The New Paradigm of AI Shopping and Cross-Border E-Commerce: From "Submitting a Feed" to "Being Understood by AI"

I. A Paradigm Reckoning Set Off by One Email

One evening in April 2026, a promotional email from ChatGPT landed in the inbox. The subject line was plain and direct: "Shop smarter with ChatGPT." The accompanying image showed a Nordic-style wooden table, with a button underneath that read "Start shopping."

This was no ordinary marketing email. It signaled something: OpenAI is systematically steering the conversational sessions of its hundreds of millions of weekly active users toward shopping.

As a Chinese factory seller running my own brand on Amazon, my first reaction was not "How do I submit my products?" but a more fundamental question:

In an age where AI becomes the gateway to shopping, can my brand be seen by AI?

The answer to this question determines the direction of everything that follows.

II. The Global Landscape of AI Shopping: Five Platforms Enter at Once

In 2025–2026, the world's five largest tech giants rolled out AI shopping features almost simultaneously. They all saw the same trend: the consumer's shopping starting point is migrating from the search box to the chat box.

ChatGPT Shopping: Discovery Happens in AI, Checkout Returns to the Merchant

OpenAI's AI shopping feature has gone through three phases—and the third phase overturned the assumption of the second, which is itself an industry signal worth noting.

• April 2025: ChatGPT Search displayed product cards for the first time—structured product information with images, ratings, prices, and purchase links.
• September 2025: Instant Checkout launched. OpenAI and Stripe jointly developed the Agentic Commerce Protocol (ACP), attempting to let users complete purchases directly inside a ChatGPT conversation. Shopify announced that 1 million merchants could connect.
• November 2025: Shopping Research launched—a deeper experience: ChatGPT first asks about budget, use case, and preferences, then spends 3–5 minutes researching before offering structured purchase recommendations.

But by March 2026, the Instant Checkout narrative was overturned by reality.

Walmart was one of Instant Checkout's largest test merchants—connecting some 200,000 SKUs starting in November 2025. Five months later, Walmart executive Daniel Danker publicly disclosed to WIRED: the conversion rate for checkouts completed inside ChatGPT was only one-third of that for redirects to Walmart.com (i.e., −66%). The reasons were blunt:

• A single-item ordering model (5 products arriving as 5 separate shipments)
• Inaccurate inventory and pricing
• Missing merchant brand signals
• No way to handle returns and exchanges

As of February 2026, CNBC reported that only about 30 Shopify merchants had actually connected to Instant Checkout—a far cry from the "1 million" picture promised at launch. OpenAI had not even built a system to collect and remit sales tax across states.

OpenAI has confirmed it is phasing out Instant Checkout, pivoting to a "merchant's own app embedded in ChatGPT" model. Walmart's next move is to embed its own chatbot, Sparky, into ChatGPT and Gemini, with users logging into their Walmart accounts and completing payment inside Walmart's system.

A Forrester survey from March 2026 corroborated this direction: "completing a purchase inside an answer engine" is the use case consumers are least willing to adopt.

The core takeaway for cross-border sellers: discovery can happen inside AI, but checkout must be pulled back to your own turf. Your job is not to "sell things inside ChatGPT," but to "get ChatGPT to recommend you, then have the consumer go to Amazon or your own website to complete the purchase."

The Other Four Platforms

Five players entering at once says one thing: AI shopping is not an experiment but an infrastructure-level migration. Yet the Instant Checkout lesson also reminds us: what AI platforms excel at is discovery and recommendation, not checkout and fulfillment.

III. Let the Data Speak: Consumer Shopping Behavior Has Already Changed

Talking trends in the abstract is pointless. Look at the data.

Consumer Adoption

Traffic Growth

Note: the absolute base of GenAI referrals is still small. A 4,700% year-over-year jump sounds staggering, but it starts from a low base—in 2025, AI referrals still accounted for a single-digit percentage of total US e-commerce traffic.

