In February 2024, Gartner predicted that traditional search volume would drop 25% by 2026. It didn’t. Google’s search revenue accelerated to 17% year-over-year growth, crossing $63 billion in Q4 2025 alone. But clicks per search are falling while query volume explodes. The pie got bigger. The slices got redistributed. And most search teams are still optimizing for the old pie.

Are you still poring over spreadsheets full of organic keyword rankings like it’s 2003? Your customers don’t care where they’re getting their answers. They’re just looking for answers they can trust. And they’re finding those answers across more surfaces than your rank tracker knows exist.

If your organic strategy lives in one spreadsheet, your paid strategy in another, and your AI search strategy in a third (or nowhere), you’re optimizing for a search experience that no longer exists.

What “search” actually looks like now

Google “best tax software” right now. Go ahead, I’ll wait.

Count the surfaces on that single results page. Sponsored ads across the top. An AI Overview with its own recommendations and citations. A Reddit thread (because Google knows people trust other people more than brands). Organic listings from CNET, H&R Block, and others. A video carousel. Discussion forum links. A product carousel with images and prices. More sponsored results at the bottom. And a “People also search for” section feeding the next query.

That is one search. One keyword. And nobody owns it.

Now think about how different people actually use that page. I scroll past everything to find the Reddit thread, because I want to know what real humans recommend. My dad clicks the first sponsored ad because he doesn’t understand paid advertising (sorry, dad!) and just trusts Google to surface the best option up top. Someone else reads the AI Overview, gets a good-enough answer, and never clicks anything at all. A fourth person watches the Smart Family Money video and leaves.

Same query. Four completely different paths. Four different “winners.” And if you’re the brand celebrating a number-three organic ranking on this page, you may be missing that most of the real estate, and most of the user attention, lives somewhere other than those blue links.

This is what I mean by the total SERP experience. Your customer sees the whole page. You should too.

The AI layer changes the math

AI Overviews now appear on roughly 25% to 48% of Google queries, depending on the study. ChatGPT processes 2.5 billion prompts a day. Perplexity is up 239% year over year. These are real numbers from real platforms where real buyers are forming opinions about your brand, or not forming opinions because you’re nowhere to be found.

But before the panic sets in: AI tools still account for less than 1% of U.S. web traffic. Google sends 300x more referral traffic than all AI platforms combined. The sky isn’t falling, but the ground is shifting.

The shift that matters most is behavioral. Wynter’s 2026 research found 68% of B2B buyers now start their research in AI tools before they ever open Google. They ask ChatGPT to narrow the field, then Google the shortlist to validate. AI evaluates, Google verifies, and your website converts. If your brand is missing from that first AI conversation, you’re not even on the shortlist when the Googling starts.

Why the click data is more interesting than scary

A Search Engine Land analysis of 25 million organic impressions across 42 clients found organic CTR drops 61% when an AI Overview appears. In addition, paid CTR drops 68%.

EVERYBODY FREAK OUT!!! Right? Not quite.

Here’s what the panicked LinkedIn posts leave out: brands cited inside AI Overviews see 35% more organic clicks and 91% more paid clicks. Being in the AI Overview doesn’t cannibalize your traffic. If anything, it amplifies it. The AI Overview functions like a trust signal, a stamp of “this brand is relevant to your question” that makes people more likely to click your listing below.

The real twist, though, is that ranking well in organic doesn’t guarantee you show up in AI. Tom Capper’s research at Moz found 88% of AI Mode citations are NOT in the organic SERP for the same query. Organic and AI are pulling from different source pools. You can be number one in Google and completely invisible in ChatGPT’s answer to the same question.

And the small amount of traffic that does come from AI? It converts at more than quadruple the rate of organic, according to Semrush. These visitors arrive more informed, more intentional, and more ready to buy. Which makes sense, because they’ve already done the evaluation inside the AI interface. By the time they click, they’re just confirming and often converting.

The org chart is the problem

Most companies have SEO reporting to content, PPC reporting to demand gen, and AI search reporting to nobody. BrightEdge found 54% of organizations have handed AI search to the SEO team alone, which is a little like asking your plumber to also handle the electrical work because, hey, it’s all in the same house.

The waste from this setup is real. One branded Performance Max campaign paid roughly $500,000 for clicks that would have come through organic anyway. Google’s own research confirms: when you rank number one organically, only half your paid clicks are truly incremental. The other half? You bought what you already owned.

