The traditional search-compare-buy model is being replaced by an AI agent-driven intelligent purchasing experience.

Have you ever wondered why Google has become a $2 trillion giant while Wikipedia remains a nonprofit organization? The answer is simple: the magic of commercial search. When you search for "how many protons are in a cesium atom," Google doesn't make a dime. But when you search for "the best tennis racket," it starts printing money. This asymmetry defines the essence of the entire search economy. Now, with the rise of AI, this balance is being completely disrupted.

I recently read a deep analysis by a16z partners Justine Moore and Alex Rampell, and their insights on how AI is reshaping the e-commerce landscape left me deeply impressed. They not only analyzed the threats Google may face but, more importantly, painted a new picture of e-commerce in the AI era. In this picture, the traditional search-compare-buy model is being replaced by an AI agent-driven intelligent purchasing experience. I spent a lot of time reflecting on their views and, combined with my observations of the industry, I want to share some deeper thoughts.

Google's Real Crisis: Not Search Volume, But Value Migration

Justine mentioned a point in the article that impressed me: even if Google loses 95% of its search volume, its revenue could still grow as long as it retains those commercially valuable queries. This point sounds counterintuitive but actually reveals the core secret of the search economy. After deep reflection, I found that there is a deeper issue behind this: AI is changing the locus of value creation.

In the traditional model, Google plays the role of an information intermediary. Users have purchasing intent, Google provides search results and ads, merchants gain traffic, and Google collects advertising fees. This is a relatively simple three-party game. But the emergence of AI agents has disrupted this balance. When ChatGPT or Perplexity can directly answer the question "what is the best tennis racket" and provide specific recommendations, why would users still need to click on Google's ad links?

More critically, AI is not just answering questions; it is redefining "search" itself. Our previous search behavior was: ask a question → get a list of links → click to view → compare information → make a decision. The AI agent's process is: describe needs → get recommendations → make a direct purchase. The intermediate comparison and research stages are significantly compressed or even eliminated. This means traditional search engines have not only lost query volume but also their critical position in the decision-making chain.

The clues can be seen from Apple Senior Vice President Eddy Cue's testimony in the DOJ antitrust trial in May 2025. He stated that Safari's search volume has declined for the first time in over twenty years, and this news directly caused Alphabet's stock price to drop nearly 8% in a single day, evaporating over $150 billion in market value. Although Google's Q2 earnings report shows that search revenue is still growing, indicating that the main loss is currently low-value queries, the direction of this trend is clear.

I believe that Google is facing not just a simple competitive threat but a structural challenge to its business model. When AI can directly complete the entire process from intent recognition to purchase decision, the traditional "traffic → advertising → conversion" model becomes inefficient or even outdated. What Google needs is not a better search algorithm but a completely new business model to adapt to AI-driven consumer behavior.

Five Types of Purchasing Behavior Transformed by AI: From Impulse to Reflection

Justine categorized purchasing behavior into five types, from impulse buying to major life purchases, each of which will undergo varying degrees of change in the AI era. I find this classification framework very precise, but I want to analyze the psychological mechanisms behind each type of purchasing behavior and how AI reshapes these mechanisms at a deeper level.

Impulse buying seems to be the area least affected by AI, as impulse implies a lack of rational research process. However, I believe this judgment may be too superficial. The true power of AI lies in predicting and guiding impulses. Imagine when you see a funny T-shirt on TikTok, AI has already analyzed your browsing history, purchase records, social media activity, and even your emotional state, then pushes the product that best meets your current psychological needs at the most precise moment. This is not just a simple algorithm recommendation but a deep understanding and manipulation of human impulsive psychology. I think this personalized impulse guidance may make impulse buying more frequent and precise.

The AI transformation of routine essentials is the easiest to understand and implement. However, I have observed an interesting phenomenon: as AI begins to take over our daily purchasing decisions, our consumption habits may undergo subtle changes. For example, AI might adjust your purchasing timing and quantity based on price fluctuations, inventory levels, or even weather forecasts. A smart AI agent might discover that a certain brand is on sale a week before your laundry detergent runs out, thus purchasing it in advance and suggesting you try it. This "smart arbitrage" behavior may allow consumers to unknowingly achieve better cost-effectiveness while also forcing brands to rethink their pricing and promotional strategies.

Lifestyle purchases are the area where I believe AI will have the greatest impact. These purchases are characterized by a certain price threshold, involve personal taste, and require a degree of research. Justine mentioned products like Plush, but I think this is just the tip of the iceberg. The real revolution will come from AI's deep learning of personal style and preferences. Imagine an AI assistant that not only knows what you have bought in the past but also understands your body type, skin tone, lifestyle, social circles, and even your aspirations. It can recommend not just individual products but entire outfits or even a lifestyle upgrade path. This level of personalization is unattainable by traditional e-commerce platforms.

The AI transformation of functional purchases is the most complex and challenging. These purchases typically involve large expenditures and long-term use, and consumers need not only product recommendations but also expert consultations. I believe a new category of AI applications will emerge here: AI consultants. These AIs will not only possess extensive product knowledge but also engage in deep conversations similar to human sales experts. They can inquire about your specific needs, usage scenarios, budget constraints, and even your future plans, then provide highly personalized advice. More importantly, these AI consultants will be cross-brand and will not favor a specific product due to commissions or inventory.

