Findings in the March edition of the PYMNTS Intelligence “Share of Wallet: Amazon vs. Walmart” report reveal that modern consumer is increasingly no longer one coherent decision-maker. Instead, shoppers are two psychologically distinct selves: a disciplined survival optimizer, and an aspirational reward-seeker.

In this emerging landscape, retailers like Amazon and Walmart may no longer be fighting the same war. Total share of wallet as a concept is increasingly bifurcating distinctly into the two perennial retail categories of essentials and discretionary spending. The result is not a zero-sum retail contest, but a segmentation of dominance.

The report’s findings frame this as a “consumer wallet reset,” a structural reallocation of spending driven by macroeconomic pressure, changing consumption patterns and the maturation of digital commerce.

Retail’s New Balance of Power

Consumers are not choosing between Amazon and Walmart. They are choosing both, and relying on each for a specific purpose. The PYMNTS Intelligence report found that Amazon is the go-to for discretionary purchases, while Walmart anchors essential spending.

Amazon’s ability to convert browsing into buying through one-click checkout, personalized recommendations and near-instant fulfillment makes it uniquely suited to capture discretionary spend. When consumers decide to indulge, Amazon is increasingly the default interface.

The report’s findings show that, by the fourth quarter of 2025, Amazon captured a record 11.1% of total U.S. retail spending, with outsized dominance in categories like hobby goods (35% share), electronics (32%), and apparel and furniture, where its share has more than doubled over time.

Yet Amazon’s dominance has limits. In grocery, a category defined by frequency, perishability and price sensitivity, its share remains a modest 3%. There, and across other gaps, is where Walmart is focusing.

If Amazon has mastered discretionary spending, Walmart has perfected the opposite: the steady, unglamorous capture of essentials. The retailer’s share of overall retail spending has remained remarkably stable over time, a reflection of its entrenched role in everyday consumption.

Walmart’s strength is most pronounced in grocery, where its share of food and beverage spending has increased by 13% since 2019.

But that strength comes with a trade-off. As Walmart has deepened its hold on essentials, it has lost ground in discretionary categories like clothing, electronics and hobby goods, where growth is faster and margins are often higher.

Read the report: Consumer Wallet Reset: How Amazon Wins Discretionary Spend and Walmart Holds Necessities

The modern consumer may feel conflicted, but their behavior is increasingly patterned. Survival mode and splurge mode are no longer anomalies; they are the defining rhythms of contemporary consumption.

Amazon wins when consumers want. Walmart wins when they need.

For the broader retail sector, this bifurcation presents both a challenge and a constraint. Companies that cannot clearly position themselves within either survival mode or splurge mode risk becoming less relevant. The traditional “middle” retailer offering a mix of value and aspiration without excelling at either may become a model that is increasingly difficult to sustain.

The deeper implication of this shift may also be that retail is evolving from a collection of channels into a set of behavioral platforms. Walmart and Amazon are not just destinations; they are becoming default responses to different types of consumer needs.

When a household considers how to restock the pantry, the answer is increasingly predetermined. When an individual feels the impulse to treat themselves, the pathway is similarly preconfigured.

Ultimately, in a world where consumers are more constrained, more selective and more digitally enabled, the battle for the wallet is no longer singular. It is becoming segmented, strategic and increasingly asymmetric.

At PYMNTS Intelligence, we work with businesses to uncover insights that fuel intelligent, data-driven discussions on changing customer expectations, a more connected economy and the strategic shifts necessary to achieve outcomes. With rigorous research methodologies and unwavering commitment to objective quality, we offer trusted data to grow your business. As our partner, you’ll have access to our diverse team of PhDs, researchers, data analysts, number crunchers, subject matter veterans and editorial experts.

The payments industry is locked into a race toward speed. Faster rails, real-time settlement, one-click checkout, invisible authentication and more innovations are all pointed toward one assumption: that friction is the enemy, and technology’s job is to eliminate it.

But as the payments ecosystem matures, the next frontier is one of understanding where speed alone is no longer a differentiator.

“If you think about the payments industry and what we’ve been building for the last decade, it’s faster rails, smarter algorithms. But we hit a wall when those algorithms do not have the full picture,” Entersekt Chief Strategy Officer and Co-Founder Dewald Nolte told PYMNTS  during a conversation for the “What’s Next in Payments” series February edition, “Word of the Year.”

That wall is becoming more visible to banks, merchants and FinTech platforms grappling with a paradox: They have more data than ever, yet are often less certain about what it means. Fraudsters exploit that ambiguity, while legitimate customers are caught in the crossfire of overly cautious risk models.

“We are drowning in data, but we’re starving for wisdom,” Nolte said, noting that for 2026, his word of the year is “context.”

When Fraud Prevention Becomes a Customer Experience Problem

Historically, fraud prevention has been measured by how much bad activity gets blocked. But that metric obscures an uncomfortable truth that the same systems designed to stop criminals can frequently alienate loyal users.

“It’s almost an insult,” Nolte said of repeat customers forced through unnecessary verification. “It’s like, you should know me.”

Consider a high-value transaction flagged by traditional controls. A $5,000 payment to an online casino, for example, may automatically trigger alarms. Yet without historical insight into the user’s behavior, the system cannot distinguish between fraud and a known, affluent customer acting exactly as they always have.

“If you have the full context of this user, such as the fact that there’s been 10 transactions over the last six months with no issues, suddenly that changes from fraud to, ‘This is a VIP customer, and how can we enable that?’” Nolte said.

This is where context transforms from an analytical enhancement into a competitive necessity. Payments leaders are realizing that false declines, the legitimate transactions rejected by risk engines, can carry hidden costs around lost revenue, damaged trust and diminished brand loyalty.

One of the most visible outcomes of context-driven payments, Nolte predicted, may be the decline of blanket authentication challenges such as one-time passwords and step-up verification prompts applied indiscriminately.

“It’s going to be the death of the automatic step-up,” he explained.

Instead of treating every transaction as suspicious until proven otherwise, contextual systems can continuously interpret behavioral signals, device intelligence and historical patterns to determine when security should be visible, and when it should disappear entirely.

The Real Barrier Isn’t Technology. It’s Fragmentation.

Despite advances in machine learning, cloud infrastructure and real-time processing, the payments industry’s biggest constraint is not computational power. It’s fragmentation.

Data remains trapped inside institutional silos: Issuing banks guard transaction histories, merchants hold behavioral insights and identity signals are scattered across third-party providers. Each participant sees only a slice of the customer journey.

“If you’re only looking at your little silo, that’s not good enough anymore,” Nolte said, describing the emerging baseline capability as “orchestrated ingestion,” or the ability to gather signals from multiple sources in real time and synthesize them into a unified risk decision.

“Sometimes the biggest challenge is that we want to build our little kingdom,” he added. “But there’s someone out there who has a perspective that you don’t have, and you need it.”

The urgency for collaboration is amplified by the evolving sophistication of financial crime. Fraud networks already operate as highly coordinated information-sharing ecosystems, trading techniques and data in real time.

“The criminals do better than us in one area,” Nolte said. “They actually share data.”

To counter that threat, institutions should consider adopting what Nolte called “collective defense” by pooling intelligence across organizational boundaries.

“There is a regulatory recognition around making sure that the guardrails and mechanisms for sharing data in a responsible way are being addressed,” he said.

Evolving Trust From Transactions to Relationships

If the payments industry’s first digital era was defined by velocity, its next phase may be defined by interpretation. Context turns isolated transactions into continuous relationships, allowing systems to recognize intent rather than merely process inputs.

“You want to move from just the fraud rate of how much fraud did I block to the trust rate,” Nolte said.

The shift reframes security as a customer experience discipline as much as a risk function and means quantifying how effectively an institution approves legitimate activity while still maintaining high protection levels.

Nolte suggested the real benchmark is the reduction of false declines without sacrificing fraud prevention.

“If I can maintain 99.9% fraud prevention by slashing my false declines by 50%, that’s the measure,” he said. “How much can I reduce these false declines where I make my most loyal customers feel like strangers?”

At the end of the day, in 2026, the winners in payments may not be those who move money the fastest, but those who understand the moment best.

Susan shopped exactly the way the mall designers intended. The department store anchors were the magnets motivating her to make the trip in the first place. The smaller shops lining the corridors between them turned her walk from point A to point B into a series of serendipitous purchases that kept the whole ecosystem humming, including a stop or two at the food court to grab lunch or dinner.

Fifty years before that, Susan’s grandmom was one of the millions who found the lure of the department store irresistible. A New York Times article published on February 2, 1927 cited Federal Reserve data showing department store sales setting new retail records in 1926. (If you do happen to click on the article link, check out the ad for life insurance on the same page.)

For the first time, curated retail items could be found under one roof for granny to touch, feel and try on. Knowledgeable salespeople demonstrated products and answered questions, turning store browsers into buyers. Stores offered credit to make buying easier, and retail sales took off. And like shopping at the mall, going to the department store was an experience that was part social, part shopping, but mostly lots of fun.

Julie Satow’s book When Women Ran Fifth Avenue documents the role department stores played in shaping commerce and influencing fashion in their heyday. It’s a great read.

Today, Susan’s granddaughter, Ellie, starts her shopping trip with a scroll.

Her smartphone and apps give her access to the equivalent of every mall and every department store ever built with a tap and a swipe. PYMNTS Intelligence data shows that nearly two-thirds of American consumers start their shopping trips that way, making purchases 12 days each month on average and window shopping for another 12. For retailers, this is pretty good news. Mobile window shoppers convert to a purchase at a rate three times higher than the casual mobile phone user. Younger consumers and parents skew that number even higher.

Read More: The 2025 Global Digital Shopping Index: The Rise of the Mobile Window Shopper and What It Means for Payments

The digital world gives Ellie tons of options to buy whatever strikes her fancy without ever visiting a store. She has certainty about product quality and inventory availability even if she decides to make the trip just to see the item for herself. If she doesn’t, the logistics of getting her items delivered are transparent, predictable and efficient.