Meanwhile, McKinsey found that a brand’s own website makes up only 5% to 10% of the sources AI references. AI pulls from Reddit, review sites, affiliates, publishers, and user-generated content. You can have the best SEO program in your category and be completely absent from AI search results because AI is reading what other people say about you, not what you say about yourself.

The unified approach works. Level cut acquisition costs 18% and boosted SEO leads 22% by merging paid and organic for a B2B SaaS client. And we can use tools in our Level Intelligence Suite to connect performance signals across search surfaces. The channels compound each other. Treating them as separate line items on separate P&Ls leaves that compounding on the table.

Three audits you can run Monday morning

You don’t need a six-month transformation to start seeing the gaps. Three lenses, applied to your top 20 keywords, will show you where the opportunities and the waste are hiding.

Lens 1: Where do you actually appear? Check your organic rankings, paid ad coverage, and AI visibility across ChatGPT, Perplexity, and Gemini for the same set of keywords. Semrush has a free AI visibility checker. Most teams have never looked at all three surfaces side by side, and the gaps are almost always larger than they expect.

Lens 2: Where are you paying for traffic you already own? Cross-reference your number-one organic rankings with active PPC bids on the same terms. Start with branded keywords, where the waste is usually largest and the test is cleanest. If you rank first and you’re still bidding, you’re probably buying your own clicks.

Lens 3: Where is AI ignoring you? Compare your organic rankings with your AI citation presence. Only 11% of domains get cited by both ChatGPT and Perplexity, so strength in one guarantees nothing in the other. And check your robots.txt while you’re at it. If you’re blocking AI crawlers like OAI-SearchBot or PerplexityBot, you’ve pulled yourself off those shelves entirely.

This diagnostic shows you the full picture. What to do about it, the actual unification framework, is what I’m laying out at SMX Advanced.

The window won’t stay open

Generative Engine Optimization (GEO) keyword difficulty currently averages 15 to 20, compared to 45 to 60 for equivalent SEO terms. That gap will close. Once an LLM selects a trusted source, it reinforces that choice across related prompts. The brands getting cited now are training the models to keep citing them. Winner-takes-most dynamics are being baked into the weights.

Many companies are seeing search traffic drop significantly. Those same brands, the ones that get it right, are seeing the inverse when it comes to business growth. Rankings and revenue have decoupled. The brands that win from here are the ones that stopped measuring channels in isolation and started measuring the search experience their customers actually have.

We’re presenting a search unification framework at SMX Advanced in our session, “Organic, paid, and AI search: one strategy to rule them all.” If you want to stop optimizing for three separate channels and start compounding performance across every search surface, join us for the session or come find the Level team at Booth #9.

Remember: The search experience that existed in 2023 is gone. The strategy should be too.

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There’s a fundamental battle happening in search right now.

• On one side is topical authority — the darling phrase of every SEO consultant who needs to sell more content.
• On the other is brand authority — something marketers have talked about for decades, while much of search treated it as optional, vague, or something the brand team could handle after the sitemap was fixed.

Now AI has walked into the room, kicked over the furniture, eaten half the traffic, and exposed the real problem.

Search still matters. The global economy runs on people looking, comparing, buying, and solving problems through it. But the industry has a marketing problem.

And it shows. Too many SEOs have lost the plot on why people choose, remember, trust, search for, recommend, and buy from brands. AI search is making that ignorance harder to hide. That’s why brand authority wins — but not in the way most SEO dashboards suggest.

Topical authority was never supposed to mean content landfill

Before we get to AI, we need to define what topical authority was meant to be. At its best, it’s simple. 

You publish useful work, create evidence, and share expertise. Others cite you, journalists mention you, communities discuss you, and customers search for you. Over time, your brand becomes associated with the topic. That’s authority. It’s also brand building.

The problem is that much of the SEO industry hasn’t sold it that way. In practice, topical authority became a convenient commercial wrapper for content production.

SEO retainers were built around three pillars: technical, content, and links. Technical SEO became more specialized. Links were outsourced, packaged, renamed, earned through digital PR, or bought in one way or another. 

Content, meanwhile, remained the dependable agency engine — easy to sell, scope, and report. Think 4-8 blog posts a month, a topical map, a content hub, a cluster, a pillar page, and another 2,000 words on something nobody asked to read.

This wasn’t always wrong. In the pre-AI search world, content had real labor behind it. A decent article required research, writing, editing, optimization, internal linking, and promotion. That work had value. Good content could rank, attract links, build email lists, support commercial pages, and create some advertising effect through exposure.

