Why Does AI Design All Look the Same? The "ChatGPT Taste" Problem

AI design looks generic, the so-called "ChatGPT taste", because most people run a general tool like ChatGPT, Midjourney, or Canva on default settings with a vague prompt and no brand direction. The model then returns its safest, most average output. The fix is not a better generator but direction: a locked palette, chosen typefaces, a reference set, and a person correcting each round until the work looks like one business rather than everyone's.

The tell is never the software. It is the absence of a human deciding what the brand should look like before the software is switched on.

What is "ChatGPT taste" in design?

"ChatGPT taste" is the recognizable house style of un-directed AI: soft gradients, a purple or blue palette, glowing shapes, symmetrical layouts, and copy that reads smooth but says little. People sense it before they can name it, and once seen it is hard to unsee.

The look is not unique to ChatGPT's own image generation, the DALL-E lineage. Un-directed output from Midjourney or Canva's AI tools drifts to the same safe middle, because the cause lives in how the generators are used, not in any one product. For a business, that look quietly says "generic", which is the opposite of what a brand is meant to do.

We've seen the same drift in our own production runs: the moment direction slips, the output slides straight back toward this look.

Why does AI-generated design all look the same?

AI-generated design converges because a general model with no direction returns the average of its training data, and the average is the same for everyone. Two businesses typing similar short prompts get near-identical output, the same way two sites on one template look alike: the starting point is shared, and nobody moved it.

There is a mechanism under this. A general image model keeps no memory of your brand between prompts, so with nothing to anchor it, it predicts the most statistically likely image for your words. The most likely image is, by definition, the one closest to everything else it has seen.

The convergence has been measured, beyond the impression alone. A 2024 study in Science Advances by Doshi and Hauser found that writers given GPT-4 ideas produced individually better-rated stories that were, collectively, more similar to each other than stories written without AI. Individual quality went up; group diversity went down.

A 2024 paper in Nature by Shumailov and colleagues described "model collapse". When generative models train on model-generated content, the rare tails of the original distribution disappear first: the distinctive material goes, the common material stays. Different studies, one direction of travel. Left un-directed, generative AI pulls output toward the middle. This is the design version of the point in why AI being free doesn't replace an agency: the generator is available to all, the judgment is not.

Can you fix the generic AI look?

The generic AI look is fixable, and the fix has nothing to do with finding a "better" AI. You give the generator something to obey. Decide the brand first, its colors, fonts, tone, and visual references, then make the AI work strictly inside those rules, correcting the output over several rounds until it stops looking like the default and starts looking like the brand.

A skilled director with an ordinary generator beats an unskilled prompter with the best one, almost every time. The starting decision, one clear direction rather than a scatter of styles, is covered in why one design direction beats ten.

Most tools expose direction controls if you are working in them yourself. Midjourney accepts style-reference images that pull new output toward a look you supply. Canva's Brand Kit pins your palette and fonts across its AI features. GPT-image models take reference images so new generations stay inside an existing system. The controls only help if a defined brand exists to feed into them, which is the real gap in most DIY setups.

What does a directed AI prompt actually look like?

The difference is easiest to see side by side. The vague prompt below is an illustration of what most people type. The directed version is the real constraint set Storming Solutions ran for its own social feed in our July 2026 production batch.

	Vague prompt (illustration)	Directed prompt (our real constraint set)
Prompt text	"Create a modern, professional social media post for a web design company."	"Clean, modern composition with generous negative space, premium-tech feel. Palette locked to ink #0B0E14, off-white #F5F6F8, accent blue #2F6BFF. No human faces. No text baked into the image — typography is added later in layout."
What the model decides	Everything: color, mood, layout, subject	Almost nothing: a person has already decided
Result	The model's safest average, "ChatGPT taste"	Output that starts inside the brand

Source: the directed prompt is Storming Solutions' own July 2026 production constraint set; the vague prompt is an illustrative composite, not a real client brief.

