Okay, I need to talk about something uncomfortable. And not the usual kind of uncomfortable, like realising you've been on mute for the first three minutes of a client call. This is the kind of uncomfortable where an entire industry is doing the thing it swore it would never do again — and somehow convincing itself it's innovation.

AEO has entered its black-hat era. And here's the part that's going to sting: most of what passes for standard AEO practice right now? It already qualifies.

We've seen this film before. Early 2000s SEO had keyword stuffing and link farms. A decade later, content mills and private blog networks. Every time a new search system emerges, an industry springs up to game it, scales the gaming until it becomes "best practice," and then acts shocked — shocked — when the correction arrives. AEO is speedrunning the same cycle, except this time the correction hits everywhere at once.

Let's start with the most obvious casualty, because it's already dead and some people haven't noticed yet. (It's giving "Weekend at Bernie's but for content strategy.")

Throughout 2024 and 2025, the default AEO move for SaaS and B2B brands was the self-promotional "best of" listicle. You know the format: "Best Project Management Software for 2026," your product mysteriously ranked first, a handful of competitors included for the illusion of objectivity, and the year in the title swapped every January without changing a single word of substance. Research analysing over 26,000 URLs cited by ChatGPT found that "best X" formats accounted for roughly 44% of all page types referenced. Some brands published over 200 of these articles. It was practically an assembly line.

Then January 2026 happened, and the assembly line caught fire. Analysis from Amsive found multiple well-known brands lost 30% to 50% of their organic visibility within weeks — the losses concentrated almost entirely in the blog subfolders where these listicles lived. One company with an estimated $8 billion valuation lost 49% of its organic visibility in twelve days. Twelve days. That's less time than most of us spend deciding whether to reply to an email.

Here's why this matters beyond Google: because ChatGPT, Perplexity, and Gemini all draw on Google's organic index as part of their retrieval pipelines, a drop in Google rankings cascades across the entire AI search ecosystem. As we covered in our piece on the three-engine problem, 91% of URLs cited by AI engines appear in only one engine — but Google's organic index is the shared upstream source feeding all of them. Lose your position there and the downstream effect is immediate.

But the deeper reason the listicle was always doomed is more interesting. Research from Demand Genius into how LLMs actually surface brands found something that should make every "best of" publisher slightly nauseous: by the time a buyer reaches the kind of decision-oriented prompt these listicles target — "what's the best CRM for mid-market companies?" — the AI has already narrowed its option pool to a small set of brands it considers viable. The researchers call this convergence: the model progressively applies constraints from earlier interactions until it arrives at a handful of high-confidence choices repeated with near-perfect consistency.

The self-promotional listicle was optimising for a stage where the outcome had already been decided. It was claiming credit for visibility that was structurally inevitable. Which is, if you think about it, the content marketing equivalent of standing at a finish line and pretending you ran the race.

While the listicle targeted the visible tip of AI search — the citation, the brand mention — a different class of manipulation targets the retrieval layer itself. The sources AI systems actually pull from when building their answers.

New research from Cornell University makes the scale of this vulnerability properly uncomfortable. Researchers found that a user-generated text snippet as short as 13 words can manipulate the output of deep-research agents powering tools like ChatGPT and Google's AI search. Their study showed these agents cite user-generated content from platforms like Reddit, Wikipedia, and Quora in roughly half of all queries, with nearly a quarter of all citations coming from user-generated platforms. A single poisoned Reddit comment can influence generated outputs for an entire cluster of related queries.

The mechanism is almost insultingly simple. LLMs often use lexical similarity as a proxy for relevance. If a brand can identify what questions users are asking AI tools about its category, it places content on Reddit that closely mirrors those queries. The retrieval system sees text that resembles the user's question and treats the similarity as relevance — regardless of whether the content is authentic or written by someone whose sole qualification is a financial incentive.

This is not hypothetical. Companies now openly advertise Reddit-seeding services for AI search. One firm describes itself as deploying "an army of agents publishing blog content and Reddit posts" to influence both Google and ChatGPT rankings. (I'd call this brazen, but it's less "brazen" and more "has its own landing page with testimonials.")

The r/biohackers subreddit — roughly 830,000 members — became a visible example when moderators banned new standalone posts about peptides and HRT after discovering coordinated vendor campaigns. The accounts were built to look human, with established posting histories and brand mentions buried in high-traffic threads. One poster created a thread about cholesterol management, generated engagement, then edited the original post to include a product link. Build the trust, then redirect it.

