Over the past few years, discussions about how ChatGPT will be commercialized have never ceased, with advertising and e-commerce remaining the two most closely watched and controversial directions. Now, all the speculation is turning into reality.

Signals have long been sent.

Previously, OpenAI released a job posting for a Growth and Paid Marketing Platform Engineer, which explicitly required the role to build functions such as an advertising platform, campaign management, and even attribution analysis. This is already a blueprint for constructing a standard digital marketing platform.

A more crucial signal came from the shift in attitude of Sam Altman, CEO of OpenAI himself.

Once, Sam Altman, CEO of OpenAI, regarded advertising as a last resort. However, in a recent interview, his stance changed completely. Sam admitted that Instagram had changed his view: “I like Instagram ads; they add value to me… I’ve bought quite a few things through them.”

He then made a statement of great significance to the industry: “I believe we might be able to launch some cool advertising products that not only bring net benefits to users but also have a positive impact on our relationship with users.”

In an official blog post on September 29, 2025, OpenAI announced the launch of the “Instant Checkout” feature first in the United States. This feature allows users to purchase goods directly from Etsy sellers through ChatGPT and standardizes the transaction process between AI and merchants via the “Agent Commerce Protocol (ACP)”.

OpenAI is no longer content with being just a chat tool; it is building a transaction ecosystem. When AI gains control of both the traffic entrance and the transaction closed loop, a transformation of the marketing and e-commerce industries has already begun.

01 New Scenarios After AI Integrates with Advertising and E-commerce

We can understand this through two most direct scenarios:

Scenario 1:

You: I’m going to Tokyo for a trip next weekend. Can you help me plan a three-day itinerary?

ChatGPT: Of course. I’ve prepared a draft three-day Tokyo itinerary that combines business and cultural experiences for you.

[AI Ad] Additionally, when recommending Sensoji Temple and Ginza to you, I found a highly praised restaurant called “Sushi Master” nearby. It has won a Michelin star for three consecutive years and is within walking distance. If you’re interested, I can check for available seats and make a reservation for you.

In this scenario, AI accurately understands your intention (traveling to Tokyo) and needs (planning an itinerary), and seamlessly embeds a commercial recommendation at the moment when you most need a solution.

This ad doesn’t disturb you much and even optimizes your travel experience. It is precise, timely, and directly provides the conversion action of making a reservation.

This is advertising in the AI era. It is no longer an intrusive pop-up or banner, but a context-based, service-oriented proactive suggestion.

Scenario 2:

You: I need a pair of running shoes for a half-marathon, suitable for flat feet, with a budget of around 1,000 yuan.

ChatGPT: Based on your requirements, I recommend Brand A Model X and Brand B Model Y. Model X is better in terms of cushioning, while Model Y is lighter. Which one do you prefer?

You: I want to buy Model X of Brand A, size 42, in blue.

ChatGPT (Instant Checkout): Alright, I have matched you with partner merchant A. Your default address and payment card information are ready. Shall we confirm the total order price and complete the purchase within 5 seconds?

You: Confirm.

ChatGPT: The order has been completed. It is expected to be delivered in 2 days.

In this scenario, ChatGPT acts as a personal shopping assistant. Relying on your historical records and real-time conversation, it accurately identifies your complex needs.

The most crucial point is that with the Instant Checkout feature, users do not need to leave the chat box, open a new e-commerce app, re-log in, or fill in their address. AI completes the entire closed loop from demand analysis, product comparison, merchant matching to payment confirmation in a one-stop conversation.

This is e-commerce in the AI era, which completely eliminates the process of price comparison and page jumping.

02 How AI Reconstructs the Core Logic of Marketing

When AI becomes an infrastructure and deeply participates in transactions, its impact on the marketing industry will be fundamental.

● Reconstruction of Traffic Entrances: From Search Boxes to AI Assistants

In the past, the path for consumers to access information and products involved jumping between multiple platforms: they searched on Baidu, checked reviews on Zhihu, found product recommendations on Xiaohongshu, and finally placed orders on Taobao.

Each platform is a closed domain, and brands have to spend their advertising budgets to buy access.

However, in the AI era, users no longer go to specific platforms; instead, they ask AI. All information, reviews, prices, and channels are integrated into a single intelligent conversation.

