When AI Takes Over the 'Shopping Journey,' How Much Time Does PayPal Have Left?

By: blockbeats|2026/02/15 18:00:02

Original Article Title: AI: PayPal's $200M Wake-Up Call in AI Commerce
Original Author: LUKE SPILL, FintechBlueprint
Translation: Peggy, BlockBeats

Editor's Note: As AI agents begin to replace humans in product discovery, decision-making, and ordering, the traditional e-commerce funnel is rapidly compressing, and payment is no longer the end of a transaction but part of embedded infrastructure. This article takes PayPal's acquisition of Cymbio as a starting point, outlining the new competitive landscape under the rise of Agentic Commerce: Google and Shopify are attempting to control the routing layer with UCP, OpenAI and Stripe are seizing the agent execution layer through ACP, and PayPal is striving to shift from the "payment button" to a key node in the "business workflow."

For fintech companies like PayPal and Stripe, whether they can embed in the underlying protocols of AI commerce will determine if they can still stay at the table; and for the banking and crypto industries, the window of opportunity is equally brief.

The following is the original article:

Earlier last week, PayPal acquired Cymbio, a platform that helps merchants complete sales across multiple AI interfaces, with supported channels including Microsoft Copilot and Perplexity. Market insiders estimate the transaction amount to be between $150 million and $200 million. It is widely believed that this acquisition is a key strategic move by PayPal to maintain competitiveness in the field of Agentic Commerce.

When AI Takes Over the 'Shopping Journey,' How Much Time Does PayPal Have Left?

Thus, as AI agents continue to compress and restructure the traditional e-commerce funnel, PayPal is shifting from a typical Web2 payment tool to more upstream and core business processes such as product discovery, product catalog distribution, and order orchestration. This shift almost entirely validates our analysis in January this year regarding exponential growth, power law effects, and scale returns in Agentic Commerce.

At the same time, the industry's infrastructure is rapidly taking shape:
Google and Shopify are driving the Universal Commerce Protocol (UCP);
OpenAI and Stripe are collaborating to advance the Agentic Commerce Protocol (ACP);
and Microsoft is embedding settlement capabilities directly into Copilot.

The shopping infrastructure around "machines" rather than "human users" is being rapidly rewritten at an unprecedented pace. Agentic Commerce is fulfilling the expectations of exponential growth in a real-world manner. The forecasts provided by all parties are both astounding and increasingly aligned:

McKinsey predicts: By the end of this decade, Agentic Commerce is poised to generate $1 trillion in revenue in the U.S. retail market, accounting for roughly one-third of all online retail sales.

Morgan Stanley predicts: By 2030, Agentic Commerce will drive U.S. e-commerce spending to $190 billion to $385 billion, representing a market penetration of 10%–20%.

Bain predicts: By 2030, the market size of Agentic Commerce will reach $300 billion to $500 billion, accounting for 15%–25% of total online retail.

Existing adoption data indicates that we are at the inflection point of an exponential growth curve: By November 2025, 23% of U.S. consumers had made a purchase using AI.

Cymbio Could Become the "Middle Layer" for PayPal in AI Commerce

For PayPal, Cymbio's potential positioning is as an intermediate infrastructure layer in AI commerce. Its core value proposition includes:

Synchronizing product catalogs across different markets and channels

Real-time management of inventory availability

Routing orders to merchants' existing OMS (Order Management System) and fulfillment systems

Allowing merchants to remain the legal entity of the transaction (Merchant of Record)

Among these, the Store Sync product enables a merchant's product catalog to be directly discovered by AI agents such as Microsoft Copilot, Perplexity, with potential future integrations with ChatGPT and Google Gemini.
For AI agents to execute transactions, the prerequisite is that product data, pricing, inventory, and fulfillment information must be machine-readable and highly reliable.

From "Checkout" to "Agentic Commerce Workflow"

PayPal processes over $1.7 trillion in payment volume annually, with over 142 million monthly active accounts. In the traditional model, PayPal's key leverage point is at the moment of payment.

However, in the Agentic Commerce system, AI systems can assist users in product discovery, scheme comparison, and even placing orders on their behalf, while PayPal handles identity verification and payment authorization.

After integrating Cymbio, PayPal covers the entire chain:

Discovery: Products are recommended and presented within the AI agent

Decisioning: Options are continuously narrowed down through conversational interaction

Checkout: Identity verification and payment are handled by PayPal

Fulfilment: Orders are directly injected into the merchant's system for execution

Protocol Wars: Service vs Standard

While PayPal is advancing Agentic Commerce in the form of "Product and Services," Google and Shopify are building a cross-functional, standardized Agentic Commerce protocol system.

