Visa creates stablecoin advisory team as onchain dollars go mainstream

cointelegraph2025-12-15 tarihinde yayınlandı2025-12-15 tarihinde güncellendi

Özet

Visa has launched a global Stablecoins Advisory Practice to assist banks, merchants, and fintechs in designing and managing stablecoin products. The initiative focuses on enhancing payment efficiency through training, market analysis, and technical integration support. This move reflects the growing significance of onchain dollar transactions, with Visa already processing billions in USDC settlements annually. The trend highlights a broader industry shift toward stablecoins for faster, cheaper payments, as seen with companies like Stripe and PayPal. This evolution is reshaping Bitcoin’s role, positioning it more as a store of value rather than a medium for everyday transactions.

Visa has launched a global Stablecoins Advisory Practice, a new unit that will help banks, merchants, and fintechs design, roll out, and manage stablecoin products.

The payments giant said Monday that the new advisory arm will focus on practical questions that traditional players struggle with, and offer stablecoin training and market trends programs, go-to-market planning, and technology enablement for stablecoin integration.

“Stablecoins may represent an opportunity to enhance speed and lower cost in payments, so with the support of Visa, we are evaluating how this technology could fit into our broader strategy to deliver meaningful value to our 15 million members worldwide,” Matt Freedman, senior vice president, Navy Federal Credit Union, said.

The move indicates that onchain dollars are now significant enough to warrant their own dedicated business line within one of the world’s largest payment networks, and it’s not a greenfield bet.

With the Stablecoins Advisory Practice, Visa is wrapping a consultancy around infrastructure it has been building out quietly for several years, including more than 130 stablecoin‐linked card programs across 40‐plus countries and billions of dollars in annualized USDC (USDC) settlement volume on its network.

Visa has launched a global Stablecoins Advisory Practice. Source: Visa

Related: Visa doubles down on stablecoins in Europe, Middle East, Africa with new partnership

A broader pivot toward stablecoin rails

The timing fits a broader pivot in how mainstream firms approach crypto. Stablecoins, rather than volatile assets like Bitcoin (BTC), are becoming the default way to use blockchains for payments.

Stripe has rolled out stablecoin payouts and accounts, pitching them as faster, cheaper options for global creators and platforms.

PayPal is pushing its PayPal USD (PYUSD) dollar token deeper into its own ecosystem, including YouTube creator payouts in the United States, and JPMorgan’s JPM Coin continues to expand as an institutional settlement rail.

Related: Spark integrates PayPal USD into its stablecoin lending markets

What this means for Bitcoin’s role

That rise of onchain dollars is starting to eat into narratives that once belonged to Bitcoin. In November, ARK Invest CEO Cathie Wood trimmed her 2030 Bitcoin price target from $1.5 million to $1.2 million, explicitly citing stablecoins taking over some of the functions she once expected Bitcoin to fulfill in payments and emerging markets.

The change doesn’t kill her long‐term “digital gold” thesis for BTC, but it does acknowledge that, in practice, the asset people want to spend or use to escape broken local banking systems is often a dollar on a blockchain rather than a volatile bearer asset.

Visa’s new stablecoin advisory business underlines this shift. Household‐name processors are now coaching banks and fintechs on stablecoin strategy, which means they’re betting that stablecoins will dominate the transactional “money” use case. At the same time, Bitcoin is settling into a more defined role as macro collateral and a long-term store of value.

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