The real shift: search isn’t dying, it’s being intermediated
Look past the AI hype cycle and the headlines all point to one structural shift:
your marketing is no longer just competing inside Google and social feeds.
It’s competing inside AI aggregators.
ChatGPT Shopping, AI “best” lists, LLM-optimized content (AEO/GEO/LLMO),
misinformation experiments, and Time Magazine’s AI overhaul are all the same story:
large language models are becoming the interface between demand and supply.
For performance marketers and media buyers, this isn’t a thought experiment.
It changes how traffic is created, who owns the relationship, and what “optimization”
even means when a model is rewriting your pitch on the fly.
The operators who win won’t be the ones who write the most prompts.
They’ll be the ones who treat LLMs as a new distribution channel with its own:
- Ranking factors (but not the SEO ones you’re used to)
- Attribution gaps (worse than “view-through” ever was)
- Trust dynamics (users outsource judgment to the model)
From SERPs to “answer pages”: where your brand now gets filtered
Historically, your funnel looked something like:
Query → SERP / feed → Click → Landing page → Conversion.
In the AI aggregator era, the path increasingly looks like:
Question → LLM answer → Curated options → Maybe a click → Maybe a conversion.
That middle step is new. The model is now:
- Summarizing your content
- Normalizing your positioning vs competitors
- Filtering out anything that looks low-trust or low-signal
You’re no longer just “ranking” in a list. You’re being compressed into a sentence.
Your 40-page comparison guide becomes “Brand A is cheaper, Brand B has better support.”
What this does to performance marketing math
- Click-through becomes a second-order metric.
The LLM’s selection and summary of you is now the real first impression. - Attribution gets fuzzier.
A user might ask ChatGPT, read its answer, then Google your brand name
and convert on a branded search or direct visit. Good luck crediting that. - Brand and performance blend.
If the model doesn’t recognize you as a credible option, your ROAS ceiling
drops no matter how good your bidding is.
LLMO is not SEO: how models actually “decide” to show you
A lot of content is trying to rebrand SEO as “AEO” (answer engine optimization),
“GEO” (generative engine optimization), or “LLMO” (LLM optimization).
Most of it is just old SEO advice with new letters.
Under the hood, LLMs are not crawling and ranking pages the way search engines do.
They are:
- Trained on large corpora (which may include your content)
- Fine-tuned with human feedback and preference data
- Sometimes augmented with live search or curated sources
That means the “ranking factors” are different:
- Clarity over cleverness.
Models are better at reproducing patterns than decoding nuance.
Clear, structured, unambiguous claims are easier to surface and restate. - Consensus over originality.
If you’re the only site saying something, the model is less likely
to present it as the default answer. It tends toward the center of gravity. - Reputation over raw authority.
Mentions across trusted sources, reviews, and third-party lists
matter more than your domain rating alone.
What the “best” list experiments are really telling you
Studies on self-promotional “best” lists and AI visibility show a pattern:
models often pick up and repeat structured, list-like content
(“Top X tools for Y”, “Best Z for A”) even when it’s self-serving.
Not because the model “trusts” you, but because:
- The format matches how users ask questions
- The content is easy to chunk and restate
- It fits the “answer a shopping query with a list” pattern
That’s not a license to spam fake “best” lists.
It’s a signal that structure and intent alignment matter more than ever.
Trust is the new performance constraint
Search Engine Journal is talking about a B2B trust deficit and negativity bias.
Ahrefs is showing how easy AI misinformation is to produce.
Time Magazine is rebuilding its operation around AI.
Put those together and you get a simple reality:
users are increasingly skeptical, but increasingly outsourcing judgment
to systems that are very confident and occasionally wrong.
For performance marketers, that creates three practical problems:
- Your claims will be fact-checked by a model, not a human.
If your pricing, features, or guarantees don’t match what’s on trusted
third-party sites, expect the model to “correct” you in its own words. - Negativity travels faster than your ads.
One bad review cluster or controversy can become the default summary
of your brand in an AI answer, even if your ads are spotless. - Thin funnels get punished.
If all you have is a landing page and some ad copy,
the model has nothing to work with except what others say about you.
What to actually change in your strategy in the next 12 months
Here’s how to adapt without burning your whole playbook.
1. Design for “being summarized”
Assume an LLM will compress your entire offer into one or two sentences.
Make those sentences obvious.
- Write a plain-language, one-sentence positioning statement
on your homepage and key landing pages:
“We are X for Y that does Z better by A, B, C.” - Add a short, structured “At a glance” block:
use bullets for who it’s for, key features, and proof points. - Standardize product names, plan names, and pricing language
across your site, docs, and help center so the model sees consistency.