Back in the day, we built what were often called power pages — strategic assets designed to earn links, rank, get shared, and pass equity to commercial pages. They had a purpose. They weren’t created just because the spreadsheet had another empty cell.

Topical authority changed that logic. It turned “let’s create something worth citing” into “let’s cover every possible keyword in the topic map and hope Google mistakes volume for expertise.” That was the original sin.

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Authority is what others say about you

Authority isn’t created by what you publish on your own site. It’s created when you become a recognized source.

Former Google engineer Jun Wu described this in terms of “mention information” — how search engines analyze natural language, identify topic phrases and sources, cluster related terms, and map associations between sources and topics. 

In plain English, they can recognize when certain brands, people, domains, and entities are repeatedly mentioned in relation to specific topics.

Today, SEOs call that brand co-occurrence. The idea isn’t new. When authoritative sites, journalists, communities, reviewers, experts, and customers consistently mention your brand in relation to a topic, you become associated with it — not because you published hundreds of near-identical articles, but because the wider web treats you as relevant.

Topical coverage is what you say about yourself. Authority is what the market says about you. AI search makes that difference hard to ignore.

The smash burger test

Suppose you want to become an authority in the smash burger industry. You probably don’t, but some topical authority consultant calling themselves a “semantic SEO” is likely pitching it to a fast food brand right now.

An SEO version of topical authority would probably begin with a map:

• What is a smash burger?
• Best meat for smash burgers.
• History of smash burgers.
• Smash burger recipes.
• Smash burger toppings.
• Smash burger glossary.
• Best smash burger restaurants.
• How to make a smash burger at home.

There’s nothing inherently wrong with that. If you run a serious smash burger publication, restaurant group, food brand, or equipment business, some of it might be useful. But authority doesn’t come from publishing those pages.

Real authority looks different. You create original data on the fastest-growing smash burger chains. You publish an index of the best-rated smash burger restaurants in the U.S. and U.K. You interview chefs, test meat blends, and produce videos people actually watch. 

You become a source journalists use when covering the category. Food creators reference your data. Restaurant owners subscribe to your newsletter. People search for your brand plus “smash burger report.”

That’s topical authority. It’s also brand authority.

The thin SEO version is publishing thousands of keyword pages and internally linking them until your CMS starts begging for death. The real version is becoming known.

AI has broken the old content economics

The old commercial defense of topical authority was traffic.

Brands didn’t hire search marketers because they had a deep spiritual yearning to become encyclopedias. They hired them for organic revenue growth — to appear when customers searched, and to drive clicks, leads, and sales.

Informational content was sold, in part, as advertising. Someone searches a question, lands on your article, and sees your brand. Maybe they join your email list, return later, or buy.

That model was always more fragile than the industry admitted. Most users don’t sit around thinking about your B2B SaaS platform, your dog food brand, or your running shoe category page. 

Ask someone to name 10 toothpaste brands, and they’ll struggle, despite a lifetime of exposure. Ask them to recall the last ten TikToks they watched, and watch their face collapse.

Advertising works through memory structures, distinctive assets, repeated exposure, and relevance. A single accidental visit to a generic “what is” article was never the brand-building miracle some content marketers claimed.

Now AI has made the economics worse. For many informational searches, answers are increasingly synthesized before the click. From the user’s point of view, that’s often a better experience.

My dad is in his 70s. He loves AI Overviews. He doesn’t want to click through three ad-infested recipe pages, dodge newsletter popups, reject cookies, scroll past a life story, and finally find how long to boil an egg. He wants the answer.

Users aren’t mourning your lost organic session. They’re getting on with their lives. That’s the uncomfortable truth.

If the click disappears, much of the supposed advertising effect of informational content disappears with it — no logo exposure, no distinctive assets, no remarketing pixel, no email capture, and no carefully designed journey. Just your content absorbed into a synthesized answer, and maybe a small source link on the side.

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AI citations aren’t the same as human citations

This brings us to another emerging industry obsession: AI citations. 

The small source boxes in ChatGPT, Gemini, Perplexity, AI Overviews, and other AI search experiences are being treated as the new holy metric. Agencies, tools, and consultants are already building around it.

The SEO industry loves a single metric — domain authority, traffic, keyword positions, share of voice, and now AI visibility. The problem is that an AI citation isn’t the same as a human citation.

