Visa Deepens Crypto Push With New Stablecoin Advisory Unit

bitcoinist2025-12-17 tarihinde yayınlandı2025-12-17 tarihinde güncellendi

Özet

Visa has launched a new stablecoin advisory service through its Visa Consulting & Analytics division to help banks, fintechs, and merchants integrate stablecoins into payments, treasury operations, and other business processes. The service includes market research, strategy development, technical support, and training via Visa University. Early clients include US financial institutions like Navy Federal Credit Union and Pathward. Visa reports an annualized stablecoin settlement volume of $3.5 billion and supports over 130 stablecoin-linked card programs globally. The move comes as the stablecoin market exceeds $250 billion, reflecting growing institutional and retail adoption.

According to reports, Visa has created a new stablecoin advisory service through its Visa Consulting & Analytics (VCA) division.

The initiative is designed to help banks, fintechs, merchants, and other large companies explore how stablecoins can be used for payments, treasury operations, and other business processes.

The program combines market research, technology integration support, and training to give clients practical guidance as they test and adopt stablecoin solutions.

Visa Expands Advisory Services

Sources indicate the advisory practice will provide market-fit assessments, strategy development, go-to-market planning, and technical enablement.

Visa has even launched a specialized stablecoin course via Visa University to train clients on the fundamentals and practical application of these digital assets.

The service aims to help organizations move from pilot programs to fully operational stablecoin systems while maintaining compliance with regulatory requirements.

Early Clients And Market Context

Based on reports, several US financial institutions are among the first clients, including Navy Federal Credit Union, VyStar Credit Union, and Pathward.

The digital payments technology company has reported that its stablecoin settlement volume has reached an annualized run rate of roughly $3.5 billion as of late November 2025. The company supports over 130 stablecoin-linked card programs across more than 40 countries.

The overall stablecoin market has surpassed $250 billion in total value, highlighting strong interest from both retail and institutional participants.

Partnerships And Pilot Programs

Reports show Visa has been piloting stablecoin settlements for several years, including early work with USDC in 2023. Partnerships with firms like Aquanow have expanded settlement capabilities in regions such as Central and Eastern Europe, the Middle East, and Africa.

Visa also experimented with initiatives allowing businesses to make cross-border payments using stablecoins for pre-funding, in an effort to lower transaction costs and manage liquidity.

Total crypto market cap at $2.94 trillion on the daily chart: TradingView

The Importance And Rise Of Stablecoins

According to analysts quoted in news reports, the use of stablecoins by banks and fintechs for various purposes such as cross-border payments and business-to-business payments is being explored.

The advisory service offered by Visa assists the traditional firm in understanding the options available to them while implementing controls and incorporating new technology into existing payment systems. Such overall expertise in technology and regulations puts the company on their advisory panel to pilot their stablecoin offering.

Featured image from Wikimedia, chart from TradingView

İlgili Okumalar

a16z: Top Talent Flows to AI Infrastructure, Infrastructure Design Will Be 'Redesigned from Scratch'

a16z Unveils "Machine Age Fund": AI Infrastructure Faces Massive Overhaul Silicon Valley VC giant a16z (Andreessen Horowitz) has launched a new "Machine Age Fund" dedicated to AI infrastructure, citing a vast and growing "supply-demand fracture." Key takeaways: * **Unlimited Demand vs. Constrained Supply:** AI demand is growing exponentially (estimated near 1000% annually for tokens), while supply chains for chips, memory, data centers, and power are booked through 2027-2028. GPU prices are rising against historical trends. * **A Resource Problem, Not Engineering:** The bottleneck is no longer software engineering but physical resources (hardware, power, cooling). Money and compute directly translate to intelligence output, removing traditional scaling limits. * **Complete Infrastructure Rebuild Needed:** Existing data centers and computing stacks, designed for a different era, are hitting physical limits. Everything needs rethinking from first principles: chip architecture, memory hierarchy, networking, power delivery (shifting to 800V DC), and cooling (moving to liquid). * **Investor & Founder Shift:** Top entrepreneurs are increasingly moving into hardware, with deals in the space rising from ~3-5% to over 20-30% of a16z's top-tier deal flow. Founders need to be "systems thinkers" who understand manufacturing and supply chains. * **Massive Economic Scale:** Training a frontier model now costs $3-5B. With inference needing to recoup ~$10B, saving 20% in efficiency ($2B) can justify developing a custom ASIC for a single model—a previously unthinkable economic dynamic. * **Long-Term Horizon:** a16z believes we are in the very early stages of a decades-long era where compute is applied to vast new domains (science, materials, biology, creative work). The firm re-frames AI as "Machine Intelligence," emphasizing the critical, foundational role of hardware in this new age.

marsbit31 dk önce

a16z: Top Talent Flows to AI Infrastructure, Infrastructure Design Will Be 'Redesigned from Scratch'

marsbit31 dk önce

OpenAI Buys Tens of Thousands of Macs for Reinforcement Training! NVIDIA's Job Gets Snatched by Apple

