Zodia Custody Enables Institutional Access to Australia’s First Regulated Stablecoin

TheNewsCryptoPublicado em 2026-01-09Última atualização em 2026-01-09

Resumo

Zodia Custody, a global institutional custodian, now supports AUDM, Australia's first regulated Australian dollar stablecoin. Issued by the licensed Marcopod, AUDM is pegged 1:1 to the AUD and backed by fiat in segregated trust accounts. Zodia provides secure cold storage custody, enabling banks and large institutions to hold AUDM without managing private keys. This enhances regulatory confidence and supports use cases like real-time payments, settlements, and smart contract automation. Australia is advancing stablecoin integration into regulated finance, as seen with regulatory simplifications and participation in the RBA’s Project Acaci, shifting stablecoins from crypto trading to mainstream financial infrastructure.

Zodia Custody, a highly secured vault for Crypto, has become the first global crypto custodian to support AUDM, which is Australia’s first regulated Australian dollar stablecoin. This move makes it safer and more secure for the Banks, funds, and large institutions to safely use stablecoins in Australia.

AUDM Gains Institutional-Grade Custody as Zodia Enables Secure Access

AUDM is a stablecoin pegged 1:1 to the Australian dollar. It is issued by Marcopod, which is Australia’s first licensed stablecoin issuer that holds an Australian Financial Service License (AFSL). Every token is backed by the real AUD, which is stored in segregated trust accounts at major Australian banks. These can be used in smart contracts and automated payments. The stablecoin was launched in October 2025 as an ERC-20 token on Ethereum with the plan to expand to other blockchains.

Zodia now offers cold storage custody for AUDM in the highest security standards. Institutions can now hold AUDM safely without managing Private keys themselves. This gives regulatory confidence to banks and large Financial firms. Zodia is backed by major financial institutions and national Australian banks, which adds more trust.

Basically, AUDM is programmable money, and it can be used for real-time payments and settlements, tokenized cash, on-chain financial products, cross-border transactions in AUD, and Automated payments using smart contracts. This helps both the traditional finance and crypto-native businesses.

Australia Pushes Stablecoins Into Regulated Financial Infrastructure

Stablecoins are moving into mainstream financial infrastructure, and Australia is positioning itself as a regulated and institutionally friendly crypto market. Australia’s Regulator ASIC recently simplified stablecoin rules that remove the need for a separate financial services license just to handle stablecoins. AUDM was also tested through the Reserve Bank of Australia’s Project Acaci, which is focused on tokenized settlements. This regulatory clarity made it easier for the institutions to adopt AUDM.

This shows that Australia is moving Stablecoins into the regulated financial system, and institutions now have safe and compliant access to AUD-based digital money. Stablecoins are now shifting from the Crypto trading tools to real-time payments and settlement infrastructure.

Highlighted Crypto News:

World Liberty Financial (WLFI) Struggles to Recover: Is a Turnaround Still on the Table?

TagsAustraliaStablecoinZodia

Perguntas relacionadas

QWhat is AUDM and who is the issuer?

AAUDM is Australia's first regulated Australian dollar stablecoin, pegged 1:1 to the Australian dollar. It is issued by Marcopod, Australia's first licensed stablecoin issuer holding an Australian Financial Service License (AFSL).

QHow does Zodia Custody's support for AUDM benefit institutional users?

AZodia Custody provides institutional-grade cold storage for AUDM, allowing banks, funds, and large institutions to hold the stablecoin securely without managing private keys themselves, thereby enhancing regulatory confidence and security.

QWhat regulatory developments in Australia have facilitated the adoption of stablecoins like AUDM?

AAustralia's regulator ASIC simplified stablecoin rules by removing the need for a separate financial services license to handle stablecoins. AUDM was also tested in the Reserve Bank of Australia's Project Acaci, providing regulatory clarity and easing institutional adoption.

QWhat are the primary use cases for AUDM as programmable money?

AAUDM can be used for real-time payments and settlements, tokenized cash, on-chain financial products, cross-border transactions in AUD, and automated payments using smart contracts, benefiting both traditional finance and crypto-native businesses.

QHow is AUDM backed to ensure its 1:1 peg to the Australian dollar?

AEach AUDM token is backed by real Australian dollars stored in segregated trust accounts at major Australian banks, ensuring a 1:1 peg and providing transparency and security for users.

