Answer engines changed materially through July and August 2026. They now name candidate brands before fetching a single page, state how fresh they want a result to be, disagree with each other about which sources count, and retrieve content from sites that block them by rule. This briefing sets out the mechanism, then a playbook for acting on it.
Being named in the model's own opening query is worth roughly 33 times more than being fetched during the search. That splits AEO into two separate games with two separate budgets: getting into the shortlist, and winning the citation once you are in it. Most of what agencies sell as AEO only plays the second game, and the second game is the smaller one.
Seven things that changed, in the order they affect you.
When someone asks an answer engine for a recommendation without naming brands, the model writes the first search query itself. That query already contains brand names. In one census, 21 of 27 conversations had brands in the opening query that the user never typed, across 11 of 13 product categories tested.field
The consequence is the single most important number in this briefing.
Read that second line carefully. Doing everything right on your website, getting crawled, getting fetched, and still not being in the model's head, buys you a 2.1% chance of appearing. That is the ceiling on pure technical AEO.
| The shortlist is pre-written when | It is not when |
|---|---|
| The ask is for a recommendation with no brand named | The user names specific brands |
| The model has to generate candidates itself | No web search is needed at all |
| The intent is comparative: best, alternatives, instead of | It is an open complaint with no buying intent |
What it means. Your highest-value queries are exactly the ones where the shortlist is pre-written, because "best X in Y" is comparative by definition. For those, the work that moves the needle happened months ago and happened off your website. That is uncomfortable to sell and it is what the data says.
Of 3,554 retrieved pages in that census, only the top 110 were cited. Position inside the grouping for your own domain decides which of your pages survives.field
If you have six near-duplicate pages targeting one intent, five of them are competing against the sixth for a slot at roughly a fifteenth of the odds. Consolidation is not tidying. It is the highest-leverage on-site fix available.
Until mid-August, a search issued inside a ChatGPT answer was a JSON object. Four days later, on the same account, it was a flat pipe-delimited line.field
| Call type | What it does | Can you win it with content? |
|---|---|---|
| fast | Web search, successor to the old fan-out | Yes the main event |
| product | Catalogue lookup, names semicolon-separated | Feed merchant data, not pages |
| business | Places lookup with a location parameter | Profile maps entity, not pages |
| image | Image search | Partly rarely audited |
| genui_run | Renders a widget from JSON arguments | No nothing is citable |
There is also a response-length directive taking long, medium or short. Fewer sentences means fewer claims, and every claim is a citation slot.
On 21 July the pipe label field result_source was replaced by search_result_groups, and the pipe labels were dropped.field On 20 August the search_queries field that mirrors the fan-out disappeared entirely.
Any tool reading the old fields now returns an empty result rather than an error. A blank report reads like a clean bill of health, which is the more dangerous failure. If you use any AEO tool that shows a fan-out, check it is not quietly showing you zero.
The third field is a day window, and observed values track how fast the underlying facts move. Content age stopped being a soft signal you argue about and became a filter with a number on it.field
What it means. A comparison page untouched for 30 days can fall outside the window a pricing query is willing to look at. Not outranked. Not considered. That converts "keep it updated" into a schedule with dates on it, which is the version a client can actually be held to. The inverse matters too: a ten-year window on community content means a 2019 thread mentioning you is still live inventory.
ChatGPT issues subreddit-level queries by name, with the widest window in the system:
What gets retrieved and what gets credited are now very different numbers, and the gap moves by query type.field
Reddit's overall citation share fell from 3.83% to under 1% in mid-August while retrieval held. That is a filter applied after the fetch, not a loss of relevance.field
A source can shape an answer while appearing nowhere in it. Every visibility score built on counting citations, ours included, reads that source as absent. The number is not wrong. It is answering a narrower question than the one the client is asking.