OpenAI is reported to have purchased tens of thousands of Mac mini and Mac Studio computers for reinforcement learning tasks, specifically to train "computer-use agents." This surge in demand from AI companies like Anthropic as well has made Mac Apple's fastest-growing product line. Apple is promoting its hardware's suitability for local AI processing, highlighting advantages like the unified memory architecture of M-series chips and robust cooling in desktop Macs. However, this unexpected enterprise demand has led to supply shortages for high-end configurations. Nvidia has taken notice, launching its DGX Spark desktop AI computer as a direct competitor. While Apple has begun building internal servers with its chips for AI tasks, it currently does not sell them, relying instead on partners to expand its enterprise reach.

marsbit43 dk önce

OpenAI Buys Tens of Thousands of Macs for Reinforcement Training! NVIDIA's Job Gets Snatched by Apple

marsbit43 dk önce

ChatGPT's New Model Bel Rumored to Have Completed Pre-training with a Staggering 10 Trillion Parameters

ChatGPT's new model "Bel," reportedly pre-trained with a staggering 10 trillion parameters, signals OpenAI's aggressive push toward AGI. This "monster" model, succeeding the "Doug" base model and targeting the post-GPT-6 era, allegedly surpasses the upcoming Astra model in coding, reasoning, and long-horizon agent tasks. It can operate autonomously for days, self-recover, and coordinate hundreds of sub-agents. The report claims OpenAI has consolidated computing power, even pausing projects like Sora, to focus on these massive models. With its proprietary "Jalapeño" chips and significant computational advantage, OpenAI reportedly believes competitors like Anthropic cannot keep pace. Internally, OpenAI is said to be achieving recursive self-improvement (RSI), using AI to optimize its own systems and drastically cut costs. Future visions include ultra-fast, low-latency AI enabling real-time "flow state" interaction, a shift to cloud-based agent clusters over local compute, and the emergence of a "Personal AGI" that passively understands and proactively assists users. While the specifics of Bel remain unconfirmed, the leaks portray an AI race where scale and compute are key, with OpenAI positioning itself to maintain a dominant lead through 2026-2027.

marsbit51 dk önce

ChatGPT's New Model Bel Rumored to Have Completed Pre-training with a Staggering 10 Trillion Parameters

marsbit51 dk önce

Claude's Safety Mechanism Backfires Spectacularly, AI Deletes Developer's 700GB Home Directory in a Fit of Rage

Claude's safety mechanisms backfired spectacularly, leading an AI to delete a developer's entire 700GB home directory. The developer, Guillemot, asked Claude Fable 5 to write a script to create isolated sandbox folders in /tmp for AI agents and clean them up afterward, ensuring files in use wouldn't be deleted. Because the task involved risky delete operations, Claude's built-in safety system triggered an "adversarial review," automatically downgrading the model from the capable Fable 5 to the more conservative Opus 5, and then to Opus 4.8, to perform a security check. The Opus 4.8 model successfully tested the script, correctly identifying the /tmp and home directories as off-limits for deletion. However, during the cleanup phase that followed the test, it reused a variable name from the testing phase. This variable contained the path to the user's home directory. Consequently, the model executed a delete command on that exact path it had just deemed unsafe, wiping out 700GB of data. The intended /tmp directory remained untouched. The incident highlights critical flaws in Anthropic's safety downgrade system, which community developers have criticized for being overly sensitive, reducing model capability for complex tasks, and being "sticky" for an entire session once triggered. The irony is that a mechanism designed to hand off dangerous tasks to a safer, more conservative model instead delegated it to a weaker model that made a catastrophic error in variable scope and path handling.

marsbit52 dk önce

Claude's Safety Mechanism Backfires Spectacularly, AI Deletes Developer's 700GB Home Directory in a Fit of Rage

marsbit52 dk önce

Starting from Anthropic, Dissecting the Hyperliquid Perpetual Contract Sector

The article explores the emergence of pre-IPO perpetual synthetic asset markets on Hyperliquid's HIP-3 protocol, focusing on platforms like Ventuals, Trade.xyz, and Entropy. It begins with the intense secondary market demand for Anthropic stock, as illustrated by Jesse Leimgruber's experience. The HIP-3 protocol allows anyone to launch a perpetual DEX by locking $40M, with 30% of Hyperliquid's volume flowing through it. The piece details the rise and fall of Ventuals, the first major platform for trading pre-IPO synthetics like Anthropic and SpaceX. Its failure was due to extreme funding rates (reportedly hitting 8,700% annualized for Anthropic) and a pricing model vulnerable to thin liquidity, leading to a 45% crash in a SpaceX contract. Trade.xyz succeeded by using a simple 30-minute internal TWAP for pricing and dominates HIP-3 volume. Entropy, backed by a $14M Ribbit Capital-led round, attempts to improve on Ventuals by capping funding rates and using a hybrid oracle that blends its order book with private market valuations, while pricing Anthropic by total market cap. However, it struggles with accurately pricing private companies like Anthropic, unlike public stocks like SanDisk (SNDK) where arbitrage bots align prices. The article concludes by noting a potential Kraken testnet deployment on HIP-3, suggesting regulated entities may adopt its technology within permissioned frameworks. The evolution of these platforms highlights the challenges and iterative progress in creating decentralized markets for private company exposure.

marsbit1 saat önce

Starting from Anthropic, Dissecting the Hyperliquid Perpetual Contract Sector

marsbit1 saat önce

İşlemler

Spot
活动图片