Walmart, Amazon Take Divergent Approaches to Agentic Commerce

Walmart and Amazon are taking apparently opposite approaches to agentic commerce. Walmart leans to towards an “open” approach while Amazon prefers a more-closed approach. 

Walmart is trying to make its products available wherever consumers choose to shop with AI, partnering with ChatGPT and Gemini, while also building its own agentic agent Sparky,  while Amazon is trying to ensure that AI shopping remains inside an environment Amazon controls by barring most third-party access and relying on its own Rufus agent and other related features.  

It’s an old set of choices. Should an incumbent embrace a new technology platform that may weaken its control over customers, or defend its existing ecosystem and risk losing customers to the new platform?

Neither strategy is universally successful or applicable, though. Much hinges on a firm’s position within an industry. Openness tends to help a company when the new technology shifts value toward a platform or distribution channel it does not control. 

Closed ecosystems tend to work better when the company can make its own platform the preferred destination. 

Open approaches also tend to enable new competition, while closed approaches might tend to favor incumbents with market power. 

But it also is fair to say that both firms attempt to control the interface, the data, and the transaction.

Strategic issue	Walmart's calculation	Amazon's calculation
Customer acquisition	Third-party AI can introduce Walmart to consumers who do not begin their shopping journey on Walmart.com.	Outside agents could divert existing Amazon customers and reduce the importance of Amazon's own app and website.
Competitive position	As a retailer competing against Amazon, Walmart benefits if AI makes retailer choice more fluid.	Amazon benefits from the frictionless integration of its catalog, reviews, payments, Prime benefits, and delivery network.
Customer data	Cooperation can provide incremental demand, although Walmart risks losing some browsing and intent data to AI intermediaries.	Outside agents could capture valuable signals about what customers want, compare, reject, and ultimately buy.
Advertising and sponsored listings	AI recommendations could create another route to product discovery, but may weaken Walmart's control over advertising inventory and ranking.	Amazon's sponsored product placements and retail-media economics could be undermined if an independent agent chooses products using its own ranking criteria.
Price competition	Walmart can compete on price, convenience, grocery, and physical-store reach if agents make comparisons easier.	Agents that systematically compare alternatives could expose price differences and weaken Amazon's ability to retain customers through convenience and ecosystem benefits.
Transaction economics	A new channel is attractive if incremental sales outweigh integration costs and any commissions or loss of control.	Keeping transactions in-house protects more of the economics and preserves control over checkout, customer service, and fulfillment.
Principal risk	Becoming a supplier to someone else's AI platform rather than owning the customer relationship.	Protecting the current platform too aggressively and allowing a new AI intermediary to become the consumer's default shopping destination.

Beyond all that, there can be other issues driving a policy choice. Walmart, for example, encountered issues with a native ChatGPT checkout experience, and now integrates its own Sparky agent. And Amazon might eventually allow some access by external agents, if it can keep control of the customer relationship, or conversely, if it finds it must cede some control to protect sales volume.

Historically, computer operating systems, browsers and mobile phones illustrate cases where distribution, customer relationships or the economic value of the ecosystem shaped choices, generally involving choices between degrees of openness versus closed approaches. 