Major life purchases may be the area least affected by AI but also the most important. Decisions like buying a house, getting married, or education are too significant and personal to be fully entrusted to AI. However, AI can play an important role in information gathering, option comparison, and risk assessment. The AI coach I envision is not there to make decisions for you but to help you make better decisions. It can organize vast amounts of information, identify potential pitfalls, simulate the long-term consequences of different choices, and even assist you in contract negotiations. I believe the value of this AI coach lies in its neutrality and comprehensiveness, unlike human consultants who may have conflicts of interest.

The Moat of Amazon and Shopify: Dual Advantages of Data and Infrastructure

Justine pointed out in her analysis that Amazon and Shopify have stronger defensive capabilities compared to Google, and I completely agree with this view, but I want to analyze the source and sustainability of this advantage at a deeper level. Amazon's advantage lies not only in its control over the entire chain from search to delivery but, more importantly, in its mastery of the most valuable behavioral data.

Amazon knows what you bought, when you bought it, how quickly you received it, whether you returned it, and whether you repurchased it, among other things. The value of this data far exceeds search history because it directly reflects real purchasing behavior and satisfaction. When AI agents need to make purchasing decisions for users, this data becomes the most precious training material. Although Google knows what you searched for, it does not know what you ultimately bought, nor does it know whether you are satisfied with the purchase outcome. This data gap will be further amplified in the AI era.

More importantly, the Amazon Prime loyalty program creates a unique economic phenomenon: sunk cost bias. When you have already paid to become a Prime member, you tend to buy more products on Amazon to "recoup" your costs. This psychological mechanism may become even stronger in the AI era. When AI agents are looking for the best purchasing options for you, they may naturally lean towards Amazon because they know you are a Prime member and can enjoy free shipping and other benefits.

Shopify's defensive logic is completely different but equally strong. It does not build a moat by controlling consumers but by empowering merchants to create network effects. As more and more D2C (Direct-to-Consumer) brands choose Shopify, the platform becomes increasingly irreplaceable. In the AI era, this decentralized advantage may become even more apparent. AI agents may need to gather information and complete purchases from hundreds of different brand websites simultaneously, and if all these sites run on Shopify, it will create a standardized API ecosystem.

I believe Shopify has another underestimated advantage: it is closest to brand stories. In the AI era, the functional differences of products may be quickly identified and compared by AI, but the emotional connection to brands still requires human perception. Brands on Shopify often have unique stories and cultures, and these soft values are difficult for AI to fully quantify but are important factors influencing purchasing decisions.

Four Major Infrastructure Challenges for AI Commercialization

Justine mentioned four foundational conditions necessary for AI to realize its full potential in the business realm at the end of the article, and I believe each of them deserves in-depth exploration, as they are not only technical challenges but also opportunities for business model innovation.

First, there is the issue of better data. The current product review system indeed has serious problems: fake reviews, polarization, and a lack of contextual information. However, I believe the root of the problem lies in the misalignment of incentives. Consumers typically write reviews when they are extremely satisfied or extremely dissatisfied, and few people document the middle ground. Moreover, existing review systems fail to capture the product's usage scenarios, user expectations, and changes over time.

The ideal data system I envision is one where the AI agent not only collects users' subjective evaluations but also monitors the actual usage of products through IoT devices. For example, a smartwatch should not only consider whether the user gave a five-star rating but also how frequently and for how long the user actually wears it. A coffee machine's evaluation should not only look at textual feedback but also consider the user's actual usage frequency, maintenance status, and so on. This combination of objective usage data and subjective feedback can form a truly valuable product evaluation system.

The challenge of a unified API is more political than technical. Each e-commerce platform has its own API structure, data format, and authentication mechanism, and these differences are largely intentional, aimed at creating platform lock-in effects. However, in the era of AI agents, this fragmentation may become an efficiency bottleneck for the entire industry. I predict that specialized API aggregation services will emerge, similar to the global distribution systems in the travel industry. These services will standardize the interfaces of different platforms, allowing AI agents to seamlessly compare and purchase across platforms.

Identity and memory are the most complex challenges because they involve balancing privacy, accuracy, and adaptability. I believe that future AI shopping assistants will need to establish a multi-layered preference model. This model should not only record your historical purchases but also understand your values, life stage, financial constraints, and more. For instance, it needs to know that you prioritize convenience during weekday lunches but focus more on quality and presentation during weekend gatherings. This context-aware recommendation requires AI to possess a social understanding capability similar to that of humans.

Embedded capture may be the most innovative area. Traditional data collection is passive and delayed: reviewing after purchase, providing feedback after use. But AI agents can achieve real-time preference learning. For example, when you linger on a certain feature while browsing a product, the AI can infer that you are particularly interested in that feature. When you quickly skip over certain color options, the AI can learn your color preferences. This micro-interaction analysis can give AI a more nuanced understanding of your preferences.

The Reshuffling of E-commerce Platforms: Who Will Prevail?

