Ripple takes RLUSD multichain: Stablecoin expands to L2s with Wormhole

ambcryptoPublicado em 2025-12-15Última atualização em 2025-12-15

Resumo

Ripple has expanded its U.S.-regulated stablecoin, RLUSD, to Layer 2 networks including Optimism, Base, Ink, and Unichain using Wormhole’s Native Token Transfers (NTT) standard. This enables native multichain issuance, reducing bridge risks and ensuring regulatory compliance. RLUSD is the first U.S. Trust-regulated stablecoin on these L2s. Backed by a NYDFS Trust Charter and with a pending OCC application, it aims to be the first dual-regulated stablecoin. The expansion enhances utility for both XRP and RLUSD, strengthening Ripple’s role in institutional DeFi and cross-chain liquidity.

Ripple has expanded its U.S.-regulated stablecoin, Ripple USD [RLUSD], to Layer 2 networks for the first time. This marks a major step toward a multichain strategy ahead of its full launch next year.

The testing rollout begins on Optimism, Base, Ink, and Unichain, and is powered by Wormhole’s Native Token Transfers [NTT] standard — a system designed to move assets across chains without relying on wrapped tokens or traditional bridge architectures.

The move positions RLUSD as the first U.S. Trust-regulated stablecoin to deploy natively on these L2 ecosystems.

RLUSD goes multichain with native issuance on L2s

Unlike bridged assets, Wormhole’s NTT framework enables RLUSD to maintain native issuance and control across every supported chain.

This approach reduces bridge risk, preserves liquidity integrity, and creates a regulatory-compliant pathway for institutional DeFi expansion.

According to the announcement, Optimism serves as the entry point, with Base, Ink, and Unichain interconnected through the same NTT infrastructure — allowing Ripple to scale RLUSD across multiple environments without fragmentation.

Ripple’s SVP of Stablecoin, Jack McDonald, said the expansion reflects rising institutional demand for a fully compliant stablecoin that can move across chains with predictable oversight.

“Stablecoins are the gateway to DeFi and institutional adoption,” he said. “By launching RLUSD on these L2 networks, we are setting the standard where compliance and on-chain efficiency converge.”

Why regulation matters more than ever

RLUSD launched under a New York Department of Financial Services [NYDFS] Trust Charter, one of the most stringent regulatory frameworks in crypto.

Last week, AMBCrypto also reported that Ripple has applied for an OCC charter. If approved, this would make RLUSD the first stablecoin simultaneously overseen at both the state and federal levels.

No existing major stablecoin, including USDC or USDT, operates under this dual structure.

Ripple now holds more than 75 licenses globally, with recent approvals in Dubai and Abu Dhabi further strengthening RLUSD’s international reach.

Utility boost for both Ripple XRP and RLUSD

The multichain expansion is designed to strengthen XRP’s role in cross-chain liquidity.

Hex Trust recently issued wrapped XRP [wXRP] to support interoperability, enabling XRP holders to pair wXRP with RLUSD on supported chains for swaps, payments, lending, or yield-generating applications.

Data from DefilLama shows that Ethereum currently has the highest share of RLUSD [79.2%] worth over $1 billion, while the remaining 20.8% is on XRPL.

Final Thoughts

  • RLUSD’s L2 expansion sets a new regulatory benchmark for multichain stablecoins and positions Ripple as a direct challenger to USDC’s dominance.
  • The move enhances utility for both XRP and RLUSD, creating a deeper role for Ripple in institutional DeFi and cross-chain liquidity.

Leituras Relacionadas

Iran’s Crypto Lifeline Hit As US Freezes $344 Million In Funds

The US Treasury has frozen $344 million in cryptocurrency tied to Iranian military and political groups, specifically targeting two Tron blockchain wallets connected to the Islamic Revolutionary Guard Corps and Hizballah. This action follows reports that Iran was collecting Bitcoin payments from ships for safe passage through the Strait of Hormuz. The freeze occurred just one day after stablecoin issuer Tether locked the same amount of funds at the request of US law enforcement. The move is part of broader US efforts to disrupt Iran's ability to generate and move funds, highlighting the limitations of using cryptocurrency as a sanctions workaround when centralized issuers comply with enforcement actions.

bitcoinistHá 3m

Iran’s Crypto Lifeline Hit As US Freezes $344 Million In Funds

bitcoinistHá 3m

Amidst the Volatility in the Crypto Market, Who is Buying Against the Trend?

