ChatGPT

In June, every web source ChatGPT pulled carried a result_source label naming the pipeline that fetched it: serp, labrador, bright, oxylabs, and later bing. That field was the centrepiece of the first teardown.

Re-read the traffic today, on the same account, and it is gone. A live commercial search retrieved 203 sources across 80 domain groups, with zero pipeline labels anywhere in the stored payload. The sources are still there, grouped by domain with object references, but the “which scraper fetched this” tag is no longer exposed.

What replaced it is sharper. Each cited source is now bound to the exact answer sentence it supports, by character offset (matched_text, start_idx, end_idx), alongside a list of runner-up pages that backed the same claim and lost the visible citation. So the engine no longer tells you how a page was fetched, but it now tells you the precise line each page won.

Read this as: any tool or claim that depended on the pipeline label is now reading nothing on current traffic.

The model behind the “Instant” label used to be gpt-5-5. Today the default model slug is auto: the router that silently picks the model is now the default selection, not a hidden fallback.

From the model endpoint, the current line-up is gpt-5-3 (labelled “our latest and most advanced”), gpt-5-5, and mini variants of each, plus auto. Reasoning resolves to gpt-5-6-thinking, and it carries a new thinking_effort field. On a hard puzzle the router set that effort to "extended", so there are tiers of reasoning depth chosen for you per turn.

The practical consequence is the one from the original teardown, now sharper: your “Instant” benchmark is not stable, because the turn you think you are testing may be silently escalated to a reasoning model at an effort level you did not pick.

This one is why the source-pipeline finding took real work to confirm. A plain web-search turn’s request no longer returns the answer. It returns a small handoff (a token and a topic id), and the actual answer streams over a persistent WebSocket that opened when the page first loaded.

Previously this async pattern was reserved for the slow “Thinking” answers. Now it is the default path for ordinary search turns too. A pre-flight step mints a fresh transport token on every single turn.

Why it matters beyond plumbing: the readable layer for this kind of work has always been the browser network panel, after decryption. Moving the answer onto a socket opened before you can attach a listener makes it materially harder to watch a normal answer being built, unless you hook the page before it loads. The window is quietly closing, which is part of why a one-click tool that reads your own session, rather than a DevTools recipe, is now the realistic way in.

The clearest single demonstration that this is a moving target. Over 21 and 22 July, ChatGPT changed the shape of its stored retrieval payload twice.

First the flat per-source pipeline label began dropping out, and the runner-up pages moved onto the citation items instead. A day later the whole retrieval block moved again, nesting under a domain-grouped structure, and the source reference changed from a plain string into an object. Anything reading the old shape by a fixed path suddenly matched nothing and showed an empty result.

The lesson for anyone building on this: never read these payloads by a hard-coded path. Collect by the presence of a field, so a rename breaks one small thing instead of the whole read. This drift week is what set up the bigger change three days later, when the pipeline label vanished from the stored data entirely.

Two findings from the follow-up sweep, covered in full in part 2 of the teardown.

The runner-up layer. A field that was empty in June is populated now. For each fact ChatGPT states, the traffic lists the other pages that backed the same claim and lost the visible citation, sometimes a sibling page on the winner’s own domain, sometimes a direct competitor. You can read, claim by claim, exactly who you lost a citation to. (structural)

A fifth pipeline, Bing, but cohort-gated. Someone was served pages through a bing pipeline that had never appeared in my own traffic. The same review page reached them through Bing and reached me through a commercial scraper, in the same week; a free account in a third country saw a third setup again. So the source pipelines are per-cohort plumbing, load-balanced across accounts, not a single global system. (structural that it exists; directional on who gets which)

This is also the finding that first proved you cannot build a tier theory off one account, a rule that only became more important as the whole pipeline label later disappeared.

Where this all started, written up in full in the first teardown.

Reading the raw network traffic, every web source ChatGPT pulled carried a hidden result_source label naming one of four fetch pipelines: an open-web baseline, a licensed-content pipe, and two commercial web scrapers. ChatGPT’s “open web” was being fulfilled by competing scraping vendors, and the field told you which one served each result. (structural)

Before any search ran, a classifier filed the query into a use case, and that decided which pipelines fired. Some query types (how-to, definitions) never touched the web at all and were answered from training data. (structural)

And the directional half, from one account over a few days: on commercial queries a handful of review hubs dominated, Reddit was cited heavily while YouTube was retrieved and almost never cited, and for pricing the model opened the vendor’s own page and read the number straight from the HTML. (directional)

Almost every specific here has since changed, which is the whole reason this log exists.