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How to spot a design made by AI (and the questions to ask before you sign)

Three agencies, three proposals, the same site: purple gradient, blurred glass, a headline a competitor could paste verbatim. It isn't copying: it's the default setting of a generative tool, and the cause is documented. Here is how to spot it at a glance, and eight questions to put to the vendor before you sign, none of which requires technical knowledge.

Method 9 min read
Written by the Innesti Digital team
In this article

An agency sends you three proposals for the new site. They're good-looking. They're clean. Every one of them has a purple-blue gradient across the top, three identical cards with rounded corners, a semi-transparent blurred panel floating over a dark background, and a headline that says something like “build faster”. You like them. You sign.

Six months later you find out that your competitor's site, built by a different agency, looks like the exact same site. Not because anyone copied: because neither of them decided anything. Both inherited the default setting of a generative tool — and that default is identical for everybody.

This article won't turn you into an art director. It gives you what a business owner actually needs: the ability to look at a proposal and tell a decision apart from a default, and to know which questions to put to the vendor before you sign. It's a buyer's skill, not a designer's.

“AI slop”: from meme to word of the year

The term AI slop describes mass-generated AI content, produced without care and recognisable precisely by that absence of care. It entered common use in August 2024 and exploded after the Ghibli-style image craze of March 2025. In 2025 slop was picked as word of the year by Merriam-Webster and by Australia's national dictionary (Wikipedia, PBS News, Euronews).

Once a word reaches the dictionary, the thing it describes has stopped being an insider curiosity: it's something your customers recognise, even without being able to name it. A site that “looks AI-made” communicates one very specific thing to the person looking at it — that nobody paid attention to it.

First wave: the signals you can see with the naked eye

The first wave (2024-2025) has a narrow, repetitive visual vocabulary. These are the easiest signals to recognise, and you can check them off one by one on any proposal put in front of you:

  • Purple-indigo-blue gradients, almost always diagonal, almost always in the header.
  • “Glass” panels, semi-transparent and blurred (glassmorphism), over dark backgrounds.
  • Glowing borders — a coloured halo around the boxes, on black.
  • Floating 3D spheres, animated gradient blobs, particles and swirls used to illustrate “artificial intelligence”.
  • Three identical cards side by side with the same thin icon on top: the most reproduced feature layout on the web.
  • A single typeface (usually Inter) with no real hierarchy: headings and body text differ only in size.
  • Interchangeable copy: “build faster”, “ship smarter”. Lines a competitor could paste onto their own site without changing a comma.

The fullest catalogue of these patterns is impeccable.style/slop, which currently lists 64 of them by name — “Purple Gradients Everywhere”, “Inter Everywhere”, “Identical card grids”. The British studio 925studios turned it into a buyer's checklist that adds generic copy — “Build faster. Ship smarter.” — to the list of tells.

The cause is more interesting than the symptom, and it's worth understanding because it explains why the phenomenon doesn't correct itself. The world's most widely used CSS framework, Tailwind, ships a default accent that is a purple-indigo; that purple saturated the public code the models were trained on; the models therefore generate purple interfaces; those interfaces go online and feed the next round of training. It's a loop that tightens on its own (prg.sh, Kai Ni, Indie Hackers).

The reverse holds too: 925studios names three digital products that escaped the generated average — Linear, Stripe, Duolingo — and the common factor isn't budget. It's deliberate constraint: a palette chosen and then respected, a typographic hierarchy actually picked, a visual language held for years. All of it is within reach on a small business budget, because it costs decisions, not rendering hours.

Second wave: when the cliché turns “tasteful”

Here comes the part almost no vendor will tell you, and it's the reason a first-wave-only checklist would protect you just halfway. Since mid-2026 the cliché has moved. Generative tools got better, they learned “good taste”, and the result is a new set of tells — restrained, warm, editorial this time:

  • beige/cream palettes with terracotta-rust accents;
  • huge italic serif typefaces used as headlines;
  • widely letterspaced uppercase eyebrows;
  • ticker-style scrolling text bars.

The person documenting it is Kyle Chayka, New Yorker staff writer and author of Filterworld, in “The Generic Style of AI Web Design” (also picked up in this reprint). In the piece, designer Celine Nguyen sums up the reaction of people working in the field: “I find myself instinctively repulsed by the warm tones”.

