XRP Wins Rare Recognition From Former US Regulator

bitcoinistОпубліковано о 2026-02-06Востаннє оновлено о 2026-02-06

Анотація

Former CFTC Chair Chris Giancarlo praised XRP for maintaining market relevance despite prolonged U.S. regulatory pressure, particularly during the SEC v. Ripple case from 2020 to 2025. He emphasized that XRP's resilience, community support, and continuous operation were key factors. Giancarlo argued that clearer regulations are needed for major banks to accelerate blockchain adoption, citing use cases like cross-border transfers and asset tokenization. He envisions a multi-chain financial future with roles for Ethereum, XRPL, Canton, and others. Despite recent price volatility and selling pressure, XRP's on-chain activity remains strong, indicating sustained network usage.

Former CFTC Chair Chris Giancarlo has given public praise to XRP, calling attention to how the token stayed active and relevant through extended US regulatory pressure.

According to reports, he singled out the period tied to regulators like Gary Gensler and Senator Elizabeth Warren as especially hostile, and asked observers to acknowledge XRP’s ability to hold its place in the market.

Giancarlo On Regulatory Pressure

Based on reports, Giancarlo used plain language to make a point about tough oversight and market endurance. He described XRP as having been treated as the figurehead for aggressive enforcement moves.

The SEC v. Ripple case, which began in December 2020 and ended with a settlement in August 2025, was put forward as a turning point.

Community backing and continuous network operation during that long legal fight were mentioned as reasons the token remained in the conversation.

He urged respect for that outcome. The line of thought was clear: rules matter before big banks will fully commit.

Banks Are Waiting For Clear Rules

Reports say Giancarlo expects banks to speed up blockchain adoption once legal guidelines become clearer. He highlighted use cases that banks already test, like faster cross-border transfers, faster settlement, and tokenized assets.

Big financial players are experimenting with institutional chains. Examples include a collaboration that resulted in the Canton blockchain, which was built with input from firms such as Goldman Sachs, BNP Paribas, and Deutsche Börse.

That project aims at handling real-world asset tokenization and institutional workflows. Adoption, he suggested, has been postponed more than it should have been because of regulatory fog in the US.

XRP market cap currently at $84.2 billion. Chart: TradingView

A Multi-Chain Outlook For Finance

Giancarlo argued that the next phase of finance will not be led by one chain. Reports note he sees a multi-chain future where different systems serve different needs.

Ethereum, XRPL, Canton and others will each play roles. Some functions will fit one ledger better than another. That idea reduces the odds of single-chain dominance and opens space for competition. It also allows institutions to pick tools that match their risk and compliance needs.

XRP Price Action

Meanwhile, XRP’s market has been hit by broader selling, with prices dipping toward multi-month lows. The token traded nearer to the $1.30–$1.60 band in recent sessions while some traders watched the $1.80 Fibonacci support as a key level.

Volatility rose and technical support was tested. Still, on-chain measures and network traffic showed pockets of strength, a sign that usage did not always mirror price moves. In short, network activity remained meaningful even as sentiment swung.

Featured image by Ron Sachs/Zuma Press, chart from TradingView

Пов'язані питання

QWhat did former CFTC Chair Chris Giancarlo praise XRP for?

AHe praised XRP for staying active and relevant through extended US regulatory pressure, particularly highlighting its ability to hold its place in the market during a hostile period tied to regulators like Gary Gensler and Senator Elizabeth Warren.

QWhat was the outcome and timeline of the SEC v. Ripple case mentioned in the article?

AThe SEC v. Ripple case began in December 2020 and ended with a settlement in August 2025. It was described as a turning point.

QAccording to Giancarlo, what is preventing big banks from fully committing to blockchain adoption?

AGiancarlo stated that banks are waiting for clearer legal guidelines and rules before they will fully commit and speed up blockchain adoption, as regulatory fog in the US has postponed adoption.

QWhat is the Canton blockchain and which institutions were involved in its development?

AThe Canton blockchain is a project built with input from firms like Goldman Sachs, BNP Paribas, and Deutsche Börse. It aims to handle real-world asset tokenization and institutional workflows.

QWhat is Giancarlo's view on the future structure of financial blockchain networks?

AGiancarlo sees a multi-chain future where different systems like Ethereum, XRPL, and Canton will each play roles, serving different needs. He argues that the next phase of finance will not be led by one single chain.

