The internet was already converging on a single aesthetic before AI arrived, and AI is its high-speed photocopier.
Open twenty cybersecurity company websites and you will find dark navy backgrounds, electric blue accents, a shield or padlock somewhere in the logo, and abstract network diagrams suggesting both complexity and control. The products are different but the design language is almost identical.
It's not just a cybersecurity design issue. There are many others where you can no longer tell the brands apart:
- Wellness brands: Muted earth tones, lowercase wordmarks, generous white space and a sans-serif that whispers rather than shouts. You'll be wondering if it's a supplement, a mattress or oat milk that you are buying. Whatever it is, you know you'll be calmer and look ten years younger after using it.
- Fintech: The original companies worked hard to say 'we are basically your clever but groovy uncle' rather than 'we are a bank'. They have gradients suggesting movement, abstract geometric marks gesturing at currency without committing to it, purples and blues chosen to feel modern rather than institutional. What was once different has long since curdled into its own kind of uniform.
- AI companies: The new kids on the block have orbital shapes, a shared corporate blue, and names that end in AI, ly, or a vowel. The technology is supposed to be reshaping human creativity. The branding looks like it was generated by a committee that had seen too much Behance. See our why do all AI companies look the same for more.
- SaaS: The same Inter typeface on every homepage, the same hero section with a floating dashboard mockup, the same headline about workflows and teams. The only differentiator is the accent colour, and there are only so many hex codes to go round.
AI is adding to this sea of sameness, and here is why.
How we got here
Before generative AI, the visual landscape was already narrowing. Design systems built by Google, Apple and Microsoft were designed to bring order and accessibility to digital interfaces. The side effect was a shared standard of safe defaults including bottom navigation bars, magnifying glass search icons and standardised spacing grids. Following these guidelines gets you to 'acceptable' without effort, which means nobody gets fired and nobody gets noticed either.
Meanwhile, luxury fashion spent the late 2010s converging on identical all-caps sans-serif wordmarks. Burberry, Balenciaga, Celine, Calvin Klein, Saint Laurent. The phenomenon got a name: 'the blanding'. A trained eye could spot the differences but everyone else saw the same logo in different colours.
That was the aesthetic landscape when AI arrived, and AI, it turns out, is an averaging engine.
The copy of a copy problem
Generative AI does not invent. It identifies the most statistically probable output based on what it has seen. What it has seen is the internet, already narrowed by cautious brands, already trending toward the same palettes with the same typefaces and layout conventions. Research published in 2026 confirmed that AI tools used to build websites reproduce dominant style conventions from their training data, accelerating convergence toward a visual mean.
The feedback loop is the real problem. AI-generated output returns to the web, the next model trains on it, and the range of what counts as 'good design' narrows further. AI company logos are themselves the most pointed illustration of this: the technology supposedly reshaping creativity has produced some of the most derivative visual identities in recent memory, built largely on orbital shapes and a shared corporate blue.
Think of photocopying a document and then photocopying the copy. The fine detail disappears, and what remains is a high-contrast, simplified version of the original.
What gets lost
The Coca-Cola AI Christmas ad has been picked over enough, but it is worth stating plainly what went wrong. When Coca-Cola recreated its iconic 1995 'Holidays Are Coming' ad using generative AI in 2024, the trucks had wheels that did not rotate correctly and lighting that did not behave like light. They redid it in 2025 with the same studio. The second version was technically better, but critics noted it felt like an imitation of nostalgia rather than the thing itself. The brand's entire commercial value rests on emotional warmth accumulated over decades, and none of that is in the training data.
This is what sameness actually costs, and it is not just aesthetic boredom, though there is plenty of that. When everything in a category looks identical, buyers default to familiarity and price. Brand equity, the accumulated weight of distinctive visual decisions made over time, becomes worthless. As one design strategist put it at last year's Upscale conference: 'We were making soulless work long before AI came along. The difference now is that AI can make that soulless work in seconds.'
What to do about it
The answer is not to avoid AI tools but to understand what they are doing when left to their own defaults, and to interrupt that process deliberately.
- Start your references outside the algorithm. If your moodboard comes entirely from Behance, Dribbble and Pinterest, you are drawing from a pool already shaped by what performed well on those platforms. Books, film, architecture, material culture and anything that predates the current trend cycle will give you inputs the model has not averaged to death.
- Use AI for the generative phase, not the finished article. The tools are genuinely useful for rapid exploration of structure and variation, but they are poor judges of what makes something distinctive, because distinctiveness is by definition statistically unusual. The 80 per cent that AI produces cheaply is a starting point, and the 20 per cent that makes it worth remembering still requires a human eye.
- Treat friction as intentional. The counter-movement already has a name: tactile rebellion, code brutalism, visible imperfection. These are not trends to follow but proof that audiences notice when something has been made with care, and that distinctiveness is still viable. When AI makes generic slickness cheap, the things that cannot be generated become the most valuable things a designer can make.