US Market-Size Forecasts

Note: eMarketer's $20.9 billion and $144 billion are US market figures, not global. The global numbers are larger, but there is currently no authoritative global-basis forecast.

In a sentence: AI shopping has gone from "some people are dabbling" in 2025 to "half of Americans are using it" in 2026—but within the total e-commerce pie, the AI channel accounts for only 1.5% in 2026, and even by 2029 only 8.8%. This is a fast-growing incremental layer, not a substitute that replaces traditional channels.

IV. The Fundamental Shift in the Buyer Journey

The traditional e-commerce buyer journey is:

Search keywords → Browse results page → Click a product → Compare → Place order

The AI shopping buyer journey becomes:

Describe the need in natural language → AI understands intent → AI recommends products → Redirect to the merchant channel → Place order

Note the last step: after Instant Checkout's failure, redirecting to the merchant's own channel to close the sale is the mainstream path. Consumers research inside AI and complete the purchase on Amazon or the brand's own website. Semrush's research supports this too: 39% of consumers, after getting a recommendation from AI, go to platforms like Amazon to buy.

This shift carries several deeper implications:

1. Keywords Give Way to Semantics

Traditional SEO optimizes for keywords. AI shopping optimizes for semantics—a consumer will say, "My son is 8, he sweats easily, I need something cotton, ideally with a cartoon print."

AI does not match keywords; it matches intent. If your product description lacks the semantic chain "sweats easily" → "breathable" → "combed cotton," AI will not recommend you.

2. Titles Give Way to Descriptions

On Amazon, the title is king. In AI shopping, the description is king.

What ChatGPT reads is the description field in your site's JSON-LD structured data, along with the natural-language content on the page. A keyword-stuffed Amazon title works for the A9 algorithm but is nearly useless to AI. What AI needs is a description that answers questions.

3. Being Indexed Gives Way to Being Understood

The core question of traditional SEO was "Can Google index me?" The core question of AI shopping is "Can AI understand what I'm selling?"

This brings us to the standing of structured data—and the controversy around it.

V. Structured Data: Important, but Not a Panacea

JSON-LD Product Schema: Worth Doing, but Its Effect Is Still Debated

In March 2025, Google and Microsoft officially confirmed: they actively use schema markup when generating AI answers. A Microsoft principal product manager stated plainly: "schema markup helps Microsoft's LLMs understand your content."

But the conclusions from academia and industry field tests contradict each other:

The supporters:

• The accuracy with which LLMs extract information from structured data rises from 16% for natural text to 54% (Data World / GPT-4 study, referring to field-extraction accuracy)
• Microsoft Bing officially confirms that schema helps Copilot understand content
• BrightEdge claims schema lifts AI citation rates by 44%

The skeptics:

• A Search/Atlas study (2024.12): AI citation rates for comprehensive schema versus minimal schema show no significant difference
• SE Ranking analyzed 300,000 domains: citation frequency with versus without llms.txt shows no statistically significant difference
• A ZipTie report: pages with FAQ schema were actually cited less often (a slight negative correlation)
• Google's John Mueller has publicly stated: no AI system reads llms.txt at inference time

My judgment: the value of schema lies not in "getting AI to cite you," but in "getting AI to accurately understand what you sell." These are not the same thing. Whether AI cites a given brand may depend on many factors (third-party reviews, Reddit discussions, authoritative sources), but whether it can correctly describe your product specs when it does cite you—there, schema helps.

Practical advice: if you do schema, do it completely. Incomplete structured data may be read by AI as inconsistent information. But there's no need to treat schema as the silver bullet of AI shopping—it is part of the infrastructure, not the whole of it.

llms.txt: A Low-Cost Play—Don't Take It Too Seriously

llms.txt was proposed by Answer.AI's Jeremy Howard in 2024. As of October 2025, over 844,000 websites had deployed it. But so far no mainstream AI platform has officially confirmed that it reads llms.txt at inference time. The deployment cost is near zero—doing it costs you nothing—but don't hold unrealistic expectations for it.

