Original Title: A Year Inside Agentic Payments: The Uncomfortable Truth

Original Author: @13yearoldvc

Translation: Peggy

Editor's Note: This article offers a relatively calm builder’s perspective: over the past year, agentic payments have become a hot narrative at the intersection of AI, payments, and crypto. Companies like Stripe, Visa, Coinbase, Google, and dozens of startups are making moves. Concepts like stablecoin micro-payments, x402, machine-to-machine settlement, and agent e-commerce are also gaining traction. But after actually building products and engaging with merchants and developers, the author finds that real demand has not yet emerged at scale.

The article breaks down several typical scenarios: agent shopping isn’t better than traditional e-commerce in most categories because users still want images, comparisons, and browsing; machine API payments seem suitable for stablecoin micro-payments, but most developers have already solved this through subscriptions, point recharges, and existing billing systems; payments between agents are a long-term vision but remain in early stages with no real transaction volume.

Relatively speaking, agentic finance is one of the few directions with existing demand. Funds, treasury teams, and DeFi users already pay for financial tools, and AI can bring real capabilities like real-time monitoring and automatic rebalancing. But this market also favors traditional institutions with licenses, compliance, and established customer relationships.

The author’s final judgment is: what the agentic economy truly lacks isn’t just a payment layer, but more complex coordination—how to enable agents to collaborate with humans, verify task completion, and settle results. Payments are just one part. For giants, early deployment is a defensive move; but for startups, the key is to find markets that already exist today.

Below is the original text:

Over the past year, I’ve been building infrastructure for the agent economy and communicating with teams at Stripe, Visa, Coinbase, Google, and dozens of startups pushing agent-based commerce. I’ve mapped out this field, launched products, and tried to find the real market.

But the reality is: genuine demand has not yet appeared. For startups wanting to enter this space, many structural issues remain.

Last month, Stripe announced 288 new products at the Sessions conference, with nearly 40% of all agent-related documentation views. Its agent business market has onboarded over 1,000 merchants. Yet, at the conference, only a handful of agents had registered and completed transactions.

Visa mentioned that its agent token currently requires 3 to 9 months of KYC approval, with a minimum enterprise annual revenue of $250 million to qualify. Today, only companies like Amazon and Walmart can close the identity verification loop.

Coinbase reported that as of April, there were 69k active agents and 165 million transactions on x402. But independent on-chain analysis shows real daily transaction volume is about $17k, with roughly half being test transactions (CoinDesk, March 2026).

What We Learned Building shop.fast.xyz

Agent to Merchant, or Agent-Based Commerce

We built shop.fast.xyz to directly verify agent-based commerce: real products, real merchants, real transactions.

But for most product categories, current AI shopping experiences are clearly inferior to traditional e-commerce. When buying clothes, electronics, or furniture, users want to see images, browse options, and compare side-by-side. Chatbot-style conversations are a regression: replacing rich visual interfaces with text dialogues. Human shopping first and foremost is visual.

The agent’s performance in what we thought was the hardest part is promising. It understands what users want and handles requests like “something similar but cheaper” well. The model layer works effectively. But it can’t replace the experience of “view ten products at once and pick one.” Chat interfaces can add product carousels and interactive displays, but at that point, you’re essentially rebuilding an e-commerce frontend inside a chat window. For shopping scenarios that require visual comparison, we haven’t yet found a convincing reason why a chat shell would outperform the original e-commerce interface.

We do see merchant demand, but it’s mostly defensive. Merchants want their stores to be queryable by agents—not because many consumers are shopping via agents today, but because they worry that if agent-based channels become mainstream, they’ll be left behind. This is the so-called agentic engine optimization (AEO) opportunity, but it’s currently just “better” rather than “must-have.” Merchants are preparing for a wave that hasn’t arrived yet.

Conversational commerce can truly improve experiences in high-frequency, low-decision-cost scenarios where users already know what they want. The clearest example is ordering food. The market is large, the frequency high, and decisions quick—like “Order me the pad thai I liked last time.” In such scenarios, chat-based agents might win. But major delivery platforms don’t open APIs. The only path is computer use—making AI operate app interfaces visually like humans do. This process is slow, fragile, and for a $15 lunch, the reasoning cost simply doesn’t add up.

Another opportunity lies in complex online stores that cause user pain—layers of discounts, promo codes, loyalty points, chaotic checkout flows. An agent that understands “apply coupons, deduct points, find the cheapest shipping, and complete the operation in my language” could significantly simplify today’s broken shopping experience. This is especially important for elderly users, non-native speakers, cross-region shopping, or niche, complex needs.

But both opportunities require massive B2C distribution capabilities. You’re competing with DoorDash, Amazon, and others for user entry points. Consumer-scale distribution is an advantage for existing giants. The supply side of agent-based commerce is ready; demand is limited by user experience and distribution channels, and more infrastructure alone can’t solve these issues.

