Ripple chases AI’s machine economy as XRPL stablecoins near $1 billion

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Ripple chases AI’s machine economy as XRPL stablecoins near  billion


Stablecoin liquidity on the XRP Ledger (XRPL) has nearly doubled over the past month, putting the network within reach of a $1 billion supply milestone as Ripple tries to position its blockchain for automated payments.

The surge gives Ripple a stronger base for one of its most ambitious pitches yet: that artificial intelligence agents will need dollar-denominated payment rails that can settle transactions in seconds, enforce spending rules, and operate without manual approval at every step.

Data from DeFiLlama shows stablecoin supply on the XRPL at about $770 million, up roughly 97% over the past 30 days.

XRPL Stablecoin SupplyXRPL Stablecoin Supply
XRPL Stablecoin Supply (Source: DeFiLlama)

RWA.xyz, which tracks a broader set of tokenized real-world assets and stablecoins, places XRPL’s stablecoin market capitalization at about $901.7 million, with 30-day transfer volume rising 122% to $4.95 billion.

The gap between the two data providers reflects differences in methodology, but both show the same trend. Dollar-pegged assets on the XRP Ledger are growing quickly, and RLUSD, Ripple’s own stablecoin, is driving most of that increase.

DeFiLlama data show RLUSD accounting for nearly 99% of stablecoin supply on the XRP Ledger, with about $761.7 million issued on the network. RWA.xyz lists RLUSD’s total market capitalization across supported blockchains at roughly $1.65 billion.

Ripple RLUSD StablecoinRipple RLUSD Stablecoin
Ripple RLUSD Stablecoin Supply (Source: DeFiLlama)

That concentration gives Ripple unusual influence over the XRPL’s dollar layer and strengthens its argument that RLUSD can become a settlement tool for institutions, developers, and software agents that need predictable access to dollars on-chain.

The harder question is whether the current growth reflects durable payment demand or early positioning ahead of a market that is still taking shape.

AI agents could decide XRPL’s next payment boom — and RLUSD is the bottleneckAI agents could decide XRPL’s next payment boom — and RLUSD is the bottleneck
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The rise of agentic commerce

Artificial intelligence agents are moving beyond passive chat interfaces into software systems that can take actions on behalf of users and businesses.

In payments, that shift creates a practical problem. An agent that needs to access an API, pay for cloud computing, purchase data, settle an invoice or complete a multi-step workflow cannot always wait for a person to approve each transaction. It also cannot easily function on payment rails designed around card forms, billing accounts, batch settlement, and delayed reconciliation.

That is the opening Ripple is trying to exploit.

The company this week released the XRPL AI Starter Kit, a developer package designed to make it easier to build AI-agent payment flows on XRPL. The first phase includes an MCP server that lets compatible AI coding tools query XRPL documentation, Claude skills for wallet creation and payments, and new tutorials for building agentic transactions.

Ripple is also tying the toolkit to x402, an open payment standard built around the web’s HTTP 402 “Payment Required” status code. Through a contribution from t54, XRPL now supports x402 payments using XRP or RLUSD, allowing agents to pay for API calls, model inference, and other digital services.

The pitch is straightforward. Instead of creating accounts, storing API keys, buying prepaid credits, or waiting for billing relationships to clear, an agent can receive a payment request, send a small amount of value, and continue the workflow.

Ripple argues that the XRPL has several properties suited to that design. Transactions settle in seconds, fees are predictable, and payments are handled at the protocol layer rather than through arbitrary smart contract code.

The ledger also supports controls such as escrow, multi-signing, deposit authorization, and trust lines, giving institutions ways to limit who an agent can pay and under what conditions.

Those features are central to the RLUSD strategy. XRP can move value on the network, but many commercial workflows still need a dollar unit. Invoices, software subscriptions, payroll, treasury transfers, and API pricing are usually denominated in fiat terms.

RLUSD gives Ripple a stablecoin it can tie directly to those use cases while keeping the payment activity within the XRPL ecosystem.

Cartoon image of Ripple’s RLUSD coin riding an XRPL Express train toward an AI-driven machine commerce district.Cartoon image of Ripple’s RLUSD coin riding an XRPL Express train toward an AI-driven machine commerce district.

Mastercard gives the thesis a mainstream venue

Ripple’s AI-agent push also gained a broader payments backdrop this week after Mastercard launched Agent Pay for Machines, a service aimed at machine-speed payments across software agents, connected devices, and automated business workflows.

Mastercard described the system as a way for businesses to let agents transact continuously while still operating within permissioning, governance, and settlement controls. Ripple was named among the participating companies, alongside a wider group that includes Coinbase, Stripe, Solana Foundation, Polygon, OKX, Cloudflare, and others.

For Ripple, the Mastercard initiative helps move RLUSD’s adoption beyond a crypto-native audience. The company can now place XRPL and RLUSD inside a larger institutional discussion about how autonomous software should be allowed to spend money.

Markus Infanger, senior vice president at RippleX, said enterprises will only allow autonomous agents to move at machine speed if the necessary controls move with them. He argues that XRPL and RLUSD can provide settlement, predictable costs, compliance parameters, and audit trails inside the transaction flow itself.

Other Ripple executives have framed the launch in similarly long-term terms.

Jazzi Cooper, head of product at RippleX, said payments over the next decade may no longer be run mainly by humans. In her view, that means autonomous agents have to be treated as a primary user group for financial infrastructure rather than an edge case.

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