OpenAI's ad platform has two halves. On the ChatGPT side, the backend injects structured single_advertiser_ad_unit objects into the conversation SSE stream while the model is responding. On the merchant side, a tracking SDK called OAIQ runs in the visitor's browser and reports product views back to OpenAI. The two are tied together by Fernet-encrypted click tokens, four of them per ad.
I captured both halves on a consented mobile-traffic research fleet. Everything below comes from observed traffic.
How an ad gets into a conversation
When you send a message to ChatGPT, the backend opens an SSE response at chatgpt.com/backend-api/f/conversation. Most events in that stream are model-output. Some are ad units. They look like this:
event: delta
data: {
"type": "single_advertiser_ad_unit",
"ads_request_id": "069e89b3-c038-7764-8000-6e5a193e5f69",
"ads_spam_integrity_payload": "gAAAAABp6Js_<...redacted...>",
"preamble": "",
"advertiser_brand": {
"name": "Grubhub",
"url": "www.grubhub.com",
"favicon_url": "https://bzrcdn.openai.com/cabfae7ead26b03d.png",
"id": "adacct_6984ed0ba55481a29894bb192f7773b4"
},
"carousel_cards": [{
"title": "Get Chinese Food Delivered",
"body": "Satisfy Your Cravings with Grubhub Delivery.",
"image_url": "https://bzrcdn.openai.com/cabfae7ead26b03d.png",
"target": {
"type": "url",
"value": "https://www.grubhub.com/?utm_source=chatgptpilot&utm_medium=paid&utm_campaign=diner_gh_search_chatgpt_kw_traffic_nb_x_nat_x&utm_content=nbchinese&oppref=gAAAA<...>&olref=gAAAA<...>",
"open_externally": false
},
"ad_data_token": "eyJwYXlsb2<...>"
}]
}
Notes:
single_advertiser_ad_unit is a typed schema. The naming implies siblings (multi-advertiser, etc.).advertiser_brand.id is adacct_<32-hex> — a stable per-merchant account identifier.- Brand favicon and ad image both load from
bzrcdn.openai.com. OpenAI hosts the advertiser's creative, not the merchant.
target.open_externally: false opens the link in ChatGPT's in-app webview, so OpenAI observes the post-click navigation on top of any pixel signal.- Four Fernet tokens per ad:
ads_spam_integrity_payload, oppref, olref, and a base64-wrapped ad_data_token. Each is AES-128-CBC under a server-only key with HMAC-SHA256 integrity.
How ads get selected
A single account in the panel received six different ads across six conversations on six different topics. The targeting is contextual to the chat:
Conversation topic
Advertiser delivered
Beijing trip planning (Great Wall, Forbidden City)
Grubhub — "Get Chinese Food Delivered"
Beijing tour bookings
GetYourGuide — Great Wall tour, ad_id=beijing003
Beijing flights
Axel — utm_term=vflight_beijing_03
NBA playoffs
Gametime — utm_campaign=nba&utm_content=playoffs
Spring fashion/trends
Aritzia — utm_campaign=chatgptpilot_trav3
Productivity / slides
Canva — utm_campaign=…link-clicks_products
Same account, different topic, different brand. I didn't find evidence one way or the other on whether targeting also incorporates prior conversation history.
The four-token attribution chain
Every ad ships with four distinct Fernet-encrypted blobs. Their roles, based on where they appear:
ads_spam_integrity_payload sent inside the SSE data, never on the click URL. Server-side integrity check against forged ad clicks.**oppref** present on the click URL and copied verbatim by the OAIQ pixel into the cookie __oppref (TTL 720 hours / 30 days). The forward attribution token. Travels with every subsequent merchant pixel event.**olref** paired with oppref on the click URL but not stored by the SDK we observed. Likely impression-side / outbound-link-reference logging on OpenAI's servers.ad_data_token base64-wrapped JSON containing yet another Fernet token. Carried in the SSE payload, presumably reconciled server-side at click time.
Fernet's first nine bytes are public: version byte 0x80 plus an 8-byte big-endian Unix timestamp. So the mint time of any of these tokens is recoverable without OpenAI's key:
import base64, struct, datetime
b = base64.urlsafe_b64decode("gAAAAABp7fdA" + "==")
print(datetime.datetime.utcfromtimestamp(struct.unpack(">Q", b[1:9])[0]))
# → 2026-04-26 11:30:08 UTC
The Home Depot click URL I captured was minted at 11:30:08; the browser fetched the merchant page at 11:31:43. Click latency: 95 seconds.
How the loop closes on the merchant side
User taps the card. Browser opens:
https://www.grubhub.com/?utm_source=chatgptpilot&...
&oppref=gAAAA<...>
&olref=gAAAA<...>
The merchant page loads the OAIQ SDK:
<script src="https://bzrcdn.openai.com/sdk/oaiq.min.js"></script>
<script>
oaiq('init', { pid: '<merchant pixel ID>' });
oaiq('measure', 'contents_viewed', { ... });
</script>
oaiq.min.js is at version 0.1.3. On init it reads ?oppref= from window.location, writes it into the first-party cookie __oppref with a 720-hour TTL, and sets a probe cookie __oaiq_domain_probe. Every subsequent measure call POSTs JSON to:
POST https://bzr.openai.com/v1/sdk/events?pid=<merchant>&st=oaiq-web&sv=0.1.3
Two domains to add to your filter list if you want to block ChatGPT ad events: bzrcdn.openai.com, bzr.openai.com. Two cookie names to inspect after any ChatGPT-recommended click: __oppref, __oaiq_domain_probe.