What that means for you, and it's the most useful thing in this whole article: if a vendor offers you cream-and-terracotta with the italic serif as the alternative to the purple gradient, they aren't offering you a decision. They're offering you the next default. Escaping the first wave by landing in the second isn't standing out: it's reaching the cliché six months late.

It isn't the first time: three precedents that show how it ends

The cycle — a visual language works, gets adopted en masse, becomes indistinguishable, provokes a backlash — has already run its full course at least three times. Knowing how each ended is what stops you making the opposite mistake.

Skeuomorphism → flat design (2013). The leather and felt textures of early mobile interfaces went from charming to embarrassing in four years; iOS 7 stripped them out, following Microsoft's flat Metro of 2010. But flat design provoked a backlash of its own — with no shadows or relief you could no longer tell what was clickable — which produced “Flat 2.0”, semi-flat, with shadows and depth reintroduced deliberately (Vermeulen Design, Nielsen Norman Group). The moral: a mechanical reaction to a cliché just produces the next one.

Corporate Memphis (2017-2021). The illustration style with the flat, disproportionate human figures, created by the agency Buck and spread by Facebook, became the default look of every tech company inside three years. WIRED called it “a massive homogenisation and dulling down of the internet's visual culture”. It's the closest precedent to AI slop: friendly signal → mass adoption → homogenisation → the coordinated designer backlash that killed it (AIGA Eye on Design, Creative Bloq).

Web brutalism (from around 2014). A term coined by Pascal Deville with brutalistwebsites.com, described as a youthful rebellion against the soft, corporate, crowd-pleasing styles — flat and material design first among them (UX Collective, Nielsen Norman Group).

Three cycles, one rule: the problem was never the style. It was adoption without decision. And that's exactly what you're buying, or avoiding, when you assess a proposal today.

Glassmorphism isn't banned: it's almost always badly implemented

One honest caveat, because a checklist that turns into a list of prohibitions is useless. Blurred glass panels were already declared “dead” by the designer debate back in 2023, and yet Apple and Microsoft kept using them across their own systems. The serious criticism isn't about the style itself, but about how it gets built: the legibility of text over a blurred background, and the computational cost on low-end devices — that is, the three-year-old phone half your customers open your site with (Axonix's 2026 CSS guide).

So the right question to put to the vendor isn't “did you use blurred glass?”. It's: which devices did you test it on, and what happens to the text on top when the background changes?

The backlash has a name: “Anti-AI Crafting”

Graham Sykes of Landor gave the 2026 counter-movement a name: Anti-AI Crafting, human craft as the antidote to AI's hyper-polished visual language. It is explicitly against laziness, not against technology — a distinction that matters, because the question was never “use AI or don't” (Design Magazine).

Meanwhile several AI companies have built deliberately anti-generic identities: Normal Computing with an assertive red sans and pixel detailing, Faculty with an ambigram monogram in a carved serif, Cohere with an organic, cell-like identity. The people selling AI, in other words, are the first ones who don't want to look AI-made (ebaqdesign, freelogodesign).

The Mistral logo: why “flat colour” was a decision

A concrete, dated example of what “chosen, not inherited” means. In February 2025 Mistral AI adopted a new mark designed by Sylvain Boyer Studio: a modular, blocky “M” in solid flat colour, explicitly dropping the gradients. The stated rationale isn't aesthetic but practical — more contrast and simpler use across every medium, from the favicon to signage. Hidden in the mark is a pixel cat, a nod to the models' early training datasets (designyourway, logos-world; the change is also on record at Brand New).

One limit worth stating plainly: this concerns the mark, and it doesn't imply a website has to be free of colour or of gradients. It isn't “gradients are banned”. It's that this gradient, in this mark, was removed for a reason someone can state out loud. And a reason someone can state out loud is exactly what you're looking for in a proposal.