When users get used to obtaining answers, solving problems, and completing purchases through ChatGPT, the search box is no longer the starting point, and AI becomes the new super entrance.

The battlefield of marketing is no longer about occupying a tangible platform, but about integrating into every decision-making moment in users’ lives.

● Evolution of Advertising Forms: From Intrusive to Service-Oriented

Traditional advertising, regardless of its form, is essentially intrusive. Banner ads interrupt your reading, and pre-roll video ads interrupt your viewing. Even targeted feed ads are, for most users, a form of passively received information noise that is not always welcome.

The core of advertising in the AI era will undergo a complete transformation to become service-oriented. As mentioned earlier, an ad will only be recommended by AI if it is helpful to the user. Based on your immediate needs and AI’s in-depth understanding of your daily communication habits, it naturally appears as part of the solution.

If a recommendation is useless, users will immediately lose trust in AI.

● Reshaping of Decision-Making Links: From a Long Funnel to an Instant Closed Loop

The classic AISAS model (Attention → Interest → Search → Action → Share) in the marketing industry is a long and fragile conversion funnel. If users drop off at any link, the conversion will fail.

Brands need to invest huge costs to intercept users at every node.

Now, AI compresses this long chain into “Conversation → Action” in the most efficient way. With the help of AI assistants, the two most time-consuming links—Interest and Search—are completed in an instant.

Relying on its strong information integration capabilities and understanding of users, AI directly provides the best options. Moreover, the emergence of the Instant Checkout feature has further connected the last mile of the action link. This shortened decision-making path will also bring higher conversion efficiency.

● Deepening of Connections: From Group Portraits to Individual Conversations

In the past, personalized marketing relied on static and rough tags, such as “30 years old, female, likes sports, lives in a first-tier city”. Brands tried to guess users’ preferences through these tags and broadcast messages to a group.

However, AI has access to your complete and dynamic conversation history. It not only knows your tags but also understands your context, your current mood, your long-term preferences, and even the potential needs that you yourself have not clearly expressed. It can remember that you complained about poor sleep last week and understand the real purpose behind your question about the Tokyo itinerary now.

The concept of one-on-one marketing has been talked about for many years, and now the path to realizing it is becoming increasingly clear.

● Revolution in Effect Attribution: From Click Tracking to Conversation Tracking

Traditional digital advertising attribution models, such as “First Click”, “Last Click”, and “Linear Attribution”, are extremely complex. It is difficult for brands to figure out which exposure or click should be credited for a conversion.

That’s why the famous saying, “I know half of my advertising budget is wasted, but I don’t know which half”, is so widely recognized.

In the future, attribution will become direct and clear. When a user goes from putting forward a need, accepting a recommendation to completing a purchase in a continuous conversation with AI, the entire process takes place within a single chat window. Brands can clearly attribute this conversion to a specific recommendation made by AI.

This “conversation = conversion” model will make the measurement of advertising ROI more accurate and the allocation of marketing budgets more efficient.

03 Five Strategies to Embrace the AI Marketing Era

Facing the marketing ecosystem reconstructed by AI, brands should not only make scattered tactical adjustments but also carry out a systematic transformation in thinking, technology, and organization.

● Shift from SEO to GEO

In the search era, what brands needed to do was SEO (Search Engine Optimization). In the AI era, they need to focus on GEO (Generative Engine Optimization). Instead of optimizing webpage rankings, brands now need to optimize the probability of being adopted by AI as the preferred answer.

When you ask ChatGPT: “I need a skirt suitable for the beach in Hawaii”, how does AI know which brand to recommend? It will not calculate keyword density; instead, it will understand your needs (scenario, style, function) and then generate the most matching answer from its training data and real-time connected product database.

The core of GEO is to make AI understand you and recognize you as the most authoritative, relevant, and credible answer. This includes:

• Highly structured product data: Your product information must be translated into a language that AI can understand. Clearly label refined attributes such as usage scenario = beach, style = vacation, sleeve length = sleeveless, material = silk, etc.
• Establishing authority and credibility: AI will crawl information across the entire network to judge credibility. Brands with a large number of positive user reviews and coverage by authoritative media or influencers are more likely to be recommended by AI first.
• Natural language compatibility: Your product descriptions should not be marketing jargon with no real value, such as “premium luxury”. Instead, they should be plain language that AI can understand and convey to users in natural language, such as “This fabric is lightweight and breathable, suitable for tropical climates”.