"In a volatile crypto market downturn of Q1 2026, institutional players demonstrated divergent strategies. While Bitcoin fell over 25% and Ethereum dropped 35%, key institutions accumulated: corporate treasuries like Strategy (MicroStrategy) added over $10 billion in BTC, sovereign wealth funds like Mubadala increased IBIT holdings by 46%, and major banks launched crypto services and ETFs. Notably, 26 new crypto ETFs were filed or launched under streamlined SEC rules, including bank-led products from Morgan Stanley and BlackRock’s staking ETH ETF. VC funding saw concentration: total investment reached ~$5 billion, but deal count plummeted 49%. Three mega-deals—BVNK ($1.8B), Kalshi ($1B), and Polymarket ($600M)—accounted for half the quarter’s funding. Investment shifted from DeFi/NFTs to payments, prediction markets, and regulated CeFi infrastructure. Selling pressure came from hedge funds (e.g., Brevan Howard cut IBIT by 85%) and Bitcoin miners liquidating holdings. The U.S. strategic Bitcoin reserve saw zero additions, highlighting that real buying came from corporations and sovereign funds, not governments. The institutional crypto landscape is bifurcating: long-term holders accumulate while short-term traders exit, signaling a foundation for the next cycle."

marsbitHá 26m

Amidst the Volatility in the Crypto Market, Who is Buying Against the Trend?

marsbitHá 26m

Hands-on with Hunyuan Hy3 Preview: Tencent's AI, Finally Competitive?

Tencent's Hunyuan AI team has released its latest language model, Hy3 preview, marking a significant step forward for the company's AI capabilities. With 295B total parameters and support for 256K context length, the model employs a mixture-of-experts architecture. It shows improvements in complex logic, instruction following, contextual learning, code generation, and agent task execution. In testing, Hy3 preview demonstrated strong performance in multi-step logical reasoning but showed occasional instability in identifying traps in trick questions. It performed well in extracting key information from disordered meeting transcripts and accurately followed new linguistic rules. As an AI agent, it successfully built functional applications like a Snake game and generated data analysis dashboards, though it sometimes fell short in fully completing complex open-ended tasks. In natural language use, it produced coherent and stylistically appropriate narratives with reduced “AI-like” tone. Priced competitively, Hy3 preview is already integrated into Tencent’s key products, including Tencent Cloud and WorkBuddy. While not leading in every benchmark, it represents a solid, practical model that signals Tencent’s renewed momentum in AI development.

marsbitHá 2h

Hands-on with Hunyuan Hy3 Preview: Tencent's AI, Finally Competitive?

marsbitHá 2h

From Theft to Re-entry: How Was $292 Million "Laundered"?

A sophisticated crypto laundering operation was executed following the $292 million hack of Kelp DAO on April 18. The attack, attributed to the North Korean Lazarus group, began with anonymous infrastructure preparation using Tornado Cash to fund wallets untraceably. The hacker exploited a vulnerability in Kelp’s cross-chain bridge, stealing 116,500 rsETH. To avoid crashing the market, the attacker used Aave and Compound as laundering tools—depositing the stolen rsETH as collateral to borrow $190 million in clean, liquid ETH. This move triggered a bank run on Aave, causing an $8 billion drop in TVL. After consolidating funds, the attacker fragmented them across hundreds of wallets to evade detection. A major breakpoint was THORChain, where over $460 million in volume—30 times its usual activity—was processed in 24 hours, converting ETH into Bitcoin. This shift to Bitcoin’s UTXO model exponentially increased tracing complexity by shattering funds into countless untraceable fragments. The final destination was Tron-based USDT, the primary channel for illicit crypto flows. From there, funds were cashed out via OTC brokers in China and Southeast Asia, using unlicensed underground banks and UnionPay networks outside Western sanctions scope. Ultimately, the laundered money supports North Korea’s weapons programs, which rely heavily on crypto hacking for foreign currency. The incident underscores structural challenges in DeFi: its openness, composability, and lack of central control make such laundering not just possible, but inherently difficult to prevent.

marsbitHá 2h

From Theft to Re-entry: How Was $292 Million "Laundered"?

marsbitHá 2h

Behind DeepSeek V4's Stunning Debut: Silicon Valley Is 'Building Walls,' China Is 'Paving Roads'

China's AI landscape is witnessing a strategic divergence from Silicon Valley’s closed-source competition to a collaborative open-source ecosystem. On April 24, DeepSeek released V4, a top-ranked open-source model on Hugging Face, featuring breakthroughs like million-token context length with minimal KV cache and native support for domestic chips like Huawei’s Ascend. Similarly, Kimi’s K2.6, released days earlier, also adopted open-source principles. Unlike U.S. giants such as OpenAI and Anthropic—locked in revenue disputes and tactical product clashes—Chinese firms embrace shared innovation. DeepSeek and Kimi openly build on each other’s advances, like the MLA architecture and Muon optimizer, avoiding redundant R&D and driving down costs. DeepSeek V4 focused on pushing base model capabilities, while Kimi specialized in Agent-based applications. Although U.S. firms lead in revenue and valuation, China’s open-source models achieve comparable performance at a fraction of the cost (e.g., DeepSeek V3 trained for $5.58M vs. GPT-5’s $500M+). With token usage growing exponentially, China’s collaborative model promises scalable, affordable AI built on domestic hardware, shaping a more accessible path to AGI.

marsbitHá 3h

Behind DeepSeek V4's Stunning Debut: Silicon Valley Is 'Building Walls,' China Is 'Paving Roads'

marsbitHá 3h

Trading

Spot
Futuros
活动图片

Categorias popularesright-arrow

Etiquetas Popularesright-arrow