Written by: Deep Thought Circle
Have you ever wondered why Google could 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 earns nothing. But when you search for 'the best tennis racket,' it starts printing money. This asymmetry defines the very nature of the search economy. Now, with the rise of AI, this balance is being completely disrupted.
Recently, I read a deep analysis by a16z partners Justine Moore and Alex Rampell. Their insights on how AI is reshaping the e-commerce sector left me deeply impressed. They not only analyzed the threats Google might 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 intelligent purchasing experience driven by AI agents. I have spent a lot of time thinking about their viewpoints and, combined with my observations of this industry, I want to share some deeper thoughts.
Google's Real Crisis: Not Search Volume, But Value Migration
Justine mentioned a point in her article that impressed me: even if Google loses 95% of its search volume, revenue could still grow as long as it retains those commercially valuable queries. This point seems counterintuitive but actually reveals the core secret of the search economy. After deep reflection, I found that a deeper issue lies beneath: AI is changing the locus of value creation.
In the traditional model, Google plays the role of an information intermediary. Users have purchasing intentions, 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 disrupts this balance. When ChatGPT or Perplexity can directly answer the question 'what is the best tennis racket' and give a specific recommendation, 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: pose a question → obtain a list of links → click to view → compare information → make a decision. The process for AI agents is: describe needs → receive recommendations → purchase directly. The intermediate stages of comparison and research are significantly compressed or even disappear. This means traditional search engines not only lose query volume but also lose their critical position in the decision-making chain.
Hints can be seen from the testimony of Apple's Senior Vice President Eddy Cue in the DOJ antitrust trial in May 2025. He stated that the search volume of Safari had declined for the first time in over twenty years, a piece of news that directly led to Alphabet's stock price falling nearly 8% in a single day, evaporating over $150 billion in market value. Although Google's Q2 financial report shows that search revenue is still growing, indicating that the main loss is low-value queries, the direction of this trend is clear.
I believe that Google faces not just simple competitive threats but a structural challenge to its business model. When AI can directly complete the entire process from intent recognition to purchasing decision, the traditional 'traffic → advertising → conversion' model will become inefficient or even obsolete. What Google needs is not a better search algorithm but an entirely new business model to adapt to AI-driven consumer behavior.
The AI Transformation of Five Types of Buying Behavior: From Impulse to Reflection
Justine categorized purchasing behavior into five types, from impulse buying to significant life purchases, with each type undergoing varying degrees of change in the AI era. I find this classification framework very precise, but I want to analyze the underlying psychological mechanisms behind each type of purchasing behavior and how AI will reshape these mechanisms.
Impulse buying may seem like the area least affected by AI since impulse implies no rational research process. However, I believe this judgment may be overly 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 simple algorithmic recommendation; it is a deep understanding and manipulation of human impulse psychology. I think this kind of personalized impulse guidance may make impulse buying more frequent and precise.
The AI transformation of routine essentials is the easiest to understand and achieve. However, I have noticed an interesting phenomenon: as AI begins to act as our daily purchasing decision agent, our consumption habits may subtly change. For instance, AI might adjust your purchasing timing and quantity based on price fluctuations, inventory status, or even weather forecasts. A smart AI agent might discover a brand is on sale a week before your laundry detergent runs out, prompting an early purchase and suggesting you try it out. This kind of 'smart arbitrage' behavior might allow consumers to unknowingly gain better value for money 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, involving personal taste, and requiring a degree of research. Justine mentioned products like Plush, but I believe that 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 color, lifestyle, social circles, and even your aspirations. It can recommend not just single products but a whole set of outfits or even an upgrade path for your lifestyle. This level of personalization is unattainable for traditional e-commerce platforms.
The AI transformation of functional purchases is the most complex and challenging. These purchases typically involve significant expenditures and long-term use, where 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 have extensive product knowledge but will 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 recommendations. More importantly, these AI consultants will be cross-brand and will not favor any specific product due to commissions or inventory.
Significant life purchases may be the area least affected by AI but also the most important. Decisions about buying a house, getting married, or education are too significant and personal to be fully entrusted to AI. However, AI can play a crucial role in information gathering, option comparison, and risk assessment. I imagine an AI coach not 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 with contract negotiations. I believe the value of such an AI coach lies in its neutrality and comprehensiveness, unlike human advisors who may have conflicts of interest.
The Moat of Amazon and Shopify: A Dual Advantage of Data and Infrastructure
Justine pointed out in her analysis that Amazon and Shopify have stronger defenses compared to Google, and I completely agree with this view. However, I want to analyze the source and sustainability of this advantage on 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. This data gap will be further amplified in the AI era.
More importantly, Amazon Prime’s loyalty program has created a unique economic phenomenon: sunk cost bias. When you have already paid to become a Prime member, you tend to purchase more items on Amazon to 'recoup' your investment. This psychological mechanism may become even stronger in the AI era. AI agents, when searching for the best purchasing options for you, may naturally lean towards Amazon as they know you are a Prime member and can enjoy free shipping and other benefits.
Shopify's defensive logic is completely different but equally powerful. It does not build a moat by controlling consumers but by empowering merchants to create network effects. As more and more direct-to-consumer (D2C) brands choose Shopify, the platform becomes increasingly irreplaceable. In the AI era, this decentralized advantage may become even more pronounced. AI agents may need to gather information and complete purchases from hundreds of different brand websites simultaneously, and if those websites all operate on Shopify, it will create a standardized API ecosystem.
