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Amol Ghemud Published: February 18, 2026
Summary
AI search is shrinking brand discovery. Instead of showing many results, platforms like ChatGPT and Google AI Overviews give one direct answer with only a few citations, usually favoring established brands.
New brands struggle because AI engines rely on existing authority and third-party mentions. This makes organic growth harder and increases dependence on paid ads.
To stay visible, brands must focus on Generative Engine Optimization by creating structured, citation-ready content and building strong off-site credibility before competitors dominate AI answers.
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I’ve spent 8 years helping brands grow through search. And I’m watching the playbook I built my career on become obsolete. Not slowly. Fast.
Last month, I ran an experiment that changed how I think about growth. I asked ChatGPT, Perplexity, and Google’s AI Overview the same question: “Best growth marketing agency for Fintech in India.” The answers cited 3-4 brands. All established. All with years of content indexed across the web.
New agencies with better work, sharper strategies, and stronger client results? Nowhere to be found. Not because they’re worse at what they do. Because the AI simply doesn’t know they exist.
That experiment forced me to confront something uncomfortable: the growth model that built my company, and hundreds of others like it, is fundamentally breaking. And most founders haven’t noticed yet.
This article breaks down exactly why brand discovery is about to get brutally harder for new entrants, what’s driving the shift, and what growth teams need to do differently starting now.
How Brand Discovery Worked for Two Decades (And Why It’s Breaking)
For 20 years, digital brand growth followed a predictable sequence. You created content, optimized it for search, ranked on Google, and got discovered by people actively looking for what you offered. The system was imperfect, but it was open.
A bootstrapped D2C brand in Pune could outrank a multinational on the right long-tail keyword. A two-person Fintech startup could steal clicks from an enterprise player with a better blog post. Google’s 10 blue links created a competitive arena where quality and strategy could beat budget and brand size.
That arena is closing.
AI answer engines don’t show 10 options. They synthesize information from across the web and present one answer, sometimes with one or two citations. The user gets their answer without ever scrolling, browsing, or clicking through to your site.
According to research from AirOps and Kevin Indig’s 2026 State of AI Search report, only about 23% as many URLs appear in AI Overviews compared to traditional search results. That’s a massive contraction in the number of brands that get any visibility at all from a single query.
The browsing phase, where new brands used to get their shot, is disappearing. And with it goes the primary discovery mechanism that an entire generation of startups relied on.
The Incumbency Bias Problem: Why AI Search Favors Established Brands
Here’s the part that should concern every founder building a new brand right now.
AI answer engines are trained on existing web content. The models that power ChatGPT, Perplexity, Claude, and Google’s AI Overviews have ingested billions of pages. But they’re not ingesting content equally. They’re weighted toward established, authoritative, frequently cited sources.
If your brand launched 18 months ago, your content isn’t deeply represented in the training data. Your case studies haven’t been picked up by third-party publications. Your domain authority hasn’t had time to compound. You’re not in the AI’s knowledge graph.
And that creates a structural problem that’s fundamentally different from the old SEO challenge.
In traditional search, a new brand competed on a level playing field. Better content, smarter keyword targeting, and faster site speed could close the gap with incumbents. The ranking algorithm rewarded relevance and quality, regardless of how long you’d been around.
AI answer engines operate differently. They don’t just look at relevance. They look at how well-established, well-cited, and well-represented a source is across the web. Edelman’s research on AI search visibility found that earned media is the single most important driver of brand visibility in AI-generated responses. New brands, by definition, have less earned media, fewer citations, and thinner authority signals.
The result? Incumbency bias becomes structural, not just competitive. The AI isn’t choosing to ignore new brands. It was never trained to recognize them.
The Discovery Funnel Is Collapsing
To understand the full impact, think about how the customer discovery funnel used to work.
Someone has a problem. They search for solutions. Google shows them 10 results. They click on 3-4 of them. They browse, compare, evaluate. Over the course of that research session, they might discover a brand they’ve never heard of. Maybe it’s a startup with a compelling landing page. Maybe it’s a niche player with a killer case study.
That middle-of-funnel exploration was where new brands earned their first customers.
