Many business owners believe that if they publish something truly original, AI assistants will notice it and credit them. It sounds fair. You did the thinking, you named the idea, you published it first. Surely ChatGPT will say, “According to your company…”
In practice, the opposite often happens. AI answers tend to blend many sources into one smooth, average explanation. Your sharp, unusual point of view can get folded into the general definition of a topic, with no name and no link back to you. Some people call this LLM flattening.
Let me explain simply why this happens, what research says about it, and what you can do so your distinctive ideas survive the trip through an AI summary. I will also be honest about when you shouldn’t worry about it at all.
How an AI answer turns ten sources into one “average” view
To understand flattening, you need a rough picture of how AI answers are built. There are two sources of knowledge at play.
The first is the model’s training data. A large language model learns patterns from huge amounts of text. It doesn’t store a neat list of who said what. It learns which ideas and phrases tend to appear together. If a hundred pages describe a topic one way and your page describes it another way, the model mostly learns the hundred-page version.
The second is live retrieval. Many assistants now search the web before they answer. Google describes this clearly in its guide to generative AI features in Search: its ranking systems retrieve relevant pages from the index, the AI reviews them, and the answer shows links to supporting sources. I explain the mechanics in more detail in my guide to retrieval-augmented generation (RAG).
Both routes push toward consensus:
- Training favors repetition. Ideas that appear in many places are learned more strongly than ideas that appear once.
- Retrieval favors agreement. When five retrieved pages agree and one disagrees, the summary usually follows the five.
- Summaries favor short, safe wording. A nuanced framework becomes “experts recommend considering several factors.”
- Attribution is optional. The model can use your idea and cite a different page, or cite nothing.
Despina Gavoyannis at Ahrefs put it in one line that I keep coming back to:
“LLMs don’t reward firsts. They reward consensus.”
— Despina Gavoyannis, Ahrefs, LLMs Flatten Originality
What happened to Ahrefs’ “Multilingual SEO Matrix”
The best-known example of flattening comes from Ahrefs itself. In an April 2025 article, Gavoyannis describes coining a framework called the Ahrefs Multilingual SEO Matrix while writing about multilingual SEO. She then asked ChatGPT, ChatGPT Search and Perplexity about it.
Here is what she reports, in short:
- The article ranked first in Google for “multilingual SEO matrix,” and its image appeared in Google’s AI Overview, which linked to the article.
- The chat assistants used the exact term several times.
- But they produced an invented definition built from generic multilingual SEO advice.
- They never named the author or Ahrefs.
- When re-prompted, they often didn’t surface it at all.
She is clear that this was an informal test, not a controlled study, and I treat it the same way: a useful warning, not hard science. But the pattern matches what many marketers see when they check their own coined terms. The AI keeps the label, loses the meaning, and drops the owner.
What researchers have found about AI and sameness
Flattening isn’t only a marketer’s complaint. Several research teams have measured how AI affects the variety of ideas. Here are three studies I found useful, with their limits.
| Study | What they tested | What they found |
|---|---|---|
| Padmakumar and He, “Does Writing with Language Models Reduce Content Diversity?” (2023, revised 2024) | People wrote argumentative essays alone, with GPT-3, or with InstructGPT. | Writing with InstructGPT (but not GPT-3) led to a statistically significant drop in diversity. Essays by different people became more alike, mostly because of the text the model contributed. |
| Anderson, Shah and Kreminski, “Homogenization Effects of LLMs on Human Creative Ideation” (2024) | 36 participants generated ideas with ChatGPT or with another creativity tool. | ChatGPT users produced more ideas, but different users’ ideas were less semantically distinct from each other. |
| Wright et al., “Epistemic Diversity and Knowledge Collapse in LLMs” (2025) | 27 models, 155 topics, about 69.5 million claims, compared with a basic Google search baseline. | Nearly all models were less diverse in the claims they made than a basic web search. Retrieval (RAG) increased diversity, and larger models were less diverse than smaller ones. |
“nearly all models are less epistemically diverse than a basic web search”
— Wright et al., Epistemic Diversity and Knowledge Collapse in Large Language Models
Two points from the Wright study matter for brands. First, retrieval helps: answers grounded in live search results were more varied than answers from memory alone. So being findable in search still gives your idea a better chance. Second, the authors note that diversity has been improving in several model families since 2023, so this isn’t a fixed law. It is a tendency you can work with.
There is a related worry: AI tools learning from AI-written pages, which can make the web even more uniform. I cover that in my article on AI answers that cite AI-written content.
Give your idea a name people can repeat
If repetition drives what models learn, your first job is to make your idea easy to repeat accurately. A vague idea gets paraphrased into mush. A named, well-defined idea has a handle that people and machines can hold on to.