Historical case	Strategic choice	What happened	Lesson for Walmart and Amazon
IBM PC vs. the Microsoft/Intel ecosystem (1980s–1990s)	IBM initially benefited from a PC architecture built around widely available components and outside software. Microsoft and Intel occupied increasingly powerful positions within that ecosystem.	IBM helped establish a market whose economics ultimately favored independent software and chip platforms. IBM lost much of its control over the architecture it had helped popularize. IBM history of e-business; U.S. DOJ Microsoft findings.	A company can create a successful ecosystem without capturing the largest share of its eventual value. Walmart could help normalize AI-mediated shopping while an AI provider captures customer attention and economic rents.
IBM OS/2 vs. Microsoft Windows (late 1980s–1990s)	IBM and Microsoft initially cooperated on OS/2, then diverged. Microsoft concentrated on Windows, while IBM continued to develop OS/2.	Windows gained a decisive advantage from software availability, hardware compatibility, and distribution through PC manufacturers. OS/2 struggled to match that ecosystem. OS/2 history.	Cooperation alone does not win. A company must attract developers, users, and complementary services around its platform. Walmart's partnerships will matter most if they generate durable customer relationships and sales.
Microsoft Internet Explorer vs. Netscape (1990s)	Microsoft initially treated the browser as a threat to Windows and responded by integrating Internet Explorer with Windows and distributing it without a separate charge.	Internet Explorer displaced Netscape's leading position. The U.S. antitrust case documented Microsoft's efforts to protect its operating-system monopoly from the emerging browser platform. U.S. DOJ findings.	Amazon has a rational reason to defend its shopping interface. But incumbents may also use integration and distribution advantages to defend a threatened business model, potentially inviting regulatory scrutiny.
AOL's walled garden vs. the open Internet (1990s–2000s)	AOL initially offered a curated online environment; the wider Internet offered access to independently operated websites and services.	The open web became the broader platform for online activity. AOL later adapted its business rather than retaining its former position as the primary gateway to online life. IBM's history of e-business.	A retailer's controlled environment can be attractive until consumers prefer a more convenient intermediary that searches across competing destinations. Amazon must consider whether its walls add enough value to offset the friction they impose.
Apple iPhone vs. Android (2007 onward)	Apple chose a tightly integrated hardware and software ecosystem. Android enabled a broader network of device manufacturers to participate in a shared platform.	Both strategies succeeded, but in different ways: Apple captured substantial value through integration and premium devices, while Android spread across a much wider range of manufacturers. Apple's developer ecosystem; Android.	This is a crucial counterexample to the idea that openness always wins. Amazon could preserve a highly profitable closed ecosystem if its convenience, trust, and fulfillment advantages remain compelling.
The open web vs. proprietary online services (1990s onward)	Proprietary services controlled the user experience; web standards allowed independent sites, search engines, and new intermediaries to emerge.	Open protocols enabled new categories of businesses and made it possible for companies such as Google to become major gateways to information without owning all the underlying content.	An AI shopping agent could become the equivalent of a search engine for purchases, directing demand across retailers rather than requiring consumers to visit each retailer first.

It might be tempting to conclude that “open” tends to win, but not always. Apple’s  example suggests openness is not a universal rule. Closed ecosystems can thrive when consumers value a coherent experience enough to accept the restrictions. 

And “control” might exist in a few different parts of the value chain:

• The user's AI agent, which understands intent, compares products, recommends and negotiates

• Retailers and their systems who supply product catalogs, prices, inventory, promotions, loyalty and checkout experiences

• Physical commerce infrastructure including warehouses, stores, picking, delivery, returns and customer service. 

It is possible that ultimate success for Walmart or Amazon will hinge on dominance in a couple, but not all, of these parts of the value chain. And either open or closed strategies could work. 

Indicator	Evidence favoring Walmart's strategy	Evidence favoring Amazon's strategy
Where shopping begins	A rising share of purchase journeys originates in general-purpose AI assistants.	Consumers continue to begin most high-intent shopping journeys on Amazon.
Incremental customer economics	AI referrals generate profitable new customers and larger baskets at reasonable acquisition cost.	Amazon's own assistant increases conversion, frequency, and retention more effectively than external agents.
Who controls recommendations	Independent agents can compare retailers and rank products without retailer restrictions.	Retailer-controlled agents provide sufficiently superior recommendations, loyalty benefits, and convenience to keep customers inside their ecosystems.
Advertising and monetization	Retailers can monetize AI-driven demand without surrendering too much margin or data.	Amazon retains valuable sponsored placements, purchase-intent data, and other retail-media revenue within its own environment.
Transaction reliability	Common standards make inventory, payment, substitution, delivery, and returns work across retailers.	Complex transactions work materially better inside each retailer's integrated systems.