I believe Shopify has another underestimated advantage: it is closest to brand stories. In the age of AI, the functional differences of products may be quickly identified and compared by AI, but the emotional connection of a brand still needs to be felt by humans. Brands on Shopify usually have unique stories and cultures; these soft values are difficult for AI to fully quantify but are essential factors influencing consumer decisions.
The Four Major Infrastructure Challenges of AI Commercialization
Justine mentioned four foundational conditions needed for AI to realize its full potential in the business realm at the end of the article. I believe each of these is worth exploring in depth, as they are not only technological challenges but also opportunities for innovation in business models.
First, there is the issue of better data. The current product review system indeed has serious problems: fake reviews, polarization, and lack of background information. However, I believe the root of the problem lies in misaligned incentives. Consumers write reviews typically due to extreme satisfaction or extreme dissatisfaction, with few recording neutral states. Moreover, existing review systems fail to capture the context of product usage, user expectations, and changes over time.
The ideal data system I envision is one where AI agents not only collect users' subjective evaluations but also monitor the actual usage of products through IoT devices. For instance, a smartwatch should not only consider whether the user gave a five-star rating but also track how often 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 unified APIs is more political than technical. Each e-commerce platform has its own API structure, data format, and authentication mechanisms; these differences are largely intentional and designed to create platform lock-in effects. However, in the age of AI agents, this fragmentation may become an efficiency bottleneck for the entire industry. I predict that dedicated API aggregation services will emerge, similar to the global distribution systems in the travel industry. These services will standardize the interfaces of different platforms, enabling AI agents to seamlessly compare and purchase across platforms.
Identity and memory are the most complex challenges as they involve balancing privacy, accuracy, and adaptability. I believe 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 stages, financial constraints, and so on. For example, it needs to know that you prioritize convenience for weekday lunches but focus more on quality and presentation for weekend gatherings. This situationally aware recommendation requires AI to have a social understanding capability similar to that of humans.
Embedded capture may be the area with the most innovative potential. Traditional data collection is passive and retrospective: evaluate after purchase, provide feedback after use. However, AI agents can achieve real-time preference learning. For example, when you linger on a particular feature while browsing a product, AI can infer that you are particularly interested in that feature. When you quickly skip certain color options, AI can learn your color preferences. This micro-interaction analysis can allow AI to have a more detailed 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 platform reshuffling, but the logic of winning will differ from the past.
In the traditional e-commerce era, competition primarily revolved around three dimensions: variety of choices, convenience, and price. Amazon won in terms of variety 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 web and act as purchasing agents, the price advantages of a single platform will be diluted. When AI can intelligently process and fulfill orders across platforms, the definition of convenience will also change. True competitive advantages will shift to data quality, AI capabilities, and ecosystem integration.
I predict that several new 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 with the needs of AI agents in mind, 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 foundational technology 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 subscribe to one or more AI shopping agents, which will act on their behalf for all purchasing decisions. These agents will charge subscription fees instead of commissions, thus avoiding conflicts of interest and truly standing on the consumer's side. This model could redefine the distribution of value chains in e-commerce.
The AI Reconstruction of Brand Marketing: From Mass Marketing to Individual Dialogue
The changes AI brings to business extend beyond purchasing behavior, fundamentally reshaping the logic of brand marketing. In the age of AI agents, the effectiveness of traditional mass marketing will significantly decline as consumers no longer actively search for and compare products but rely on AI agents' recommendations.
This means brands need to learn to converse with AI, rather than with humans. AI agents are more rational and data-driven when evaluating products; they are not swayed by attractive packaging or emotional advertising but focus on objective performance metrics, cost-effectiveness, and user satisfaction ratings.
But this does not mean brand stories become unimportant. On the contrary, I believe authentic brand narratives will become even more important because AI agents will deeply analyze a brand's consistency and credibility. If a brand conveys contradictory information across different platforms and at different times, AI can easily identify this and lower its recommendation weight.
I predict a new marketing role will emerge: AI Relationship Manager. The job of these managers is to ensure that various aspects of a brand's product information, pricing strategy, inventory management, etc., can be correctly understood and assessed by AI. They will need to optimize product data, manage API integrations, monitor AI recommendation patterns, and so on.
Another significant change is the extreme personalization. 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 tells a clothing brand your exact size, color preferences, material requirements, and budget range; the brand can create a unique item just for you. This kind of mass customization becomes economically feasible in the AI era.
The Next Ten Years: What Are We Witnessing?
After deeply contemplating Justine's analysis and my own observations, I feel we are witnessing not only a transformation in the e-commerce industry but a deeper shift in economic behavior.
Traditional economics assumes that consumers are rational actors who actively collect 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' because AI can process more information, avoid emotional biases, and consistently apply decision-making standards.
The proliferation of rational consumption may have far-reaching effects. First, market efficiency will greatly improve as consumers can more accurately assess product value. Second, product quality will become more important than marketing ability, as AI agents will not be swayed by flashy ads. Finally, price transparency will increase as AI can easily compare prices across the web.
However, I am also concerned that this 'super-rational' consumption might lead to some negative consequences. The joy of discovery in shopping may diminish because AI agents always recommend the 'optimal' choice rather than surprising or delightful ones. Impulse buying, while less rational, is also part of the joy of life. If everything is optimized by AI, life may become too predictable.
From a broader perspective, I believe the application of AI in business will accelerate the digitization 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 more accurately predict economic trends, identify market failures, and design targeted interventions.
I predict that within the next ten years, we will see AI-driven commerce evolve from experimental applications to mainstream practices. Early adopters will gain significant competitive advantages, but as technology becomes widespread, these advantages will gradually commoditize. The true long-term winners will be those businesses that can redefine customer value in the AI era.
