a16z Latest Insights: Is Traditional E-commerce Dead? AI Native Platforms Are Redefining "Shopping"

Written by: Deep Thought Circle

Have you ever wondered why Google could become a $2 trillion giant while Wikipedia is 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 very nature of the search economy. Now, with the rise of AI, this balance is being completely disrupted.

Recently, I read an in-depth analysis by a16z partners Justine Moore and Alex Rampell, and their insights on how AI is reshaping the e-commerce sector 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 - comparison - purchase model is being replaced by an intelligent purchasing experience driven by AI agents. I have spent a lot of time reflecting on their viewpoints and, combined with my own observations of the industry, I want to share some deeper thoughts.

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

Justine mentioned an impressive point in the article: even if Google loses 95% of its search volume, its revenue could still grow as long as it retains those queries with commercial value. This point sounds counterintuitive, but it 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 intentions, Google provides search results and ads, merchants gain traffic, and Google collects advertising fees. This is a relatively simple three-party game. However, 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 importantly, 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 process of the AI agent is: describe needs → receive recommendations → make a purchase directly. The comparative and research stages in between have been greatly compressed or even disappeared. This means that traditional search engines have not only lost query volume but also their critical position in the decision-making chain.

The testimony of Apple Senior Vice President Eddy Cue in the DOJ antitrust trial in May 2025 reveals some clues. He stated that Safari's search volume has declined for the first time in over twenty years, which 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 financial report shows that search revenue is still growing, indicating that the majority of the current losses are from low-value queries, the direction of this trend is clear.

I believe that what Google faces is not a simple competitive threat, but a structural challenge to its business model. When AI is able to 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 a brand new business model to adapt to AI-driven consumer behavior.

AI Transformation of Five Types of Purchasing Behaviors: From Impulsive to Thoughtful

Justine categorizes purchasing behavior into five types in the article, ranging from impulse purchases to significant 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 on a deeper level, as well as how AI reshapes these mechanisms.

Impulse buying (Impulse buy) seems to be a field with minimal AI influence, 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 activities, and even your emotional state, then pushes products that best meet your current psychological needs at the most precise moment. This is not just simple algorithmic recommendations, but a deep understanding and manipulation of human impulsive psychology. I feel that this personalized impulse guidance may make impulse buying more frequent and precise.

Everyday essentials ( The AI transformation of routine essentials ) is the easiest to understand and implement. However, I have observed an interesting phenomenon: when AI begins to take over our daily purchasing decisions, our consumption habits may undergo subtle changes. For instance, 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 the week before your laundry detergent runs out, thus purchasing it in advance and suggesting you try it. This "intelligent arbitrage" behavior may enable consumers to unknowingly achieve better cost-performance ratios, 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. The characteristics of these purchases include: a certain price threshold, involving personal taste, and requiring a certain degree of research. Justine mentioned products like Plush, but I think that’s just the tip of the iceberg. The real revolution will come from AI's deep learning of personal styles and preferences. Imagine an AI assistant that not only knows what you've bought in the past but also understands your body type, skin tone, lifestyle, social circles, and even your aspirations ). It could recommend not just individual products, but a whole set of outfits, or even an upgrade path for your lifestyle. This level of personalization is something traditional e-commerce platforms can't achieve.

Functional purchases ( are the most complex and challenging to AI-ify. These purchases often involve large expenditures and long-term usage, and consumers need more than just product recommendations; they require expert consultations. I believe a new category of AI application will emerge here: AI advisors. These AIs not only possess extensive product knowledge but can 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, and then provide highly personalized suggestions. More importantly, these AI advisors are cross-brand and will not favor any specific product due to commissions or inventory.

Major life purchases )Life purchases( may be the area where AI has the least impact but is also the most important. Decisions like buying a house, getting married, and education are too significant and personal to be completely 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 meant 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 advisors who may have conflicts of interest.

The Moat of Amazon and Shopify: The Double Advantage of Data and Infrastructure

Justine pointed out in the analysis that Amazon and Shopify have stronger defensive capabilities compared to Google, and I completely agree with this view. However, I would like to analyze the source and sustainability of this advantage at a deeper level. Amazon's advantage lies not only in its control over the complete 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, whether you repurchased it, and so on. The value of this data far exceeds search history, as it directly reflects real purchasing behavior and satisfaction. When an AI agent needs to make purchasing decisions for users, this data becomes the most valuable training material. Although Google knows what you searched for, it doesn't know what you ultimately bought, nor does it know whether you were satisfied with the purchase outcome. This data gap will be further amplified in the AI era.

Moreover, the Amazon Prime 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 costs." This psychological mechanism may become even stronger in the AI era. When an AI agent is looking for the best purchasing options for you, it may naturally lean towards Amazon because it knows you are a Prime member and can enjoy free shipping and other benefits.