The latest PYMNTS Intelligence data finds that 48% of clothing and accessories, 61% of electronics purchases and 75% of sporting goods and hobby purchases made online now happen at Amazon. Overall, almost two in every ten retail purchases now starts and ends online.

Read More: Walmart Aims at Closing Amazon Online Sales Gap

But Ellie’s generation may be the last to make even that digital journey themselves.

Do Consumers Still Want to Shop at a Store?

Warren Buffett said in a 1977 Wall Street Journal interview that the best business to own is a toll bridge. His thesis: Once capital is invested to build one, you can keep collecting revenue and raising prices since you control access to the places people want to go.

If you can’t be the destination and monetize it, the next best thing is to own the metaphorical bridge everyone has to cross to get to one. Platforms are a great example of the art and science of being a massively successful toll booth. Or a complete disaster, if you get the platform economics wrong.

Buffett’s aphorism is particularly appropriate to shopping in an age of AI agents.

The retail industry is in the throes of a heated debate about whether to open storefronts to AI models so that consumers (or their agents) who start their product journeys there can find their merchandise and buy. That’s the OpenAI, Stripe, Adyen, Fiserv, Perplexity, Shopify, Google, PayPal and Walmart thesis.

Read More: Department Stores of the Future Are AI Agents

The other side of the argument: if you’re already the destination, what’s the point of the bridge? If consumers already come to you to search, discover, compare and buy, then connecting to someone else’s toll bridge is not in your best interest. As the destination, you’re the bridge and the toll booth all wrapped up into one. That’s the Amazon and eBay thesis. (Amazon, I get. eBay is the ultimate head-scratcher, if you ask me.)

But there’s a more fundamental question at the heart of this issue even before getting to the strategic question of open versus closed.

In a world where agents can buy, do consumers still want to shop at a store?

Will Ellie, her kids or her grandkids need to start at “the store” in order to gather product information, read reviews, evaluate options and decide what, when and whether to buy so that they will have confidence the order will be right and show up on time? If so, consumers won’t be willing to fully delegate to an agent because too much can go wrong.

Or will shopping, as we know it, shift from stores and apps to a collection of hundreds of billions of digital SKUs that agents shop and buy on a consumer’s behalf? In that scenario, consumers say shopping across endless aisles and multiple drives to physical stores is too much of a hassle. Agents can do it smarter and more efficiently.

This turns out to be the $5.5 trillion question for retail in the U.S.

Because if AI agents do the shopping, nobody (or many millions of nobodies) will “go” anywhere. Instead, shopping’s theoretical bridge leads to another bridge and another bridge and another with their own tollbooths.

The destination that was once the store becomes irrelevant because it becomes invisible. The new destination becomes the prompt and the agent dispatched to do the shopping and buying. The bridges and the tollbooths connect to and from this new agentic storefront.

The Shift That’s Already Here, Sort Of

The data says this isn’t so much of a theoretical debate anymore.

PYMNTS Intelligence research shows that more than six in ten U.S. consumers used AI in the past year to do something. More than a third of Gen Z consumers and power users now start their daily tasks on dedicated AI platforms first, including content discovery. And not in addition to Google search, but as a replacement.

As of January 2026, not only have 41% of consumers used dedicated AI platforms for product discovery, but 33% say they have fully replaced their prior methods. They’re not layering AI on top of old habits. They’re shutting the door and leaving them behind.

Read More: Smart Agents Replace Super Apps

This behavior is more predominant among the early adopters who are all in on AI and agents, with 51% having replaced their old methods of product discovery. True to their early adopter roots, they are willing to tolerate untold friction to try something new. Among AI power users, the 24 million Americans who use AI and agents to do everything from building shopping lists to doing research on what stocks to buy, the share who reported replacing their previous search and discovery methods continues to increase since November 2025, when it stood at 46%.

For them, the front door of commerce is already in motion.

But here’s the caveat.

The growth is being driven overwhelmingly by use cases that are not yet letting agents make high-stakes, complex purchasing decisions on their behalf.

In part that’s because the inventory of products and merchants to shop remains nascent. In part it’s because the experience is largely circa the early days of internet commerce: functional, but not exactly one-click amazing. And mostly it’s because consumers still have to trust that their purchase discovery and outcomes are as good as the content discovery and outcomes when the starting point is the physical or virtual store.

For the moment, the interest in doing these things is higher than the reality of actually doing them.

And yet about 82% of these power users, who are the most likely to have replaced their old discovery methods, say they would use AI agents for big, complex purchases where the stakes are high and getting it wrong has financial consequences. These are not impulse or everyday buys. They are the high-consideration, high-research decisions that used to take hours, even days or weeks. And where a lot of spend hangs in the balance.

That’s consumer intent at the very leading edge of AI and agents. And it’s pointed directly at the heart of how retail works today.

Why Shopping is Not a One-Size Fits All Experience

The mistake in the current debate about agents versus stores is treating “shopping” as a one-size fits all activity. It isn’t. There are several distinct reasons for how, why, when and where people buy. And the relevance of AI agents will vary tremendously based on those buying triggers.

Take replenishment, AKA subscriptions.

The dog food. Laundry detergent. Paper towels. Toothpaste. The purchases where the consumer made the real decision once, maybe even years ago, and has been on autopilot ever since. There’s no discovery here. No joy. Just the mild annoyance of remembering to place an order before it runs out. And the catastrophe of running out when they forget something important.

This is where an AI agent doesn’t just help, it can take over entirely and reinvent the subscription experience along the way.

No more boxes of paper towels that pile up in the basement because someone in the household forgot to pause an order. Instead, an agent can notice and nudge.

“It’s been six weeks since you ordered dog food. Time to reorder?” “You haven’t refreshed your white short-sleeve t-shirts for the summer. Want the same ones you bought last year, or take a look at the five most popular styles in your size?” “Time for new flip flops. Same pair as last June, or want to see what’s out there?”

In truth, consumers don’t want to spend their time shopping for these things. They want the buying process to disappear. That’s what the agent can do, with a prompt and one-tap confirmation.

Read More: Why 30 Million US Consumers No Longer Search

Amazon already does a version of this with Subscribe & Save, which I live by. Alexa+ attempts to take it further by adding context, knowing that summer is coming, that the dog food bag lasts roughly this many weeks, that last year’s flip flops ran a half-size too small because of a return and a reorder.

Early access data from Amazon on Alexa+ finds that users tripled their shopping activity and had two to three times more conversations compared to the original Alexa. Amazon made Alexa+ fully available to all U.S. users this month (February 2026), and it’s free for its 250 million Prime members. That’s a lot of people who already live with an agent on their kitchen counter or inside an Amazon app.

Walmart’s play with its One Pay banking, credit, shopping and rewards app tries to capture a piece of this layer by turning the grocery trip into a financial flywheel that expands into replenishment. It remains an aspirational goal. Walmart’s ecommerce business has seen strong growth over the last year (from 16.4% to 19.9%), with much of that growth coming from groceries, which drive nearly 60% of their sales. Its Subscribe and Save service, which launched in 2023, is positioned as a counter to Amazon’s in capturing recurring sales for groceries and essentials.

In an agentic world, whoever owns this layer owns the most frequent, most predictable and most invisible transactions in a consumer’s life.

Then there are the bigger-ticket, once-every-so-often, more considered purchases.

A new camping tent. A stroller. A laptop. A dishwasher. A new car. These are the decisions where people currently spend hours reading reviews, comparing specs, toggling between browser tabs and going back and forth over whether the extra hundred dollars is worth it. And this is exactly the layer where the PYMNTS Intelligence data gets most interesting. It’s precisely these complex, high-stakes decisions where consumers are most eager to hand the tedious task of evaluation those options to AI.

Read More: From Assistive to Agentic AI: Consumers Wade Into Autonomous Commerce

It makes sense. Typing in (or speaking) a detailed prompt is just easier than scrolling through forty-seven open tabs. A consumer can describe what they need in plain language and the agent does in seconds what used to eat up an entire afternoon or more.

Amazon’s Rufus is the most visible example of what this looks like inside a closed ecosystem. The numbers Amazon reports tell you why Amazon is pushing so hard on it.

They report that some 250 million shoppers used Rufus in 2025, with monthly active users growing 140% year over year. Amazon says it’s on pace to drive more than $10 billion in incremental annualized sales with it. Rufus users are 60% more likely to complete a purchase than non-Rufus shoppers. During Black Friday 2025, sessions involving Rufus that ended in a purchase doubled compared to the trailing thirty-day average, while non-AI sessions grew just 20%, they say. That doesn’t feel like a marginal improvement. It suggests that a fundamentally different shopping behavior is happening inside its ecosystem.

Then there are the LLMs such as ChatGPT, Claude, Gemini that want to be the destination where that journey starts and ends, routing consumers to the right merchant with the right product at the right price. It’s also where the strategic tension begins to get, well, a little tense.

Read More: Why the ‘Person’ of the Year in 2025 Should Be the Chatbot

Amazon can end the shopping journey because it owns what I’d call logistics certainty. A consumer knows when the product is arriving: same hour, same day, next day. They know what the shipping costs are: mostly free with Amazon Prime, which is a bonus and eliminates the uncertainty of not knowing the final cost. A consumer knows how to return an item if there’s a problem. And where to do it for free. The entire post-decision experience is part of their destination’s appeal.

Perhaps one of the most underrated variables in the entire agentic commerce debate: What exactly happens once an agent clicks “buy.”

Then there are the highly complex, product/service blended purchases that support some of life’s biggest moments.

The 25th anniversary trip. Having a baby. Sending a kid to college. Buying a first house. Getting a puppy. Planning a wedding. Moving to a new city. Each of these involves dozens of purchases, but none of them is really about filling a cart. They are about making a life passage happen.

