Shopify CEO Tobi Lütke: AI Is Moving Shopping Decisions Forward, Traditional Advertising Model Faces Trust Issues

Recently, something quite interesting happened in the global e-commerce industry.

Faced with Meta's AI Agent product Muse, Amazon chose to restrict its access, while Shopify took a completely opposite stance and actively embraced it.

Both are e-commerce giants, so why do the two companies have such different attitudes toward AI Agents?

Shopify co-founder and CEO Tobi Lütke believes that behind this lies a fundamental difference between two business models.

Amazon is more like a digital department store, building its competitive advantage on a vast product inventory, traffic, and transaction system. Shopify, by contrast, serves millions of independent merchants, enabling them to sell products through any channel.

For the former, AI Agents may mean losing control over the shopping entry point; but for the latter, Agents may instead bring new transaction opportunities.

Recently, Tobi Lütke appeared on the Sources podcast hosted by Alex Heath for an 84-minute interview covering AI shopping, the Agent business model, and the future of the entire e-commerce industry.

Among them, several points struck me as very worth paying attention to.

Second, Tobi is also skeptical of the business model, widely assumed today, of taking a commission on personal Agents. Because existing e-commerce already has a complete, mature profit-sharing system, the profit margin that can truly be freely allocated may not be as large as people think.

Third, in the Agent era, the traditional advertising model may face a fundamental trust problem. If an AI assistant takes money from a merchant and then recommends products to users, does it represent the buyer or the seller?

From product consumption to intent consumption, the point of shopping decisions has moved earlier

Over the past two decades, the e-commerce industry has been competing around one thing: how to make it easier for consumers to find the products they want to buy.

From the earliest product categories and keyword search to later personalized recommendations and livestream selling, all of it has essentially been about optimizing the matching efficiency between products and consumers.

But the emergence of AI Agents may fundamentally change this logic. In the interview, Tobi Lütke mentioned that Shopify has already observed a very interesting phenomenon.

At present, shopping transactions completed through AI Agents are still on a relatively low base, so discussing growth rates alone is not very meaningful. What is truly worth paying attention to is that consumer purchasing behavior is changing.

A typical case is the home gym.

In the past, someone who wanted to build a home gym might first search for a treadmill, then research dumbbells, training benches, floor mats, and other equipment, and finally buy them one by one according to budget and space.

Throughout the process, the e-commerce platform was mainly responsible for providing product information and completing transactions. As for what to buy, how to combine it, and how to allocate the budget, that was still up to the consumer.

But now, some consumers have begun making completely different requests to Agents:

Help me buy everything needed to set up a home gym.

This may look like just a change in the prompt, but the business logic behind it is actually completely different.

Because what the Agent faces is no longer a purchase need for a single product, but a project that requires a complete solution. It needs to understand the user's budget, space, and fitness goals, determine which equipment is worth buying, and then complete product selection, combination, and ordering.

Tobi even envisions that in the future consumers may only need to upload a photo of a room or a floor plan, and AI can participate in the entire procurement planning process.

There are many similar scenarios. For example, on a weekend, you want to complete a 3D printing project with your child. In the past, parents might first research the project plan, then figure out which electronic components, magnets, and printing consumables are needed, and finally place orders separately with different merchants.

Now, you only need to tell the Agent what project you want to complete, and it has the opportunity to handle the entire materials list, product procurement, and delivery arrangements.

This is an important trend Tobi has observed: more and more complex, high-ticket purchases are beginning to be completed through Agents.

In the past, between intent and a specific product there was still a huge decision-making step, and this work was often done by consumers themselves.

This means that the value of Agents may not only be to improve existing shopping efficiency, but also to create consumption that did not happen in the past because the decision-making cost was too high.

Why does Amazon block Agents while Shopify opens the door?

When consumers begin to hand shopping decisions to Agents, a new question follows:

What should e-commerce platforms that originally controlled the consumer entry point do?

The different choices of Amazon and Shopify happen to provide two very representative cases.

In Tobi's view, this divergence is actually not surprising at all.

Over the past two decades, the two companies have represented two different paths of e-commerce development.

Amazon's business model is closer to a digital department store. Consumers enter Amazon, search for products, compare prices, check reviews, and finally complete a purchase. The platform attracts consumers through a vast product supply, logistics fulfillment capabilities, and transaction system, and then uses traffic to help merchants complete sales.

Therefore, where consumers search, which products they see, and what they ultimately choose to buy are very important to the platform's commercial value.

Especially the advertising business. When merchants need to buy on-site ads to gain exposure, consumers' search and browsing behavior becomes an important monetization resource for the platform.

As a result, many core steps of traditional e-commerce platforms may be bypassed. Consumers may not need to open an e-commerce app, browse products one by one, or make decisions in the order recommended by the platform.

For platforms that rely on traffic distribution and advertising monetization, AI Agents may be not just a new shopping channel, but a potential competitor for the entry point.

