We’re informed to “suppose totally different,” to coin new phrases, to pioneer concepts nobody’s heard earlier than and share our thought management.
However within the age of AI-driven search, originality will not be the boon we expect it’s. It would even be a legal responsibility… or, at finest, a protracted sport with no ensures.
As a result of right here’s the uncomfortable reality: LLMs don’t reward firsts. They reward consensus.
If a number of sources don’t already again a brand new thought, it could as nicely not exist. You possibly can coin an idea, publish it, even rank #1 for it in Google… and nonetheless be invisible to giant language fashions. Till others echo it, rephrase it, and unfold it, your originality gained’t matter.
In a world the place AI summarizes reasonably than explores, originality wants a crowd earlier than it earns a quotation.
I didn’t deliberately got down to check how LLMs deal with unique concepts, however curiosity struck late one night time, and I ended up doing simply that.
Whereas writing a put up about multilingual website positioning, I coined a brand new framework — one thing we referred to as the Ahrefs Multilingual website positioning Matrix.
It’s a net-new idea designed so as to add data acquire to the article. We handled it as a chunk of thought management that has the potential to form how folks take into consideration the subject in future. We additionally created a customized desk and picture of the matrix.
Right here’s what it seems to be like:
The article ranked first for “multilingual website positioning matrix”. The picture confirmed up in Google’s AI Overview. We have been cited, linked, and visually featured — precisely the type of website positioning efficiency you’d count on from unique, helpful content material (particularly when trying to find an actual match key phrase).
However, the AI-generated textual content response hallucinated a definition and went off-tangent as a result of it used different sources that discuss extra usually in regards to the mum or dad subject, multilingual website positioning.
Following my curiosity, I then prompted varied LLMs, together with ChatGPT (4o), GPT Search, and Perplexity, to see how a lot visibility this unique idea may truly get.
The overall sample I noticed is that every one LLMs:
- Had entry to the article and picture
- Had the capability to quote it of their responses
- Included the precise time period a number of instances in responses
- Hallucinated a definition from generic data
- By no means talked about my title or Ahrefs, aka the creators
- When re-prompted, would incessantly give us zero visibility
General, it felt academically dishonest. Like our content material was appropriately cited within the footnotes (generally), however the unique time period we’d coined was repeated in responses whereas paraphrasing different, unrelated sources (nearly at all times).
It additionally felt just like the idea was absorbed into the final definition of “multilingual website positioning”.
That second is what sparked the epiphany: LLMs don’t reward originality. They flatten it.
This wasn’t a rigorous experiment — extra like a curious follow-up. Particularly since I made some errors within the unique put up that doubtless made it troublesome for LLMs to latch onto an specific definition.
Nevertheless, it uncovered one thing fascinating that made me rethink how straightforward it is likely to be to earn mentions in LLM responses. It’s what I consider as “LLM flattening”.
LLM flattening is what occurs when giant language fashions bypass nuance, originality, and progressive insights in favor of simplified, consensus-based summaries. In doing so, they compress distinct voices and new concepts into the most secure, most statistically strengthened model of a subject.
This will occur at a micro and macro stage.
Micro LLM flattening
Micro LLM flattening happens at a subject stage the place LLMs reshape and synthesize information of their responses to suit the consensus or most authoritative sample about that subject.
There are edge instances the place this doesn’t happen, and naturally, you possibly can immediate LLMs for extra nuanced responses.
Nevertheless, given what we find out about how LLMs work, they may doubtless proceed to battle to attach an idea with a definite supply precisely. OpenAI explains this utilizing the instance of a instructor who is aware of so much about their subject material however can’t precisely recall the place they discovered every distinct piece of data.
So, in lots of instances, new concepts are merely absorbed into the LLM’s normal pool of data.
Since LLMs work semantically (primarily based on which means, not precise phrase matches), even if you happen to seek for an actual idea (as I did for “multilingual website positioning matrix”), they may battle to attach that idea to a particular particular person or model that originated it.
That’s why unique concepts are likely to both be smoothed out so that they match into the consensus a few subject or not included at all.
Macro LLM flattening
Macro LLM flattening can happen over time as new concepts battle to floor in LLM responses, “flattening” our publicity to innovation and explorations of latest concepts a few subject.
This idea applies throughout the board, masking all new concepts folks create and share. Due to the flattening that may happen at a subject stage, it implies that LLMs might floor fewer new concepts over time, trending in the direction of repeating essentially the most dominant data or viewpoints a few subject.
This occurs not as a result of new concepts cease accumulating however reasonably as a result of LLMs re-write and summarize information, typically hallucinating their responses.
In that course of, they’ve the potential to form our publicity to information in methods different applied sciences (like search engines like google) can’t.
Because the visibility of unique concepts or new ideas flattens out, meaning many more moderen or smaller creators and types could battle to be seen in LLM responses.
The pre-LLM establishment was how Google surfaced data.
Usually, if the content material was in Google’s index, you could possibly see it in search outcomes immediately anytime you looked for it. Particularly when trying to find a novel phrase solely your content material used.
