AEO isn’t killing SEO. The pie is just getting bigger.

Everyone’s asking: is AI search killing Google?

The better question: are we still only playing for one pie?

AEO isn’t replacing SEO. It’s adding a second game. Search traffic isn’t disappearing; LLM usage is creating more queries. You have traditional search (SEO) and the new, conversational layer (AEO). Different intents, different tactics. SEO still owns “best project management tool” and the classic 6‑word query. AEO owns the 25‑word, messy, follow-up questions: “Which meeting transcription tool integrates with Looker?” Those are net-new. That’s the additive part.If you only do SEO, you’re leaving that second pie on the table.

On the “head,” you don’t win by being #1

In SEO, the game is simple: get your URL to rank #1. You’re #1, you win the click.

In AEO, being the top cited source doesn’t mean you “win.” LLMs don’t pick one winner. They synthesize. They read a bunch of sources—reviews, Reddit, YouTube, articles—and build a kind of consensus. To be recommended as “the best” product, you need to show up in multiple trusted places. One link isn’t enough. The model has to see you again and again, in different contexts, before it’ll say “use this one.”

So the head strategy shifts: it’s not “rank one page.” It’s “get cited everywhere that counts.”

The long tail is actually long now

Classic SEO long tail: a few extra words, still one query per search.

AEO long tail: real conversations. Follow-up questions. “What about for a team of 50?” “Does it work with Notion?” Average prompt length is way longer than the average search query. That’s a different kind of long tail—and a big opportunity. Small teams can win on hyper-specific questions that have almost no Google volume and no dedicated content. If you can answer that exact need, you can show up in the answer. No need to outrank a decade of existing articles.The long tail in AEO is deeper and more conversational. Play there.

Off-site is where AEO becomes its own thing

On-site AEO looks a lot like good SEO: clear pages, structure, data. The big split is off-site.

SEO off-site = backlinks. Authority. Domain rating.

AEO off-site = citations. Getting your brand into the places LLMs treat as sources of truth. Reddit, YouTube, review sites, press. Not link equity—being mentioned. Reddit and other UGC get weighted heavily because they’re human, messy, and hard to spam. So the play isn’t “build links to our site.” It’s “show up where the model reads.” That means real participation in threads, real videos with real transcripts, real mentions in real articles and “best of” lists. Each of those is a citation. Citations feed consensus.

The traffic converts better. Attribution is worse.

Early data from teams measuring this: traffic that comes after someone used an LLM often converts way better than standard search traffic. By the time they land on your site, they’ve already narrowed down. They’re not browsing; they’re checking you out.

The catch: attribution is a mess. Lots of answers are zero-click. The user gets the answer in the chat. When they do convert, they often open a new tab and type your name. So your analytics say “direct” or “unknown.” You can’t rely on the same click-based funnels as SEO.

The metric that actually matters in AEO isn’t “position” or “clicks.” It’s share of voice: how often your brand appears in the answer. That’s what you optimize for.

Information gain beats the sea of sameness

SEO taught a generation to rewrite what already ranks. Same intents, same angles, same structure. Result: a lot of interchangeable content.

LLMs (and modern search) are built to filter that out. They want information gain—something new. Unique data. Real expertise. Original research or quotes. If your content is just a remix of the top 10 results, it doesn’t add much. Pure AI slop that summarizes public info gets ignored or deprioritized. The content-farm play doesn’t work here.

To win in AEO, your content has to add something the model doesn’t already have. That’s the bar.

Citation influence: you’re only in the answer if you’re in the sources

RAG runs in two steps. First it retrieves stuff it trusts. Then it generates from that. So the only way you get into the answer is by being inside those retrieved sources. Not by having a great site. By being mentioned in the places the model pulls from.

In classic SEO, ranking #1 usually means you get the click. In an LLM, being the top result doesn’t matter if your product never shows up in the summary. The model doesn’t “click” your link. It reads the citations and writes. So the game is: get your brand into the text of those trusted third-party sources. That’s citation influence. No citation, no answer.

Models want variety and real human signal. They weight “wisdom of the crowds” so they don’t just echo other AI content. So they lean on sources that look like real people talking. Reddit and similar UGC get heavy weight because they’re messy, multi-voice, and hard to fake at scale. The play isn’t to spam with fake accounts—that gets banned and adds no real signal. It’s to have real people from your team show up in the right threads, identify themselves, and give genuinely useful answers. Video is in the same bucket: YouTube and Vimeo transcripts are dense, unique, and often not duplicated in text. So they count as strong citations. Be present where humans are actually discussing your space, in a way that adds information gain.