Of course, Tobi did not assert that Amazon restricted Muse specifically to protect advertising revenue. He also acknowledged that aggregator-type platforms do have a stronger incentive to maintain control, and that the current technical implementation of Agents also has problems.

But from a business model perspective, the potential conflict between the two is already quite clear.

By contrast, Shopify's situation is completely different. Shopify does not operate a large e-commerce marketplace in the traditional sense; instead, it provides independent merchants with website building, payments, order management, and sales tools.

Consumers can place orders through merchants' independent websites, or complete purchases through social media, offline stores, and other channels.

Tobi has a very simple judgment about this: the more places merchants can sell products, the greater Shopify's value.

Therefore, whether the person who ultimately places the order is the consumer themselves or an AI Agent representing the consumer, it is acceptable to Shopify. What it really cares about is whether the transaction can be completed smoothly and whether merchants can get more orders.

This is also why Shopify is building more APIs and transaction systems so that Agents can more conveniently call product and checkout capabilities.

The difference between the two business models is thus revealed.

Amazon needs to consider how to maintain the relationship between consumers and the platform, while Shopify is more focused on how to help merchants connect to new consumer entry points.

Of course, this does not mean Shopify will definitely become a winner in the Agent era. But at least from a business model perspective, the conflict of interest between Shopify and Agents may be much smaller than that of traditional traffic-based platforms.

I think this is also the key to understanding the next round of e-commerce competition.

When AI begins to take charge of shopping decisions, the focus of competition among e-commerce platforms may shift from how to keep users on their own platforms to how to make their products and services preferentially used by Agents.

Agent wants to take 2% of every transaction, but who pays?

How exactly should Agents make money?

In this interview, host Alex Heath mentioned a common idea among current Silicon Valley Agent startups.

Many entrepreneurs believe that in the future, personal Agents can establish a business model by taking a commission on transactions. For example, if AI helps a consumer complete a $100 order and takes 2% of it, it can earn $2 in revenue.

At first glance, this model seems quite reasonable.

After all, Agents help users complete product research, comparison, and purchasing, creating real value, so receiving a certain percentage of transaction revenue also makes sense.

But Tobi directly raised a question: who exactly pays this 2% commission?

There is a reality here that is easy to overlook. E-commerce transactions themselves already have a complete and mature profit distribution system: payment institutions, credit card networks, e-commerce service providers, and other participants all need to receive corresponding revenue from transactions.

According to Tobi, of Shopify's roughly 3% transaction fee, a large portion actually goes to credit card companies. In other words, it looks like there are several percentage points of transaction revenue, but the profit margin that can truly be freely allocated may not be that large.

Now, with Agents suddenly joining the transaction chain and wanting to take 2% from it, the problem becomes more complicated.

If merchants bear it, that is equivalent to further increasing customer acquisition costs. If the platform bears it, that means existing participants need to give up their own profits. If consumers bear it, then Agents must prove that the value they provide is enough for users to pay extra.

Therefore, Tobi is not in a hurry to judge that commissions will definitely become the final business model for Agents.

In his view, the more important question is how much value Agents can actually create for users.

For example, an Agent can help consumers compare the fee plans of different banks, discover unreasonable long-term payment fees, and even automatically complete a service switch. If it can help users save hundreds of dollars each year, then consumers have every reason to pay a certain fee for it.

Under this model, Agents do not necessarily need to participate in product transaction commissions; instead, they can charge users directly. In the future, there may also be usage-based pricing, subscription fees, or hybrid models related to transactions.

But more worth discussing than the business model is another question raised by Tobi: when Agents truly make decisions on behalf of consumers, the traditional advertising model may encounter a trust crisis.

Suppose you have a personal shopping assistant that knows you very well, knows your budget, lifestyle habits, and purchasing preferences, and can help you compare different brands and choose the most cost-effective product.

But one day, you discover that it recommends a certain product not because this product is the most suitable for you, but because the merchant paid it an advertising fee.

Then, would you still trust it?

In Tobi's view, this constitutes a destruction of user trust.

The traditional advertising model mainly stands on the seller's side. Merchants pay for exposure, hoping to influence consumers' purchasing decisions. But the value of a personal Agent is precisely built on the premise that it represents the buyer's interests.

If an Agent must both represent the consumer in choosing the most suitable product and decide the recommendation order based on fees paid by merchants, a conflict of interest may arise between the two.

This is also why the advertising business model in the Agent era may need to be redesigned.

However, Tobi does not believe that advertising will disappear because of this.

In the past, the advertising systems of Facebook and Google helped a large number of small merchants find their first batch of users. In the future, the Agent world will likewise need some mechanism for product discovery and matching supply with demand.

This may be one of the most noteworthy directions for Agent commercialization. Whoever can find a balance between sellers' customer acquisition needs and buyers' decision-making trust may be able to build a new generation of commercial distribution platforms.

This article comes from the WeChat public account "Silicon-Based Observation Pro", author: Silicon-Based Jun, published by 36Kr with authorization.

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