Circle moves toward privacy-focused stablecoin with USDCx project

cointelegraph2025-12-09 tarihinde yayınlandı2025-12-09 tarihinde güncellendi

Özet

Circle is developing a privacy-enhanced version of its USDC stablecoin, called USDCx, in partnership with blockchain firm Aleo. Aimed at banking and enterprise users, USDCx will offer "banking-level privacy" by concealing transaction details onchain, while still allowing compliance with regulatory requests. This addresses a key hurdle for institutional adoption, as many financial firms hesitate to use public blockchains due to transparency concerns. The move comes amid growing corporate interest in stablecoins following the US GENIUS Act, with major players like Citigroup, JPMorgan, and Visa expanding their stablecoin initiatives. USDC and USDT currently dominate the dollar-pegged stablecoin market.

Stablecoin issuer Circle is developing a privacy-enhanced version of its US dollar-pegged USDC token, aiming to spur institutional adoption by offering greater confidentiality than traditional public blockchains allow.

The new stablecoin, called USDCx and targeting banking and enterprise users, is being built in partnership with the privacy-focused blockchain company Aleo, Fortune reported on Tuesday, citing Aleo co-founder Howard Wu.

Unlike most existing stablecoins, which have wallet addresses and transaction details fully visible onchain, USDCx is designed to provide “banking-level privacy.” Circle would still be able to provide a compliance record if law enforcement or regulators request information on specific transactions, according to the report.

The initiative aims to address a key hurdle for major financial institutions, many of which have been hesitant to utilize blockchain-based payment rails because their transaction flows would be publicly visible.

Source: Circle

Aleo has long argued that privacy is essential for the next phase of stablecoin adoption. In a May post, the company wrote that while transparency is often promoted as a core blockchain advantage, “it becomes a liability when dealing with sensitive, confidential payment data.”

Aleo isn’t the only company pushing for privacy in stablecoins. As Cointelegraph reported, digital asset infrastructure provider Taurus has developed a private smart-contract system for stablecoins, designed to enable anonymous transactions. This approach aims to boost the use of stable assets for intracompany payments and employee payrolls.

Related: Bank lobby is ‘panicking’ about yield-bearing stablecoins

Stablecoins take center stage in corporate America

Circle’s move into privacy-focused stable assets comes as more major institutions begin exploring stablecoins in the wake of the US GENIUS Act, the new regulatory framework governing US dollar–pegged tokens.

As Cointelegraph reported, a corporate stablecoin race is emerging in the wake of GENIUS. Citigroup has partnered with Coinbase to test stablecoin-based payment rails for its clients, while other Wall Street companies, including JPMorgan and Bank of America, are reportedly in the early stages of experimenting with similar technologies.

Global remittance provider Western Union is also building a digital asset settlement system on Solana, with plans to introduce a US Dollar Payment Token as part of its infrastructure overhaul. Meanwhile, global payments giant Visa has expanded its stablecoin offerings amid growing competition in the space.

Average stablecoin supply by issuer. Source: Visa Onchain Analytics

The US dollar underpins the vast majority of global stablecoin activity. USDC (USDC) and Tether’s USDt (USDT) together account for roughly 85% of the market, while other dollar-linked tokens, including synthetic dollars and PayPal USD (PYUSD), also rank among the largest.

Related: Crypto Biz: Wall Street giants bet on stablecoins

İlgili Okumalar

Ethereum Q1 2026 Report: Fees Decline, Users and Transaction Volume Hit New Highs

Ethereum Q1 2026 Report: Fees Down, Users & Transactions Hit New Highs Token Terminal's Q1 2026 report on Ethereum presents a pivotal development: the network achieved record highs in monthly active users (13.2M, +85.9% YoY), total transactions (200.4M, +81.5% YoY), and throughput (25.78 TPS), while transaction fees on the mainnet plummeted by 47.9% quarter-over-quarter. This shift is attributed to the network's strategic move into a "low fees for scale" phase, exemplified by the Fusaka upgrade which increased data capacity and lowered block space costs, releasing pent-up demand (a manifestation of Jevons's Paradox). The report highlights a core narrative shift for Ethereum: from a DeFi-centric blockchain to a global financial settlement layer. It maintains a dominant position in tokenized assets, holding majority market shares among top chains in stablecoins (61.8%), tokenized funds (73.0%), and tokenized commodities (84.0%). Growth in tokenized funds (+73.1% YoY) and commodities (+325.9% YoY) was particularly strong, driven by institutions like BlackRock and JPMorgan entering the space. Contrasting these usage gains, several USD-denominated value metrics declined in Q1: fully diluted market cap fell 30.3% QoQ, total value locked (TVL) dropped 11.0%, and ecosystem transaction volume decreased 24.0%. The report interprets this as Ethereum prioritizing long-term network expansion and cementing its role as the default settlement layer for finance over short-term fee capture. The commentary from Etherealize argues that, much like the early internet, Ethereum's open, permissionless model is poised to win over closed alternatives as institutional tokenization accelerates.

