Why Fan-Out Data Disappeared From So Many AI Visibility Dashboards in 2026

Key takeaways

• In early March 2026, when GPT-5.3 became ChatGPT's default model, the search_model_queries field that powered most fan-out tracking tools quietly disappeared from the browser's network response. It wasn't deleted, just hidden.
• The disappearance coincided with a real behavioral shift: Resoneo and Meteoria tracked 27,000 responses and found unique domains cited per response dropped 20.5% and unique URLs dropped 21% after the GPT-5.3 switch.
• By August 2026 the field started reappearing on newer model versions, but inconsistently, and fan-out behavior itself kept changing (shorter queries, then a sudden site: operator surge on August 8 that raised fanout queries per response from ~1.08 to ~1.83 almost overnight).
• A workaround exists: pulling fan-out queries directly from OpenAI's Responses API instead of scraping the browser UI, though this introduces its own mismatch since the API and web interface run different system prompts.
• The core lesson isn't about one bug. It's that any AI visibility strategy built on scraping a competitor's undocumented internals is going to break, repeatedly, without warning.

The morning the queries vanished

Sometime around early March 2026, a bunch of SEOs opened their favorite ChatGPT Conversation Analyzer bookmarklet, checked the "Queries" column like they'd done for over a year, and found it empty. Not broken, exactly. Just blank. German outlet SEO Südwest flagged it first, noting the ChatGPT Chromium Inspector output had no query fields at all for GPT-5.3 sessions, even though the same tool worked fine on GPT-5.2.

This mattered because for more than a year, extracting fan-out queries had been almost embarrassingly easy. You'd open Chrome DevTools, go to the Network tab, filter for the conversation endpoint, and read the search_query or search_model_queries field straight out of the JSON response. Extensions like ChatGPT Conversation Analyzer and a handful of bookmarklets from agencies like The SEO Pub packaged this into a one-click workflow. It wasn't glamorous but it worked, and it became the backbone of how a lot of GEO and AI visibility tools showed clients "here's exactly what ChatGPT searched for before answering."

Then the field just stopped showing up. Marketer Chris Long was among the first to call it out publicly, describing GPT-5.4 as hiding fan-out queries entirely and publishing a Python script that hit the Responses API directly to get the data back. Jérôme Salomon independently confirmed the same behavior. The data hadn't gone anywhere, it turns out, it just stopped being exposed in the interface that everyone's scraper depended on.

It wasn't just a UI change, the behavior itself shifted

Here's where it gets more interesting than a simple bug report. The disappearance of the field lined up almost exactly with a real, measurable drop in how many sources ChatGPT was citing.

Resoneo and Meteoria ran a joint study, tracking 400 prompts a day for 14 weeks, comparing roughly 27,000 responses before and after the GPT-5.3 default switch on March 4, 2026. The numbers:

They nicknamed it the "Bigfoot Effect," borrowing the term from Moz's old 2013 Google Bigfoot update, where fewer domains ended up dominating a bigger share of results. The URLs-per-domain ratio staying flat is the telling detail: ChatGPT wasn't crawling less deeply within each site it visited, it was simply visiting fewer sites in the first place.

Resoneo's explanation is mundane but convincing. Most ChatGPT usage runs on the free tier, and once GPT-5.3 Instant became the default there, the majority of real-world queries started triggering fewer searches, fewer sub-queries, and fewer citations. This wasn't an isolated glitch in a dashboard, it was a platform-wide change in how the free tier of the world's most-used chatbot decides who gets cited.

Promptwatch's own citation tracking backs this up from a different angle. Its data on the ChatGPT citation drop after GPT-5.3 shows average citations per search-enabled response falling from about 6.4 the week before the rollout to somewhere around 4.7-4.9 by late March, a drop of roughly 27%, and it happened within a day of the model rollout rather than fading in gradually. A month later there was still no recovery. That's a useful diagnostic habit worth adopting: before you blame a content or SEO issue for a citation dip, check the date against known model rollouts first.