It’s also where the question of whether consumers still want to go to stores gets interesting. The answer might be yes, but in a different way than it happens today.

Nobody wants to spend a week researching car seats, strollers, nursery furniture, baby monitors, bottle warmers and the thirty-five other things your friends, relatives and in-laws insist you absolutely must have before the baby arrives. Then there’s the physical infrastructure necessary to support the baby. The diaper service. The pediatrician. The day care. The preschool. It’s exhausting, often contradictory and largely anxiety-producing.

The agent’s role in these moments isn’t to shop for those parents. It’s to be the smart and efficient concierge that helps to simplify the massive complexity around this important life moment.

Today preparing for the new baby (or any of life’s biggest moments) looks like parallel processing: researching across endless tabs, juggling competing recommendations, stitching together a plan from fragments.

Tomorrow it might look like this.

Congratulations! You’re having a baby! Here’s a curated registry across eight retailers based on your budget, your house or apartment size, and what parents with similar lifestyles actually used and loved. Here are the retailers that have most of these items in stock if you want to go in and check them out IRL. Here are the three pediatricians near you accepting new patients with strong reviews. Here’s a timeline of what to buy to avoid the panic-ordering purchase of any car seat in stock at 2 a.m. three days before the due date.

The agent handles the things that take time and create frustration. The hassle of research, comparison and logistics coordination is stripped away, and what’s left is the part that actually matters to people. The choosing, experiencing and satisfaction of making a good decision in the context of something very important about to happen.

Who Plays Where and Why It Matters

So, who owns the toll bridge and who becomes the destination? That depends on which version of shopping you’re talking about. And the four most consequential players in this space, Amazon, Google, Walmart and the LLM platforms, are each building from very different starting positions.

Amazon: The Everything Store wants to become the Everything Concierge.

Amazon pulls in roughly 2.5 billion visits a month to its site, commands roughly 9.1% of all U.S. retail spending according to the latest PYMNTS Intelligence report, serves more than 300 million active customers, with 250 million-plus Prime members locked into an ecosystem that has become the defacto starting point for product purchases. Amazon reported Q4 2025 revenue hit $213 billion, up 14% year over year. These are not the numbers of a company that needs to reinvent itself. These are the numbers of a company that can afford to make big bets from a position of massive retail strength.

That appears to be what Amazon is doing.

Subscribe & Save handles replenishment. Alexa+ is hoping to extend that by contextualizing it, learning the consumer’s purchase velocity, anticipating what they need before they think of it and eventually ordering ahead of time with its auto-buy feature.

For more considered purchases, Amazon has built the perhaps most complete environment in retail: search, reviews, product information, price comparison, and then the killer, logistics certainty, all in a one-stop shop. Consumers start and end the journey on Amazon and reliably know that the product will arrive when promised. Rufus and Help Me Decide don’t replace the shopping journey, but instead compress it.

And at a nearly 10% advertising conversion rate, roughly five times Google Shopping’s rate, the math for brands selling on Amazon is hard to argue with.

Amazon’s bigger bet with Alexa+ is to push beyond shopping and into life. The ambition is not to be just a shopping assistant but a life operating system.

Alexa+ already integrates with Uber, Grubhub, Ticketmaster, Vagaro for spa and fitness, Thumbtack for home services, Square for merchant services, Expedia for travel, Yelp for local discovery, and Amazon Autos for car sales. Amazon’s Panos Panay has described a vision where Alexa becomes the consumer’s personal shopper, butler and home manager. The more she understands about the consumer’s life, the better she can serve the customer.

Add Amazon’s healthcare play through One Medical and pharmacy, its grocery infrastructure through Whole Foods, Fresh, and the Uber Eats delivery partnership, Prime Video for entertainment, and Alexa embedded in hundreds of millions of homes, and you start to see the outlines of a company that could plausibly become the life concierge, not just the destination as the store. What we once called the Super App.

Here’s the catch. Amazon may make more trips to my home in a week than I’d like to admit, but Alexa+ still has a long way to go, at least in my experience, before she can live up to the claim of a personal assistant. She is what most consumers say they’d trust, according to PYMNTS Intelligence data. For me, it’s a crap shoot as to whether she can reliably turn on Bloomberg TV in the morning on my Fire TV, never mind organizing my day-to-day.

The conundrum is that Amazon is trusted today for efficiency, reliability and price. But no one is thinking of Amazon curating their wedding registry. The Everything Store may struggle to become the Everything Concierge precisely because life moments require warmth and judgment that an efficiency machine may not inspire. And Alexa+ hasn’t proven she can deliver.

Then there’s Google, which is playing a fundamentally different game.

Google is a massive advertising machine that generated more $400 billion in revenue in 2025, with advertising driving the lion’s share of those numbers. Google says its Shopping Ads drive 76% of all retail search ad spending. Google is, by a wide margin, the world’s largest digital advertising platform.

But Google’s position in commerce is that it is really nowhere. It’s always been the bridge, never the destination. Consumers search on Google, discover products, and then leave to buy somewhere else. Google gets paid for the referral.

That’s what Google is trying to change.

In January 2026, CEO Sundar Pichai unveiled the Universal Commerce Protocol at NRF, an open standard designed to let AI agents navigate the full shopping journey from discovery through checkout, all within Google’s own ecosystem. Google co-developed UCP with Shopify, Etsy, Wayfair, Target and Walmart, and got endorsements from Visa, Mastercard, Stripe, American Express and Best Buy. The message was clear.

Google wants to stop being the bridge and become the destination.

Read More: The Protocol Power Struggle Reshaping AI-Driven Commerce

This announcement came shortly after Google rolled out agentic checkout in November 2025, letting users set a target price and authorize Google to auto-purchase via Google Pay when the price drops. Its Shopping Graph now indexes more than 50 billion product listings, with 2 billion updated every hour. The new Business Agent feature lets retailers like Lowe’s, Michaels, Poshmark, and Reebok deploy branded AI assistants directly inside Google Search to chat with shoppers and close sales. Direct Offers, a new ad pilot, offers exclusive discounts to high-intent shoppers in AI Mode.

It’s nothing if not ambitious. But Google’s challenge remains closing the gap between intent and conversion by turning itself into the internet’s marketplace.

Google Shopping Ads convert at roughly 1.91%. Amazon’s marketplace converts at nearly 10%. That’s a five-to-one ratio, and it tells you everything about the difference between a platform where people go to browse and a platform where people go to buy.

Open standards and broad retailer partnerships are a compelling pitch, but until more shoppers are completing purchases inside Google rather than clicking away to finish somewhere else, the toll bridge metaphor still holds. And merchants need to consider how much, and when, to put effort into exposing their entire product catalog to Google, without a clear understanding of how it plans to monetize those sales. And who owns the customer relationship. More on that point later.

Walmart is the wildcard with one truly irreplaceable asset: its physical storefront.

Roughly 100 million people walk into Walmart for the most frequent, most habitual, most non-discretionary purchase in retail: their groceries. That foot traffic is massive. But it’s also their greatest Achilles’ Heel.

Few people who go to Walmart for groceries buy anything else. The foot traffic is enormous, but their slice of the retail basket is narrow. PYMNTS Intelligence data shows Walmart’s share of retail declining in nearly every category except food.

Walmart’s AI strategy seems two-fold. With its AI partnerships, Walmart appears comfortable being a destination where the LLM toll bridges send traffic because its physical infrastructure is something no AI platform can replicate. And digital is where they lack meaningful share.

Physically, the One Pay credit and rewards play is an attempt to expand what the in-store shopper buys — to turn a grocery trip into a broader financial relationship. If you are already in the store buying food and the Walmart app knows you need new school supplies for your 10-year old,  it can offer you credit and rewards to buy them right then, and might expand the basket. It’s a theory, for now.

Like Amazon, the challenge for Walmart is in playing the life concierge role.

Walmart is not a lifestyle brand. It is not where a consumer goes to curate their baby registry or furnish their first apartment or plan their wedding. It is not a Super App.  Unless Walmart can leap from “where I buy groceries” to “where my Walmart agent manages my life,” it remains powerful but confined to a single, if highly defensible, position as the world’s biggest grocery store. With a little retail eCommerce on the side.

And then there are the LLM platforms that are building the toll bridges that want to own the road. And become the destination.

Consumers are increasingly starting their discovery journeys on these platforms, and the agents can route them to any merchant. That brings with it enormous disintermediation power. The PYMNTS Intelligence data confirms it. Consumers are replacing traditional search and discovery methods with AI-first approaches, and the replacement rate is accelerating.

Adobe reported that AI-driven traffic to retail sites surged 693% during the 2025 holiday season, and shoppers arriving from AI services were 38% more likely to convert. This sounds amazing. But transaction volume is miniscule; this is still very early days.

OpenAI has already launched its own Instant Checkout feature. Microsoft Copilot is partnering with Shopify for embedded checkout. Perplexity was the first to launch one-click checkout within its app. Everyone wants in on commerce.

But the LLMs have a structural vulnerability that the current hype obscures. They have no logistics. They can send you to a store, but they cannot guarantee when the product arrives, what shipping costs, or how returns work. Every merchant on the other side of their toll bridge is a different experience with a different uncertainty profile. And the selection of those merchants is pretty limited right now.

Being discoverable by an agent does not solve the fulfillment gap. And that is why Amazon’s position is structurally stronger than the LLM toll bridge position, at least for now. Amazon is the one destination where the post-agent experience is already solved.

My sense is that the LLMs’ real aspiration is the life concierge.

Not to sell you things, but to be the agent that manages complexity across your entire life.

Getting a puppy? It finds the vet, orders the food, finds and books the trainer and the dog walker,  schedules the groomer, arranges for doggie daycare and the transportation to and from. The LLM becomes the relationship layer. Merchants, including Amazon, become the inventory.