Here is how I suggest you name and define a framework:
- Pick a short, specific name. Two to four words. Avoid generic labels like “The Growth Framework.”
- Attach your brand to it, at least at first. Gavoyannis recommends including the brand in the label. “The Acme Three-Zone Check” carries attribution inside the name.
- Write a one-sentence definition. Put it near the top of the page under a heading like “What is the Acme Three-Zone Check?”
- Explain the parts in plain words. If there are three steps, list them with a sentence each.
- Use the exact name in headings, body text and internal links, not only in an image or caption.
- Show it working. One clear example with numbers makes the idea concrete and harder to replace with generic advice.
| Weak version | Stronger version | Why it survives better |
|---|---|---|
| “Our approach to pricing pages” | “The Acme Price-Anchor Test” | A unique name can be searched, quoted and linked. |
| Idea explained only in an infographic | Idea defined in text, with the image as support | Text is easier for crawlers and models to read and quote. |
| Framework mentioned once in a long post | A dedicated page for the framework, linked from related posts | One clear URL becomes the obvious source to cite. |
| Different wording every time you mention it | The same name and definition everywhere | Consistency is what turns your phrasing into the “consensus.” |
This connects to a broader principle I discuss in my piece on information gain: adding something new to the conversation is what makes a page worth ranking or citing. Naming is how you make that new thing portable.
Proprietary data is the hardest thing to flatten
Opinions get blended easily. Numbers that only you have are much harder to absorb without credit, because an accurate answer needs to say where the number came from.
Examples of data a normal business can produce:
- A survey of your own customers, for example “what made you call us instead of a competitor?”
- Anonymized patterns from your jobs or orders, such as average repair time by appliance age.
- Prices you see in your market each quarter.
- Before-and-after results from a process you run, reported honestly with sample sizes.
To make attribution more likely when that data is reused:
- Put the key number and your brand in the same sentence: “In Acme’s 2026 survey of 412 homeowners, 38% said…”
- Include the year and sample size, so the number is easy to cite properly.
- Publish the method on the same page. Journalists and AI systems both prefer sources that explain themselves.
- Give it a stable URL that you update each year rather than a new post every time.
Google’s own helpful content guidance asks a question every content team should print out: “Does the content provide original information, reporting, research, or analysis?” Its newer AI guide is even more direct:
“Don't just recycle what others on the internet have already said”
— Google Search Central, Google’s Guide to Optimizing for Generative AI Features on Google Search
That same guide says unique content will likely have more influence on AI search visibility over time than any other suggestion it gives. So original data helps twice: it is harder to flatten, and it is what Google says it wants.
Repeat the idea across channels until it becomes the consensus
Here is the uncomfortable truth. If AI rewards consensus, then your idea has to become part of the consensus. One article, however brilliant, is one voice. You need other people and other places repeating it, accurately and with your name attached.
A simple distribution plan I suggest for a new framework:
- Week 1: Publish the definitive page on your site, with the name, definition, steps and example.
- Week 2: Post a short version on LinkedIn from the founder’s or expert’s profile, linking to the page. My LinkedIn personal branding playbook covers how to do this without sounding like an ad.
- Week 3: Send it to your email list and invite replies with examples.
- Weeks 4 to 8: Pitch it to two or three podcasts or newsletters in your industry. Spoken and written mentions by others count.
- Weeks 4 to 12: Use the term consistently in related blog posts, sales decks, webinars and videos, always linking back to the main page.
- Ongoing: Invite partners and customers to use it. A framework that other people adopt becomes a shared idea with you as the clear origin.
This is the same work that builds brand mentions in general, which is one of the strongest signals for AI visibility. I cover that in my guide to factors behind brand visibility in AI answers and in my post on earning brand mentions. Expect this to be slow. Gavoyannis suggests treating early AI pickup as a bonus, and I agree.
How to check what AI assistants do with your idea
You can’t fix what you don’t measure. Here is a simple monthly audit that takes about an hour.
- Write 10 to 15 prompts. Include the exact name (“What is the Acme Three-Zone Check?”), the problem it solves (“How do I find hot and cold spots in my house?”) and comparison prompts (“Best ways to check home heating balance”).
- Run each prompt in two or three assistants you care about, such as ChatGPT, Google AI Mode and Perplexity.
- Score each answer with the table below.
- Save the answers in a spreadsheet, because AI answers change daily.