GPT-5.3 and GPT-5.4 don't even agree on who to cite

If you're still tempted to treat "ChatGPT citations" as one stable thing to optimize for, a study from Writesonic should change your mind. Samanyou Garg's team ran 50 prompts across GPT-5.2, GPT-5.3 Instant, and GPT-5.4 Thinking, producing 119 conversations, 532 extracted fan-out queries, and 1,161 classified citations.

The headline number: average citation overlap between GPT-5.3 and GPT-5.4 responses to the same prompt was just 7%. On 22 of the 50 prompts, overlap was exactly zero. These are meant to be the same product answering the same question, and they're pulling from almost entirely different sources.

The pattern behind it is even more striking:

GPT-5.3, running essentially one query per prompt, leaned hard on third-party gatekeeper sites. GPT-5.4, decomposing into 8-plus sub-queries with domain restrictions, went straight to brand websites instead. Neither is "wrong," they're just different retrieval strategies wearing the same product name. Testing only one model and assuming the results generalize is a good way to make confident recommendations that are half-true at best.

Fan-out queries got longer, then got a lot shorter, then came back with a twist

Separately from the model-version story, the shape of fan-out queries themselves has been shifting all year. Peec AI analyzed more than 20 million fan-out queries between October 2025 and January 2026 and found average word count per query roughly doubled, from about 6 words to around 12, peaking near 16 words in week 49. The number of queries per prompt stayed roughly flat around 2.3 to 2.8, so ChatGPT wasn't asking more questions, it was asking more precise ones. Peec found this trend was uniform across every country and language they tested, which rules out a regional experiment and points to a deliberate architectural change instead.

Promptwatch's own query fanout volume data tells a related but distinct story through the winter and into spring 2026: average queries per response held around 2.15 in early December, eased to about 1.84 by early March, then after the gap in visibility, resurfaced in April at exactly 1.0 query per response, a much leaner pattern. Average query length over that same window dropped from roughly 117 characters in early December to around 53 characters by April, less than half. Fan-out queries are drifting toward terse, entity-first phrasing that looks more like a Google keyword search than a full sentence.

Then August happened. On August 8, 2026, Promptwatch recorded fanout queries using the site: operator jumping from 0.37% to 16.8% of all fanout queries overnight, a roughly 46x increase in a single day, tracked in its report on ChatGPT's site-operator fanout surge. Queries per response nearly doubled at the same time, from about 1.08 to 1.83. Because both numbers moved together, Promptwatch reads this as domain-scoped searches layering on top of generic ones rather than replacing them, which means a brand's own domain is now directly interrogated by ChatGPT via site:yourdomain.com [topic] style queries. Crawlability and indexation of your own site now gate inclusion in ChatGPT answers just as much as how well you rank externally.

Is it actually back now?

Sort of. Little Green Agency reported in mid-2026 that on GPT-5.6, the search_model_queries field reappeared in the browser's network response, and a separate source-tracking project called GPTSpy found the same thing independently in late-August data. Chris Long posted an update confirming fan-out queries were "officially" back, adding that the newer model was leaning heavily on site: searches within those fan-outs, consistent with what Promptwatch's crawler data shows.

But treat this as a truce, not a peace treaty. The consistent message across every source tracking this is that field visibility comes and goes across model versions without warning. If you're relying on browser scraping, the only sane approach is to check the Network tab every time OpenAI ships a new default model, rather than assuming whatever worked last month still works today.

The workaround that survives model updates

The data was never actually deleted, it just stopped showing up in the browser UI. It's still fully retrievable through OpenAI's Responses API when you enable the web_search tool. Fan-out queries live under the output array, in web_search_call items, specifically in action.queries (an array, not the singular action.query field, which only captures the first query and will undercount everything else). Citations show up under message.content[].annotations[].url_citation.