Now consider the same journey through AI search. Someone has a problem. They ask ChatGPT or Perplexity. The AI gives them a direct answer, citing 1-2 sources. The user gets what they need and moves on. No browsing. No discovery. No accidental encounter with a brand they didn’t already know.
Gartner predicts traditional search engine traffic will drop 25% by 2026. The AirOps research found that only 30% of brands maintain visibility from one AI answer to the next. And 85% of brand mentions in AI answers originate from third-party pages rather than the brand’s own website.
The consideration set is shrinking from seven options to one or two. If you’re not in that answer, you’re not in the conversation. You’re invisible.
The Inversion: Brand Now Comes Before Traffic
This is the shift that most growth teams haven’t processed yet. And it might be the most consequential change in digital marketing since Google launched AdWords.
The old growth equation was straightforward: create great content, drive traffic, build brand recognition over time. Traffic came first. Brand followed.
The new equation is inverted. You need brand recognition before the AI will cite you. You need to be known to get known.
Think about why. When an AI model decides which sources to cite in its answer, it’s evaluating authority signals: how often your brand appears in high-quality publications, how many other credible sources reference you, whether your content has been cited in places the model trusts.
For established brands, these signals already exist. They’ve accumulated over years. For new brands, they don’t. And there’s no shortcut to manufacturing them overnight.
This inversion creates a catch-22 that didn’t exist in the Google-dominant era. To get AI citations, you need brand authority. To build brand authority efficiently, you used to rely on organic search traffic. But organic search traffic is declining. The system that used to bootstrap new brands into visibility is losing its power precisely when new brands need it most.
What This Means for Customer Acquisition Costs
Let’s follow the economics.
If organic discovery declines and AI answer engines favor incumbents, new brands lose their most cost-effective growth channel. What’s left?
Paid acquisition. And paid acquisition costs are already moving in one direction: up.
When organic discovery was strong, a new brand could build a content engine, rank for dozens of keywords, and acquire customers at a fraction of the cost of paid ads. Content marketing served as a counterweight to paid media, keeping blended CAC manageable.
As that counterweight weakens, new brands become increasingly dependent on paid channels where they’re competing against incumbents with bigger budgets, more data, and better economies of scale.
The result: customer acquisition cost goes up, the capital required to launch and scale a brand goes up, and the barrier to entry for new market entrants gets higher.
This isn’t speculation. It’s the logical consequence of a discovery system that concentrates visibility among established players while making it structurally harder for new ones to break through.
How AI Engines Actually Decide What to Cite
Understanding how AI answer engines select sources is critical for any brand trying to maintain or build visibility. While each platform has its own approach, the research points to several common signals.
Freshness matters significantly. The AirOps study found that pages not updated quarterly are three times more likely to lose AI citations. Over 70% of all pages cited by AI have been updated within the past 12 months. Stale content doesn’t just underperform. It actively loses ground once fresher alternatives appear.
Structured content gets cited more. Pages with sequential headings and rich schema markup showed 2.8 times higher citation rates. AI models need to extract clean, self-contained answers from your content. If your page is a wall of text with no clear structure, the model will skip you for a competitor whose content is easier to parse.
Off-site credibility drives visibility more than on-site content. About 48% of AI citations come from community platforms like Reddit and YouTube. And 85% of brand mentions originate from third-party pages rather than owned domains. Your own blog matters, but what others say about you matters more.
Dual visibility (mentions plus citations) creates stability. Brands earning both signals showed 40% higher likelihood of reappearing across AI answers. But only 28% of answers include brands with both types of visibility. Most brands are either mentioned or cited, rarely both.
The GEO Imperative: What New Brands Need to Do Differently
If you’re building a brand right now, the single most important thing you can do isn’t optimize for Google rankings. It’s figure out how to get into the AI’s answer before your competitors permanently lock you out.
This discipline has a name: Generative Engine Optimization, or GEO. It’s the practice of optimizing your brand’s presence specifically for AI-generated search results across platforms like ChatGPT, Perplexity, Google AI Overviews, Claude, and Gemini.
