A fast be aware up entrance, so we begin on the correct foot.
The analysis I’m about to reference will not be mine. I didn’t run these experiments. I’m not affiliated with the authors. I’m not right here to “endorse” a camp, decide a facet, or crown a winner. What I’m going to endorse, loudly and with out apology, is measurement. Replication. Actual-world experiments. The form of work that teaches us in actual time, in actual life, what modifications when an LLM sits between prospects and content material. We want extra examined information, and that is a type of beginning factors.
When you do nothing else with this text, do that: Learn the paper, then run your personal check. Whether or not your outcomes agree or disagree, publish them. We want extra receipts and fewer scorching takes.
Now, the explanation I’m penning this.
During the last 12 months, the trade has been pushed towards a neat, comforting story: GEO is simply website positioning. Nothing new to be taught. No want to alter how you’re employed. Simply maintain doing the basics, and the whole lot will likely be high quality.
I don’t purchase that.
Not as a result of website positioning fundamentals stopped mattering. They nonetheless matter, and so they stay crucial. However as a result of “crucial” will not be the identical as “ample,” and since the incentives behind platform messaging don’t at all times align with the operational realities companies are strolling into and coping with.
The Narrative And The Incentives
When you’ve paid consideration to public steering coming from the main search platforms currently, you’ve in all probability heard a model of: Don’t concentrate on chunking. Don’t create “bite-sized chunks.” Don’t optimize for the way the machine works. Deal with good content material.
That’s been echoed and amplified throughout trade protection, although I wish to be exact about my place right here. I’m not claiming a conspiracy, and I’m not saying anybody is being deliberately deceptive. I’m not doing that.
I’m saying one thing a lot easier. It’s my opinion and occurs to be based mostly on precise expertise – when messaging repeats throughout a number of spokespeople in a good window, it alerts an inside alignment effort.
That’s not an insult neither is it an ethical judgment. That’s how massive organizations function when they need the market to listen to one clear message. I used to be a part of precisely that kind of setting for properly over a decade in my profession.
And the message itself, on its face, will not be mistaken. You possibly can completely damage your self by over-optimizing for the mistaken proxy. You possibly can completely create brittle content material by attempting to sport a system you don’t totally perceive. In lots of instances, “write clearly for people” is strong baseline steering.
The issue is what occurs when that baseline steering turns into a blanket dismissal of how the machine layer works in the present day, even when it’s unintentional. As a result of we’re not in a “10 blue hyperlinks” world anymore.
We’re in a world the place reply surfaces are increasing, search journeys are compressing, and the unit of competitors is shifting from “the web page” to “the chosen portion of the web page,” assembled into a solution the consumer by no means clicks previous.
And that’s the place “GEO is simply website positioning” begins to interrupt in my thoughts.
The Improper Query: “Is Google Nonetheless The Greatest Site visitors Driver?”
Executives love comforting statements: “Google nonetheless dominates search. Conventional website positioning nonetheless drives essentially the most visitors. Due to this fact this LLM-stuff is overblown.”
The primary half is true, however the conclusion is the place firms get damage.
The most important threat right here is asking the mistaken query. “The place does visitors come from in the present day?” is a dashboard query, and it’s backward-looking. It tells you what has been true.
The extra essential questions are forward-looking:
- What occurs to your corporation when discovery shifts from clicks to solutions?
- What occurs when the shopper’s journey ends on the outcomes web page, inside an AI Overview, inside an AI Mode expertise, or inside an assistant interface?
- What occurs when the platform retains the consumer, monetizes the reply floor, and your content material turns into a supply enter slightly than a vacation spot?
In order for you the habits trendline in plain phrases, begin right here, with the 2024 SparkToro examine, then check out what Danny Goodwin wrote in 2024, and as a follow-up in 2025 (spoiler – zero click on cases elevated Y-o-Y). And whereas some sources are a few years outdated, you may simply discover newer information displaying the development rising.