The ChatGPT Merchant Portal

OpenAI has opened a merchant entry point at chatgpt.com/merchants. Once approved, you can push product data every 15 minutes. But given that Instant Checkout is being phased out, the merchant portal's center of gravity is also shifting from "checkout" to "discovery"—letting ChatGPT cite your product information more accurately when answering shopping questions, and then steering users toward your sales channel.

VI. What Amazon Sellers Most Need to Know: Two Disconnected Data Universes

This is the most important section in the entire article.

Amazon Has Blocked OpenAI's Crawlers

Amazon has blocked all of OpenAI's crawlers (OAI-SearchBot, GPTBot). ChatGPT Shopping Research cannot see products on Amazon.

The OpenAI Shopping Research team demonstrated this openly at its November 2025 launch: when a user asks about a specific Amazon product, ChatGPT tells the user to "please confirm on Amazon yourself"—tantamount to admitting it is blind to Amazon.

This means:

• Optimizing only your Amazon listing leaves you all but invisible in the ChatGPT channel—unless the user explicitly asks to see Amazon
• Rufus optimization (in-Amazon AI) and ChatGPT optimization (off-Amazon discovery) are two things that barely overlap
• They inhabit two disconnected data universes

Rufus Optimization: The AI Battlefield Inside Amazon

Amazon's AI shopping assistant Rufus already has 250 million users. During Black Friday 2025, 38% of shopping sessions involved Rufus, and consumers who used Rufus were 60% more likely to complete a purchase.

Rufus reads the entire content of your listing: the title, the five bullet points, the A+ Content copy, the backend keywords, customer reviews, and Q&A. The average query length in a Rufus session is 2.4 times that of traditional search—users no longer search short keywords but describe their needs in full, natural language.

Key action: answer specific use-case questions in your listing. Rufus matches on "whether this product can meet the specific need the user describes."

The Standalone Site: A Path Around Amazon's Block to Being Discovered by ChatGPT

For Chinese sellers who sell on Amazon, building a standalone brand site and deploying Product Schema is, in practice, all but the only path to being seen by ChatGPT.

The flow is:

Consumer asks ChatGPT a question → ChatGPT crawls your standalone site's product page (schema + description)
→ ChatGPT recommends your product → Consumer searches your brand name on Amazon and completes the purchase

This explains why "build a standalone site + Product Schema + links pointing to the Amazon store" matters more than it looks—it is not merely brand-building; it is a tactical move to bypass Amazon's data blockade and win incremental exposure in the age of AI shopping.

VII. The Overlooked Key: Third-Party Platform Signals

What ChatGPT Shopping Research trusts first is not the brand's own website but "unpaid review" sources.

OpenAI's Manuka Stratta put it this way at the launch: "Generally a lot of reviews on Reddit are pretty trustworthy."

This means that, for a factory brand, another important battlefield for AI visibility is getting real users to discuss your product on third-party platforms.

SE Ranking data from November 2025:

• Domains with heavy brand mentions on Quora/Reddit are 4 times more likely to be cited by ChatGPT
• Brands with profiles on Trustpilot/G2/Capterra are 3 times more likely to be cited

What this means in practice for cross-border sellers:

• If your product has been spontaneously recommended by users in relevant Reddit subreddits (e.g., r/BuyItForLife, r/parenting), ChatGPT will most likely include you among its recommendation candidates
• Building a brand profile on Trustpilot and accumulating genuine reviews may be more effective than piling up schema on your own site
• The prerequisite is "spontaneous discussion by real users"—fake reviews and paid shills are easier to detect and penalize in the AI era than in the traditional SEO era

VIII. The Evolution of SEO: From SEO to AEO to GEO

Three tiers of optimization concepts are emerging in the industry:

• SEO (Search Engine Optimization): the traditional optimization of search-engine rankings
• AEO (Answer Engine Optimization): optimization aimed at AI answer engines, covering structured data, E-E-A-T signals, content architecture, and product-feed submission
• GEO (Generative Engine Optimization): optimization aimed specifically at visibility within LLM output

The strongest strategy is to run all three in parallel: SEO and AEO share a technical foundation (schema, site speed, E-E-A-T) and do not conflict. In 2026, about 31.3% of the US population will use GenAI search—but 77% use both AI and traditional search. You have to walk both roads.