Walmart has an incentive to open distribution because it can gain from a change in the shopping gateway. 

Amazon, on the other hand, has an incentive to control distribution because it is the existing gateway.

But much hinges on where users experience value. If starting a shopping journey inside Amazon continues to make sense, Amazon wins. If users start to trust their agents, then perhaps Walmart wins. And it seems inevitable that neither Amazon nor Walmart approaches win all the time. 

It might matter whether the user values selection across retailers (perhaps choices are narrower but price differences can be significant), compared to the value of curation when the product universe is large. 

The key variables might include:

• how much comparison matters

•  how costly a mistake is

• how much context the agent needs

• whether the retailer has a unique advantage in price, assortment, or fulfillment.

Not every shopping experience favors each strategy. 

Shopping experience	Likely advantage	Why
Routine grocery replenishment	Walmart openness	An agent can compare prices across grocers, assemble a basket, and optimize substitutions, store proximity, and delivery. Walmart's stores and grocery scale are valuable if the agent can access them.
Repeat purchases of known products	Amazon curation, with a caveat	One-click reordering, purchase history, reviews, and reliable delivery make Amazon convenient. But if an independent agent can automatically find the cheapest equivalent across retailers, Walmart benefits from openness.
Staples and commodity products	Walmart openness	For paper towels, detergent, batteries, and pantry staples, product differences may be small relative to price, availability, and delivery cost. Cross-retailer comparison is valuable.
Novelty and product discovery	Amazon curation	A broad catalog, customer reviews, recommendations, and product comparisons can help consumers discover unfamiliar products. A strong independent AI agent could, however, reproduce much of this capability across retailers.
Rare, complex purchases	Amazon curation, potentially	For cameras, specialized tools, electronics, or hobby equipment, product specifications, compatibility, and reviews matter. Amazon has extensive product information and shopping history. An independent agent may win if it searches specialist retailers and manufacturer sources more comprehensively.
Urgent purchases	Depends on local fulfillment	The winning retailer is the one that can actually deliver the right item fastest. Walmart's physical-store network may be decisive for local essentials; Amazon may win on products already stocked in its fulfillment network.
Large, mixed baskets	Walmart openness	An agent can optimize the entire basket across retailers, rather than selecting the best individual product at one retailer. Walmart's grocery, household, pharmacy-related merchandise, and general merchandise breadth can help.
Premium or highly differentiated goods	Depends on brand access	Product authenticity, warranties, authorized distribution, expert advice, and service may matter more than the lowest price. Neither Walmart's openness nor Amazon's curation automatically wins.
Gift shopping	Open agents, potentially	An agent can search multiple stores using recipient interests, budget, delivery deadlines, and gift availability. Amazon's reviews and recommendations still help, but cross-retailer discovery may offer more distinctive choices.
Fashion, beauty, and home décor	Mixed	Personal taste, visual discovery, sizing, returns, and brand identity complicate pure price optimization. Retailer-specific expertise can matter, but a cross-retailer agent could broaden discovery and compare return policies.

And different types of purchases might create value in ways that either approach can tend to capture:

• Frequency favors whichever platform has the best history, automation, and fulfillment.

• Commodity products favor open, multi-retailer agents.

• Decision complexity might favor the agent with the best data and reasoning

• Fulfilment (delivery speed, returns, warranties, local availability matter favors the provider with  strongest operational capabilities.

And there also are the issues around user preferences. Some users will want to delegate more; some less. Some will trust agents more; some less. 

The point: it is not certain which approach is “better” or “more successful” long term. Both can work, sometimes, for some purchases.

AI Impact on Revenue, Profits Strongest in IT

A new study by Torsten Slok, partner and chief economist at Apollo Global Management, finds evidence of artificial intelligence financial benefits in the information technology industry, but not elsewhere. “Revenue per worker is rising for the Mag 7 but is flat for the S&P 493,” he says. “Profit margins are rising for the Mag 7 but are flat for the S&P 493” as well. 

source: Apollo

 

source: Apollo

That might fuel concern that AI overinvestment is occurring. On the other hand, it seems also the case that AI investment is highly concentrated in relatively few IT firms. In other words, AI actually is not being deployed at scale outside IT. 

source: Apollo

So observers can take their pick of explanations: either AI is not generally producing measurable results or AI is not yet producing results because few firms outside IT have implemented at scale. 