An AI citation is often a helpful link — a reference, a retrieval artifact. It’s directionally useful. It can show what sources a system uses to support an answer, and whether your content is accessible, relevant, and being surfaced in certain contexts.

But it’s not the same as:

• A journalist choosing to cite your research. 
• A customer recommending you in a forum.
• A creator reviewing your product.
• A trade publication naming your brand as an expert source.

Human citations are evidence of market recognition. AI citations are evidence of machine retrieval. Don’t confuse the two.

The goal isn’t to be scraped. It’s to be recommended.

Brand search is the cleaner signal

If you want a better proxy for whether your authority is growing, look at brand search.

People search for brands they know, are considering, have bought from, or were recommended. Brand search isn’t perfect, but it’s much closer to commercial reality than counting how often a chatbot footnotes your blog post.

That’s why share of search matters. It gives you a directional view of market demand and mental availability. If more people are searching for your brand relative to competitors, something is happening. Your advertising, PR, product, reviews, word of mouth, content, partnerships, social presence, and customer experience are creating demand.

This is where the “but this is just SEO” crowd starts clearing its throat.

It’s not “just SEO.” Or rather, it’s only SEO if you define it so broadly that it includes every activity that might influence a search result. That’s strategic ambiguity. It lets everyone claim they were doing the future all along.

Most SEO retainers weren’t building brand fame. They were producing content, fixing technical issues, buying or earning links, and reporting rankings. Sometimes it worked — sometimes very well. But the average topical authority strategy wasn’t a sophisticated brand visibility program.

Traditional SEO still matters

None of this means you abandon traditional SEO. Buyer-intent rankings, category pages, product pages, local pages, technical SEO, internal linking, structured data, reviews, and crawlability matter. 

Search still works as a shelf. Many brands are discovered for the first time in supermarkets. The same is true in Google. If someone searches “emergency locksmith near me,” “best trail running shoes,” or “meeting intelligence software,” you want to appear.

Being found still matters, but it’s not the same as being recommended. Traditional SEO helps you get found, while brand authority drives recommendation. 

AI search shifts the balance toward the latter, synthesizing options, reducing uncertainty, and often naming brands, products, and solutions directly.

The new job is meaningful visibility

Semrush accidentally said the quiet part out loud with its April Fools’ “Brand Visibility Expert” stunt, where employees changed their titles on LinkedIn. It was a joke, but not entirely. 

The company later described AI visibility tools that track brand visibility, mentions, prompts, perception, and competitor presence in AI search. That’s where the market is going.

The future of search marketing isn’t just search engine optimization. It’s brand visibility across the network.

That means increasing meaningful visibility in the places where humans and AI systems encounter information: 

• Search engines.
• AI answers.
• Review sites.
• Communities.
• YouTube. 
• Reddit.
• Trade media.
• News sites.
• Podcasts.
• Influencers.
• Comparison pages.
• Customer reviews.
• Social platforms.
• Partner ecosystems.
• Your own site.

The web is now the surface, and your website is just one part of it. This is the shift many SEOs don’t want to face. Many are used to optimizing owned pages for search engines. 

The next era is about optimizing a brand’s presence across the web. That requires different work.

Start with positioning

If you want to build brand authority in AI, start with positioning.

• Who are you for?
• What problem do you solve?
• How do you solve it better?
• What should the market associate with you?
• What proof supports that claim?

These aren’t fluffy brand questions. They’re search questions now.

• A locksmith isn’t only an emergency locksmith. They may install commercial locks, repair window locks, replace garage locks, secure doors, and provide security advice. 
• A running shoe retailer may want to be known for trail running expertise, fast delivery, wide range, gait analysis, competitive pricing, or specialist advice. 
• A SaaS platform may want to be known for extracting meeting intelligence that helps sales teams improve conversion.

These are performance attributes — the reasons people choose you. Your search strategy should reinforce them.

If your pet food brand specializes in sensitive stomachs, you need to be visible around dog dietary problems — not just on your blog, but in vet commentary, buyer guides, reviews, creator content, journalist coverage, customer stories, comparison pages, and data studies. 

These are the places where humans and AI systems learn what’s credible. That’s brand authority.

Create things worth being cited by humans

The rule for AI-era content is simple. Every piece of content should have real-world marketing value at publish.

If one person encounters it, they should understand your brand better, feel more positively about it, remember something useful, or be more likely to trust you.

If content only makes sense as an SEO asset after it ranks, it’s probably weak.

