoerotic aesthetics — not because their desire has changed, but because technology has lowered the barrier between impulse and image. The phone allows modern men to behave exactly like Egon Schiele, but to do so instantly, repeatedly, and effortlessly.

Thumbnail of the same man rendered in black and white - synthographic artwork by davidname.

My Camera Never Lies | How AI Reshapes Photography

For almost two centuries, photography has carried a quiet contract with the viewer: a photograph may be composed, cropped, even manipulated, but at its core it records something that once existed. Light strikes a surface — first metal plates, then film, then digital sensors — and the result is an index of reality. Even as photography evolved from daguerreotypes to 35mm film to the era of digital cameras and smartphones, the underlying logic remained unchanged. A photograph was still anchored to a moment, a body, a place.

That idea has shaped how we trust images. We know photographs can mislead, but we also know they begin with something the lens encountered. Digital photography blurred this slightly — pixels replaced chemistry, and editing tools became routine — but the image still depended on a camera and a real-world subject. The photograph remained tethered to experience.

Synthography breaks that tether. Generative AI does not record light; it generates synthetic imagery that simulates the appearance of light. The result may resemble a photograph, behave like one, and even be mistaken for one, yet it has no origin in the world. It is not captured. It is conjured.

Flux.2 Pro made this distinction uncomfortably clear for me. Before its release, I avoided portraiture because earlier models struggled with anatomy and expression. Now, suddenly, the rendering of human form is astonishing — precise, convincing, and strangely intimate. I can create portraits without a camera, without a subject, without a studio. I still take photos with my iPhone, but I no longer require a camera to make images that look photographic.

Synthography does not replace photography; it transforms its context. Photography retains its strength as witness: it records time, presence, and unpredictability. A synthographic portrait cannot capture the nervous gesture before the shutter clicks or the atmosphere of a street just after the rain. It can only simulate those moments. Photography remains a human encounter. Synthography is a human intention.

Generative artificial intelligence extends what an image can be. It offers control impossible in traditional photography: light without lamps, models without bodies, locations without geography, impossible moments made plausible. It collapses barriers of cost and access, giving artists the ability to work from the coffee shop at scales once reserved for commercial studios.

If the camera never lies, it is only because it can only show what stood before it. My tools now show what I imagine. The truth of the image is no longer about whether it happened, but whether it speaks. AI hasn't ended photography; it has widened the field in which images can exist, inviting us to reconsider what it means to create — and to believe — what we see.