AEO Is Selling Control That Doesn't Exist
The trend is real. The promise underneath the marketing is not.
A new service category has formed in the last year, and it is being sold with a familiar promise. Optimize for the AI answer the way you once optimized for the Google ranking, and you can move where your brand lands when someone asks ChatGPT, Gemini, or Perplexity about your category. The category goes by a few names. Some call it GEO, generative engine optimization. Some call it AEO, answer engine optimization. The names themselves are a small tell, which I will come back to.
I want to be precise about what is true here, because the easy version of this argument is wrong, and the accurate version is more useful.
It is true that AI-mediated discovery matters, and it matters more every quarter. McKinsey, in an October 2025 analysis, put roughly half of consumers already using AI-powered search, projected $750 billion in US revenue flowing through it by 2028, and warned of a 20 to 50 percent decline in traditional search traffic for unprepared brands. That is not hype. That is a real shift in how people find and choose. And a brand that ignores it is making a mistake.
This is not an argument that AI search is a fad, or that you cannot influence it. You can. The argument is narrower and more important: you cannot control it, and the category is being sold as if you can. The word optimization carries a promise it cannot keep.
Here is the distinction that the marketing glosses over. Traditional SEO was, at its core, a control game played against a knowable interface. There was an index. There were ranking signals. You could improve your position against those signals with enough technical work, content volume, authority building. The relationship between effort and outcome, while never perfect, was real and repeatable. That is what optimization means. A lever you pull that moves a result in a predictable direction.
AI-mediated discovery does not work that way, and the people building the tools say so plainly. The system does not retrieve your page and rank it. It synthesizes an answer from a wide and shifting field of sources, then compresses that into a response that varies by model, by phrasing, by the user’s context, and over time. You are not optimizing a position. You are one input among many into a process whose output you do not hold.
The numbers make the point concrete. By McKinsey’s own measure, a brand’s own website typically accounts for only 5 to 10 percent of the sources AI search draws on. The other 90 percent and up is third-party material, publishers, reviews, forums, user-generated content, affiliates, none of which you own and most of which you cannot direct. In some categories, McKinsey found, publishers, user-generated content, and affiliate sites make up more than 65 percent of the sources. The plain consequence: “Visibility is not guaranteed,” in McKinsey’s own words, even for established market leaders. Traditional brand strength does not carry over. The lever SEO offered does not exist here, because the thing you would pull on is mostly not yours.
The honest players in this space already concede the point, and the clearest example is one of the biggest. HubSpot now ships an AEO product, and its own documentation describes, accurately, something much weaker than the word optimization implies. It tells users that AI “responses change over time,” that “each answer engine may generate different responses” to the same prompt, and that after you act on its recommendations, improvements “may not appear immediately” and should be reviewed “across multiple analysis cycles.” Read the recommendations the tool actually produces and they are not a control panel. They create content, engage relevant Reddit threads, post on LinkedIn and YouTube, and reach out to third-party publishers. That is content production, public relations, and reputation work. It is genuinely useful. It is also, by the tool’s own account, measurement and influence with no guaranteed outcome.
That is the gap between the documentation and the label. HubSpot’s help pages describe influence. The category name promises optimization. Those are not the same word, and the difference is the whole problem. A buyer who reads “optimization” hears SEO, hears a lever, hears control. What they are actually buying is the disciplined management of evidence they mostly do not own, feeding a system whose output no one can dictate.
Which brings me back to the names. A field confident enough to charge for outcomes has not yet agreed on what to call itself. McKinsey says GEO. HubSpot says AEO. Others say AI engine optimization. When the most credible voices in a category cannot settle its vocabulary, that is not a branding quirk. It is a sign the practice is younger and less defined than the confidence of its sales pitch suggests.
So if optimization is the wrong frame, what is the right one?
The right frame is governance, not marketing. The problem an organization actually faces in AI-mediated discovery is not a visibility problem to be optimized. It is a risk problem to be governed: the risk that the public evidence an AI system retrieves about you is inconsistent, outdated, unsupported, or contradictory, and that the system, doing exactly what it is designed to do, synthesizes those contradictions into an answer you would never have authorized.
That is not a marketing trick. It is reputation risk created by an ungoverned evidence layer, and the discipline that addresses it is the same discipline any governance problem requires. The principles are not exotic. They are the ones that govern any system of record. Define the standard: what the organization’s authoritative claims actually are, and which claims are not defensible and should never be made. Establish a single source of truth, so the public record states those claims consistently rather than five different ways across five channels. Assign ownership, a named person accountable for the coherence of that record, not a responsibility scattered across marketing, PR, and whoever updated the website last. And monitor, because the answer layer is not stable and drift is the normal condition, not the exception.
Notice what that discipline does and does not promise. It does not promise to make the model say what you want. Nothing can promise that and a vendor who does is selling you the one thing the technology does not offer. What it does is reduce the number of reasons an AI system has to get you wrong. You cannot control the output. You can govern the inputs you own and influence the ones you do not, and you can know, continuously, what the systems are actually saying so that you are managing a current picture rather than an assumed one. That is the difference between governing your evidence and optimizing your ranking. One is honest about the limits of control. The other sells a lever that was unplugged the moment discovery moved from the index to the synthesis.
The organizations that will navigate this well are not the ones that buy the most aggressive optimization promise. They are the ones that treat their public evidence as a governed asset: consistent, owned, monitored, and defensible. That is unglamorous, it does not come with a guarantee of where you land in tomorrow’s answer, and it is the only version of this work that survives contact with how the systems actually behave.
AEO is selling control. The honest product underneath it is governance. Buy the second, and be skeptical of anyone charging you for the first.
The Evolving Mindset publishes weekly on AI governance and organizational accountability. If a vendor has promised to control what AI systems say about your organization, that is a claim worth testing before you buy it. Reach out through the link below.
Sources and notes
AI-search adoption and economics: McKinsey & Company, “New front door to the internet: Winning in the age of AI search,” October 16, 2025. Cited figures: roughly 50 percent of consumers already use AI-powered search; projected $750 billion in US revenue flowing through AI-powered search by 2028; 20 to 50 percent potential decline in traditional search traffic for unprepared brands; a brand’s own sites typically comprise only 5 to 10 percent of the sources AI search references; in some categories publishers, user-generated content, and affiliate sites exceed 65 percent of sources; “Visibility is not guaranteed”; just 16 percent of brands systematically track AI search performance. Note: this is a pro-GEO analysis; it is cited here for its data, not its recommendation.
Output variability and the nature of the service: HubSpot, “Set up and analyze AEO” (product documentation, last updated May 21, 2026). Cited language: AI “responses change over time”; “each answer engine may generate different responses”; improvements “may not appear immediately” and should be reviewed across multiple analysis cycles. The tool’s recommendations consist of creating content and engaging earned, social, and third-party channels.
Terminology: the category is referred to variously as GEO (generative engine optimization), AEO (answer engine optimization), and AI engine optimization, with no settled industry taxonomy as of this writing.
All figures verified against primary or top-tier sources prior to publication. Nothing in this piece constitutes legal, financial, or marketing advice.