GEO isn’t just repackaged SEO. While the underlying principles share DNA (authority, relevance, trustworthiness), the execution is different in several critical ways.
1. Build Citation-Worthy Content, Not Just Ranking Content
In SEO, the goal was to rank on page one. In GEO, the goal is to create content that an AI model would want to quote, cite, or reference when answering a query.
That means every key section of your content should include self-contained, extractable statements with specific data. If an AI could pull one sentence from your article and use it to answer a user’s question accurately, you’ve written a citation-worthy sentence.
Example of an extractable sentence: “In our work with 150+ growth clients, we’ve found that brands investing in GEO alongside SEO see 40-60% higher citation rates in AI-generated answers within 6 months.”
Example of a non-extractable sentence: “Our approach helps brands grow their visibility significantly.”
The first sentence can be quoted by an AI. The second can’t, because it lacks specificity, data, and context.
2. Win the Off-Site Game
Since 85% of brand mentions in AI answers come from third-party sources, your owned content alone won’t be enough. You need a deliberate strategy for earning mentions on platforms that AI models trust and frequently cite.
That includes industry publications, community platforms like Reddit and Quora (where real users discuss real experiences), podcast appearances, guest articles on authoritative sites, and collaborative content with other brands or experts in your space.
Think of it this way: every external mention of your brand is a vote in the AI’s knowledge graph. The more votes you have from credible sources, the more likely you are to appear in AI answers.
3. Update Relentlessly
Freshness is now a survival requirement. Quarterly content updates aren’t optional. They’re the minimum bar for maintaining AI visibility.
Build a content refresh calendar. Prioritize your highest-traffic pages and most citation-worthy content. Update statistics, add new case studies, refresh examples, and ensure your publish dates reflect the most recent edit.
Pages that go stale don’t just lose ranking. They lose AI citations. And once a fresher competitor takes your spot, you rarely get it back without a significant update.
4. Structure for Extraction, Not Just Reading
AI models don’t read articles the way humans do. They extract. They parse. They look for self-contained sections that answer specific questions.
Structure your content with question-based headings that match real search queries. Start each section with a direct, quotable answer in the first one or two sentences. Then add supporting evidence, nuance, and context.
Use schema markup (Article, FAQ, HowTo) to help AI crawlers understand the structure and intent of your content. Pages with proper schema showed significantly higher citation rates in multiple studies.
5. Audit Your AI Presence Monthly
Most growth teams track their Google rankings religiously but have no idea how their brand appears in AI answers. That’s a blind spot you can’t afford.
At minimum, once a month, search your core queries in ChatGPT, Perplexity, Google AI Overview, and Gemini. Document whether your brand appears, what’s being said about you, which competitors are cited, and what gaps exist that you could fill.
This isn’t just a monitoring exercise. It’s a strategic input. Every gap you identify is a content opportunity. Every competitor citation is a playbook you can reverse-engineer.
The Compounding Advantage: Why Starting Now Matters
Here’s the part that makes this urgent rather than merely important.
AI visibility compounds. The brands that get cited early build stronger authority signals, which leads to more citations, which leads to stronger authority signals. It’s a flywheel.
Conversely, brands that ignore this shift fall into a negative spiral. Less visibility means fewer citations, which means less authority, which means even less visibility. The gap between AI-visible brands and AI-invisible brands will widen every quarter.
Edelman’s research on GEO found that early movers have a significant advantage, comparable to the early days of SEO when forward-thinking brands could establish dominant positions before their competitors understood the game.
Three years from now, we’ll look back and realize the brands that survived weren’t necessarily the ones with the best product. They were the ones that figured out how to exist in the AI’s answer while the window was still open.
That window is open right now. It won’t stay open forever.
A Real-World Example: What GEO Impact Looks Like
To ground this in real numbers, consider what we’ve observed across our client work at upGrowth.
One Fintech client in the fintech space was invisible in AI answers despite having strong Google rankings for 30+ keywords. After a 90-day GEO sprint focused on structured content, third-party mentions, and monthly freshness updates, they started appearing in ChatGPT and Perplexity answers for their three most important buyer-intent queries.