Their advantage is being platform agnostic, smart and a massive time saver. The disadvantage is that a concierge without logistics and without fulfillment is ultimately dependent on others to deliver. And without access to all of the right products, creates buyer uncertainty.

In a world where the uncertainty tax on fulfillment determines which agent recommendation the consumer actually trusts, that dependency may be the LLMs’ defining limitation.

And  why “just open your storefront to the AI agents” is not the simple answer it appears to be for merchants.

Who Owns the Relationship When No One Goes Shopping Anymore?

Let’s close with how we started. The destination and bridge/toll booth metaphor assumed a world with fixed destinations and fixed paths to get to and from. The destination was valuable because there were a finite number of destinations, and lots of  people wanted to get there. There were only a few ways to  get in and out. The bridge was valuable because it made the trip possible. And people and businesses paid the tolls.

The agentic world makes the destination and the bridges with tollbooths pretty fluid. For the replenishment shopping example, the destination is invisible, handled by an agent on autopilot that noticed a consumer was running low before they did.

For considered purchases, the destination is whoever collapses the research journey fastest while maintaining the certainty that the product will arrive when promised and can be returned if it’s wrong.

For life moments, the destination is wherever the people feel a connection and a sense of trust to start the conversation.

The real strategic question comes down to how consumers view the shopping experience for each of those use cases.

Do they want an agent that makes shopping disappear? Or do they want an agent that makes the shopping experience at a store they know and trust  better?

The answer, almost certainly, is both. The company that figures out how to deliver both will own the most valuable thing in commerce.

Not the product, not the price, not the storefront, and not the toll bridge.

The relationship. The real starting point of any shopping journey.

 

Until NEXT time.

Join the 19,000 subscribers who’ve already said yes to what’s NEXT.

PYMNTS CEO Karen Webster is one of the world’s leading experts in payments innovation and the digital economy, advising multinational companies and sitting on boards of emerging AI, healthtech and real-time payments firms, including a non-executive director on the Sezzle board, a publicly traded BNPL provider.

She founded PYMNTS.com in 2009, a top media platform covering innovation in payments, commerce and the digital economy. Webster is also the author of the NEXT newsletter and a co-founder of Market Platform Dynamics, specializing in driving and monetizing innovation across industries.

When Windows 95 Becomes a History Class

A recent Fast Company story argued that retro tech isn’t just nostalgia merch, it’s becoming curriculum. In 2021, University of Wisconsin–Milwaukee history professor Thomas Haigh began teaching a course on the history of computers after noticing that many classic histories of the 1980s–2000s assume students already know what it was like to live with desktop PCs, early consoles, and floppy disks. His students didn’t.

So Haigh built what amounts to a time machine with power cords: a lab stocked with machines from the 1980s, 1990s and 2000s so students can experience “normal” computing from those eras: saving to disks, booting up creaky operating systems and learning how much patience it used to take just to open a file.

That hands-on approach matters because modern tech is designed to disappear into the background. The cloud is “just there.” Subscriptions renew themselves. Devices update while you sleep. Retro tech forces the opposite: friction you can touch and systems you can understand. It’s technology with visible cause-and-effect, and, for a generation raised inside opaque platforms, that’s basically a superpower.

The impulse is bigger than one campus. The Media Archaeology Lab at the University of Colorado Boulder invites the public to “turn on, open up, play and create” with still-functioning obsolete media, and it hosts events (including repair-focused workshops) that treat old devices as something you can actually tinker with.

And there are plenty of “learn the past” programs for younger kids, too: the Computer History Museum publishes activities and classroom resources for learners of all ages; the National Museum of Computing in the United Kingdom runs learning visits and “Digital Future Days”; and the Vintage Computer Federation’s Commodore Classroom teaches BASIC (and even 6502 assembly) on real Commodore 64 hardware.

A Quick Sweep of the Retro-Tech Market

Of course, retro isn’t only educational, it’s commercial. The same digital economy that made everything instant has also made “slow” feel premium. Consumers are buying limitations on purpose: fewer apps, fewer notifications, fewer ways for a device to phone home (or to accidentally approve a one-click checkout).

Retailers have noticed. Fast Company has chronicled the rise of nostalgia-fueled gadgets and peripherals: new hardware engineered to look like old hardware, but (usually) with fewer mysterious beeps. And businesses like Retrospekt have built serious operations restoring and selling classic instant cameras and expanding into other throwback formats like cassettes and Tamagotchis.

6 Retro-Tech Trends That Are Charming, Weird or Both

1. Landlines, but for kids. A startup wants the home phone back in the kitchen: voice calls only, no texts, no doomscrolling, with parents approving contacts. The most disruptive feature is that it can’t “like” anything.
2. The dumbphone renaissance. Some Gen Zers are ditching smartphones for simpler phones as a digital detox, for less dopamine, more presence and the satisfying clamshell snap of a tiny rebellion.
3. Mechanical keyboards as identity. Retro-styled, loud, clicky keyboards are a booming subculture because, apparently, the future of productivity sounds like a 1992 computer lab.
4. Instant cameras and the luxury of consequences. Retro photography restores scarcity: you get one shot, you wait, you keep the print. In an era of infinite cloud storage, limitation reads as intimacy.
5. Typewriters as anti-surveillance tech. Young people are gravitating toward tech they can see into and understand, and nothing says “data minimization” like a device whose only analytics are ink on paper.
6. Retro gaming as a social ritual. The point isn’t perfect frame rates; it’s gathering in the same room, sharing a controller and arguing about who “really” won, like nature intended.

The Future, With a Little More Friction

Retro tech is having a moment because it offers something our always-on economy rarely does: boundaries. Devices that do fewer things. Media that can’t ping you. Systems you can understand (or at least open up without violating a warranty).

Teaching kids how recent tech used to work won’t turn them into Luddites. It will make them sharper consumers in the cloud era. They learn that “seamless” is a design choice with trade-offs, and that convenience always has a cost, sometimes paid in money, sometimes in attention, sometimes in data.

Also, it finally clears up a major misconception: the floppy disk is not, in fact, “the save emoji.”

Live shopping channels for fashion are growing fast, with over 500,000 hours being viewed per month in the UK

Whatnot

As we scan our social media feeds on our phones, we are constantly being served some version of commerce disguised as entertainment – a “what I ordered vs what I got” reel, a creator unboxing a haul, or a get-ready-with-me video. Fashion retail has already been living inside video for years, and now Whatnot’s live shopping app is poised to grab the majority share.

The classic e‑commerce product page (flat images, rigid sizing charts, templated copy) was never a natural home for something as tactile, social and identity‑laden as clothing. It is an interface built for certainty, applied to an industry built on nuance; now live shopping is the market that aims to address this mismatch.

I view this change as a potential infrastructure shift, not just a clever gimmick. This is why live shopping giant Whatnot’s rise matters to Western fashion right now; not simply because “live shopping is growing” (it is), but because it reveals what the next product page, the next returns strategy, and the next “store” might actually look like. As Grant LaFontaine, co‑founder & CEO of Whatnot states, “live shopping is no longer the future of retail. It’s the present.”

Live Shopping: The New Product Page Is A Person

At a Whatnot live shopping briefing I attended in London, Whatnot’s UK General Manager, Daniel Fisher, used an analogy that resonated because it so clearly captured what fashion has been trying to recreate since the first “add to basket” button: the feeling of being known in a store.

“It’s really about bringing the best of an in-store experience directly to your phone. If you were to head to Soho right now, and walk into a boutique… Imagine that boutique owner closing the store for you, and you’re there with 50 of your friends.”

This frames the store as a relationship, rather than just a square footage metric. This is the part many brands misunderstand when they test livestream shopping like it is a seasonal campaign format. They treat it as a channel, but live shopping acts more like a service layer: it collapses the distance between discovery, education and transaction.

For fashion, this is a big deal because the most expensive problems in the category (anxiety around fit, authenticity, trust, styling confidence) are all communication problems, rather than inventory problems.

A good host is effectively a human “UX layer”: answering questions in real time, showing fabric movement, narrating fit, giving context, building social proof in public. This transforms the live chat from just a comment section into the new fitting room.

Sellers getting this format right does not just lead to more sales, it changes what “conversion” even means. It becomes less like clicking “buy now” and more like joining a habit.

Daniel Fisher, Whatnot UK General Manager

Solo Mio Photography

“Live shopping is a combination of commerce and entertainment, with buyers tuning in for over two million hours weekly and an average Whatnot user spending over 95 minutes a day watching live shopping [Netflix’s average user time is 120 minutes a day].” Fisher went on to say that, “the key to this entertainment factor lies in leveraging the tools available – be it auctions, flash sales or ‘buy it now’s, combined with authenticity (knowing about your product and audience) and consistency. We see sellers who go live daily make on average 166 times more in revenue than sellers who go live once a month.”

Whatnot’s own report positions live commerce as becoming mainstream fast, with Whatnot holding close to 60% market share across North America and Europe, and reporting $8B in live sales GMV in 2025. These are huge numbers, but the sharper fashion implication is that the winner is not just the brand that “does a live,” but the brand (or seller) that builds a repeatable show.

As Fisher described, “consistency is very important. The more you go live, the more success you will see. Daily sellers now average £30,000 a month in sales in the UK and surveyed sellers told us that live selling accounts for two thirds of their total business sales.”

What Live Shopping Means For Fashion Returns

As has been well reported, fashion has a returns problem that it rarely treats like the strategic crisis it is. Returns are not just margin leakage; they can entail reverse logistics, fraud risk, waste, and operational drag, all at enormous scale. In the U.S. alone, the National Retail Federation and Happy Returns estimate total retail returns could hit $890 billion in 2024, with 16.9% of annual sales returned.

