James Dooley crowned himself King of AEO in a sports stadium outside Manchester. The search industry is supposed to treat this as a breakthrough. SEO consultant Jesper Nissen staged a mock coronation, complete with robes and a throne, at Leigh Sports Village. The production aimed to prove that enough optimized content can train algorithms and large language models to parrot a fabricated title as established fact. It succeeded. Google’s AI Overviews and various chatbots now repeat the phrase with the same confidence they apply to actual monarchies. The stunt worked precisely as intended, and that is the problem.
The New Acronym Same Old Tactics
Answer Engine Optimization has been marketed as a distinct discipline, a necessary evolution now that AI systems summarize results instead of listing links. Dooley has already moved on to the next coinage, Decision Engine Optimisation, targeting the layer where AI models recommend products and services directly. His agencies, FatRank and PromoSEO, operate on a rank-and-rent model. They capture high-intent commercial traffic in UK construction and finance verticals, then sell pre-qualified leads to businesses that actually do the work. The model is sound; the branding is noise.
What Dooley calls AEO and DEO has been standard practice for experienced SEO practitioners since before the acronym existed. Doug Kemp, a consultant with two decades in search, has spent those years implementing structured data, building entity recognition into site architecture, and optimizing for information retrieval mechanics that now get repackaged as cutting-edge AI readiness. The work did not change when ChatGPT launched; the conference slides did.
Entity recognition, schema markup, semantic search architecture, and E-E-A-T signals are not responses to generative AI. They are the foundation generative AI inherited. When an LLM extracts a concise answer from a webpage, it performs a more sophisticated version of the same parsing that Google’s featured snippets have done since 2014. The industry’s recent habit of rebranding featured snippet optimization, voice search preparation, and semantic markup as AEO or GEO serves marketing purposes; it does not reflect new technical requirements.
What the Coronation Actually Proved
The Leigh Sports Village ceremony demonstrated something useful, just not what its organizers claimed. It proved that algorithms remain vulnerable to coordinated content campaigns targeting phrases with zero commercial competition. “King of AEO” has no search volume from actual customers. No business loses revenue if it fails to rank for it. There is no competitive field, no comparison shopping, no intent to transact. The phrase exists only as a demonstration, which makes the demonstration hollow.
Compare that to a query with commercial weight. “What is the best commercial insurance provider for a small business in Cape Town?” carries intent. The searcher has a budget, a timeline, and a genuine need. Ranking for that requires competing against every insurer and broker with a digital presence in the Western Cape. It requires trust signals, local relevance, comparative depth, and technical performance that keeps a user from bouncing to the next result. The LLM summarizing that query must weigh conflicting claims, assess authority, and risk being wrong in a way that affects a real decision. That is the actual frontier. A vanity title in an empty semantic space proves nothing about navigating it.
Dooley’s own business model acknowledges this distinction. FatRank and PromoSEO do not make money from coronation videos. They make money from capturing high-intent traffic in competitive verticals and converting it to leads. The AEO spectacle is a loss-leader for credibility, a way to get invited onto podcasts where the real services get pitched. There is nothing illegitimate about this; it is simply not the technical revolution it pretends to be.
The Veteran View from the Trenches
Kemp is currently auditing an older site’s legacy structure against modern AI scraping capabilities. The work is concrete: checking whether decade-old URL architectures confuse current crawlers, whether schema implementations written for Google circa 2017 still parse correctly for LLM training pipelines, and whether entity disambiguation that worked for Knowledge Graph inclusion still signals clearly in an AI summary context. This is adaptation, not reinvention. The core inputs from marketers have not changed: clear, authoritative, well-structured, verifiable information. The packaging shifts; the requirements do not.