Another client, a D2C food brand in Dubai, went from 20K to 2M AED/month in revenue growth partly by building the kind of authoritative, citation-rich content that AI engines now reward. Their case study has been cited in multiple AI-generated answers about growth strategies in the GCC market.
The pattern is consistent: brands that deliberately optimize for AI citation earn compounding returns. Brands that don’t, lose ground to those that do.
What Growth Teams Should Do This Week
If you’ve read this far, you’re probably thinking about what this means for your own brand. Here’s a practical starting point.
Day 1-2: Run a baseline AI audit. Search your top 10 buyer-intent queries in ChatGPT, Perplexity, Google AI Overview, and Gemini. Document who gets cited, whether you appear, and what the AI says about your category.
Day 3-5: Identify your three biggest citation gaps. Where are competitors showing up and you aren’t? What content do they have that you don’t? What off-site mentions are they earning?
Week 2: Create (or restructure) one cornerstone content piece designed specifically for AI citation. Use question-based headings, extractable sentences with specific data, and proper schema markup.
Week 3-4: Launch one off-site credibility initiative. This could be a guest article on an industry publication, a collaborative study with a partner brand, or active participation in relevant community platforms.
Ongoing: Set a monthly AI visibility review. Track your citation share across all major AI platforms. Update your highest-value content quarterly at minimum.
The brands that treat this as a one-time project will fall behind. The ones that build it into their ongoing growth operations will own their category’s AI narrative.
The Bottom Line
The growth game just changed. Not in a “things are evolving, stay tuned” way. In a “the rules you learned no longer apply the same way” way.
AI answer engines are replacing the discovery mechanism that new brands depended on for two decades. The browsing, comparing, exploring phase of the buyer journey is compressing into a single AI-generated answer. And that answer overwhelmingly favors established brands with deep authority signals.
For new brands, this means growth is about to get harder, more expensive, and more dependent on brand-building than ever before.
But there’s a window. GEO is still early. Most brands aren’t optimizing for AI citation yet. Only 22% of marketers currently track their LLM brand visibility. The opportunity for forward-thinking brands to establish dominant positions is real, but it’s closing fast.
The question isn’t whether AI will reshape brand discovery. It already has. The question is whether you’ll be visible when your customer asks the AI for help.
FAQs
1. What is Generative Engine Optimization (GEO)?
Generative Engine Optimization is the practice of optimizing a brand’s content and online presence to appear in AI-generated search answers from platforms like ChatGPT, Perplexity, Google AI Overviews, Claude, and Gemini. GEO focuses on building citation-worthy content, earning off-site brand mentions, maintaining content freshness, and structuring pages so AI models can easily extract and cite them.
2. How is GEO different from traditional SEO?
While GEO and SEO share foundational principles like authority, relevance, and trustworthiness, GEO focuses specifically on earning AI citations rather than Google rankings. Key differences include greater emphasis on extractable sentences, off-site brand mentions (85% of AI citations come from third-party sources), quarterly content freshness, and schema markup that helps AI models parse and cite content accurately.
3. Why are new brands at a disadvantage in AI search?
AI answer engines rely on training data and authority signals that established brands have accumulated over years. New brands lack deep representation in AI training data, have fewer third-party mentions and citations, and haven’t had time to build the domain authority that AI models use to evaluate source credibility. This creates a structural incumbency bias where new brands are invisible by default.
4. How can I check if my brand appears in AI search answers?
Search your most important buyer-intent queries in ChatGPT, Perplexity, Google AI Overview, and Gemini. Document whether your brand is mentioned, which competitors are cited, and what information the AI provides about your category. Doing this monthly creates a baseline for tracking your AI citation share over time.
5. How long does it take to see results from GEO?
Based on our work with 150+ clients, brands that implement a focused GEO strategy typically begin appearing in AI answers for targeted queries within 60-90 days. Consistent effort over 6 months usually produces measurable improvements in AI citation share. However, the compounding nature of AI visibility means that the earlier you start, the greater the long-term advantage.