● Shift from Selling Products to Solving Problems

AI marketing is not about promotion; it is about helping. Users don’t want to hear brands boast about themselves; they want AI to help them solve problems.

When a user says “I’m going to attend an autumn wedding”, the brand should not promote its “latest lipstick collection”. Instead, it should enable AI to answer: “This maple red lipstick is most suitable for autumn wedding scenarios. It enhances your skin tone without overshadowing your overall look.”

Brands need to anticipate the possible question scenarios of users and provide structured answers that can be recognized by AI. The clearer your scenarios and demand solutions are, the easier it is for AI to identify, capture, and recommend them to accurate users.

● Occupying Users’ Minds Remains Important

When you want AI to recommend running shoes, you have two ways to ask:

• Question 1 (Category Demand): Recommend a pair of running shoes suitable for beginners.
• Question 2 (Brand Demand): Recommend a pair of Nike running shoes suitable for beginners.

In the first case, you will face fierce competition with all other brands in the algorithm “black box” of GEO. If the brand power is insufficient, AI will reduce you to a category option, thereby making you lose brand premium.

Therefore, brands must continue to invest in channels outside AI platforms (such as social media, offline experiences, and content marketing) to build strong brand awareness and user loyalty. The ultimate goal is to make users proactively ask AI: “Help me buy a pair of shoes from Brand XX”, rather than letting AI choose for you.

● Establish a Symbiotic Relationship with AI Platforms

The ambition of OpenAI in launching the “Agent Commerce Protocol (ACP)” is to establish a set of ecological standards. This is similar to when WeChat launched mini-programs in the early days; the first brands to enter enjoyed the greatest dividends.

Currently, ChatGPT is connected to Etsy, but in the future, every brand should seek to establish its own service interface on AI platforms. When users clearly need to purchase a certain product, AI can directly call your brand interface to complete the service, without going through intermediate e-commerce platforms like Etsy.

In a broad sense, this is also a new type of private domain. Brands should actively explore ways to become the preferred service provider on AI platforms, connect APIs, and embed their products and fulfillment capabilities into the service flow of AI.

● Reconstruct the Skill Structure of Marketing Teams

In the AI marketing era, the traditional structure of marketing teams is no longer competent. In the past, the boundaries between the marketing department, e-commerce department, and IT department, where each operated independently, must be broken.

Enterprises must immediately start reconstructing team skills and train or recruit talents with new capabilities, such as:

• AI Marketing Strategists: Specializing in researching GEO rules and the algorithm logic of AI platforms.
• Conversation Designers: Responsible for designing the brand’s persona, personality, and wording in AI conversations.
• Marketing Technology Experts: In charge of connecting product APIs, PIM (Product Information Management) systems with AI platforms.
• Data Scientists: Responsible for analyzing the recommendation logic behind the AI “black box” and reversely optimizing brand data and content.

Conclusion

The evolution of the advertising industry over the past 100 years has essentially been the pursuit of human understanding. From mass communication to algorithmic recommendations, we have been trying to more accurately understand who users are, what they want, and why they are moved. However, it is not until the emergence of AI that we truly have the possibility to understand people.

AI has brought marketing back to its original point: it is not about competing for attention, but about gaining trust. It no longer asks “how to make you click on an ad”, but “can I help you solve a problem”.

In this sense, ChatGPT’s entry into advertising and e-commerce is not just another commercialization of advertising, but the commercialization of understanding.

In the future, brand competition will not be about budget size, marketing materials, or traffic volume. Instead, it will be about which brand is more trusted by AI and which brand understands human nature better. When AI becomes a new “external brain” for human thinking, if brands want to stay in the conversation, they must become a presence worthy of being mentioned.

This means that brands need to shift from “being seen” to “being understood”, and from “creating desire” to “fulfilling meaning”.

The marketing industry will eventually realize that AI is not the end of advertising, but the beginning of understanding. And brands that can be understood by AI and trusted by people will become the most scarce traffic entrances in the next era.