Veterans of the industry have watched this cycle repeat. Mobilegeddy was going to kill desktop SEO. Voice search was going to make screens obsolete. Featured snippets were going to zero out click-through rates. Each development mattered, but none replaced the fundamentals. Each required adjustments to implementation, not abandonment of principle. The current moment with generative AI search follows the same pattern. LLMs scrape and summarize using the same signals that determined rankings before. They are harder to game with keyword density alone. They reward topical depth and explicit entity relationships more directly. This is an algorithm update with broader consequences, not a new profession.
The practitioners who thrived through prior shifts did so because they understood the underlying mechanics. Kemp’s two decades span the transition from directory submissions to PageRank manipulation to Panda and Penguin recoveries to the current AI summarization era. The throughline is technical competence applied to information retrieval. Calling one phase of this “AEO” and awarding crowns for it is like declaring a monarch of HTTPS migration. The work was necessary; the theatre is optional.
Measuring What Actually Counts
The commercial value of traffic remains the only metric that cannot be faked. A ranking for “King of AEO” generates no revenue. It may generate speaking engagements, podcast invitations, and LinkedIn visibility. These have indirect value. But they are not the same as capturing a user who searched for a service, compared options, and submitted a contact form.
Genuine optimization demonstrates revenue-generating visibility for high-intent queries. In the South African market, this means showing up when a procurement manager in Johannesburg searches for enterprise CRM implementation, or when a Durban restaurant owner looks for card payment systems with same-day settlement. The queries have competition. The answers require specificity. The LLM selecting a source for its summary performs a more compressed version of the same relevance judgment that search engines have always performed.
Dooley’s rank-and-rent model works because it accepts this reality. The agencies build assets that rank for commercially valuable terms, then monetize the attention through lead generation. The AEO coronation is marketing for the marketing. It is effective marketing, judged by the coverage it received. But conflating that coverage with technical innovation misleads businesses about what they actually need. A Cape Town accounting firm wondering how to appear in AI summaries does not need a ceremonial title. It needs clean structured data, clear service descriptions, local business markup, and content that demonstrates expertise on South African tax obligations. The implementation is unglamorous; the results are measurable in client acquisition cost and lifetime value.
The Harder Problem Nobody Crowns
The real challenge in current search optimization is not getting an LLM to repeat a phrase. It is getting an LLM to select your content as the basis for a summary when multiple credible sources exist, and when the summarization itself may reduce or eliminate direct traffic to your site. This is a genuinely new tension. Google’s AI Overviews and competitors like Perplexity aim to answer directly, which breaks the old model where ranking first meant capturing the click.
Addressing this requires more than schema markup. It requires strategic decisions about what information to give away in easily scrapable form versus what to reserve for deeper engagement. It requires building brand recognition so that users seek you out even when a summary satisfies their initial query. It requires diversified traffic sources so that search dependence does not become fatal. These are business strategy questions, not purely technical ones. They deserve attention that vanity metrics distract from.
Kemp’s current audit exemplifies the practical response. An older site with years of accumulated content, legacy plugins, and outdated markup cannot pivot to AI-optimized presentation without systematic review. The work is specific: which pages still carry weight, which structures confuse modern crawlers, and where entity relationships need clarification. There is no ceremony for this. There is only the incremental improvement that compounds over time.
What to Actually Do
For businesses and practitioners watching the AEO noise, the response is straightforward. Ignore the acronyms. Verify that your structured data validates. Confirm that your entities are disambiguated and linked to authoritative references. Ensure your content answers specific questions with specific information rather than keyword-stuffed generalities. Build topical authority through depth, not breadth. Measure outcomes in commercial terms: qualified leads, cost per acquisition, revenue attributed to search visibility.
The practitioners who have done this through multiple algorithm shifts will continue doing it through the current one. They do not need new titles. They need accurate tooling, clear documentation from platforms, and the patience to test what actually moves metrics. Everything else is stadium spectacle.
James Dooley proved that search algorithms can be manipulated into repeating fiction. That was already known. The more relevant question is whether the industry can resist being manipulated into treating old practices as new disciplines, and self-promotion as substance. The crown is empty; the work remains.