For Curious Minds
AI answer engines are transforming brand discovery by replacing the traditional list of ten blue links with a single, synthesized answer. This consolidation severely limits visibility, creating a winner-take-all environment where only a few brands get mentioned, posing a major hurdle for new entrants who relied on ranking for niche keywords to get found. The primary challenge is that the user's browsing phase, once a key opportunity for discovery, is being eliminated. Research from AirOps and Kevin Indig shows that AI Overviews feature only about 23% as many URLs as traditional results, a massive contraction of digital shelf space. For two decades, a new company could compete by creating better content for a specific query. Now, it must compete for a coveted spot within the AI's single response, a fundamentally different and more difficult objective. To learn how to build a strategy for this new reality, you must first understand the mechanics behind it.
Incumbency bias is the systemic preference AI answer engines show for established, well-cited brands over newer ones. This is not just a competitive disadvantage, it's a structural barrier because AI models are trained on existing web data, which is heavily weighted toward older, more authoritative domains. Unlike traditional SEO where a new brand could outmaneuver an incumbent with smarter keyword targeting or higher-quality content, AI search prioritizes historical authority. The models, including those from Google and Perplexity, equate being well-represented across the web with being correct or relevant. This means new companies, which by definition lack years of citations and earned media, are often invisible to the AI's knowledge graph. Overcoming this requires more than just great content; it demands a strategic effort to build authority signals outside of your own website, a topic explored deeper in the full analysis.
The primary difference lies in the balance between on-page relevance and off-site authority. Traditional SEO rewards a balanced approach, where a highly relevant blog post from a new domain could rank well, while AI-driven discovery heavily skews toward domain-level authority and the volume of third-party citations. For a new fintech startup, this means a strategic pivot is essential. Your focus should shift from simply optimizing content for keywords to systematically building brand credibility across the web. Key priorities should include:
Securing Earned Media: Getting mentioned in reputable publications is a powerful authority signal for AI.
Building Citations: Ensure your brand is consistently cited in relevant industry discussions and reports.
Developing Expert-Led Content: Position your team as authorities whose insights get referenced by others.
This move from a content-first to an authority-first mindset is crucial for being recognized by systems like ChatGPT, a strategy detailed further in the complete guide.
The experiment showed that AI answer engines act as aggregators of established consensus, not as discoverers of emerging quality. When queried, tools like Perplexity and ChatGPT cited a few well-known agencies with years of indexed content and brand mentions, completely overlooking newer agencies with potentially better results. This outcome proves that AI models primarily rely on the existing, indexed web, which creates a significant visibility barrier for new firms. Their knowledge graph is built on historical data, meaning a brand's current performance or innovation is irrelevant if it hasn't been widely documented over time. The key takeaway is that merit alone is no longer enough for discovery. New companies must actively work to get into the data set that AIs train on, a challenge that requires a new playbook.
The 23% figure starkly illustrates the radical consolidation of online visibility; for every 100 opportunities to be seen in traditional search, only 23 now exist in an AI-generated answer. This shrinking digital shelf space means competition is fiercer and the cost of being ignored is higher than ever. It's no longer about appearing on the first page, it's about being one of the two or three sources the AI chooses to cite. Supporting this, Edelman's research on AI search visibility identified earned media as the single most important driver for getting included in these responses. This finding confirms that off-site validation and third-party credibility have overtaken on-site content optimization as the primary lever for growth. This shift demands a fundamental re-evaluation of marketing budgets and tactics, which the full article explores in detail.
A D2C brand must pivot from a volume-based content strategy to an authority-based brand strategy. The goal is no longer just to rank, but to become a citable source for an AI engine like Google's AI Overview. A practical plan involves these steps:
Identify Citable Topics: Instead of just targeting keywords, identify industry questions where your unique data or expertise can become a definitive source.
Launch a Digital PR Program: Actively pitch your insights, data, and expert commentary to industry journalists and publications to build earned media mentions.
Invest in Original Research: Create proprietary reports or surveys that others in your industry will need to cite, directly building your brand's authority.
Collaborate with Established Voices: Partner with recognized influencers or companies to co-create content, borrowing their authority to build your own.