Live shopping potentially puts a new spin on this: in the Whatnot ecosystem, returns and end‑of‑line stock do not just get written down, they get re‑contextualised. At the briefing announcing Whatnot’s 2026 State of Live Selling Report, Mike and Annabel Winter of fashion channel Weardeadstock described working directly with brands to take returns and end‑of‑line stock and auctioning items with low starting prices, turning reverse logistics into a format the audience actually wants to watch. This turns live shopping from “QVC for Gen Z” to something more operationally interesting for fashion: returns become programming.

In a traditional brand model, returned inventory is often hidden in warehouses, offloaded through opaque channels, or dumped into off‑price flows that train consumers to wait for discounts. In live shopping, that same inventory can become a time‑boxed event (“returns rail”, “warehouse clearout”, “reverse drop”) or a transparent story (“here’s why this is a return, here’s what’s imperfect, here’s why it’s still great”).

Fashion items being sold on live shopping streams

Whatnot

Although this does not solve overproduction, it turns one of fashion’s most painful cost centres into a relationship moment, all while clearing inventory fast. It also hints at a future where the best live operators do not just sell, but can stabilise the messy middle of fashion’s supply chain.

“The biggest concern from brands was product oversaturation,” Winter told me. “Brands didn’t want products appearing across multiple marketplaces at once, which can dilute perceived value. We address this by offering a controlled, channel-exclusive environment. When items are sold on Whatnot, brands can move stock directly to engaged customers very quickly – often getting hundreds of pieces into real wardrobes within days, not months.”

Winter adds, “that speed and visibility is particularly valuable for smaller or emerging brands. Instead of sitting, their products are worn, shared, and seen almost immediately, helping to build awareness and social proof organically.”

Why China Made Live Shopping Infrastructure

If you want to understand where live shopping goes when it becomes “normal,” you need look no further than China.

Renowned entrepreneur Gary Vaynerchuk (also a seller on Whatnot) writes in the foreword for their report that “China has been the global proof point for years. Livestream commerce there surpassed $700B+ in GMV (gross merchandise value) last year, and top creators sell millions in a single night. In the U.S., we’re now at the same cultural and technological inflection point China hit a decade ago. Community-driven platforms are mainstream, consumers crave authenticity more than polished perfection.”

McKinsey famously pointed to Alibaba’s Singles’ Day presales on Taobao Live, where the first 30 minutes generated $7.5 billion in total transaction value; an almost absurd demonstration of what happens when entertainment, retail and logistics are fused. More importantly, McKinsey’s analysis suggested that, if China is a guide, sales initiated through live commerce could account for 10% to 20% of all e‑commerce by 2026.

China has now shifted live shopping from a feature to an entire layer; live commerce has matured into an ecosystem with specialised roles (hosts, moderators, operators), supply chains built for speed, and platforms designed to convert attention into purchase without friction.

Whatnot’s Live Shopping Treats Trust As The Format

This is where Whatnot deserves more credit than it sometimes gets in surface-level coverage. Yes, the numbers are astonishing: the Whatnot report positions the live shopping market at an estimated $22B across North America and Europe, more than doubling and projected to double again, with Whatnot leading market share.

Live sellers build trust with their audiences

Whatnot

But the more telling signal is behavioural: Whatnot is training consumers to treat shopping as something you attend, not something you click.

At the UK briefing, seller stories described hours‑long streams, intense community rituals, and “event retail” energy, right down to viewers booking time off work to watch Black Friday streams.

This sounds like entertainment, which it is. But it is also a trust mechanism: the more time you spend with a host, the less “anonymous internet risk” you feel. You are not buying from a page; you are buying from a person with receipts, reputation, and a live audience watching.

For fashion, where trust is both the problem and the moat, this matters hugely. The report also notes that Women’s Fashion is among Whatnot’s fastest-growing lifestyle categories year-on-year (UK buyers watch over 500,000 hours of women’s fashion shows on Whatnot each month). This signals that live is not just staying in collectibles, but is moving into the messy, subjective categories where taste and fit dominate.

The Way Ahead For Fashion Brands

Live shopping is not just marketing; it is an operating model that fuses merchandising, media, community, logistics and also the future of search. It rewards items with narrative weight: provenance, scarcity, visible quality, and the kind of styling transformation that would be hard to communicate on a static page. This is why resale, deadstock and “drop” culture translate so naturally; those products arrive pre-loaded with context, and context is the thing a good host can amplify in real time.

Live also switches returns from being a cost centre, where the goal is to make them disappear into the warehouse and off-price channels with as little brand damage as possible, into a front-of-house format; inventory you can explain, show, measure, and move transparently. The most obvious operational benefit of this is velocity, but the more strategic benefit is subtler; you can rebuild trust by making condition, fit and value visible in public, rather than leaving customers to gamble alone.

And crucially, none of this works without the human layer. The host is more than the “talent,” they are the interface. They replace the product page, the fitting room, and the customer service in one fell swoop. Without the key benefits of this role (product knowledge, taste, community management), live streams become more like a stiff advert, and the audience will treat it like one.

Live selling becomes a new “third space” for retail

Whatnot

Live Shopping Is Becoming A “Third Place” Again

For years, fashion has been trying to win on convenience: one-click checkout, next-day delivery, and endless choice. But culture keeps drifting back toward something more familiar: shopping as social life.

Live shopping is about more than just buying, it is about being there when it happens. In a fragmented attention economy, where Deloitte notes social platforms and creator-led video are becoming a new centre of gravity for entertainment, commerce formats that feel like entertainment will keep pulling budget, time and cultural “heat” away from static retail experiences.

Could this be seen as the return of the digital high street? Not the high street as real estate, but as a habit: familiar faces, community rituals, and the feeling that taste is being made live, in public, with other people watching.

China has proved this at scale and this is what Whatnot is building in a Western context: live shopping as an infrastructure layer for trust, community and commerce…one person, one show, and one audience at a time.

The company on Tuesday (Jan. 13) announced the debut of the latest iteration of its radio frequency identification (RFID) lanes, designed for things like pop-up shops and festivals.

“This builds on the RFID technology we pioneered in 2023 for merchandise: walk-through lanes that use RFID tags to automatically detect what customers are carrying, so they can simply grab items and walk out by tapping their card to pay,” Amazon said on its blog.

The new lanes feature enhancements designed to speed checkout, such as in-lane screens “with an intuitive user interface guide shoppers through the checkout process while displaying cart totals.”

They also feature motorized gates that automatically open and close to help the flow of traffic, along with “dynamic pre-authorization gives customers greater cart visibility” so they know what they’re spending before finishing their purchase.

Amazon also cites the impact Just Walk Out has had for its corporate users. For example, Lumen Field in Seattle increased total sales per game by 47%, while BayCare’s St. Joseph’s Hospital in Florida shrank wait times from 25 minutes down to 3 minutes.

The technology also helped UC San Diego in California serve 11% more students while reducing retail theft by 83%.

In addition to third-party retail locations such as stadiums, Amazon said it is also adding Just Walk Out technology to its own operations, including more than 40 Just Walk Out-enabled stores at Amazon fulfillment centers, with more slated to go live this year.

“This internal deployment demonstrates our confidence in the technology while creating additional opportunities for innovation and scale,” the company said.

In other Amazon news, PYMNTS wrote on Tuesday about the company’s rivalry with Walmart at a time when “retail is being stretched by two different and increasingly incompatible forces that shape consumer behavior, capital allocation and competitive advantage … Call them essential gravity and discretionary gravity.”

Amazon, that report said, has found its strength is not any one category, but the ability to absorb demand as it appears. Search, recommendations, reviews, Prime membership and fulfillment all come together to ease friction at the moment of intent.

“Still, discretionary gravity is riskier. It is exposed to consumer sentiment, macro cycles and promotional intensity,” that report said. “But it is also where growth lives. When consumers feel confident, discretionary spending expands rapidly. When new categories emerge, platforms, and not stores, can capture the upside first.”

The question before retailers now, PYMNTS added, is not whether they can compete with giants on price or scale. It is whether they grasp which gravity they serve, and whether they are willing to commit fully to it.

We have been here before, and the examples are familiar. Uber didn’t simply fix those “broken” taxi card readers. It replaced the entire mobility landscape with cleaner cars on demand and payments that disappeared into the background. Amazon didn’t just build a better checkout experience. It collapsed millions of products and a dozen checkout steps into a one-click-purchase marketplace that became the default starting point for eCommerce.

Both solved massive pain points. Both redefined values. Both forced the rest of the industry to decide whether to adapt or get left behind.

We are now approaching that same moment again.

This time, the shift is not about making payments invisible or shaving a few seconds off the checkout flow. It is about something much bigger: who — or what — makes the decision about what to buy and how to pay.

And where that journey begins.

The End of Human-Only Decision Making

Agent-led commerce is often described as a revolution. It is actually the next logical step. Systems designed for human attention and human decision cycles are now interacting with autonomous agents that operate with far more context, speed, precision and consistency than people ever could. Researchers estimate that AI can process quintillions (hundreds of million trillions) of calculations per second. Humans, roughly 10 bits per second. Put simply, it would take hundreds of people working nonstop to match what a single AI model can analyze in under a minute.

Naturally, when agents become part of the buying decision, the old interfaces break down. Screens, forms and flows built for people introduce friction that agents neither need nor use efficiently. That mismatch becomes the next competitive battleground.

Two models are emerging, and both point in very different directions.

The question is no longer whether agents will transact. It is how they will do it. And who owns the data that informs those decisions and the liability when something goes haywire. And, importantly, who captures the economics when an agent completes a purchase.

Whoever solves it will shape the next era of commerce.

Two models are emerging, and both point in very different directions.

OpenAI and Stripe launched the Agentic Commerce Protocol (ACP) in September 2025. It puts merchants and their existing infrastructure at the center. That same month, Google introduced the Agent Payment Protocol (AP2), which positions discovery and product data as the control point.