The key points are:

Google is embedding the UCP (Universal Commerce Protocol) directly into Search and Gemini

Shopify is ensuring that its millions of merchants only need to integrate once to reach multiple AI agents

This means that the underlying infrastructure of AI commerce is evolving from "point capabilities" to a "networked protocol."

The goal of UCP is to control the "routing layer" of AI commerce, rather than directly owning or operating the commerce itself.
This is more like a defensive layout: by making this layer a "free" public protocol, introducing strong network effects, and preventing any single player from monopolizing the core control of the AI commerce system.

Therefore, PayPal is not directly competing with UCP, but is proactively embedding itself within this system.
Google has explicitly stated that the checkout capability based on UCP will support multiple payment service providers, including PayPal and Google Pay.

In other words, UCP is attempting to become a "neutral expressway," while PayPal aims to be an indispensable toll booth and payment node on this expressway.

OpenAI and Stripe are key competitors in this field.
As early as September, Stripe and OpenAI announced the launch of Instant Checkout in ChatGPT, powered by the Agentic Commerce Protocol (ACP).

ACP enables AI agents to actively initiate purchase requests through a structured API and receive shared payment tokens from Stripe to achieve payment confirmation under agent authorization. This allows AI to complete a full set of transaction processes from ordering to payment on behalf of the user once authorized.

In December 2025, Stripe introduced the Agentic Commerce Suite, enabling merchants to:

Publish a product catalog for direct access by AI agents

Select which AI agents to sell through

Handle payments, risk management, and dispute resolution through Stripe

Send order events back to existing business systems

In 2024, Stripe processed over $1 trillion in payments, serving millions of businesses worldwide. Its competitive strategy is clear: to become the "default wallet" and "action execution layer" for AI agents—a path reminiscent of its journey to becoming the default payment API for internet businesses years ago.

In this context, it is evident that PayPal and Stripe are in direct conflict:
What they are vying for is not just the payment itself, but the key control points when AI agents truly "take action to execute transactions."

Comparing the Three Systems Together

(This is where a horizontal comparison of UCP/ACP/PayPal + Cymbio typically comes in:
Who controls the routing layer, who controls the protocol, who controls payment and fulfillment execution — and where their respective network effects come from.)

If you'd like, I can directly help you organize the next paragraph into a comparison table or a highly summarized "pattern judgment," clarifying the division of labor and game theory of the three parties at once.

Key Takeaways

Three key impacts are particularly prominent:

Commercial actions will become dialogic and agentable
Purchases will no longer be a process of gradual user clicks completion but will be understood by AI in conversations and carried out under authorization.

Merchant "one-time access, everywhere distribution"
Merchants will not need to adapt to each platform individually; they will only need to complete one integration, and products will be reached to users through multiple AI agents and channels.

Payments will become embedded infrastructure, no longer a transaction endpoint
Payments will no longer be the "last-step button" but will be deeply embedded as a fundamental capability in the discovery, decision-making, and fulfillment process.

Early Response of Payment Networks

By the way, in January 2026, Mastercard announced that it is researching "AI Business Rules," essentially attempting to take a proactive step and participate in defining the governance framework of this transformation.
The payment networks clearly recognize that the authority to establish rules and standards will determine their position in the future before AI agents massively complete transactions.

As we pointed out in our analysis in January of this year, banks, fintech companies, and the crypto industry must ensure that they are "at the table" rather than being included afterward.

If financial institutions cannot embed themselves in these platforms in advance, their financial functions may ultimately be internalized by Big Tech.

Situation and Choices of Different Camps

For Banks

Traditional banks lack the technological infrastructure to compete head-to-head with Google, OpenAI, or Microsoft in Agentic Commerce. However, they still hold three key resources: payment settlement channels, customer credit relationships, compliance, and regulatory experience.

These assets determine that banks will not disappear but must reposition themselves.

For Fintechs

Companies like PayPal, Stripe, Adyen realized early on that merely focusing on payments was not enough to solidify their long-term position.

Therefore, they are proactively moving upstream into: commerce orchestration, merchant services, and the infrastructure layer of the AI era.

For Crypto

So far, the Agentic Commerce protocol system that has been disclosed is almost entirely following the traditional financial path: credit cards, Google Pay, PayPal, Stripe, etc., occupy a central position.