Executing this pivot from content production to authority building is essential for survival, and the full piece offers more advanced techniques.
Founders must prepare for a future where brand is the new ranking factor. As AI Overviews become the default discovery tool, strategic planning needs to shift from a channel-specific focus (like SEO or social) to an integrated brand authority model where all activities contribute to making the company a recognizable and citable entity. Budgets should be reallocated accordingly. This means reducing spend on high-volume, low-impact content creation and increasing investment in areas that build long-term credibility, such as original research, digital PR, and partnerships with established industry voices. The key is to think like a publisher and create assets that others want to reference. This proactive shift ensures your brand is part of the foundational knowledge AIs use, a critical preparation the article outlines further.
The most common mistake is continuing to invest heavily in a high volume of keyword-targeted blog posts while ignoring the signals that AI engines actually value. This old playbook assumes that more content equals more traffic, a formula that is breaking as AI Overviews intercept users before they click. Forward-thinking companies avoid this by reallocating resources from content quantity to authority-building quality. They understand that a single piece of original research that earns 50 media citations is now more valuable than 50 blog posts that earn none. They prioritize activities like getting their executives quoted in major publications, publishing unique industry data, and securing product mentions on trusted review sites. This strategic shift from content producer to citable authority is the key to staying visible, a process examined more closely in the complete analysis.
This assumption is flawed because AI models do not discover quality; they reflect documented history. An AI like ChatGPT or Google's AI Overview cannot know about your great results if those results do not exist in its training data, which is composed of public web content, news articles, and other citable sources. Your internal success is invisible to it. The proactive solution is to externalize your proof points. You must translate your private client success into public authority signals that an AI can ingest. This can be done by:
Publishing detailed case studies that get picked up by industry media.
Encouraging clients to review your company on trusted third-party sites.
Turning internal data into a public report that others can reference.
Without this deliberate effort to create public-facing validation, even the best new companies will remain unknown. Understanding how to create these signals is the first step.
The browsing phase was critical because it created a level playing field where new brands could win on merit. When a user scrolled through Google's 10 blue links, they were open to discovering a company they had never heard of if its headline or description was compelling enough, giving startups a direct path to compete with incumbents. AI-generated answers eliminate this phase by presenting a pre-digested summary, removing the user's need to browse at all. The fact that AI Overviews contain only 23% as many URLs as a classic search results page is hard evidence of this disappearing opportunity. This isn't a small change; it's the removal of the primary mechanism that fueled organic growth for an entire generation of digital-native companies. Navigating this new landscape requires a completely different approach to getting noticed, as the rest of this analysis explains.
The long-term implications could be a significant reduction in market dynamism and innovation. If new companies are structurally blocked from reaching their audience by AI gatekeepers that favor incumbents, it raises the barrier to entry and stifles competition, potentially leading to market stagnation. The startup ecosystem might shift away from a model that rewards scrappy, SEO-savvy innovators toward one that favors well-funded ventures with the resources for large-scale digital PR and authority-building campaigns from day one. This could mean fewer bootstrapped success stories and a greater concentration of market power among established players. This potential future underscores the urgency for new brands to adapt their growth playbooks now to ensure they can break through. The article provides a starting point for building that new strategy.
A new agency must focus on manufacturing credibility signals that AI can easily recognize. The key is to create assets that get amplified and validated by third parties, moving beyond self-published blog posts to build a portfolio of external proof points. A targeted approach would be to:
Systematize Case Study Promotion: Don't just publish case studies. Turn the key results into a press release and pitch it to relevant industry publications.
Conduct Niche Industry Surveys: Survey a specific market segment and publish the unique data. This creates a citable asset that positions the agency as an authority.
Pursue Executive Commentary: Proactively offer expert commentary from your leadership team to journalists writing about your industry.
This earned media-first approach directly counters incumbency bias by creating the very citations AI models like Google's are trained to look for. Exploring these tactics further is essential for any new player.
Amol has helped catalyse business growth with his strategic & data-driven methodologies. With a decade of experience in the field of marketing, he has donned multiple hats, from channel optimization, data analytics and creative brand positioning to growth engineering and sales.