These two approaches reflect fundamentally different ideas about where value and control should sit in agentic commerce. ACP is a checkout-centric model that routes agents into the merchant’s existing infrastructure. AP2 is a mandate- and knowledge-graph-centric model that defines how agents express intent and authorization across ecosystems.

To understand what could scale and what may stall, you have to look at what each model demands from the ecosystem to get commerce run by agents off the ground.

The Infrastructure That Had to Be Built First

Before any commerce model could flourish, agents needed something foundational: a governance and control layer. They needed a safe way to take actions across systems without breaking workflows or tripping security boundaries. That foundation is the Model Context Protocol (MCP), launched by Anthropic in November 2024 as a neutral layer that lets agents operate inside enterprise systems securely and at scale.

MCP is simply the plumbing that allows ACP and AP2 to make agentic commerce a reality.

Banks, acquirers, processors and networks have already begun using MCP for identity verification, chargeback triage, settlement, merchant onboarding and fraud. These early initiatives signal that enterprises are preparing agents to run real workflows inside their systems. The analogy is simple. HTTP made reliable communication possible across the early internet. MCP standardizes safe and structured actions for agents.

MCP is not trying to control the demand funnel or the transaction economics. It is simply the plumbing that allows ACP and AP2 to make agentic commerce a reality. While ACP and AP2 compete for influence over transaction flows and merchant relationships, MCP ensures that agents can interact with banks, processors and merchant back ends without introducing new operational or security risks.

Only once MCP established this baseline could real competition begin.

Model One: Merchant-Centric Agentic Commerce Protocol (ACP)

The Agentic Commerce Protocol emerged from the partnership between OpenAI and Stripe. It brings together the dominant conversational interface and one of the most widely adopted payment platforms. Together they built a protocol that lets agents complete purchases inside the AI experience while keeping the merchant’s current tech and payment stack intact.

Stripe brings decades of experience in digital payments, compliance and settlement workflows. OpenAI brings deep understanding of how conversational intent becomes transaction ready. Their bet is straightforward. ACP gives agents a standardized way to send orders into systems that already work for human checkout. Agents discover products, build carts, authorize payments and trigger fulfillment through flows merchants already trust.

ACP gives agents a standardized way to send orders into systems that already work for human checkout.

This low coordination burden is why ACP is drawing partners like Instacart and DoorDash. Stripe aggregates millions of merchants. OpenAI aggregates hundreds of millions of users. ACP bridges the two without forcing merchants to rebuild catalog structures or operational logic. Other processors are now treating ACP as the template for “agent-ready commerce.”

But ACP’s advantages come with new challenges.

Delegated payments, a key part of ACP, are fully implemented only by Stripe. Other PSPs need months to reach parity.

In the meantime, many processors and networks are launching ACP-style variants with the same agentic thesis: tokenized credentials running over existing card and account-to-account rails, with disputes resolved inside current network rules. That means that merchants still have to implement ACP or a variant, or more than one, stitching multiple flavors of agentic commerce into their stack. And integrating with and supporting them.

Model Two: Data Centric Agentic Payments Protocol (AP2)

Google’s Agent Payments Protocol starts somewhere entirely different. It is a mandate and knowledge-graph framework, not an end-to-end transactional rail. Google has spent decades building a global product graph through Shopping, Merchant Center and schema.org. AP2 formalizes that graph and asks merchants, PSPs and platforms to feed it structured data and adopt a common language for agent mandates. It centers on cryptographically signed purchase mandates, verifiable credentials and a universal product index. Payment rails are treated as interchangeable plumbing.

AP2 launched with 60 partners across eCommerce, payments, crypto and enterprise software. Its goal is to be a shared protocol for how agents express intent and authorization. Crypto platforms see AP2 as a quiet path to participate at scale. If agents can settle over crypto rails behind the scenes, merchants never need to support those payment methods directly.

AP2 asks merchants, PSPs and platforms to feed it structured data and adopt a common language for agent mandates.

But this design creates a much higher coordination burden.

AP2 expects merchants to maintain a structured catalog that may not match their internal schemas. For merchants with large SKU counts, this becomes significant operational work.

And AP2 cannot guarantee that agents will query its index or that merchants will see incremental demand. Agents decide whether to use AP2. Merchants cannot treat it as a steady demand source.

How Merchants Will Decide

From the merchant’s perspective, ACP and AP2 represent very different tradeoffs.

In theory, ACP fits cleanly into today’s checkout and order-management environment. Merchants keep their product data, processors and fulfillment logic. Agentic becomes simply another way that orders enter systems they already trust. The primary effort lies in implementing the protocol(s) and managing delegated payments, not rebuilding catalogs.

AP2 prioritizes global consistency and agent portability across transacting environments. It emphasizes mandates, discovery through a universal product index and cryptographically verifiable intent. It needs merchants to align their data to this structure without promising demand in return. AP2 asks merchants to build and maintain a new structured product layer and ongoing data hygiene consistent with their knowledge graph. Conversion happens through processers that have adopted the AP2 protocol.

AP2 also inherits Google Shopping’s persistent limitation: strong discovery, weak transaction confidence. And a business model that is built to optimize advertising, not commerce.

AP2’s Google Shopping Hangover — and Headache

Google Shopping has existed since 2002. It has one of the largest product listings in the world and appears in billions of search queries. Yet it never became a marketplace. It never built the trust layer, fulfillment infrastructure, dispute processes or vetted seller network needed for end-to-end commerce. Google Shopping remains what it is intended to be: a lucrative, ad-driven discovery engine that directs traffic to the merchant, who will hopefully convert that visit to a sale.

Merchants exposed some of their inventory to Google Shopping because it drove human clicks, often supported by search ads. But agent-led commerce removes the ad-funded funnel entirely. If AP2 can’t show that it drives completed transactions, merchants may not be enthusiastic about investing in another product feed. Especially one that is disconnected from their existing order and payments systems. And one where the transacting economics remains TBD.

If AP2 can’t show that it drives completed transactions, merchants may not be enthusiastic about investing in another product feed.

To make AP2 work, Google would need to build (or rely on partners to build) something it has historically avoided: the hard, unglamorous, low-margin infrastructure that takes a product listing and turns it into a completed purchase, inside a Google Marketplace.

As all of this unfolds, ACP will continue to evolve. Not every platform will update on the same timeline. Merchants will find themselves supporting multiple protocol versions and PSP-specific variants whether they planned to or not.

Meanwhile, the number of agents seeking access to merchant systems will keep growing. Each one must be vetted, contracted and cleared from a security perspective. An abstraction layer that smooths out these differences and sets consistent standards for agent behavior may give merchants a way to participate without getting buried in agent fragmentation.

Igniting Agentic Commerce

Commerce innovations have never ignited quickly. Card networks, eCommerce, mobile wallets and one-click checkout all took years to evolve from something new into everyday behavior, even with familiar rails and payment methods to anchor the “new.”

Agentic commerce will follow that same pattern. The difference now is that the rails, the interface and the decision-maker are all changing at the same time.

ACP and AP2 face very different ignition paths. ACP rides on existing merchant systems, processors, issuers and risk frameworks, so the jump from pilot to scale is, at least in theory, short. Agents route orders into infrastructure merchants already trust, and delegated payments settle inside compliance flows everyone understands.

AP2 has a tougher lift. It requires the ecosystem to adopt a new discovery and representation layer, restructure catalogs around Google’s schema and rely on agents voluntarily querying Google’s product graph. At the same time, Google must build transactional and dispute-handling capabilities it has never owned. The more coordination required, and the less guaranteed the demand, the tougher the slog to ignition becomes.

Then there is the quiet elephant in the room: liability.

Igniting agent-led commerce is not just a technical milestone; it is an economic one. Agents don’t simply transact, they choose. And the path they choose depends on who they trust, what incentives shape those choices and how the ecosystems behind them intend to make money. Every major commerce shift has been anchored by a shift in trust: card networks scaled when liability became predictable; Amazon became the default marketplace when it proved more reliable than individual sellers; and Uber took off when riders trusted an algorithm to deliver a more reliable outcome than a dispatcher. Agentic commerce will follow the same pattern, but with a twist. Here, trust extends beyond safety and liability into something deeper. The business model that determines how discovery is surfaced and prioritized.

Search results and product recommendations have always reflected monetization models, but agents heighten the stakes. They compress discovery, evaluation and checkout into a single autonomous action. If an agent’s choice is influenced by advertising, sponsorship or retailer-owned media networks, then trust may be called into question. Google has refined the advertising-driven discovery model for decades, and AP2 naturally sits atop that legacy. Walmart is rapidly expanding its retail media capabilities, and agents could easily become the next frontier of monetizable demand shaping. OpenAI and Stripe, on the other hand, designed ACP around transaction flows rather than discovery economics, which, at least for now, limits the concern that paid placement might distort what an agent recommends. That could certainly change.

This matters because agents operate at machine scale. A small economic nudge, when applied across millions of autonomously completed transactions, can shift market share overnight. For agentic commerce to ignite, ecosystem participants must trust that agent recommendations reflect user intent, not necessarily platform incentives. When an agent surfaces a product because it has been paid to do so, but presents the action as optimal, consumer trust can erode. When merchants suspect that discovery is shaped by advertising fees rather than relevance, their appetite to invest in new protocols weakens. When issuers or networks cannot understand why an agent made a choice, their ability to manage risk and liability diminishes. Ignition stalls whenever the incentives of the system diverge from the expectations of the ecosystem.

In the end, the real competitive differentiator may not be technical at all. It may be the model that convinces the ecosystem that trust, incentives and outcomes are aligned. Business models will ultimately determine whether agent-led commerce reaches ignition. And until the economics of discovery become as transparent and aligned as the mechanics of the transaction itself, any model that cannot demonstrate that alignment will find ignition, and long-term success, are harder to achieve.