In UCP, ACP, and Store Sync, cryptocurrencies and stablecoins are largely absent, except for sporadic experiments involving Stripe or Coinbase.

Whether this is a massive strategic oversight or a deliberate exclusion remains to be seen.

For crypto companies, the opportunity window is very clear: if they can build a payment rail that natively integrates with AI agents (real-time settlement, programmable money, global reach) and successfully embed it into an AI platform before the protocol solidifies, they may achieve a leapfrog over traditional finance; otherwise, they risk being permanently excluded from the system.

Conclusion

Fundamentally, PayPal is striving to catch up with Stripe and adapt to rapidly changing consumer behavior.
As people increasingly make everyday life decisions within AI platforms, these platforms will gradually evolve into the brand's "default virtual storefront."

Whoever can embed the infrastructure behind these storefronts will be able to stay at the table.

PayPal's stock price has been in a slump for a while, down about 37% from its 52-week high. Investors continue to question whether the company still has structural relevance in the long term, and the rise of the Crypto + AI narrative has only exacerbated these concerns.

In this context, the diversification around Agentic Commerce is not an aggressive choice but a "necessary cost" to maintain relevance. For PayPal, this is not a luxury but an entrance fee that must be paid: only by completing this shift can it hope to remain at the core of the next-generation business infrastructure.

[Original Article Link]

Editor's Note: Citrini7's cyberpunk-themed AI doomsday prophecy has sparked widespread discussion across the internet. However, this article presents a more pragmatic counter perspective. If Citrini envisions a digital tsunami instantly engulfing civilization, this author sees the resilient resistance of the human bureaucratic system, the profoundly flawed existing software ecosystem, and the long-overlooked cornerstone of heavy industry. This is a frontal clash between Silicon Valley fantasy and the iron law of reality, reminding us that the singularity may come, but it will never happen overnight.

The following is the original content:

Renowned market commentator Citrini7 recently published a captivating and widely circulated AI doomsday novel. While he acknowledges that the probability of some scenes occurring is extremely low, as someone who has witnessed multiple economic collapse prophecies, I want to challenge his views and present a more deterministic and optimistic future.

Never Underestimate "Institutional Inertia"

In 2007, people thought that against the backdrop of "peak oil," the United States' geopolitical status had come to an end; in 2008, they believed the dollar system was on the brink of collapse; in 2014, everyone thought AMD and NVIDIA were done for. Then ChatGPT emerged, and people thought Google was toast... Yet every time, existing institutions with deep-rooted inertia have proven to be far more resilient than onlookers imagined.

When Citrini talks about the fear of institutional turnover and rapid workforce displacement, he writes, "Even in fields we think rely on interpersonal relationships, cracks are showing. Take the real estate industry, where buyers have tolerated 5%-6% commissions for decades due to the information asymmetry between brokers and consumers..."

Seeing this, I couldn't help but chuckle. People have been proclaiming the "death of real estate agents" for 20 years now! This hardly requires any superintelligence; with Zillow, Redfin, or Opendoor, it's enough. But this example precisely proves the opposite of Citrini's view: although this workforce has long been deemed obsolete in the eyes of most, due to market inertia and regulatory capture, real estate agents' vitality is more tenacious than anyone's expectations a decade ago.

A few months ago, I just bought a house. The transaction process mandated that we hire a real estate agent, with lofty justifications. My buyer's agent made about $50,000 in this transaction, while his actual work — filling out forms and coordinating between multiple parties — amounted to no more than 10 hours, something I could have easily handled myself. The market will eventually move towards efficiency, providing fair pricing for labor, but this will be a long process.

I deeply understand the ways of inertia and change management: I once founded and sold a company whose core business was driving insurance brokerages from "manual service" to "software-driven." The iron rule I learned is: human societies in the real world are extremely complex, and things always take longer than you imagine — even when you account for this rule. This doesn't mean that the world won't undergo drastic changes, but rather that change will be more gradual, allowing us time to respond and adapt.

The Software Industry Has "Infinite Demand" for Labor

Recently, the software sector has seen a downturn as investors worry about the lack of moats in the backend systems of companies like Monday, Salesforce, Asana, making them easily replicable. Citrini and others believe that AI programming heralds the end of SaaS companies: one, products become homogenized, with zero profits, and two, jobs disappear.

But everyone overlooks one thing: the current state of these software products is simply terrible.

