XRP Wins Rare Recognition From Former US Regulator

bitcoinistPublicado a 2026-02-06Actualizado a 2026-02-06

Resumen

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

Preguntas relacionadas

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.

Lecturas Relacionadas

Has the 'Digital Gold' Narrative for BTC Failed?

**Title: Has the "Digital Gold" Narrative for Bitcoin Failed?** The article argues that Bitcoin's "digital gold" narrative remains valid despite a recent sharp price decline (from a peak near $126k in Oct 2025 to briefly under $61k in Feb 2026). It presents a long-term investment framework based on three core points: **1. Viewing Bitcoin as an Asset:** Bitcoin is presented as a superior potential store of value compared to gold. Key arguments are its absolute scarcity (21 million cap), superior portability, and transparent auditability via its public ledger. While acknowledging its current use in early, volatile stages (~3-4% global adoption), the author draws parallels to the early, disruptive phases of the internet and e-commerce. **2. Understanding the Recent Downturn:** The current ~50% correction is framed as a predictable, consensus-driven cycle following its post-halving peak (the 2024 halving preceded the Oct 2025 high). A crucial factor is a historic "changing of hands": the influx of new institutional buyers via ETFs allowed early, low-cost holders (miners, OG believers) to take profits. The author notes that while severe, Bitcoin's historical drawdowns (e.g., 93% in 2011, 77% in 2021-22) have been progressively smaller, suggesting maturing holder structure and decreasing volatility over time. **3. The Long-Term Perspective:** The long-term thesis hinges on Bitcoin capturing a portion of gold's market value. With Bitcoin's market cap at ~$1.4 trillion (at $70k) versus gold's ~$20 trillion, significant upside potential exists if the "digital gold" narrative is partially realized. However, the author strongly cautions that short-term risks remain, the bottom is unpredictable, and high volatility is inherent. The real risk is not Bitcoin failing but poor personal position management (over-leverage, wrong capital) and a lack of deep understanding, which can force investors out during severe downturns. The conclusion uses Amazon's 95% crash post-2000 dot-com bubble and subsequent 42x recovery as an analogy. The ultimate question is not if Bitcoin's price will rise, but if an investor's strategy and conviction can withstand the volatility to see the long-term play out. The recent divergence (gold up, Bitcoin down) is posed not as a narrative failure, but as potential evidence of this ongoing, painful transition from a speculative asset to a mainstream allocation.

marsbitHace 23 min(s)

Has the 'Digital Gold' Narrative for BTC Failed?

marsbitHace 23 min(s)

Has BTC's 'Digital Gold' Narrative Failed?

The article discusses Bitcoin's "digital gold" narrative, its recent price drop, and long-term outlook through the perspective of "Jason". It argues the narrative is not a failure but that Bitcoin represents a superior, new asset class due to its fixed supply (21 million), portability, and auditability. The piece compares its current ~3-4% global adoption rate to early internet/e-commerce, suggesting significant growth potential. Regarding the 2025-2026 price decline (from ~$126k to briefly under $61k), the author views it as a predictable, consensus-driven sell-off within Bitcoin's ~4-year cycle post-halving, exacerbated by a major "handover" from early, low-cost holders to new institutional buyers via ETFs. A key observation is that historical peak-to-trough drawdowns have lessened over time (e.g., 93% in 2011 to ~50% in 2026), indicating maturing volatility as holder structure changes. For the long term, the author uses a simple framework: Bitcoin's total market cap (~$1.4T at $70k) is only about 7% of gold's (~$20T). Even capturing 30-50% of gold's value would imply substantial upside. However, the article strongly cautions against viewing this as investment advice, emphasizing extreme volatility and the critical importance of risk management, position sizing, and deep fundamental understanding to survive severe drawdowns. It concludes by drawing a parallel to Amazon's 95% crash in 2000 and subsequent 42x recovery, stressing that the key is surviving market cycles to realize long-term potential.

链捕手Hace 33 min(s)

Has BTC's 'Digital Gold' Narrative Failed?