The Zero-Click Threat: The Vast Gap Between Informational and Commercial Queries

An important distinction:

Source: Ahrefs 2025.11

This means that, for e-commerce sellers, AI's impact on organic search traffic is far less severe than it is for content sites. Product searches are still dominated by traditional results pages. But this ratio is changing fast—it should be monitored continuously.

IX. The Future: Agentic Commerce and MCP

Conversational Commerce Is No Longer a Concept

84% of brands consider conversational commerce more strategically important than a year ago. 82% believe it will become mainstream within two years. But the failure of Instant Checkout reminds us: conversational "discovery" has matured; conversational "checkout" still has a long way to go.

MCP: AI Agents Connecting Directly to Merchant Back Ends

Anthropic released the Model Context Protocol (MCP) in November 2024; by March 2026 it had been adopted by Google, Microsoft, OpenAI, and Shopify alike. There are now more than 10,000 active public MCP servers worldwide, with 97 million monthly SDK downloads.

In its Summer 2025 edition, Shopify automatically enabled an MCP endpoint for every store—three MCP servers handling product discovery, customer accounts, and the checkout flow respectively.

McKinsey forecasts that by 2030 the global agentic commerce market will reach $3–5 trillion.

From "Submitting a Feed" to "Becoming AI's Source of Information"

The traditional model:

Upload product feed → Google Merchant Center → Wait for indexing → Optimize ranking

The new model:

Make your entire digital presence (website, schema, third-party reviews, Reddit discussions, content matrix)
machine-readable and semantically rich → AI agents can understand and recommend your products
→ Consumer redirects to your sales channel and completes the purchase

Note the step "redirect to your sales channel"—this is the consensus the industry reached after Instant Checkout's failure. AI handles discovery and recommendation; the merchant handles checkout and fulfillment.

X. A Field Retrospective: One Factory Brand's AI Shopping Overhaul

Let me illustrate with a real case. A certain Chinese factory brand, in operation for over 20 years, has multiple ASINs on Amazon North America, already runs a brand website, and operates an SEO content matrix.

The diagnosis that night revealed: the website already had solid SEO infrastructure (daily news articles, NewsArticle JSON-LD, IndexNow, an RSS feed), but was missing a critical link—there were no product catalog pages on the site and no Product schema. Given that Amazon blocks OpenAI's crawlers, this meant the brand was all but invisible in ChatGPT's world.

The Overhaul Completed That Night

1. Sort out all factory SKUs, organized by the physical product rather than by Amazon ASIN
2. Create a dedicated product detail page for each SKU, displayed grouped by series
3. Deploy complete Product JSON-LD + BreadcrumbList schema on every page
4. Factory product images (white-background hero shots + lifestyle shots + detail shots) all published
5. Create an llms.txt plain-text product catalog
6. Purchase links all point to the Amazon brand store (not to specific ASINs), with inventory/supply-chain notes attached
7. Deploy + use IndexNow to notify search engines to crawl the new pages

Why This Matters More Than It Appears

Against the backdrop of Amazon blocking OpenAI's crawlers, these product pages in effect establish a discovery path around the blockade:

ChatGPT user asks a question → Crawls the brand website's product page (with Product schema)
→ Recommends the brand → User searches the brand name on Amazon → Completes the purchase on Amazon

The website is no longer merely a brand-image page. It is the brand's only public ID card in the world of AI shopping.

