XRP: ETF ‘green days’ fade as leverage hits $450mln – This hints at…

ambcryptoPublished on 2025-12-28Last updated on 2025-12-28

Abstract

The initial wave of ETF-driven demand for XRP is fading, as daily inflows have slowed significantly despite total assets remaining elevated. This slowdown coincides with key support structures weakening. Long-term holders (LTHs) have been reducing their holdings, with one cohort's share of supply dropping sharply from 14.26% to 5.66% in a month, indicating profit-taking or exit. Concurrently, Open Interest (OI) on Binance has plummeted to $450 million, its lowest since November 2024, signaling widespread closure of leveraged long positions and a reduction in speculative trading. With both institutional accumulation and steady holder support thinning, XRP is losing its structural price support. Unless demand returns, the next price move may be driven by a lack of confidence, leaving it fragile and reactive.

Despite a lovely (yet trimmed) patch of green ETF inflows, Ripple’s XRP is moving against the grain.

Key cohorts are cutting down exposure, and Open Interest (OI) is in freefall. This change in tone may be one that is worth watching, before the next move unfolds.

ETFs calm down

After strong early inflows that pushed Total Net Assets to around $1.24 billion, recent data showed that the pace was slowing. Daily inflows have tapered, with several sessions barely registering new capital.

While price has held, accumulation has clearly hit the brakes.

Assets remain elevated, so it’s not an exit. However, it does mean that the initial wave of ETF-driven demand is fading.

AMBCrypto previously reported that despite inflows, XRP’s price hadn’t changed much; institutional buying was happening in quiet. At the time, whale wallets were rebuilding exposure and helping stabilize price.

What’s changed now is that this support is thinning.

LTHs are letting go

Glassnode’s HODL Waves showed that wallets holding XRP for 2-3 years (typically considered those that stick) have reduced their share of supply.

This cohort fell from 14.26% in late November to around 5.66% by late December, a big drawdown in just one month. That kind of exit is LTHs taking profits or stepping aside.

When steady holders leave alongside fading inflows, it weakens structural support. This leaves price more exposed to impulse changes.

Derivatives step back

XRP’s OI on Binance has dropped to around $450 million, its lowest level since November 2024. That’s a big decline from earlier peaks, so there’s widespread position closures – especially from leveraged longs.

When OI falls this quickly, it usually means traders are stepping aside rather than staying for potential surges. Lesser leverage means less speculation, and in this case, it further shows that confidence is thinning.

Until participation rebuilds, price action may remain fragile and reactive.

Final Thoughts

XRP is losing the structural support that once held price steady.
Unless demand returns, XRP’s next move may be driven by a lack of confidence.

Jensen Huang 'Saves' South Korean Stock Market: Locks In SK Hynix Memory, Chip Shortage to Continue

On June 5th, South Korea's stock market experienced a sharp decline, with major chipmakers like Samsung and SK Hynix dropping nearly 10%. Amidst the turmoil, NVIDIA CEO Jensen Huang's visit to Seoul played a dramatic role in boosting market sentiment. Following a dinner meeting with SK Group Chairman Chey Tae-won and SK Hynix CEO Kwak Noh-Jung, Huang confirmed that NVIDIA's new Vera CPU will utilize SK Hynix DRAM. The companies announced a multi-year technical partnership to co-develop next-generation memory for NVIDIA's AI infrastructure, covering products from data centers to personal AI and robotics. This collaboration extends beyond memory supply. SK Hynix is integrating NVIDIA's AI and Omniverse platform into its own semiconductor design and manufacturing processes, including computational lithography and creating digital twins of its fabrication plants for autonomous operation. While strengthening ties with SK Hynix, NVIDIA is diversifying its supply chain for the upcoming HBM4 memory, with Samsung, SK Hynix, and Micron all certified as suppliers for its Vera Rubin platform. Despite this, Huang warned that the global chip shortage, driven by relentless demand from AI factory construction, is expected to persist for several years across the entire supply chain. His visit underscores NVIDIA's systematic effort to deepen integration with South Korea's broader tech industry.

marsbit27m ago

Jensen Huang 'Saves' South Korean Stock Market: Locks In SK Hynix Memory, Chip Shortage to Continue

