Morph Integrates USDT0, Unlocking Access to the World’s Largest Stablecoin Liquidity Pool

TheNewsCryptoОпубліковано о 2026-02-13Востаннє оновлено о 2026-02-13

Анотація

Morph, an Ethereum-based payments settlement network, has integrated USDT0, the omnichain Tether liquidity network powered by LayerZero. This integration provides Morph with direct access to unified USDT liquidity across more than 18 blockchains, eliminating the need for traditional bridges and wrapped tokens. USDT0 uses a burn-and-mint mechanism, creating a single consistent asset across all supported networks and reducing liquidity fragmentation and counterparty risk. Designed specifically for payments, Morph offers sub-300ms block times and zero-fee stablecoin transfers, targeting merchant settlement, remittances, crypto card issuance, and treasury management. With USDT's market cap exceeding $185 billion, developers on Morph can now tap into the world’s largest stablecoin liquidity pool from day one. This enables seamless cross-border transactions, deeper DeFi liquidity, efficient merchant payment processing, and predictable cross-chain operations for financial institutions. The collaboration between USDT0 and Morph aims to advance unified omnichain liquidity, making stablecoins truly borderless and supporting next-generation financial applications.

Singapore, Singapore, February 13th, 2026, Chainwire

Ethereum-based payments settlement network Morph has integrated USDT0, the omnichain Tether liquidity network powered by LayerZero. The move gives Morph, which aims to become the settlement layer for everyday money, direct access to unified USDT liquidity across 18+ blockchains.

For developers building payment apps, merchant tools or even DeFi protocols on Morph, this means they can tap into a massive, ready-made liquidity pool from day one without the headache of managing a dozen different bridged token contracts.

No more bridges. No more wrapped tokens

Traditionally, using USDT on another blockchain requires a bridge. This process locks the original tokens and mints a new, “wrapped” version on the destination chain.

These wrapped variants are not the same asset. They are separate tokens backed by assets held in complex smart contracts, leading to liquidity fragmentation — where the same currency is trapped in isolated pools — and introducing counterparty risk if a bridge fails.

USDT0 proposes a different model. Instead of locking and minting, it uses a burn-and-mint mechanism. To move USDT from Chain A to Chain B, tokens are burned on Chain A and minted directly from Tether’s canonical supply on Chain B.

As a result, USDT0’s Omnichain Fungible Token (OFT) standard creates a single, consistent asset across all supported networks.

What USDT0 enables for builders on Morph

While many L2s compete for general DeFi activity, Morph is engineered for a specific vertical: payments. Its architecture — featuring sub-300ms block times and zero-fee stablecoin transfers — targets merchant settlement, remittances, crypto cards issuance, and treasury management.

For such use cases, deep and frictionless liquidity is non-negotiable. USDT, with a market cap exceeding $185 billion, represents the largest pool of stablecoin liquidity in crypto.

As the USDT0 integration is now live on Morph mainnet, developers on Morph can integrate what is effectively a universal USDT, slashing technical overhead and simplifying cross-chain user experience, which means:

  • Payment applications can process cross-border transactions with instant settlement and minimal overhead.
  • DeFi protocols can access deeper liquidity without managing multiple stablecoin variants.
  • Merchant platforms can accept stablecoin payments with seamless conversion and settlement.
  • Financial institutions can execute treasury operations with predictable behavior across chains.

The combination of USDT0’s unified liquidity and Morph’s payment-optimized infrastructure lays a powerful foundation for next-generation financial applications.

We’re excited to work alongside the USDT0 team in advancing the vision of unified, omnichain liquidity that makes stablecoins truly borderless.

Money at the speed of life.

About Morph

Morph is an Ethereum-based, payments-first settlement layer and the native onchain home of BGB, focused on building the foundation for global consumer finance onchain. Morph supports real-world financial activity across payments, savings, identity, and rewards, enabling scalable, onchain settlement for consumer and business use. Guided by the Morph Foundation, the network connects more than 120 million users through the Bitget and Bitget Wallet ecosystems.

Contact

Andrew Azarias
Andrew.azarias@morphl2.io

Пов'язані питання

QWhat is the main benefit of Morph integrating USDT0 for developers building on its network?

ADevelopers gain direct access to a massive, unified USDT liquidity pool across 18+ blockchains from day one, eliminating the need to manage multiple bridged token contracts and reducing technical overhead.

QHow does USDT0's mechanism differ from traditional cross-chain token transfers?

AUSDT0 uses a burn-and-mint mechanism instead of locking and minting wrapped tokens. It burns tokens on the source chain and mints them directly from Tether's canonical supply on the destination chain, creating a single consistent asset across networks.

QWhat specific vertical is Morph's architecture optimized for, and what features support this?

AMorph is engineered for payments, featuring sub-300ms block times and zero-fee stablecoin transfers to target merchant settlement, remittances, crypto card issuance, and treasury management.

QWhat are some use cases enabled by the USDT0 integration on Morph for different types of platforms?

APayment apps can process cross-border transactions instantly; DeFi protocols access deeper liquidity without multiple stablecoin variants; merchant platforms accept stablecoin payments with seamless conversion; financial institutions execute predictable cross-chain treasury management.

QWhat is the role of the Morph Foundation and how many users does the network connect through its associated ecosystems?