The structural problem runs deeper. AI systems effectively export their trust to the content moderation of platforms like Reddit — moderation systems never designed to bear that weight. Reddit is simultaneously licensing its data to AI companies and struggling to keep manipulation out of the communities that make that data valuable. Analytics show Reddit is cited in roughly 21% of Google AI Overview responses and was the most-cited domain by both Google AI Overviews and Perplexity between August 2024 and June 2025. That citation share is precisely what makes it a target. (The irony is so thick you could spread it on toast.)

Reddit seeding and self-promotional listicles are the headliners, but the supporting cast is equally creative. Schema markup manipulation — injecting structured data with no visible counterpart on the page, fabricated credentials hidden in code no human visitor sees. As we covered in our schema piece, AI systems ignore information that exists only in schema with no visible page match. Fabricating it gains nothing and risks being flagged, but people keep doing it, presumably because invisible code feels like it should be a cheat code. (It isn't.)

Then there's synthetic E-E-A-T — fabricated author personas with AI-generated headshots and invented credentials. AI Overview poisoning, which distributes misleading information across enough websites that repetition reads as consensus to systems that don't independently verify claims. Confirmed cases have included fraudulent contact details appearing in AI-generated answers for legitimate businesses — turning AEO from a marketing concern into a security threat. And content flooding — thousands of AI-generated articles published purely to overwhelm retrieval systems with volume — remains the bluntest instrument in the toolkit, which has never stopped anyone from swinging it.

What connects all of these is a shared assumption: that AI search is a system to be gamed through signal manipulation, the same way early Google was gamed through links. The assumption is wrong. Understanding why requires looking at what AI is actually doing, which is — and I cannot stress this enough — not what most AEO agencies think it's doing.

The Demand Genius research provides the clearest picture available. AI systems don't arrive at recommendations by matching content to queries the way search engines match pages to keywords. They converge on options through progressive narrowing. Early in a buyer's journey — when they're exploring problems, comparing approaches, defining requirements — the model operates in exploratory mode. It draws on internal knowledge, varies responses significantly between runs, and rarely cites external sources.

Later, as intent becomes more decision-oriented, the model invokes external retrieval, surfaces citations, and becomes highly conservative. This is convergence. And the criteria driving it are formed upstream, during the exploratory phase that generates no trackable signal.

The researchers call this invisible upstream influence "Dark AI" — the conversations where problems are framed, requirements are built, and category narratives are established, all without producing clicks or any metric current AEO tracking captures. It is the vast majority of how AI shapes decisions. Black-hat AEO ignores it entirely. It's like trying to influence an election by standing outside the polling station with a sign, when the voters made up their minds six months ago.

If the black-hat playbook is structurally misaligned with how AI actually works, what does sound practice look like? The answer is straightforward, demanding, and — fair warning — profoundly unscalable.

Contribute genuinely new knowledge to your category's problem space.

The concept is information gain: does your content add something meaningful to an AI system's understanding of the problems your audience faces, or does it just repackage what already exists? The self-audit is brutal: could an AI generate this piece from existing sources? If yes, you're competing with an infinite supply of material that says the same thing. You are, to put it delicately, a commodity in a market with zero barriers to entry.

Original research, proprietary data, novel frameworks grounded in real experience, detailed first-party case studies — these pass the test. They give AI systems something they can't produce themselves. When that knowledge becomes part of how models understand your category, the effect isn't a single citation in a single answer. It's a shift in the direction of convergence itself — influencing which criteria get applied and which brands survive the narrowing process across thousands of buyer interactions.

None of this is quick. It doesn't scale the way a batch of AI-generated listicles or a network of aged Reddit accounts scales. That is precisely the point. The tactics that scale fastest are the ones most easily detected and most quickly corrected. The work that endures is harder to produce and harder to replicate. In a landscape where corrections cascade across every AI platform simultaneously, that difficulty isn't a drawback. It's the moat.

The age of black-hat AEO is here. So is the age of its correction — and the correction cycle is moving faster than it ever did in SEO. The brands that come out strongest won't be the ones who gamed the most citations or seeded the most Reddit threads. They'll be the ones who never mistook manipulating the surface for influencing the system.

Which, yes, means the answer to "how do I win at AEO?" is "produce genuinely valuable original work." I know. I'm as annoyed about it as you are.

Jo Lambadjieva is an entrepreneur and AI expert in the e-commerce industry. She is the founder and CEO of Amazing Wave, an agency specializing in AI-driven solutions for e-commerce businesses. With over 13 years of experience in digital marketing, agency work, and e-commerce, Joanna has established herself as a thought leader in integrating AI technologies for business growth.

😍It was great, all I needed

🙂Good, not great

🙄It sucked