I’m not utilizing these sources to assert “the sky is falling.” I’m utilizing them to bolster a easy operational actuality: If the clicking declines, “rating” is not the top objective. Being chosen into the reply turns into the top objective.
That requires further considering past basic website positioning. Not as a substitute of it. On high of it.
The Platform Footprint Is Altering, And The Enterprise Mannequin Is Following
If you wish to perceive why the general public messaging is conservative, you must take a look at the platform’s strategic path.
Google, for instance, has been increasing AI reply surfaces, and it’s not refined. Each AI Overviews and AI Mode noticed bulletins of huge expansions throughout 2025.
Once more, discover what this means on the working degree. When AI Overviews and AI Mode increase, you’re not simply coping with “rating alerts.” You’re coping with an expertise layer that may reply, summarize, advocate, and route a consumer and not using a click on.
Then comes the half everybody pretends to not see till it’s unavoidable: Monetization follows consideration.
That is not hypothetical. Search Engine Journal lined Google’s official rollout of adverts in AI Overviews, which issues as a result of it alerts this reply layer is being handled as a sturdy interface floor, not a brief experiment.
Google’s personal Adverts documentation reinforces the identical level: This isn’t simply “one thing folks observed,” it’s a supported placement sample with actual operational steering behind it. And Google famous mid-last-year that AI Overviews monetize at an identical charge to conventional search, which is a quiet sign that this isn’t a facet characteristic.
You don’t want to be cynical to learn this clearly. If the reply floor turns into the first floor, the advert floor will evolve there too. That’s not a scandal a lot as simply the truth of the place the mannequin is evolving to.
Now join the dots again to “don’t concentrate on chunking”-style steering.
A platform that’s actively increasing reply surfaces has a number of official causes to discourage the market from “engineering for the reply layer,” together with high quality management, spam prevention, and ecosystem stability.
Companies, nonetheless, would not have the luxurious of optimizing for ecosystem stability. Companies should optimize for enterprise outcomes. Their very own outcomes.
That’s the stress.
This isn’t about blaming anybody. It’s about understanding misaligned goals, so that you don’t make selections that really feel protected however price you later.
Discovery Is Fragmenting Past Google, And Early Indicators Matter
I’m on file that conventional search continues to be an essential driver, and that optimizing on this new world is additive, not an in a single day substitute story. However “additive” nonetheless modifications the workflow.
AI assistants have gotten measurable referrers. Not dominant, not decisive on their very own, however significant sufficient to trace as an early indicator. Two examples that seize this development.
TechCrunch famous that whereas it’s not sufficient to offset the lack of visitors from search declines, information websites are seeing progress in ChatGPT referrals. And Digiday has information displaying visitors from ChatGPT doubled from 2024 to 2025.
Why do I embody these?
As a result of that is how platform shifts look within the early phases. They begin small, then they turn out to be regular, then they turn out to be default. When you look ahead to the “huge numbers,” you’re late constructing competence and in taking motion. (Bear in mind “directories”? Yeah, Search ate their lunch.)
And competence, on this new setting, will not be “how do I rank a web page.” It’s “how do I get chosen, cited, and trusted when the interface is an LLM.”
That is the place the “GEO is simply website positioning” framing stops being a useful simplification and begins turning into operationally harmful.
Now, The Receipts: A Paper That Exams GEO Ways And Exhibits Measurable Variations
Let’s discuss concerning the analysis. The paper I’m referencing right here is publicly obtainable, and I’m going to summarize it in plain English, as a result of most practitioners would not have time to parse educational construction through the week.
At a excessive degree, the (“E-GEO: A Testbed for Generative Engine Optimization in E-Commerce”) paper assessments whether or not frequent human-written rewrite heuristics truly enhance efficiency in an LLM-mediated product choice setting, then compares that to a extra systematic optimization method. It makes use of ecommerce because the proving floor, which is sensible for one purpose: Outcomes could be measured in ways in which map to cash. Product rank and choice are economically significant.