This means you stop creating “dead” content. Instead:

• Create original research. 
• Publish category data. 
• Build useful tools. 
• Share expert commentary. 
• Produce strong product comparisons. 
• Release reports journalists can cite. 
• Create opinionated guides. 
• Review products properly. 
• Explain problems better than competitors. 
• Make videos people want to watch. 
• Turn internal data into public insight. 
• Build assets that earn links and mentions.

Do fewer things. Make them better. Promote them harder.

Brands have limited budgets — smaller ones have even less room for waste. Spending thousands on a content library that repeats known information may be less effective than using the same budget to create one excellent data study, seed it with journalists, get creators talking, earn reviews, improve product pages, and run ads that make people search for your brand.

Ask yourself, “What use of this budget is most likely to increase brand search, links, mentions, reviews, and recommendations?”

Fitness times visibility equals success

A useful idea from network science applies here: success is driven by fitness multiplied by visibility.

• Fitness is your ability to outperform alternatives — product, service, price, expertise, speed, range, design, convenience, proof, reviews, and customer experience.
• Visibility is how often and how meaningfully the market encounters those signals.

Fitness without visibility is a brilliant brand nobody knows. Visibility without fitness is hype — and it usually collapses. 

That’s how preferential attachment starts. Brands that are talked about get talked about more. Brands that are searched get searched more. Brands that earn links earn more links. Brands that become default sources are cited more often. Brands that sell more get more reviews, more mentions, more data, and more presence.

AI accelerates this dynamic, consuming the web faster than humans and reinforcing those signals at scale. If your brand has dense, consistent, and credible associations with the problems you solve, you reduce uncertainty that you’re a good recommendation.

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What actually wins in AI search

Brand authority wins in AI — because real topical authority was always brand authority.

The version of topical authority that deserves to survive is the one where a brand becomes a genuine source in its category — creating useful information, earning mentions, building demand, getting searched, getting cited, and becoming associated with the problems it solves.

The version that deserves to die is the one where a brand publishes endless keyword-targeted sludge and calls the result authority.

AI hasn’t killed SEO. It’s killed the illusion that mediocrity deserves traffic.

The search marketers who win next won’t be the ones who publish the most. They’ll be the ones who make brands more meaningfully visible across the internet. They’ll understand positioning, PR, content, technical SEO, reviews, creators, category demand, links, mentions, and brand search as one connected system.

The goal isn’t to optimize for search engines, but for the network they use to understand the world.

Build the brand. Make it visible. Make it worth recommending. Everything else is just content with delusions of grandeur.

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Lincoln-Way East and Sandburg both move up, while Providence joins rankings topped by St. Laurence.

Top 10

With records through Sunday and previous rankings in parentheses.

1. St. Laurence 24-1 (1): In a game played over two days due to rain, Oakton commit Sean Popp finishes the Vikings’ 5-4 comeback win over Mount Carmel with a walk-off RBI single.

2. Lincoln-Way East 21-2 (2):Rocco Triolo homers twice and drives in four runs as the Griffins complete two-game sweep of Homewood-Flossmoor with 9-6 victory.

3. Lockport 17-7 (3):Anthony Farina throws complete-game gem, striking out eight and allowing just one unearned run on three hits in 5-1 win over Lincoln-Way Central.

4. Brother Rice 17-8 (4): Have a week, Luca Agne. The junior infielder announces his commitment to Boston College and drives in eight runs over four games, all wins.

5. Lincoln-Way West 16-4 (6): Wisconsin-Platteville recruit Matthew Oberts drives in three runs and scores two more for the Warriors, who roll to 12-0 win over Bradley-Bourbonnais.

6. Mount Carmel 16-9 (5): Wake Forest commit TJ McQuillan finishes 2-for-4 with triple and three RBIs to lead Caravan to 13-3 win over Fenwick in six innings.

7. Lemont 18-3 (7):Zane Schneider triples and scores in the first inning to provide quick spark as Lemont goes on to 3-0 victory over Hanover Central from Indiana.

8. Sandburg 15-6 (10): The Eagles sweep two-game series with district rival Andrew as senior right-hander Peter Jurcenko dominates Game 2, throwing five shutout innings in 4-0 win.

9. St. Rita 15-6 (8):Jayden Ibarra throws four strong innings and allows just one run as the Mustangs beat Providence 6-3, salvaging win in tough 1-3 week.

10. Providence 13-9 (NR): Junior righty Kobe Jordan delivers five shutout innings in 2-1 victory over Lincoln-Way East for the Celtics, who also pick up 4-2 victory over St. Rita.