Little Green Agency published a working n8n workflow built around this, and it's a reasonable template if you want to build your own tracking rather than depend on a UI scrape:

1. A trigger node with your list of test prompts, pulled from a spreadsheet
2. An HTTP Request node posting to https://api.openai.com/v1/responses, specifying the model, the web_search tool, and critically, an explicit user_location (country, city). Skip this and the API defaults to a US-based searcher, which will quietly skew any local or international testing.
3. A code node that parses the output array for fan-out queries and citations

The cost is negligible, running 20 or 30 prompts through this costs pennies. The catch worth remembering is that the API and the web interface run under different system prompts, so what the API returns is a close approximation of what a real ChatGPT user's browser session would generate, not an exact transcript. It's still the most durable option available, because it doesn't depend on OpenAI continuing to expose an undocumented field in a JSON payload meant for rendering a chat window, not for third-party analytics.

Why so many dashboards broke, and why that Reddit thread exists

There's a reason a recent Reddit thread in r/DigitalMarketing, titled roughly "Is anyone else's AI visibility dashboard just making numbers up," struck a nerve. The complaint wasn't really about any single tool, it was about the difficulty of explaining unexplainable swings in a visibility score to a client or a boss who wants a clean number and a clean story.

That difficulty has a root cause. A huge share of GEO and AI visibility tools that launched fast in 2024 and 2025 were built on scraping the browser conversation payload, either through headless browser automation or a bookmarklet-style extension. The moment OpenAI stopped exposing search_model_queries for GPT-5.3-and-later sessions, those tools either broke outright or started quietly returning incomplete data, which is arguably worse because it looks fine until you compare it against something more reliable. Tools already built on the official API were less exposed to this specific break, but they still carry the API-versus-web-interface mismatch.

If you're evaluating AI visibility platforms and want to know which ones are less likely to leave you guessing the next time a model ships, it's worth asking vendors directly how they extract fan-out and citation data, not just what dashboards they show you.

Promptwatch's approach is worth calling out here because it sidesteps a chunk of this fragility by pulling AI crawler logs directly from your own infrastructure (Cloudflare, AWS CloudFront, Fastly, Vercel and others), rather than depending entirely on scraping a chat interface that can change its output format overnight. That gives you a second, independent signal, actual bot visits to your pages, alongside prompt-level tracking, so a UI change on OpenAI's end doesn't leave you completely blind.

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What to actually do about this

A few practical habits, based on everything above:

First, stop assuming "ChatGPT" is one consistent thing to measure. GPT-5.3 and GPT-5.4 cited almost entirely different sources for the same prompts. If your visibility audit only tests whichever model happens to answer by default that day, you're getting a partial picture at best.

Second, before panicking about a citation or traffic drop, check it against the calendar. The March 4, 2026 default-model switch and the August 8, 2026 site: operator surge both caused overnight, platform-wide shifts that had nothing to do with anyone's content quality.

Third, if you're extracting fan-out data yourself, build on the Responses API rather than browser scraping, and set user_location explicitly if location matters to your testing. It's more stable across model updates, even if it's not a perfect mirror of the web interface.

Fourth, treat your own site's crawlability as part of the answer now, not just an SEO afterthought. With site: operator fanouts becoming common, ChatGPT is directly searching your domain before deciding what to cite, so a page that isn't indexed or is buried behind a slow-loading template is invisible to that query regardless of how good the writing is.

And finally, keep some healthy skepticism about any dashboard, including your own, that shows suspiciously smooth trend lines through periods when the underlying platform was visibly in flux. The fan-out data didn't disappear because someone lost it. It disappeared because a third party controls the pipe it flows through, and that pipe changes shape whenever they feel like it. Building a measurement strategy that assumes otherwise is how you end up explaining unexplainable numbers to a client at 9am on a Tuesday. If you want to compare more platforms built to handle this kind of volatility, the GEO software directory at bestgeosoftware.com is a reasonable place to keep tabs on who's adapting fastest.