UK tests stablecoins in regulatory sandbox while proposing limits on user holdings

ambcryptoОпубліковано о 2026-02-25Востаннє оновлено о 2026-02-25

Анотація

UK regulators are advancing a dual approach to stablecoin regulation, combining live testing within the Financial Conduct Authority’s sandbox with proposed limits on user holdings. The sandbox includes firms like Revolut and Monee Financial Technologies, trialing applications in payments, settlement, and crypto trading under strict supervision. Concurrently, the Bank of England has proposed temporary caps—£20,000 per stablecoin for individuals and £10 million for businesses—to mitigate systemic risks such as bank deposit flight. This cautious "test and contain" strategy aims to foster innovation in pound-denominated stablecoins while preventing rapid scaling until the market matures. The UK’s approach reflects the minor global role of non-dollar stablecoins and focuses on ensuring safe integration into the existing financial system.

UK regulators have begun live testing of stablecoin use cases under a controlled sandbox programme. This comes even as policymakers move to impose limits on the amount of stablecoin users can hold domestically.

The parallel initiatives underscore a cautious regulatory approach: encouraging experimentation with pound-denominated stablecoins while putting guardrails in place to prevent them from becoming systemically significant too quickly.

FCA sandbox moves stablecoin testing into live trials

The testing is taking place under a sandbox run by the Financial Conduct Authority, which allows firms to trial new financial products under regulatory supervision.

The sandbox includes a small group of participants assessing potential applications such as payments, wholesale settlement, and crypto trading. Revolut, Monee Financial Technologies, ReStabilise, and VVTX would be part of the testing.

Work is expected to begin this quarter, with testing conducted in tightly controlled conditions rather than through public rollouts.

Notably, the trials do not include major high-street banks, reflecting the continued caution among large UK lenders toward issuing or distributing stablecoins.

Proposed holding caps signal prudence

Running alongside the sandbox is a separate proposal from the Bank of England to cap the amount of stablecoins that UK users can hold, at least during the early phase of adoption.

Under the proposal, individuals would face limits on holdings of a given systemic stablecoin. At the same time, businesses would be subject to higher thresholds.

Individuals will be allowed up to about £20,000 per coin, while companies will be allowed up to about £10 million per coin.

The measures are intended as temporary safeguards to mitigate risks such as deposit flight from traditional banks and rapid outflows during periods of stress.

The caps would apply only to stablecoins deemed systemic and are designed to be revisited as the market matures.

UK takes a “test and contain” approach

Taken together, the sandbox and holding-limit proposals reflect a regulatory strategy that prioritises learning and containment over rapid scale.

While regulators acknowledge that stablecoins could improve efficiency in areas like payments and settlement, they remain wary of their potential to blur the line between bank deposits and crypto-based instruments.

The Bank of England has previously encouraged banks to focus on tokenised deposits rather than issuing stablecoins directly.

Pound stablecoins remain marginal globally

The UK’s measured stance also reflects the limited global footprint of non-dollar stablecoins.

Industry data shows that pound- and euro-denominated stablecoins account for a fraction of global circulation, with dollar-pegged tokens continuing to dominate volumes.

Against that backdrop, the sandbox is less about accelerating mass adoption and more about determining whether sterling stablecoins can operate safely within the existing financial system.


Final Summary

  • The UK is allowing stablecoin testing to proceed, but only within tightly controlled regulatory boundaries.
  • Proposed holding limits suggest policymakers want to manage systemic risk before stablecoins reach meaningful scale.

Пов'язані питання

QWhat is the purpose of the UK's regulatory sandbox for stablecoins?

AThe purpose is to allow firms to trial new financial products, including stablecoin use cases for payments, wholesale settlement, and crypto trading, under the supervision of the Financial Conduct Authority (FCA) in a controlled environment.

QWhich companies are mentioned as participants in the FCA's stablecoin sandbox testing?

AThe participants mentioned are Revolut, Monee Financial Technologies, ReStabilise, and VVTX.

QWhat are the proposed holding limits for stablecoins in the UK according to the Bank of England?

AIndividuals would be allowed to hold up to about £20,000 per systemic stablecoin, while companies would be allowed up to about £10 million per coin.

QWhy are UK regulators proposing limits on stablecoin holdings?

AThe limits are proposed as temporary safeguards to mitigate risks such as deposit flight from traditional banks and rapid outflows during periods of stress, managing systemic risk before stablecoins reach a significant scale.

QWhat does the UK's regulatory approach to stablecoins prioritize, according to the article?

AThe regulatory strategy prioritizes learning and containment over rapid scale, encouraging experimentation with pound-denominated stablecoins while implementing guardrails to prevent them from becoming systemically significant too quickly.

Пов'язані матеріали

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.

marsbit20 хв тому

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

marsbit20 хв тому

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.

marsbit26 хв тому

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

marsbit26 хв тому

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.

marsbit2 год тому

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

marsbit2 год тому

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.

marsbit2 год тому

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

marsbit2 год тому

Торгівля

Спот
Ф'ючерси
活动图片