That is essential as a result of the GEO dialog typically will get caught in “vibes.” In distinction, this work is attempting to quantify outcomes.
Right here’s the important thing punchline, simplified:
A whole lot of frequent “rewrite recommendation” doesn’t assist on this setting. A few of it may be impartial. A few of it may be damaging. However once they apply a meta-optimization course of, prompts enhance constantly, and the optimized patterns converge on repeatable options.
That convergence is the half that ought to make each practitioner sit up. As a result of convergence suggests there are steady alerts the system responds to. Not mystical. Not magical. Not purely random.
Steady alerts.
And that is the place I come again to my earlier level: If GEO had been really “simply website positioning,” then you definitely would count on basic human rewrite heuristics to translate cleanly. You’ll count on the successful playbook to be acquainted.
This paper suggests the truth is messier. Not as a result of website positioning stopped mattering, however as a result of the unit of success modified.
- From web page rating to reply choice.
- From persuasion copy to choice copy.
- From “learn the entire web page” to “retrieve the very best section.”
- From “the consumer clicks” to “the machine chooses.”
What The Optimizer Retains Discovering, And Why That Issues
I wish to watch out right here, as I’m not telling you to deal with this paper like doctrine. You shouldn’t settle for it on face worth and out of the blue undertake this as gospel. It is best to deal with it as a public experiment that deserves replication.
Now, essentially the most helpful output isn’t the precise numbers of their setting, however slightly, it’s the form of the answer the optimizer retains converging on. (The identify of their system/course of is optimizer.)
The optimized patterns repeatedly emphasize readability, explicitness, and decision-support construction. They cut back ambiguity. They floor constraints. They outline what the product is and isn’t. They make comparisons simpler. They encode “selection-ready” data in a type that’s simpler for retrieval and rating layers to make use of.
That could be a totally different objective than basic advertising and marketing copy, which frequently leans on narrative, model really feel, and emotional persuasion.
These issues nonetheless have a spot. However if you wish to be chosen by an LLM appearing as an middleman, the content material must do a second job: turn out to be machine-usable choice assist.
That’s not “anti-human.” It’s pro-clarity, and it’s the form of element that can come to outline what “good content material” means sooner or later, I feel.
The Common LLM-Optimization Rewrite Recipe, Framed As A Reusable Template
What follows will not be me inventing a course of out of skinny air. That is me reverse-engineering what their optimization course of converged towards, and turning it right into a repeatable template you may apply to product descriptions and different decision-heavy content material.
Deal with it as a place to begin, then check it. Revise it, create your personal model, no matter.
Step 1: State the product’s goal in a single sentence, with specific context.
Not “premium high quality.” Not “finest in school.” Goal.
Instance sample:
It is a [product] designed for [specific use case] in [specific constraints], for individuals who want [core outcome].
Step 2: Declare the choice standards you fulfill, plainly.
That is the place you cease writing like a brochure and begin writing like a spec sheet with a human voice.
Embody what the client cares about most in that class. If the class is knives, it’s metal kind, edge retention, upkeep, stability, deal with materials. If it’s software program, it’s integration, safety posture, studying curve, time-to-value.
Make it specific.
Step 3: Floor constraints and qualifiers early, not buried.
Most advertising and marketing copy hides the “buts” till the top. Machines don’t reward that ambiguity.
Examples of qualifiers that matter:
Not superb for [X]. Works finest when [Y]. Requires [Z]. Appropriate with [A], not [B]. This issues if you happen to [C].
Step 4: State what it’s, and what it isn’t.
This is among the easiest methods to scale back ambiguity for each the consumer and the mannequin.
Sample:
That is for [audience]. It isn’t for [audience].
That is optimized for [scenario]. It isn’t supposed for [scenario].
Step 5: Convert advantages into testable claims.
As a substitute of “sturdy,” say what sturdy means in apply. As a substitute of “quick,” outline what “quick” appears to be like like in a workflow.