The questions to ask the vendor before you sign

Everything above boils down to a single criterion, well put by 925studios: distinctiveness comes from making the decision, not from inheriting it. You don't have to judge whether you like a proposal — you have to work out whether anyone decided. These eight questions reveal it inside one meeting, and none of them requires technical knowledge:

  • Why this colour and not another? An answer that talks about your sector, your competitors or your materials is a decision. “It works well / it's modern” isn't.
  • Who chose the typefaces, and why two voices instead of one? A deliberate pairing — a typeface with personality for headings, a neutral readable one for body text — is a sign of work done. One typeface for everything is, as a rule, the default.
  • What, in this identity, is specific to my company? If the answer holds identically for any other client, you aren't buying an identity.
  • Show me the alternatives you discarded. Whoever decided has some; whoever generated usually doesn't. It's the single most discriminating question of all.
  • Which devices did you test the blurs and shadows on? It turns an aesthetic question into a verifiable one.
  • Does the copy say anything a competitor of mine couldn't paste verbatim? Actually try it: read the headline with your name swapped for theirs. If it still holds up, it's generic copy.
  • What constraints did you set yourselves, and who wrote them? Palette, hierarchy, spacing: if the vendor uses generative tools — perfectly legitimate — they should be able to show you the rules they briefed them with.
  • In two years, will this site look dated to 2026? An uncomfortable and useful question: every visual wave carries its own date stamped on it.

None of these questions requires you to tell a serif from a sans. It's the same skill you use to assess a quote from any other supplier: telling apart the ones who thought about your problem from the ones who handed you their own default output. In design, since 2024, that default output is recognisable with the naked eye — and now you know what it's called.

Sources

The twenty-three sources cited above, in the order they appear.

Method Written by the Innesti Digital team

Every resource grows out of the research we do for SMEs and the products we build ourselves: cited sources, a method we state openly, no claim you cannot check.

The sources are cited in the text. We encourage you to always check them directly at the original source.

More deep-dives on AI adoption in an SME.

  1. Publishing with AI in five languages: what actually breaks, and how you catch it before the reader does This site ships in Italian, English, French, Spanish and Dutch through an AI transcreation pipeline: here is the defect register that came out of it, with names and causes. Five real classes — the French verb that, on a legal subject, turns a statement of fact into something very close to an accusation; the substantive error propagated identically across all five languages because it sat upstream of the per-locale pass; datelines written in a format none of the five languages actually uses; the missing revision marker visible to the reader (that one is already fixed and in production); and the best of the lot, the only class where the copy is right and the code is wrong — two components searching the headline for the string "AI" and never finding "IA", leaving French and Spanish with a flat title while raising no error and leaving no translation key missing. Then the controls the localization industry already runs, any of which would have caught them: the MQM taxonomy with its seven dimensions across three severity levels, the structure underneath ISO 5060:2024 and ISO 11669:2024; back-translation, which is not round-trip machine translation but an independent linguist retranslating without ever having seen the original; the three-tier glossary enforced before the text reaches a reviewer, with a named owner and a review calendar; the 10% native-reviewer sample, raised on high-risk content instead of held flat; language-aware date, number and currency formatters, and pseudolocalization. It closes with a checklist ordered by risk and the rule that cost us most to skip: a pattern defect is closed on the repository, not on the file. 10 min
  2. You don't need more AI: you need to redesign a department The reason almost no AI project reaches the P&L isn't the model: it's that companies gave everyone access to the tools without redesigning the processes around them. With data from Deloitte (State of AI in the Enterprise 2026), MIT NANDA (The GenAI Divide) and McKinsey (State of AI 2025) as third-party evidence of the gap, and why for an SME redesigning a single department is genuinely within reach today. 6 min
  3. Why the AI giants aren't calling your SME (and who is) Anthropic, OpenAI, Google DeepMind and Mistral sell to large enterprises through the Big Four and the big integrators, not to your company: seven dated deals (Anthropic × Deloitte with Claude to 470,000 people and 15,000 certified, October 2025; Accenture × OpenAI reselling the playbooks to clients; the forward-deployed engineering practices with Microsoft and Google/DeepMind; PwC and KPMG × Anthropic; Capgemini × Mistral) show the channel is the reseller, not the SME. The pricing keeps you out too: Claude's Enterprise plan starts at 20 seats, and the forward-deployed-engineer model — roughly $5.5 billion combined spend by Anthropic and OpenAI in May 2026 — is by explicit admission reserved for "marquee accounts", not the mid-market (Forbes, PYMNTS). From the SME side the gap shows: only 14% of small businesses have fully integrated AI, held back by privacy (50%), technical expertise (49%), tool selection (48%) and training (73%) — an implementation gap, not one of awareness (Goldman Sachs). Our reading: the giants feed large enterprises via integrators and embedded engineers, nobody productizes hands-on implementation for the SME — that's the seat an "innesto" implementation occupies, forward-deployed at the scale of a small company. 10 min

From theory to your business. We graft AI in.

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