2. Treat third-party validation as an AI ranking factor
You already know reviews and mentions help with human trust.
Now assume they also shape how models talk about you.
- Prioritize a small set of high-signal platforms in your category
(G2, Capterra, Trustpilot, niche directories) and build real review volume there. - Pitch inclusion in credible “best X for Y” lists where the editorial bar is real,
not pay-to-play. These lists are often scraped, cited, or used as training data. - Keep your profiles and listings clean, consistent, and up to date.
Outdated pricing or positioning will be repeated back to users.
3. Build “LLM-friendly” content, not just SEO content
You don’t need a new content department.
You need to adjust how you package what you already create.
- For key commercial queries (“best X for Y”, “X vs Y”, “X alternatives”),
create pages that:- Use the exact query language in plain English
- Offer clear, honest pros and cons (models like balanced takes)
- Include simple tables and bullet lists that are easy to parse
- Avoid over-optimized, keyword-stuffed content.
It confuses models and looks low-trust to humans. - Add short, direct answers near the top of pages:
“If you’re [persona], choose [option] because [reason].”
4. Re-think performance reporting in an AI-influenced world
Your existing dashboards won’t show “ChatGPT-assisted conversions.”
You’ll need to infer.
- Watch for rising branded search volume and direct traffic
in markets where AI usage is high, especially when you’re
not increasing brand spend proportionally. - Add “How did you hear about us?” fields that explicitly include
options like “ChatGPT / AI assistant” and “Recommended in an article or list.” - Segment performance by query type:
generic vs brand vs comparison vs competitor terms.
Expect generic to decay faster as AI answers get better.
5. Use AI to scale operations, not to flood the ecosystem
The temptation is to crank out infinite AI content.
That’s a fast way to become training data mulch.
Better use cases for performance teams:
- Creative and copy iteration.
Use custom GPTs or Claude Projects to generate variants,
then test ruthlessly. AI is your intern, not your strategist. - Audience and query mining.
Feed in your search term reports, social comments, and reviews.
Have the model cluster themes and surface new angles to test. - Landing page surgery at scale.
Like the “8,000 title tag rewrites” case study, but for
headlines, FAQs, and objection handling blocks that align
with how users actually ask questions.
Media buying in the age of auto-apply and black-box models
While LLMs are changing demand capture, ad platforms are
quietly pushing more automation: Google Ads recommendations,
auto-apply, Advantage+ everything.
You’re now dealing with two black boxes:
- The ad platform’s optimization system
- The AI layer that shapes what users see before they even click
The response is not to turn everything to manual.
It’s to get very clear on what you control.
What you still own as a media buyer
- Offer and positioning.
No algorithm can fix a weak offer or a confused value prop.
In an AI-summarized world, clarity wins. - Audience definition.
Even with broad targeting, your creative, hooks, and landing pages
define who actually responds. That’s your real targeting. - Guardrails.
Turn off auto-apply recommendations that expand match types,
add junk audiences, or loosen brand controls without clear upside. - Testing cadence.
You decide what gets tested, how long it runs, and what “good” looks like.
The platform optimizes within your test design.
Practical 90-day plan for AI-aggregator readiness
To turn this from theory into an actual roadmap, here’s a simple 90-day plan.
Days 1-30: Audit and baseline
- Ask ChatGPT, Claude, and Gemini 10-15 core buying questions in your category.
- Document how often your brand appears, how it’s described, and who else is named.
- Audit your top 10 landing pages for “summarizability” and consistency of claims.
- Pull branded search and direct traffic trends for the last 12-18 months.
Days 31-60: Fix the obvious gaps
- Rewrite key pages to include clear, one-sentence positioning and “at a glance” blocks.
- Create or update 3-5 high-intent comparison and “best X for Y” pages.
- Standardize product naming and pricing language across site, docs, and listings.
- Launch or refresh profiles on 2-3 high-signal review or directory sites.
Days 61-90: Integrate into performance ops
- Add AI-related options to “How did you hear about us?” in your forms.
- Update dashboards to segment performance by query type and brand vs non-brand.
- Use a custom GPT or similar to generate and test 10-20 new creative angles.
- Set a quarterly review ritual: re-run your AI queries, track changes, and adjust.
The platforms, models, and acronyms will keep changing.
The operators who win will be the ones who treat AI not as magic,
but as just another messy, powerful distribution layer that can be understood,
instrumented, and exploited with clear thinking and tight execution.