marsbit1 saat önce

Ethereum Q1 2026 Report: Fees Decline, Users and Transaction Volume Hit New Highs

marsbit1 saat önce

He Just Raised 2.7 Billion, and Li Fei-Fei Also Invested

Pete Florence, a former senior research scientist at Google DeepMind and a key contributor to the Vision-Language-Action (VLA) model architecture, is deliberately distancing his startup, Generalist AI, from the trendy "world model" label. He argues that the industry should prioritize concrete goals over buzzwords. His goal is to create robots that can perform a vast range of unseen tasks with high speed and success rates, without needing task-specific training data. Recently, his company raised $400 million (¥2.7 billion) at a $2 billion valuation. Notable investors include NVIDIA's NVentures, Bezos Expeditions, NFDG, as well as Xiaomi co-founder Lin Bin, Zoom founder Eric Yuan, and renowned AI scientist Fei-Fei Li. Florence's approach stems from his academic background at MIT under Professor Russ Tedrake, focusing on understanding the physical world. After joining DeepMind, he developed models like Transporter Network and co-created the VLA framework. He left in 2025 to found Generalist AI. The company has launched two models: GEN-0, which demonstrated that scaling laws apply to physical motion, and GEN-1. GEN-1 was trained on over 500,000 hours of physical interaction data collected via a specialized wearable device. It achieves a 99% success rate on precise mechanical tasks like folding boxes and maintains performance three times faster than its predecessor. Florence believes GEN-1 is reaching a commercial utility threshold similar to the GPT-3 inflection point. The substantial funding round, following GEN-1's release, signifies strong investor confidence in Generalist AI's practical, goal-driven path to creating versatile, useful robots, regardless of the "world model" terminology.

marsbit1 saat önce

He Just Raised 2.7 Billion, and Li Fei-Fei Also Invested

marsbit1 saat önce

Two Legends Lost in Three Days: Is Google's AI Talent Dam Cracking?

In three days, Google lost two AI legends. On June 18, Noam Shazeer, co-author of the seminal "Attention is All You Need" paper and Gemini co-lead, left for OpenAI. Just 48 hours later, John Jumper, 2024 Nobel laureate and AlphaFold lead, departed DeepMind for Anthropic. This follows Andrej Karpathy joining Anthropic in May. These moves highlight a structural trend: top AI talent is concentrating at mission-driven, pre-IPO firms like OpenAI and Anthropic, while Google becomes a primary source. The exodus stems from a core mission mismatch. Google's ad-centric model often subordinates AI research to product and revenue goals, creating friction for pioneers like Shazeer, who returned in 2024 only to leave again. In contrast, OpenAI and Anthropic offer singular focus on pushing AI boundaries, whether towards AGI or safety-aligned models, which deeply appeals to top researchers like Jumper. Financial incentives amplify the pull. With both OpenAI and Anthropic nearing IPO, employees stand to gain immensely from equity, an upside Google's mature stock cannot match. Furthermore, the 2023 merger of Google Brain and DeepMind, intended to consolidate strength, has instead created cultural tension and slowed the path from research to product, as evidenced by Gemini's pace. This talent redistribution is reshaping the AI landscape. While Google retains vast data and compute resources, its true crisis is the quiet, continuous loss of the people who define the field's future. The real moat in AI is not infrastructure, but the concentration of brilliant minds—a battle Google is currently losing.

marsbit3 saat önce

Two Legends Lost in Three Days: Is Google's AI Talent Dam Cracking?

marsbit3 saat önce

Behind the AI Report Card, Lies a Chinese 'Exam Setter'

Beyond the familiar performance charts like MMLU-Pro and MMMU, which major AI models strive to ace, stands a key "examiner": Chinese-Canadian researcher Wenhu Chen. An assistant professor at the University of Waterloo and founder of TIGERLab, Chen addresses the crucial need for more rigorous AI evaluation. As models like GPT-4 began scoring near-perfect results on older benchmarks like MMLU, it became difficult to distinguish their true capabilities. In response, Chen introduced MMLU-Pro in 2024, featuring harder, more reasoning-focused questions with more answer choices, successfully reintroducing meaningful performance gaps. His work extends to multi-modal evaluation with MMMU and its enhanced version, MMMU-Pro. These benchmarks test a model's ability to understand and reason with complex information from images, charts, and text across diverse academic subjects, exposing the significant challenges even top models face in genuine comprehension. Chen's background in complex QA, table reasoning, and his experience at Google DeepMind on projects like Gemini inform his approach. He understands that effective benchmarks must anticipate how models might "cheat" by memorizing data or avoiding visual analysis. His lab also actively researches video understanding and generation models (e.g., UniVideo, Vamba), ensuring his evaluation work is grounded in practical model-building challenges. Now at Meta's Super Intelligence Lab, Chen continues his focus on multi-modal data and evaluation, representing the deep yet often unseen contributions of Chinese talent in shaping the fundamental tools of the AI industry.

marsbit3 saat önce

Behind the AI Report Card, Lies a Chinese 'Exam Setter'

marsbit3 saat önce

İşlemler

Spot
Futures
活动图片