Until NEXT time.

Join the 19,000 subscribers who’ve already said yes to what’s NEXT.

PYMNTS CEO Karen Webster is one of the world’s leading experts in payments innovation and the digital economy, advising multinational companies and sitting on boards of emerging AI, healthtech and real-time payments firms, including a non-executive director on the Sezzle board, a publicly traded BNPL provider.

She founded PYMNTS.com in 2009, a top media platform covering innovation in payments, commerce and the digital economy. Webster is also the author of the NEXT newsletter and a co-founder of Market Platform Dynamics, specializing in driving and monetizing innovation across industries.

Merchants chasing global growth are learning that cross-border sales succeed only when payments feel as local as possible. That is the new message emerging from the latest Payments Optimization Tracker, which shows that consumer expectations have shifted faster than many retailers’ payment strategies.

Christmas 2025 is set to be the most technologically advanced Christmas yet

getty

By the time peak holiday shopping deals land in inboxes this year, a lot of shoppers will already have a digital co-pilot. Some will be asking AI tools to shortlist gifts, while others will be planning Buy Now, Pay Later (BNPL) repayments before they ever hit “buy.”

Christmas 2025 is shaping up to be the most digitally assisted festive season yet, with software influencing how people decide what to buy and how to pay for it.

What’s new isn’t just the technology, but the way people are using it. Convenience, confidence, and control now sit at the center of the holiday journey. Shoppers are happy to let algorithms shoulder some of the work—as long as they keep their hands on the steering wheel.

AI’s Big Moment in Holiday Shopping

That appetite for digital backup isn’t hypothetical; it’s already showing up in the numbers.

Checkout.com’s latest research finds that 46% of UK consumers would be open to using an AI agent to help with their Christmas shopping, and those who are interested would allow an average “trust threshold” of £200 for the AI to spend on their behalf. In fact, 42% of respondents admit to already using tools like ChatGPT for gift ideas.

Jenny Hadlow, chief operating officer at Checkout.com, believes this could mark a real turning point.

“Every year there’s a new trend shaping how we shop for Christmas, and this year, agentic AI could transform how we find, choose and buy our festive essentials,” she says. “The real potential of agentic shopping lies in its ability to navigate the vast landscape of options—comparing deals, scanning holiday sales, and optimising for the best consumer outcomes, from price to personal preference.”

That doesn’t mean the concerns have disappeared though.

Checkout.com’s research highlights fear of losing control, missing the personal touch, and worries about fraud and privacy as the main reasons people hesitate to let AI do more. When asked what would build trust, shoppers point to secure systems, easy returns and refunds, spending limits they can set themselves, and instant purchase alerts.

In other words, they want AI to filter the noise and surface smarter choices—not to make opaque decisions in the background.

Discovery Is Getting Smarter

Beyond fully “agentic” systems, AI is already reshaping how people research products long before they reach the checkout.

Jen Pollard, Senior Analyst at Visualsoft, believes that generative AI shopping, like Shopify-ChatGPT Instant Checkout and Google AI search, “may have an early impact.” However, Pollard is hesitant to label this shopping season as “transformational” and is instead leaning more towards “experimental.”

But Klaviyo takes a different stance, describing Black Friday and Cyber Monday 2025 as the first truly “AI-fueled” holiday shopping season. The company reports that more than half of consumers say they’ll use AI tools to compare deals or plan purchases. Instead of one frantic sales day, Klaviyo sees spending spread across a 16-day window that begins roughly 10 days before Black Friday.

Shopify’s Global Holiday Retail Report points in a similar direction.

The company finds that 64% of shoppers expect to use AI for at least one task in their holiday journey, while 88% of businesses are investing in AI-driven discovery tools. At the same time, 73% of consumers still value human interaction somewhere in the process.

Even as more shoppers rely on AI tools and digital wallets, they still anchor decisions in trust, loyalty, and connection.

Research from LoyaltyLion shows that 71% of Gen Z and Millenials say they feel more emotionally connected to brands through loyalty programs, while Antavo’s data suggests roughly seven in ten shoppers join those programs primarily for rewards, discounts, or cash back, with a meaningful share also motivated by personalized offers and perks like free shipping or early access.

For smaller businesses, that blend of automation, loyalty, and humanity is particularly important.

Andrew Goodacre, CEO of the British Independent Retailers Association, argues that artificial intelligence has moved from buzzword to practical tool.

“AI is no longer a distant concept requiring huge investment or technical expertise,” he says. “It’s readily accessible, often affordable, and increasingly user-friendly. For independent retailers, this is transformative.”

He also stresses that AI should sit alongside human service, not replace it. The strongest model is “AI plus human”: digital tools handle stock management, basic queries, and marketing, while staff focus on personalized service which keeps people coming back.

Buy Now, Pay Later as a Planning Tool

If AI is changing how people decide what to buy, BNPL is quietly changing how they decide when and with what means to pay.

Clearpay’s Festive Insights Wrapped report finds that more than a third of shoppers are planning to use BNPL this season to give them extra flexibility, with adoption strongest among Gen Z and Millennial consumers.

Their motivations are mostly pragmatic.

Clearpay reports that 63% of those who plan to use BNPL cite convenience as a key reason, 58% point to reliability, 52% to flexibility, and 51% to speed. Many shoppers are not trying to expand their overall budget; they are looking to smooth out the impact of big-ticket purchases or spread the cost of multiple family commitments over several paychecks.

Rich Bayer, CEO at Clearpay, sees that as a defining pattern for 2025.

“The festive season is always a special time, but 2025 brings fresh financial challenges for many households,” he says. “What we’re seeing is a pragmatic approach; people are still planning to celebrate, but they’re planning earlier, being practical with their budgets, prioritising self-care, and turning to tools like BNPL to help them manage their spending responsibly.”

However, that pragmatism doesn’t eliminate risk.

Used thoughtlessly, BNPL can still encourage overspending or lead to missed payments and fees. As BNPL becomes standard at checkout, retailers and providers will both be judged on how transparent they are with shoppers.

Technology, Trust, and the Holiday Playbook

This holiday season, AI, BNPL, and one-click checkout are no longer nice-to-haves; they’re must-haves as the modern-day consumer expects smart recommendations, flexible ways to pay, and a checkout that feels instant.

But even still, shoppers gravitate toward brands they recognize, clear terms at checkout, and real humans when something goes wrong. The retailers that will stand out aren’t the ones with the flashiest tech, but the ones that combine digital speed with transparency, loyalty, and a human tone, making holiday shopping feel both effortless and genuinely worth returning to.

For more than a century, the Hatfields of West Virginia and the McCoys of Kentucky fought. Courtroom battles reached all the way to the U.S. Supreme Court. Newspapers across the country covered every move. The feud became legendary as a symbol of stubborn, unending conflict.

Both families were caught in a cycle where every act demanded a tit-for-tat retaliation. Where the original cause, a stolen pig, was buried under decades of grievances. Where neither side could remember how to stop fighting, or in some cases, why they started fighting at all.

In 1891, the violence finally ended. But it would take another 85 years before, in 1976, both families shook hands. Then another 27 years before descendants gathered in Pikeville, Kentucky on June 14, 2003, to sign an official peace treaty. The truce declared “an official end to all hostilities, implied, inferred and real, between the families, now and forevermore.”

It took 140 years to move from a stolen pig verdict to a peaceful outcome.

While the legal teams on both sides were focused on the past, the future of commerce showed up.

Today, the families hold joint reunions and have turned their history into a story of reconciliation.

Both saw that clinging to century-old grievances was less valuable than building something new together.

After two decades of litigation, multiple rejected proposals and hundreds of millions in legal fees, the Visa-Mastercard merchant settlement is heading toward final court approval. Merchants fought for changes to interchange fees and card acceptance rules. They won concessions: lower interchange rates, modified acceptance requirements and expanded surcharge rights.

Soon, the courts are expected to close the book on a legal battle that began in 2005, before the iPhone existed, before digital wallets, before consumers could complete a transaction with their face or their thumbprint. Before tokenized credentials, one-click checkout and embedded payments inside of every app and software platform made payments invisible while making the infrastructure that powered them even more critical to igniting commerce in the U.S. and across the globe.

Hopefully, this lengthy chapter in the history of payments is finally over. (I say hopefully because the judge still has to agree with the settlement provisions, and the merchants have to agree as well.) And that leaves us with a new question: what does the payments ecosystem build now?

Because while the legal teams on both sides were focused on the past, the future of commerce showed up.

And it will require the ecosystem to work together to reimagine the role of payments, and the business models underpinning it, to scale and ignite a future that looks very different than it did in 2005.

A Commerce Future That’s Already Here

We are standing at the edge of the most significant transformation in commerce since the invention of the general-purpose credit card 67 years ago. Not a new channel, not a new device, not a new interface, but a fundamental rewiring of how consumers shop and buy.

I call it The Prompt Economy.

It’s a world where consumers and merchants are about to lose direct contact with each other as consumers, at the prompt, decide the what, the why and the how of their shopping and payment experiences.

Agentic commerce is coming. Not eventually but now, even though it’s very early days. Agents are being built by every major technology company and platform, designed to act as intermediaries between consumers and the merchants they shop. These agents will evaluate options, compare total costs, assess merchant reliability, analyze return policies, and complete purchases. All in milliseconds, all with minimal (maybe even without any) human intervention aside from the initial prompt.

Consumers are doing more than simply leaning in.

Consumers and merchants are about to lose direct contact with each other as consumers, at the prompt, decide the what, the why and the how of their shopping and payment experiences.

PYMNTS Intelligence data shows that nearly 70% of consumers are interested in using AI agents to simplify shopping tasks. Forty-one percent say they want an agent to find the best deal for every purchase. One in three say they would trust an agent to pick the merchant. Nearly half of Gen Z and millennials say they expect AI to make purchasing decisions for them within the next five years. Thirty million consumers are what we call “Pros” – consumers who use Gen AI and Agentic Commerce techniques to complete 45 of 75 everyday tasks that PYMNTS Intelligence studies now track monthly.