At a high level, it also is likely that standard statistics miss some meaningful AI-related benefits. In other words, value is created, but not in ways that show up immediately, in every firm or industry, in the form of gross domestic product and productivity growth.

Type of benefit or measurement problem	Example involving AI	What conventional statistics may miss
Free or inexpensive services	A person uses a free chatbot instead of paying for tutoring, research, or basic professional advice.	Consumer value beyond the price paid.
Quality improvement	AI helps a physician explain a diagnosis more clearly or helps a lawyer produce a better analysis.	Better output when the number of services and their recorded prices barely change.
Time savings	A worker completes research in 20 minutes instead of two hours.	The value of saved time if it doesn't translate into recorded production, earnings, or leisure-related output.
New capabilities	A small business gains access to analytical capabilities previously affordable only to large firms.	New economic opportunities that have not yet generated recorded sales.
Intangible investment	A company spends months reorganizing workflows, developing proprietary data, and training employees.	Some of the investment and the resulting organizational capital.
Innovation spillovers	AI accelerates product development, scientific research, or software creation.	Future benefits that have not yet become market transactions or measurable output.
Unpaid or household activity	A consumer uses AI to plan a trip, learn a language, or manage household tasks.	Some of the resulting value because much household production lies outside GDP.

Measurement is itself a problem. The OECD, for example,  identifies intangible investment, cloud-computing accounting, and improvements in the quality and scope of services as important measurement challenges.

Also, value can be produced in economic welfare without showing up in GDP statistics. Such has been the case for many internet-supplied “no extra cost” services used by consumers, for example.

Economists call the difference between what a consumer would be willing to pay and what the consumer actually pays consumer surplus. So a2025 paper by Erik Brynjolfsson and coauthors, GDP-B: Accounting for the Value of New and Free Goods, develops an alternative approach to capturing benefits from free and new goods that standard GDP measures poorly. 

A 2026 Stanford Digital Economy Lab study, What Is Generative AI Worth?, used surveys and incentive-compatible choice experiments to estimate the value U.S. consumers place on access to generative AI tools. Its estimated aggregate consumer surplus rose from $116 billion in 2025 to $172 billion in 2026.

That is unlikely to satisfy skeptics insistent on visible AI impact on firm results. The International Labour Organization's May 2026 research brief, The Aggregation Paradox of AI , finds that strong task-level gains have not yet translated consistently into measurable gains at the firm, sector, or macroeconomic levels. Uneven adoption, workflow constraints, and the cost of organizational change help explain the gap. 

AI might produce large benefits for consumers and workers while capturing relatively little revenue for the companies providing it, as the Apollo analysis suggests is presently the case. 

Or, productivity gains might accrue primarily to a small number of firms, with limited diffusion to the rest of the economy, as the Apollo study also suggests. 

The absence of an immediate productivity boom is not strong evidence that AI is failing, or will fail. Past historical evidence suggests an important new technology takes some time to show benefits. 

But the existence of a historical productivity lag is not strong evidence that a productivity boom is inevitable, either.

Still, a delay in measured economic or firm benefits is not at all unusual. Past innovations such as steam power, electricity,  information technology and the internet have taken some time to generate clear economic value, because:

• Complementary investments in organizational processes, software, training, and new business models must be made

• Benefits may initially be concentrated in particular tasks, firms, and industries

• Some gains, particularly in consumer welfare and intangible assets, are imperfectly captured by conventional statistics.

Financial observers will still remain skeptical. But, as a historical matter, important new general-purpose technologies have almost-always, perhaps always, experienced a lag time between introduction and impact. AI is showing the same pattern.