Speed: why AEO can work for early-stage

SEO is slow. You’re building domain authority for your own site. That’s a multi-year game. Early-stage startups usually don’t have the backlink profile or the content depth to win that quickly.

Citation influence is different. You’re borrowing the authority of the source. Get mentioned in a solid Reddit thread, a YouTube video, or a decent blog post today, and you can show up in answers tomorrow. The model trusts Reddit or Forbes or that blog; you’re just in the text. So a new company can “win” in AEO before it can rank in SEO. You don’t need years of link-building. You need to be in the right citations.

Why AEO actually matters

The best reason to care isn’t volume. It’s who shows up.Traffic from LLMs converts way better than traffic from classic search. The difference is intent. In search, people often click to explore. In a chat, they’ve already been narrowing it down: “Does it integrate with X?” “Is it cheaper than Y?” By the time they hit your link, they’re not browsing. They’re checking you out. So even if the channel is smaller today, the value per visit is higher.

The story isn’t “AI is killing Google.” It’s “there’s more query volume overall.” People use LLMs for things they didn’t used to do in a search box: long summaries, coding help, hyper-specific questions. That’s new demand. The total pie grows. LLM-driven traffic is still a fraction of search today, but it’s already meaningful for some brands—and the curve is steep. It’s additive. You’re playing in a second game that’s still expanding.

Winning the answer isn’t winning the link. In Google, rank #1 and you usually get the click. In an LLM, the model summarizes. So even if your site is the top search result, the model might leave you out of the answer if you’re not in the other stuff it pulled—Reddit, G2, a review, a video. If you’re only on your own site, you might not make it into the summary. AEO forces you to think in terms of ubiquity: show up in a mix of trusted third-party places so the “wisdom of the crowds” logic includes you. Share of voice in citations is what gets you into the answer.

AEO tooling and tracking: where we are today

Answer engines don’t give you a Search Console. There’s no API for “how many people asked this” or “where you rank.” So you’re working with proxies, probabilities, and experimentation. The problem isn’t that there are too many similar tools—it’s that most teams have no systematic visibility. They don’t know which prompts they show up for, which sources are feeding the answers, or whether share of voice is going up or down. You can’t improve what you don’t measure.

The real metric is share of voice. In SEO you care about position. You’re #1 or you’re not. In AEO the output is probabilistic. Ask “what’s the best ice cream?” and the model might say vanilla 90% of the time and chocolate 10%. So you can’t run a prompt once and call it your “ranking.” You have to run the same prompt many times and count how often your brand shows up. That percentage is your share of voice. Different models and different contexts give different results, so you need to track across surfaces and accept variance.

Prompt volume is a guess. Nobody publishes “search volume” for LLM prompts. A simple heuristic: LLM query volume is often in the ballpark of 1/25th of Google search volume for the same topic. To find high-intent prompts, one approach is to take a competitor’s top paid keywords, export them, and turn those keywords into questions. For the long tail—questions with zero Google volume—mine internal data: sales calls, support tickets, Reddit threads.

Attribution is messy. A lot of LLM answers are zero-click. The user gets the answer and leaves, or opens a new tab and types your brand name. So you split the problem. Top of funnel: visibility tracking. Are we in the answer? Share of voice over time. Bottom of funnel: post-conversion surveys. “How did you hear about us?” You won’t get clean last-click attribution. You get “we show up in answers” plus “some of our signups say they found us via AI.”

Best practices are unproven. So the move is to test. Define a control group (prompts you don’t touch) and a test group (prompts where you intervene—Reddit, video, PR, reviews). Run both for a few weeks. Compare share of voice. If the test group goes up and the control stays flat, you’ve got signal. Rigorous experiment design beats copying what someone else did.

Why we built Orvi AI. Without a way to measure, you’re optimizing in the dark. We built Orvi AI to give you that visibility. We track your brand across the major AI platforms—ChatGPT, Perplexity, Gemini, Claude, Google AI—and surface the metrics that matter for the generative web: share of voice in answers, which sources are feeding those answers, and how sentiment lands. You see where you show up, which domains the models cite, and whether you’re being recommended in a way that helps or hurts. It’s built for the reality we just described: probabilistic outputs, no official APIs, and a need to measure and improve your presence where answers are generated, not just listed. If you’re serious about AEO, you need visibility and source intelligence. That’s what we’re building.

What to do next

Do both. Keep doing SEO for the queries that still happen on Google. Start doing AEO for the new ones: get into the right citations (Reddit, video, reviews, press), aim for share of voice, and create content that adds real information gain. The pie got bigger. The game is retrieval and consensus, not just ranking. You need to be in the sources, and you need to know if it’s working. Measure visibility, run experiments, and double down where you see lift. Time to have a strategy for both slices.