And that’s today, even as the agentic commerce experience itself is still clunky, still pretty laden with friction. But these figures foreshadow mainstream shifts in consumer intent.

When that happens, everything changes.

A sale will be made to an algorithm deciding, in milliseconds, which merchant best satisfies the consumer’s instructions. A brand that has spent decades building loyalty will compete, not for the consumer’s attention, but for the agent’s evaluation. Unless they are part of the prompt. The more complexity associated with a purchase, the more valuable the agent, and this experience, becomes for the consumer.

Building the Modern Payments Ecosystem  

A sale will be made to an algorithm deciding, in milliseconds, which merchant best satisfies the consumer’s instructions. A brand that has spent decades building loyalty will compete, not for the consumer’s attention, but for the agent’s evaluation. Unless they are part of the prompt. The more complexity associated with a purchase, the more valuable the agent, and this experience, becomes for the consumer.

That idea took shape in 1958 when Bank Americard introduced the general-purpose credit card. Eight years later, this turned into the four-party network model now called Visa. Over the last sixty-plus years, that new credit card for bank customers in California has scaled into an infrastructure that moves trillions of dollars annually in the United States alone and connects consumers and merchants across every commerce channel.

But as those of us in payments know, what looks deceptively effortless at checkout is anything but. It requires coordination among institutions with different incentives, massive ongoing investment in security and fraud controls, global interoperability and the instant authorization, clearing and settlement of billions of payment transactions. Interchange emerged as the mechanism that aligned those incentives and funded the rewards, protections and capabilities that made credit cards the payment method many consumers prefer and merchants accepted to complete their purchases.

Consumers will rely increasingly on virtual assistants to strip out friction and surface the best combination of convenience, price, payments choice and value every time they buy.

The model worked because every stakeholder received value from participating. And importantly, because merchants wanted to eliminate any friction that would prevent them from making a sale.

Over time, the way consumers shop and buy has changed. The path to purchase no longer begins in a store or even on a merchant’s website. It unfolds across ecosystems, platforms, apps, embedded experiences. And now, algorithmic recommendations. PYMNTS Intelligence data shows that 83% of consumers shop digitally across at least four channels each month, and more than half begin product discovery inside platforms merchants do not control. More than half (58%) expect purchases to be seamless across devices without reentering information.

Now, the payments ecosystem that has supported commerce for decades has to evolve for a world where consumers will rely increasingly on virtual assistants to strip out friction and surface the best combination of convenience, price, payments choice and value every time they buy.

The Shift From Merchant-Designed to Consumer-Directed Acceptance

For nearly seven decades, one principle has defined the relationship between merchants and consumers. Merchants support the methods of payment that a critical mass of their customers want to use. They do that because it is good for business.

If a consumer walked into a store or put items in a cart online and their preferred payment method was accepted, the sale happened. If it was not, the consumer would make a choice. Depending on how badly she wanted the item or how much she liked the merchant, or how many other payment options she had access to, she either pulled out a different card or abandoned the purchase. Or bought but never came back to that store again.

The dynamics of acceptance will shift in a way that is both subtle and profound.

Every new payment method has lived through the slog of that ultimate chicken-and-egg dynamic. Merchant acceptance is the holy grail. Without it, driving sales and margin and scale are nearly impossible. Acceptance has always been the oxygen that allows any payment method to breathe.

In the age of AI and agentic commerce, acceptance will still matter. But the dynamics of acceptance will shift in a way that is both subtle and profound.

Consumers will still care about paying with the method they prefer, but instead of deciding at checkout, they will specify their preferences once, through the agent acting on their behalf at the prompt. In the Prompt Economy, those instructions then become the rules that govern which merchants an agent considers. If a merchant does not accept the credential the consumer has directed their agent to use, the agent simply moves on to another merchant who does accept it. Merchants may not ever know the sales they missed as a result.

Consumers are already demonstrating how intensely payment preference shapes behavior. PYMNTS Intelligence finds that 72% of cardholders say rewards influence their payment choice. More than half (58%) choose cards strategically to maximize those rewards. One in four rotates between cards to extract the highest possible value in each category. Consumers are scripting their payment behavior today. Soon, they will literally script it through their agents.

This shift has implications for everyone. Consumers gain more control with less friction. They will not manually navigate checkout flows or toggle between wallets. Payment disappears into the background even more than it has today.

For merchants, the stakes change. Friction is no longer a point of irritation. It is a filter that determines their visibility. A merchant who limits acceptance or introduces fees may not have a chance to nudge consumers toward cheaper payment methods. On the other hand, merchants who support a consumer’s directed preferences, regardless of where or how the agent interacts, position themselves to be chosen more often.

For issuers, the expectation becomes one of flexibility and intelligence. Consumers want credentials that work everywhere, carry rules across all contexts and adapt to their liquidity needs. Two in three consumers say that matching payment to cash flow is more important than the size of the reward. That demand is highest among high-income consumers who have the most payment options.

Where New Business Models Are Emerging

What makes the proposed merchant settlement so consequential is not what is ending, but what has the opportunity to start.

Commerce is moving into a phase where the value of a consumer and the value of a transaction can be monetized in ways that were not possible when payments were simply a means to make a sale for a merchant. As consumers move across digital ecosystems, as agents mediate decisions, and as transactions become the output of a much larger and richer behavioral context, the opportunity is to rethink how payment credentials anchor value for every participant across the payments ecosystem.

We are already seeing the early signs of this creative rebuild.

The opportunity is to rethink how payment credentials anchor value for every participant across the payments ecosystem.

The debit card, once a straightforward access point to funds on hand, is becoming a programmable financial tool. Consumers increasingly want credentials that adapt to their cash flow and their lives, not the other way around. PYMNTS Intelligence research shows that 14% of all consumers have used BNPL over the last three months. Adoption is highest among consumers earning more than one hundred thousand dollars a year, the same consumers who also maximize rewards, juggle multiple cards strategically and expect flexibility on their terms.

Retail media networks are another indicator of this shift. The moment of purchase is becoming the moment of value creation. Platforms such as Amazon, Walmart, Klarna, Affirm and Chase have demonstrated that merchants will invest in reaching high-intent consumers at the precise point of decision. Players like FIS with Smart Basket, Bilt with Banyan, SKUx and Lynx power retail media ecosystems that create new brand/merchant/consumer economics using personalized rewards and promotions funded by the brand. That creates immediate value for consumers, who receive targeted savings instead of delayed rewards. It creates measurable value for merchants, who can improve conversion and fund promotions from brands that want access to their customers. That, in turn, creates new revenue models for issuers and platforms that participate in this path to purchase.

At the same time, new ecosystems are forming around intelligent credentials. Banks and networks are experimenting with payment instruments that are far more dynamic than a static sixteen-digit number. These credentials can carry consumer preferences across contexts, apply rules automatically, authorize and route transactions dynamically, and deliver the transparency consumers increasingly expect. Item-level and category-level purchase data enable merchant-funded offers with surgical precision. These ecosystems are not dependent on traditional funding structures. They grow because they address consumer needs that legacy models were not designed to satisfy.

What ties these developments together is a new set of hypotheses and assumptions. That the next phase of commerce will reward participants who create value throughout the consumer journey, not just at the moment of payment. A smart credential becomes the connective tissue linking discovery, decisioning, fulfillment and loyalty. The sale becomes the strategic outcome of an ecosystem in which merchants, issuers, networks, platforms and now agents all play a role in shaping consumer value.

These new business models are not reactions to what came before. They are the natural evolution of a world where commerce becomes more autonomous, more dynamic and more contextual and personalized. They are the building blocks of a future where the economics of payments reflect the broader value consumers generate as they move through digital and agent-driven environments. They are the beginning of a much larger transformation in how payments create and capture value.

What we are seeing across these developments is a form of creative destruction in real time.

What we are seeing across these developments is a form of creative destruction in real time. The business models that sustained merchants, issuers and networks for decades are being unbundled and rebuilt under new economic assumptions. The subsidy structures that once supported rewards programs are shifting. Merchant acquisition models are changing as platforms and retail media networks influence discovery. Issuer economics are being pressured by new forms of competition for consumer preference. Even networks, long anchored by universal acceptance, are navigating a world where acceptance is not just a merchant decision, but a consumer directive executed by agents.

And that’s the opportunity for reinvention. The parts of the system that no longer fit the way consumers shop are stripped away, and the parts that still create value are becoming the foundation for what comes next.

What’s Next

The end of the Hatfields and McCoys’ century-long feud didn’t erase their history. It simply cleared the way for something new to take its place. The merchant settlement that hopefully concludes shortly offers the payments ecosystem a similar opportunity. The creative destruction already reshaping the business models of merchants, issuers and networks is part of a much larger transformation. As AI and agents change how consumers shop and how industries operate, the opportunity is to rethink how value will be created when discovery, choice and payment follow an entirely different logic. All players across the ecosystem have an incentive to rethink, reimagine and rebuild.

That broader transformation is the one of the areas of focus of PYMNTS’ first live event in eight years, CHNGNetwork on May 5–6 in New York. One of the topics of conversation is the extent to which AI and agents are reshaping business models, incentive systems and competitive dynamics across the economy at large, not just payments and commerce. These discussions across the most relevant pillars of the connected economy, including payments and commerce, will help define what comes next.

And what comes next is the start of a new era in commerce.

One that will be mediated by agents, shaped by algorithms and executed by systems that behave differently than people. The settlement will close the longest chapter in payments history. The next one will determine whether the ecosystem adapts to the world emerging around it or remains tethered to the one that is disappearing.

You might even say that now, the real work begins.

 

Until NEXT time.