Player of the Week

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The trial is live, limited to the U.S. for now, and moving faster than you likely expected. ChatGPT ads launched Feb. 9 for logged-in users on Free and Go tiers, with 600+ advertisers already in. 

With 800 million weekly active users, a global rollout of ChatGPT ads is a matter of when, not if. 

OpenAI has confirmed the next expansion to Australia, New Zealand, and Canada. The latest update from Adthena trialists suggests the UK could see ads as early as mid-May.

We’ve tracked ChatGPT ad placements since rollout. With an index of 50,000+ daily placements across B2B software, ecommerce, fintech, and consumer verticals, we’ve had a front-row view of how this format is evolving. Here’s what we’ve found.

What ChatGPT ads actually look like

ChatGPT ads appear inline within conversation responses. When you ask something with commercial intent like “best weekend getaway” or “top running shoes under $100,” a sponsored result can appear alongside the AI’s answer, clearly labeled “Sponsored.”

This isn’t a search bar. It’s a conversation. Users arrive already engaged, already researching, often close to a decision. 

The format is tighter than traditional search: no sitelinks or extensions — just a headline, short body copy, and a destination.

But here’s what we didn’t expect. Our data shows what we’re calling the Adthena “Double Parked” phenomenon: a single brand appearing twice in the same response.

We spotted New Balance with two separate sponsored placements in one ChatGPT answer. This raises a key question around visibility, frequency, and what it means to own a conversation on this platform.

10 things we’ve learned from 50,000+ daily placements

If you move fast, this is a rare moment: a new format, an uncontested landscape, and data most competitors don’t have yet. Here’s what it shows.

1. Headlines follow a “Brand: Benefit” formula. A name, a colon, a value claim. Think “Betterment: 5.25% APY Cash Account.” Dominant across top performers.
2. Almost every ad leads with the brand name. Awareness thinking for a format where users are already deep in a conversation, not just entering a search bar.
3. Headlines average just 30 characters, with a ceiling around 36. The constraint forces hyper-concise messaging and every word earns its place.
4. Body copy runs around 19 words, structured as two tight sentences. One lead proof point, one offer or nudge. One reason to click.
5. Context mirroring is a defining feature. The strongest ads echo the user’s query directly. A running shoe ad referencing “the transition from 5k to 21.1k” isn’t a coincidence.
6. The $ symbol drives conversion. Specific dollar figures, precise APY rates, credit amounts. Concrete claims consistently outperform vague promises in intent-heavy environments.

7. Numbers dominate body copy. Specs, trial lengths, rates. Hard numbers feel more native and trustworthy than soft superlatives in a research-led environment.
8. “Free” is the most common conversion lever. It removes friction for users already in research mode and close to a decision.

9. CTAs are action-specific and generic “Learn More” is virtually absent. “Open Account,” “Shop Cell Phones,” “Claim Credits.” Every CTA names the brand, offer, or next step.

10. Tone is confident and measured. Exclamation marks are rare. The best ads mirror ChatGPT’s calm register—hype punctuation kills trust here.

What this means for your paid search strategy

Top-performing brands in ChatGPT don’t repurpose Google ad copy and hope for the best. They write for a conversational, intent-rich environment where users are already halfway through a decision before the ad appears.

Lead with your brand name. Anchor value in specifics. Make low-friction offers central to your creative. If you’re not thinking about context mirroring, you’re leaving performance on the table.

The bigger question is visibility. If your competitors show up in ChatGPT conversations and you don’t, you’re not just missing clicks — you’re missing the conversation.

See exactly what’s happening with Adthena’s ChatGPT Ads Intelligence

Knowing the trends is one thing. Knowing what your competitors are doing on your exact prompts is another. That’s the problem we set out to solve.

Right now, ChatGPT ads give you impressions and clicks — nothing more. No competitive context, no prompt-level visibility, no insight into who else appears in the same conversations or where you’re missing coverage. You’re optimizing blind.

Adthena’s ChatGPT Ads Intelligence changes that. Here’s what you get.

Your performance, in context

The Ads Performance tab gives you a live snapshot of your ChatGPT activity: ad presence rate, top-performing intent group, total impressions, average CTR, and unique competitors detected. The trend chart shows your presence over time so you can clearly see whether you’re gaining or losing momentum.