The vague prompt leaves every decision to the model, so the model answers with its average. The directed prompt leaves almost no room to drift. Color, mood, spacing, and what must not appear are all decided by a person before generation starts.

How do you get on-brand results from AI tools?

Four steps, in order.

1. Lock the brand identity first. A defined palette with exact hex values, chosen typefaces, and a reference set the new work must sit beside.
2. Feed constraints, not open-ended requests. The identity goes into every prompt as rules, so each generation starts inside the brand instead of inside the model's average.
3. Direct and correct across rounds. Treat the first output as a rough draft. Generate several candidates, select one, and re-run whatever misses.
4. Keep a human judging every result against the brand, killing anything that drifts back toward the generic default.

Here is what that looked like in our own July 2026 production run, generating a month of social assets for the Storming Solutions feed. Masters were generated text-to-image with Recraft V4.1 under the constraint set shown above, two to three candidates per image with one selected.

For a five-slide carousel, the chosen master was passed back into a GPT-image model as an image reference. Every slide then stayed inside one visual system instead of being five unrelated generations. Headline text was kept out of every prompt, because AI text rendering is approximate; real typography was added at the layout stage.

The run still needed corrections. One model would not output the 4:5 feed ratio, so we generated 3:4 and cropped. Native output came in under the 1080px target, so masters were upscaled and then sized down for crisp finals. At review, anything off-palette or carrying accidental faces was killed.

That is a production process, not a single prompt. For where a real camera still beats generation, see AI visuals versus a photographer.

Brand-direction checklist

Before you publish an AI-generated design:

• Palette locked (exact hex values, not "blue-ish")
• Typefaces chosen — type added in layout, not generated
• Reference set collected (images the output must sit beside)
• Constraints written into the prompt, not implied
• Several candidates generated, one selected
• Output reviewed against the brand; anything that drifts is killed

Frequently asked questions

Why does everything from ChatGPT and Midjourney look alike?

They look alike because an un-directed model returns the statistical average of its training data, and that average is the same for every user. Give two brands the same vague prompt and they get near-identical results. The look changes only when a person feeds the generator a specific palette, references, and rules to work inside.

Is "ChatGPT taste" only a problem with ChatGPT?

No, it appears across every general generator run without direction, including Midjourney and Canva's AI tools. The cause is the workflow, not the product: a vague prompt and no brand constraints. The same tools, given a locked identity and human correction, produce on-brand work, so the fix is direction rather than switching apps.

Can I fix generic AI design without hiring a designer?

Sometimes, if a defined brand kit already exists and someone in-house has the eye to enforce it. You feed the palette, fonts, and reference images into every prompt and reject anything that drifts. Where no brand has been properly defined yet, that is the missing piece a studio supplies, and no amount of prompting replaces it.

Does using a locked palette really change the output that much?

Yes, because most of the "generic" look comes from the model choosing color and composition freely. Pin the exact hex values and reference images, and you remove the biggest decisions the model would otherwise average. The output stops drifting to the safe middle and starts sitting inside your visual system.

Should headline text be generated inside the image?

Usually not, because AI text rendering is still approximate and often misspells or warps letters. The reliable method is to keep text out of the prompt and add real typography at the layout stage. That also lets you use your chosen typefaces exactly, which is part of staying on-brand.

Is it dishonest to use AI for client design work?

Not if you are open about it and the judgment is real. We use AI in production and say so; what a client pays for is the art direction and quality control that decide what the generator makes. Hiding it is the problem, not using it.

Direction is the part that is not free

The generator is available to everyone, which is exactly why it cannot be your edge. What separates one business's feed from the generic wash is a person deciding the palette, holding every output to it, and killing what drifts.

Storming Solutions runs a generative creative studio in Kuala Lumpur, making on-brand social content, product visuals, and motion with AI under human art direction, used openly, exactly as described above. If your feed has started to look like everyone else's, send us what you are posting or see how a directed production run works on our social media page.