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The Nasdaq plunged 4.18% on June 5, 2026, its worst single-day drop in over a year, as a much stronger-than-expected US jobs report triggered fears of economic overheating and delayed Federal Reserve interest rate cuts. The selloff, centered on high-valuation tech and AI stocks like Nvidia and Broadcom, spread across major indices. The article examines whether this signals a market top. The strong May non-farm payrolls data, nearly double expectations, pushed bond yields higher, directly hurting rate-sensitive tech stocks. This exposed vulnerabilities in the crowded AI trade, where valuations had soared on narratives of infinite growth, despite emerging signs of slowing order momentum and corporate AI monetization challenges. Prior to the drop, market indicators flashed warning signs: historically high valuations (e.g., Shiller CAPE ratio near 39.5), extreme bullish sentiment, and high levels of leverage. Technical charts showed key support levels being breached. Wall Street is divided on the outlook. Bears, citing risks of "stagflation" and AI bubble comparisons to the dot-com era, warn of a potential significant correction. Bulls view the drop as a healthy correction within a bull market, underpinned by a strong economy and expected corporate earnings growth of around 7% in 2026. The immediate future hinges on upcoming key events: the May CPI inflation data and the mid-June FOMC meeting. Their outcomes will critically shape market expectations for the Fed's rate path. The article concludes that conditions for a major market top are aligning, marking a fragile transition from narrative-driven gains to a phase demanding validation from macroeconomic data and corporate fundamentals. Caution is advised.

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Nasdaq Plunges 4.2% in a Single Day: Does "Black Friday" Burst the U.S. Stock Market Bubble?

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Nasdaq Plunges 4.2% in a Single Day, Did 'Black Friday' Pop the U.S. Stock Bubble?

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The First Case on AI Agents: What Was Adjudicated?

"The First 'Agent' Ruling: What Was Decided?" On April 30, the Guangzhou Internet Court issued a ruling—China's first behavior preservation order in the intelligent agent (AI agent) field. The defendant, an open-source AI agent software, was ordered to stop downloads, cease actions that bypassed a platform's technical protection measures, and delete related tutorials and data. The core issue: the software used the operating system's "accessibility service" permissions to automate user interactions within other apps without those platforms' authorization. This mirrors a recent US case where Amazon sued Perplexity for similar reasons—bypassing Amazon's API to directly scrape and interact with its pages—and won a preliminary injunction. Both rulings establish a crucial legal boundary for the AI agent era: agents cannot operate unchecked. The article argues the fundamental legal principle emerging is one of **dual authorization**. An AI agent requires both **user consent** AND **platform consent** to operate legitimately within that platform's ecosystem. Bypassing platform rules through system-level permissions, even with user permission, undermines platform responsibilities for content moderation, data security, and user privacy, creating liability issues. The piece uses the evolution of "Doubao Phone" (an AI-integrated smartphone) as a case study. Its initial, aggressive version that bypassed platform controls faced roadblocks. Its upcoming 2.0 version is reportedly pivoting to negotiate API access and authorization deals with major platforms (like Alibaba's ecosystem), seen as a strategic adaptation to the new regulatory reality. A global trend is identified: the era of unregulated, "wild west" growth for AI agents is ending, replaced by a **compliance race**. This raises barriers to entry, as securing platform authorizations becomes a new cost. Open-source status is also not a legal shield if the code facilitates bypassing technical protections. In conclusion, these first rulings target not the largest, but the most **aggressive and representative** cases. By setting precedent with them, regulators are efficiently steering the entire industry towards a new, more regulated operating paradigm defined by dual authorization and platform cooperation.

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Fired by Google Over a 14-Page Paper, Over 4,000 Rallied for Her. 6 Years Later: She Almost Predicted the Entire AI Era Back Then.

In late 2020, Google AI researcher Timnit Gebru was effectively dismissed following a conflict over a 14-page, unpublished research paper she co-authored titled "On the Dangers of Stochastic Parrots." The paper, which has since been cited over 14,000 times, raised critical early warnings about the risks of large language models (LLMs). It argued that these models, trained on vast, biased internet data, are essentially "stochastic parrots" that mimic language without true understanding, potentially amplifying societal biases, generating plausible but false information (later termed "AI hallucination"), consuming massive energy, and obscuring their training data contents. Gebru's stance led to a clash with Google management, who requested the paper's withdrawal. Her subsequent internal criticism of the company's diversity efforts and handling of the matter culminated in her termination, which sparked protests from over 4,000 Google employees and researchers. Six years later, the paper's predictions have proven remarkably prescient. Issues like AI hallucination, embedded bias (evident in resume screening and healthcare algorithms), soaring energy consumption from AI data centers, unvetted training data containing harmful content, and the risk of "model collapse" from AI-generated internet content have become central industry challenges. The incident also highlighted concerns about AI development being driven primarily by commercial competition within a handful of powerful tech companies, often at the expense of ethical considerations. After leaving Google, Gebru founded the Distributed AI Research Institute (DAIR) to explore these issues independently. The controversy underscores how her early, critical insights into the fundamental limitations and societal impacts of LLMs anticipated many of the most pressing dilemmas in today's AI era.

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