Know which topics you’re winning and where to close the gap

The Topics and Keywords Analysis view breaks down performance by intent group, showing your ad presence rate against the competitor average. Each group includes a built-in tactical recommendation, so you always know your next move.

See your own ads as users see them

The Ads Sampling tab shows all your ChatGPT creatives with their headline, description, image, and format. The insight panel highlights your top-performing creative and surfaces optimization opportunities, like pairing a price anchor with a time-limited offer.

Understand exactly what competitors are running

The Competitor Creative Analysis panel breaks down rival ads across your tracked prompts: the images they use, the dominant copy themes, and their format mix. No more guessing what your competition is doing.

Never miss a shift in the competitive landscape

The Ads Benchmarking tab shows who’s advertising on your prompts and how their presence changes week to week. The “What changed this week?” feed flags new entrants and share shifts in plain language before your next campaign review.

Find the gaps before your competitors do

The Competitor Gap Analysis table shows every prompt where competitors have presence and you don’t, flagged by intent group and competitor count. A clear, prioritized view of where to expand your ChatGPT coverage.

The first prompt is the new first click

We’re tracking early-stage data from a platform still in limited rollout. As OpenAI expands to new countries and the advertiser base grows, the competitive landscape will shift fast. Brands building their ChatGPT presence now — learning the format, testing creative, mapping competitive gaps — will have a meaningful head start over those who wait.

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Across 90 prompts we tested in ChatGPT, commercial prompts triggered web searches 78.3% of the time. Informational prompts did so just 3.1%.

That gap changes what you should write if you want to appear in a ChatGPT answer.

ChatGPT doesn’t pull every response from the same place. Some answers come from training data; others use live web search — a behavior called query fan-out. The model expands your prompt into multiple background searches, then retrieves and synthesizes across those subtopics. If your page isn’t on those branches, it won’t be pulled in.

So the question is no longer just how to rank. It’s which pages open the fan-out door in the first place.

In our sample, informational pages didn’t. Read on to discover where the system went instead.

We tested 90 prompts across three industries: beauty, legaltech/regtech, and IT. We analyzed prompt intent, downstream query expansion, and the intent those expansions reflected.

Here’s the breakdown and the core finding: most queries aligned with commercial intent, not purely informational prompts.

Why this question matters now and how query fan-outs come into play

Query fan-outs change the content game because the system isn’t limited to the literal prompt.

It expands the request into multiple background searches, then retrieves and synthesizes across those subtopics.

Fan-outs trigger parallel web searches tied to the initial prompt, creating opportunities for retrieval, mention, and link citation.

Multi-query expansion is a core design pattern in modern generative search systems. Google describes AI Mode this way: it breaks a question into subtopics, searches them in parallel across multiple sources, then combines the results into a single response.

That raises a strategic SEO question: should you invest more in top-of-funnel educational content, or in lower-funnel comparison, shortlist, and recommendation content?

This experiment framed that problem.

The objective was to test, across selected industries, where fan-out appears by intent category: informational, commercial, transactional, or branded.

The initial hypothesis was direct: informational prompts wouldn’t trigger fan-out, while commercial prompts would, and those fan-outs would stay at the same funnel level or move lower.

We found that ChatGPT-generated fan-outs are overwhelmingly associated with commercial intent.

Disclaimer: This experiment measures observed prompt expansion behavior in ChatGPT. Google AI Mode is cited only as context to show multi-query expansion as a broader pattern in generative search, not as proof of ChatGPT’s internal architecture.

The setup: what we tested

The core sample includes 90 numbered prompts, heavily weighted toward informational intent.

Prompt intent	Prompts	Share of sample	Prompts with fan-out	Fan-out rate
Informational	65	72.2%	2	3.1%
Commercial	23	25.6%	18	78.3%
Branded	1	1.1%	0	0.0%
Transactional	1	1.1%	0	0.0%

The sample skews heavily toward informational prompts, with some commercial ones and minimal branded and transactional queries.

We structured the experiment around the sectors in the brief: beauty/personal care, legaltech/regtech, and IT/tech.

The result: commercial prompts triggered almost everything

The main finding is clear.

Out of 90 prompts, 20 triggered fan-out. Of those, 18 were commercial and 2 informational.

Informational prompts made up about 10% of fan-out triggers (2 of 20). When they did trigger expansion, they were rewritten into more evaluative, solution-seeking subqueries.

In other words, 90% of fan-out-triggering prompts in the core sample came from commercial intent.