Don’t fabricate. Don’t inflate. This isn’t about hype. It’s about readability.
Step 6: Present structured comparability hooks.
LLMs typically behave like comparability engines as a result of customers ask comparative questions.
Give the mannequin clear hooks:
In comparison with [common alternative], this gives [difference] as a result of [reason].
When you’re selecting between [A] and [B], decide this when [condition].
Step 7: Add proof anchors that enhance belief.
This may be certifications, supplies, guarantee phrases, return insurance policies, documented specs, and different verifiable alerts.
This isn’t about including fluff. It’s about making your claims attributable and your product legible.
Step 8: Shut with a choice shortcut.
Make the “in case you are X, do Y” second specific.
Sample:
Select this if you happen to want [top 2–3 criteria]. In case your precedence is [other criteria], contemplate [alternative type].
That’s the template*.
Discover what it does. It turns a product description into structured choice assist, which isn’t how most product copy is written in the present day. And it’s an instance of why “GEO is simply website positioning” fails as a blanket assertion.
website positioning fundamentals show you how to get crawled, listed, and found. This helps you get chosen when discovery is mediated by an LLM.
Totally different layer. Totally different job.
Saying GEO = website positioning and website positioning = GEO is an oversimplification that can turn out to be normalized and result in folks lacking the truth that the main points matter. The variations, even small ones, matter. They usually can have impacts and repercussions.
*A a lot deeper-dive pdf model of this course of is obtainable for my Substack subscribers without cost by way of my sources web page.
What To Do Subsequent: Learn The Paper, Then Replicate It In Your Surroundings
Right here’s the half I wish to be specific about. This paper is fascinating as a result of it’s measurable, and since it suggests the system responds to repeatable options.
However it is best to deal with it as a place to begin, not a legislation of physics. Outcomes like this are delicate to context: trade, model authority, web page kind, and even the mannequin and retrieval stack sitting between the consumer and your content material.
That’s why replication issues. The one manner we be taught what holds, what breaks, and what variables truly matter is by working managed assessments in our personal environments and publishing what we discover. When you work in website positioning, content material, product advertising and marketing, or progress, right here is the invitation.
Learn the paper right here.
Then run a managed check on a small, significant slice of your web site.
Preserve it sensible:
- Choose 10 to twenty pages with comparable intent.
- Cut up them into two teams.
- Go away one group untouched.
- Rewrite the opposite group utilizing a constant template, just like the one above.
- Doc the modifications so you may reverse them if wanted.
- Measure over an outlined window.
- Monitor outcomes that matter in your corporation context, not simply self-importance metrics.
And if you happen to can, observe whether or not these pages are being surfaced, cited, paraphrased, or chosen within the AI reply interfaces your prospects are more and more utilizing.
You aren’t attempting to win a science honest. You are attempting to scale back uncertainty with a managed check. In case your outcomes disagree with the paper, that’s not failure. That’s sign.
Publish what you discover, even when it’s messy. Even when it’s partial. Even when the conclusion is “it relies upon.” As a result of that’s precisely how a brand new self-discipline turns into actual. Not by way of repeating platform speaking factors. Not by way of tribal arguments. By way of measurement.
One Closing Degree-Set, For The Executives Studying This
Platform steering is one enter, not your working system. Your working system is your measurement program. website positioning continues to be crucial. When you can’t get crawled, you may’t get chosen.
However GEO, which means optimizing for choice inside LLM-mediated discovery, is an extra competence layer. Not a substitute. A layer. When you determine to disregard that layer as a result of a platform mentioned “don’t optimize,” you’re outsourcing your corporation threat to another person’s incentive construction.
And that’s not a method. The technique is straightforward: be taught the layer by testing the layer.
We want extra folks doing precisely that.
Extra Sources:
This submit was initially printed on Duane Forrester Decodes.
Featured Picture: Rawpixel.com/Shutterstock