- Repeat monthly and compare the totals, not individual answers.
| Score | What you see in the answer |
|---|---|
| 0 | Idea not present at all. |
| 1 | Idea present in generic form, no name, no credit. |
| 2 | Name used, but definition wrong or generic. |
| 3 | Name and definition correct, but no brand or link. |
| 4 | Name, correct definition and your brand named or linked. |
If you track many prompts, an AI visibility platform can automate the running and storing. I compare the options in my AI visibility tools comparison. And when an answer cites a page that misrepresents you, find out whether that source is one you can influence. My guide to influenceable AI citations explains how.
A worked example: tracking a coined framework over four months
Here is a hypothetical example to show how the scoring works in practice.
A heating and cooling company publishes a guide to “The Acme Three-Zone Comfort Check,” a simple way for homeowners to test whether their system is balanced. It writes 12 prompts and runs them in 3 assistants each month: 36 answers per month, with a maximum possible score of 36 × 4 = 144.
| Month (example) | What the company did | Answers with score 4 | Total score out of 144 |
|---|---|---|---|
| Month 1 | Published the guide only | 0 of 36 | 22 (15%) |
| Month 2 | Added a “What is…” section and internal links from 6 related posts | 2 of 36 | 41 (28%) |
| Month 3 | Founder posted it on LinkedIn; one trade newsletter featured it | 5 of 36 | 58 (40%) |
| Month 4 | Added original survey data from 300 customers to the page | 9 of 36 | 74 (51%) |
These numbers are invented for illustration. The point is the method: a fixed prompt set, a fixed score, and changes logged next to the results. With 36 answers a month, I would still treat a change of a few points as noise. A steady climb over three or four months is a signal.
When blending into the consensus is perfectly fine
Not every page needs a coined framework. Some content exists to answer a common question correctly, and being part of the consensus is exactly the goal.
- Basic definitions and how-tos. “How often should I change my furnace filter?” has a standard answer. Your job is to be accurate and trustworthy, not original.
- Safety, legal and medical basics. Here, agreeing with expert consensus is a feature.
- Product facts. Prices, specs and opening hours should be consistent everywhere. Flattening is not a risk; inconsistency is.
Save your originality budget for the ideas that make customers choose you: your method, your point of view, your data. That is where thought leadership content earns its place, and where it is worth fighting flattening.
There is also a fairness issue worth mentioning. Large, well-known brands are repeated more often across the web, so their ideas reach consensus faster. If you are a smaller business, read my notes on big-brand bias in AI search. The answer is usually to go narrower: own one specific idea in one niche rather than competing on broad topics.
Questions marketers ask about AI and original ideas
Should I stop publishing original ideas because AI won’t credit them?
No. Original ideas still help you rank, earn links and win customers who read your site directly. AI pickup is slower and less reliable, so treat it as a bonus on top of those benefits.
Does putting my brand name in a framework look too self-promotional?
It can if overdone. One branded framework for your core method is normal. Branding every tip is not. Once the idea is widely adopted, you can use the shorter name and let your page remain the source.
Will schema markup or llms.txt make AI credit my ideas?
There is no evidence of that. Google’s 2026 AI guide says you can ignore llms.txt files for Google’s AI features. Clear text, consistent naming and third-party mentions matter far more.
How long does it take for AI assistants to pick up a new term?
There is no fixed timeline. Retrieval-based answers can pick up a page quickly once it ranks, while what a model learns in training changes only when it is retrained. Plan for months, not weeks.
What if an AI assistant gets my framework wrong?
Check which sources it cites. Strengthen your own definition, and ask anyone who described it wrongly to correct it. Then keep repeating the correct version across channels.
Where to start protecting your point of view this quarter
In brief: AI answers blend sources into consensus, so a single original article is easy to flatten. Give your best idea a clear name, define it in plain text, back it with data only you have, and get it repeated by others with your name attached. Then measure monthly with a fixed prompt set.
If you want help turning your expertise into named frameworks, original research and content that AI assistants can attribute correctly, our content writing team can help. Tell us what makes your business different on our get a quote page, and we will suggest where to start.
Further reading on llms flatten originality
- Ranking Yourself #1 in Your Own 'Best' Lists: Does It Work in AI Search?
- Content Engineering: What It Is and How to Start
- AI Marketing Tools Worth Using: Picks by Task
Sources for this guide to originality in AI answers
- Ahrefs: LLMs Flatten Originality (Despina Gavoyannis, April 2025)
- Google Search Central: Optimizing for generative AI features on Google Search
- Google Search Central: Creating helpful, reliable, people-first content
- Padmakumar and He: Does Writing with Language Models Reduce Content Diversity?
- Anderson, Shah and Kreminski: Homogenization Effects of Large Language Models on Human Creative Ideation
- Wright et al.: Epistemic Diversity and Knowledge Collapse in Large Language Models