链捕手Hace 33 min(s)

From Code to Cognition: A Ten-Thousand-Word Guide to the Evolution of the Robot Brain

"From Code to Cognition: The Evolution of Robot Brains" The journey of robotic intelligence has shifted dramatically from manually coded systems to AI-driven brains. For decades, robots relied on layered software stacks—perception, state estimation, planning, control—each handcrafted. While predictable, they lacked adaptability. The 2010s saw deep learning revolutionize perception (e.g., object detection) and control (via reinforcement learning), but learned skills remained narrow. The arrival of Large Language Models (LLMs) marked a turning point. LLMs acted as high-level planners, interpreting natural language instructions and generating sequences of actions for traditional robotic systems to execute. However, true integration came with Visual-Language-Action (VLA) models, which fused vision, language, and motion prediction into a single network. Pioneered by models like RT-2 and open-source projects like OpenVLA, VLAs enable robots to reason and act directly from visual input and commands. The most advanced humanoid robots now employ a "dual-brain" architecture: a slow-thinking, large VLA (System 2) for reasoning and planning, and a fast-reacting, small network (System 1) for high-frequency motion control, sometimes with an even lower-level System 0 for balance. This split balances cognition with the physics of real-time movement. Computation is split between onboard hardware (e.g., NVIDIA Jetson) for safety-critical control loops and cloud/edge servers for non-critical tasks like learning and interfaces. A crucial driver is the open-source ecosystem—models like GR00T and OpenVLA allow startups to build upon pre-trained brains and fine-tune them with their own data, accelerating development. Despite progress, current systems struggle with recovery from errors, sample inefficiency, and long-horizon tasks. This has spurred the rise of **World Models**—neural networks that predict the consequences of actions. By simulating possible futures before acting (like NVIDIA Cosmos or Meta V-JEPA), robots can plan, recover, and generalize better. This represents the next frontier: shifting intelligence from learned reactions to an internal model of physics and cause-and-effect. The field is rapidly evolving. While not yet at its "ChatGPT moment," the convergence of cheaper hardware, scalable simulation, and world models points toward robots that are increasingly capable, adaptive, and useful. The question is shifting from "what can robots do?" to "what *should* they do?"

marsbitHace 1 hora(s)

From Code to Cognition: A Ten-Thousand-Word Guide to the Evolution of the Robot Brain

marsbitHace 1 hora(s)

AI Bubble Is Bursting

The AI Bubble is Bursting: A Necessary Purge on the Path to Ubiquitous Intelligence Market volatility has reignited debates about an AI bubble, with figures like Ray Dalio pointing to high valuations. However, this parallels the dot-com bubble, which, despite its crash, laid the physical infrastructure for today's internet era. The current AI investment frenzy, with tech giants planning trillions in infrastructure spending far outstripping current AI application revenues, appears similarly imbalanced. This 'bubble' is seen as an inevitable phase for a disruptive technology, paying the "innovation tax." Critically, AI inference costs have plummeted over 99.7% since 2023, making intelligence nearly free at the margin. This hasn't reduced spending but has instead unlocked massive new demand, as seen in enterprise AI cloud expenditure tripling. This follows the Jevons Paradox: efficiency gains lead to greater total consumption. The market is now entering a cleansing phase, weeding out speculative ventures lacking real moats. The deeper shift is a move from capital expenditure (CapEx) on hardware to value creation in operational expenditure (OpEx) through AI applications that solve real industry problems. While infrastructure valuations are high, rapid earnings growth from widespread AI adoption across sectors—from manufacturing and finance to law and healthcare—may digest these valuations over time. Ultimately, this creative destruction will leave behind robust infrastructure and optimized models, cheaply powering an AI-augmented future for all industries, much as the internet became indispensable after its own bubble burst. The core productive potential remains undiminished.

链捕手Hace 1 hora(s)

The AI Bubble is Bursting

The article argues that while a bubble in the AI sector is real and is currently showing signs of deflation, this is a typical, even necessary, phase for a transformative technology. It draws parallels to the dot-com bubble, where speculative excess was followed by the consolidation of internet infrastructure, enabling the digital age. Similarly, today's massive investments in AI infrastructure (data centers, power, cooling) far outpace current application-layer revenue, creating a valuation imbalance. However, the author contends this apparent bubble is not a sign of failure but of a transition. Key drivers include the plummeting cost of AI inference (over 99.7% in two years), which, per Jevons Paradox, has unleashed massive new demand as AI moves from simple tasks to complex agentic workflows across all industries. The market is now cleansing itself of shallow, speculative ventures, shifting value from capital expenditure (CapEx) on hardware to operational expenditure (OpEx) on transformative AI applications. The conclusion is that the underlying productive force of AI is undeniable. The current financial correction will eliminate weak players, leaving behind robust infrastructure and efficient models. This will ultimately fuel an "AI+" era where intelligence becomes as ubiquitous and essential as the internet, embedding itself into every sector of the economy.

marsbitHace 1 hora(s)

marsbitHace 1 hora(s)

Trading

Spot

Futuros