The Dirty Secret Behind Text-to-Image AI

Everyone’s obsessed with the magic of text-to-image AI right now. It feels like we’ve entered a world where you can summon anything — a dragon on Wall Street, a neon forest, a portrait of Cleopatra sipping a cappuccino — just by typing a sentence. Tools like DALL-E, DaVinci, Picture AI, Copilot, and Firefly have thrown open the creative floodgates. But while most people are busy applauding the possibilities, very few are talking about the ugly flaws hiding right in plain sight.

Let’s start with the most obvious one: foreground and background often don’t match. At all. It’s like those bad Photoshop jobs from the early 2000s where someone would crop out a person and plop them onto a random beach scene with zero concern for lighting, perspective, or shadows. You get a shiny, studio-lit subject floating awkwardly in a misty forest — a visual version of nails on a chalkboard. It’s distracting. It breaks the illusion. It instantly screams “fake.”

And here’s the thing: most text-to-image generators struggle with this, badly.

Midjourney, to its credit, seems to be one of the few tools that somehow understands how light and atmosphere are supposed to behave. You look at a Midjourney piece, and the foreground blends naturally into the background, like a real-world photo. But everywhere else, it’s still an ongoing mess.

Then there’s the weird, uncanny valley effect when it comes to images of human beings. So often, the people AI generates don’t actually look human — they look like mannequins or statues covered in a thin layer of latex. Their skin is too smooth. Their eyes have a lifeless, glassy quality. You know it when you see it: close enough to a person to be eerie, but wrong enough that you immediately pull back.

And that’s just when the AI manages to get the human anatomy somewhat right. All too often, it doesn’t. There are way too many fingers, or not enough. Toes growing sideways. Two left legs. Heads twisted in ways that would make chiropractors weep. It’s still shockingly easy to get an AI-generated image of someone with six fingers on one hand and a thumb growing out of their palm — and the AI doesn’t seem to notice anything is wrong.

But why do these issues happen, when AI is supposed to be “so smart”?

It’s because AI doesn’t actually “understand” images the way humans do. When you ask a human to imagine a beach scene, we instantly grasp physics, anatomy, light, shadows — because we’ve lived it. AI models, on the other hand, are just crunching patterns. They are trying to statistically guess what pixel or shape should come next, based on millions of examples they’ve been fed. They don’t have an internal model of reality. They don’t know that a light source from the left should cast a shadow to the right, or that a hand should have exactly five fingers. They’re playing a giant, supercharged game of “what looks likely?” — and sometimes they guess wrong.

Another reason you get weird results, especially with groups of people, is that the difficulty level skyrockets when the scene becomes more complex. A single person? Maybe the AI can manage enough correct guesses to create a convincing image. Five people standing together, each in different poses, with overlapping limbs, clothing folds, perspective shifts? It becomes a minefield of potential mistakes. Every new person introduced multiplies the complexity exponentially — which is why group shots almost always fall apart on closer inspection. It’s not because the AI is lazy. It’s because it was never really built to truly “understand” scenes in three dimensions the way our brains do.

Now, here’s an interesting twist you may have noticed if you’ve spent enough time with these tools: it’s much easier to get a convincing AI image of a woman than a man.

That’s not a coincidence. Most models were trained on huge datasets scraped from the internet — and surprise, surprise, the internet is flooded with images of women. AI doesn’t invent visuals out of thin air — it learns by absorbing mind-boggling amounts of data scraped from the internet. Every photo, every piece of artwork, every stock image it’s fed teaches it patterns: how a face is shaped, how shadows fall, how a smile curves.

But here’s the catch — the internet is not a balanced place.

There are far more high-quality images of women than men floating around, thanks to fashion photography, beauty advertising, influencer culture, and plain old human bias. So when AI learns, it’s swimming in oceans of female imagery and only puddles of male examples. As a result, it gets really good at generating women — with better skin textures, more natural poses, and richer details — simply because that’s what it practiced the most. Men, on the other hand, often come out looking flat, awkward, or even strangely proportioned because the model had fewer clean examples to learn from. In short, the bias of the training data shapes what AI can and can’t do well — and until someone trains models on a more balanced diet, we’re going to keep seeing these lopsided results.

So, what can be done? For users, it’s all about smarter prompting and managing expectations. You can improve results by specifying light sources, moods, color palettes — anything that nudges the AI toward a more coherent vision. Some users even blend multiple generations together manually, cleaning up hands and shadows by hand afterward. For developers, the path forward is harder. It involves training models on better-curated datasets, incorporating 3D spatial understanding into their architecture, and perhaps eventually combining text-to-image with physics engines to truly ground AI creations in something resembling real-world rules.

We’re not there yet. And honestly, it’s important that we recognize that — because when we forget the flaws, we stop pushing for better.

AI image generation is impressive, yes. But it’s still halfway between magic and illusion. And sometimes, if you look closely, the cracks are wide enough to drive a truck through.