XI. An Action Checklist for Cross-Border E-Commerce Sellers

Do It Now (This Week)

• [ ] Check whether robots.txt allows OAI-SearchBot and GPTBot access
• [ ] Deploy complete Product JSON-LD for every core product on the brand website
• [ ] Rewrite product descriptions in natural language—assume the buyer is talking to an AI, not typing into a search box
• [ ] Answer every plausible natural-language question in the A+ Content and Q&A of your Amazon listing
• [ ] Check whether the brand has organic discussion in relevant Reddit subreddits (don't astroturf—just observe)

Medium Term (1–3 Months)

• [ ] Apply to the ChatGPT merchant portal (chatgpt.com/merchants)—the emphasis is on "being discovered," not "in-platform checkout"
• [ ] Build a brand profile on Trustpilot and encourage real buyers to leave reviews
• [ ] Build a Q&A-style content matrix—each article answering one real consumer question
• [ ] Monitor the impact of AI Overview on organic traffic in Google Search Console
• [ ] Deploy llms.txt (zero cost, nothing to lose)

Long Term (3–12 Months)

• [ ] Watch the evolution of the MCP standard—in the future, AI agents may call product APIs directly
• [ ] Prepare an automated update pipeline for your product data feed
• [ ] Watch the development of ChatGPT Ads (already down from $60 CPM to about $25 CPM, shifting to a CPC model at $3–5/click, with the threshold lowered from $200k to $50k)

Conclusion: AI Is an Added Layer of Opportunity, Not a Replacement

The competitive landscape of cross-border e-commerce is layering on a new stratum:

• Amazon/platforms: still the absolute main battlefield. In 2026 the AI channel accounts for only 1.5% of US e-commerce
• Standalone sites/SEO: serving both traditional search traffic and AI discovery—two birds with one stone
• The AI discovery layer: a fast-growing new increment. Whoever lays the groundwork first eats first

This is not a "search vs. AI" opposition. 77% of consumers use both. But AI is becoming the new "first touchpoint"—consumers ask AI first, then go to Amazon or the brand website to confirm.

For Chinese cross-border sellers who own factories and brands, the greatest advantage is authenticity. The core logic of AI recommendation is semantic matching plus trust signals. Years of factory history, large volumes of genuine reviews (especially on third-party platforms), a registered trademark—in AI's eyes, these weigh more than any technical trick.

There is also an underrated fact: Amazon's blocking of OpenAI's crawlers has, paradoxically, created a structural advantage for sellers who have a standalone site. Competitors who sell only on Amazon and have no brand website are invisible in ChatGPT's world. Your standalone site + Product Schema is the "ticket in" for the age of AI shopping.

Getting AI to understand who you are matters more than getting AI to see you. But first, you have to let AI see you.

Data sources: OpenAI, Google, Adobe, Semrush, eMarketer (US), McKinsey, Capital One Shopping, Capgemini, Dataslayer, Seller Labs, Gartner, Forrester, WIRED, CNBC, Ahrefs, SE Ranking, Position Digital, and others.

Appendix: Key Sources

• OpenAI - Introducing Shopping Research (2025.11)
• OpenAI - Buy it in ChatGPT (2025.9)
• OpenAI - Powering Product Discovery (2026.2)
• OpenAI Merchant Portal
• WIRED - Walmart Instant Checkout Conversion Data (2026.3)
• CNBC - Only ~30 Shopify Merchants on Instant Checkout (2026.2)
• Forrester - Consumer Willingness to Buy in AI (2026.3)
• Stripe - Agentic Commerce Protocol
• Semrush - AI Tools and Modern Buyer Journey (2025.12, n=1030)
• Adobe - Holiday 2025 Record $257.8B
• eMarketer - AI Commerce 2026 (US)
• Capital One Shopping - AI Shopping Statistics 2026
• Seller Labs - Amazon Rufus AI Optimization 2026
• SE Ranking - Third-party Signal Impact on AI Citations (2025.11)
• Ahrefs - AI Overview by Query Type (2025.11)
• Shopify - Agentic Commerce Momentum
• Ecommerce Fastlane - Shopify MCP
• GEORaiser - Schema Markup for AI (2025.3)
• llmstxt.org
• McKinsey - Agentic Commerce $3-5T by 2030