Perplexity is not a variant of ChatGPT. It picks sources on different rules, and the biggest split is the one nobody optimises for.field
Two more Perplexity mechanics worth knowing. skip_search is always false, so every query hits the web and every query is therefore winnable, which is not true of ChatGPT. And its fan-out is conservative, using the words people actually type rather than a cloud of adjacent topics, so exact-phrase targeting still works there.
| Intent | What takes the citations | Your play |
|---|---|---|
| Commercial | Fresh current-year listicles. Brand size is not the gate, freshness is. | Get onto the "best [category] [year]" lists and keep them current |
| Comparison | The named vendor's own page | Own your comparison pages, they are winnable |
| News | The official primary source. Freshness alone is not enough. | Be the primary source or do not bother |
| Local | Maps place-entities take everything, roundups get nothing | Fix the maps entity, not the content |
| How-to | Video, cited like any text source | Publish the video, not just the post |
Perplexity also carries scoped trust entries per domain, at level 1 (credible) or level 2 (trusted), and the scope is first-party only. A domain is trusted about its own products and services, not about the category. That is a clear instruction: the page about your own thing is the one you can win, and the "ultimate guide to the industry" is the one you probably cannot.
We probed four domains from New Zealand on 25 August, testing fourteen crawler user agents against robots.txt and then against the live server.ours The results say something the field research implies but does not state outright.
| Domain | Verdict | robots.txt | Live server |
|---|---|---|---|
| reddit.com | BLOCKED | Disallow: / for all 14 agents | Mixed, see below |
| gerrards.co.nz | OPEN | All allowed | All 200 |
| aisearch.marketing | OPEN | Explicit Allow | All 200 |
| janprocleaning.co.nz | OPEN | All allowed | All 200 |
Its robots.txt refuses every AI crawler we test, including OAI-SearchBot. Its live server then disagrees with its own robots file, and disagrees per agent.ours
| Agent | robots.txt | Live fetch | Reading |
|---|---|---|---|
| Real browser | n/a | 200 | Control |
| OAI-SearchBot | Disallow | 200 | Server allows what robots forbids |
| ChatGPT-User | Disallow | 403 | Refused twice over |
| PerplexityBot | Disallow | 200 | Server allows what robots forbids |
| Bingbot | Disallow | 403 | Hard refusal |
| python-requests | n/a | 200 | An anonymous scraper walks straight in |
Three things fall out of that table, and all three are practical.
Our probe proves the observable, client-controlled gates only: robots.txt directives and WAF behaviour toward named agents. It cannot see residential-proxy traffic and never claims to.
Which means: a BLOCKED verdict is a real, fixable problem you should fix first. An OPEN verdict is not proof you are being retrieved, and a block is not proof you are not. Anyone selling "we made you visible to AI" off a robots.txt edit alone is overselling a gate they can only half see.
The finding that matters commercially: three of our four domains were already wide open. For a typical NZ business, reachability is not the problem. Selling a robots.txt fix to a client whose door is already open is selling nothing. The problem is upstream in the shortlist, or downstream in the citation.
Fragments of AI Mode conversations are recorded as ordinary queries in the performance report. Not summaries, the literal strings people typed mid-conversation.field
They are recognisable because nobody types them into a search box. The tells are conversational, not topical.
| Signal | Looks like | Why it cannot be a search |
|---|---|---|
| Assistant instruction | give me step by step | Addressed to a listener, not an index |
| First-person context | i am using lmstudio | Pointless to tell a search engine |
| Dangling pronoun | is it free | The referent exists only in a prior turn |
| Politeness marker | please clarify | Nobody says please to an input field |
| Bare reply | yes · sure · really? | An answer, not a question |
Seven categories in total: bare replies, pivot follow-ups such as what about resend?, conversational questions, synthetic tracker probes, pasted agent instruction sets, pasted error strings and CSV headers, and long uncategorised strings.
The floor problem caps how far this goes. Conversational strings almost never repeat, so most sit at one to three impressions, and 57.7% of impressions are anonymised rows with no string at all. Treat it as a qualitative sample with real volume behind it, not a keyword list.
