Standard Chartered, AirAsia parent to test ringgit stablecoin in Malaysia

cointelegraphPublicado a 2025-12-12Actualizado a 2025-12-12

Resumen

Standard Chartered Bank Malaysia and Capital A, the parent company of AirAsia, have signed a letter of intent to jointly explore a ringgit-pegged stablecoin. The initiative falls under Malaysia’s Digital Asset Innovation Hub, a regulatory framework introduced by the central bank. Standard Chartered will serve as the issuer, while Capital A and its ecosystem partners will develop and pilot wholesale use cases. This move aligns with Malaysia’s broader effort to modernize its financial system with digital assets, supported by recent regulatory developments including a central bank roadmap for asset tokenization and the formation of an industry working group.

Standard Chartered Bank Malaysia and Capital A, the parent company of AirAsia, plan to jointly explore a stablecoin pegged to Malaysia’s local currency, the ringgit.

In a statement Friday, the bank’s Malaysian arm and Capital A said they signed a letter of intent to explore a ringgit-pegged stablecoin under the country’s Digital Asset Innovation Hub, an initiative announced by Bank Negara Malaysia (BNM) in June.

This is Capital A’s first interaction with the regulated digital asset space. The initiative will rely on Standard Chartered’s infrastructure and financial expertise, as well as Capital A’s ecosystem, to pilot the stablecoin in a wholesale fashion, rather than focusing on the retail market.

Standard Chartered Malaysia will serve as the issuer of the stablecoin, while Capital A and companies within its ecosystem will be tasked with developing, testing, and piloting wholesale use cases.

Source: Air Asia

Related: Malaysian regulator proposes easing crypto asset listing process

Malaysia is not getting left behind

Malaysia is moving to ensure it is not left behind as more countries weave crypto and stablecoins into mainstream finance. Capital A’s announcement said the effort “supports the aspirations of Malaysia,” positioning the stablecoin work as part of a broader national push to modernize payments and capital markets with digital asset technology.

That direction appears to have backing at the highest levels. The eldest son of Malaysia’s billionaire king recently launched a stablecoin pegged to the national currency. The Digital Asset Innovation Hub allows fintech and digital asset firms to test new technologies under BNM oversight.

Related: Illegal crypto mining surges in Malaysia amid unclear policies

Last month, BNM also unveiled a three-year roadmap to explore and test asset tokenization across the financial sector while building on the regulatory sandbox framework. The roadmap anticipates that the institution will launch proof-of-concept projects and conduct live pilots.

The central bank also decided to create an Asset Tokenization Industry Working Group to coordinate industry-wide exploration, share knowledge and identify regulatory and legal challenges in the country.

Malaysia has been considering a change in its approach to the digital asset industry since the beginning of 2025. In mid-January, the local government reportedly began exploring the possibility of establishing a cryptocurrency policy that could recognize the industry and modernize the nation’s financial system.

Lecturas Relacionadas

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.

marsbitHace 1 hora(s)

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

marsbitHace 1 hora(s)

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.

marsbitHace 1 hora(s)

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

marsbitHace 1 hora(s)

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.

marsbitHace 3 hora(s)

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

marsbitHace 3 hora(s)

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.

marsbitHace 3 hora(s)

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

marsbitHace 3 hora(s)

Trading

Spot
Futuros
活动图片