ChatGPT launches visual shopping built on the Agentic Commerce Protocol

ChatGPT can now shop. Instead of plain links, it shows visual cards with photos, prices and a "buy" button. OpenAI has launched the Agentic Commerce Protocol: stores connect directly, and users compare products side by side and buy without leaving the chat. The feature is rolling out in the US. It is a direct challenge to Google Shopping and Amazon, as product search shifts toward ChatGPT.

AI-processed from OpenAI Blog; edited by Hamidun News
ChatGPT launches visual shopping built on the Agentic Commerce Protocol
Source: OpenAI Blog. Collage: Hamidun News.
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ChatGPT is no longer just an adviser. It can now sell. OpenAI has launched visual shopping inside ChatGPT, built on a new open standard called the Agentic Commerce Protocol.

Previously, when a user asked ChatGPT "which headphones should I buy," they got a text list of names and sometimes links, and had to head to a browser on their own. The interface has now changed fundamentally: answers include visual product cards with photos, prices, ratings and purchase buttons. Several options can be compared side by side, without switching tabs or leaving the chat.

The key element of the new feature is the Agentic Commerce Protocol (ACP). It is an open standard through which online stores and brands pass an up-to-date catalog directly to ChatGPT: products, prices, stock availability, delivery terms and timelines. Unlike ordinary web scraping, ACP delivers real-time data: if an item sells out or a price changes, ChatGPT sees the current state immediately. This is a fundamental difference from search, which indexes data with a delay.

OpenAI has not yet disclosed a list of specific merchant partners, but said large retailers are already integrating through the protocol. The feature is rolling out gradually, starting with users in the US, first in the ChatGPT web interface and then in mobile apps.

From the user's perspective, it works like this: you type "I need running shoes under $150 for pavement," ChatGPT asks clarifying questions about foot width or brand preferences, and then shows several options as visual cards. When you click "buy," you land directly on the merchant's payment page. The whole path from query to cart happens in one interface, with no extra hops.

For the advertising and e-commerce market, this is a serious signal. Google spent years building Shopping as a separate product with paid listings. Amazon bet on trust through reviews and a closed marketplace. ChatGPT offers a different model: a personalized conversation that leads to a purchase on its own, without pages of dozens of ad results.

Monetization remains an open question. OpenAI has not said whether it takes a commission on transactions or offers paid placement within results. Analysts expect a mixed model: free basic integration via the protocol and paid promotion for priority display, an evolution reminiscent of Google Shopping's path from organic listings to mostly paid ones.

Technically, the Agentic Commerce Protocol continues OpenAI's strategy of standardizing agent integrations. The company previously introduced the Model Context Protocol (MCP) for connecting tools to AI agents. ACP follows the same logic: instead of an agent scraping merchant sites itself, merchants publish a structured feed built specifically for agents. Standardization benefits both sides: agents do not have to parse each site separately, and merchants do not have to worry about their data being processed incorrectly.

Shopping in ChatGPT is not just a new feature but a bid to become a new layer between users and online retail. If OpenAI manages to bring enough merchants into the protocol, the next step is purchases completed entirely inside ChatGPT, without redirects. For Google, Amazon and Perplexity, it is another signal: AI interfaces are rewriting the rules of product discovery and search faster than the market expected.

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Zhemal Khamidun — CPO at Alpina Digital, product manager of AlpinaGPT. Building AI products since 2019. Hamidun News is a pet project that grew into a production aggregator with 6,000+ readers.
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