Shopify's defensive logic is completely different, yet equally powerful. It does not establish a moat by controlling consumers, but rather 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 pronounced. AI agents may need to gather information and complete purchases from hundreds of different brand websites simultaneously, and if these sites all operate on Shopify, it will create a standardized API ecosystem.

I believe Shopify has an underestimated advantage: it is closest to the brand story. In the age of AI, the functional differences of products can be quickly identified and compared by AI, but the emotional connection of a brand still needs to be felt by humans. Brands on Shopify often have unique stories and cultures, and these soft values are difficult for AI to fully quantify, yet they are important factors that influence consumer decisions.

Four Fundamental Infrastructure Challenges of AI Commercialization

Justine mentioned four fundamental conditions required for AI to realize its full potential in the business sector at the end of the article. I believe each of them deserves in-depth discussion, as they are not only technical 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 a lack of background information. However, I believe the root of the problem lies in the misalignment of the incentive mechanism. Consumers typically write reviews because they are extremely satisfied or extremely dissatisfied, with few documenting the middle ground. Moreover, the existing review system fails to capture the usage scenarios of the product, the users' expectations, and the changes over time.

The ideal data system I envision is as follows: the AI agent not only collects users' subjective evaluations but also monitors the actual usage of the product through IoT devices. For example, a smart watch should not only consider whether the user gave a five-star rating but also look at the frequency and duration of actual wear. The evaluation of a coffee machine should not only focus on written feedback but also take into account the user's actual usage frequency, maintenance status, and so on. This combination of objective usage data and subjective feedback is what can create 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 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 as they involve balancing privacy, accuracy, and adaptability. I believe that future AI shopping assistants need to establish a multi-layered preference model. This model should not only record your purchase history but also understand your values, life stage, financial constraints, and so on. For example, it needs to know that you prioritize convenience during lunch on weekdays, but focus more on quality and presentation during weekend gatherings. This contextual awareness in recommendations requires AI to possess human-like social understanding capabilities.

Embedded capture may be the most innovative potential area. Traditional data collection is passive and delayed: reviews are given after purchase, feedback is provided after use. But an AI agent can enable 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 allows the AI to have a more nuanced understanding of your preferences.

The reshuffling of e-commerce platforms: Who will emerge victorious?

After considering Justine's analysis, I have formed some of my own judgments about the future landscape of the e-commerce industry. I believe that AI will trigger a new reshuffle of platforms, but the logic of winning will be different from before.

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

When AI agents can automatically compare prices across the entire network and act as purchasing agents, the price advantage of a single platform will be diluted. When AI can intelligently process in bulk and fulfill across platforms, the definition of convenience will also change. The real competitive advantage will shift towards data quality, AI capabilities, and ecological 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, 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 that a new business model will emerge: AI agent subscriptions. Consumers may no longer shop directly on various e-commerce platforms, but rather subscribe to one or more AI shopping agents, which will make all purchasing decisions on their behalf. These agents will charge a subscription fee instead of commissions, thereby avoiding conflicts of interest and truly standing on the side of consumers. This model could redefine the distribution of value chains in e-commerce.

AI-Driven Reconstruction of Brand Marketing: From Mass Marketing to Individual Dialogue

The changes brought about by AI in business are not limited to purchasing behavior; they 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 recommendations from AI agents.

This means that brands need to learn to communicate with AI rather than with humans. AI agents are more rational and data-driven when evaluating products; they are not influenced by fancy packaging or emotional advertising, but rather focus on objective performance metrics, cost-effectiveness, and user satisfaction ratings.

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

I predict that a new marketing role will emerge: AI Relationship Officer. The job of these officers is to ensure that all aspects of a brand's product information, pricing strategies, inventory management, etc., can be correctly understood and evaluated by AI. They need to optimize product data, manage API integrations, monitor AI recommendation patterns, and so on.

Another important change is the ultimate personalization. When an AI agent has 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, that brand can create a unique piece just for you. This kind of mass customization becomes economically feasible in the AI era.

The Next Ten Years: What Are We Witnessing?

After deep reflection on 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. However, 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 criteria.

The popularization of rational consumption may 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.

But I also worry that this kind of "super-rational" consumption may bring some negative consequences. The joy of discovering while shopping may decrease because the AI agent always recommends the "optimal" choice instead of surprising or pleasurable options. Impulsive buying, while not very rational, is also a part of the joy of life. If everything is optimized by AI, life may become overly predictable.

From a more macro perspective, I believe that the application of AI in the business field will accelerate the digitalization of the economy. More and more business activities will be digitally recorded and analyzed, which will provide 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 intervention measures.

I predict that in the next decade, we will see AI-driven businesses evolve from experimental applications to mainstream practices. Early adopters will gain significant competitive advantages, but as the 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.