Пов'язані матеріали

How to Do Research Well: Deliberately Practice the Real Skills That Matter

No one truly teaches you how to do research. You're often given a desk, a pre-selected problem, and vague instructions to "create something new." Consequently, many people reverse-engineer the job based on visible outputs—papers, posts, announcements—learning only how to *appear* like a researcher rather than how to *become* one. True research capability is built from stacking small, trainable skills, nearly all of which can be developed through deliberate practice. **Pick Your Own Problem:** Most researchers absorb problems from advisors or trends, lacking the underlying reasoning. Choosing a problem you genuinely care about, as John Schulman advises, leads to original work. Develop "taste" like a muscle: predict experiment outcomes, guess paper results from methods, and track which findings remain important over time. **Upgrade Your Inputs:** Relying on shared reading lists (arXiv hot lists, filtered group chats) leads to unoriginal conclusions. Undervalued old literature often holds crucial insights (e.g., MoE, LSTM, backpropagation). Richard Sutton's "The Bitter Lesson" or Claude Shannon's 1952 talk on creative thinking are more predictive than lengthy modern surveys. Breadth matters as much as depth: draw from neuroscience, mechanism design, hardware knowledge, and honest statistics. Read papers directly, especially appendices and limitations sections. **Write Everything Down:** As Paul Graham noted, writing exposes flaws in seemingly mature ideas. Writing is the cheapest defense against self-deception. Following Feynman's principle, Darwin programmatically wrote down facts contradicting his theory to combat memory bias. Maintain a detailed log of hypotheses, setups, predictions, results, and updated understandings. Reviewing past logs fosters essential humility.

marsbit1 год тому

How to Do Research Well: Deliberately Practice the Real Skills That Matter

marsbit1 год тому

Following US Ban on Fable 5, Zhipu AI's Stock Soars 47%

On June 15th, shares of Zhipu AI surged dramatically on the Hong Kong stock market, peaking at a 47.6% gain before closing 32.82% higher. This sharp increase was directly triggered by two recent industry events. On June 12th, Anthropic announced it was suspending global access to its latest flagship models, Claude Fable 5 and Claude Mythos 5, to comply with a U.S. government export control order. The next day, Zhipu AI announced it would open access to its latest open-source flagship model, GLM-5.2, under the permissive MIT license. The Anthropic incident highlighted a critical issue beyond raw model capability: the risk of sudden, unpredictable loss of access to advanced AI models, especially for developers and enterprises deeply integrated with them. This has shifted industry and market focus toward factors like stability, sustainable access, and controllability. Zhipu's move, promoting "frontier intelligence for all," positions its openly available model as a reliable and accessible alternative. The GLM-5.2 model emphasizes "Long Horizon Task" capabilities with a 1M context window, targeting complex, multi-step coding and engineering workflows where maintaining context is crucial. Analysts note this event exposes the risk of dependency on closed-source models subject to single jurisdictional controls, potentially accelerating a shift toward domestic base models and localized deployments. The market's reaction signals a new valuation dimension in AI: providers who can offer stable, long-term, and sustainably accessible AI capabilities are gaining strategic importance.

marsbit1 год тому

Following US Ban on Fable 5, Zhipu AI's Stock Soars 47%

marsbit1 год тому

Fully Entering the AI Era: Alipay Bets on Conversation, WeChat Holds Fast to Social

In May 2026, Alipay announced over 300 million AI payment transactions. Shortly after, WeChat opened its mini-programs for AI integration, sparking controversy by requiring developer source code access. This highlights their diverging approaches to AI integration. Alipay is testing "Project Treasure," an optional AI-native interface replacing traditional app grids with a conversational window. Users can command complex tasks (e.g., "book a ride and order coffee") handled end-to-end by AI. This shift follows an abandoned standalone AI app, focusing instead on enhancing its existing user base. For unmodified mini-programs, Alipay's AI uses "screen-reading" to simulate user interactions, bypassing the need for developer overhaul. It also introduced "Token Pay" for micro-transactions and "AI Wallets" for autonomous agent spending. WeChat, prioritizing its core social function, is taking an embedded approach. Its AI agent will operate within existing contexts like group chats and official accounts, assisting without a separate interface. To enable this, WeChat offers developers two paths: granting source code access for direct AI control ("Automatic Mode") or manually encapsulating services into standardized "Skills." Both place significant burden on developers. Key differences emerge in handling legacy services: WeChat demands developer cooperation (code or labor), while Alipay's screen-reading offers immediate, if potentially less stable, compatibility. Alipay's 3 billion AI transactions demonstrate user acceptance of AI-driven commercial actions. The divergent strategies may reshape mini-program ecosystems—Alipay passively "AI-fying" services, WeChat potentially favoring resource-rich developers—and set competing technical standards. Ultimately, the competition centers on where users entrust the command to "help me get things done."

marsbit1 год тому

Fully Entering the AI Era: Alipay Bets on Conversation, WeChat Holds Fast to Social

marsbit1 год тому

Торгівля

Спот
Ф'ючерси
活动图片