Your model’s itemizing in search outcomes would show the components of your content material that match the question verbatim:
That’s because of the “lexical” a part of Google’s search engine that also works primarily based on matching phrase strings.
However now, even when an thought is appropriate, even when it’s helpful, even when it ranks #1 in search — if it hasn’t been repeated sufficient throughout sources, LLMs typically gained’t floor it. It could additionally not seem in Google’s AI Overviews regardless of rating #1 organically.
Even if you happen to seek for a novel time period solely your content material makes use of, as I did for the “multilingual website positioning matrix”, generally your content material will present up in AI responses, and different instances it gained’t.
LLMs don’t attribute. They don’t hint information again to its origin. They simply summarize what’s already been stated, once more and once more.
That’s what flattening does:
- It rounds off originality
- It plateaus discoverability
- It makes innovation invisible
That isn’t a knowledge challenge. It’s a sample challenge that skews towards consensus for many queries, even these the place consensus makes no-sensus.
LLMs don’t match phrase strings; they match which means, and which means is inferred from repetition.
That makes originality tougher to search out, and simpler to neglect.
And if fewer unique concepts get surfaced, fewer folks repeat them. Which implies fewer possibilities for LLMs to find them and decide them up sooner or later.
LLMs seem to know all, however aren’t all-knowing. They’re confidently flawed so much.
One of many greatest criticisms of AI-generated responses is that they’re typically utterly inaccurate… nicely, that is why. In the event that they’re incapable of attributing an unique idea to its creator, how else are they to calculate the place else their interpretation of their information is flawed?
Massive language fashions will more and more have entry to all the pieces. However that doesn’t imply they perceive all the pieces.
They gather information, they don’t query it.
They collapse nuance into narrative.
And so they deal with repetition as reality.
And right here’s what’s new: they are saying all of it with confidence. LLMs possess no capability for reasoning (but) or judgment. However they really feel like they do and can outright, confidently let you know they do.
Living proof, ChatGPT being a pal and reinforcing this idea that LLMs simulate judgment convincingly:
How meta is it that regardless of having no possible way of figuring out this stuff about itself, ChatGPT convincingly responded as if it does, in reality, know?
Not like search engines like google, which act as maps, LLMs current solutions.
They don’t simply retrieve data, they synthesize it into fluent, authoritative-sounding prose. However that fluency is an phantasm of judgment. The mannequin isn’t weighing concepts. It isn’t evaluating originality.
It’s simply pattern-matching, repeating the form of what’s already been stated.
With out a sample to anchor a brand new thought, LLMs don’t know what to do with it, or the place to put it within the cloth of humanity’s collective information.
This isn’t a brand new downside. We’ve at all times struggled with how data is filtered, surfaced, and distributed. However that is the primary time these limitations have been disguised so nicely.
So, what can we do with all of this? If originality isn’t rewarded till it’s repeated, and credit score fades as soon as it turns into a part of the consensus, what’s the technique?
It’s a query value asking, particularly as we rethink what visibility truly seems to be like within the AI-first search panorama.
Some sensible shifts value contemplating as we transfer ahead:
- Label your concepts clearly: Give them a reputation. Make them straightforward to reference and search. If it appears like one thing folks can repeat, they may.
- Add your model: Together with your model as a part of the concept’s label helps you earn credit score when others point out the concept. The extra your model will get repeated alongside the concept, the upper the prospect LLMs can even point out your model.
- Outline your concepts explicitly: Add a “What’s [your concept]?” part straight in your content material. Spell it out in plain language. Make it legible to each readers and machines.
- Self-reference with goal: Don’t simply drop the time period in a picture caption or alt textual content — use it in your physique copy, in headings, in inner hyperlinks. Make it apparent you’re the origin.
- Distribute it broadly: Don’t depend on one weblog put up. Repost to LinkedIn. Discuss it on podcasts. Drop it into newsletters. Give the concept a couple of place to dwell so others can speak about it too.
- Invite others in: Ask collaborators, colleagues, or your neighborhood to say the concept in their very own work. Visibility takes a community. Talking of which, be at liberty to share the concepts of “LLM flattening” and the “Multilingual website positioning Matrix” with anybody, anytime 😉
- Play the lengthy sport: If originality has a spot in AI search, it’s as a seed, not a shortcut. Assume it’ll take time, and deal with early traction as bonus, not baseline.
And at last, determine what sort of recognition issues to you.
Not each thought must be cited to be influential. Generally, the largest win is watching your considering form the dialog, even when your title by no means seems beside it.
Remaining ideas
Originality nonetheless issues, simply not in the way in which we have been taught.
It’s not a progress hack. It’s not a assured differentiator. It’s not even sufficient to get you cited these days.
However it’s how consensus begins. It’s the second earlier than the sample varieties. The spark that (if repeated sufficient) turns into the sign LLMs ultimately study to belief.
So, create the brand new thought anyway.
Simply don’t count on it to talk for itself. Not on this present search panorama.
