The most important AI application after ChatGPT! The era of the “shopping agent” is coming—who will be the winners and losers?
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Author of this article: Bao Yilong
Source: Hard AI
Following ChatGPT, the next major breakthrough in generative AI—the era of “online shopping agents”—is about to arrive.
On November 17, Morgan Stanley published a research report predicting that, by 2030, AI-driven personalized shopping agents may add up to $115 billion in incremental spending to the U.S. e-commerce market, accounting for about 6% of total e-commerce expenditures at that time and contributing over 100 basis points of annual growth to the industry.
The report emphasizes that not all retailers will profit. Companies with strong infrastructure, unique inventory, and innovative capabilities, such as Amazon and Walmart, may emerge as winners. However, those relying on high commission models, product homogeneity, or heavy dependence on search traffic, such as Etsy, Chewy, and Lululemon, will face challenges.
Additionally, Morgan Stanley believes traffic gateways will be reshaped. The value of platforms with massive user reach (such as META and YouTube) will be highlighted, while retail media and open web advertising face the risk of being bypassed for traffic.
The highly profitable search advertising model of search giant Google may be hit by the low-commission Agent model, and whether it can successfully convert large volumes of free traffic into paid transactions will be crucial for its future growth.
What is an "Online Shopping Agent"?
Three years after the launch of ChatGPT, investment and innovation in the AI field are accelerating.
Morgan Stanley’s research team predicts that from 2025 to 2027, tech giants are expected to invest a total of about $1.7 trillion in data center capital expenditures. These investments are spawning a new generation of generative AI products, with AI shopping agents seen as the next major breakthrough. This “always-on” “online shopping agent” can:
Conduct complex product research and price comparison across platforms and websites.Recommend product bundles based on personalized user needs. For example, curate an entire “Top Gun” outfit for a Halloween party.Automatically track product prices and inventory, and automatically place orders when conditions are met.Enable automated and periodic procurement for items like fresh groceries.
Currently, platforms and retailers including Alphabet, OpenAI, Amazon, and Walmart have begun launching early versions of "online shopping agents."
(Major platforms and retailers have launched early-stage "online shopping agents")
This will evolve the e-commerce “shopping funnel” from traditional search, social, and direct visits into a more conversational, personalized, and interactive new model.
(Shopping agents can help users search and discover products more efficiently across multiple shopping platforms through providing relevant context information and personalized services)
“Online shopping agents” will further drive the digitalization of consumers’ wallets by improving consumer utility. According to Morgan Stanley’s model, by 2030, “online shopping agents” will bring an incremental spending of $50–115 billion to the U.S. e-commerce market. Specifically:
Market share forecast: In the base scenario, agent-driven consumption will account for 10% of e-commerce spending; in the optimistic scenario, it will reach 20%.
(Under the base and optimistic scenarios, e-commerce spending will account for about 10% and 20% of total expenditure respectively)Growth contribution: By 2030, “online shopping agents” will contribute over 100 basis points per year to the e-commerce sector in the base scenario, or 300 basis points under the optimistic scenario.Key areas: Personalized fresh grocery shopping is considered a key trigger point. Between 2026-2030, fresh grocery and consumer packaged goods (CPG) will contribute 48% (base) to 53% (optimistic) of total agent-driven shopping.
Currently, grocery has an e-commerce penetration rate of only about 12%. Morgan Stanley believes that the multi-step, multimodal shopping capabilities of AI agents (such as analyzing refrigerator stock via photos and automatically generating shopping lists) will be a key driver for increasing penetration rates. After groceries, home goods, personal care, and apparel are the next categories to benefit.
Major Changes in Digital Advertising and Redistribution of Traffic Gateways
Morgan Stanley believes that "online shopping agents" will reshape the landscape for digital advertising traffic allocation.
Platforms with enhanced value: Platforms with massive users and strong reach, such as META (Facebook/Instagram), YouTube, and AppLovin, will see further increases in value for branding and product discovery. These platforms are developing tools to allow SMEs to automatically create and manage entire ad campaigns through AI agents.
(META and Google's platforms already hold significant advantages in distribution scope and coverage)Areas facing risk: Retail media and open web advertising will face the greatest risks. As consumers increasingly make shopping decisions using agents, direct traffic to retailer websites will decrease, thus weakening the value of retail media advertising.
(Currently, user traffic entering e-commerce platforms via search, social networks, and direct visits will all be impacted by "online shopping agents")Google faces transformation: Google's search is at a crossroads. The report estimates that currently, Google search ads have an “actual commission rate” (the percentage of ad spend to overall GMV) of about 33% on average, while the early agent transaction commissions offered by OpenAI and others are only in the single digits—a factor of five to ten difference. If Google cannot dominate agent-driven shopping, its high-profit model will be severely eroded.
(The early agent transaction commissions offered by OpenAI and others are just in single digits)
However, the report stresses that more than 80% of the online traffic for retailers on Google is still free, via direct access and organic search.
If Google can leverage its agent products, such as Gemini integrated in the Chrome browser, some free traffic may be converted to paid transactions. Even if monetization per transaction drops, overall revenue may remain unaffected or even grow.
Analysis shows that shifting 5% of free traffic to paid channels can offset a 14% decline in effective monetization rate.
Winners and Losers Among Retailers
Currently, high-margin retail media ad revenue averages about 6% of e-commerce GMV, and often constitutes most or all profits for e-commerce platforms.
The report suggests that when consumers complete purchases via third-party agents (such as OpenAI’s ChatGPT), retailers not only lose the opportunity to directly reach customers but may also need to pay commissions to the agents—a double hit to their profit statements.
According to Morgan Stanley’s analysis using the “break-even curve,” assuming a 5% commission to third-party agents, retailers need to ensure at least 50% of agent transactions are “incremental” (i.e., transactions that wouldn’t have occurred otherwise) to break even on EBIT (Earnings Before Interest and Taxes).
(Break-even points for agent transaction EBIT at various companies)
Additionally, Morgan Stanley proposes the “Five I’s” framework for evaluating retailers’ AI agent positioning:
Inventory: Unique inventory is increasingly important, as agents make cross-platform price comparison and personalized matching easier.Infrastructure: Logistics infrastructure is key to improving inventory availability and shortening delivery times.Innovation: Investments in GPU-driven machine learning, better matching, and supply management.Incrementality: Ability to drive incremental revenue growth and boost market share.Income Statement: Risk that agents erode high-margin core businesses and retail media ad revenue.
Based on this framework, Amazon, eBay, and Revolve are rated as best positioned, while Etsy, Chewy, and FIGS face greater challenges. Specifically:
Potential winners:Amazon: Possesses a deep fulfillment network and Prime member moat, especially with significant opportunities in fresh groceries.Walmart: Strong scale, robust supply chain, membership base, and limited downside risk for its retail media business.eBay: Has a large and unique inventory of second-hand and out-of-season goods, with agents helping better connect buyers to long-tail products.Other strong performers: Revolve Group, Macy’s, Wayfair also show strong advantages in the framework.
Potential losers:Etsy: Faces huge pressure under the price transparency trend brought by agents due to its high transaction commission rate (over 20%).FIGS: Highly dependent on search traffic and has premium pricing that is vulnerable to shocks.Chewy: Mainly sells homogenized pet products, and although its subscription model is a moat, the products themselves are easy to compare and substitute.Other disadvantaged players: Lululemon, PVH Corp, Dollar Tree, and Five Below are in tough positions due to low product differentiation, low digitization, or over-reliance on wholesale channels.
This article comes from WeChat Official Account "Hard AI". For more AI frontier news, go here
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