After reflecting on Justine's analysis, I have formed some of my own judgments about the future landscape of the e-commerce industry. I believe AI will trigger a new reshuffling of platforms, but the logic of winning will differ from the past.

In the traditional e-commerce era, competition mainly revolved around three dimensions: variety of choices, convenience, and price. Amazon won in terms of selection with its "Everything Store" concept while establishing an advantage in convenience through Prime. However, in the AI era, the importance of these advantages will change.

When AI agents can automatically compare prices across the internet and act as purchasing agents, the price advantage of a single platform will be diluted. When AI can intelligently handle bulk processing and cross-platform fulfillment, the definition of convenience will also change. The real competitive advantage will shift to data quality, AI capabilities, and ecosystem integration.

I predict that several new types of platform players will emerge: AI-native e-commerce platforms, vertical AI agents, and commercial infrastructure providers. AI-native platforms will be designed from the ground up, centered around the needs of AI agents, providing structured product data, standardized APIs, and AI-friendly user experiences. Vertical AI agents will focus on specific categories, such as fashion AI, digital product AI, or home renovation AI, establishing competitive advantages through deep specialization. Commercial infrastructure providers will offer underlying technical services to help traditional e-commerce platforms become AI-enabled.

I also believe a new business model will emerge: AI agent subscriptions. Consumers may no longer shop directly on various e-commerce platforms but instead subscribe to one or more AI shopping agents, which will act on their behalf for all purchasing decisions. These agents will charge subscription fees rather than commissions, thus avoiding conflicts of interest and truly standing in the consumers' shoes. This model could redefine the distribution of value in e-commerce.

The AI Reconstruction of Brand Marketing: From Mass Marketing to Individual Dialogue

The impact of AI on business extends beyond purchasing behavior; it will fundamentally reshape the logic of brand marketing. In the era of AI agents, the effectiveness of traditional mass marketing will significantly decline, as consumers will no longer actively search for and compare products but will rely on AI agents' recommendations.

This means brands need to learn to communicate with AI rather than with humans. AI agents will evaluate products in a more rational and data-driven manner; they will not be swayed by attractive packaging or emotional advertising but will focus on objective performance metrics, cost-effectiveness, and user satisfaction ratings.

However, this does not mean that brand stories become unimportant. On the contrary, I believe authentic brand narratives will become even more crucial, as AI agents will deeply analyze a brand's consistency and credibility. If a brand conveys contradictory messages across different platforms and at different times, AI can easily identify this and reduce its recommendation weight.

I predict a new marketing role will emerge: AI relationship managers. These managers will ensure that all aspects of a brand's product information, pricing strategies, inventory management, and more are correctly understood and evaluated by AI. They will need to optimize product data, manage API integrations, and monitor AI recommendation patterns, among other tasks.

Another significant change will be the extreme personalization of products. When AI agents have a deep understanding of each consumer, brands can offer customized products for everyone. This is not just personalized recommendations but personalized products themselves. Imagine when your AI agent informs a clothing brand of your exact size, color preferences, material requirements, and budget range, that brand can then create a unique item just for you. This kind of mass customization becomes economically feasible in the AI era.

The Next Decade: What Are We Witnessing?

After deeply contemplating Justine's analysis and my own observations, I feel that what we are witnessing is not just a transformation in the e-commerce industry but a deeper shift in economic behavior.

Traditional economics assumes that consumers are rational actors who actively gather information, compare options, and make optimal decisions. But in reality, we all know that human decision-making is filled with biases, emotions, and cognitive limitations. The emergence of AI agents may make consumers more "rational," as AI can process more information, avoid emotional biases, and consistently apply decision-making standards.

The widespread adoption of this rational consumption could have far-reaching effects. First, market efficiency will significantly improve, as consumers will be able to assess product value more accurately. Second, product quality will become more important than marketing ability, as AI agents will not be deceived by flashy advertisements. Finally, price transparency will increase, as AI can easily compare prices across the internet.

However, I also worry that this "super-rational" consumption may bring some negative consequences. The joy of discovery in shopping may diminish, as AI agents always recommend the "optimal" choice rather than surprising or delightful options. Impulse buying, while not very rational, is also part of the joy of life. If everything is optimized by AI, life may become overly predictable.

From a broader perspective, I believe that the application of AI in the business realm will accelerate the digitalization of the economy. More and more business activities will be digitally recorded and analyzed, providing an unprecedented data foundation for economic planning and policy-making. Governments may be able to predict economic trends more accurately, identify market failures, and design targeted interventions.

I predict that within the next decade, we will see AI-driven business evolve from experimental applications to mainstream practices. Early adopters will gain significant competitive advantages, but as technology becomes widespread, these advantages will gradually be commoditized. The true long-term winners will be those companies that can redefine customer value in the AI era.

免责声明：本文章仅代表作者个人观点，不代表本平台的立场和观点。本文章仅供信息分享，不构成对任何人的任何投资建议。用户与作者之间的任何争议，与本平台无关。如网页中刊载的文章或图片涉及侵权，请提供相关的权利证明和身份证明发送邮件到support@aicoin.com，本平台相关工作人员将会进行核查。