I'm qualified to say this because I've spent hundreds of thousands of dollars on Salesforce and Monday. Indeed, AI can enable competitors to replicate these products, but more importantly, AI can enable competitors to build better products. Stock price declines are not surprising: an industry relying on long-term lock-ins, lacking competitiveness, and filled with low-quality legacy incumbents is finally facing competition again.

From a broader perspective, almost all existing software is garbage, which is an undeniable fact. Every tool I've paid for is riddled with bugs; some software is so bad that I can't even pay for it (I've been unable to use Citibank's online transfer for the past three years); most web apps can't even get mobile and desktop responsiveness right; not a single product can fully deliver what you want. Silicon Valley darlings like Stripe and Linear only garner massive followings because they are not as disgustingly unusable as their competitors. If you ask a seasoned engineer, "Show me a truly perfect piece of software," all you'll get is prolonged silence and blank stares.

Here lies a profound truth: even as we approach a "software singularity," the human demand for software labor is nearly infinite. It's well known that the final few percentage points of perfection often require the most work. By this standard, almost every software product has at least a 100x improvement in complexity and features before reaching demand saturation.

I believe that most commentators who claim that the software industry is on the brink of extinction lack an intuitive understanding of software development. The software industry has been around for 50 years, and despite tremendous progress, it is always in a state of "not enough." As a programmer in 2020, my productivity matches that of hundreds of people in 1970, which is incredibly impressive leverage. However, there is still significant room for improvement. People underestimate the "Jevons Paradox": Efficiency improvements often lead to explosive growth in overall demand.

This does not mean that software engineering is an invincible job, but the industry's ability to absorb labor and its inertia far exceed imagination. The saturation process will be very slow, giving us enough time to adapt.

Redemption of "Reindustrialization"

Of course, labor reallocation is inevitable, such as in the driving sector. As Citrini pointed out, many white-collar jobs will experience disruptions. For positions like real estate brokers that have long lost tangible value and rely solely on momentum for income, AI may be the final straw.

But our lifesaver lies in the fact that the United States has almost infinite potential and demand for reindustrialization. You may have heard of "reshoring," but it goes far beyond that. We have essentially lost the ability to manufacture the core building blocks of modern life: batteries, motors, small-scale semiconductors—the entire electricity supply chain is almost entirely dependent on overseas sources. What if there is a military conflict? What's even worse, did you know that China produces 90% of the world's synthetic ammonia? Once the supply is cut off, we can't even produce fertilizer and will face famine.

As long as you look to the physical world, you will find endless job opportunities that will benefit the country, create employment, and build essential infrastructure, all of which can receive bipartisan political support.

We have seen the economic and political winds shifting in this direction—discussions on reshoring, deep tech, and "American vitality." My prediction is that when AI impacts the white-collar sector, the path of least political resistance will be to fund large-scale reindustrialization, absorbing labor through a "giant employment project." Fortunately, the physical world does not have a "singularity"; it is constrained by friction.

We will rebuild bridges and roads. People will find that seeing tangible labor results is more fulfilling than spinning in the digital abstract world. The Salesforce senior product manager who lost a $180,000 salary may find a new job at the "California Seawater Desalination Plant" to end the 25-year drought. These facilities not only need to be built but also pursued with excellence and require long-term maintenance. As long as we are willing, the "Jevons Paradox" also applies to the physical world.

Towards Abundance

The goal of large-scale industrial engineering is abundance. The United States will once again achieve self-sufficiency, enabling large-scale, low-cost production. Moving beyond material scarcity is crucial: in the long run, if we do indeed lose a significant portion of white-collar jobs to AI, we must be able to maintain a high quality of life for the public. And as AI drives profit margins to zero, consumer goods will become extremely affordable, automatically fulfilling this objective.

My view is that different sectors of the economy will "take off" at different speeds, and the transformation in almost all areas will be slower than Citrini anticipates. To be clear, I am extremely bullish on AI and foresee a day when my own labor will be obsolete. But this will take time, and time gives us the opportunity to devise sound strategies.

At this point, preventing the kind of market collapse Citrini imagines is actually not difficult. The U.S. government's performance during the pandemic has demonstrated its proactive and decisive crisis response. If necessary, massive stimulus policies will quickly intervene. Although I am somewhat displeased by its inefficiency, that is not the focus. The focus is on safeguarding material prosperity in people's lives—a universal well-being that gives legitimacy to a nation and upholds the social contract, rather than stubbornly adhering to past accounting metrics or economic dogma.

If we can maintain sharpness and responsiveness in this slow but sure technological transformation, we will eventually emerge unscathed.

Source: Original Post Link

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