What We Learned from x402 and MPP

Agent to Web/API, or Machine Commerce

We’ve spoken with dozens of developers about their real payment needs. The pattern is almost identical: today’s agent API usage is mainly for recurring consumption—computing power, inference, data sources. Developers already have subscriptions, API keys, linked accounts, and billing relationships with core service providers.

A typical argument for stablecoin payments is that credit card payments on Stripe have a minimum effective cost of about 2.9% plus $0.30, making API calls under $1 uneconomical. But with today’s low transaction volumes, recharging points solves this problem. Developers pre-fund accounts, and this issue disappears.

Deeper issues lie in the vendor market. Most large SaaS companies don’t want to offer tiny API access at fractions of a cent. Their business models rely on multi-year enterprise contracts. Companies dependent on large commitments resist new pricing models that bypass this.

Machine commerce is structurally a long-tail market. It serves small services, vertical data sources, independent developers, MCP servers, etc. Protocols like MPP and x402 are well-suited for this niche. But by definition, it’s a market for specialized needs; developers have historically been among the least willing to pay.

When Stripe Projects launched, it integrated 32 service partners, including Vercel, Supabase, Cloudflare, Twilio, covering most core services used by developers to build and deploy software, all accessible via existing billing systems. The developer tech stack is well served. The opportunity for new payment tracks lies beyond those top 30 providers: it’s real but inherently smaller than the markets implied by grand narratives.

Content access follows the same logic. Agents are already crawling and summarizing articles, publishers are fighting back. But when content monetization scales massively, it will likely happen through existing CDN providers between publishers and the internet—like Cloudflare’s AI audit tools—or via bulk licensing agreements between publishers and AI labs. Infrastructure opportunities will flow to those with existing distribution capabilities.

What We Learned from Agent-to-Agent Payments

Agent-to-agent commerce is a long-term vision, but currently almost purely theoretical. No one has yet achieved meaningful transaction volume. The real challenges are being advanced by startups—agent discovery, trust establishment, terms negotiation, dispute resolution.

Once such transaction structures truly form, they will look very different from existing payment rails. Neither party will have human identities; latency will be under a second; transaction amounts could range from fractions of a cent to millions of dollars; multi-party settlement will be involved, not just the traditional bilateral buyer-seller model. When it happens, we believe it will explode rapidly and at scale.

This is the long-term bet on dedicated settlement infrastructure, and it’s a real one. But “real long-term bet” and “current market” are not the same. We were among those claiming for months that this market would arrive, and over the past years, built a whole infrastructure around it, including our distributed network. Theoretically, it could scale to over 1 billion TPS, with latency below 50 ms, and average finality of 10 ms. But we must return to where the market is now.

What We Learned from Agent Finance

This is arguably the only category with genuine existing demand. Customers already exist and are paying. Fund managers, treasury teams, and DeFi users are already spending on financial tools today. Integrating AI into existing workflows is a natural product path.

Agent finance will also create entirely new behaviors. Autonomous agents that monitor and rebalance hundreds of positions in real-time can operate in ways impossible for humans to manually replicate. This offers real productivity gains, not just automation.

The challenge lies in the competitive landscape. Finance is highly regulated and relies on existing relationships. Established institutions have licenses, compliance infrastructure, and customer bases. Startups can enter lightly regulated areas like DeFi or seek segments where incumbents are slow, or where AI can create capabilities giants lack. But overall, this field favors existing players, since adding AI on top of current products and clients is easier than building from scratch.

Honest Summary

So why are people still doing this? Two reasons.

First is incentives. Large companies have enough cash flow to bet on a future that takes years to materialize. For them, entering five years early is just a small decimal point error; entering a year late could be disastrous. So they must do it.

Second is cognitive bias. When your business is payments, every problem looks like a payments problem. The agent economy needs a payment layer, so everyone builds one.

But payments are only part of a bigger problem. The real challenge isn’t just moving money between agents, but coordinating work between agents and humans, verifying completion, and settling results. Payments are just one part of settlement. Settlement is part of coordination. And coordination is the real prize.

Large-scale coordination naturally creates demand for settlement mechanisms. Payments will become one instrument in this orchestration, not the entire composition. Companies that truly solve coordination will eventually incorporate payments into it, rather than being acquired by payment companies that dominate coordination.

Most existing giants are defensively building a future of “machine-scale transactions.” For them, timelines don’t matter much—they have nearly unlimited runway.

But startups don’t have that luxury. We must find where the market is right now. We can’t wait forever for the wave to arrive.

A year of building has taken us in an unexpected direction. There is activity, and rapid growth, but underserved. It exists outside the four categories we’ve outlined.

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