The contrast is stronger than the raw totals suggest. Commercial prompts triggered fan-out 78.3% of the time; informational prompts did so just 3.1%.

This supports the working hypothesis: in this sample, fan-out was overwhelmingly a commercial phenomenon.

Those 20 prompts produced 42 fan-out queries — an average of 2.1 per triggered prompt.

Of those 42 fan-out queries:

• 39 were commercial.
• 2 were branded.
• 1 was informational.

Even when a prompt triggered expansion, the system usually shifted toward comparison, product evaluation, feature filtering, shortlist creation, or brand-specific exploration — not broad educational discovery.

Methodology: how we performed the analysis

The experiment used 90 prompts across three industries, mostly informational, with a smaller set of commercial prompts and minimal branded and transactional queries.

In the analysis, we have:

• Selected a representative battery of prompts.
• Identified the fan-outs.
• Classified each fan-out by intent.
• Observed distribution by prompt metadata.

The analysis then followed three steps:

1. Each prompt was classified according to prompt-intent labels.
2. We counted the prompts triggering fan-out (at least one).
3. We inspected the observed expansion queries and their assigned fan-out intent labels.

That produced two distinct but complementary views:

• A prompt-level view, asking whether a given prompt triggered fan-out at all.
• A fan-out-query view, asking what kind of intent the downstream expansion actually took.

That distinction matters: the first shows which prompts open the fan-out path, while the second shows where the system goes once it opens.

Interpreting the results: fan-out tends to move down-funnel

The cleanest interpretation is that, in this sample, fan-outs behave less like open-ended topic expansion and more like assisted decision support.

Commercial prompts almost always opened the door.

Once they did, fan-outs usually stayed commercial.

The system expanded into comparisons, feature-based filtering, product lists, pricing-adjacent queries, and brand-specific evaluations.

A few examples make that concrete.

• “Suggest the best accounting software for small business and explain why” expanded into a commercial comparison query around features.
• “What are the top AI document management systems for lawyers?” expanded into multiple product-oriented legaltech queries.
• “What are the best products for skin care?” expanded into a shortlist-style query around product categories and reviews.

The two informational exceptions are even more revealing than the rule.

• “I need an open-source document management system. What can you suggest?” was labeled informational at prompt level, but the resulting fan-out moved into solution recommendation.
• “AI tools for legal research and document automation” also moved into a clearly commercial/evaluative downstream query.

So, even when the prompt starts broad, fan-out often translates that breadth into a lower-funnel retrieval path.

What this means for content strategy

The takeaway isn’t to stop writing informational content.

It’s this: informational content alone is unlikely to align consistently with fan-out expansion, at least in this dataset.

If your goal is visibility in AI answers tied to product selection, vendor discovery, or option narrowing, you need stronger coverage of pages and passages that match those downstream commercial branches.

That may include:

• best-of and shortlist pages
• comparison pages
• “which tool should I choose” pages
• feature-led category explainers
• alternatives pages
• evaluation FAQs
• recommendation-oriented paragraphs embedded inside broader educational pages

In practical terms, your content model shouldn’t be just ToFU or BoFU, but ToFU with commercial bridges.

A broad article can still help, but it should include passages the system can easily reformulate into decision-support subqueries.

A purely educational piece that explains a category without naming products, tradeoffs, features, use cases, pricing logic, or selection criteria is much less likely to align with the fan-out paths seen here.

Put simply: Don’t just answer the obvious question — anticipate the next evaluative step the system is likely to generate in the background.

Limitations

This result is directional, not universal.

• 90 prompts reveal a pattern, but not a stable law of AI retrieval behavior.
• The prompt mix is uneven. Informational prompts dominate the sample, while branded and transactional prompts are barely represented. That means those findings aren’t proof of absence.
• The dataset spans industries but isn’t normalized by brand, wording style, or use case. Some sectors may be easier to express in product-discovery language.
• This is an observational analysis of recorded fan-outs, not a controlled platform-level test. It shows what happened in this prompt set, not how ChatGPT always behaves.
• Google’s description of fan-out provides context, but this isn’t a Google AI Mode test. It’s a ChatGPT-focused prompt and fan-out dataset. The takeaway is strategic, not architectural.

What to test next

The next version of this experiment should isolate the question more aggressively and expand the dataset.

A follow-up should map triggered fan-outs back to specific content formats.

The goal isn’t just to confirm that commercial intent wins. It’s to identify which page templates and passage structures best cover the fan-out branches AI systems prefer.

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