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

Upbit Rebalances 864B SHIB In Internal Wallet Move

South Korean cryptocurrency exchange Upbit moved a total of 864 billion SHIB (worth approximately $4 million) between its internal wallet addresses. The activity, involving transfers of 384 billion SHIB outbound and 480 billion SHIB inbound, occurred following a 36% SHIB price rally. On-chain data indicates this was a routine internal wallet rebalancing operation by the exchange, not a sell-off or liquidation event. The article cautions that while large on-chain transfers are highly visible, they require proper context and wallet labeling to interpret correctly. In this case, the movement between known Upbit addresses does not inherently signal a change in market supply or selling pressure for SHIB.

bitcoinistHá 9m

Upbit Rebalances 864B SHIB In Internal Wallet Move

bitcoinistHá 9m

Can Generative Models Finally Be Trained End-to-End? The Core Is a For Loop

This article introduces a novel training paradigm for generative models called Explorative Modeling (XM), which enables true end-to-end training. Traditionally, powerful generative models like autoregressive and diffusion models are not trained end-to-end. They are trained to predict a single small step but require iterative multi-step sampling for inference. This "exposure bias" leads to error accumulation and limits performance. The core challenge XM addresses is "mode blurring." In generative tasks, a single input (e.g., "generate a dog") corresponds to many valid outputs (multiple modes). Standard training objectives like reconstruction loss force the model to average these modes, producing unrealistic, blurry outputs. To avoid this, existing models break generation into many small, almost deterministic steps, sacrificing end-to-end training. XM tackles this by restructuring the training loop itself. Its key insight is to amplify "generative expressivity." For each training input, instead of generating one sample, the model generates K candidate outputs. Only the candidate closest to the real data is used for computing the loss and updating the model via backpropagation. This simple "best-of-K" mechanism is implemented as a short for-loop. By exploring multiple possibilities, the model learns to distribute its guesses across different modes rather than collapsing to their uninformative average. The paper demonstrates that "exploration" acts as a new, powerful scaling axis. Gains from XM increase with model size, data scale, and compute. Experiments show improvements in FID scores for image generation and significant efficiency gains, sometimes outperforming larger models without exploration. When pushed to the limit, XM enables fully single-step, end-to-end generative models. In robotics tasks, an "Explorative Policy" matched the performance of a 100-step Diffusion Policy with a single forward pass, drastically improving inference speed. While the best-of-K concept is not entirely new, the authors' contribution lies in formally understanding it as a direct method to boost generative expressivity without fragmenting the generation process. This work suggests that as models scale, enhancing exploration during training may become crucial for overcoming fundamental performance bottlenecks.

marsbitHá 9m

Can Generative Models Finally Be Trained End-to-End? The Core Is a For Loop

marsbitHá 9m

A Maximum of 20 Submissions Per Person? DeepMind Researcher Satirically 'Petitions' Against ICLR's New Rule

The International Conference on Learning Representations (ICLR) 2027 has introduced a new submission rule limiting authors to a maximum of 20 papers per conference. This policy, announced due to a 68% surge in submissions for ICLR 2026 that strained the review system, aims to ensure fair and informative peer review. Google DeepMind researcher Dan Roy responded with a sarcastic petition on X, calling the 20-paper cap "absurd" and claiming it would slow AI progress. His post mockingly argued that AI agents can now produce incremental research and that large language models (LLMs) already handle all reviewing, so output should be maximized. This satire critiques the broader trend of AI's role in academia, referencing a 2025 study that found about 21% of ICLR 2026 reviews were likely fully AI-generated. Roy's underlying point questions whether simply capping submissions addresses the systemic issue of AI potentially flooding conferences with agent-written papers and AI-assisted reviews. The debate highlights tensions between managing submission volume and maintaining research quality as AI tools become pervasive.

marsbitHá 14m

A Maximum of 20 Submissions Per Person? DeepMind Researcher Satirically 'Petitions' Against ICLR's New Rule

marsbitHá 14m

Coldcard Vulnerability Leads to $89 Million Theft, Sparking Largest On-Chain Migration Since FTX Collapse

A security vulnerability in Coldcard hardware wallets led to the theft of 1,367.05 BTC (approx. $89 million) from 4,585 addresses, according to Galaxy Research. The flaw, caused by insufficient randomness in seed phrase generation for specific firmware versions, triggered a massive migration of funds as users moved assets to new, secure wallets. This event caused the largest on-chain movement of small Bitcoin transactions since the FTX collapse, distorting key market indicators like active addresses and exchange deposits, and significantly dampening market sentiment. The incident also highlighted challenges in cybersecurity investigations, as researchers reported that safety guardrails on mainstream U.S. AI models hindered the analysis of attack patterns, forcing them to use an open-source Chinese AI model for forensic work. This underscores a dilemma in balancing AI security restrictions with the need for rapid response in tracing stolen crypto assets.

marsbitHá 59m

Coldcard Vulnerability Leads to $89 Million Theft, Sparking Largest On-Chain Migration Since FTX Collapse

marsbitHá 59m

Annual Salary of Millions Competing for Electricians, Meta Rushes to Open Its Own Technical School

The AI boom is facing an unexpected bottleneck: a severe shortage of skilled construction workers and electricians. As tech giants like Meta, OpenAI, and Alphabet race to build massive data centers—such as OpenAI's $16 billion "Stargate" project—they are hitting a critical labor wall. The U.S. needs an estimated 130,000 more electricians, 240,000 construction workers, and 150,000 supervisors by 2030 for AI infrastructure alone, but tens of thousands of electrician jobs go unfilled each year. While AI companies offer high premiums, with electricians earning up to $280,000 annually, worker scarcity still causes massive losses—delays on a single project can cost $14.2 million per month. The complexity of building AI data centers, which require immense power (equivalent to powering hundreds of thousands of homes), sophisticated electrical systems, and advanced liquid cooling solutions, demands highly skilled technicians who are in short supply. To combat this, companies are investing heavily in training. Meta has committed $115 million to a free training school offering tuition, housing, and stipends, targeting 5,000 new workers. OpenAI is partnering with unions to secure skilled labor. These efforts are paying off, with a significant rise in Gen Z interest in trade schools over college. However, the power demands are staggering. AI data centers are driving a rapid surge in electricity consumption, projected to account for up to 12% of U.S. power use by 2028 and raising costs for consumers. Furthermore, the construction boom is project-based, leading to a potential future glut of trained workers once building peaks, which could depress wages industry-wide. The race for AI supremacy now depends as much on skilled hands as on advanced chips.

marsbitHá 1h

Annual Salary of Millions Competing for Electricians, Meta Rushes to Open Its Own Technical School

marsbitHá 1h

Trading

Spot
活动图片

Categorias popularesright-arrow

Etiquetas Popularesright-arrow