AThe Morph Foundation guides the network, which connects over 120 million users through the Bitget and Bitget Wallet ecosystems.

Пов'язані матеріали

Claude's Major New Feature: Screen Recording + Voice, Distilling Your Skills into AI Tasks in One Click

Claude has introduced a major new feature called "Record a Skill," available for Pro, Max, and Team users. This function, found in the Claude desktop app's CoWork menu, allows users to create reusable AI skills simply by recording their screen and providing voice narration while performing a task. Claude then automatically analyzes the recording and generates a functional Skill. A hands-on test confirmed the feature works seamlessly. Users start recording via the Skills manager, perform their workflow while verbally explaining the steps and logic, and avoid including sensitive information. After recording, Claude processes the content and creates the Skill, which can be saved and later invoked with a slash command (/). This eliminates the need for manual adjustments or writing complex instruction files. The innovation goes beyond mere efficiency. Previously, creating a Skill required writing a detailed SKILL.md file in Markdown—a significant barrier for non-technical users. "Record a Skill" bypasses this by directly capturing both actions and the implicit reasoning shared in the narration. This lowers the barrier to knowledge transfer and automation, addressing a core challenge in corporate knowledge management: the difficulty of getting experts to write and maintain documentation. However, the feature also highlights a shift in the nature of work. A case study from March 2026 showed a freelancer whose five-year client relationship was effectively replaced by a hand-coded Claude Skill automating their content workflow. With the even lower barrier of screen recording, the ability to distill personal expertise into automatable skills accelerates this trend. The "moat" for work is moving from simply knowing how to do a task to mastering tasks that are difficult or impossible to automate.

marsbit6 хв тому

Claude's Major New Feature: Screen Recording + Voice, Distilling Your Skills into AI Tasks in One Click

marsbit6 хв тому

Feeding AI "Noise" Can Also Boost Scores, This Work Enables Positive Transfer with Noise

Feeding "Noise" to AI Can Improve Performance: A Method Enables Positive Transfer from Noise This work, Semi-Supervised Noise Adaptation (SSNA), introduces a Noise Adaptation Framework (NAF) that challenges traditional transfer learning. Instead of requiring a labeled source domain of real data (e.g., images, text), NAF uses randomly generated Gaussian noise as the source. For a target task with C classes, it constructs C noise clusters by sampling from Gaussian distributions. Although this synthetic noise contains no semantic meaning, NAF trains it to form a discriminative class structure in a shared representation space—clustering same-class noise and separating different classes. The key is aligning this learned structure from the noise domain to the real, sparsely labeled target domain. A small number of target labels are still essential to establish the correspondence between noise clusters and actual classes. The training objective combines: 1) supervised loss on the few labeled target samples, 2) classification loss for the noise to build its structure, and 3) a distribution alignment loss (using Negative Domain Similarity) to minimize the gap between the noise and target domains in the shared space. Experiments show significant gains in few-label settings. With just 4 labels per class, NAF with a ResNet-18 backbone improves accuracy over a standard supervised baseline (ERM) by +12.35% on CIFAR-10, +7.61% on CIFAR-100, +4.38% on DTD-47, and +2.74% on Caltech-101. It also benefits fine-grained datasets and scales to ImageNet-1K (with 100 labels/class) and text classification (AG News). NAF can be integrated into existing semi-supervised methods like FixMatch for further gains. Ablation studies confirm the transferred benefit comes from the discriminative structure of the noise, not randomness itself. Collapsing all noise into a single point causes negative transfer, while increasing separation between noise cluster centers improves performance. The amount of noise per class is less critical once a basic structure forms. In conclusion, this work demonstrates that for positive transfer, the semantic content of source data may not be necessary. What can be effectively transferred is the *organizational structure* of categories within a representation space. This offers a promising alternative for scenarios where real source data is unavailable due to privacy, copyright, or procurement constraints.

marsbit7 хв тому

Feeding AI "Noise" Can Also Boost Scores, This Work Enables Positive Transfer with Noise

marsbit7 хв тому

The White House's "Exclusive" Teleprompter Operator Makes Over $100,000 by Profiting from Insider Information Predictions

"White House Speech Prompt Operator Earns Over $100,000 Using Insider Information on Prediction Markets" U.S. White House staffer Gabriel Perez, a long-time teleprompter operator for former President Donald Trump, has been suspended without pay for using non-public information to profit on prediction markets. As one of the few individuals with advance access to Trump's prepared speech texts, Perez placed bets on specific words or phrases Trump would mention in speeches over a three-month period, earning over $100,000. His activities were flagged by the prediction platform Kalshi, which froze over $90,000 in his account and reported him to the Commodity Futures Trading Commission (CFTC). While Perez avoided criminal charges, he is required to return his profits and cease such trading. This case marks the third major instance of insider trading on prediction markets involving government or corporate insiders, following earlier cases involving a special forces soldier and a Google engineer. The incident highlights the vulnerability of "mention" markets on prediction platforms, where individuals with advance knowledge or even the speakers themselves can easily manipulate outcomes. In response, platforms like Kalshi are tightening rules, now requiring users to disclose their employers to help prevent similar abuses.

marsbit1 год тому

The White House's "Exclusive" Teleprompter Operator Makes Over $100,000 by Profiting from Insider Information Predictions

marsbit1 год тому

Торгівля

Спот
活动图片