What to actually do, in the order that pays. Every play below traces to a finding above.
The 33x gap in Finding 01 means these are separate budgets with separate timelines. Running the second without the first is the most common and most expensive mistake in AEO right now.
Be a brand the model names before it searches. This is reputation across the open web: mentions, reviews, listicles, directories, community threads, analyst and press coverage.
It is slow, it is mostly off your own website, and it is not technical SEO. It is the one that carries the 68.9%.
Once you are in the retrieved pool, be the page that gets bound to a claim. Page consolidation, answer placement, server-side rendering, freshness cadence, first-party scope.
It is fast, it is fully under your control, and its ceiling is 2.1% if you are not also playing game one.
Five failure states. Each has a different test and a different fix, and they are ordered: fix the lowest one that is failing before touching anything above it.
robots.txt or the WAF refuses the crawlers that feed the engines. Real, but uncommon: three of the four domains we probed were already wide open.
Your name never appears in the query the model writes for itself. You are competing for the 2.1% lane no matter how good the site is, and nothing in a rank tracker or an ads report will show you this.
The model has you in mind but does not go to your site, or goes to the wrong domain. Entity confusion, a stale domain slot after a rebrand, or a name that collides with a bigger entity.
Your pages are being retrieved and then filtered out. Sixth position or lower in your own domain grouping is a 0.3% citation rate against 5.2% for first.
You are shown as a source and no claim is bound to you. Usually the answer to the question is not present as a liftable sentence, or it sits too deep in the HTML, or the page is client-rendered.
The engines reward different assets. Optimising for one average of them all is how you end up strong nowhere.
| ChatGPT | Perplexity | Google AI Mode | |
|---|---|---|---|
| Does every query search? | No | Yes, so every query is winnable | Varies |
| Community threads | Retrieved heavily, credited on niche intent only | Retrieved, cited close to never | Present in the index |
| Video | Cannot extract transcripts, weak | Cited at scale on product and how-to | Surfaces separately |
| Freshness | Explicit day window in the call | Current-year listicles win commercial | Not exposed |
| Local intent | Separate places lookup | Maps entities take everything | Maps-led |
| What you can measure | Full retrieval funnel from the conversation | Streamed router scorecard | Leaked fragments in Search Console |
| Your first move | Get named in the pre-search shortlist | Get on the fresh listicle, publish the video | Mine pivot follow-ups for content gaps |
Ten checks. Everything here is either a command we already have or a report you already have access to.
Not a single yes or no. Allowing the search crawler while refusing the live fetch is a specific and currently invisible failure.
Default bot rules block AI crawlers without the owner ever choosing it. Look at the rule, not the intention.
Memory and custom instructions off, or a clean profile. Ask "best [category] in [place]". Write down every brand named. That is the shortlist you are trying to join.
Find the dominant subreddit, retrieve the threads, count brand mentions. Your share of that count is your game-one scoreboard.
More than one page for one intent means they are competing with each other at a fifteenth of the odds. This is usually the fastest win on the list.
Anything with a price or a comparison on it, last modified more than 30 days ago, is outside the window for pricing queries. List them, schedule them.
If the answer sentence is not in the raw HTML, a meaningful share of the retrieval pipeline never sees it.
Search results, add filter, query, custom, regex. Then read the pivot follow-ups as a content brief and bin the machine probes.
Two field renames in five weeks. A tool reading the old fields returns zero, and zero looks like a clean result. Confirm it is reading the current format.
The plays diverge sharply between ChatGPT and Perplexity. Optimising for the average of both is how you end up strong on neither.
Stated plainly, because the alternative is selling a number we cannot defend.
Retrieval and credit have come apart, and the decision that matters now happens before retrieval starts. Systems read far more than they cite, they are explicit about how fresh a page must be, and they arrive at a shortlist built from years of open-web reputation rather than from your website. Measurement built on citation counting reads a shrinking slice of that, and will keep returning confident numbers about a narrowing question.