Leituras Relacionadas

From South Korea to the United States: Blue-Collar Jobs Are Becoming Increasingly Popular, Thanks to AI

AI is reshaping the labor market's value proposition. The traditional four-year college degree is losing its appeal as a guaranteed career path, while skilled blue-collar trades like electricians, welders, and plumbers are experiencing historic demand and wage premiums. This shift is driven by dual pressures: AI's displacement of certain white-collar roles and a booming need for physical infrastructure and data center construction. Data confirms the trend. In the U.S., vocational school revenue surged, and a significant portion of recent layoffs are AI-related. Surveys show a majority of Gen Z adults plan to pursue blue-collar work, citing better job security against AI automation. Vocational education interest has exploded recently. Experts cite a psychological shift as younger generations seek tangible, AI-resistant careers and avoid high student debt. In many cases, salaries for skilled trades now match or exceed those requiring a bachelor's degree. In South Korea, semiconductor vocational high schools boast near-total employment, with graduates securing high-paying roles at companies like Samsung. The shortage is structural, exacerbated by a retiring baby boomer workforce and massive infrastructure projects. Companies like JPMorgan Chase, Meta, and Lowe's are investing heavily in training programs. However, overcoming historical stigma and a "perception gap" around trade careers remains a key challenge to closing the talent gap.

marsbitHá 36m

From South Korea to the United States: Blue-Collar Jobs Are Becoming Increasingly Popular, Thanks to AI

marsbitHá 36m

Qualcomm: AI Hype Subsides, When Will Smartphones Emerge from the Gloom?

Qualcomm reported its Q3 FY2026 results (ending June 2026), with revenue of $9.95B, down 4% YoY but above expectations. Gross margin declined to 53.1%, pressured by rising costs across manufacturing and memory. Key business segments showed mixed performance: Handset revenue fell 19.6% YoY to $5.09B, dragged by an 11% decline in non-Apple Android shipments and weaker high-end mix. Conversely, Automotive revenue surged 61% to $1.59B, and IoT grew 9% to $1.83B. Core operating profit dropped 41% YoY due to margin compression and higher expenses. Management's Q4 FY2026 guidance projects revenue of $9.7B-$10.5B, in line with consensus, but Non-GAAP EPS guidance of $2.05-$2.25 fell short of expectations. Amidst persistent weakness in its core handset market, Qualcomm is pursuing growth in AI, focusing on Edge AI (smartphones, PCs, automotive) and Data Center AI. Its data center strategy includes four pillars: AI accelerators (e.g., AI200), commercial CPUs (Dragonfly C1000), custom silicon, and connectivity solutions. While these initiatives initially boosted its stock, concerns over AI capital expenditure sustainability have since erased those gains. The company targets $5B in data center revenue for FY2027 and $15B for FY2029. The report concludes that with the traditional handset business still under pressure, the data center opportunity is currently viewed as a longer-term option, and a more conservative valuation based on core operations may be warranted until AI contributions materialize.

marsbitHá 40m

Qualcomm: AI Hype Subsides, When Will Smartphones Emerge from the Gloom?

marsbitHá 40m

From TPU to Self-Evolving Agents: How Jeff Dean Predicts the Next Step in AI

At the 2026 YC Startup School, Jeff Dean outlined his vision for AI's next phase, shifting focus from simply scaling models to building intelligent, autonomous systems. He believes AI's progress is no longer just about creating smarter models, but about integrating them into systems capable of long-term, iterative work, automated experimentation, and continuous learning. This evolution moves the competition from "who has the bigger model" to "who can best organize intelligence." Dean suggests AI capabilities are now comparable to a junior engineer, enabling the automation of complex workflows. However, the true challenge and opportunity lie in managing these AI "workers" at scale. He emphasizes the importance of **context engineering**—structuring tools, memory, and feedback loops—over raw model power. For startups, this means building deep expertise in niche domains where general models currently fail (near 0-1% success rates), leveraging proprietary data, specialized tools, and domain-specific evaluators. A recurring theme is re-examining fundamental constraints. Dean's past work, like moving Google's search index to memory or creating the TPU, stemmed from questioning outdated assumptions about hardware and cost. He sees similar inflection points today, particularly in **specialized inference hardware** to drastically reduce latency and energy consumption for real-time Agent operation. Notably, he points out that in modern AI systems, the dominant cost is often not computation but **data movement**. Reliable, long-running Agents require robust system design, borrowing concepts from distributed computing like checkpointing, state management, and parallel exploration to handle failures and maintain progress over days or weeks. As AI automates execution, the scarcest human skills will shift to **defining clear specifications**, **judging what problems are worth solving** (taste), and designing effective feedback loops. Ultimately, Dean's framework prioritizes understanding the problem deeply, identifying the true bottlenecks, and systematically building closed-loop systems where AI can not only perform tasks but also improve AI itself.

marsbitHá 40m

From TPU to Self-Evolving Agents: How Jeff Dean Predicts the Next Step in AI

marsbitHá 40m

Trading

Spot
活动图片